From 12662905b1fb99cd6334e7826893755928572255 Mon Sep 17 00:00:00 2001
From: reveurmichael <reveurmichael@users.noreply.github.com>
Date: Thu, 30 Nov 2023 15:14:44 +0000
Subject: [PATCH] deploy: afb6871f929f27fe6f4580209cf9d9a66d8a946c

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 .../retrying-some-regression.ipynb            |    2 +-
 .../working-with-data/numpy.ipynb             |  696 ++---
 _sources/ml-advanced/kernel-method.ipynb      | 1562 +++++-----
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 ...pervised-learning-pca-and-clustering.ipynb | 1264 ++++++++
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 ...web-app-to-use-a-machine-learning-model.md |  358 ---
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 .../gradient-descent.ipynb                    |  636 ++++
 .../loss-function.ipynb                       |  291 ++
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 .../build-your-own-custom-vis.html            |  408 +--
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 .../write-a-data-ethics-case-study.html       |  408 +--
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 ...ising-autoencoder-dimension-reduction.html |  510 ++--
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 ...nguage-digits-classification-with-cnn.html |  466 +--
 .../dqn/dqn-on-foreign-exchange-market.html   |  420 +--
 assignments/deep-learning/gan/art-by-gan.html |  478 +--
 .../deep-learning/gan/gan-introduction.html   |  492 ++--
 ...model-with-tweet-volume-and-sentiment.html |  424 +--
 .../nn-classify-15-fruits-assignment.html     |  436 +--
 .../nn-for-classification-assignment.html     |  504 ++--
 ...ification-classify-images-of-clothing.html |  486 ++--
 .../google-stock-price-prediction-rnn.html    |  406 +--
 .../intro_to_tensorflow_for_deeplearning.html |  478 +--
 .../time-series-forecasting-assignment.html   |  520 ++--
 ...rintuitive-challenges-in-ml-debugging.html |  444 +--
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 .../debugging-in-classification.html          |  504 ++--
 .../debugging-in-regression.html              |  528 ++--
 ...d-random-forests-more-ensemble-models.html |  486 ++--
 .../ensemble-learning/decision-trees.html     |  522 ++--
 ...-forest-classifier-feature-importance.html |  598 ++--
 .../random-forests-for-classification.html    |  456 +--
 .../random-forests-intro-and-regression.html  |  480 +--
 .../boosting-with-tuning.html                 |  630 ++--
 .../gradient-boosting-assignment.html         |  420 +--
 ...perparameter-tuning-gradient-boosting.html |  648 +++--
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 .../decision_trees_for_regression.html        |  450 +--
 .../kernel-method-assignment-1.html           |  834 +++---
 ...rt_vector_machines_for_classification.html |  444 +--
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 .../dropout-and-batch-normalization.html      |  432 +--
 .../lasso-and-ridge-regression.html           |  444 +--
 ...ng-curve-to-identify-overfit-underfit.html |  504 ++--
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 .../linear-regression/california_housing.html |  546 ++--
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 .../linear-regression-metrics.html            |  516 ++--
 .../linear-regression/loss-function.html      |  456 +--
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 .../ml-logistic-regression-1.html             |  486 ++--
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 .../ml-fundamentals/ml-overview-iris.html     |  516 ++--
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 ...duction-to-statistics-and-probability.html |  386 +--
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 deep-learning/autoencoder.html                |  456 +--
 deep-learning/cnn.html                        |  482 ++--
 deep-learning/difussion-model.html            |  570 ++--
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 ml-advanced/kernel-method.html                |  971 ++++---
 ml-advanced/model-selection.html              |  552 ++--
 ...upervised-learning-pca-and-clustering.html | 2563 +++++++++++++++++
 ml-advanced/unsupervised-learning.html        |  504 ++--
 ...b-app-to-use-a-machine-learning-model.html |  705 +++--
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 .../getting-started-with-classification.html  |  398 +--
 .../introduction-to-classification.html       |  448 +--
 .../classification/more-classifiers.html      |  496 ++--
 .../classification/yet-other-classifiers.html |  666 +++--
 ml-fundamentals/ml-overview.html              |  598 ++--
 ml-fundamentals/ml-summary.html               | 1733 +++++++++++
 .../gradient-descent.html                     | 2272 +++++++++++++++
 .../parameter-optimization/loss-function.html | 1920 ++++++++++++
 .../parameter-optimization.html               | 1647 +++++++++++
 .../linear-and-polynomial-regression.html     |  492 ++--
 .../regression/logistic-regression.html       |  516 ++--
 ml-fundamentals/regression/managing-data.html |  494 ++--
 ...egression-models-for-machine-learning.html |  402 +--
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 slides/deep-learning/cnn.html                 |  474 +--
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 slides/ml-advanced/unsupervised-learning.html |  456 +--
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diff --git a/_images/kernel-method_11_0.png b/_images/kernel-method_22_0.png
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diff --git a/_images/kernel-method_26_0.png b/_images/kernel-method_26_0.png
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diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
index fae97e3344..29e514dd72 100644
--- a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
+++ b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0d88f1d0",
+   "id": "305a7498",
    "metadata": {},
    "source": [
     "# Create a regression model\n",
diff --git a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
index 12acc90456..e3db9fb6e8 100644
--- a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
+++ b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ff7cec9a",
+   "id": "d6d78fce",
    "metadata": {},
    "source": [
     "# Exploring visualizations\n",
diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
index b58caebd88..042c11bb64 100644
--- a/_sources/assignments/ml-fundamentals/parameter-play.ipynb
+++ b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "625a83ea",
+   "id": "548d9208",
    "metadata": {},
    "source": [
     "# Parameter play\n",
diff --git a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
index f7c1872c2a..653ee17d92 100644
--- a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
+++ b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "32d9bc30",
+   "id": "5ec84e9c",
    "metadata": {},
    "source": [
     "# Regression with Scikit-learn\n",
diff --git a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
index 3fa29fe687..fc4360d640 100644
--- a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
+++ b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5b747e8f",
+   "id": "0b9e3e87",
    "metadata": {},
    "source": [
     "# Retrying some regression\n",
diff --git a/_sources/data-science/working-with-data/numpy.ipynb b/_sources/data-science/working-with-data/numpy.ipynb
index 5556ac9e64..17220bc4dc 100644
--- a/_sources/data-science/working-with-data/numpy.ipynb
+++ b/_sources/data-science/working-with-data/numpy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6d9d1ce0",
+   "id": "294046b4",
    "metadata": {},
    "source": [
     "# NumPy\n",
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e1d1eec7",
+   "id": "2534b327",
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5373df66",
+   "id": "9362869f",
    "metadata": {},
    "source": [
     "### Create a basic array\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9ee641e8",
+   "id": "ca3eea24",
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3f6f353",
+   "id": "502439df",
    "metadata": {},
    "source": [
     "Besides creating an array from a sequence of elements, you can easily create an array filled with `0`’s:"
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "db13ae06",
+   "id": "dd953576",
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f17edf7b",
+   "id": "e21b8985",
    "metadata": {},
    "source": [
     "Or an array filled with 1’s:"
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "005908f9",
+   "id": "d60ae805",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9369ea1",
+   "id": "94d25668",
    "metadata": {},
    "source": [
     "Or even an empty array! The function `empty` creates an array whose initial content is random and depends on the state of the memory. The reason to use `empty` over `zeros` (or something similar) is speed - just make sure to fill every element afterwards!"
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "620f3841",
+   "id": "3c9d5d7e",
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4e977f5",
+   "id": "e27f7fb0",
    "metadata": {},
    "source": [
     "You can create an array with a range of elements:"
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5e743152",
+   "id": "efaeb42d",
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8882db37",
+   "id": "c6db992a",
    "metadata": {},
    "source": [
     "And even an array that contains a range of evenly spaced intervals. To do this, you will specify the **first number**, **last number**, and the **step size**."
@@ -200,7 +200,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "0a8b9cb5",
+   "id": "b6f2c853",
    "metadata": {},
    "outputs": [
     {
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d2b3d32",
+   "id": "0aa0e5a9",
    "metadata": {},
    "source": [
     "You can also use `np.linspace()` to create an array with values that are spaced linearly in a specified interval:"
@@ -229,7 +229,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "e9bafe92",
+   "id": "1828a248",
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "982f3ac2",
+   "id": "f2c11a33",
    "metadata": {},
    "source": [
     "While the default data type is floating point (`np.float64`), you can explicitly specify which data type you want using the `dtype` keyword."
@@ -258,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "68c60568",
+   "id": "29eb71ee",
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d0bf8a9",
+   "id": "b3ad5396",
    "metadata": {},
    "source": [
     "### Adding, removing, and sorting elements\n",
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "8417eb3f",
+   "id": "a1829b50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "357f3ad6",
+   "id": "aee59712",
    "metadata": {},
    "source": [
     "You can quickly sort the numbers in ascending order with:"
@@ -309,7 +309,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "eb00afa9",
+   "id": "d0cd9491",
    "metadata": {},
    "outputs": [
     {
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c212a7fb",
+   "id": "f34ffd2d",
    "metadata": {},
    "source": [
     "In addition to sort, which returns a sorted copy of an array, you can use:\n",
@@ -345,7 +345,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "0166fd8a",
+   "id": "d265b521",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fe53193",
+   "id": "3cb430c4",
    "metadata": {},
    "source": [
     "You can concatenate them with `np.concatenate()`."
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "3b4d4bde",
+   "id": "29e4e207",
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83ebe0ad",
+   "id": "f89f5d4d",
    "metadata": {},
    "source": [
     "Or, if you start with these arrays:"
@@ -393,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "fc11179c",
+   "id": "039f106b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a5c9839",
+   "id": "f1421e49",
    "metadata": {},
    "source": [
     "You can concatenate them with:"
@@ -412,7 +412,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "1efedec3",
+   "id": "4edbc3e4",
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "171b6700",
+   "id": "5e132be4",
    "metadata": {},
    "source": [
     "In order to remove elements from an array, it’s simple to use indexing to select the elements that you want to keep.\n",
@@ -450,7 +450,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "1c4758ee",
+   "id": "84edfbda",
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "82b58af9",
+   "id": "61fba9a7",
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "661e0607",
+   "id": "1e041af8",
    "metadata": {},
    "source": [
     "- ndarray.shape\n",
@@ -505,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "6b33c9ac",
+   "id": "0399a901",
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6c56963",
+   "id": "d1df4cf0",
    "metadata": {},
    "source": [
     "- ndarray.size\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "b520b8a5",
+   "id": "0b8ade4b",
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98fe5815",
+   "id": "a7b3c878",
    "metadata": {},
    "source": [
     "- ndarray.dtype\n",
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "a881026a",
+   "id": "86006356",
    "metadata": {},
    "outputs": [
     {
@@ -586,7 +586,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "f8fb61cf",
+   "id": "fa6bad80",
    "metadata": {},
    "outputs": [
     {
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d887da3",
+   "id": "fb1a62eb",
    "metadata": {},
    "source": [
     "- ndarray.itemsize\n",
@@ -616,7 +616,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "ca0a3919",
+   "id": "3077e3e0",
    "metadata": {},
    "outputs": [
     {
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "233f4f5f",
+   "id": "6eb7f606",
    "metadata": {},
    "source": [
     "- ndarray.data\n",
@@ -646,13 +646,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "4b178c40",
+   "id": "8c4f2e3e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<memory at 0x7f94e40bdd40>"
+       "<memory at 0x7efe7227dd40>"
       ]
      },
      "execution_count": 23,
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c008c5ae",
+   "id": "8cbb9111",
    "metadata": {},
    "source": [
     "### Reshape an array\n",
@@ -679,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "832e3dd7",
+   "id": "d73486ff",
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "441fe36b",
+   "id": "b28b7693",
    "metadata": {},
    "source": [
     "You can use `reshape()` to reshape your array. For example, you can reshape this array to an array with three rows and two columns:"
@@ -709,7 +709,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "d585bcb0",
+   "id": "3fd54b49",
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68974bdd",
+   "id": "dddb6833",
    "metadata": {},
    "source": [
     "With `np.reshape`, you can specify a few optional parameters:"
@@ -741,7 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "1c4f6e73",
+   "id": "559239a6",
    "metadata": {},
    "outputs": [
     {
@@ -761,7 +761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b01978d",
+   "id": "e15df32d",
    "metadata": {},
    "source": [
     "`a` is the array to be reshaped.\n",
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "88b22fba",
+   "id": "520e3c94",
    "metadata": {},
    "outputs": [
     {
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a12a9684",
+   "id": "fab36e70",
    "metadata": {},
    "source": [
     "You can use `np.newaxis` to add a new axis:"
@@ -812,7 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "ac0b5b87",
+   "id": "7c6924b1",
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "846b8ea0",
+   "id": "1087bed9",
    "metadata": {},
    "source": [
     "You can explicitly convert a 1D array with either a row vector or a column vector using `np.newaxis`. For example, you can convert a 1D array to a row vector by inserting an axis along the first dimension:"
@@ -842,7 +842,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "d30b82ce",
+   "id": "38ebac75",
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +863,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3439a3f",
+   "id": "5141222d",
    "metadata": {},
    "source": [
     "Or, for a column vector, you can insert an axis along the second dimension:"
@@ -872,7 +872,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "987b0814",
+   "id": "e616f42f",
    "metadata": {},
    "outputs": [
     {
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e917772",
+   "id": "289172a9",
    "metadata": {},
    "source": [
     "You can also expand an array by inserting a new axis at a specified position with `np.expand_dims`.\n",
@@ -904,7 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "07e8f5c3",
+   "id": "e1649f82",
    "metadata": {},
    "outputs": [
     {
@@ -925,7 +925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c767b374",
+   "id": "51507a9f",
    "metadata": {},
    "source": [
     "You can use np.expand_dims to add an axis at index position 1 with:"
@@ -934,7 +934,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "4e50322a",
+   "id": "71ab2006",
    "metadata": {},
    "outputs": [
     {
@@ -955,7 +955,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85b437c2",
+   "id": "98b4d586",
    "metadata": {},
    "source": [
     "You can add an axis at index position 0 with:"
@@ -964,7 +964,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "f51ea5ed",
+   "id": "68594e13",
    "metadata": {},
    "outputs": [
     {
@@ -985,7 +985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1791fd7b",
+   "id": "5a929ab1",
    "metadata": {},
    "source": [
     "### Indexing and slicing\n",
@@ -996,7 +996,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "e7d8f8a9",
+   "id": "3f052c44",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1006,7 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "bf491a89",
+   "id": "6ac7e251",
    "metadata": {},
    "outputs": [
     {
@@ -1027,7 +1027,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "5973bc92",
+   "id": "5bde1cd8",
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "6e40373e",
+   "id": "e000bda3",
    "metadata": {},
    "outputs": [
     {
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "4f7199b7",
+   "id": "9648e317",
    "metadata": {},
    "outputs": [
     {
@@ -1089,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28986b03",
+   "id": "d5586737",
    "metadata": {},
    "source": [
     "You may want to take a section of your array or specific array elements to use in further analysis or additional operations. To do that, you’ll need to subset, slice, and/or index your arrays.\n",
@@ -1102,7 +1102,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "67b36827",
+   "id": "896d7f0e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdda8006",
+   "id": "eb0b667a",
    "metadata": {},
    "source": [
     "You can easily print all of the values in the array that are less than 5."
@@ -1120,7 +1120,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "d7368706",
+   "id": "c3d892c2",
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0d08a22",
+   "id": "c8c3fef9",
    "metadata": {},
    "source": [
     "You can also select, for example, numbers that are equal to or greater than 5, and use that condition to index an array."
@@ -1149,7 +1149,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "a197d891",
+   "id": "cc49291a",
    "metadata": {},
    "outputs": [
     {
@@ -1170,7 +1170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a6c9ced",
+   "id": "57aa56e1",
    "metadata": {},
    "source": [
     "You can select elements that are divisible by 2:"
@@ -1179,7 +1179,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "8c0ab03c",
+   "id": "d017eb15",
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d756701a",
+   "id": "9654b67f",
    "metadata": {},
    "source": [
     "Or you can select elements that satisfy two conditions using the `&` and `|` operators:"
@@ -1209,7 +1209,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "298a2f92",
+   "id": "b72d40ca",
    "metadata": {},
    "outputs": [
     {
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed7b7987",
+   "id": "38acfeb0",
    "metadata": {},
    "source": [
     "You can also make use of the logical operators `&` and `|` in order to return boolean values that specify whether or not the values in an array fulfill a certain condition. This can be useful with arrays that contain names or other categorical values."
@@ -1239,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "ce337551",
+   "id": "6632d44f",
    "metadata": {},
    "outputs": [
     {
@@ -1262,7 +1262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b7cb0a3",
+   "id": "d692ad57",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to select elements or indices from an array.\n",
@@ -1273,7 +1273,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "3b6a5b2b",
+   "id": "5af70340",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1282,7 +1282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0165554",
+   "id": "91609593",
    "metadata": {},
    "source": [
     "You can use `np.nonzero()` to print the indices of elements that are, for example, less than 5:"
@@ -1291,7 +1291,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "c8da4077",
+   "id": "5b1b29c7",
    "metadata": {},
    "outputs": [
     {
@@ -1312,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02608ff6",
+   "id": "6cc1b2ea",
    "metadata": {},
    "source": [
     "In this example, a tuple of arrays was returned: one for each dimension. The first array represents the row indices where these values are found, and the second array represents the column indices where the values are found.\n",
@@ -1323,7 +1323,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "7ec6b013",
+   "id": "821a7561",
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afd58a50",
+   "id": "b3a5f10a",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to print the elements in an array that are less than 5 with:"
@@ -1354,7 +1354,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "ae326ec1",
+   "id": "033a1707",
    "metadata": {},
    "outputs": [
     {
@@ -1374,7 +1374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33f62dd4",
+   "id": "9038f1c6",
    "metadata": {},
    "source": [
     "If the element you’re looking for doesn’t exist in the array, then the returned array of indices will be empty. For example:"
@@ -1383,7 +1383,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "2afaf9f9",
+   "id": "aef8e21c",
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e759e8e",
+   "id": "ebc2730d",
    "metadata": {},
    "source": [
     "### Create an array from existing data\n",
@@ -1417,7 +1417,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "cb4e7789",
+   "id": "4a7aceae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1426,7 +1426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d38c10cd",
+   "id": "f3e3b26b",
    "metadata": {},
    "source": [
     "You can create a new array from a section of your array any time by specifying where you want to slice your array."
@@ -1435,7 +1435,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "7a0cab9e",
+   "id": "5bb15b64",
    "metadata": {},
    "outputs": [
     {
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aadc5fa3",
+   "id": "28c104b8",
    "metadata": {},
    "source": [
     "Here, you grabbed a section of your array from index position 3 through index position 8.\n",
@@ -1467,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "b15af3da",
+   "id": "4718a50a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd436075",
+   "id": "3333f872",
    "metadata": {},
    "source": [
     "You can stack them vertically with `vstack`:"
@@ -1488,7 +1488,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "226e292e",
+   "id": "07e39e1e",
    "metadata": {},
    "outputs": [
     {
@@ -1511,7 +1511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00b4dff2",
+   "id": "180bce99",
    "metadata": {},
    "source": [
     "Or stack them horizontally with hstack:"
@@ -1520,7 +1520,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "35e4f03e",
+   "id": "4c2a4b51",
    "metadata": {},
    "outputs": [
     {
@@ -1541,7 +1541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d86b7d9f",
+   "id": "f1ac837c",
    "metadata": {},
    "source": [
     "You can split an array into several smaller arrays using `hsplit`. You can specify either the number of equally shaped arrays to return or the columns after which the division should occur.\n",
@@ -1552,7 +1552,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "95d85ba0",
+   "id": "890dcabe",
    "metadata": {},
    "outputs": [
     {
@@ -1574,7 +1574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "373e106f",
+   "id": "03918095",
    "metadata": {},
    "source": [
     "If you wanted to split this array into three equally shaped arrays, you would run:"
@@ -1583,7 +1583,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "41409cd2",
+   "id": "5e4fa735",
    "metadata": {},
    "outputs": [
     {
@@ -1608,7 +1608,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "433207b5",
+   "id": "45a40f55",
    "metadata": {},
    "source": [
     "If you wanted to split your array after the third and fourth column, you’d run:"
@@ -1617,7 +1617,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "5bdc5ed1",
+   "id": "6359b8fd",
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +1642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5513953",
+   "id": "02cef3c9",
    "metadata": {},
    "source": [
     "You can use the `view` method to create a new array object that looks at the same data as the original array (a shallow copy).\n",
@@ -1655,7 +1655,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "1cfb97ff",
+   "id": "764986eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1664,7 +1664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6e080e6",
+   "id": "f6f1fead",
    "metadata": {},
    "source": [
     "Now we create an array `b1` by slicing `a` and modify the first element of `b1`. This will modify the corresponding element in `a` as well!"
@@ -1673,7 +1673,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "cdbf76eb",
+   "id": "3b87eedc",
    "metadata": {},
    "outputs": [
     {
@@ -1695,7 +1695,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "5c97387b",
+   "id": "74ec18eb",
    "metadata": {},
    "outputs": [
     {
@@ -1717,7 +1717,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "948d96fb",
+   "id": "57e35c2c",
    "metadata": {},
    "outputs": [
     {
@@ -1739,7 +1739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78b7f46b",
+   "id": "095d5cf3",
    "metadata": {},
    "source": [
     "Using the `copy` method will make a complete copy of the array and its data (a deep copy). To use this on your array, you could run:"
@@ -1748,7 +1748,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "e46274d5",
+   "id": "70f66529",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1757,7 +1757,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ece94ce9",
+   "id": "d5be9931",
    "metadata": {},
    "source": [
     "## Array operations\n",
@@ -1770,7 +1770,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "9fbc88b5",
+   "id": "b8a5fcbb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1780,7 +1780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85e00f3e",
+   "id": "8bb91d7e",
    "metadata": {},
    "source": [
     "You can add the arrays together with the plus sign."
@@ -1789,7 +1789,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "0c1190dc",
+   "id": "70fce857",
    "metadata": {},
    "outputs": [
     {
@@ -1809,7 +1809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "586bddac",
+   "id": "43370432",
    "metadata": {},
    "source": [
     "You can, of course, do more than just addition!"
@@ -1818,7 +1818,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "a1868565",
+   "id": "7b94a66e",
    "metadata": {},
    "outputs": [
     {
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3589b67b",
+   "id": "87474e10",
    "metadata": {},
    "source": [
     "Basic operations are simple with NumPy. If you want to find the sum of the elements in an array, you’d use `sum()`. This works for 1D arrays, 2D arrays, and arrays in higher dimensions."
@@ -1848,7 +1848,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "c8b4490b",
+   "id": "c9374bd2",
    "metadata": {},
    "outputs": [
     {
@@ -1869,7 +1869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "611d150e",
+   "id": "786acede",
    "metadata": {},
    "source": [
     "To add the rows or the columns in a 2D array, you would specify the axis.\n",
@@ -1880,7 +1880,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "1c1c5010",
+   "id": "151020bf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de615bab",
+   "id": "ad38a939",
    "metadata": {},
    "source": [
     "You can sum over the axis of rows with:"
@@ -1898,7 +1898,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "a009c64d",
+   "id": "e1dc0bff",
    "metadata": {},
    "outputs": [
     {
@@ -1918,7 +1918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ab7bc41",
+   "id": "7ae26c5c",
    "metadata": {},
    "source": [
     "You can sum over the axis of columns with:"
@@ -1927,7 +1927,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "60d0450e",
+   "id": "e2b8b733",
    "metadata": {},
    "outputs": [
     {
@@ -1947,7 +1947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6613fe2f",
+   "id": "c0d551fa",
    "metadata": {},
    "source": [
     "### Universal functions(ufunc)\n",
@@ -2038,7 +2038,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "a1908b5b",
+   "id": "9c729399",
    "metadata": {},
    "outputs": [
     {
@@ -2060,7 +2060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d50168f",
+   "id": "83d66d6b",
    "metadata": {},
    "source": [
     "NumPy’s broadcasting rule relaxes this constraint when the arrays’ shapes meet certain constraints. The simplest broadcasting example occurs when an array and a scalar value are combined in an operation:"
@@ -2069,7 +2069,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "9a59a50e",
+   "id": "f764cedc",
    "metadata": {},
    "outputs": [
     {
@@ -2091,7 +2091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d773d4fc",
+   "id": "8bd31c37",
    "metadata": {},
    "source": [
     "The result is equivalent to the previous example where `b` was an array.  NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible.\n",
@@ -2133,7 +2133,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "f862c32f",
+   "id": "5a18ad9f",
    "metadata": {},
    "outputs": [
     {
@@ -2154,7 +2154,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "34488462",
+   "id": "f0ef5c50",
    "metadata": {},
    "outputs": [
     {
@@ -2175,7 +2175,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "85316056",
+   "id": "58a9c694",
    "metadata": {},
    "outputs": [
     {
@@ -2195,7 +2195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b0304bf",
+   "id": "18407877",
    "metadata": {},
    "source": [
     "Let’s start with this array, called “a”."
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "80a64ff3",
+   "id": "859d39f7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a79d42e",
+   "id": "5250d55f",
    "metadata": {},
    "source": [
     "It’s very common to want to aggregate along a row or column. By default, every NumPy aggregation function will return the aggregate of the entire array. To find the sum or the minimum of the elements in your array, run:"
@@ -2224,7 +2224,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "354dc739",
+   "id": "0248e318",
    "metadata": {},
    "outputs": [
     {
@@ -2244,7 +2244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a8bd43d",
+   "id": "19f999f5",
    "metadata": {},
    "source": [
     "Or:"
@@ -2253,7 +2253,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "fbb84280",
+   "id": "db116c7a",
    "metadata": {},
    "outputs": [
     {
@@ -2273,7 +2273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce4c3339",
+   "id": "ede99754",
    "metadata": {},
    "source": [
     "You can specify on which axis you want the aggregation function to be computed. For example, you can find the minimum value within each column by specifying `axis=0`."
@@ -2282,7 +2282,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "cc79bf47",
+   "id": "32ef200c",
    "metadata": {},
    "outputs": [
     {
@@ -2302,7 +2302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "969a866e",
+   "id": "d836f9f9",
    "metadata": {},
    "source": [
     "The four values listed above correspond to the number of columns in your array. With a four-column array, you will get four values as your result.\n",
@@ -2325,7 +2325,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "b5461d8c",
+   "id": "234541e1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2335,7 +2335,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "1a45a638",
+   "id": "6624f629",
    "metadata": {},
    "outputs": [
     {
@@ -2356,7 +2356,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "a273f75e",
+   "id": "3a1cdd60",
    "metadata": {},
    "outputs": [
     {
@@ -2376,7 +2376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3465fb9",
+   "id": "b31c56eb",
    "metadata": {},
    "source": [
     "It is not necessary to separate each dimension’s index into its own set of square brackets."
@@ -2385,7 +2385,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "4546d8f1",
+   "id": "8990ffad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2395,7 +2395,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "31959fce",
+   "id": "2c39baa0",
    "metadata": {},
    "outputs": [
     {
@@ -2416,7 +2416,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "d58a393b",
+   "id": "7e029fbe",
    "metadata": {},
    "outputs": [
     {
@@ -2436,7 +2436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f210ae60",
+   "id": "e3b55ba2",
    "metadata": {},
    "source": [
     "Note that If one indexes a multidimensional array with fewer indices than dimensions, one gets a subdimensional array. For example:"
@@ -2445,7 +2445,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "1e756920",
+   "id": "16440d1f",
    "metadata": {},
    "outputs": [
     {
@@ -2465,7 +2465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0de1156",
+   "id": "38228e10",
    "metadata": {},
    "source": [
     "That is, each index specified selects the array corresponding to the rest of the dimensions selected. In the above example, choosing 0 means that the remaining dimension of length 5 is being left unspecified, and that what is returned is an array of that dimensionality and size. It must be noted that the returned array is a view, i.e., it is not a copy of the original, but points to the same values in memory as does the original array. In this case, the 1-D array at the first position (0) is returned. So using a single index on the returned array, results in a single element being returned. That is:"
@@ -2474,7 +2474,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "537d0a49",
+   "id": "09aad5cf",
    "metadata": {},
    "outputs": [
     {
@@ -2494,7 +2494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "018174b1",
+   "id": "be3f43f7",
    "metadata": {},
    "source": [
     "So note that `x[0, 2] == x[0][2]` though the second case is more inefficient as a new temporary array is created after the first index that is subsequently indexed by 2.\n",
@@ -2521,7 +2521,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "d7d3dc66",
+   "id": "2ee26d39",
    "metadata": {},
    "outputs": [
     {
@@ -2542,7 +2542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47813580",
+   "id": "e40bbf82",
    "metadata": {},
    "source": [
     "- Negative *i* and *j* are interpreted as *n + i* and *n + j* where *n* is the number of elements in the corresponding dimension. Negative *k* makes stepping go towards smaller indices. From the above example:"
@@ -2551,7 +2551,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "0397a343",
+   "id": "4bab58f0",
    "metadata": {},
    "outputs": [
     {
@@ -2572,7 +2572,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "20b18cd3",
+   "id": "cbfc773a",
    "metadata": {},
    "outputs": [
     {
@@ -2592,7 +2592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f7e53e7",
+   "id": "8970e8be",
    "metadata": {},
    "source": [
     "- Assume *n* is the number of elements in the dimension being sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and *n - 1* for *k < 0*. If *j* is not given it defaults to *n* for *k > 0* and *-n-1* for *k < 0*. If *k* is not given it defaults to 1. Note that `::` is the same as : and means select all indices along this axis. From the above example:"
@@ -2601,7 +2601,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "cd78bfd8",
+   "id": "5f597003",
    "metadata": {},
    "outputs": [
     {
@@ -2621,7 +2621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "318d5178",
+   "id": "93adf52d",
    "metadata": {},
    "source": [
     "- If the number of objects in the selection tuple is less than N, then `:` is assumed for any subsequent dimensions. For example:"
@@ -2630,7 +2630,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "id": "1b1175ed",
+   "id": "0e791aec",
    "metadata": {},
    "outputs": [
     {
@@ -2652,7 +2652,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "2068cc8d",
+   "id": "ca5dc7f6",
    "metadata": {},
    "outputs": [
     {
@@ -2674,7 +2674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a420403e",
+   "id": "17abd363",
    "metadata": {},
    "source": [
     "- An integer, *i*, returns the same values as `i:i+1` **except** the dimensionality of the returned object is reduced by 1. In particular, a selection tuple with the *p*-th element an integer (and all other entries *:*) returns the corresponding sub-array with dimension *N - 1*. If *N = 1* then the returned object is an array scalar.\n",
@@ -2699,7 +2699,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "28299877",
+   "id": "c219d658",
    "metadata": {},
    "outputs": [
     {
@@ -2720,7 +2720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea0277c9",
+   "id": "fa43be71",
    "metadata": {},
    "source": [
     "This is equivalent to:"
@@ -2729,7 +2729,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "3fa8ff8c",
+   "id": "490610b5",
    "metadata": {},
    "outputs": [
     {
@@ -2750,7 +2750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c81b2481",
+   "id": "1461d981",
    "metadata": {},
    "source": [
     "Each `newaxis` object in the selection tuple serves to expand the dimensions of the resulting selection by one unit-length dimension. The added dimension is the position of the `newaxis` object in the selection tuple. `newaxis` is an alias for `None`, and `None` can be used in place of this with the same result. From the above example:"
@@ -2759,7 +2759,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "1e5a9080",
+   "id": "d7e2cc77",
    "metadata": {},
    "outputs": [
     {
@@ -2780,7 +2780,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "49aa984d",
+   "id": "18586923",
    "metadata": {},
    "outputs": [
     {
@@ -2800,7 +2800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25687342",
+   "id": "1552e8f6",
    "metadata": {},
    "source": [
     "This can be handy to combine two arrays in a way that otherwise would require explicit reshaping operations. For example:"
@@ -2809,7 +2809,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "3ff635f3",
+   "id": "400818ee",
    "metadata": {},
    "outputs": [
     {
@@ -2834,7 +2834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f265c602",
+   "id": "4dc1eb6f",
    "metadata": {},
    "source": [
     "### Advanced indexing\n",
@@ -2857,7 +2857,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "16177ea2",
+   "id": "50a2d329",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2867,7 +2867,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "3d9203d6",
+   "id": "0f2f8598",
    "metadata": {},
    "outputs": [
     {
@@ -2888,7 +2888,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "3b354876",
+   "id": "e74649c9",
    "metadata": {},
    "outputs": [
     {
@@ -2909,7 +2909,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "id": "689b38fe",
+   "id": "6142e214",
    "metadata": {},
    "outputs": [
     {
@@ -2929,7 +2929,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57268869",
+   "id": "f0c27fce",
    "metadata": {},
    "source": [
     "If the index values are out of bounds then an `IndexError` is thrown:"
@@ -2938,7 +2938,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "0c8986c5",
+   "id": "411afe63",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2948,7 +2948,7 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "id": "24091760",
+   "id": "b22be777",
    "metadata": {},
    "outputs": [
     {
@@ -2969,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c35eeec7",
+   "id": "cf19e4b5",
    "metadata": {},
    "source": [
     "```py\n",
@@ -2999,7 +2999,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "d873ac94",
+   "id": "ad05c49f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3009,7 +3009,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "id": "d6deb56b",
+   "id": "3d495a7e",
    "metadata": {},
    "outputs": [
     {
@@ -3034,7 +3034,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "9ea05216",
+   "id": "b547695a",
    "metadata": {},
    "outputs": [
     {
@@ -3054,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01122682",
+   "id": "af047bb1",
    "metadata": {},
    "source": [
     "In this case, if the index arrays have a matching shape, and there is an index array for each dimension of the array being indexed, the resultant array has the same shape as the index arrays, and the values correspond to the index set for each position in the index arrays. In this example, the first index value is 0 for both index arrays, and thus the first value of the resultant array is `y[0, 0]`. The next value is `y[2, 1]`, and the last is `y[4, 2]`.\n",
@@ -3077,7 +3077,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "id": "136e2612",
+   "id": "837fe7df",
    "metadata": {},
    "outputs": [
     {
@@ -3097,7 +3097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6571dc6",
+   "id": "2079c2c7",
    "metadata": {},
    "source": [
     "Jumping to the next level of complexity, it is possible to only partially index an array with index arrays. It takes a bit of thought to understand what happens in such cases. For example if we just use one index array with y:"
@@ -3106,7 +3106,7 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "id": "78759660",
+   "id": "3290acc0",
    "metadata": {},
    "outputs": [
     {
@@ -3128,7 +3128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee8e481b",
+   "id": "2011e41a",
    "metadata": {},
    "source": [
     "It results in the construction of a new array where each value of the index array selects one row from the array being indexed and the resultant array has the resulting shape (number of index elements, size of row).\n",
@@ -3143,7 +3143,7 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "id": "eef71eb5",
+   "id": "05d6334e",
    "metadata": {},
    "outputs": [
     {
@@ -3164,7 +3164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e7e895d",
+   "id": "95e4429c",
    "metadata": {},
    "source": [
     "To achieve a behaviour similar to the basic slicing above, broadcasting can be used. The function `ix_` can help with this broadcasting. This is best understood with an example.\n",
@@ -3177,7 +3177,7 @@
   {
    "cell_type": "code",
    "execution_count": 110,
-   "id": "34487b46",
+   "id": "ae007ca3",
    "metadata": {},
    "outputs": [
     {
@@ -3206,7 +3206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82a7e62f",
+   "id": "762c4c15",
    "metadata": {},
    "source": [
     "However, since the indexing arrays above just repeat themselves, broadcasting can be used (compare operations such as `rows[:, np.newaxis] + columns`) to simplify this:"
@@ -3215,7 +3215,7 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "id": "49b8dd24",
+   "id": "ca140c7c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3226,7 +3226,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "9ced9155",
+   "id": "ba951733",
    "metadata": {},
    "outputs": [
     {
@@ -3248,7 +3248,7 @@
   {
    "cell_type": "code",
    "execution_count": 113,
-   "id": "0ab14479",
+   "id": "871d300e",
    "metadata": {},
    "outputs": [
     {
@@ -3269,7 +3269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9738f32a",
+   "id": "2bb0f591",
    "metadata": {},
    "source": [
     "This broadcasting can also be achieved using the function `ix_`:"
@@ -3278,7 +3278,7 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "id": "96080625",
+   "id": "19a09903",
    "metadata": {},
    "outputs": [
     {
@@ -3299,7 +3299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50bde8a6",
+   "id": "b9d22cf6",
    "metadata": {},
    "source": [
     "Note that without the `np.ix_` call, only the diagonal elements would be selected:"
@@ -3308,7 +3308,7 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "id": "cf366f1c",
+   "id": "f06a529f",
    "metadata": {},
    "outputs": [
     {
@@ -3328,7 +3328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7701272",
+   "id": "77129c0f",
    "metadata": {},
    "source": [
     "This difference is the most important thing to remember about indexing with multiple advanced indices.\n",
@@ -3349,7 +3349,7 @@
   {
    "cell_type": "code",
    "execution_count": 116,
-   "id": "adf5c632",
+   "id": "e020787e",
    "metadata": {},
    "outputs": [
     {
@@ -3370,7 +3370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ca79e74",
+   "id": "db11f96a",
    "metadata": {},
    "source": [
     "Or wish to add a constant to all negative elements:"
@@ -3379,7 +3379,7 @@
   {
    "cell_type": "code",
    "execution_count": 117,
-   "id": "91907464",
+   "id": "55daf8de",
    "metadata": {},
    "outputs": [
     {
@@ -3401,7 +3401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efb0ae3f",
+   "id": "3c4dbf46",
    "metadata": {},
    "source": [
     "In general if an index includes a Boolean array, the result will be identical to inserting `obj.nonzero()` into the same position and using the integer array indexing mechanism described above. `x[ind_1, boolean_array, ind_2]` is equivalent to `x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]`.\n",
@@ -3414,7 +3414,7 @@
   {
    "cell_type": "code",
    "execution_count": 118,
-   "id": "21ea5048",
+   "id": "fd899798",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3425,7 +3425,7 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "id": "7f45cb94",
+   "id": "e6913800",
    "metadata": {},
    "outputs": [
     {
@@ -3446,7 +3446,7 @@
   {
    "cell_type": "code",
    "execution_count": 120,
-   "id": "ac28d3e9",
+   "id": "69a1a637",
    "metadata": {},
    "outputs": [
     {
@@ -3467,7 +3467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a2cd287",
+   "id": "7d2fecf3",
    "metadata": {},
    "source": [
     "Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array.\n",
@@ -3480,7 +3480,7 @@
   {
    "cell_type": "code",
    "execution_count": 121,
-   "id": "6fde295c",
+   "id": "de50718b",
    "metadata": {},
    "outputs": [
     {
@@ -3503,7 +3503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94756280",
+   "id": "95ffcf5f",
    "metadata": {},
    "source": [
     "Combining multiple Boolean indexing arrays or a Boolean with an integer indexing array can best be understood with the `obj.nonzero()` analogy. The function `ix_` also supports boolean arrays and will work without any surprises.\n",
@@ -3516,7 +3516,7 @@
   {
    "cell_type": "code",
    "execution_count": 122,
-   "id": "22d0324a",
+   "id": "d3fa87aa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3530,7 +3530,7 @@
   {
    "cell_type": "code",
    "execution_count": 123,
-   "id": "49f065c6",
+   "id": "80d9060c",
    "metadata": {},
    "outputs": [
     {
@@ -3551,7 +3551,7 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "id": "c6782968",
+   "id": "476f719f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3561,7 +3561,7 @@
   {
    "cell_type": "code",
    "execution_count": 125,
-   "id": "4811bad5",
+   "id": "742dbb01",
    "metadata": {},
    "outputs": [
     {
@@ -3582,7 +3582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9c3e7b7",
+   "id": "c945a8dc",
    "metadata": {},
    "source": [
     "Without the n`p.ix_` call, only the diagonal elements would be selected.\n",
@@ -3593,7 +3593,7 @@
   {
    "cell_type": "code",
    "execution_count": 126,
-   "id": "508b0cf4",
+   "id": "cb046219",
    "metadata": {},
    "outputs": [
     {
@@ -3615,7 +3615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "546b356d",
+   "id": "09ab69c9",
    "metadata": {},
    "source": [
     "##### Example 3\n",
@@ -3626,7 +3626,7 @@
   {
    "cell_type": "code",
    "execution_count": 127,
-   "id": "a9af3686",
+   "id": "d1386be7",
    "metadata": {},
    "outputs": [
     {
@@ -3654,7 +3654,7 @@
   {
    "cell_type": "code",
    "execution_count": 128,
-   "id": "1a1ba536",
+   "id": "0184c268",
    "metadata": {},
    "outputs": [
     {
@@ -3678,7 +3678,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a3a1599",
+   "id": "f0b6b5ed",
    "metadata": {},
    "source": [
     "#### Combining advanced and basic indexing\n",
@@ -3691,7 +3691,7 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "id": "5e402d34",
+   "id": "ed7512fc",
    "metadata": {},
    "outputs": [
     {
@@ -3714,7 +3714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75193d01",
+   "id": "60815064",
    "metadata": {},
    "source": [
     "In effect, the slice and index array operation are independent. The slice operation extracts columns with index 1 and 2, (i.e. the 2nd and 3rd columns), followed by the index array operation which extracts rows with index 0, 2 and 4 (i.e the first, third and fifth rows). This is equivalent to:"
@@ -3723,7 +3723,7 @@
   {
    "cell_type": "code",
    "execution_count": 130,
-   "id": "409a29dd",
+   "id": "61381ac1",
    "metadata": {},
    "outputs": [
     {
@@ -3745,7 +3745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecefd3bd",
+   "id": "bfd73532",
    "metadata": {},
    "source": [
     "A single advanced index can, for example, replace a slice and the result array will be the same. However, it is a copy and may have a different memory layout. A slice is preferable when it is possible. For example:"
@@ -3754,7 +3754,7 @@
   {
    "cell_type": "code",
    "execution_count": 131,
-   "id": "0463e8e9",
+   "id": "54a09a5d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3767,7 +3767,7 @@
   {
    "cell_type": "code",
    "execution_count": 132,
-   "id": "72dbb400",
+   "id": "b2edcad6",
    "metadata": {},
    "outputs": [
     {
@@ -3788,7 +3788,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "id": "5bf8f5b1",
+   "id": "78adeec7",
    "metadata": {},
    "outputs": [
     {
@@ -3808,7 +3808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98dfe63e",
+   "id": "3fe94999",
    "metadata": {},
    "source": [
     "The easiest way to understand a combination of multiple advanced indices may be to think in terms of the resulting shape. There are two parts to the indexing operation, the subspace defined by the basic indexing (excluding integers) and the subspace from the advanced indexing part. Two cases of index combination need to be distinguished:\n",
@@ -3834,7 +3834,7 @@
   {
    "cell_type": "code",
    "execution_count": 134,
-   "id": "2486cd86",
+   "id": "4a83106d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3845,7 +3845,7 @@
   {
    "cell_type": "code",
    "execution_count": 135,
-   "id": "871567dc",
+   "id": "2d47ce0e",
    "metadata": {},
    "outputs": [
     {
@@ -3870,7 +3870,7 @@
   {
    "cell_type": "code",
    "execution_count": 136,
-   "id": "d31c3724",
+   "id": "09d31689",
    "metadata": {},
    "outputs": [
     {
@@ -3891,7 +3891,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40ae753d",
+   "id": "313e4c1f",
    "metadata": {},
    "source": [
     "### Field access\n",
@@ -3908,7 +3908,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "84351c2d",
+   "id": "07c4035f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3918,7 +3918,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "8fd94ff3",
+   "id": "4ebde01d",
    "metadata": {},
    "outputs": [
     {
@@ -3939,7 +3939,7 @@
   {
    "cell_type": "code",
    "execution_count": 139,
-   "id": "3e49a3a2",
+   "id": "882f818d",
    "metadata": {},
    "outputs": [
     {
@@ -3960,7 +3960,7 @@
   {
    "cell_type": "code",
    "execution_count": 140,
-   "id": "5cf2b194",
+   "id": "d0fa8dbb",
    "metadata": {},
    "outputs": [
     {
@@ -3981,7 +3981,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
-   "id": "db1341e6",
+   "id": "d69f7364",
    "metadata": {},
    "outputs": [
     {
@@ -4001,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "488e2c56",
+   "id": "79237e99",
    "metadata": {},
    "source": [
     "### Flat Iterator indexing\n",
@@ -4016,7 +4016,7 @@
   {
    "cell_type": "code",
    "execution_count": 142,
-   "id": "a626bc2d",
+   "id": "b5a18c60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4026,7 +4026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16b058c5",
+   "id": "2d6df149",
    "metadata": {},
    "source": [
     "Or an array of the right size:"
@@ -4035,7 +4035,7 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "id": "0b0a3d62",
+   "id": "15651bd4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4044,7 +4044,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5766fac6",
+   "id": "d6e3d3e4",
    "metadata": {},
    "source": [
     "Note that assignments may result in changes if assigning higher types to lower types (like floats to ints) or even exceptions (assigning complex to floats or ints):"
@@ -4053,7 +4053,7 @@
   {
    "cell_type": "code",
    "execution_count": 144,
-   "id": "a35a4e00",
+   "id": "b87dbf7b",
    "metadata": {},
    "outputs": [
     {
@@ -4074,7 +4074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ddcd9e8",
+   "id": "583f84fd",
    "metadata": {},
    "source": [
     "```py\n",
@@ -4093,7 +4093,7 @@
   {
    "cell_type": "code",
    "execution_count": 145,
-   "id": "fb21c79b",
+   "id": "a879612d",
    "metadata": {},
    "outputs": [
     {
@@ -4115,7 +4115,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "id": "96d8aad3",
+   "id": "02f4191a",
    "metadata": {},
    "outputs": [
     {
@@ -4136,7 +4136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "660828eb",
+   "id": "7d8f3dfd",
    "metadata": {},
    "source": [
     "Where people expect that the 1st location will be incremented by 3. In fact, it will only be incremented by 1. The reason is that a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus the value of the array at `x[1] + 1` is assigned to `x[1]` three times, rather than being incremented 3 times.\n",
@@ -4149,7 +4149,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "e65e48ec",
+   "id": "31cf7eea",
    "metadata": {},
    "outputs": [
     {
@@ -4171,7 +4171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ac9262a",
+   "id": "1dd672ed",
    "metadata": {},
    "source": [
     "So one can use code to construct tuples of any number of indices and then use these within an index.\n",
@@ -4182,7 +4182,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "76422eae",
+   "id": "4cd4e7db",
    "metadata": {},
    "outputs": [
     {
@@ -4203,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2b8cd56",
+   "id": "552091f3",
    "metadata": {},
    "source": [
     "Likewise, ellipsis can be specified by code by using the Ellipsis object:"
@@ -4212,7 +4212,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "56552834",
+   "id": "b2d16c60",
    "metadata": {},
    "outputs": [
     {
@@ -4235,7 +4235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42fa7ab5",
+   "id": "4a9728b9",
    "metadata": {},
    "source": [
     "For this reason, it is possible to use the output from the `np.nonzero()` function directly as an index since it always returns a tuple of index arrays.\n",
@@ -4246,7 +4246,7 @@
   {
    "cell_type": "code",
    "execution_count": 150,
-   "id": "6d723ae5",
+   "id": "27c9d77e",
    "metadata": {},
    "outputs": [
     {
@@ -4316,7 +4316,7 @@
   {
    "cell_type": "code",
    "execution_count": 151,
-   "id": "6016414f",
+   "id": "69bb5b91",
    "metadata": {},
    "outputs": [
     {
@@ -4336,7 +4336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "101c28fd",
+   "id": "1d3828d4",
    "metadata": {},
    "source": [
     "## Structured arrays\n",
@@ -4349,7 +4349,7 @@
   {
    "cell_type": "code",
    "execution_count": 152,
-   "id": "21a37178",
+   "id": "39bb399d",
    "metadata": {},
    "outputs": [
     {
@@ -4372,7 +4372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7512ba5",
+   "id": "d9f10ae1",
    "metadata": {},
    "source": [
     "Here `x` is a one-dimensional array of length two whose datatype is a structure with three fields: 1. A string of length 10 or less named `'name'`, 2. a 32-bit integer named `'age'`, and 3. a 32-bit float named `'weight'`.\n",
@@ -4383,7 +4383,7 @@
   {
    "cell_type": "code",
    "execution_count": 153,
-   "id": "16961ff7",
+   "id": "84981aa3",
    "metadata": {},
    "outputs": [
     {
@@ -4403,7 +4403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c82bde82",
+   "id": "266359f7",
    "metadata": {},
    "source": [
     "You can access and modify individual fields of a structured array by indexing with the field name:"
@@ -4412,7 +4412,7 @@
   {
    "cell_type": "code",
    "execution_count": 154,
-   "id": "7cba13b9",
+   "id": "8d8139cd",
    "metadata": {},
    "outputs": [
     {
@@ -4433,7 +4433,7 @@
   {
    "cell_type": "code",
    "execution_count": 155,
-   "id": "162625f8",
+   "id": "c0df64b4",
    "metadata": {},
    "outputs": [
     {
@@ -4454,7 +4454,7 @@
   {
    "cell_type": "code",
    "execution_count": 156,
-   "id": "c87da6f4",
+   "id": "f47b25f3",
    "metadata": {},
    "outputs": [
     {
@@ -4475,7 +4475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40c3c768",
+   "id": "5a51df58",
    "metadata": {},
    "source": [
     "Structured datatypes are designed to be able to mimic 'structs' in the C language, and share a similar memory layout. They are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.\n",
@@ -4498,7 +4498,7 @@
   {
    "cell_type": "code",
    "execution_count": 157,
-   "id": "8ebed3e7",
+   "id": "6ada089f",
    "metadata": {},
    "outputs": [
     {
@@ -4518,7 +4518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8364591f",
+   "id": "97a94406",
    "metadata": {},
    "source": [
     "If `fieldname` is the empty string `''`, the field will be given a default name of the form `f#`, where `#` is the integer index of the field, counting from 0 from the left:"
@@ -4527,7 +4527,7 @@
   {
    "cell_type": "code",
    "execution_count": 158,
-   "id": "5f3a2e03",
+   "id": "ff7ae9e1",
    "metadata": {},
    "outputs": [
     {
@@ -4547,7 +4547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a410ef9",
+   "id": "21c3a1b7",
    "metadata": {},
    "source": [
     "The byte offsets of the fields within the structure and the total structure itemsize are determined automatically.\n",
@@ -4560,7 +4560,7 @@
   {
    "cell_type": "code",
    "execution_count": 159,
-   "id": "563f36b7",
+   "id": "0dc98566",
    "metadata": {},
    "outputs": [
     {
@@ -4581,7 +4581,7 @@
   {
    "cell_type": "code",
    "execution_count": 160,
-   "id": "c8af119c",
+   "id": "07e1a703",
    "metadata": {},
    "outputs": [
     {
@@ -4601,7 +4601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b617c114",
+   "id": "138bb0b4",
    "metadata": {},
    "source": [
     "- A dictionary of field parameter arrays\n",
@@ -4614,7 +4614,7 @@
   {
    "cell_type": "code",
    "execution_count": 161,
-   "id": "9d686cbb",
+   "id": "d8b3106c",
    "metadata": {},
    "outputs": [
     {
@@ -4635,7 +4635,7 @@
   {
    "cell_type": "code",
    "execution_count": 162,
-   "id": "41b11aa3",
+   "id": "c69c18d9",
    "metadata": {},
    "outputs": [
     {
@@ -4658,7 +4658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d42f6816",
+   "id": "d95d85d9",
    "metadata": {},
    "source": [
     "Offsets may be chosen such that the fields overlap, though this will mean that assigning to one field may clobber any overlapping field’s data. As an exception, fields of `numpy.object_` type cannot overlap with other fields, because of the risk of clobbering the internal object pointer and then dereferencing it.\n",
@@ -4673,7 +4673,7 @@
   {
    "cell_type": "code",
    "execution_count": 163,
-   "id": "cc4c0368",
+   "id": "44993229",
    "metadata": {},
    "outputs": [
     {
@@ -4693,7 +4693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c81d4ae",
+   "id": "c059b8d3",
    "metadata": {},
    "source": [
     "#### Manipulating and Displaying Structured Datatypes\n",
@@ -4704,7 +4704,7 @@
   {
    "cell_type": "code",
    "execution_count": 164,
-   "id": "f570e2d2",
+   "id": "202d0103",
    "metadata": {},
    "outputs": [
     {
@@ -4725,7 +4725,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caf4c574",
+   "id": "79b4aae8",
    "metadata": {},
    "source": [
     "The field names may be modified by assigning to the `names` attribute using a sequence of strings of the same length.\n",
@@ -4736,7 +4736,7 @@
   {
    "cell_type": "code",
    "execution_count": 165,
-   "id": "573fe622",
+   "id": "ff34ff6a",
    "metadata": {},
    "outputs": [
     {
@@ -4756,7 +4756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "270f24a3",
+   "id": "6825a51f",
    "metadata": {},
    "source": [
     "Both the `names` and `fields` attributes will equal `None` for unstructured arrays. The recommended way to test if a dtype is structured is with `if dt.names is not None` rather than `if dt.names`, to account for dtypes with 0 fields.\n",
@@ -4773,7 +4773,7 @@
   {
    "cell_type": "code",
    "execution_count": 166,
-   "id": "e88dc838",
+   "id": "68f79b52",
    "metadata": {},
    "outputs": [
     {
@@ -4793,7 +4793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ee3900e",
+   "id": "575355a3",
    "metadata": {},
    "source": [
     "When using the first form of dictionary-based specification, the titles may be supplied as an extra `'titles'` key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple `(datatype, offset, title)` instead of the usual 2-element tuple:"
@@ -4802,7 +4802,7 @@
   {
    "cell_type": "code",
    "execution_count": 167,
-   "id": "76314273",
+   "id": "3ce81cb2",
    "metadata": {},
    "outputs": [
     {
@@ -4822,7 +4822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff1df0a1",
+   "id": "0a56d640",
    "metadata": {},
    "source": [
     "The `dtype.fields` dictionary will contain titles as keys, if any titles are used. This means effectively that a field with a title will be represented twice in the fields dictionary. The tuple values for these fields will also have a third element, the field title. Because of this, and because the `names` attribute preserves the field order while the `fields` attribute may not, it is recommended to iterate through the fields of a dtype using the `names` attribute of the dtype, which will not list titles, as in:"
@@ -4831,7 +4831,7 @@
   {
    "cell_type": "code",
    "execution_count": 168,
-   "id": "641b9322",
+   "id": "284570a3",
    "metadata": {},
    "outputs": [
     {
@@ -4850,7 +4850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a045f320",
+   "id": "57d73335",
    "metadata": {},
    "source": [
     "### Indexing and Assignment to Structured arrays\n",
@@ -4867,7 +4867,7 @@
   {
    "cell_type": "code",
    "execution_count": 169,
-   "id": "b45893cc",
+   "id": "bc115c7a",
    "metadata": {},
    "outputs": [
     {
@@ -4890,7 +4890,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96dd359a",
+   "id": "7b5e910d",
    "metadata": {},
    "source": [
     "##### Assignment from Scalars\n",
@@ -4901,7 +4901,7 @@
   {
    "cell_type": "code",
    "execution_count": 170,
-   "id": "3b39fe14",
+   "id": "3006b834",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4911,7 +4911,7 @@
   {
    "cell_type": "code",
    "execution_count": 171,
-   "id": "47c57685",
+   "id": "0757d711",
    "metadata": {},
    "outputs": [
     {
@@ -4934,7 +4934,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "id": "ac3adbcb",
+   "id": "22fc7055",
    "metadata": {},
    "outputs": [
     {
@@ -4956,7 +4956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d700015",
+   "id": "11454638",
    "metadata": {},
    "source": [
     "Structured arrays can also be assigned to unstructured arrays, but only if the structured datatype has just a single field:"
@@ -4965,7 +4965,7 @@
   {
    "cell_type": "code",
    "execution_count": 173,
-   "id": "9b5871fd",
+   "id": "e478dcd4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4976,7 +4976,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "e297ef69",
+   "id": "599da67f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4985,7 +4985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41de4970",
+   "id": "bb1804bb",
    "metadata": {},
    "source": [
     "```py\n",
@@ -5006,7 +5006,7 @@
   {
    "cell_type": "code",
    "execution_count": 175,
-   "id": "396d597a",
+   "id": "75acad31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5017,7 +5017,7 @@
   {
    "cell_type": "code",
    "execution_count": 176,
-   "id": "3630f077",
+   "id": "f205325c",
    "metadata": {},
    "outputs": [
     {
@@ -5039,7 +5039,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b7d6dd2",
+   "id": "8dfe8198",
    "metadata": {},
    "source": [
     "##### Assignment involving subarrays\n",
@@ -5056,7 +5056,7 @@
   {
    "cell_type": "code",
    "execution_count": 177,
-   "id": "1a7747a7",
+   "id": "e7ff88ba",
    "metadata": {},
    "outputs": [
     {
@@ -5078,7 +5078,7 @@
   {
    "cell_type": "code",
    "execution_count": 178,
-   "id": "9170261b",
+   "id": "1a64a9c2",
    "metadata": {},
    "outputs": [
     {
@@ -5099,7 +5099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d08976ef",
+   "id": "d785433e",
    "metadata": {},
    "source": [
     "The resulting array is a view into the original array. It shares the same memory locations and writing to the view will modify the original array."
@@ -5108,7 +5108,7 @@
   {
    "cell_type": "code",
    "execution_count": 179,
-   "id": "830977e2",
+   "id": "dcbd6351",
    "metadata": {},
    "outputs": [
     {
@@ -5130,7 +5130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "306737c0",
+   "id": "719b75ea",
    "metadata": {},
    "source": [
     "This view has the same dtype and itemsize as the indexed field, so it is typically a non-structured array, except in the case of nested structures."
@@ -5139,7 +5139,7 @@
   {
    "cell_type": "code",
    "execution_count": 180,
-   "id": "43d14c2f",
+   "id": "8a83c1cc",
    "metadata": {},
    "outputs": [
     {
@@ -5159,7 +5159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a38e64ad",
+   "id": "ea3f8ed4",
    "metadata": {},
    "source": [
     "If the accessed field is a subarray, the dimensions of the subarray are appended to the shape of the result:"
@@ -5168,7 +5168,7 @@
   {
    "cell_type": "code",
    "execution_count": 181,
-   "id": "5833c3e8",
+   "id": "2b4119c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5178,7 +5178,7 @@
   {
    "cell_type": "code",
    "execution_count": 182,
-   "id": "acd83150",
+   "id": "5074b1c4",
    "metadata": {},
    "outputs": [
     {
@@ -5199,7 +5199,7 @@
   {
    "cell_type": "code",
    "execution_count": 183,
-   "id": "369287fa",
+   "id": "eefe2bf0",
    "metadata": {},
    "outputs": [
     {
@@ -5219,7 +5219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea7b76e1",
+   "id": "cf62c47e",
    "metadata": {},
    "source": [
     "##### Accessing multiple fields\n",
@@ -5232,7 +5232,7 @@
   {
    "cell_type": "code",
    "execution_count": 184,
-   "id": "942a4381",
+   "id": "b70bd472",
    "metadata": {},
    "outputs": [
     {
@@ -5254,7 +5254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46ea9ec8",
+   "id": "2b1bc48c",
    "metadata": {},
    "source": [
     "Assignment to the view modifies the original array. The view’s fields will be in the order they were indexed. Note that unlike for single-field indexing, the dtype of the view has the same itemsize as the original array, and has fields at the same offsets as in the original array, and unindexed fields are merely missing.\n",
@@ -5265,7 +5265,7 @@
   {
    "cell_type": "code",
    "execution_count": 185,
-   "id": "cc264ddc",
+   "id": "961fd6ea",
    "metadata": {},
    "outputs": [
     {
@@ -5287,7 +5287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44f42f71",
+   "id": "f0c3ca61",
    "metadata": {},
    "source": [
     "This obeys the structured array assignment rules described above. For example, this means that one can swap the values of two fields using appropriate multi-field indexes:"
@@ -5296,7 +5296,7 @@
   {
    "cell_type": "code",
    "execution_count": 186,
-   "id": "2af22795",
+   "id": "ec093d50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5305,7 +5305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "712054ea",
+   "id": "fb7f930a",
    "metadata": {},
    "source": [
     "##### Indexing with an integer to get a structured scalar\n",
@@ -5316,7 +5316,7 @@
   {
    "cell_type": "code",
    "execution_count": 187,
-   "id": "d15c0b6c",
+   "id": "6a38a507",
    "metadata": {},
    "outputs": [
     {
@@ -5339,7 +5339,7 @@
   {
    "cell_type": "code",
    "execution_count": 188,
-   "id": "84ccc8ec",
+   "id": "a63a293b",
    "metadata": {},
    "outputs": [
     {
@@ -5359,7 +5359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83d752ff",
+   "id": "1940637c",
    "metadata": {},
    "source": [
     "Unlike other numpy scalars, structured scalars are mutable and act like views into the original array, such that modifying the scalar will modify the original array. Structured scalars also support access and assignment by field name:"
@@ -5368,7 +5368,7 @@
   {
    "cell_type": "code",
    "execution_count": 189,
-   "id": "ab8fe10e",
+   "id": "42ad6ec6",
    "metadata": {},
    "outputs": [
     {
@@ -5391,7 +5391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc97ac07",
+   "id": "3bb463b3",
    "metadata": {},
    "source": [
     "Similarly to tuples, structured scalars can also be indexed with an integer:"
@@ -5400,7 +5400,7 @@
   {
    "cell_type": "code",
    "execution_count": 190,
-   "id": "9fd5a8b1",
+   "id": "b278c6c4",
    "metadata": {},
    "outputs": [
     {
@@ -5422,7 +5422,7 @@
   {
    "cell_type": "code",
    "execution_count": 191,
-   "id": "1aa405d5",
+   "id": "7750d24e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5431,7 +5431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e86b0e6",
+   "id": "3fd2f50b",
    "metadata": {},
    "source": [
     "Thus, tuples might be thought of as the native Python equivalent to numpy’s structured types, much like native python integers are the equivalent to numpy’s integer types. Structured scalars may be converted to a tuple by calling `numpy.ndarray.item`:"
@@ -5440,7 +5440,7 @@
   {
    "cell_type": "code",
    "execution_count": 192,
-   "id": "b47456cb",
+   "id": "8e3df6fc",
    "metadata": {},
    "outputs": [
     {
@@ -5460,7 +5460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "387302ef",
+   "id": "bb75bc69",
    "metadata": {},
    "source": [
     "#### Viewing structured arrays containing objects\n",
@@ -5475,7 +5475,7 @@
   {
    "cell_type": "code",
    "execution_count": 193,
-   "id": "44fa0e7d",
+   "id": "4c181f9d",
    "metadata": {},
    "outputs": [
     {
@@ -5497,7 +5497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0db0b192",
+   "id": "7fb3d82e",
    "metadata": {},
    "source": [
     "NumPy will promote individual field datatypes to perform the comparison. So the following is also valid (note the `'f4'` dtype for the `'a'` field):"
@@ -5506,7 +5506,7 @@
   {
    "cell_type": "code",
    "execution_count": 194,
-   "id": "b15e16e7",
+   "id": "115f72aa",
    "metadata": {},
    "outputs": [
     {
@@ -5527,7 +5527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1905ca1d",
+   "id": "338f273d",
    "metadata": {},
    "source": [
     "To compare two structured arrays, it must be possible to promote them to a common dtype as returned by `numpy.result_type` and `np.promote_types`. This enforces that the number of fields, the field names, and the field titles must match precisely. When promotion is not possible, for example due to mismatching field names, NumPy will raise an error. Promotion between two structured dtypes results in a canonical dtype that ensures native byte-order for all fields:"
@@ -5536,7 +5536,7 @@
   {
    "cell_type": "code",
    "execution_count": 195,
-   "id": "82a6108d",
+   "id": "4cbe1b37",
    "metadata": {},
    "outputs": [
     {
@@ -5557,7 +5557,7 @@
   {
    "cell_type": "code",
    "execution_count": 196,
-   "id": "0f0b3d33",
+   "id": "b3b7f0ab",
    "metadata": {},
    "outputs": [
     {
@@ -5577,7 +5577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e51af95c",
+   "id": "566b9474",
    "metadata": {},
    "source": [
     "The resulting dtype from promotion is also guaranteed to be packed, meaning that all fields are ordered contiguously and any unnecessary padding is removed:"
@@ -5586,7 +5586,7 @@
   {
    "cell_type": "code",
    "execution_count": 197,
-   "id": "bdd4ef5d",
+   "id": "76ed757f",
    "metadata": {},
    "outputs": [
     {
@@ -5608,7 +5608,7 @@
   {
    "cell_type": "code",
    "execution_count": 198,
-   "id": "a2a9a6e6",
+   "id": "e93538a9",
    "metadata": {},
    "outputs": [
     {
@@ -5628,7 +5628,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da50a6db",
+   "id": "5890cb8b",
    "metadata": {},
    "source": [
     "Note that the result prints without `offsets` or `itemsize` indicating no additional padding. If a structured dtype is created with `align=True` ensuring that `dtype.isalignedstruct` is true, this property is preserved:"
@@ -5637,7 +5637,7 @@
   {
    "cell_type": "code",
    "execution_count": 199,
-   "id": "36f4c0f5",
+   "id": "c86b3fff",
    "metadata": {},
    "outputs": [
     {
@@ -5659,7 +5659,7 @@
   {
    "cell_type": "code",
    "execution_count": 200,
-   "id": "d69a4ea7",
+   "id": "debd83a9",
    "metadata": {},
    "outputs": [
     {
@@ -5680,7 +5680,7 @@
   {
    "cell_type": "code",
    "execution_count": 201,
-   "id": "f09da238",
+   "id": "61c1c5e1",
    "metadata": {},
    "outputs": [
     {
@@ -5700,7 +5700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01831f4c",
+   "id": "4983815d",
    "metadata": {},
    "source": [
     "When promoting multiple dtypes, the result is aligned if any of the inputs is:"
@@ -5709,7 +5709,7 @@
   {
    "cell_type": "code",
    "execution_count": 202,
-   "id": "81c875d9",
+   "id": "2854512d",
    "metadata": {},
    "outputs": [
     {
@@ -5729,7 +5729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77e8b74f",
+   "id": "e7bb0e17",
    "metadata": {},
    "source": [
     "The `<` and `>` operators always return `False` when comparing void structured arrays, and arithmetic and bitwise operations are not supported.\n",
diff --git a/_sources/ml-advanced/kernel-method.ipynb b/_sources/ml-advanced/kernel-method.ipynb
index bb9c353158..12a9659da4 100644
--- a/_sources/ml-advanced/kernel-method.ipynb
+++ b/_sources/ml-advanced/kernel-method.ipynb
@@ -1,71 +1,209 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
-   "id": "eea5b482",
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install --quiet numpy pandas matplotlib scikit-learn jupyterlab-myst ipython seaborn ipywidgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# Kernel method\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.datasets import make_circles\n",
+    "from mpl_toolkits import mplot3d\n",
+    "from ipywidgets import interact, fixed\n",
+    "from sklearn.datasets import make_blobs\n",
     "\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kernel method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "SVMs are a powerful and flexible class of algorithms used for classification and regression. In this section, we will explore the intuition behind SVMs and their use in classification problems.\n",
     "To start with, let's understand the basic concept of SVMs. \n",
     "Support Vector Machines (SVMs) are supervised learning algorithms that can be used for classification and regression tasks. SVMs try to find the best decision boundary that separates data points of different classes. The decision boundary is chosen such that it maximizes the margin between the data points of different classes.\n",
     "The margin is defined as the minimum distance between the decision boundary and the closest data points of each class. This makes SVMs very robust to outliers as the decision boundary is chosen based on the data that is closest to it.\n",
-    "In classification problems, the goal is to find a decision boundary that separates the data into two or more classes. SVMs can be used for binary classification, where the classes are only two, or for multiclass classification, where there are more than two classes.\n",
-    "\n",
-    "## Motivating Support Vector Machines\n",
+    "In classification problems, the goal is to find a decision boundary that separates the data into two or more classes. SVMs can be used for binary classification, where the classes are only two, or for multiclass classification, where there are more than two classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo1/demo.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "A demo of SVM. <a href=\"https://cs.stanford.edu/~karpathy/svmjs/demo/\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
     "\n",
-    "In this section we will consider differentiated classification: rather than modelling each category, we simply find a line or curve or flowform that separates these categories from each other."
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo1/demo.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "A demo of SVM. <a href=\"https://cs.stanford.edu/~karpathy/svmjs/demo/\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "47f7f487",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo2/index.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "A demo of SVM. <a href=\"http://macheads101.com/demos/svm-playground/\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy import stats\n",
+    "from IPython.display import HTML\n",
     "\n",
-    "# use seaborn plotting defaults\n",
-    "import seaborn as sns\n",
-    "sns.set()"
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo2/index.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "A demo of SVM. <a href=\"http://macheads101.com/demos/svm-playground/\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Motivating Support Vector Machines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we will consider differentiated classification: rather than modelling each category, we simply find a line or curve or flowform that separates these categories from each other."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "6f32ad62",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<matplotlib.collections.PathCollection at 0x237e2ea9548>"
       ]
      },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-advanced/kernel-method_2_0.png"
-      }
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from sklearn.datasets import make_blobs\n",
-    "X, y = make_blobs(n_samples=50, centers=2,\n",
-    "                  random_state=0, cluster_std=0.60)\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');"
+    "# use seaborn plotting defaults\n",
+    "\n",
+    "X, y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap=\"autumn\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2febdf39",
    "metadata": {},
    "source": [
     "A linear discriminant classifier may draw straight lines to separate two sets of data, but there may be multiple split lines that perfectly distinguish between the two categories. \n",
@@ -75,132 +213,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "48e4eeef",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "(-1.0, 3.5)"
       ]
      },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-advanced/kernel-method_4_0.png"
-      }
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "xfit = np.linspace(-1, 3.5)\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
-    "plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap=\"autumn\")\n",
+    "plt.plot([0.6], [2.1], \"x\", color=\"red\", markeredgewidth=2, markersize=10)\n",
     "\n",
     "for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\n",
-    "    plt.plot(xfit, m * xfit + b, '-k')\n",
+    "    plt.plot(xfit, m * xfit + b, \"-k\")\n",
     "\n",
-    "plt.xlim(-1, 3.5);"
+    "plt.xlim(-1, 3.5)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a166d13f",
    "metadata": {},
    "source": [
     "All three separators are good if we want to distinguish between these samples. But they have some significant differences. In the following, I will explain these separators with an example to show the difference between them. Now, suppose you want a new data point, which is marked with an \"X\" in this graph. So, which separator would you choose? This is a very interesting question! Because you can choose to give this new data point a different label. It's up to you!\n",
-    "Obviously we can't simply \"draw a line between classes\", but we do need to think a bit deeper.\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Support Vector Machines: Maximizing the _Margin_\n",
-    "\n",
+    "Obviously we can't simply \"draw a line between classes\", but we do need to think a bit deeper."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Support Vector Machines: Maximizing the _Margin_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "SVM proposes a new solution algorithm which finds the nearest straight line by applying an appropriate boundary fill to each line. The following is an example of an improvement to the SVM algorithm."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "ff93d885",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "(-1.0, 3.5)"
       ]
      },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-advanced/kernel-method_6_0.png"
-      }
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "xfit = np.linspace(-1, 3.5)\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap=\"autumn\")\n",
     "\n",
     "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n",
     "    yfit = m * xfit + b\n",
-    "    plt.plot(xfit, yfit, '-k')\n",
-    "    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\n",
-    "                     color='#AAAAAA', alpha=0.4)\n",
+    "    plt.plot(xfit, yfit, \"-k\")\n",
+    "    plt.fill_between(\n",
+    "        xfit, yfit - d, yfit + d, edgecolor=\"none\", color=\"#AAAAAA\", alpha=0.4\n",
+    "    )\n",
     "\n",
-    "plt.xlim(-1, 3.5);"
+    "plt.xlim(-1, 3.5)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e27e7a9b",
    "metadata": {},
    "source": [
-    "In SVM, the best models are those that maximise this margin, and SVM is an optimal model that achieves the maximum margin and can effectively use the boundary information in the dataset to predict the distribution of new data points, thereby improving prediction accuracy. Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data.\n",
-    "\n",
-    "\n",
-    "## Fitting a support vector machine\n",
-    "\n",
-    "\n",
-    "\n",
-    "```{figure} ../../images/svm/svm2d.png\n",
+    "In SVM, the best models are those that maximise this margin, and SVM is an optimal model that achieves the maximum margin and can effectively use the boundary information in the dataset to predict the distribution of new data points, thereby improving prediction accuracy. Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting a support vector machine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm2d.png\n",
     "---\n",
     "name: 'SVM for Linear classification : 2D case'\n",
     "width: 90%\n",
     "---\n",
     "SVM for Linear classification : 2D case. [source](https://online.stat.psu.edu/stat555/node/102/)\n",
-    "```\n",
+    ":::\n",
     "\n",
-    "```{figure} ../../images/svm/svm3d.gif\n",
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm3d.gif\n",
     "---\n",
     "name: 'SVM for Linear classification : 3D case'\n",
     "width: 90%\n",
     "---\n",
     "SVM for Linear classification : 3D case. [source](https://www.youtube.com/watch?v=3liCbRZPrZA)\n",
-    "```\n",
-    "\n",
+    ":::\n",
     "\n",
     "Now we will use a linear kernel and set the C parameter to a large size:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "9ce20625",
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=10000000000.0, kernel='linear')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from sklearn.svm import SVC # \"Support vector classifier\"\n",
-    "model = SVC(kernel='linear', C=1E10)\n",
-    "model.fit(X, y);"
+    "model = SVC(kernel=\"linear\", C=1e10)\n",
+    "model.fit(X, y)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bb72f7ca",
    "metadata": {},
    "source": [
     "Let's create a quick and easy function to draw the SVM decision boundary:"
@@ -208,8 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "c38627f2",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,57 +387,55 @@
     "        ax = plt.gca()\n",
     "    xlim = ax.get_xlim()\n",
     "    ylim = ax.get_ylim()\n",
-    "    \n",
+    "\n",
     "    # create grid to evaluate model\n",
     "    x = np.linspace(xlim[0], xlim[1], 30)\n",
     "    y = np.linspace(ylim[0], ylim[1], 30)\n",
     "    Y, X = np.meshgrid(y, x)\n",
     "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
     "    P = model.decision_function(xy).reshape(X.shape)\n",
-    "    \n",
+    "\n",
     "    # plot decision boundary and margins\n",
-    "    ax.contour(X, Y, P, colors='k',\n",
-    "               levels=[-1, 0, 1], alpha=0.5,\n",
-    "               linestyles=['--', '-', '--'])\n",
-    "    \n",
+    "    ax.contour(\n",
+    "        X, Y, P, colors=\"k\", levels=[-1, 0, 1], alpha=0.5, linestyles=[\"--\", \"-\", \"--\"]\n",
+    "    )\n",
+    "\n",
     "    # plot support vectors\n",
     "    if plot_support:\n",
-    "        ax.scatter(model.support_vectors_[:, 0],\n",
-    "                   model.support_vectors_[:, 1],\n",
-    "                   s=300, linewidth=1, facecolors='none');\n",
+    "        ax.scatter(\n",
+    "            model.support_vectors_[:, 0],\n",
+    "            model.support_vectors_[:, 1],\n",
+    "            s=300,\n",
+    "            linewidth=1,\n",
+    "            facecolors=\"none\",\n",
+    "        )\n",
     "    ax.set_xlim(xlim)\n",
     "    ax.set_ylim(ylim)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "70c3a097",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-advanced/kernel-method_11_0.png"
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
-    "plot_svc_decision_function(model);"
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap=\"autumn\")\n",
+    "plot_svc_decision_function(model)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7729cbd0",
    "metadata": {},
    "source": [
     "This is a maximum expansion of the space between the two points. Note that some of the practice points just reach the boundary and are marked with black circles. Those points are important in the matching process and are called, support vectors, which is the name of this algorithm. In Skeeter-learning, the identification of these points is stored in the support_vectors_ property of the classifier at:"
@@ -277,8 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "0231952b",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -289,7 +454,7 @@
        "       [2.06156753, 1.96918596]])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -300,7 +465,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf056504",
    "metadata": {},
    "source": [
     "One of the main points of this classification method is that just the space in which the support vector is located has a large effect on the matching result; on the correct side, no point away from the boundary can correct the match. This is due to the fact that during the fitting process those points do not affect the loss function used in the model. Theoretically, this is due to the fact that during the fitting process, those points do not affect the loss function used in the fitting process, and therefore, if they do not cross the boundary, then their location and number are irrelevant.\n",
@@ -310,67 +474,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "9750dd07",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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1y3vqCUiBQHSAyp4kXYfk8cA2c1r9zvPgfXDccgPkkpLdx60otpu+XQH3iYMh/7WxXufYk+Oe26Gu+rHGzYjK88Mw4Dx/IqQdO1J6XiIiosaEhXCiOrK9OCPSAiUOSddh/uh9yFu3ZCgqIiIiIqLUMH/8EeTy8oTbCVmG5e05GYgoDr8f1jdfizlIpZJk6LC9OBOoY4dQ5bd1sD8Se6CLpOuQSkrguO3GOh2/1mOWemF99aWYBX6gYhCOFob19ZdTdl4iIqLGhoVwojpSNm6IOzqmkiQE5L/+ykBERERERESpIxftSmo7yTAg78zuSGR521ZIwWBy2+7aCfj9dTqP9cUZEIoSdxtJ12Feuhjy5k11OseeTMs/h5RM6xnDgOXdd1JyTiIiosZIzXYARA2VMJkhwZfcxhZzeoNp4qQdO2Cd+ybkf/+FsOdBP+FE4PhB2Q6LiIiIqEEz3IVJbSdkGUbz5mmOJgGrde+2N5nqdBrz8s8TjjoHIjcH1O+/Rahtuzqdp9qxykqT2w6AVFZW7/OlgrL+d1gWzIdUVARRWAhtzKnAQQdkOywiImriWAgnqqPQ4CGwLJwPSdPibme43dD27ZOhqJqYcBj222+Cbdb0yPRWRQGEgPS/h4D+/SFPmwV06prtKImIiIgapNDgIRB5eZB88Qd/SIaB4JixGYqqdkar1tC6doOy4c+4szaFoiB88KF1LoQjHEp600TfE5JlJFlMF7ICo32HlJyzrqRiD/IvnQrL+0siI+dlGTAMSPffDYwcCemJZwBHQVZjJCKipoutUYjqyD9lasLkVsgy/BOnABZLhqJqQoRA/iUXwDZjWnSBIikc3v1/sno18k8YDPlvtqUhIiIiqhO7Hf4pUyHirIsjFAV6y1YInjwmg4HVQpLgP/+ixJvpOvznX1jn02j77Q+hJDeeTOu1b53PU1X4sCOgt22HRE0ZJUNH4KzxKTlnnZSXo+CUkTB/9EEkHl2P5OeVI+gXL4Zj1HAgib7zRERE6cBCOFEdaYccCt9lVwFArUmpkBVo+x8A3+XXZDawxioYhOXtN5F/0XlwnnsW8iedDev8t2OP+NE0SCUlsN9/d2bjJCIiImpEym+4BaEhJ0AgMsijKqEoEPlOlMyeu/etSdIgMGESQsceByHV/jVXSBICY89AaOTJ9TjHZEh64sEw4X4HQu+dolmhigLftTcg9u0IQCgqtK7dEBx+UmrOmQSppBi2559B/vkT4Zx0DgomnAH1lzWxW8foOpSf10QWKyUiIsoCSYg6LpedYR5POTTNyHYYlENUVYbbbc/utSEErDOfR96jD0HZvm33w2YzAmeeg7Lb7wEcjuzE1oioX3+FgklnQd61KzLF0jAiLVCS2FeoKnat/h2iWbO0x0m5LyfeNyhn8fqgWAoL7VAUjh+pij8nTYymwfrGa7BNewbqrz8DAIz8fATOmQj/1IujrTty4n00FIL9v/fAOut5yFVGHhsuF/wXXQbfFddE2nXUlRBwnj0O5mUfQDJqvkYhSYAso+TthQgfcWTdz1PLefMevA/2Rx6AUNRoMV5IEiAEjE6dUTz3XRgdOqbunHFYZr+K/OuuBEIhQJIi+XmFuAV7SYLRvgOKvlsd2Y+avJx436CcxGuD4qlrfs5CODVYOfWmqGkwff4p5K1bIBwOhI8eBFHgym5MjYSy+ie4hx8HhMO1ftlIRvFbCxA+elBqA6MGKafeNyjn8PqgWFgIr4k/J02XVFIMBEMQhYWAWr1FSE69j5aXw/zpx5BKiiGaNUPomMGpa1fo8yH/0qmwvvsOhKoCmlbRC1tA2O0ofW4GQkNPTM259qCu/A62mc/D/OFSIBiC3rkzAudOQeC00zM2AMcy/204L5gEgfhF73h2rtsIkeRirNS45dT7BuUUXhsUT13zcy6WSZQKqorwscdlO4pGyX7fnZE2J3UsggOILKRJRERERPXWYAZ72O0IDR+ZnmPn5aF05svwrVkN62svQdm4EbBaERo0GIFTxwF2e3rOC0A78CCUHnhQ2o6fOAAN9ltvrFcRHADzcyIiygoWwokoZ8n//gPzsg9j9wFPglAUaPv0TmFURERERESAvt/+KL/voWyHkVHmjz6Asm1rnfcXAIzWrSFc7tQFRURElCTO8SSinKX8trbeRfDg8JEQrVqlMCoiIiIioqZJWftLZM2eupJlBCZfUL8+7URERHXEEeFElLvkeiTZigLYbPDdeFvq4iEiIiIiasrqk5+rKoxOneGffH7q4iEiItoLvA1LRDlL63sAhMmUcDuByAr0QlUjCxYBQMeOKF30PvTuPdIbJBERERFRExE++FBIup7UtkKWIRRld35+yCEofXcphLMgjRESERHFxhHhRJSzRGEzBEefCsvcOXETbgmA//SzIAEQNhu0YcORf9rJ0Ev8AFeXJiIiIiJKCe3Qw6D13AfKH7/FXMxeAICqInDOuZD8fhjuQminjYXzuKMhPOXMz4mIKGtYCCeinFZ+yx0wffox5F07ay2GCwCBiVNQ9uCj0cdUVWbfwcbOMABdB5KYMZBSug7zsg+grFsHmFSEDx8IrW+/zMZARERElC2ShNLH/g+u0cMhNK1GMVwgMkil9L+PIDBhUvRxVWVu3uhVflerTw/5uvD5YFmyCPKmTRB5eQgNGQqjU+fMxkBEDQYL4USU04w2bVG8+CPkX3ohzF8th5ArityaFukBfskV8F17Q9LHk8pKYf5gKaRdOyFcboSOPwGiwJW+F0CpIwTMSxfDNv1ZmJZ/BskwoLdug8DEKfCfOwWiWbO0nt4ydw7sd9wMZevWyCJRQkAyDIQP6I/SR/8P+n77p/X8RERERLlAO+gQFM9bhPwrLoH6x2+RvEiSIGkaRLPmKL3zXgTHnZn08eRtW2H6+CNIZaUw2rZH6LjjAYslja+AUiYYhPWtN2Cd/hxMP68GAGi99oV/ygUInH4WYLOl79yGgbzHH4HtyUchl5VFrkPDAG66DqEhJ6D0f09CtGqVvvMTUYMkCSFEtoNIhsdTDo1TqKgKVZXhdtt5bTQhyrq1MH/4PiRfOfQOHREaOQrCkV9ju1qvjXAY9vvugm3GNEgBP4QkQRICwmKBf/xElN92N2C1ZvgVUdIMA46rL4PttZchFCU6O0AAgCzDaN4CJfPfS9gTvq7vG9ZXXkT+1ZdFRzlVJRQFMFvgWfQBi+ENHD9XKJbCQjsUhaMZq+LPCdWG76NNjBAwffMV1G++gqRp0Hrti9AJJ9Y6Y6+2a0PyFMFx/TWwLJwPSdej+bnhcsF35XXwX3QpIO2ZeVGukMpKUXD6GKjffgPIcnR2gJAkQADaAf1Q8tY7CQcd1el9Qwg4brwWtpnP1/60osBo2w6epZ9ANG++Ny+Lcgg/UyieuubnLIRTg8U3RYqlxrWh63BOPBvm9xdDquUtT8gywgOPQsnrbwNmcxYipkRsT/wP9nvuqFGEriQUBUbrNij6+oe4I4jq8r4h7dqFZn17AuFw3PNr+/VF8QefJnVMyk38XKFYWAiviT8nVBu+j1Ise14bUkkxXMOHQPlzfcy1gHxTL0H53fdnOFJKVv7k8bAsfjfm/59QFIQGDYb39bfjHqcu7xvq11/BPeqEuNsIRUHgnIkoe+jRuNtR7uJnCsVT1/ycGT0RNXqWObNhWfperUVwAJAMA6bln8H68guZDYySEwwi76nHYxahAUDSdSib/oXl3XdSfnrr668Aup7w/KZVP0D96ceUn5+IiIioscl76P64RXAAyHvuKagrvslgVJQsecOfsCxaEPf/T9J1WD76AMq6tSk/v23WNAglfqdfSddhfeNVSKXelJ+fiBouFsKJqNGzTX820ls8ie3QMCbJNCnmzz6G7PEk3E7IMixvvp7y85u+/jLSbzCJ85u+/jLl5yciIiJqVMrLYX31pbhFVAAQqgrbzGkZCor2hnXeW5F1mxIQigrL22+m/Pym5Z9D0rWE20mBANQ1q1N+fiJquLhYJhE1bj4fTD+tSriZJATU9X9A2rkTokWLDARGyZJ37EhqO8kwoGzdmvLzS+FQ3NHguzeUAC3+F7pqyspgfftNmBe/C9lbAr1tewTPOAuhY4cAilLXcImIiIhymvrzGsjl5Qm3kzQN5k8/zkBEtLeknTsihfAENzMgAfL2bak/vxZOfuNw8ttK27fD9uqLMH32CaRQCFrPXgiMnwit/wD2qydqJFgIJ6JGTQqH9np7jgnPLYazIKnthCTBcLtTfn6t176RZDhBoi/pOvRevZI6punL5XBOOAOS1wtULAylKithXTAPWu/9UDL7bRit26QifCIiIqKcslf5+V4UMSlzhNOZ9EzaRItl1oW2z74wffN1dIHOmOeWJOjdeyR1TOurL8Fx3ZWRmaCGAQmAuvJ72F59CcETR8L7zHQgL6/+wRNRVrE1ChE1aiLfCaOwWVLbGg4HjOYcDZ5rwoOOhbAlkXQKgeDJY1J+/sCESYmn7gLQ27RBaNBxCY+n/LwGBaefAqmsDBIQ7V1feQ5l3a8oGDMSSGKkFBEREVFDo3fpCpHE6Fohy9B79sxARLS3giNOhqQl0ZpE0xA86eSUnz8w6fzERXBFQWjIUBht2yU8nnnhfORfdSmgaZAqiuAAou1XzEvfg/Oi8+obNhHlABbCiahxk2X4J05O2CNcKAoCZ58LmM0ZCoySJRz58J8b//9QyDKEswDBsaen/Px69x7wn3NuzC9sAoAEoPzO+5JqaWJ/5L/RJLs2kq5D+eN3WNPQT5GIiIgo24y27RAaPAQiQd4kGQb8507JUFS0N/T9+yJ0+BFx/w+FoiB8QH9oAw5O+fmDI0Yh3H9AzPMLWQZUFb4bbkl8MMOA445bojl9bSTDgGXxu1B/+L7OMRNRbmAhnIgaPf+UC2E0bxE7UVIUCKcT/gsvyXBklKzym25DeOBREJJUoyAtFAWwWFHy6hwIR35azl/2wP8QOOfc3edDpAAuJAkwm1H66P8hOPrUhMeRduyA+b13E44whyRxcSgiIiJqtHzX3wzISsyBDkJRoPXuk5bZfpQapdNegNGufa3fsYSiwGjdGt5Zr6Snt7bJhJI35iJ86OGR86mRrr+V15NwFqDkjXnQ9j8g8aG++BzKP38nXBNIKAqsL86sV9hElH3sEU5EjZ5o0QIl899DwemnQPnnbwhZgWToELIMyTBgtGiJktlzYbRrn+1QKRarFSWvvw3bjGmwTX8Wyj9/AwCEyYzgqWPhu+wq6D3SOHXWZELZI0/Af9FlsL78ApTf1gKqCeHDByJwxlkQSbbfUf7akHAaJxBpl6L8ub6+URMRERHlJK3fgSh5bQ6ck84Gysqia6YIRYGk69D2PwAlr84BrNZsh9rgSbt2wfz+YsgeD4zCQoSGDYdw1X9dHaNVa3g++BR5Tz0B60szIRcXRx7PdyIwYRJ8F18O0SJ9bSeFy42SeYugfrcC1tmvQvn3Hwi7A8GhwyI3UGy2pI6j/PE7RMX1F4+k61B/W5uK0Ikoi1gIJ6ImQe/eA0Vf/wDzkvdgnfsm5O3bYDRrgeAppyI4YhRbojQEZjP8F10K/9SLIf/7D6RQCEbr1mkbBV4bvXsPlN95b90PsBfXmVBNdT8PERERUY4LH3Msin5aB8tbb8K85D3IpV7oHToicOY5CB91DJCgtSEl4PPBcduNsL7+SmTRUUUBdD2SU0+YjPLb7wYslnqdQrgLUX7LHSj/z01Q/o0MVNHbdaj3cZMmSdAOPhRlBx9a92OYzQmL4EDFbFBzhl4XEaUNC+FE1HSYTAiddDJCaViwhTJIlmF07JTtKOpE67UvjAIX5JLiuNsJRUX46GMyExQRERFRlghHPgITpyAwkb3AUyoYRMGZp8L0zVe7ZyNWtuYLhWCbOQ3Kn+vhfeUNQE1BWchsht61e/2PkwWhwwcmt6EsI3z0oLTGQkTpl5ZbrJs2bUKvXr1q/JozZ046TkdERNQwWCwInDsZQk6wOJSuwT9laoaCIqLGjrk5UQppGiRPERAKZTsSophsL82E6esvYy/ObhgwL/sAljmzMxxZ7jG6dkPomGPjL/wJAJIM/9nnZiwuIkqPtIwIX7duHSwWCz788ENIVRZGyM/P3PR1IiKiXOS78hqYP3wfyrpfYy6a6Z84BeEjj85wZETUWDE3J6o/5ec1sD33NKxz34QUCkHIMkJDh8F/4aUIH3FktsMj2k0I2J5/NvF2sgzb888ieOY56Y8px5U+9Bjcw44FSkpq5OcCgASg9IFHIFq1ykp8RJQ6aRkR/ttvv6FLly5o2bIlWrRoEf1l5UIXRETUxAlHPooXLEbw1HG7V7ivKEwZBQUou+UOlD3wP0BKtHY9EVFymJsT1Y954TtwDzkK1jmzIVWMBJcMA+YPlsI1ejhsTz2R5QiJdpN27ICycUPixR8NA6Y1PwE+X4Yiy11G5y7wLF5WbSBKND9v3wHeabMQmDApW+ERUQqlbUR49+4Nsz8UERFRuglnAUr/7zmU3XEvzB+9D6msDEabtggdd3zmFhcioiaDuTlRTdL27bDOmQ1l4wYIqxWhYwcjPOi4Ggs0Kr+tg3PqJMAwahQWK0eOOu68BXqvXggNOSFj8RPFImnhvd4+8VKRjZ/RpStK5rwD+c/1MH+5HAiFoHfvESmOc+FWokYjLYXw3377DS1atMBZZ52FjRs3olOnTrj44otx1FFH1fmYisI3Hqqu8prgtUF74rVBseTctdG6JfSzd09H5QrW2ZVz1wfljIY+QYO5OWVKg3gf1TTYbr8ZlmnPAkJEC1x5zz0FvWNHlM98GfqBA6Kb22Y+BwgRd3StUBTkPfU4jGEnpj38hqpBXBuNRZvWEPn5kEpLE25qtGgBxe3K6gddzl0bPXtA69kj+lfm59mTc9cG5ZS6vm1JQiSYL7OXQqEQ+vfvj3333Rf/+c9/kJeXhwULFuCll17CrFmzcPjhh6fydEREREREFANzc6I9nHceMHNmpAi+J0UBzGbgq6+AAw6IPOZ0AkkUFAEAW7YArVunLlaiurrmGuDxx4EY69EAiFzvt94K3H575uIiIsqylBfCAcDn80FVVZjN5uhjU6ZMgSRJmD59ep2O6fX6oeu1r3hMTZOiyHA6bRm7NqQtWyD/+w9gtULftzeg8t5wrtrra6O0FOYF8yH/tRGw2RA+7njofQ9Ie5yUeZl+36CGhdcHxVJQYIPcgKdFMzenTMn0+6i84U9IO3ZAFBTA6Nkr4fAwZeX3cA45Ju42QlGgDTwSZfMXAZoGd0tX0vGUfLECxr69k96+Kdnba0PauhXmBfMi/78uN8KjTobRoWMGIm0cpM2b4TzmcEjFxbUuzi4UBaJlK3g/+xKiWfMsRLgb8y+KhdcGxVPX/Dwtlby8vLwaj/Xs2RPLly+v8zF13YCm8cKnmtJ9bajffoO8/z0I87IPo1MijRYt4Z98PnyXXsl+vjks4bVhGMh79CHkPfE/wO8HVBMgDNjuvgPhAQfB+9TzMLp2y1zAlDH8TKF4eH3QnlI/bCSzmJtTpqX7+jAvfAd5jz8C008/Rh/TuveA/5IrEDhrfMyCuHX68xCKCknXYh5b0nWYPvsU4rffoHftDmHLg+RPbjFBzemCwZ+LuBJeGz4fHDdeC+ubrwOGASgqYOiw3XYTQsNPQulj/wdR4MpYvA1Wy9Yonr8YBWeeCuXffyBkBZKhQygKJF2H3qkzSl5/G0ZBIZAj12xj+1yRvCVQflsHCAG9R08IlzvbITVYje3aoNSoa36e8qEta9euRf/+/fHdd99Ve3zNmjVcpIcaHPN778I1ahjMnyyr1hdQ3rEdeQ/dj4KxJwOBQBYjpPqw33Yj7A/cC8nvh4TIQjGVIybUH3+Ae/hxkVHiREREDRRzc2ps8h57GAVTxkNd81O1x5X1fyD/qkvh+M/VMb8dm1Z+G7cIXpW6OnL8wJjTIJT448eErCB8yGEwWrEtSr2EQig4eyysb7wGSdchCRHJzysWKjUvWQTX6OFAWVm2I20Q9F77oOibH1Ey8xUETzoZoYFHIThqNEpefgOeL76D0aVrtkNslOQtm+G4+jI069Md7uFD4B5xPJr16QHH5RdD/vuvbIdH1OSlvBDes2dP9OjRA3feeSe+++47rF+/Hvfffz9+/PFHXHjhhak+HVHaSNu2wXlBxQrxtUwnkwwDphVfw/7fe7IQHdWX+tOPyJv2TMznJV2HVFICx523ZDAqIiKi1GJuTo2J6cvlsN93F4BILl5V5aAV24szYJk7J2Xn9J9/EQCBeAPPJEOH7+LLU3bOpso6ZzZMX3xe4/+2kqTrUH79BXnTn81wZA2YyYTQyFEoff4FlMxbhNLnZiF0womR/uANkOQtgXX6s3CeexacZ4yB/Zbroaz9NdthRckbN8A95GhYX38FUjAYfVwKh2Cd8zrcxx8N5fffshghEaW8EC7LMp599lnsv//+uPLKK3HKKadg1apVmDVrFnr16pXq0xGlje21lwAtHHeFeMkwYH1pJuBLbrok5Q7rzOcTju6RdB3mxYsgb9uaoaiIiIhSi7k5NSa2559JYnS2DNtzT9f6XHjAwQn3j267f2S9GL13H5Q+8QwgyzX2FRXFxPLrbkRo+Mikjkux2Z5/FkjU79UwYJ0xLf4ikNQoWea/jWb79YDj5uthXrIIlmUfwjbjeRQefSjyLzoPqFJ4zgoh4DxvAqSiXbUPpNN1SF4vnBPPbvg914gasLT0CC8sLMR9992XjkMTZYx54fxIX7oE5LIymL9ajtBxQ9MfFKWM6asvkpoaK+k61B9WIjRseAaiIiIiSj3m5tQoGAbMS96rtcBUlWQYMP24EtK2bRCtWlV7LjDpPNhefyXu/kJREB54VLV1YoJjz4DetRtsTz8Jy3sLozGEBx4F/4WXIDTkhDq+KIoKBqH+sibhZhIAZdtWyFu3wGjXPv1xUU4wf7AE+VMnA0C1gWqV3+cs894CdB2l02ZlJT4AUH/4HqafVsXdRtJ1qL+vg+nL5QgPPCpDkRFRVSkfEU7UWMheL+KvO7+bxD51DU6sKZe12pttiYiIiCj1gsGERfCq5PLSGo9p/Q6E/+wJEDEW0xSKApjNKLvj3pr7DjgYpTNews4//sWuH37BzvX/ouStBSyCp8re5tscEd50CAH7bTcBQMzZ2pJhwDr/bairfshkZNWYl7wHoSYeaypUFeYlizIQERHVJi0jwokaA71de8j//pNUwVRv3TYDETVwgQAsC+bB9O0KwNCh7dsbwdNOz9rq2dp+fSP/v0kk0do++2YgIiIiIiKKyWqF4XBATmIAipBlGM1b1Ppc2cOPQ+Q7YXv+mUh7AlkBICBpGoy27eCd/iL0/faPfXC7HYbdXscXkVukYg+sc2ZD+fUXQFERPuRQBE8aDVitmQ/GZoPevkMkP0+wqZHvhNG6TUbCouxTv/ka6vo/Em4nFAXWF2ei7H9PZiCqmqSyUiDGTbaa23IgHVG2sBBOFEPgzHNg/uqLuNsIAEaHjtAOPiQzQTVQ5oXvIP+qSyF7S3bfJdd1OO64BeX/uRn+y65MOmlIFf+k82BZtCDuNkKWET58YLWpsURERESUBZKEwFnjYZsxLe5ABqEoCJ04AsJZUPsGioLyu+6D79IrI0XgvzZAWKwIHXscwoMGJ+5R3RgIAduTj8L+wH2AFo4unGh7cQaMm/6D0seeQmjESRkPyz9lKux33QaI2AORhKIgMH4iYDZnLjDKKvW3tRBAwhskkq5D/eXnTIRUK6NNO0BPYmaDELyRQ5RFTeBTnqhugiePgd6+Q3QRnNpIALTuPbjYRRzmxYsii4aUegEAkqZFfgkBKRSC457bkff4IxmPK3zUMQgePwwixpcdIcuAqqL8trsyHBkRERER1cY/ZSpgNsfO3wBA16F17Z7wWKJlS/gvuRxlDz6K8rvvR3jwkKZRBAeQ9+hDcNxzB6RwKJKTV+TnACB5S+CcfA7M7y/OeFyBCROhd+kS8/uXUBQYzVvAd9FlGY6MskpVk25ZKkymtIYST/DUsah4F4pL0nUExp2Z/oCIqFZN45OeqC5sNpS89Q6EywWB2B9p5k+WwXHz9ZmMrOHQdThuuBpA7H5uAJD3wL2Qtm/PVFQRkgTv9BcRHHUKAEAoKoQkRRNv4XKj5M350PoPyGxcRERERFQro0tXlLz8RnQE854qi2X2J/4H66svZS6wBkTethV5D90f8/nKnN1x/TUZXydH5DtR8s5iaH37Rf6uVuTnFTNK9a7dUPzO4hqLoFLjFj70sKS2E7KM8MAj0xxNbEbbdgiccXbMG3VAJMbAyWNgdOmawciIqCoWwoni0Lt0g9G8JSBJMe9CS0LANnMalN/WZTS2hsD80ftQtmyJWwQHAAgB6+svZyaoqmw2lE6bhaLl38J/4SUIjTwZwTFj4X12BnatWovwEbUkUroO8wdL4Dz3TLiPPBiu449B3oP3Qd6yOfPxExERETUx4QMPilkIByLFcAHAfusNgM+XsbgaCusrLyaczSoJAWXTvzB98lGGotrNaNUaxUuWwbNgKQLnnIvQiFEInHE2it+cD8/nK2pvWej3wzL7VRScehLcAw+C66QTYJ3xHCRvScbjp9TTu/VA6Mij487UrhQYPykDEcVW9t9HEBp8PABUi7fyz+GBR6H0saeyEhsRRbBHOFEc6o8roa77NeF2kYU5ZqD83gczEFU9aRrM7y+B+ZOPgGAQRsdOCJxxNox27VN+KnXNaghVjU61jEkIqKt/Svn5k6X37IXy2+9OuJ20axcKzjoNph++h1CUaH9KdfUq5D36MEr/9wSCZ56T7nCJiIiImizrvLeAQCDuNhIAlJXBsmAegmecnZG46qW8HNb5b0Nd+R1gGND67Ifg2DMgClwpP5W6ZnVSbR2FosD00yqEK4p6GSVJ0A47HGWHHZ5wU2XdWhSMPRnK1i0QsgzJMCAkCY4VX8N+313wvjoH4cOOyEDQlE5lDz4K1wnHAr7ymGsElN9+T1q+0+4VqxXel2fDvHQxbLOeh/r9dwAA7YB+8E++AKETRwAqy3BE2cSfQKI41NU/Jb0wh+nHlZkIqV7Ub76G8/xzI4li5QewIZD34H0ITJiMsvseBLLRVy3DC2XWia6j4MxToa5eBQDVEjDJMCBgIP/KSyAKmyF0wonZijIt5A1/wvbiTJhWfBXpu9m3P/wTJkHfv2+2QyMiIkoPXYf5g6WwzH8b8q5dMJo1Q3DMaQgdNzTuaGRKP3X1qkghKdFAC5MJ6upVOV8It8yZDcd/roJUXr67QKbrcNx5K8pvvh3+qZdkLVcWOZ6jSzt2wHXKCEieosjfK1q5RGejlpej4PRT4Pnwc+g9emYrzLRQV34H60uzoP68BjCbERp4FAITJsFo3yHboaWF3r0Hihd/hPwrL4HpuxWR9iOSBEnXYTRrjvJb7kDg7AnZDjNCURAaPhKh4SOzHQkR1YKFcKJ49mbBHCm3Ow2pq36A67STgHAYAGqM0ra+NBNSWSlKn34+Zcl2uN+BiUeDA4AQ0PodmJJzpov5g6Vxb3ZEp+HeeydCQ4c1jOJ+IiJyk8T+yAPVR8D/tAq2F2fAf+Y5KHv48ezcPCEiIkoTZf3vKDjzNCgbN0Q//4SiwDp3DrSu3VDy+tvs75pNyeZYQuR8PmaZOwfOSy7YPfCmat4cDMJx202AAPwXXZqyc4b7Hwjz4ncTt0fR9czl50LAvOwDWGdMg3n5Z0BYg96lK/yTz0PwjLMhHPm17mZ7YTqkol3RAvieJMOACIVge/IxlD3xdDpfQeYEAsi/5AJYF86vnp+v/A55jz+C8tvuhv+Sy7McZHroPXuh+L0Pofy8BuYvPgOCIejduiN0/An8PkJEScvtyh1RloX7D0hqhWqhKAgfdEja46kP++03A5oWO1EUAta334T6/bcpO2d40GDo7TskHk2iqgjkeEsR60uzEvalk4SAuvYXqKt+yFBU6WV7+knYH3kAwB4j4Cv+bJ39KheKJSKiRkXatg2uUSdC/ufvyN8rPvMqf1f+2gjXySdC2rkzazE2deH+A5IaaCFpWm4veh4KwXHjtQlnn9rvvQNSsSdlpw2cOT7hYB8hy9A7dkL4qGNSdt6YDAOOqy5FwZmnwfzxR5ACAUi6BmX973DcfD3cg4+EvOnfWne1vjgz5nebSpKuw/r2m0BZWTqiz7j8Ky6GZdECADXzc0kIOO68BdaXX8hSdJmh99kP/gsuhv+yKyOjrlkEJ6K9wEI4URx6n/0Q7j8AQk5QANV1+M+dnKGo9p6y/neYv1wes59aJaGosM2anroTyzJKH34ckKS4xfDy2+6CaNYsdedNA+WP3xP++0W3/WtjeoPJhPJy2B+6L+4mkhCwvjgD8t9/ZSgoIiKi9Mqb9nRkhGmMz3xJ1yFv3w7bjOcyHBlVCp48Bka+E/HGMwtJguFyITjy5IzFtbcsi9+F7PEkHnQTDsP6xmspO69o0QLlt9wZ+3lJAiQpksPvzezYOsp77GFYX3sZwB6FXSEgCQH5n79RcPoYYM+fyVAIyvZtSZ1DCoegbN+aspizRfnlZ1jnvZWw+G+/987oLGAiIqqOhXCiBMoeeAQwqZE+ZDGUX3lt9qfICgF5w59QV6+CtK16UqiuWZ3UISRdg5riXufhwUPgfeUNiOYtImGqJgiTCQKAYbej9P6HI70Pc53NmvSmwmxJYyCZYVk4H/D5Em8oy9EvL0RERA2apsH60qyEN74lQ48MHEhQjKI0sdlQ9sAj0bZ0exKIFFHLHnoMsGQ5J9M0KL+tg7JmNaSS4mpPqat/gkhmJKssp3xRef/Fl6H0vgch8vIggEhurkZiMVq0RMmrcxAeNDil56w9ED9sTz0R92aApOtQf1sL80fvV39CVfeqh3ljyM+tr74IoSTubisX7YL5/SUZiIiIqOFhj3CiBLR+B6J43iI4L5wC5e+/IotMSlKkh5/FAt/V/4HvimuyF6BhwPrKi7A9+39Q//g9+nBo0GD4rrgG4YFHxS3i15CGXoqhISdg16q1MC9dDNOKrwFDh75vHwROHgPY7Sk/XzqEjhsK5bd1iUfVm0wIH3pYhqJKH+XP9ZFpholGkwhA2fhnZoIiIiJKI2nXLsh7FCtjkYt2QSophnAXpjcoqlXwtNPhlWU4brgWUrEnWlCWwmGIwkJ4H3wUoVGnZC9Anw95zzwJ24xpkHfuAAAIVUVw9Bj4rrwOes9ekfw8fpvu3dKQnwfOuxCBM8fDOv9tKGt/AWQF4UMPj6x1o2amTGD+8H3Ipd6E2wlFgeWN1xEaWmVBellG+MijYUow61VIEoyOnWC0a5+KkLNKWf8HJD1xWyChKJFcnoiIamAhnCgJ2kGHoGjFKpg++ySygEsosjBHcMxpEPnO7AVmGMi/7EJY5syukSCbPv8UBZ9+jNLHn0b48IEQkrR7BfUYhKoifPCh6YlVVREacRJCI05Kz/HTzH/uZNieejzuNkJREDx1HERhbrd5SYrJlNxINwmAyZz2cIiIiNJub4t/GSoWUu2CY8YiOGIULO8thPrTKgCA1q8/gieOBMxZzE3KyuAaMxLqTz9Wa2EhaRos8+bCsuhdFM95B1q/AyFpSbSvMAyE09Xr3G5H4OwJ6Tl2EuTtW5P6jiLpOpQtm2o87j/vQpg//zThefwXXJTzC6cmxWJN6t8LQgAW5udERLVhaxRqkKSdOyGv/RXYsiVzJ5VlhAcNRvktd6D8rvsQOHdydovgAKyzpsMyZzYkoEZCJOk6IATyr7wEUjCI0KDBiRd71DQEJp2XxogbLqNTZ5Td9xAA1DoNUygKjA4dUXbHPZkOLS1CRw1Kqie6pOsIHXl0BiIiIiJKL1FYCK1L14TtFoQkQeu5T9bzwJwiBOTNmyL5eVFR5s5rsSB4ymkov/1ulN9+N4Inj8luERyA49YboP60qtY+zpKuAcEACs4Zh9CRR8No0TLu9SYAwGpFcOzp6Qs4i0S+M3FRFxWjup2uGo+Hhg2H/5xzYw6sF5KM0DGD4Z/YOL7fhI5KLueWDAOhIzOw0CkRUQPEQjg1KKZPP0bBaaPQvHdXFBxxMNC2LfKHHgvzwneyHVrmGQbynnky7ugGCQAkCbZZz6P8rvshrNaYxXABwD9xCrS+/dIRbaMQmHIBvM+/AL1zl2qPC1VF8JTT4Fm8rHGMBgegHXpY5Et+nJsnQpJgFLgiXzqJiIgaOkmC//wLk9rUf8FFaQ6mgRACltmvwn3sQDTrt28kP2/eHPazxkFd8U3M3eSNG6B+/RWUNatrLoLYgEm7dsH65uuQjNivSTIMSMUeWN99B6X/exJAjEEWiOTyZfc80GhvuoSOG5pcn3QhEBo1uubjkoSyhx9H+a13wdgjBzccDvgvuRzeV96IzHRsBILjzgSs1vg3TxQF4YMPhd67TwYjIyJqOCQhkrgFmwM8nnJoGhekacqsM6Yh/8ZrIRSl2khVIcuQDAO+K65B+c23ZzHCzFJ+/QWFxyTXi9po1hy7fv0Tyuqf4Jw6Ceofv1fvda6a4L/4MpTfeGtGVoevt0AAloXzYZ0zG/L27TDcbgRHn4rAqeMAhwOqKsPttqfvfUMIqCu+gfLXBgibDeHDj4Ro3jz158kydfUquE46AQgGa4wOF7IMSBK8r7yB0HFDsxTh3kv7tUENGq8PiqWw0A5FaQCfjxnUaH9OgkEUjBsN0zdf1TqiV8gywgOPQsnsuY2muFZnQsBxzeWwvfJiNB+PPqUogBAo/b/nEDxt92hm84dLkfe/h2D6bkX0Mb1tO/inXhK5uZBg9mKus7z5OpyXTk24nZAkhI4dAu/st2Fe8h7yr74M8s4dkQUrpUivcyPfibJ7/ovgmedkIPL6k4o9sL7xGsyLFkL2eqG3b4/AGecgNGw4oKoxP2MdV1wcuXkQ44aIkGUIZwF2/fgrkJcXO4BwGKbPP4W8YzuEy43QUcfE376BsrwzF/kXTAJQc0awUBQIuwPFS5ZB794jG+HVCfMvioXXBsVT1/ychXBqENTvv4Vr+JCEU+dKZr6C0MhRGYoqu9QV38A98vikthVWK3b+vb3iLwKmr76A+eOPgEAAeufOCI4Z22AWe1J+W4eCcaOhbN4U/dIlJAkQAsLlRslrc4DDDuMHZooov/wMxw3XwPz1l9Ue13rvh7J7H0B44FFZiqxumExRPLw+KBYWwmtq1D8nPh8ct94A6+xXI4tGK0pk1LLJjMDZ41F21/2A1ZrtKLPO+tIs5F97RcznBQAoCjyfr4DevQess6Yj//qraxbNAUCSEDpxJLwzXmoYxfBgEPKmfwEARvsO0XYs1hnT4LjpuqTafYQHHITixcsq/hKGecl7MK38DjAMaH32Q3DUKQ3mOjMt+wAFk8cDfj8gBCQgOnhJ69ETJW/Mg9y5U62fsVJZKQpGj4C65qcaN5+EogBmC4rfnA+tESxGnyrmj96H/babof6+rtrjoWOORdl/H4bereEUwQHmXxQbrw2Kh4VwatTyp06GZeF8SFrsVbKFLEM76BAUv/t+BiPLHnnjBjQ75ICkttXbd0DRyp/THFH6STt2oPCYwyB5imodNSJkBcJmRemnX6JgQF++b6SQ8ts6qN9/C6niy5l2QP8GuegQkymKh9cHxcJCeE1N4edE2rULlsXvQioqgmjeHMFhwxtNC7R6EwLuww+EsuHPuEVfoSjwTz4fgbPPhfvYI+JvK0kov+Ne+C+6NB0Rp4S0cyfynnoc1pdfgOwtAQAYLhcCEybDd8nlMC3/HAVTxic8jlCUSOF/5svpDjnt1B9XwjXieEDTav3/rVxHx/vZV3B3aFX7e0d5OfKeehy2WdMh79oZ2U9VETx5DHxXXgu91z6ZeCkNixBQv1sBdd1aCFVF+JDDYHTtlu2o6oT5F8XCa4PiYSGcGi8h0Lx9c0jhJFZVB7Dz5/UQLVqkOajc4Bo2GOqPK2udultJyDJ8194A37U3ZDCy9Mh78D7k/e/B+K9XURA6ZwIsL8zk+wbVwGSK4uH1QbGwEF4Tf06aNmXdWhQedUhS2xrNmyM4fBSsr74UWSwyBgHAaNcORd//nJOt+uRN/8I1cijkrVtqtoxTFBjt2qP4rQVwDz4ScllpwuOVvPwGQiecmK5wM8Z5zjiYP/og7iLrAoDvkcdgv/qK+O8d4TCU9X9ACoegt+/QYGasUv0w/6JYeG1QPHXNz3MvwyDaUyCQdBEcAGRvcfpiyTG+q65LWAQXdjv84ydlMKo0EQK2F2bEfb0AIOk6zG+8Dvh8GQqM5G1bYXn7TVhfexmmLz4HEvwfERERUcMmlZQkv21pKSyL3olbBAciC0MqmzZB+fWXekaXBkLAOfkcyFu31lrwlXQd8qZ/kX/FRfBfcjnijTQTigKtRy+EhjScNVZikbdthfmDpXGL4AAASYJl5vTEBzSZoO+zL7T9D2ARvJ6U33+D5Y3XYJn9KpTVP2U7HCKinKFmOwCihKxWiLw8SEkUNoUk1VgxvDELnXAiyu6+H45bb4RQ1GpfMISsQOTZUPL6XIhWrbIYZYoEApB37khqUykQAK6/HvlffAn4/NC7dUNg/ESEjh2SkyOMGipp2zY4broOlvcWVvsCpHfoiPKbb0dwzNgsRkdERETpYuzF7EujWXPIRUVJby+VldUlpLRSV34H0w8r424j6TrMX32JsnsegPz3X7C9/kq0TzYQ+Z4CRHqKl7wxt2H0Qk9A/uuvpPqhS0JAWf8HcMklyP/sc0A3EO7XH4GJU6D1H5CBSJsO5ec1cNx0HcxffVHt8fAB/VF2zwPstU5ETR4rQpT7JAmBcWdGFkuJQygKQscd3+RGD/inXgLP0o8RHHMaRF4ehCRBb9kKviuugueL76Adcmi2Q0wNk2nvtn/6aag/rIS67leYly5GwZmnwTVyKCRP8l/EKDZp+3a4TxxcowgOAPI/f8N54RRYpz+bpeiIiIgonYwuXRHudyBEggEGQpYROONsGK1bxx0lXe3YbdrUP8AUsyx8B0JNPIZMKCosixag7LGnUPLqmwgdPQjCbIGQZehduqL8rvvgWbY8ssBmY2BOPj+XAgFg2jSoa1ZD/fVnWOfMhvuEY5F/6dTIorRUb+qqH+AePgSmFV/XfG71KrhOGQ7TZ59kPjAiohzCQjg1CP7zLwJkOTqSolaGAf+lV2Ysplyi9R+A0qemYefGrdi5tRhFa36H78bbYLRpm+3QUkdVETrsiIQ3RIBIH8Kq7TkqC7XqD9+j4KyxQKLpm5SQ4/abIG/ZXPv04Mptbr4e8l8bMxoXERERZYbvimsStuiD1YrAuZPhP3tCwll5QlYQPvRwGB07pTrUepO8SbaCkSXIxcWAJCF0/DB435iHnf/uwM4tHni+/gH+qZdA5DvTGmsmafv2geFy7cUOu2evShV/tsyZDcfN16c4siZICORfdB4QCtaenxsGYBhwTp0EhEJZCJCIKDewEE4Ngt6jJ7wzXgZUE4RSfTSGUFQISULZw48jfMSRWYowh8S7WdDA+c+/MGEPQoHdhdg9SboO0/ffwvzxhymPrSmRduyAZf7biftByjJsL83KTFBERESUUaERJ6Hs1rsAoMZoaaEogMWKklfnwGjTFoGzz4VwFkDIsQc0SIaO8quuTWvMdWU0b4GkhrQbBowWLWs+3ljzc4sF/onnJZwZEI8kBKwvzoC86d8UBtb0mL5cDvWP3+Pm55JhQN61C5ZFCzIYGRFRbmEhnBqM0LDh8Hz6FQITJ8NwOCIPWq0IjR2H4g8+RWD8xKzGR+kXGnkyAqedHndmQKKvGUJRYH2Rxdn6MH39ZeIiOCpuPCzjTQciIqLGyn/ZlfAsfB/BkaMgKtvYud0IXnQpij7/BuGBRwEARPPmKHnrHQinM7KYe5VjCEWBkCSUPvQYwoOPz/yLSEJwzNiEi30CAAwDgSa2Rorvimug7dc37k2OhGQZ1tdfSV1QTZDps4+Ta9+jqjB9/mkGIiIiyk1cLJMaFL17D5Td/zDK7n8YqtDhblEAX7EPmhZ7WiZlgRCRKXdmc2pHwEgSSp98Fnr3HrA98yTkkt3TVIXZDCmJaX6SrkP584/UxdQESeHkp1NKwUAaIyEiIqJs0w49DKWHHoZSIaDCgLulC35POYw98nOtbz8UfbUS1ldfgvWVF6Fs2wJhtyN40mj4J50PfZ99s/QKEtP32RfBIUNh/vijmIMBhKIgNGw4jC5dMxxdktKVn9vtKJm/CPY7boF19qvV8nFhywOCQUhG4gEUysYNqYupCZJC4eT+X4VI6jsTEVFjxUI4NVwmU+OdZthAyX+uh23Gc7DOfg1yqRfCZEJwxEnwn3dR6hbtVBT4rv4PfJdcAfMnyyDv3AHD5QZ8PhRccn5ShxBWa2piaaL0JL/gCUWB3qNXmqMhIiKinCBJgBp/8UTRrBn8l18F/+VXZSio1Cl9ZjoKTjsZ6qofASnS0gOo6IVuGJE1e57MvYXC1e+/he35Z2F59x1IoRAMhwPB08+C/7yp0Lv1SMk5hCMfZQ8/jvJb7oDps08gl5VBb90Gpq++QN5TjwOJxixJEoSF+Xl96F26VuvBHpNIPpcnImqMWAgnopQwffwRCiacAWhadKSMFA7DsnABrPPnouyu++C/8NLUndBiQeiEE6N/lXbuhFDV6MI7sQhFydlptw2F1u9AaL32hfLb2uiXwNpIuo7AhImZC4yIiIgoTUSBC8ULlsD65uuwTX8O6rpfAQB6r33hP28qAuPOBCyWLEdZnXX6s5GFKBUlmiPLZWWwvjAD1pdfgPfF1xA6bmjKzidcboRGnVIlACukxx9JuJ+kaQgdMyhlcWSMzwfrW2/A+sIMqH+uh5Ai/eT1jh2h994PwbFnQOvbLyOhBE85FY5brgcCiWZjCgTOPCcjMRER5SJJiDhVjBzi8ZSz/QVVo6oy3G47r40cIP/9FwqPPDgy9THOW0rJq28idPywtMWRf/F5sMyLv4ijkGUUffsTjA4d0xZHU2B+fzGc48+ITK+s5XmhKAgPOBglC5YA9VhAKdX4vkHx8PqgWAoL7VCU3HkvywX8OaHaNKn30crBF0n0Zc4G08cfwXX6KTGfF5IEmMwo+vyb9LVzEQLugQdD2bA+dksZWYYobIZdq9ZGZvw2EPKWzSgYMxLK+j8ASdo9QwCRNYtExWOho46Bd/qLEO7CtMeU9+hDsN9/d8znhSTBP/kClN//UNpj2RtN6n2D9gqvDYqnrvk5M3oiqjfbzOeBcDhuEVzICmxPPJrWOMruvB9Gu/YQSs3FeioX2Cz77yMsgqdAaOiJKH3iGUBVqy16JZTIl8HwQYfA+8obOVUEJyIiIkoZVc3ZIjgA5D35aK05cSVJCEDXYJs5LX1BSBK8z80ELNba83NZBhQV3udfaFBFcOg6CsadAmXjBkhAte9AlQNEKh8zfbkcBWNPTmKkdv35rrwWvssibYeq/ntX5ueBM89B+d33pz0OIqJcxgoFEdWb9Y3X4o7CBgDJ0GH+5ivImzelLQ7RogU8i5chdMqpNb6Y6F26omTGSwhMnJK28zc1wdPPwq4ffoXvPzdBO/hQhPfri+DIk1D81gKULFgC4XJnO0QiIiKiCCFg+uwTOCeejcL9eqCwT3c4zxoL84dLAaNxjTSUtm2DeflnifNzXYd19qtpjUXfvy88iz+CNvCoGs9pAw5G8TvvIVzLc7nM/MFSqOt+TfjvC0T+jdWfVsH69pvpD0ySUH7rnSj64jv4p1yA8AH9Ee7bD4GzJ6Doo+Uoe+ypnL55Q0SUCWyNQg0Wp8nkjuatXZCS/ALh+eBTaAf0T2s8qirDHSpD2Xvvw/D5oXfqElmsk4urNnl836B4eH1QLGyNUhN/Tqg2Ofs+Gg4j/5ILYJ3/NoSiRAuYlX8ODhkK74yXAZsty4HuPaloFyzz50LZvAkiLw/B44cBioLCQYcnfYwdW4vTPotPVWW4d21B2cefw9B0aPv1hb7Pvmk9Z7o4J54F89LFSRXCgcjId23fPij++Is0R9Yw5ez7BmUdrw2Kp675OW8HElG9CYcDkteb1LZGgSu9wVRq1Qrh0WP4gUlERETUxNlvuxGWd+YCQLXiZeWfzcs+RP7Vl6H0melZia9ONA32e26H7fnnAC0cGelrGLD/9x6E92KBRpGXl7lWdt27I9ysTYPPz+V//km6CA4AkmFAXfsrIAQH5hARZRmHthBRvQVHnxbtPReLkGVovfeD0alzZoIiIiIioiZP2r4dthdmxF3LRjIMWObOgbzhzwxGVg9CIP+Ki2F75v8ghUOQhIAUDkeLs+rPqyFUNbpGTszDKCoCo0/NRMSNinA6E/7b1iBLLIITEeUAFsKJqN78Uy4AhIF4fZYkw4Dv4suYABIRERFRxljnvhkZiZuILMP65uvpDygFTMs/g3XO7JjFfUnXAcOIv5A9ABg6/FOmpifIRiw44qS92l7ICrQDD0pTNEREtDdYCCeietP37Y3Sx54CJKnGivCiYqql77wLERx7RjbCIyIiIqImSt60CdgjP62VJEHZ9G/6A0oB28znE87GlAwjmoeLPVqfCEUBJAll/3sS+v590xZnYxUcdyZgy6vx7xqLZOiRgUNERJR1LIQTUUoEzzgbxe8sQWjI0GpTBbUD+sP73EyU3/sAR4MTERERUUYJe15yI8JR0S+7ATB98xUkXUu4nWQYKLv2Bmj9Dow+JiQJoWOHoGT+ewicPSGdYTZawlmAklmvAIpSYxBQjW1lGcHjjkfwpNGZCY6IiOLiYplElDLaYYfDe9jhkDxFkHftgsjPh9GqdbbDIiIiIqImKjTkBNj/91DC7SRNQ3DosAxElAIi+cUmtcOOgP8/N0Hatg2ytwRG8+YQ7sI0Btc0hI89DsWLPkDew/+F+YOl0TY0AogsPioEIMsInHkOyu5/OLlZCURElHYshBNRygl3IXQm2ERE1AAFg0F4PEVo1qw5TCYTAEDXdSgKJ1ISNUTagIMR3q8v1F9/ji4muSehKDDatUd40HFpj0dZ+ytsL0yHefEiSH4/9I4dERg/CYFTxwEOR1LH0PY7AKbln8Z8PZWELEPvtU/kz61aQW/Vqt7x025avwPhfeVNyFu3QP7rL8jbtkD5/TfIfj+MVq0QGDUGgv/mRET1IoSA3++D1+tF69Ztoo/XNT9nIZyIiIiImhy/349du3bC4/HA4ylCcXHkd5/PBwAYM2YcWrGAQdTwSRJKn58F14jjgZKSGsVjoSgQeXkomfVqZCRvGtmefwb2W24AZDkah1RSDMd1VyLv8UdQ/PZCGF26JjyOf/L5MH+6LO42QlEQOmF4TszOlLZvh/nTjwAjBFNBM2iDhgBWa7bDShmjdRsYVYozWeXzwfLOXFg+fB/wlcPo0BGBM8+B1n9AtiMjIkqorKwMu3btqMjPPdH8PBgMQpIknHfehVDV+pWyWQgnIsoS+a+NsL71BuRtWyEc+QieOALaQYewlzoRUYoYhoHSUi88niJ4PMXo1q07nE4nAOD339fhiy8+r3U/h8OBUCgY/TtHgxMlIRyG6aNlgGcHzFBhHDUoJ4qwAKB36wHP+5/Cft9dsCyYB0mL9NcWioLQiSNQftPt0Lv3SGsM5oXvwHHz9RUB7S7GV7bUkLdshuu0USj6fAWQoFd5aOgwhI46BqYvlkMyao4KF4oCYbWi/MZbU/cC6kAqKYbjhmthmf92pPAvSXAIgbyCAvguuwr+y67KubxXXfUDLAvfgeQtgdG8BYKnjoXeLb3XRqqYPv8UzsnjIZUUR262GAaEqsL2wgwEBx2H0hkvQuQ7sx0mETVxmqbB6y1BUVFkIEr//gOgVLSPWrHiK6xbt7bGPpIkwel0wufzRXP5uubnLIQTEWVaeTnyr7kMlnlvR0YeSTIAgbz/ewzhvgfAO+NlGJ06ZztKIqIGp6ioCOvX/x4dQVJSUgxN272gnMNhjybPhYXN4HK54HYXwuVyV/ndDbPZvMeRc6tQQ5RThIB15jTYH34A8q6dgCTBLgTyFAXBUaeg7P6HIAqbZTtKGB07ofTZGSi75wGov6wBhIC2T+/MtK4QAvYH74OQpGjhe0+SrkP+529Y3pmL4JnnxD+eoqDkpdlwXnoBLIsWQihqRU9qCZKmwWjVGt4XXoXeoydMX38J+d9/IPLsCA88EqLAlfrXV9vrKfXCNWoYlN/W7R6FX1n0LymB4547oGzeFOmfnQPFcHnLZjjPmwDTtysgVBWABAgD9of/i+CJI1D6f8/ldBFZ/eF7FJxxKqBrkU8sI9JHvvKmj/nzT+A8exxK5i1iv3IiyqjNmzfh77//is7A9Hq9MIzda1107dodhYWR1rrNmjVHs2bNozl55FchCgpctYwEr9tnhyREkktoZ5nHUw5NS35REGr8VFWG223ntUE15PS1oWkoOP0UmL74HJJRMzahqDCaNUPxh5/lzhTLRiSnrw3KOl4fuS8YDEanSFa2NNlvv77o2LETAGDjxj+xePGiavuoqhpNpvfdtw/atWu/1+ctLLRzVPge+HNClfLuuwv2xx6u9TmhKNA7d0Hxex826QUaldU/ofC4IxNuJ2QZ2oCDULzow+SPvW4trG+8BnlTpNgdGnoiQsefAMvbb8L+4H1Q/vl79/EtFgTOOAflt92Z9qKu/a7bYHvmyYR9zIvnvovwkUenNZZEpF274B56NOTNm2uNVygKtAP6o3j+eznb0qVg7MkwLf8s4b93yYuvI3TiiAxFlRzmXxQLr43cV9m/e89WJsccMzg6+OS771bg22+/qbaf2WyODkI58MABcLnce33uuubnHBFORJRBloXzYf7805jPS7oGeddO5D36EMoe+F8GIyMiyg1CCBiGEZ0iuXPnDnz55XIUF3tQXl5eY/uWLVtFC+HNmrXAvvv2iY70drvdyM93QsqB0YZEjZG66oeYRXAgMspZ2bgB9gfuRdl/H8lgZLlF2bwpqe0kw4BcpXCdDL3XPii/7a5qj9me+B8c99yBPUe8ScEgrK+8APW7FShZuATCkb9X50paIADrS7MSL+apqLDNnJb1Qnje00/ELIIDketYXfkdrHNmIzB+YmaDS4L810aYP/044XZCUWCdNT3nCuFElPuEEBBCQK5YS2PDhj/x448rUVzsQSAQqLG9x7MrWghv06Yt+vTZP5qbu91u5OXZs5afsxBORJRBtpnPQ1T07ItF0nVYX38VZbfeBTgcGYyOiChzKvt3Fxd7UFS0ewRJZa/A/hULe8mygk2b/o3u53A4qrUyadu2bfS5/Px8DBo0OOOvhaipss58HkJRIelazG0q85ryW+5IX+E1x4kEPb+rb2uv17mUn9fAcc8dAGqfNC7pOtS1vyDvwftRftd99TpXLOraXyB7SxJuJ+kaTJ99kpYYkhYKwfrSzIRFe8gybNOfy8lCuPr7uqS2k3Qd6q8/pzkaImrIdF1HSUnxHovJe1BcXIxhw4ajQ4eOAABNC2Pr1i0AIv278/OdcLvdcLncKCwsRLNmLaLHbNeufZ1mZKYLC+FERBmkrvohbhG8khTwQ/3zD2h9+6U/KCKiNNI0DSUlxVAUJTrt0ePx4K23Zlfr312Vx+OJ/rmgoADHHjskOoqkZv9uIsoW88cfxS2CV5L8PqirfkR44FEZiCr3hA86BIYjH3JZadzthKIgNGJUvc5lmzU9uZsTr7yA8htuSbgwZ52EwklvKsX4HMgU5d+/IZckUbQ3jEgRWddzrse2UE3Jb1yjxy4RNUWhUAgejwcOhx12e2Tw3fr1f+DDD5dW699dlcdTFC2Et2nTDscffwJcLjcKClwwmfbifSjL+C5I1BQYBkyffgzzsg8g+QPQO3ZEcNyZ7EFNREQpo+s6du7csccIkiJ4vV4IIdC7dx8cc0xktLbD4YCu69H+3VUXrSwsLITTWRA9rqIo2GeffbP1sogoDimcfMEToVD6Asl1NhsCE6fA9vQTMQdECAAwDAghYF60EKGhw4A6FBbMHyxJ6uaEXFYG06ofED584F6fIxG9c5eEMyCBip7o3bqn/PxNjXZAPwiTGVI4/s+YUFSEjzomQ1ERUS4Ih0PYvn17lZHdkfy8rKwMAHDUUYOw3377A4jk54ZhwGw2R2dfVrYycbnc1fJzh8OB7t17ZuU11RcL4USNnLp6FZyTx0P5a2PFCugADAP2++5CYMJklN37QJ2SbKobre8BUL/7DpKRoGei1Qa9a7cMRUVElJw9F8SxWq3o3r0HgEghfO7cObXuZ7FYqvUBNJlMOOusCXA4HNFeg0TU8Gg9esDkKUpqtltTz2vKr7sRphVfQ/1uRY1/L4FIGxMhBPKeegKSMGA0a47yW+5A4OwJe3UeKRhMfuNa+rqmgmjZEqFhw2Feujh+yxHDQGDS+WmJIVl6+44wnAUJW7kISYK+T++cGw0OAMJdiOCpY2GZMzvuv7eka/BPnJLByIgoE4QQKC0tjQ5EadGiJdq2bQcA2LFjBxYsmFfrfnl5eTCq1CWaN2+B8eMnwW7PXv/uTGAhnKgRU9atRcGoYZAqktw9px5aX5wBqaQYpc/OABrxG10u8U++AM4V38TdRigKAmec1WT7aBJR7hBCYNWqH6qNIAlWKbK0a9c+Wgg3m81o0aJltVXgCwsL4XK5al0Qp3IBHSJquAITz4P566/ibiMUBeEjjoTRqXNmgspVNhuK57yD/MsuhGXBvGr9u6Wqv4tIkVzatRP5V10KqawU/qmXJH0avWtXSMWe5G5OpPH/pPyaG2D+6AMIIWqNRSgK9G7dERgztuIBAdPHH8I243mYvvsGMAS0vv3gn3w+QsOGp68AbTYjMGESbM88Gb9oLwT8Uy5ITwwpUH7z7TB9+jHk7dtivg7f1IuhHXhQhiMjolTz+3345Zefq8zALIam7Z6hdcAB/aKFcLc7MtMyMsK78lckT7dardWOqygKHE1gjTJJCLHnYtI5yeMph6Yl/jCnpkNVZbjd9ty6NsrKYHn3HSj//A1htSF03PHQe/fJWjjOs06r6N0Yf/SxZ8FSaIcdnqGo0i8nr41KmoaCcaNh+nJ5zC8ForAZPB9+BqNN21oOQPWR09cGZV1TvD40TYPXW4Kiot2tTKxWG46qMnX6hRdmwO/3Rf8uSRKcTifc7kK0atUaBzaBL9WFhXYoCkeuV9WUfk4ojlAIrhOOhbr2l1rzTSFJgKKgeP5iaIccmoUAAWn7dlgWzoe8ayeE243giFEwKgoEGafrKOzfG/LWLbUuZFkbIcsoWvlzNGblt3Uwf/QBJL8PevsOCI4YBdh3L7Bpmf0qnJdflPCY4YMPRcnCpXV9JUkxffE5nOeeCcnrBSQJkhAQigJJ1xHusz+8s9+G0ao1EA4j/8IpsC6cH30eQPTPoaOOQclLs6u9zlSSdu2C+/ijIW/ZXPt1rCjQ+vZD8TuLgT0KR7lE3rwJjisvheWTjyBkGZBkSLoGw5EP35XXwH/ZVTk5+Kkp5l+UnKZ6bYTDoejsy8r8vG3bduhbsX5YeXkZXnppVrV9FEVBQYELLpcbXbp0Rc+evbIQeWbVNT9nIZwarLS+KWoazEveg+n7bwHDgNZnPwRPGg3YbLVvLwTyHn0IeU/8D/D5IouQGAKSoSN86GHwPvkcjM5dUhtjAvK//6BwwH6QEvyIC0VFcNTJKH1uVtztGpKc/8AsL0f+lZfAsmAeIMvRhFTSNIT36wvvzJczfr00FTl/bVBWNebrQ9M0qFUWyFq27ANs27YVXq+3xoI4Dkc+xo+fGP37t99GZrFUjiApKHBVO1ZTwEJ4TY3x54TqRtq5EwVnnQbTjyujhUtRWWyz2eB9/gWEjh9W/xOVlcH6zlwo69YCJhPChx+B0LFDYo8U9vvhuPE6WN94FTCMyHYV73fBk0aj7JHHIar0O80E8/uLUXDO6Xu1j5AV+K66FoGzJyD/sgth/uLzSJFTlgFNA/Ly4Lv8aviuvDbymN8P93FHQtnwZ+ybE5KEkrcWIHzk0al6abGVlcE67y1YFy+EqbwModZt4Tv9bIQHDY7EC8BxwzWwzpoe83uLkGUER5yE0hkvpy1MefMmOKeMh+n773a3kxQCkq4jeMKJKH1qWsavl7pS/vwDpmUfQvL5YLTvgOCwEelZFDVFGnP+RfXT2K+Nqvl5MBjEBx8sgcfjQVktCyt36dIVw4aNABCZsfnZZx9XG+ntdBY0uXaDLIRTk5OuN0Xz+4uRf9VlkHdsj6zALUUWAjLynSi/+34EzhpfYx/7rTcg77mnaz2eUBQItxuepZ/AqFhhNxPMS95DwYQzktpW79QZRd/+lOaIMqehfGDKGzfA+tYbkLduhXA4EBx+ErSDD8nJkRqNRUO5Nig7Gvr1sWf/7soRJMXFHqiqijPP3P35NW/eW9i6dQsARFuZ7G5n4kanTrwZVxUL4TU11J8TShPDgOmzT5D3+sswb/oHYWsegieciMDpZ9W/eCgEbNOehv3+uyMDTkymSIFS06C3a4fS/5uG8MCjqu8TCqHgjDFxZ+BpffZH8YIlGS0Q5j10P/IefahGu8JEwgcdAvmfvyHv3BG77cWUqSi//yEAgLxlMwrGjob629rdNyeASOFZUVD69PMInjymnq9m78T6jJW2b0ezA3olnMEKAEVffg+9oh1Xuqg/fA/LuwsgFRfDaNkSwVPHpf2cTV1Dz78ofRrDtVHZv7tqXl6Zq7dr1x5Dhw6Lbjd9+nPRFic2W161hSpbtGiJNpwxXk1d8/OmNZyHKAHzB0vgHL+7eCxV6bMklXqRf+UlgKYhMGFS9HF11Q8xi+AAIkmdxwP73behdNoLaYmbGiajcxf4rr0h22EQUQMTSai9KC0tRbt27aOPL1w4H5s2/VvrPpIkVRt1csghh0EIAbfbXWv/biKivSLLCA8ajPIhQ2B221GWwqKF7clH4bjnjt0PhHfn5/KWLSgYezJK5r6L8GFHRB+3vvk6TMs/i9l+RNJ1qGt+gm36c/BfflVK4kwnZcOfkb7fcYrFeTOeQ/C0cdAGHAyjTVt4Pv4C5iXvwfrqi1D++gvCYUfoxJHwn30uRKtWGYw+Puu8OUASY/OEosDy5uvw3XRbWuPR+g+A1n9AWs9BRI2PrusoKSlBOBxGq4r3WCEEXnhhOgIxFib2eIqif5YkCccddzxsNhtcLjdssboRUL2ltRC+YcMGjBkzBrfeeivGjMnsHWeivabrcFx9OQDUOi1PQmRFd8ct1yM4ekx0dIt15vMQigpJjz2yQ9J1WBa+g7Lt2yFatkxH9DVo+/eFqOjFF49QVYSbQH9XIqKGqKSkGDt37qw2gqS4OLIgjqIomDJlKpSKtgAOhwOSJCE/3xkdPRJZrDLy56rtTKoW0KlpYX5ODYm8dUtkJHgMkmFE8vNrroBn+YrorDrb9Ocio5/jLRhpGLDNnAb/pVdEW3Skm9av/16PBheKAslTlHDxS6GosL0wA6UDDo48YDIhdNLJCJ10cl3DzQh58+ZqbWviUTZvykBERETx7dixvWJU9+41dkpKSmAYBlq0aIHTTosMrpQkCXl5doTDYTidBRUzMHeP8na53NWO27Vrt2y8nCYnbYXwcDiMa6+9Fj6fL/HGRDnA/OH7ULZtjbuNBEAEg7DMmY3AlKmR/b5cHrcIHt1X12H68XuEhp6YinATMtq1R+j4EyKL6cQZPSJpGvyTc3cFdCKixi4UCkVbmRQXe3DwwYdGe/ytWPE1/vjj9xr7VC6IEwj4YbdHVnc//PAjcdRRg2AymTIaPzUczM+pobG+8mLC0cKSYUD9fR3UFd9AO/QwIBCA+suahMeWECmsytu2ZmyB8tDg46G3aQN569aEg1UqJdMyJLKdBtMXn9cnvKwQdntSRXBIEkQO97kmosbF7/dHC93hcBgHHNA/+txHH70Pj8dTYx+TyQRVrZ6Hjxw5ClarLTpwhbIvbYXwJ598EvY0repMlA7qD5GFURKO0pBlmFZ+j8CUir8nk7hV0jPb16r8trth+nI54PfHXCwneOo4aIccmtG4iIiasn/++Rt//bUxOoKkrKys2vP77tsbzopZRy1atITX660xgqS2BXE4hZISYX5ODY268vukcm0hyzD98F2kEJ5k4Thqb7evD0VB2cOPw3nO6cnN3JRkhI84AuYvlid3/L0cbZ4LQiecCPvD/024naRpCA4bnoGIiKgpWrduLbZu3Rzt3x0I+KPPmc1m9O3bL9pKsFWrNrBabdVmXrrdhdHZmVVVDlih3JGWQvi3336LN954A/Pnz8egQYPScQqi1DMEELOT4B6qJK1a7/0gb96U1GgNrdc+dQyubvSevVC8YCmc502A+uf6ihXQJcDQAUmCf9L5KL/rPi7OSESUIpUL4lRvZeLBkCEnwOGIJMKbN2/C6tWrqu2Xl5cXTaKrfhb163cg+vU7MJMvgRop5ufUIAkj2ew80sMQAPLyoLdtF8nPE+xiOAtgtMxsv+zQ8cPgffF15F95CaSiXRC1tXCpaLsYHHs6yq+6Ds0OT/w5IBQFWu/90hR1+mgH9Ee4/wCoP/0Y8/uUUBQY7dojPOi4DEdHRI2Bruvwer3VWpn4fD6cdNLo6DZ//PE7/v57Y7X9KtsNut1u6LoebTN47LF8L2rIUl4I93q9+M9//oNbbrkFbdq0Sdlx67ISKDVulddEqq4NccAB1RbHjMkwYBzQD6oaOW/ovAtgWfpe/GMrCrSBR0Lu2QMZv5L7HYDSb3+E+vmnMH34AeD3wejYGaFxZ0C0atUoV8xN9bVBjQevDYpnb64PXdchSVJ0VPZvv63Djz/+AI/HE13tvSqvtxgulxMA0LlzJwCiIrGOjPS2Wq2peyGUcg39fnE68nO+j1JtUv05a/TrB/HJsoQDTiTDgOh3QDQ/D553AWz33Bl3NLlQFITOnQQ1L/Pvv8bIkSgZOhSmRQuhrvgGCPohlXgh+f2ArsPo3gPBCRNh9NoHMoDwsYOhfvZp/HaHuo7w+RdE/w1yTbxrwzfzReSfMBjYtavGaxSKAmG3o/zVN6CaG+M3F2J+TrHs7bURDoertQj89tsV+OOP31BSUgK9lvfPcDgYnVHZq1dPtG7dssoaOy6YTOYUvApKl7rm55IQSTYnS9LVV18NIQQeffRRAECvXr1w//33czEeyn3hMNC2LbBzZ/ztzGZg82agWbPI3w0DGD4c+PDD2qdWyjKgqsDy5cDBB6c+biIiSptQKISioiLs2rULRUVF0V/FxcU47bTT0KFDBwDAmjVrsHTpUgCALMtwuyNJdLNmzVBYWIiOHTuyJQVlDfNzarD++gvo0iV+n3BJArp2BX7/ffe34uJiYMAA4O+/a28XoqpA8+bADz8ArVunJfSU+v57YODAyPeV2or7igIccwzw/vuRPzdE//4L3HYb8NprQDAYeUxVgdNOA+66C+jRI7vxEVHO8Pv91fLzyt9LS0tx8cUXRweYLFu2DD/88AMAQFXVaG5emZ937tyZa+s0QSm9pTp//nx89913WLhwYSoPCwDwev3QM9xfmXKboshwOm0pvTZM/30YjvMmIl6TFN/NtyMoWwFP+e4Hp78E+0Xnw/zugkj7EV0HZBmSrsMocKH8hZehde9dfZ8UkHbugHne25A3b4ZwOBA6cQSM3n1Seo6GKB3XBjUOvDYoFp/PB6+3BB06tIZhKNB1A2vX/oqPPvog5j4bN26Cw1EIACgoaIFBg45HYWFhrf27QyEgFErtZwBlTkGBrcb/aUORrvyc76NUm5R/zjqbw3rlNbA9+nCtTwtJAiQJZfc/DK246iKwJkjvLIbj7HFQf1oVyc81DahYD0jv3AVls9+CYclPeX4ub/gTpgXzIRcXw2jeAqHRYyDatavfQbvuA/Wt+bBPOBuypwhCViKtDiteT2jIUJRPmwl4A6l5EWmQ8Nqwu4FHnoR0291QfvkZEAJ6z14QzVtEnt+b/yefD+a358D0wVJIPh+Mjh0RPHsC9AMHNPwpPo0Q83OqjRACfr8P4bAPTmczSFIkD/vii+X48ceVMffbuHETWrWK3ODs0KEbmjVrHbN/d1lZCEAoba+B0quu+XlKR4SPHz8eK1euhNm8e/qAz+eD2WxGx44dsWjRojof2+Mph6bxTZF2U1UZbrc95deG5a034LjuSkjl5ZFRCECksG0yofym2+G/6NKYCZTy6y+wvvYylH/+hrBaETrueARPGg2kesp7OAz7bTfC9uLMyAKcqgIYBiRdR+jwI1D6zAwYbeuZcDdg6bo2qOHjtUHBYBDbtm2t1sO7ckEcWZZw8skj0b59V2iagc2bN+Gdd+bCZsuL9gd0uwujvbztdnuNhJoap8JCe4Odsp2u/Jzvo1SbtHzOCoG8B+9D3uOPREZDV3zplTQNRoELpU8+i1CsRRSFgOnL5bDMnQN5504YLheCJ49BeNDg6HFSRfIUIf+yC2F5fwmEokSOXzFbNDhmLEofegyo78ygYBCWBfNg/nApJL8fevsOCJw5Hvr+fev/AtIsUzmY6dOP4ZwyHpLXGxmYZBgQFT3XQ8ccC++MlyAqFqSm3MD8nMrLy7B9+/Zofh6ZfemBrmuw2cw46aQxcLubAwBWr16F5cs/g8ORH12ssnIxebe7kIvHNyF1zc9TWgjftm0bAoHqd6GHDh2Ka6+9FsOHD0e7etwJ55si7SmtH5jl5bDOfxvq998ChgG9dx8Exp0J4XKn9jx1YRjIP+9cWBYtqHWleaEoMFq1huf9TyFatsxCgNnHZIpi4bXRNOi6jpKSkuhiOG3btkObNm0BAP/++w8WLpxf634uVwGOPfboaCFc13WEw2H276YGXQhPV37O91GqTTo/Z6WdO2F94zUov60FVBPChx2O4KhTAIslpeepU2xlpXANHwLl999q7eMtZAXhQw5FyVsLIm0Wm6BM5GDqyu/gOukEQNch1dJCRigKwgcdgpL57zXcFjKNEPPzpiEcDqO42BMdiLLPPr3hdEbWz/nxx5X46qsvauyjqgratGmJQw4ZiJYtI2uchEIhSBLYv5vqnJ+ntDVKq1a1r7jdrFmzehXBiTLObkfg7AnA2ROyHUkN5g+XwvruOzGfl3Qd8ratsD98P8oefDSDkRERZUd5eRnWrFkdLXyXlJTAqPIF+MADD4oWwncvUFkYHeXtckV+2WyW6BcxAFAUBQq/KFMDx/ycGgvRvDn8l1ye7TBqZXvuaSi/rau1+AoAkqHD9PWXsL7xGgLjJ2Y2uCbEfm9kgdSY/w+6DvM3X8H8/hKEThyR4eiImpadO3fgt9/WwuMphsdThLKyUlQdh1tY2CxaCG/WrDlatGgRHdVdOQOzWTM3mjd3VrtJYm6iNxMpdbjsMlEDY50xDUJREq4ab539GspvuwvCkZ/B6IiIUs/v91drZVJc7EHHjp2w//4HAAB03cDKld9V28dkMkVXfW/RYvfsGLvdgTPOODuj8RMRUSOm67DOml77IpZVyTJs059jITxN5I0bYP7804TbCUWBddZ0FsKJ6kEIgfLy8ir5eRE8Hg/69x+Ajh07AQC8Xi9Wrfqx2n5Wqy06ECU/f3edokOHjujQoWON8zTU2XiU29JeCF+3bl26T0HUpJi+WxG3CF5JCvihrFsLbcDBGYiKiKh+hBDQtHB0mqPf78PSpYuj/bv3ZDKZo4Xw/Px89OmzP1wuV3QESW0L4hBRBPNzotSRt2yGsn1bwu0kw4D668+R1ZM5ojHl1D9+S2o7Sdcj/w9ElJBhRFoFmkwmAJF2a8uXf4riYg9CoZqLTHbo0DFaCG/evAX69u0Xzc/dbjdstryMxk9UG44IJ2pojL1o659oZAoRUYbpug6v11tjBElxsQddunTFcccNBQBYLFZs374NesWNv/x8Z7VWJlVHeUuShKOPHpSNl0NERE3d3ubbzM9TyzAAISCUvShtqCyDEFUVDodRUlJcZbHKSG5eUlKMQw45DP36HQgg0rN7e8WNP1mWUVBQEG1n4nK50bp16+gxnU4nBg48KiuvhygefgIQNTB6n/0gffctJCP+qHChqtC798hQVERE1YXDIRQXF8MwDLRqFUmKdV3HzJnPQ9PCte5TXFwc/bMsyxg6dBjsdgdcLhcXxCEiopxktG4Dw1kA2VsSdzshSTA6dAS4AHP9hcOwzJ0D2/TnoP70IwBA79YdQlYSf0dSFISPOiYDQRLlnkAggOJiD8xmCwoLCwEAu3btxJw5s6v1766qpKQ4+ueCAheGDj0RbnchCgoKuJYONUgshBM1MP7J58O54uu42whFQXD0qRDuwgxFRURN2datW1BUtCvaw9vj8aC01AsAaNWqNcaMGQsgsvikw2FHeXl5xeiR3SNI3O7C6II5lTp37prx10JERLRXzGYExk+E7dn/S9i+0D9laoaCasR8PhScMw7m5Z9ByDKkiuKdsv6PyMhwAPEao0m6Dv/k8zMSKlG2aJqGrVu3RGdeVo709vl8AIA+ffbD0UcfCyAyclsIAavVWiUvd0fz86q9vFVVRbdu3bPymohShYVwogYmeNJohGY+D9P339aabAtFgcizo/zaG7IQHRE1RkIIlJWVRVuZGIYRnSIJAB9++H608F2V1WqDdY+Rb6NHnwar1cr+3URE1Gj4Lr4clrlzIG/fFjM/17t2Q2D8uVmIrnHJ/89VMH25HECk73qlyoK4qPgVK8vwXXgptCo5DFFDZRgGvN6S6EAUh8OBHj16AYjMwly4cH6t+zkcDqiqKfp3k8mMiRPPY35OTQYL4UQNjckE7+tvIf/CKbB8sBRCVSN9w2UJkqbBaNsOJS/NhtG1W7YjJaIGbM2a1di+fSuKiiIjSMLh3e1MbLa8aoXwdu3aw+crrzGCxGaz1ThubY8RERE1ZKJFCxQvWIKC8WdAXftLRX5uALIMSdOgHXgQSma9CuHIT3wwiknevAmWt96oVgDfU9UynpCkyP+BrsPId8J35bXwX3pF+gMlSgPDMPD99yvg8UR6eZeUFEfX0gEiC1VWFsItFgvatGkLq9VarYe32+2GuZbFepmfU1PCQjhRAyTynfC+OgfKLz/D+sZrkLduhnDkIzRsOEKDjwfYq4uI4giHwygu9lRrZRIMBjBq1CnRbTZsWI9///0n+vfKBXEqE2nDMCDLMgDg2GOPy/hrICIiyiVGp87wfPoVTF8uh+WduZBKimE0a47gqeOgHXgQwJGW9WaZPzep7YSqIjDuTGj794Xk88No3x7BYSMAFvsohwWDwSqLVUZ+t9vtOOaYwQAii8OvWbMagUAguo+qmuByueB2u9GqVZtqxxs9+tSMxk/UULAQTtSA6b37oPzOe7MdBhHlqGAwCIvFEv37119/iT/++L3WNiYAEAqFoqNEevbcB23btosWvrkgDhERUQKShPDAoxAeeFS2I2mU5O3bIgN+4owIBwAIASkYRIA92SnHCCEQCoWq5edLlizC9u3bUF5eXmN7p7Mg+mdJktCv34GQJAludyHcbjfy851sZ0K0l1gIJyIiasCEECgvL4+OINm9KI4HgYAfU6ZMhckU6QMYCgWjRXCr1VaxWOXuViaVI7wBoFevfbLyeoiIiIhqIwoKEhfBAUCSItsSZYlhGCgt9Vbk5cXV8nS73Y4zzjgnum15eXm0CO5wOGq0Mqmqf/8BGX0dRI0RC+FEREQNQOWCOB6PBx07doqOzv7880/w889rYu7n9ZagWbPmAIA+ffZH9+494Xa7YbPlZSBqIiIiotQIjhgF+/13J9xO0jQETxqd/oCoydM0DSUlxSgvL0fHjp2ij8+b9xa2b99W6z6GYVRrMXjEEUdClpWY/buJKLVYCCciIsoxXm8Jtm3bWtEjMNLLu+qCOKeffhYKC5sBAPLzC6L9u/ccQeJ2u2Ey7U6oKwviRJQcwzBQVlYKl8sGRZET70BERGmj9+yF0NGDYPric0hVFgmsSigK9O49ET7iyAxHR43drl07sWPHjmqju71eL4QQUFUVU6ZMjRa3CwoKUFS0Cy6Xu6KHdyQ/LywshNNZUG0WZps2bbP1kogaJF3XUVJSUuf8nIVwIqK6CoVg+m4FpNJSGK1bQ+vbjwshUdICgUC1Vib7738A8vPzAQC///4bVqz4usY+qmqC2+1COKxFH9tvv/3Qt+8B7N9NVA+apsHrLYkuThX5kluM4mIPNE3DVVddBpMpP9thEhE1ed6np8N10lAof/9VoxguFAVGYSF8l1wO84dLoXftBr1bjyxFSg2NEAI+XzmKi4tRVFQEr7cERxxxZLQH93fffYs///yjxn4WiwVudyGCwSBsFQuyHn30sRg8+PhqBW8i2jvhcCja8nN3fu5BSUkJDMPAtddeCZNp78vaLIQTEe2tUAh5jz8C2/RnIXs80Ye1bt3hu+Z6BE87PYvBUa7atm0r1q1bG/0A9/l81Z5v06ZttBDevHkLtGnTNjqyu3Kkd35+fo0FcaqO+Cai+EKhyoS6qFpC7fV6YcToOxu5ycSbnEREuUC0bInipR8j76knYH1xBuTi4sjjeXnQO3WB8ucfcF5+UXT78KGHo/ym2xA+fGCWIqZctmHDn9i4cUM0JwgGg9We79u3XzQ/b926NUKhYDQvr5x9abPl1cjP2eKEKHl+v69Kfl4cHShWVlYacx+TyQQh6nY+FsKJiPZGOAznhDNg/vgjSHu88yp/rofz4vNR/s/f8F11XZYCpGyo7N8dSaI90Q/yQw89HO3bdwAAlJZ68fPPq6vt53A4olMlHQ5H9PFOnTqjU6fOmXwJRI2GEKJKQl19BElZWVnM/cxmc5XWQoVwu10VN6CcyMtjT30iolwhXG6U33w7yq+7Eco/f0Eq8iD/kvOh/La2xihx9dtvUDBmJLwzXkZo+MgsRUzZEGu214gRJyEvzw4gMlBl7dpfovtIkgSn01llIfndBe4DDuiPAw7on/HXQdQYCCFQWlpaLS+vzNUDAX/M/Wy2PLhcLhQWFlZrA+pwOGC1WusUCwvhRBSfzwfJ54NwOoEcubMt//sPLG+/CWXLZgi7A8Fhw6EddEhG2pLYnnu61iI4gOhj9vvvRujoQdAGHJz2eCizNE2DEEZ0FPbmzZvw+eefVuvfXdWuXTujhfCWLVuhf/8B1Xp4c7QIUd1FEmpvjREkxcUeBAKBmPvl5eXtUfCO/Dzm5dlrjOgiIspFUlkpEAxBuFxAjrRGU35eA8vCeZCLi2E0a47gmNPS35bEbIberQfyH5wM5Z+/a+0bLhkGhCTBOXUydv20FsJdmN6YKONCoRAURYm2CVy79hf88MP3MWd7eTyeaCG8cgH6ynygoMAFVWWZjKiuKvt3Vy94F8HjKYamhWPul5/vjObkVQvele2GUok/4URUK9OyD2F77imYP1kGSQgIsxnBMWPhu/BS6L37ZCeoQACO666E9c3XAVmO/BICeU8+ivB+feGd8RKMLl3Td35dh+35Z5BoDo5QVdhmPIdSFsIbrGAwGP3g3j2KxIPSUi8GDjwa++/fFwCgqiqKinZV/NlUsRjO7g/vli1bRY/pdBbgsMOOyMrrIWrIIgl18R4tTTwoLo6dUEuSFE2oKxeniixY5a7z6BEioqzSdVjmzIZt2jMwrfkJAGA4CxAYPxH+Cy6CkaUF96Rt2+CcOgnmL5dDqGpkYIphwP7Q/QiecCJK/+85iAJXWs9vWTAv5uKZQGSwiggFYZ39GvwXXZq2WCh99pztVbXAVl5ejtGjT40uOmkYBoorWuZYLJZqRbXCQne1xePbtm2Htm3bZeMlETVolf27qw5E8XiKov27ayPLMgoKXBUF78JqbUBNJlPGYmchnIhqyHvwPtgf/i+EokRHOUuhECxvvQHL229GphYOG57ZoHQdzsnnwLzsw0hMuh75VUH99We4RwyB54PPgE4d0xKC8vtvULZsTridpGkwL1mclhgodaouiGOz2VBY2AwAsG3bNsyd+2bM/UpLS6J/drsLMXz4SXC73cjPd3I0KVE9VPbv3nMESaL+3ZGEencrE5crMqIrkwk1EVFahcNwnj8RlvcWQlRZfE/2lsD27P/B+tpLKJ73XsYHq0glxXCNOgHK339F/q5p1Z43f/g+Ck4dheKFS4F8e1piMH/2cdwieJQQMC99j4XwHFd1tlfz5s1ht0daB/7yyxp89tknMffzer3RQninTp0xatQpcLlcnO1FVE9+v3+PgSiRgWKJ+ndXFrmrzsJ0Op3RmRvZxEI4EVVjXjgf9of/CwA1kkpJ0yJTC6dMQNHyFekdfb1nXEsXw/Lh+zGfl3Qd8HiQ99D9CPzfM2mJQfL7Em9UuW0w9rR8yrxwOIxNm/6B11uCcNiHf/7ZgqKi3Qvi9O3bDwMHHgUAKCgoABDp3111BEnVBXEqmUwm9vIm2gtCCAQC/mozLSoL3on6d+/ZysTlcsPpLIBcpShERNQY5T10P8yL3wUQafVRlaTrgNeLgtNPQdG3PwEZnPVie/YpKH/9BcmovRAt6TrU1atgfe1laFMvTEsMkt8PgcRLGksApPLytMSQVULA9OnHsM2YBtPXXwK6Dr13b/gnnY/gSaNzprVlbfx+HzZv3gyvt7hKfu6JzvY69tgh2GeffQEALpe7Wv/uqrO93O5CWCyW6HHtdke0gE5EiQkhUFZWtkcrk8T9u61WWzQvr1rwdjgcOX0DioVwIqom74lHIWS5RpJdSRICwtBhmzUd5Xfdl7G4bDOmRUaox5v2qOuwvvUGgvfeD7hTP+rEaNsOQpJq7Q9elajYljJL07SK9gmRD26Xy4UePXoBiEzdWrx4EWRZgs1mht8fgmEISJKEgoKCar26rVYrpkyZyv7dRPVQuSDOniNIPJ6ihP27qxe8I0m13c4RXUTURPl8sE1/Nm7+Kek6lG1bYVkwD8FxZ2YmLk2D7YUZMYvgVdlmPIfSNBXC9fYdEhbBAUAoCvTOndMSQ9boOvKvuBjWN1+v9j1J+u5bOFd8g/C0Z1DyxlwIlztrIVbO9qrMBzp27BRtRbJjxw68//7iGvm5qqooKHBBUXbf6G7dug3OO+9C9u8mqgdd1+H1emsUvIuLPQiHY/fvdjjyq9x42t0GNB39uzOB7yJEFCX/tRGmVT8k3E7SdVjnzM5oIVz98fukpj1KoRDkdeuAzqkvRButWiN07HEwf5pgCqYswz9hcsrPT9VpmoZvv/0m+kHu9XohqnxJ7NSpc7QQbrPloU2btnA689GxY1uoqg1OpwtOZ0GtCTWL4ETJqVwQp2pCHfk9cf/uqjMtKgve7N9NRFSd+eOPIMeZMVNJyDIsb7+ZsUK4vHUL5F07E24nCQH1j9+BQABA6geqhI8eBL1VK8jbtsUtiEu6jsBZE1J+/myy//ceWN6cDaD6TN7KAU3qTz/COWUCSt5emLGYvF4vfvrpx2g+sOdsL0WRo4Vwt7sQrVq1RrNmhejUqR0UxQqn04X8fGeN2V650E6BqKEIh0MVvbs91QreXm8J9Bh1jEj/7oJqI7sr178ymRrXd2MWwokoSvYUJb2tVOxJYyS5y3fVf2D+9OOYI8OFokC4CxE4e3wWoms8qvbv3t1CoQgulwtHH30sgEhC/PPPq6vdvbZYLNEP7zZt2kQflyQJo0efClWV4Xbb4fGUQ9Nqn/VARDVVLoizZw/veAviVPbvrlycqjKpZv9uIqLkyRWLciciGQbkHTvSHE0OUlX4rrsJ+ddeEXMToSjQDuiP8DHHZjCw9JK8JbA99xQkxJ8pYP78U6g/fA+t/4B6nzPWbK+uXbuhb99+FdsYWL16VbX98vLyovl5q1a78/P8/HyMGTOW+TlRHfn9/j0GokR+lZZ6Y+6jqqZq6+rsXl+noMnccGIhnIiiDHdh0tsKZ0EaI6lJ69sPpq+/TDgqXJjMMHr2TF8chx4G77RZcF44BcIwovGIiin7RrPmKHlrAUTFwosUn2EYCAYD1fpuL1gwDzt37oj2767K59vdp12SJAwYcAhMJlO1/t1sn0BUd36/r8oU5t2thhItiLNnKxO3m/27iYhSIdn8XMgyRPPmaY5mN6N1GxiFhZCL4g+kEZIEvUvXtPYuD0yYBHnHdtgfuBdCUSHpkUU7K9uFaH32Q8mrc4BG9JlkeXdBxSj7+ISqwjr7VZTtRSFc13WEw+HoLK3y8jIsXvxuzNleDsfuftz5+U7063dgtH0CZ3sR1U9l/+6qBe/IQLFi+OOsYVa1f3fVgnd+fn6T/77MQjgRRRmdOiO8/wFQf14ds0c4EEkqA2NPz2BkgH/yBTB/8XncbYSiIHDquLT3wQudNBpFBx4E68uzYHlnHqTSUhht2iBw1gQEx54O4chP6/kbot39u6vfsS4pKYbL5cbYsWdEtw0GgwgGg9EFcarerXbv8WWwf/8DM/1SiBq8yhFdtY0gibcgjs2WV+uCOOzfTUSUPqFjj4Nht0NOsNCjZBgIjBlb8wkhoP70I5RffwEkCdpBB0Pv1qP+gakq/OdORt4TjyYcqOI/b2r9z5eA75rrERw2ArYXZsC87AMgFILevSf8k6YgdOJIoJHNRJI3bwJUFdC0+BtqGuQtW2p9qrJ/d9V8oHK2V48evTB48BAAkYLarl27YBhGdLZXJC+P/N6sWYvdcckyDj98YMpeJ1FTYRgGSkpKail4J+7fvTs3d0UHp1QdaEbVsRBORNX4L7sSzgsmxXxeSBIgywhMPj+DUQGh4SMROuZYmD7/tNYivVAUiIIC+K67AZkY62G0aw/fDbfCd8OtGThbwxFJqIvg9/vQuXPX6ONvv/0mimJM7S0tjfT2riykHX30oOgiOVwQh6juKvt3Vy94F8Xt3w1ERnNV/YJbWfDmiC4ioiyw2xGYMhW2Jx+NuWCmUBQYhc0QPHlMtcdNX3wO+y03wPTz6mqPh448GmX3Pwy91z71Cs1/4aWwvj0H8qZ/ay2GC0WBtk9vBM6akJHCg95nP5Q99GgGzpR9wuEA4gxcilIUlFut2Lx5EwBE+3NrmoaZM6dVW1+nqqqtFRRFwYgRo+BwODjbi6iewuFw9KZT5UAUj6coqf7dVUd2Vw5OaWz9uzOBFQYiqiY4+lSU//Iz7I89XG31cQAQigpIgPe5WdC7ds9sYIqCkhdfR/7Vl8Iy7+3I1MaKwqmkadC79YD3hVdhdOiYkUI4Adu2bcOOHdurFdjKK0YrmUwmTJkyNVrcdrlcKC8vq/bhXdkzOD/fWW00aatWrbPyeogaqsr+3ZWtTCp/HhP173Y6C6q0NNk9hZn9u4mIckv59TdD+fUXmD9YAshytUEhQlEgHPkomT0XsNmij5s/WALnhDOBWgqdpq++gOvEwShe+D70PvvVOS7hLkTxu+/DOWU8TN+ugKgcwCAASdcQOnoQSp+dAeRxZGKqhYYOg+O2m2o8vkFRsEuWUVT5S1HgycuD/s5ctGnTFqNHnwoAUFUV+flOaFq4SlGtMJqn2+3VFzZt375DRl4XUWMRCARqDETxeIpQVlYa8wZUZf/u6gXvwibVvzsTWAgnohp8N90G7eBDYHv2aZg//wRApL9ccPSp8F10GfT9+2YnsLw8lD47E+U33ArrnNmQt26BsDsQOnEEwocdES2MU2pE2id4oz2CS0tLceSRR0eL1itXfouNGzfU2M9ut8PlciMUCsFisQAABg8+Hqqqsn0CUT34/f4aC1QVFRUl7N8dufFUfUEcp9PJhJqIqKEwmeB98TVY33gNtmnPQP31ZwCA4XAgcM5E+KdeDKNd+93b+/3Iv+h8wDBqHUUu6Xpkm0unonjZ8nrl0EbrNihe9CHUH1fCsmA+pGIPjOYtEBwzFvo++9b5uFS76GwvSAgdejhMq37AYVV6hb9vtaKsYsS2kCSIvDwYPXvB6SxAfn719o3jxp3Jm99E9SCEQHl5+R4F70ievjf9uytzdPbvzgwWwomoVqHjhyF0/DCgrAxyeRmMAldaF7nZG0bnLvBdd2O2w2iUfv99HTZu3IDi4mIUF3ug7dF3cMCAg5CXFxkh0qZNWwghaiySV1n8ropJNlFyKhfEqS2hTtS/2+Vy1Sh4OxwOJtRERI2BqiJw9gQEzhoPqaQYCIYgCgtr7X1teWcuZG9J3MNJug7Tz6uhfv8ttIMOqXd4Wr8DofXj2i3p8MMPK7Ft25Zo/+7K2V7SMYPQ/N9/cOjWLdFZvF01DX5JQiGAQlWF6fGnYT/ymFpzcebnRMkxDANeb0m1nvqVfw6FQjH3czgcVWZasH93rmAhnIjiczhgVFkJnBquygVxqhbYios9OOWUsdHev9u3b8cff/we3aeyV3fllMmqBbV+/Q5EP37hIaoTXdfh9XprFLwTLYgT6d9dcwV4W5Xp8ERE1IhJUsKF4c2ffVKjxWFthKLA/PmnKSmEU93UNtsrHA5HW5gAwN9/b4z2+AYiBexIQW0fuAccAv+SRbDNfg2S34fjg0EIRUFwxCj4/nMT9J69svGyiBqkcDiMkpLdrQaLiioXkC1Oqn/3nj28zWb2785FLIQTEaWTEBlt2SKEgN/vg8VijbY9WL16FX78cSXKyspq3ae42IPWrdsAADp37oK8vLzoCO/8fCcXxCGqh3A4VNG7u/oIksQL4riqzbSIFL5dXBCHiIgSC4Vq7Q1egyRHtm1qspCf+3zlsNt3Dy767LNPsH79HzFne4XDoehnfu/efdClS9foyFK73V5tcEr5sceh/Na7Ii1zdAN6t+4QLVqk90URNWCBQKDGzMviYg9KS73s390EsBBORJRi8r//wDZrOqyvvgS5aBeEzYbgiJPgP/8iaP0HpOQcu/t3V47s3n3nOhAIYMyYcWjVqlXF1lK0CB4pcu/+4Ha73SgsbBY9brt27dGuao9JIkqK3++vNtOiMqkuLfXG3Keyf3f1gnch+3cTEWWLEDB9uRyW+XMheYogCgsRHDMW4UMPb1Br0RhduiYXrxaG3qVr+gPKAcrqn2Cb/iys78yF5PPBcLkQOP1s+KdcAKNzl5ScI95sL03TcN55F0KtWFBU1/VoEbzqbK/KfECWd+cBPXokMarb4YB28KEpeR1EjUFl/+7qBe/Iz6bPF69/t3WPgSjs393YSCLW7Y4c4/GUQ9OMxBtSk6GqMtxuO68NqiGb14bpqy9QcOapQDBYbTqqUFRIuoayex+A//yLkj5eZEGcyGjS1q1bR0eSrFr1A778cnmt+0iShOOPH4Zu3boDAMrKylBWVgqXyx1tgdJU8X2D4kl0fVT2765a8C4qityISnZBnKoFb/bvbjgKC+1QFM6OqYrvo1Sbhvw5K//zNwrGnwH1lzUQqgoYBiDLkDQN4QP6wfvSbBht2mY7zKTIG/5Es0P7JdzOcDiwa80fQF76+9Vm89qwvvoSHFdfBsgKJH33+jdCUQBVRcmLryM8eEjSx6s626tbt+7Rm9fLln2AdevW1rqPLMsYN+4suN2RtjZFRUXQdY2zvdCw3zcovZK5Nir7d1cdiFKZpyfTv7tqwbuwsBBWq435eQNR1/ycI8KJiFJE3rIZBWeeBgQCkIzqH9SVSbfj5uuhd+mK0JATauxfXl6Gf//9t9ooEq/XG10Q5/jjT0D37j0BAC6XG4qiVLRPKITb7Yp+kBcUuKotfuNwOOBgn3eipBmGUa2Vye6Cd/z+3Q5HfpXFKnePIGH/biKi3Cbt2gXXqGGQt26N/L1ysfCKHExdsxquUcPg+eDThP25c4HRpSv8Z02A9fWXIcUZ9+a79saMFMGzyfTlcjiuvizy76BXXwRe0vX/Z+/O4+So6/zxv+ro7ur7mJncITcJEAgBAoQj3PclqIALCAIqrO5XV911f+vufl2/67G76rre4gEqguKBIp4gIIiIyn0TICSQhBzT13R39VFVn98f1VXTNd0903P1HHk9Hw8ek0xXdX8qdHV/6l3vz/sNYVmIX/k2pH//J1jLVzTtn06n8cYbO9qu9urrm+MGtxOJZMervVKp1CQdMdHs5NTvHszwtldED1e/W5Ikt3730FXRrN+972IgnIhogmg3faNlENwhAJQUBf2f/k9sm78A2WwWy5evwPx6dtGePbtxzz13Ne3nNMRpXCK5aNFiXHvtdazfTTQOtVoN2WzGvbjN57OoVIrYuXM3ajWj5T5OQ5zGybTdBT65z2d0ERHNVMGvfxnyzp2QrDbBFNOE/No2BG/6Jkrv/1CXRzc2hf/6LKRqBdqPfuCuTATgNtEsfvDD0K9/7xSPcvIFv/A/gCwD7RqHCoG8aWL3F/4HO95xLbLZDA47bIObRPLyy5vx17/+uWk3Z7WX2RBcP+SQQ7F+/eHMJiUah0ql4iai5HJZ1GolvPbaTuRyuWHqd6ueRBQnuzsWi7vliIgcfEcQEU0Q7dabmy6g0pKEv/r9SMsy+hUFZUkCNr+A0p0/g4jFoWmaGwhPpXqwYMHCpgySoQ1xALB+MNEolMvlIfU67QySQmHAM6GWZQnBoB+maboNcbwB7yQb4hARzTamieBN32obBHdZFrRv3oDS+z44M+qF+/0Y+PLXob/remjf/hbUp54EFBm1o4+FfuXVLbOfZxupvx/+e+5uyop/TVHwtM+HfllGWpZRkySIX/8Spf3tWtxLlixzA+Fz5szFfvst6Wi1FwNuRJ1xGsgOLWWSyaQ99budubmuVyGEQCAQ8JQzcbK7o9EYb0BRx/hJTUQ0Rnb97pz7xW0MDCATCuGgWg2H1csnWJKEpxqWXUlCICoE5kQiiK07FPPmzXcfi8XiuOCCi7p+HESzgdMQZ2iDqkwm03H97p6eHixduhCyrCEYbL4BRUREs4+0dy/kdP/I2wFQdr0BKZ+DiCcmfVwTxTj0MBQOPWyqh9E1jau9ck89iVoggLQs4+RKBfvVs8IHJAnPNpQRlIVAQi9h3n5LkOjtQzQacx9bsmQplixZ2u3DIJoVLMvCwEC+XmZwMOCdzWZQqVTa7heJRJBIJNHb24P99lsARdEQiyUQDIY4P6dxYyCciGgEtVoVhmEgGLRrKObzefzyl3cgl8u59bsBIBQKQSqX0WdZQD0QnrQsHFWpoMeykKr/5wOQPuMcmCtXTcXhEM1oTkOcxhrezp+Hb4gTbVhtkXAzSJzzGmCzJiKifdJoy8yxLN20UC6XIcuyW+f3tde24fe/v9dTv1sqFBCqB7z3yrIbCF9gmji2YX6esCzI/gD2nn3ezMj2J5pmDMNALpcdkt1tlzYxjNblBiVJQiwWa6rdnUgkEQgEAHBuTpODgXAiojpdLzUE1zJug7xCYQAHHngQTjjhZABAMBhENpuFEMJtiJNKpTB/43GY/+tfYk5tMBinADiuITgnJAnGipUwV6zs9uERzShOQ5zBZpUZt1Zgu4Y4Tv1ub0OcJOt3ExFRW6K3F+aixZBffw3DhUCFJMFcvgIiEm2/UaGAwC9/DmXHdliRCKqnngFr6bIJH/O+YqTVXps2nYiDDjoYAOD3+90geONqr0W/uANzNr+IeQ3BuIQQOLpxfq6oqJx/AYPgRCOoVCoNjWMHz8t8Pj9s/e54POEpLZRMJhGPJ1hOiKYE33VEtE8RQmBgYADZbAaKomDhwkUAgGq1iptu+mbb/Rprlfl8Ppx33psQi8URiUTc5VnK/IVI3vkzoM0kALBLo+jX/x0n2kR15XK56eI2m81gYGC4CbVdv3toB3jW7yYiolGTJOjXXofwv//LsHM4ANCvva71HM6yEPr0pxD60ucBvQSoqt2c8SMfRvXk0zDwuS9BzJ07SQcw8zWu9opGo+jt7QMA7Ny5Az/72U/a7lcsFt0/p1I9uOCCi5pWewXe90HE3n31CAMw7f+3ROTW785mh2Z4pz3n3FCBQMAzN0+l7MB3NBqDzJU0NI0wEE5Es5YQAq+88rLnyzubzaJWL1uyaNFiNxDu9/vrQW25fpd6sEleIpFsaojj7NfIPOBADPzPFxF9/3sgZBlSQ9aqkCQ7CH7Z21G+/MpJPGqi6cfJ6PIGvO3zsvEm01Capnkyu53JdTQaZX1AIiKaMPpV1yBw+4+gPv2kZ/7mELIC47DDUb7s7c07C4HIh94H7eZvD2aUN2Qf++/7HZJnn4LMb+6D6O2dnAOYQarVKl5++eW2q73WrVvvBsKTyWTHq718Ph8WLFjY9HqVN70ZpUcfQehrX7Ln5w1lDYWiQDJNFD7xXzAOO2KSj5xoehlav3vwenn4+t3hcHhIwNsuOxgKsb8OzQwMhBPRjFarVesZpHYJBVVVcdRRRwGw647df/99TY3y7Al1AvF43PP7v/mbt487m7Tytsth7bcEwc9/Fv777nG71Jv7r0Hpb/8OlUsvYzY4zVpORtfgksnBCfXw9bsjTQHvVCoFTQtyQk1ERJMvFELux3cg8oG/Q+DnP7PnaopiZ3UDqLzpQgx8+vOApjXt6nvwAQRv/nbbp5ZME/KO7Qh/+pMofOozk3YI08nQ1V49Pb046KADAdiB8Lvv/m3TPs5qr1BoMJs7GAzh2muvG9/8XJJQ/NgnYBy6HsEvfwG+p55wH6ptPBal974ftZNPHfvzE01zk1W/m2imYiCciGacv/zlT9i9e7dbv7tRNBpzA+EAsGzZMhiG4fkSj8ViLSfUE1VSoXbs8agdezykPXsg794FEYnA2m8JA+A0azj1uwcvcu0bUSPV7x46oXYC306jKyIioqkiYnEMfOM7KL7+GgI//xmkTBoilULl/Athtcg0dgS/dQOEokIyWweUADsYrt16M4r/8tHha4zPULVaFQ899Me2q72WLVvuBsLD4TAWL97PbWI90mqvCZmfSxIqb74YlTdfbNeCz2Yh+vpgzZ03/ucmmiaq1WrTykvW7yZqxnc2EU0bQggUCgXPsqx0Og0hBC688C3udq+99hp27XrD/XtjQ5xUqsfzRe80uJwKoq8PZl/flL0+0XhVKhXPuehkkAxfv1ttKmWSTCYRi8U5oSYiomnPWrQY+vXv7Xh73x//MGwQ3CHpOpRnnoFx1NHjGV7XtVvtlUr14KSTTgFgZ3O/8MLzMIyau1/jaq958+a7v5ckCeeee0HXj8NhLVoMLFo8Za9PNB5CCOh6qWnlJet3E3WOV6RE1HWmaaJYLCAWGyxNcs89d+GVV15263c3kiQJhmG4QbRDDlmHavUA98t8aP1ullIg6pzTEKfVhHqk+t1ODX37RpQd8I5GYzwHiYho32G0XgnVSicB86lSq9VQLpcRjdoZ60II/PjHtyGd7m+52qvxd5Ik4aijjkYgoHG1F9EEEEJgYCDf1Ew+k0kPW787FAo1lTNJJpOs303UgIFwIpo0tVrNzR5prBOYz+cAANdee517B1oIgVqt5jbEsb+4nWaVCc+d6pUr95+S4yGayZyGOI2lTJzzcqT63a0aVAWDIU6oiYhon2euWQPpr3/xNGFsRcgyzBUruzSq9obW73bm6QMDefT29uEtb7kEgB3cNk0TpmlCVX1IJIaWT0h5nveQQw6dgqMhmtkMw0A+n2tYeZlGNmuXHxyufnc0GvP01XHOS9bvJhoZA+FENG7lctkNqq1Zc6AbHLv33t/h5Zc3t9xHVX0oFAqIxWIAgMMO24D1649APB6fsFrdRPuiwYY43gvcThviDM0iYUYXERFRe/o73onYnx8edhuhKKiecVbXalI3rvYql8tYuXKV+9jtt/8Q2Wy25X66XoIQwp3Ln3zyqQgEAlztRTROdv1u78rLbDaDfD4Pq81NNEVR6vW7U0gmE+4cPR5PwOfzdfkIiGYPBsKJaFT27t2DnTt3eIJsuj5YPmHhwsVucDuZTHrqdzdmlQ5tiJNMJrt+LEQzWaVSGZLRNTihHql+t53VNZjhzYY4REREY1M5702o3fAVqE8+DqlFCREhy4Dfj+I/fmTSxrB9++vYvXt3y9VegUAAK1asdOfdyWQKhmF0tNqrr2/OpI2ZaLYRQqBc1pFOpz0rorPZDAqFQtv9/H5/UymTRMLur8P63UQTj1e9RNOdZcH30INQtrwC4fOhduzxdpOXSXs5C7lczvPFffTRxyAUCgMAXn75JTz66F+b9nM6vzdmnB5++AZs2HDUpI2VaLZzMrqy2eyQJZOZUTfESSZTiESinFATERGNV6UC/333QN71BkQshvwNNyL6vr+F/49/gFBVSIYBIcuQLAsimUTu29+HeeBBY365oau9CoWC26gSAB5//DFs2/aqZx9JkhCPx5FIJGEYNfh89gqv008/i3MBonGw63cPNNx4Ggx6l8vltvuFQqEhAW97nh4Os343UTcxEE40jfnvuB2Rj/0blG1b3d8JSUL19LNQ+NSnYS1cNCGvs23bVjz33LPIZNLI53NNDXH233+NGwifN28eli1b3pRB4kyuG3GSTdQZp353NptBOu3NIBmuIU44HG7ZEIf1u4nGR9dL8PsDbqkuy7KgKPxOI9rnWRaCX/o8Ql/8H8iZjPtr4fejfOllyHzon6D99CeQd2yHCIdRPeMsVM57EzCGur3PPfcMtmx5pe1qr6OPPsZtGL9o0SL4/b6GG+ApxGLxlqu9OD8n6oxpmsjlck3lTDKZLAyj1nIfp35343Wyc15qmtblIyCaPYQQ0PWSG5cCxj4/ZyCcaJrSvnMjoh96H4YWOJCEgP/u3yBx5qPI/uZeWAsWDvs87RrinHTSKVhQ37dYLOCVV15y91FVH5LJhBtcc7rHA8CSJcuwZMmyCTtOon3JYEZXxpNF0mn97saAdyKRZEMconFwMrq8F7eZek1dHW9966Xo7e1ztyWifZwQiPzTBxG86ZtND0nVKrSbvwP16aeQvf0XQD1A3fpp2q/2uvTSy93eHHv37sXWra+6+wUCAU8iSuMN73Xr1k/ccRLtY2q1quc62Tkfc7nciPW7nZWXzhyd9buJxseyLOTzOc/56PzZMAxce+11bqLKWOfnDIQTTUPyzh2IfPgDAIBWOZ2SaULeuweR/+9DyH/7VgghUCwW4fP53MDYli2v4P7770WpVGrxDEAmk3YD4QsWLMQxxxznCXwzm5Ro7OyGOGkMbYozUv1uuyFO0hPwZv1uovFxMrqy2QzmzZvnZpI88cRjeOihB9vuNzAw4AbCmUFJRL777mkZBHdIlgn18UcR+tL/ovShf3JXe0UiUfei/bHHHsFjjz3SdrVXJpPB3LlzAQArVqzkai+iCaTrpYa5eWOpoYG2+wzW7/bW1Gf9bqLxqdVqbsmvFStWuefTvffejRdffKHlPrIso1AYQDyecP8+FryyJpqGtO/eBLQIllkAspKEtKIgLcvYe+/v8No3v4YM7MDbSSedgjVrDgRgf2k7QfBIJOL54k4kku7FPQDE4wlmkhCNkrM8y8kgaQx4j7Z+t91ANsYJNdE4FQoF7Nix3XM+NmZ0nX76WVixYiUAIJFIQFEUxGLxpgZViUTSk9HF4BMRBb/5NQhFaWqIaQDIyDLSsox+WcbeG7+B1xYtQm5gAIZh4C1vucRtOqkoCiqVStvVXqlUyn3eBQsWukkrRNSZkVZ7tRMMhppKmSSTKdbvJpoAmUwGb7yxw7PyYmBgMEFszpy5bnDbTgKzKxQ0nouJRBLxeNy9sQyMfX7OQDjRNCTdezf2AEirKlKWhTn1C/htioIfh0KebSuPPwbjwIMgyzJ0ffDLfc6cObjooouRTCbdJZZENHr2hDrfVF4ok0mPWL/bG/BOIZFIIBTihJpoPHRd91zcrly5CnPnzgMA7Nr1Bn73u9827ePz+ZBIJD03mxYvXoJrrnm3Z0JNRNSOuP8+7BQCaVXFUtNEpH4B/5jfj/sbS5WVdeibN8Pq7YWqqiiVBm+Or1y5CgsXLuJqL6Jxalzt1Xjze6T63ZFItH7jyRtkY/1uorFzKhQ0ljI54ogN7irMzZtfwCOP/KVpP00LIplMekqEHnroYTj88A2Ter3Mb1+iKVYul/Hqq6801AjMoJbNQg7bHxpHViqYU60CAHosC6oQSFoWeiwLKcuCtmp/hC75G8TjCc/FvM/nd5dWEtHI7Al1c/3ubHb4CXUsFkOrDvCs3000MbLZDJ544rG2GV2hUMgNhPf09GD+/AX1G0+D52MkEmmaUDMATkTt5PM5bNu2zRNgE/4ApPpKkfN0HfvXL9xTpomAEEg1zM/Vo45G9KiNTau9QqGwp9EXEQ3Pqd/tlDJptdprqE5XexHR2G3f/jpeeOF597ys1mNWjmXLlrvfd3PmzMHixfvV5+UJ99wMBkNNz9uNm8QMhBNNslZ3x+bOnYfVq9cAAKrVCu6993eefQI9PQju3o1eo4ZoQ4mUiBD4P4WCp2549sijUUv1dONQiGaFSqWCXbvewN696ab63SM1xGmcUDsNcZjRRTR2pmkin88PyebKYM2aA7F27cEA7Cazzz77jGe/SCSKVMo+H+fMGbzpm0gk8aY3vbmrx0BEM49Tv7uxbMKaNQdg/vwFAOxGlQ88cJ9nn2A8jkgmjZRpQm2Yny83TbynYX4uFAX9RxwJUV/mTUQj03UdO3Zsx969/e7qy3Q6PWz9bme119Cb3/E463cTjYdTv7txpUUmk8bxx5/gluwaGBjACy885+4jy7Kn5Fc4HHEfW7p0OZYuXd7142iHV+9Ek6BSqeDBBx9oe3esUim7gfBoNIb99luKeDzu3q2es+ZAzL/kwqZGmY1/F5IEc+ky1I7aOLkHQzQDCSFQLuuelRZ29/cMTLMKXa/Csprr8A82xPFmkLAhDtH41GpVGIaJYDAIAMjlsvjVr36BfD4Hc0i9XQDo6/P2sTj88A0NfS4S8PlY8ouIRied7scjj/wFmUwGuVzWsxQbsPsGOIHwnp4eLFmy1LPaa75lIfWxf4M0pI+PZ36uKKicdwEEk1SImgghUCgUmm5+5/NZAGbb+XkwGEIikehotRcRdc7pWeGU0t22bSseeOD3nvrdjdLptBsInzdvHjZsOMr9nozF4jMmQWzCR9nf349PfepTeOCBB1CpVLBhwwb84z/+I1auXDnRL0U0JQzDcLvbNjbJmzdvHk444WQA9nKOl1560b24lyQJ8Xjc/eKeP3+++3ySJOGcc87zvsiChaidcBJ8f7i/qSEPAAgAkhAo/su/A/zyp32Y0xDHW8rEPifL5XLT9rIsIRj0IxQKD1ky6dy5Zv1uovEol8v170dvg6qBgTzWrj0Exx9/AgD7ojaTSQMYzOhqPBcbGzr7fD4ceeTRU3I8swXn5zTbVavVhrm5/fmTzWawdu0hOPjgdQDsLPCXXtrs7qOqKuLxhPv509iYMh5P4OyzvfNzccVVsL51A+QdO1rPz2UZ8PlQev8/TNJREs0M7VZ7ZbMZ1GrN5Qad+XksFkMslnATUZw5gXMTnYhGTwiBUqnoiV0552WpVMKJJ56MAw44CID9vZjP5wAAmqY11NGvJ2zOmeM+byKRxBFHHDklxzReEx4Iv/766yHLMr7+9a8jFArhf//3f3HVVVfhrrvu4gcYzSiVSgWVShmxWByAPXm+7bZbkM1mW94da6w1qigKNm48DsFgEKlUavR3xyQJ+RtvRuwdl8P/+3shVBWSYUAAgCwDkoyB//4fVM+7YJxHSTQzOA1xGr+47Z/D1++ORmMNk+kkent7sWzZQpTLFgyjdRkUIhqeU/Irk0lDURQ3eFQul3HjjV9vu1+xWHD/7Pf7cf75FyIWizOjqws4P6fZQAgBXS9BCOEuuc5mM7jjjttRLBZb7tPf3+/+OZFI4uijj3Wb5A2t3z3i68fiyN7+S8QvfhPUV16GUBRIpglR//wS4TDy3/k+zAMPGsdREs0ctVq1Xrs7MyTDu/VqL8Aun2CXGxwMdPf19WDp0oUoFmucnxONkVPyK5NJIxaLI1VfmbRjx3bcccftbfcrFAbn5319fTj//AuRSqWgacFZOz+f0EB4JpPBokWLcP3112PVqlUAgL/927/FBRdcgM2bN+OQQw6ZyJcjmhDO3bHBEgr2z2KxiHnz5uPCC98CwP7StiwLQggEAgHP3TH7P+8SyIMPHt/7XUSiyN32U/j+9Edo370RyubNQCCA6okno3zFVbDqjcGIZhOnIU7jhNouaTJ8Q5zB+t2DHeDj8URTQxxVlREMBlEut75gJiIvIQRefXVLU0aXU/Jrv/2WuIFwTdMQCoUgy0rD8uXBrK6hAdeFCxd1/Xj2RZyf00xjr/bKe1ZfOhne5XIZBx10MDZtOhGA3XzSCYKHQqGG+bm94iuVSrnPq6oq1q8/bFxjs/ZbgswDf4b/N7+C9oPvQd6xAyIeR+W8N6Hy1ksgItFxPT/RdKTr+pBElMHVXu20Wu2VTKYQi8WamlWrqgy/349isXVyCxF5VatVbNv2quc7srHk1/r1h+Poo48BYJf9Glq/uzGW5ZRFAQCfz79PzM8nNBCeTCbx2c9+1v373r178c1vfhPz5s3j0kuaUo0NcarVKlatWu0+9uMf/7BtE46htb3POONsBINBBIOh7twdkyTUNh6L2sZjJ/+1iLpI10sNS7MGSw0N1xCH9buJJketVnODTJlMBj6fD+vXH+4+fu+9d6NSqXj2sTO64ohGvUGfyy67csbUB9xXcH5O05W92svOJvX7/Vi8eD8A9qrM733vOy33kSQJ1erg55Hf78dFF12MeDwOTdO6Mm74fKieez6q557fndcj6gKnfndjwNtOFMtC10tt99O0oKeRPOt3E02MSqXiSUTp7e1141jlchl33fWbpn1UVW0qJxQKhXHNNe/m/LzBpP1L/Ou//ituu+02+P1+fOUrX0EoFBrX8ykKgxzk5bwnWr03Xn11C3bv3u1+gTfeHQsGQzjggAPcbfv6euHzqW72WiqVRCJhB9oCgYDneefOnQOa/oZ7b1D3OPW7ByfSgz91XW+5jyxLCIXCQybU9s+JqN/N9wYNZ196f/z5z3/C7t27kcmkMTAw4Cn5FY8nsGHDBvfvK1euhGmanvMxHo83ZXQBgKrOziaWs+VafiLn5/vCeUKj1+5z1LIsvPjiC5463vl83l3ttXjxfli2bCkAIBIJIR6Pwe/3ez53ksnWq70WLpwPmv72pe/Y6cyyLPcGVOP5mMm0rt8N2PPzaDTadD62Wu01FnxvUDv70nujWq3iT3/6o3s+Di35tWLFSjeOlUzGsd9+ixGLxesxrFQ9QSzW8nrZ52ues88GY52fS6JVseMJ8NJLL6FcLuPWW2/FnXfeiVtuuQUHHcR6aTQxKpUK+vv7kU6nkU7bRf7PPPNM9/HbbrsNr732mmcfRVGQTCbR09ODs846y72AF0LwbjXROJimiWw2i3Q67Z6Xzk/nBtRQkiQhFoshlUqhp6cHqVTK/Y/1aonGzsnoGno+AsAll1zibnfzzTdj165d7t+dnhapVAq9vb047LDxlQ+g6Ynzc5osdv1u3fO5EwgEcOyxx7qPf/nLX25qZO33+5FKpbB48WJs2rTJ83ycnxONXa1Wc6+VG6+bM5lM23KDsiwjkUi484HGOXpj+QQiGh37BlTOPQ+d83LOnDk49dRT3W3+93//13N+RiIR9xxctGgRVq9e3e4laBQmLRDusCwL5513Hg455BB88pOfHPPz5PM6TJONE/ZlTz75BLZseQWZTBrFYhGyLCEQ8KFSqcGyBN75zuvcL+jHH38M6XS/e3csmUyOuiEOzVyKIiMWC/JzY4I11u9uzCAZqX53IpHw3KlOpVItM7q6ge8NGs5Men9YloVCoYBYLOb+7u67f4stW15pKusF2Ofiu951vfs9+MILz6NWq7nn5XhX7s128XhwVs0hJmJ+PhPOE5p8f/jD/e7qknK57JmfR6NxXH75291t77//PgghJny1F80MM+k7diYpl8tNmd2tVns1UlVfy9WX7VZ7TTa+N6idmfbeMAwDlUrZbegshMCPfvSDtglifX1zcPHFl7p/f/zxx6BpAbfMEG9ADW+s8/MJLY3S39+Phx56yJNtK8syVqxYgd27d4/ruU3TYgfhWap9Q5wsLr/8SjdYtmfPHmzbts3dLxwOY+HCeQgEwohGEzAMC7Jsv0fWrl3X9DqWhbbBOpqd+LkxNrque5rhOCVNhqvf7fP5PBNpp/nGcPW7p/L/Dd8bNJzp9P6o1Wr1JcxOeaHBhjiSJOGaa97tnmO1moFyueLW7x7aDMf+nrSfd8WK/T2vM12Od7qa3LSRyTVZ8/PpdJ7QxLPrd+ca5gP2T0DCW94yuLrk9de3Y88e+30kSRIikRgWLZoHvz+MeDzpeY8cc8ymoS8D0xQAZvAJRqPGz47RE0KgWCx6zkfnurnT+t2Nc4JoNNryBpQQnJ/T9DTd3hvVatVz48k5L/P5PObMmYuLLnqru225XEG1WnPrdycSCfdcTKV6PMc1NI41nY55Ohrr/HxCA+G7d+/GBz/4QfT09GDjxo0A7Au4Z599FieffPJEvhTNQIZhIJ/PIR5PuBdif/nLn/D444+1LZ+Qy2XR29sHAFi1ajXmzp3nfmiEw0Ekk2FkMkV+QBCNklM+odWEulxuXb8bsGvsN2aQOJNqZnQRjU+lUnGbx65evcY9n+655y688srLLfdRVR9KpRIiETvr5PDDj8Thhx85ZRldND1xfk7DqdWqGBgYQCrV4/7u17/+BbZufbVlAoksyzBN0/2MWb/+cFiWWc8mTSAYDHB+TjRGlmUhn8+5iSjO/DybzbRc7eWIRKINc/OEm5wSDHK1F9FY2SW/SshkMqhUKli+fIX72A9/+H3k87mW+5VK3ptTp5xyOjRNQyQSnVWrC2eyCQ2Er1mzBscddxz+/d//Hf/xH/+BWCyGr371q8jn87jqqqsm8qVoGqvVqkinM00ZJE5DnEsuuQypVAqAfRFvGAZUVUU8nnCz1hqDbI4FCxZiwYKFU3VYRDOSaZrI5/NNAe9stn1DHACIRmP1SfTgHeuhHaiJaGz27t2DnTt3es7LxknzokWL3eB2MpmCpm33ZHY7N6CGZnQ5361EjTg/J2BwtZdzw82ZDxQKA1BV1bO6RFEUWJbVdrVX44X8ihUrp+qQiGas4VZ7mabZcp92q72SySR8PpZPIBqv7dtfx549uz03oiqVCgB7dUVjIDyRSMI0Dc/5aJcbTCAUCnued86cuV09DhrZhAbCJUnC5z73OXzmM5/B+9//fgwMDOCII47A9773PSxYsGAiX4qmWOPdsUwmg+XLl7sn/JNPPoE///lPLffz+/315Vv2xfrq1WuwfPkK1u8mGqdarVq/sPVmkOTzubYTakVREIvFGy5yk+5yLU6oicbOsqyGkl/2he7Gjce6N5I2b34Rjz/+aNN+kUgEyWQKhjF4k+rwwzdgw4ajuOKCxozz832HXW5wwA2orV17iPvZcf/997ZdXeLz2fNzp6bpUUdtxMaNx3G1F9E4lcvlppWX2WwGAwP5Eep3J4YEvKeufjfRbOFUKEin7RtQpVIRmzad5D7+6KN/xeuvv+bZR5IkxGIxJBJJN4ETAM4882yejzPYhAbCASAajeKjH/0oPvrRj070U9MUymQy2Lp1i5tBks1mPF3fI5Ewli5dDgBu063GL24nyBYKeSfUQ++WEdHwdF33rLRwJtUDA/m2+/h8Pk+zSufcjMVi/AInmiDbtm3F888/5waghpb8Wr16DRYuXAQAmDdvPpYtW96U1dWqIQ7PUZoInJ/PTm+8sRPbt7/eMC/IelZ7LV26HNFoFIC9YmTPnj0drfaKxeJdPQ6imcyp3+0NeDuBtuHqd2sdrfYiorF79tmnsXXrq54KBY2OOuoYBAIBAMCiRfshENA8FQri8YQb/G7E+fnMNuGBcJqZ7IY4Wc8X99q1h2DevPkAgL17d+Ohhx707CNJUr18gnc51vLlK7hMkmgchtbvdppVZrPZjhviOJ2mE4kkIpEIJ9RE4zBYvzvjZpFksxmccsrpmDt3HgBgYCCPl1/e7O7jNMRxLm6dUicAsGzZcixbtrzrx0FEM0utVq1nkA4momzadKJb93fLlleaVpc0rvayrMEVYUcccRQ2bDi6q+Mnmk2c+t2NiSjOeTl8/e5IU8A7lUpB04KcnxON0dAKBY2JYpdeepkbn9q7dw9efXWLu18gEPAkojRav/6wrh4DTR0GwvcxQgj3C3fPnt145JG/uN1th94d6+ub4wbCe3vnYMWKVZ4Mkng8AZ/P1/Qa/EIn6oxlWcjlcg0ZJKOp351syiBh/W6isbMzugoIhQanRq+88jIeeOC+thld6XS/GwhfsGARNm481l0FFY3G+H1IRB1pnJ9v2fIKnn32aaTTaRQKA03brl17CBYutAPhCxYsgK6XOlrtxc8jos449bsH5+f2jaiR6nfHYjHPiujhVnsRUWeEEMhmszBNGYD9Pfboo3/FE0885qlQ0CibzaKvbw4A1GNYPZ763fw+JAbCZyEhBMpl3c1aa7xDtn79EVi79mAAgGUJbNnyiruf3+/3lDJxlnADQDKZxOmnn9n1YyGaDWq1mptB2phBMlz9bqchjndCnWBDHKJxcup3NzancjJIarUqLrzwfCxYsBSA/b3oBMHD4bBnpUUymURvb5/7vM5qDCKiVoau9mqsGXzWWee4ySe6XsK2bVvd/YLBEBKJhFverLFsyZIly7BkybKuHwvRbNBqtZdTbrB9/W61KRElmbQTxFgqgWjsWlUocPpd+XwSLrjgrYjH7Xm2LMsol8ueCgWNZUAbM70XLlzkiWsRAQyEz2hOQxxZlt0l1/39e3HHHbe3vTuWyaTdP6dSSRx33Cb3op4NcYjGp1wuD7nAtTNICoWBERviDM0gYUMcovExDMOdUKdSKaRSPQCA117bhl/+8uct91EU2ZP9PXfuXFx00cXM6CKijpmmiXw+j2AwCE3TAAAvvfQi7rvvnrarvTKZtBsIX7hwEU444SR3PsDVXkRjJ4RAqVRsKmWSyaRHrN/tTUThai+iiVCtVt1EzcWLF7s945544jE8/PBDTdvLsoRAQEOxWHAD4atW7Y9Fixa3rVBANBIGwmcAy7I8tQEHf2ZhGDWsW3cojjnmeAB288nGu2POHevGmsEOn8+Pgw9eN1WHRTQjOQ1xWmV0dVq/uzHgzYY4RONXLpfx6qtbPOdlPj+Y0XXEERvcQHgymXIzupyLWyeLJJVKoq8vjkymCMD+npw7d+6UHRcRTV+GYXhKmjl/dlZ7nXzyaVi9eg0Aew5Qq9Xqq70S7ry8MbPUEY8nEI8npuioiGYmZ7VXYyKKsxqzUqm03S8SiTSVMkkmkwgGQ5yfE41TPp/Da69t83xPFgoF9/EzzzzH7ZmTSCSbKhTYqy97sGTJAuRyOgzDLuUbDkcQDkdaviZRJxgIn0achjiZTAbBYBD77bcEAFAu6/jBD77Xch9FUWAYhvv3YDCIiy9+G2KxOO+OEY2D0xBn6AXuyA1xog0XuIMZJE5jKyIavaENcezsyXlYtWo1APt78t57727aLxAIIJlMudkmABCNRnHNNe+GLMtN26tq8++IaN+m67o7D+jrm+PWHd2+/fW2q0t8Ph+q1cHg29y5c3HppZe3rd9NRJ0ZXO01mIiSzWaQy2U918SNJElCPB4fEvBOcbUX0Tg5FQoaS5msWXOAu8Jp9+5duP/++5r2C4VCSCSSnu/DpUuX4eqr39V0A0pV5ZZzdqLxYCB8ipimieeff9bN9M5kMp6GOEuXLnMD4cFgCJFIFKFQqCmDJBaLN30w9PT0dvVYiGYypyHOYI3AwQn1SPW7W2WQsH430cQol8t46KEH3QDU0IyuarXiBsJjsTgWL94P8XjCze5ul9ElSRKzvIioJV0vYfPmFxtqBmc9q72OOGKDGwhPJlOe1V6NAbZIJOL5nPH5/EgmOT8g6lSlUhmy8tKepzeu9hqq3WqvWCwOVWXYg2gi9PfvxWOPPeKpUNAokUi4gfCenj7st9/Spu9Jp2xYIwa7qZv4jTBJnLtjjaVMQqEQNmw4GoB9oj/00INNtQKDwVBTAy5JknD55Vfywp1oHMrlclMpk2x2pIY4dv3uwcwR1u8mmgiGYSCfzzU0p7IDTnPnzsOmTScCsC9oX3jhOff8lCQJsVjMPQ8XLFjgPp8syzj33Aum4lCIaAaxLAu5XM4zP1+wYBHWrDkAgB18e/DBB5r2i0SiSKVSiEYHG1VGo1G84x3Xdm3sRLONU787m80OmQ9kUCwW2+7nrPYaWgI0EokymEY0Do0VChrPx7Vr12Ht2oMB2Amdmze/6O6jKEpDIkoS8+cPzs+TySTOOee8rh8H0UgYCB8nIYQnQH3fffdg797dLe+OJZMpNxAuSRLWrDkQsix5Mkha3R1ztiei4Tn1uwcGstiyRce2bTuwd28/stnMCA1xvPW7nfOR9buJxqdaraJcLiMWiwGwJ88//OGtyGazLW9ANd5gUlUVxxxzHEKhMJLJJOLxBDO6iKgjjfNzXddx//331XsH5JpWewkh3EB4LBbH8uUrGzJKE21Xe3F+QNQZp373wEAOL73knZ8PV787HA43lTJh/W6i8dP1EoQQbunAdDqNX/ziDk+FgkbpdL/752QyiaOPPmbYCgVE0x2vKDvk3B0b2rTS5/PjzW++2N1u9+5d6O/fC8C+oI/F4u4Xt9Ooy3HccZu6egxEs4VTv9upRda4ZLJarUKWJQSDfuh6FZY1GGyLRCJNzalSqRTrdxONU6lUbJlBUigUMH/+ArzpTW8G4PS1MCGEgN/vH3KBm2j6njzkkEOn4GiIaKYol8tDmlfb8/SFCxfhpJNOAQD4/X68+uorsCy7yZaz2sv5/HGWcAP26pIzzjhrSo6FaKYbrN+dQWPNYKd+d6v5+dDVXs51cyKRRCAQmOIjIpq5WlUocObq5bKOgw9e58ajwuGwGwR3KhQ0ljLp6Rmcn/t8fqxff/iUHBPRRGEgfAhd11EsFjylSX7+85/i9ddfa7m9qvo8WSdHHHEkJElCIpFkQxyicTIMw+343niBO1L97mQygUWL5sPvDyEaTbiBbzbEIRo7e0KdRyaTQa1Ww8qVq9zHfvSjH7Rdxjy0ueyZZ56NYDCIUCjMjC4iGpGz2qtWqyGZTAKwV5fcfPNNbVd7ZTJp98+KouCEE05CKBRGIpHkai+icapWq021u+0VF8PX704mk1i8eD58vhBisQRXexFNANM03ZJffr8fixYtBmA3kv/e977ddr9Kpez+ORAI4KKL3op4PNG2QgHRbLLPfusUCoX6F3gajTWDy2Udfr/f07HWCZ61a4jTaPnyFV0/FqKZrlKpNDSrHLxjPZr63c7PeDyOQMCHZDKMTKYIw7C6fDREs8PWra9iz57d7vmYzQ6W/AqHw55AeCrVA0VRPSstnDJDQyfUjTeaiYgaDX7eDPb0cFZ7DV1doij2ZUwkEm2YmyfcjNJGa9Yc2PVjIZrJhBDQ9VLTystMJj1i/e7GuXkqlazfgIrB71c5PycaB8uy8NJLmz3nYy6Xc1c8LVmy1A2Ea1oQoVAIgYDmKS3kzM99Pp/nuefOndf14yGaKrM6EG6aJvL5vFsTcN269W5w+/7778XWra+23M/vD6BcLiMYDAIANm48Dps2neT+nYhGz2mI02pCPXz9bq2pnAnrdxONn53RNRhwKpfLOPHEk93HH3vsEezcucOzj9MQJ5lMwbIstybgOeecz/ORiDpSq9Xq5RPSME3Lrc8NAHfccXvLIFur+qPnnns+wuFwy/rdRNSZwdVe3mbymUx6xPrd3oC3fSOKq72IxkfXdU8pE03TcPjhGwDYpYQeeOC+ptWWPp+vKUlTkiS8/e1X83wkamFWBcK3b38d27e/7n6RD22Is3r1GrcWcE9PL/L5nCeTtF1DHKfJFxGNzGmI01jKxClvMtyEOhKJNNUGTKVS0LQgv8CJJsiTTz6Obdu2IpNJo1AoeB6TJAnHHnu8myGyZMmyep+LwZtQ7Rri8BwlonZeeulF7N692w2wNa72CofDnkB4X98chEKFlqu9hpYbTCS8Wd9E1J5hGMjncw0rL9PIZrPIZjMwDKPlPk79bm+zyhTrdxNNsAcffAB79ux2KxQ0SiaTnkD4qlX7A4DnezISibSci3N+TtTajAqE67reVOz/1FPPcL+IX311C5588nHPPo0NcQxjMCh+1FEbcdRRG7s5fKJZZbAhjjeDxGmI04okSYjH400XuKzfTTQ+TkOcxu9H+wZUFpdffqVbf7O/vx+vvbbN3S8UCnnOQ2CwFNH69Yd1+zCIaIZx6nc3ljKpVMo47bQz3W2efvqpptUlzmqvZDLpWV1y1lnndnX8RLONs9qrceVlNptBPp93yycM1bjaq7GRbDyeaCqfQESda6xQ0Pg9qSgKLrzwLe52O3Zsx969e9y/R6MxNxFlaCP5TZtO6tr4iWarGREILxQK+Na3vtlyqWQmk3a7vS9cuBCGUfME2Vg+gWh8KpWK54u7cUI9XEMcu/5YwrNkMhaLsyEO0Tg4DXESiYQbOHr44Yfw5JNPuPW7h8rnc+4kev/9V2PevHnuecmGOEQ0FuVyGT/60Q/Q359uWqItSRJOPLHqrrBcsWIlenv7uNqLaIIMrd/dGPAeutqrkd/vH5LdPfxqLyLqTK1WRaFQ9PSn+NWv7sS2bVtb3oBSVdVzE3j9+sNhWVb9nEyw5BfRJJsRESnDMKDrdg3hSCTa0IQrgWg06m63dOlyLF26fKqGSTRjOfW7s9nskCWTmREb4jTW73aWTEYiUU6oicahVqu2vMB1GuK87W2Xu2UBVFWFYdTcjK7G5lR2SYGE+7wLFy7CwoWLpuioiGi2sCwLu3btgmWJlqu9gMEg98EHr5u6gRLNYIP1uwdLmTjzgXK53Ha/oau9nPMyHGb9bqLxKJfL9eSwwRWYmYxd8svn8+Gaa97tnmOyrMCyrIb63d4V0Y3nYmMDeiKafDMiEB4KhfDWt16CaDTOu2NE4+DU785mM0invQG2kRriDC1lkkwmEQyGOKEmGofBjK40li9f4faxePzxR/HXv/6l5T4+nw/FYtENhK9ZcwBWrFjJjC4i6hq/348zzjgLsViCq72Ixsle7ZUdUtLELm/WbrWXJElu+YTBZpVJrvYiGichBAqFglticO3ag93r3XvvvRuvvrql5X6KoqJc1t25/NFHb8QxxxzXtn43EU2dGTFr9fv9mDNnLgyjdV0zIvIarN/tnVCPVL87Fos1BbzZEIdoYmQyGWzb9qong6SxIU4kEsWSJUsBAMlkCsFgyLPSwjkvh2Z0hcORbh8KEe3jVFXFypWrODcnGgWnfre351Wa9buJptAbb+zEjh3b64li9nlZqw3egFqxYgVCoTAAe36+d+9e98aTc61sz9uDnudtXJFJRNPLjAiEE1Fr9oQ6jaFNcUaq321PqJOegHc8nmBGF9E4OPW7Gy9w165dh7lz5wIAdu/ehT/+8Q+efZyMrkQi6Tn/VqxYhZUr9+/q+ImIiGh8hBAol/WGUoODgW/W7ybqvlqtVs/uHkxEOeGEk9yVE6+88hKeeOJxzz6yLLslvxqTyI46aiOOPvqYbg6fiCYBo15E09zQhjiNAe+R6nc31ut0agZHozFOqInGQQjhZmTv3r0Ljz76V0/97kZz5sxzA+F9fX1Yvnyl5wI3kUi2zOjiEkoiIqLpy67fPTCklIk9Px+ufnenq72IaHQa5+evvPIynnvu2foNqIGmBLFDDlmH+fMXAADmz18IXdc9183xeByKojS9Bs9RotmBgXCiaWKwIU4ajU3yRmqIEw6HhwS8U0gkEgiFOKEmGg9d15sucNPpNA4//AgceOBaAHbd/S1bXnH3cRriOEsm58+f7z6WSvXgjDPO6vpxEBER0dg4q70aE1Hsn8PX745Eok3lTJLJFOt3E42DEALFYtFzPjrXzWedda6bfFIsFrFt26vufpoW9CSiRCJR97Fly5Zj2bLl3T4UIppCDIQTdZlhGMjnc+6SycYg20j1u1t1gGf9bqKxcxriyLLk1tres2c37rzzDk/97kaZTMb9cyrVg2OPPd49H9kQh4iIaOap1aqeRBQn0NZqtZdDURTEYvGmcibtVnsRUWcsy0I+n0MwGHKvdV988QU88MB9qFarLffJZjNuIHzx4sXYtOlE99x0GlgSEQEMhBNNGqchztAMkuEa4jj1u53mG07Am/W7icbHsizkcjnk8xmYZhlbt25Hf/9gQ5xDDz0MGzceC8BeZeEEwaPRmOfi1p5Qp9zn9fv9OOSQQ6fikIiIiGiUBssNppHNZt2M0kJhoO0+Q1d7NZZPYLlBorEzDKN+fZyFYejYtm0H+vvTyOWyME0Tp556OlatWg0A0DQN1WrVU797aJKYw3mMiKgVRtaIxmFo/e7GgPdIDXEal0k6yyZZv5tofGq1av3CNoNQKIRFixYDsJdIfv/7N0OWJQSDfuh6FZZl1wuUZRm12mB2STAYwlvecgkSiQR8Pv+UHAcRERGNjVO/21vKxP6v3WovwP7+TyQSTQFvrvYiGp9yueyej319c9Db2wcAeP31bfjVr37Rcn6uqj6UyxX3OebPn49LLrmsbf1uIqJOMRBO1IHB+t3eDJKR6neHQqEhAW87s5T1u4nGzzRNvPDCc54L3IGBvPv48uUr3EB4JBJBKBRCLBbFokXz4feHEI3aN6BisZhnQi1JEvr65nT9eIiIiKhzTv1ub8A7PWz9bqD1aq9EIolgMNjF0RPNTrpewksvbfasjC6VSu7jRx55tBsId+rm9/T0YPHi+fD5QohG4/UEsajnetnn8yOVSjW9HhHRaDEQTtTAnlBnh5Q0ySCbHb4hjjOhHrpskvW7icbOqd/deHEbDkdwxBFHArDPvQcffKCptr7TEKenp9f9nSRJePvbr4bPpyCZDCOTKcIwWpcoIiIiounDqd/dmIiSyaSHrd9tl09IePrq2HN1rvYiGg+nfndjIsqiRYux//52CRNdL+MPf7i/ab9IJIJkMuVpVBmLxXHVVddyfk5EXcVAOO2TnPrdQzNIhqvfrShKfULt7QAfjyfYEIdoHIQQnoyP++67B3v37nHrdzfq7e1zA+GyLGP16gOgKIrnQrddRhdXYRAREU1fuq4PSUTJIJ1Od1S/2xvwbl7tRUSj0zg/1/USHnjg/voNKLt+91BOIDwej2PZsuWe1RbJZBJ+f/MNKM7NiWgqMBBOs5YQAuWyjnTaO6HOZNIj1u8eWsokkUgiFmNDHKLxqNVqyGYzngySTCYNTdPwpje92d3ujTd2IpNJA4DbEMeZSDdmeQPApk0ndvMQiIiIaBxarfbqpH63s9praMCb9buJxqdSqbjnY+N18+LFi7Fp00kA7Hrdr7zyEoQYrN+dSCTc6+T58xe4z6coCs4885wpORYiok4wEE4znhAC+Xy+KYMkk0mPWL97aKfpRCKJcJj1u4nGQ9d1lEpFT9D6jjtux44d290JdCOfz+fJOtmw4ShIklRfccGGOERERDONaZrIZLJNAe9Wq70aRSLRhjKDgzW8Wb+baOyEECiViqjVakgkkgAAwzBwyy3fQbFYbLlPOh12/+zz+XD88ScgEokimUwiGo3xepmIZiwGwmnGcBriOBPqfD6LSqWI7dt3oVqtttzHqd89uGRyMOCtaVqXj4BodikWC0in00OySLLQ9RI0TcNVV13rTpJVVYUQApqmuTefGpdMNlqxYuVUHA4RERGNUq1WrdfutoPcuVwG1WoJO3fuRq1mtNxn6Gov+6edXcr63UTjk8tl6/NybwnQarWKhQsX4fzzLwRgz82deXo4HK7Pz1Oe6+ZGBx10cNePhYhoMjAQTtOO0xBnaA3voQ1xZFlCMOiHYdSgKApisbinnEkymWL9bqJxchriZDIZDAzkccghh7qP3XffPdi2bWvL/VTVh2q16jaMPe64E3DSSacgGAx1Y9hEREQ0gXRdH9JbJ+PODRo583PTNKGqPk9fncH+OlztRTQehmEgl7NvQFmW5dbnBoDbb/8xdL3UtI8kSU29sM49900Ih8Mt63cTEc1WDITTlNH1klsj2OkCn8lkOmqIk0ql0NOTwtKliyDLAYTDMdbvJpoA27e/jh07Xkc67WR2eRvi7L//Gnc1RSrVg4GBfFMznFYZXbFYrKvHQURERKPj1O9uDHg3rvZqp7F+d09PD5YuXQhZ1hAMstwg0UR46aXN2LNnd8PK6LxbbjAajXkC4X19fSiVSvVVFin32jkWi0NVveGfoVnfRET7AgbCaVIJITAwMNAyg2S4hjjBYAiJRKKhRqD9s7EhjqrKSCbDyGSKMAyr7XMR0aByudzUnOq0085wM0FefvklPPPMU559nIyuRCIJwxhc5rxx47HYuPHYro6fiIiIxseyLORyuRYB75Hrdw+WGUy4KzEbV3txfk40OkKIhgQx+5ys1ao4+eTT3G2eeuoJvPHGTs9+gUDALWfS2GvnnHPO7+r4iYhmGgbCaUI49bu9AW87g2S4CXU0GnMzSBoD3myIQzR2ToaIMyHevPkFPPvsM8hmMyiVmjO6stkM5syZCwBYtGgxLMvynJPRaJQZXURERDNMrVZDNpvxJKJkMmnk8znPaq9GTv3uTlZ7EVHnGoPVAPDII3/Btm1bkcmkUalUPNtKkoRNm05yM7iXL1+B3t4+N7vbuQHF+TkR0egxEE6j0tgQpzHgPbR+d6NW9bsTCfs/1u8mGjunfvfQC9xsNoMLLrgIvb19AIBSqYQdO7a7+0UiEc/FbSQScR9bvnwFli9f0fVjISIiorEpl8ueeXkmY5ccLBQG3JvjQzWu9mqs4c363UTjYxgG8vmcu8rCSQ7L53N4xzve6ZbzzGYzbpa3JEmIxWKec7Hx3F23bv2UHAsR0WzEQDi11GlDnEaN9bsbJ9WxWIwTaqJxMAwD2WwG0WjMbT753HPP4IEHft82oyubzbiB8P32WwpNC7qBbzbEISIimlmEECgWi0MC3vb8fPj63Zrn5jdXexFNjGq1ikwmgzlz5rjn0oMPPoCnnnqi7Q2oXC7n1uU+8MC1WLJkGZLJJOLxRFP97tnHgs/3ByjKKxAigFptEyxr4VQPioj2QbP905aGMdaGOJ3U7yai0avVqujv7/fU6nRuQAkhcMYZZ7vZ2sFgEKZpQlV99Tqdgxe3zoTa4azEICIiounNWe2VyWQ8Ae9sNoNqtdp2v6GrvZzkFE0Lcn5ONA66riOd7veck9lsBoVCAQBw2WVvRywWBwD4/X4IIRAIBDzXyamUfU5Go4PN4+fPXzAlxzMVAoHbEA7/OxTlNfd3QsioVs9BofBpAAyIE1H3MBC+DzBNE/l8vimDpJOGOE4NMtbvJpoYQgiUSkU3i2vBggXo6ekFAGzbtg2//e2vWu6naZrnAnjhwkW47LK3IxqN8QKXiIhohqnVasjlsg3JKPbcPJfLDlu/e2j5BK72Iho/IQQGBvJumcHVqw9wr3mffPJxPProX1vuFw6Hoeu6Gwhfu/ZgHHjgQQiFwpyf12naVxGN/iOGJslLkgW//5dIJB7FwMB9AFZOweiIaF/EQPgs0li/uzHg3UlDHO+EOsGGOEQTpFAoYPPmF90L3Ww242mIc/TRx7qB8GQyhUgk0rJB1dCGOD6fn+coERHRNFcul5tKmWSzg6u9WhlutRfLDRKNX3//XmzZ8oq7+jKbzcAwDPfxnp5eLF68HwAglUp5Gsjac3N7nu6ULHQEg6GuHsd0J8tbEYl8GADQ6r6AJJmQ5TcQCn0YwI+7Ozgi2mcxED4DjaV+t9MQZ2gGCRviEI2PYRhuRpczkV66dBlWrVoNAND1Ev70pwc9+0iS5E6oY7HBJZKpVApXXPGOro6fiIiIxsep3+0NeNvz9FJp+Prd3kQUO8jG1V5E4+PU7268Zl6//gjMnTsXALBnz2785S8Pe/ZRVRXxuN1AtnGFxapVq915PY1OMHgjABlA66Q8wA6G+3w/A/AGgGiXRkZE+zIGwqepsdbv1rSgm0HaGPBmQxyi8bEsy+3yXigUcP/99yKbzSCfb87o8vv97oQ5kUhi5cpVDTUCU4jF4vtAQxwiIqLZxanf3ZiI4szTR6rf3clqLyLqnBACQgh3fr5z5w488shfkMmk3frdjRYv3s8NhPf1zcHq1QfUbz4l6g1kY+5z0cTw+X4HSWofBHfY2zwA4OxJHxMRESMxU8yyLORyuRYB75Hrdw8uyxrMIOFyLKKxc+p3Z7PZhmaV9s9ly1bg+ONPAAD4fD5s3fqqu18gEPBc3M6bN999zOfz4bTTzuz2oRAREdEYOfW7BzO87ZVfw9Xvblzt1bgCk/W7icZnaP3ubHbw3Ny48VisWXOgu91rr21z9wuFQp5zccGCRe5jPT29OPnkU7t+LPsaSSqPYuvKyJsQEU0ABsK7pFarufWBGzNIWL+bqPssy8LAQB6maSGVSgGwl1DefPNNnvrdjbLZjPvnQCCAk046BdFojBldREREM1SlUmlKRHHKDbav360ikUi6iShc7UU0MUzTRC6Xhc/nRzRql8jYtesN3HHHT2EYrRPE0um0++eenl6cdNIp9fMzCU3TujJuas80V0NRXuooKxxYNenjISICGAifcOVyuaF292AGSaEwMGxDHNbvpn2JojyLYPBbUNVHAAC12pEol6+Bae4/oa8jhEB//17P0uVMJoNcLgvDMLDffktxzjnnAbDLmciyAkmSEIvFmrK5Eomk57md7BMiIiKavpzVXkNLmWQy6WHrdw9d7eWsxIxEoiyfQLOQgM/3IDTtJijKiwA0VKunoFy+EpY1b0JfyTRN7N69u+l8zOfzsCwLhx12BI46aiMAIByOwDBqUBQF8XjCU8rEvl5OuM8bCAQ4P59mdP0dCATuGHYbISRY1mooypEA2n8mExFNFAbCx8BpiOMNeNuTa9bvJhqOiUjkwwgGb4AQipsdoKqPIxT6Ckqlv0Ox+P9gN1XpnN0Qxz4PAWDt2oPcx372s5+0zPJulbV10UVvQSgUZkYXERHRDOOs9mpMRHFWY7Zb7QUA4XC46eY3V3vRvkSS8ojFLofffx+EUCFJBoQAVPXPCIX+E4XC/6JcvmLUz6vrJTfYHY3GsHz5MgB2r50f//i2lvv4/X7PaulwOIy3ve1yxGJx3oCagWq1k1CtHgef76GWWeFOnqCufwyRCD9viag7GO0ZhtMQp7HjtPPnkRrieDNIWL+bCADC4X+Hpn0dADyTIefPodAXIEQYpdI/D/s8zz//LPbs2eOel8Vi0X0skUi4gXBJkjB37jxUKpWG5cvJ+g2o5oY4sVh8Qo6TiIiIJodhGMjlsp5ElGx2cLVXK8Ot9goEAl0+AqLpRCAWuxw+3wMAAEky6j8BwIIQFqLR98CyEqhWz2v7LLVaDc8++7SnDGi5rLuPr1ixyg2Ex2IxxGJx95p5sO9VEuFw2HMDSpKkplWZNJPIyOdvRSz2N/D7H/DcaLETn2QMDHwRpskmmUTUPQyEY7AhzmCNwMEJ9Uj1uxszu53ANxviEDWTpN0IBr8ASWpdIsgRDH4Wu3ZdhnTacifSlmXilFNOd7d5+uknsWfPHs9+4XAYiUQSPT29njJE55xz/sQeCBEREU26SqUyZOWlPU/P54ev322XT0h6At7xeIKrvYha8PkegN9/X9vHJckuXaFp/4o33jjGvfGUzWYQi8WwYcPRAABFUfDwww95rp0lSUI0GkMikcTcuXM9v7/ssrdP2jHR9CJEHLncnfD5/gBN+zYUZTOECKJWOwW6fiWEmAN+PBNRN+1THznlcrmplEk2O1JDHLt+99AO8KzfTTQ6mnYrgMHzzLKAxoTsBx7wY8sWFem0glLp/8IwDncfUxQFJ510qpvBvWrVaixcuNiTQdKY0cWlzERERNOfU787m802NKu0fzau9hoqEAh45ubDrfYiovY07UZPucLG+bkQwJ13ati7V0Y2uxvF4n/Bsha5+/b19bmBcFmWcdBBB8Pv9zVcLyfg8/m6fkw0HUmo1Y5HrXb8VA+EiGj2BcKd+t2tMkiGa4ijaZqnnInzBc763UTjY9fvzqBSeQzFYgDpNJBOyygWJbz3vQU4p1c2K2PPHgVCyFDVDOLxXk+DqsabVevWrZ+ioyEiIqLRcup3N668dObnI9Xv9ga8U0gkEgiFwpyfE42DU7/bMJ5FPq+iv9+PdFpGNCrwtrfZ18ySBOzZoyCTsSPjgUAekcic+nlor8JsdOyxDHISEdH0N2MD4U797sY6ZM6EeqT63d7sbntSrWlBTqiJxkgI4U6oFyxY6J5L9977Ozz//LMAAL+/H6rqhyRZ7n75vIR43A5wH3ZYDQccUEMqJcPvPxi6/rbuHwgRERGN2WD97sF5eSbTWf1ub7PK5tVeRDQ6QggMDAxA10uYO3ee+/sf/ej7bolBTTOhKINlPatVASHgJqps2lSGqgKplAUhTkS1eklXj4GIiGiizYhAuGma2Lz5Rezdu9ftAj9c/W5JkprqdzsTa9bvJhqfYrFQPxfTyGYHa+uXy2UAwNvf/g6EwxEAQCgUcn/29R2GhQv/hJ4eC6mUhZ4eC+HwYJb3woXO+Wwimz2hq8dEREREnRNC4I033sDevf0NAe90R/W7G1d7OeUTWL+baHxyuSz6+73nYyaThWHUEAyGcNVV17jbBoP2/DwajWHu3EMwf/429PaaSKUspFImGnPDVq605+dCyEinj+vqMREREU2GGTHrHBgYwG9/+2tYlndiraqqp5SJs2QyFotzQk00DqZpejK61q49BJqmAQCeeOJxPPHEY037OA1xdF13A+GHHHIo1q1bX9/XQCp1C2R5jycrvJEQMixrIWq1Uybt2IiIiGh8SqUSfvzj25rm5gDg9/uHJKIk6uUGWb+baDxqtWo9CSWDYrGA9esH++ncf/99eP3115r2kWUZmqahVqvC57MTwk488WT4/X74fH7I8qlIpW53a4S3IoSCavUsWNbCiT8oIiKiLpsR0WJJkjBv3nzE40kkEgk3uzsajbGcCdEE2LVrF7ZsedmT0WVZg8HqBQsWYsECe/Lb09OLVKrH05yqXUOcYDDY8DcVAwM3Ih6/oL7k0hsMF0IBoCKf/xYAXigTERFNV7IsIxQKN2R4p9wsb9bvJpoYW7duwfbtr7tlQAcG8p7H7eaUdnC7t7cPlUrFU1oomUwhFotBURTPfk7CCgBY1kIUCp9GNPr3npIoDiEUWFYfCoVPT85BEhERddmEB8Kz2Sw++9nP4r777kOhUMDq1avxwQ9+EEccccSYnzMWi+HNb34rDKN1FikRtSeEQLmsI50erNWZzWZw1FEb0dc3BwCwZ89uPPbYI579/H6/u+KisaTQ6tVrsHr1mjGNpVY7DtnsLxGJ/BN8Pu/rGcbhKBT+C4Zx2Jiem4iIiFqb6Pm5pml4xzuu4dycaIyEECgUCkNKmWRwzjnnu4klr766Bc8++4xnP00LujedDKPmztE3bjx2zGMpl6+BEAmEwx+FomxtGKOEavVMFAqfhmUtGPPzExERTScTHgj/wAc+gP7+fnz2s59FKpXCLbfcgmuuuQY/+clPsGLFijE9J7NKiEYmhIBlWW7Wx+uvv4a//vXPyGTSbv3uRitXrnID4fPmzcNBB631ZJCEw5OT0WUYRyGbvReK8iR8vicghATDWA/TPGjCX4uIiIgmfn7OuTlRZyzLgiRJ7jnz3HPP4JlnnkY2m0GtVmvaPpfLore3DwCw335Loao+twxoMpkastpy4lQqb0alciF8vgehKK9ACD9qteNhWYsm5fWIiIimyoQGwrdu3YoHH3wQt956Kw47zM7q/MhHPoL7778fd955J973vvdN5MsR7ZPs+t05TwaJ/TOLE044CfvvvxqAHRjfuXMHAPuCNRKJemp1OqVOAHs55aZNJ3X5OA6BaR7S1dckIiLa13B+TjT5arWaZ+Wlk+Gdz+fwlrdcglSqBwBQLlewZ89uAHaJoXg8PiQRZbBsybJly7Fs2fIuHoWMWu141GrHd/E1iYiIumtCA+HJZBI33HAD1q5d6/5OkiQIIZDL5SbypYhmvVqtikwmA00LIhaLAQC2b38dd975M0/97kaZTNr9c1/fHJx66uluI9mh9buJiIho9uP8nGji6LqObDaDZDLlNpJ/6qkn8OCDD0CI5uaxAJDJZNxA+LJlyxCPx5FIJBGPx5vqdxMREdHkmtBAeCwWwwknnOD53a9+9Sts27YNxx133LieW1HYPI+8nPfETH9v1Go17Nmz263h7fwcGBgAAGzYcBSOPPIoAEAiEQcgEAj43WY4jT9jsbj77xGJhHDAAQdM1WFNqdny3qCJx/cGDYfvD2pnJlcCmaz5Oc8TamW2fI6Wy2Xs3r3bzex2fup6CQBw9tnnutnasVgUkgSEQqF6M/mU52c0GnVLo/T29qC3t2fKjmsqzZb3Bk08vjeoHb43aDhjnZ9Lot2t6wnwyCOP4Nprr8XGjRvx5S9/ebJehmjaE0Ign88jnU6jv78fqVQKy5fbk+d0Oo0bb7yx5X6hUAjr1q3DMccc4z7PwMCAZ0JNRERE1CnOz4lslmUhm80inU4jnU5j6dKlmDPH7p/z/PPP4xe/+EXL/aLRKDZt2oQ1a+zm8dVqFYZhIBQKdW3sRERENDYT3izTcffdd+NDH/oQ1q1bh89+9rPjfr58XodpsjM9DVIUGbFYcFq+N6rVKp588omGLJIMDGOwIc7q1WuQTM4FAAjhg98fQjweb8ogcRriZDLFhmdXkM2Wunk4M850fm/Q1OJ7g4bD9we1E48HIcszPxtpIufnPE+olen8OZrL5fD888+6Gd65XA6mabqPH3vs8fD5wgAARQlC08JNGd7JZBI+nx/A0Pk5UKl4/05e0+e9IaCq98Pvvw2StAdCJFCrXYha7XQAM6tUjSS9jkDgRqjqnwBYMM11qFTeActaPdVDG5Xp896g6YbvDRrOWOfnkxIIv/nmm/Hxj38cp512Gj796U/D7/eP+zlN04Jh8I1PzabiveHU785ms26zylQqhQ0bjgYAWBbwpz895KkVaDfESSCZTKKvb65nzJdeennL1+F7fnz4uUHt8L1Bw+H7g4aavPWT3TPR83OeJzScqXh/lMvlhiby9n+rVu3vNpIvFEr485//7NlHVX1IJhP1EiYxd8yJRAoXX3xZy9fh+358pvKzQ5Z3IBa7GD7fkxBCBWAAUBAI3ALTXIZc7ocwzf2nZGyjIxAM/g/C4X8HIEOS7Bs6qvpHaNoXoevvRKHwX5hpgX1+r1A7fG9QK2Odn094IPyWW27B//t//w9XXHEF/vmf/3lWZM/QvsswDKiqfZpYloVf/eoXSKf7USgMNG1bKpWwYYP9Z1VVccgh66BpQbcLfCwWY0McIiIi6jrOz2m2EELANE13fp7P53Dvvb/z1O9uFI1G3UB4MpnEAQcchGQy6c7PWW5w3yFJOSQSZ0GWt9X/btQfsYPIsrwNicSZyGT+AMtaMEWj7Eww+FVEIh+t/21wVYMTENe0b0AIH4rFT3V/cERE09yEBsK3bNmCT3ziEzjttNPw7ne/G/39/e5jmqYhGo1O5MsRTQghBAqFQkMGyWA5k1QqhQsuuAiAndHdGATXtGC9UaU9ke7p6fU87zHHHN/1YyEiIiJqxPk5zUSWZSGfz9VXYA7Oz7PZDNasORDHHmvPs/3+AHbs2O7uF4lEkEym6oHuJObOnec+5vf7ceKJJ3f9WGh60LSbIMuvQpJapxDaQeQMgsEvoFj8ZHcHNyolhEL/MewWkiQQDH4Fuv53sKyFXRoXEdHMMKGB8N/85jeo1Wq46667cNddd3keu/DCC/GpT/GOJE0d0zSRz+dRLuuYP3/wLv/3v38zstlsy32y2Yzn75s2nQifz4dkMuXW7yYiIiKarjg/p+msVqshl8sCAHp7+wAAuq7ju9+90VO/u1Hj/FzTNJx22hmIRuNIJpMTUpKTZqdg8AYAw6+jlyQTmvZtFIsfBRDoxrBGLRD4GSSpeXVyMxma9l2USv806WMiIppJJjQQft111+G6666byKckGpP+/r3o7+/3ZJDk83ZDnEgkgiuueIe7bTgcQT6fRzweb8ggSSGRsOt5N1qyZGmXj4Ro6knSAAKBH0DTvgtZfgNCxFCpXIRy+SpY1vypHh4REQ2D83OaDoQQ2LXrDc/Ky2w2g4GBPIQQWLJkKc4++zwAdnDb5/NBkmR3Pu7Mz5PJJGKxuOe5V66cCTWdaWqVoSivdbSlLBcgyztgWcsmeUxjoyibAfgA1DrY9sVJHw8R0UwzKc0yibpB13WUSlls2fI6BgYKOOKII93HHnjg99i5c0fTPqrqQzAYgmmabr3u0047A35/gPW7iVpQlGeQSFwASdoDAPXlpDuhKP+JUOizyOdvQrV6ztQOkoiIiKacU24wn9+LV1/dDkDCgQeudR+/886foVZrDt45gW+HJEm4+OK3IRQKs343TZDRXudN5+tCFY11wYfnG3kTIqJ9DAPhNGO89NJm7NjxOtLpNLLZLCoVHcGgH7pehRDAunXr3Um0Uw+wMYPE7gbf3BAnGAx1/ViIZgJJ2otE4lxIUrapnqIkWRCiiljscmSzd8EwjpiiURIREdFUefLJx7F37163145h1Nz5eTyedAPhkiRhwYKFME3TU8M7mUwiGAw1zc/D4chUHA7NWj7UaodCVZ+EJFlttxICsKz507qutmXNG/YYHJJkolplzyoioqEYCKdpwbIs5HI5t5RJNptBNpvFBRdc5GZqb9u2FS+88Jy7jyxLiEaj6O0NIx5PwjRNNxC+ceOxU3IcRLNJMHgjJCnTdrItSQJCAKHQZ5DP39rl0REREdFkMgyjPid3Sg1mAQCnn36mu83zzz+H/v697t8VRUYymcS8eWEkEinP8zmlT4imgq6/G7HY9SNsJUPX34XpnBHu9/8OQgDDLZYQAgACqFTe3K1hERHNGAyEU1fVajWoqupmfTz11BN45pmn3frdQ+VyOaRS9iR66dJlCIfDbgZJX18P5sxJIpMpwjBGvitORKOjaTcCGP7ckiQTfv8vIUl7IURvdwZGREREE6ZWq8LnG2wy+cADv8e2bVvd+t2NVFWFZVmQZRkAcMABB6JSqSCRSCKVSiGVSqKvL875OU07lcolqFR+BL//3pZJHkIoMIy10PXp21NBktLw+38xbBDc3g4wzRCAYFfGRUQ0kzAQTpOiXC57GlU2NsR529suRzyeAABUq1VkMmkAdv3uZDLhKWUSDofd51y+fAWWL1/h/l1V5a4eE9G+RpZ3jDjRBuzMcEXZDsNgIJyIiGg6EkKgVCq683JnBWYmk4ZhGLj66ne5iSqlUhH5fA4AEAgEmkqZNDr44HWev3N+TtOXinz+VkQi/x807duw62wr9Z8SKpU3oVD4HIDwcE8ypWR5Z0dlUQBAUTKwE1p4ThIRNWIgnMZMCIFisYhMJo05c+YiEAgAAB599K94+OGH2u6XzWbcQPiKFSvR29uHZDLVsn43EU2lAAC9oy2FYK19IiKiqWZZFgYG8shms9hvvyXu3Pquu36Dl1/e3Ha/YrGISMSuy33ooYdh7dpD2tbvJpq5NBQK/4Ni8SMIBO6ALO+BZSVQrZ47reuCO4ToPMNbCD8YBCciasZAOHWkVCpi165dnizvbDaDarUKADjnnPOx335LAADRaBQAEIlEhmSQpNwJtSORsJtZEtH0U6mciUDg55Ako+02dlOhxTDNFW23ISIioomXz+ewe/cuz+rLXC4Lw7C/t6+88mqEQnZ2q5NwEovFGubnKbexvJPQAgw2nSearYToRbl89VQPY9QsaylMcylkeWtTI/tGQqioVs/o4siIiGYOBsLJVavVkMtl3WD38uUr0dNjlzrYtm0b7r337qZ9ZFlGLBaDZQ3W9166dDmuuebd8Pv9TdsT0cxRLr8bmnb7CFtJ0PXrwYwTIiKiieeUEXTKmRx22BFu0Pqpp57Ek08+3rSPqqqIxxMolytuIPyww47Ahg1HQVV5+Uc0c8nQ9esRDv/TsFtJkgFdf3eXxkRENLNwJrQPy2QyeO65Zzz1uxsb4mhayA2E9/T01EuYJD0ZJPF4Aori7art8/m6ehxENDlqtWNQLP4DwuH/btmdXggJ1epp07qpEBER0UyyY8d2vPzyS25iSrFY9Dy+bNlyzJs3HwDQ29uHuXPnuRneqZQ9T49GY24zS0djxjcRzVy6/k74fPfB7/91U1a4EBIkSaBU+iBqtU1TNEIioumNgfBZqlVDnEwmjQMPPAirVq0GAJTLOp544jHPfpqmecqZOPr65uCtb720q8dARFOvVPpXmOZyhMP/BUXZ4v7espLQ9XejVPoH8KuEiIhoZIP1uzNIpzNusHvTphPR29sHAOjv78fTTz/p2S8cDruJKH7/YEB79eo1WL16TVePgYimmop8/maEQp9BMPhVyHLafcSy9kOp9A8ol6+YwvEREU1vjF7McJZlwTQN+Hx2GZJMJoN7770bmUzard/dqK+vzw2EJ5MpHHzwOjfonUqloGlBNsQhIo9K5TJUKn8DVf0rZHk3hIiiVjsaAMsfERERDeXU6HbKkGzdugUPP/wnT/3uRv39/W4gfP78+Tj00MPc/jpD63cTEQE+lEr/hFLpA/D5HoYk5WFZc2AYh4PlComIhsdA+AxhGAZyuWxDjcDBhjhr1x6CjRuPBQAEAn7s2vUGAECSJMTjcTeDJJFIeprfaJqG447jkiki6oQEw9gw1YMgIiKaNuz63XZWt5Pdbc/Pczj11NOxcuX+9S0l9PfvBTBYv3uwmXwS8+cvcJ+zt7fPDYoT0UwkAHQrscyPWu34Lr0WEdHswED4NFOpVJDNZiDLMvr65gAAisUCvvvdmzz1uxvl8zn3z8FgCKeffhaSySRisTgb4hARERERjZEQArpeQiaTQTQaRSwWBwBs3foqfvnLn7fdL5vNun+eO3cezjrrHCSTqZb1u4loZpOkfmjaTQgGvwVZfh2AH9XqqdD161CrnTDVwyMiogaMkk4Ry7Kwc+cOpNPeDBKnIc7y5StxxhlnAQBCoTBUVYUsy5763c6SyUgk6j6vJElYsWLllBwTEREREdFMVa1WsXPnjvq8POvOz8vlMgDgyCOPxuGH26uj4nE7IB4KhRrm5yl3jh4Khd3n1TQNS5cu7/4BEdGkU5QXEY+fA1neA8CqN5evwO//DQKBX6BU+jsUi/+B7mWJExHRcBgIn0RDG+IEAn4ceOBa9/Ff/vLnLesEhsNhTy1ASZJw+eVXIRAIsH43EREREdEYmaZZLzdoJ6L09fVhyZJlAIBCYaBllrckSYhGY1AUxf1dPJ7A1Ve/i/W7ifZpOuLx8yHLeyFJlucRSbKv80OhL8A0l6FcvnYqBjhNmZCkIoQIA1BG3JqIaCIxED7BHn30r+jv34tMJtPUEKe3t88NhMuyjEWLFgOAJ4OkXUMcTdO6cwBERERERLNEpVLBY4894q7AzOfzsKzBgNWaNQe6gfBYLI7e3j7E4wkkkwk30zseT8Dn83meV5IkBsGJ9nGBwI8hyzswXK6aEEAo9GmUy+/Avh70VZSnEAp9GYHADyFJVQiholK5ANXqewGcONXDI6J9BAPho2A3xEl7muIoiorTTz/T3ebFF59HJpNx/+40xEkmk02Nb84669yujZ2IiIiIaDYRQqBc1htKDdrN5Pv65uCoozYCsJNPHn/8UU+vHb/f7yaiLFy4yP29qqp461sv7fpxENHMpGnfAyADsNpuI0mAouyAz/cQarXjuja26SYQ+BGi0XcCkNxseUkyEAj8DIHATwB8BcDlUzlEItpHMBA+hBAClUrFk4F9zz134/XXt7n1uxv5/X4IIdySJQcddAgMw0AqZWd3syEOEREREdHYCSFQrVbdDGzTNPHzn/8UmUzard/dqHFFps/nw/r1h3tqeYfDYZYbJKJxs7PB2wfBvdu+Mcmjmb4U5cl6ENxsyp53guLA9VDVpTCMY7o9PGogy68gGPwWfL6HAJgwjEOh61fDNA+Z6qERTZh9NhAuhMDAQL4hwzvjLpmUJAlXXXWtO0Eul3U3CB4Oh90MkkQiiVQq5Xnegw/mBwQRERER0WjZ9btz7srLwZ9ZzJs3D+ed9yYAgKIoyOdzKJfLbv3uxmbyPT29nud1ssOJiCaSEHEIgWFLowxuG5v8AU1TodBXAIz07yQhEPg8ymUGwqeGQCj0SYRC/wlAhiSZAABVfRLB4Leg65ehUPg8AN+wz0I0E8z6QLhhGMjnc8jlcli2bLBb+y9/eSe2bXu15T6SJKFcLiMYDAIAjjjiSBx++Ia29buJiIiIiKgztVoVmUwG1WrV7ZkDAN/73rdbrsAEgFwu5/n7KaecjkAg0LJ+NxFRN1QqF0FVn8RwpVEAwLKiqFaP786gph2zXhPcHGE7Cz7fryBJ+X36psFUCQa/iHD4U/W/Df6/cjL2Ne0WAAEUCp/r+tiIJtqsCoSn02ns3r3Lk0HS2BCnsbN7PB5363c3ZpAkkynE4wmo6uA/zZw5c6fkeIiIiIiIZrJdu97A3r17kM1m3ZWYhcIAACAajeHyy690t43F4qhWq24TeWcFZjKZRCwW9zxvY21vIqKpUC5fgXD4kxCiDEkSLbcRQka5fC2AYHcHN01IUhGSVO1wWwFJSjMQ3nVFhEKfGHYLSRLQtBtRKv09LGtJl8ZFNDlmVCBcCAFdL3lKmRx11NHw+fwAgKeffhLPPPNU035+vx/JZArlsu4Gwo866mgcc8xxrN9NRERERDRGdrnBATcRpVKp4Mgjj3Yff/DBB7BrV3Nt3GAwhFgsBsuy3Pn42WefB5/Px/rdRDQjCNGDXO5mxONvgxCmJ+vZ7s8roVbbhGLxn6dsjFNNiBCEkNreKBhKkrKTOyBqEgj8FJLUejWWlwxN+w5KpX+d9DERTaYZEQgvlUr48Y9/iHS6uSHO/vuvdjO258yZi2w249bwdrK8Q6HmhjhO8JyIiIiIiDpXrVZx112/QX9/PzKZLAyj5j4myzIOO+wId3XlggULEQhoSCYTDRneKU9jeoffz/k5Ec0stdppyGZ/h2Dw0wgE7nSD4Za1ELp+PXT9OgD78mebCiECkKTmxsatKMpOmOahkzsk8lCUl2HX/q6NsKWob0s0s82IQHi1WsUbb+yEZQm3IU4ymWyq2b1mzQFYs+aAKRwpEREREdHsZhgGXnzxBViWneGnKApisbibiOKUJQSAo49m4zMimt0MYx0GBr6LQqEfsrwdQBCmuQIAV58DgBAJAM0rg1pvOyNCVLNMAEAnGftSfVuimW1GfMpomobTTz8T0WicDXGIiIiIiKaQz+fD0UdvRCxmJ6bEYjEoijLVwyIimlJC9MA0e6Z6GNNOtXoqNO0WSNLwTUWFUGEYh3VpVOSoVk9AOPzxEbeTJBPV6qYujIhocs2YQPiqVfvDMIb/4CQiIiIiosnl8/lw+OEbODcnIqIWKggEfgZN+5ZbSmOkIDigolp9C4TgjYRuM4yjYBgHQFFe9NS5bySEBCFiqFTe3OXREU28GREIJyIiIho9Cz7ffdC0b9cvxIKoVk+Hrr8dQsyd6sERERERzSqStAuJxAVQ1WchhOwGwIdrmCmEAknqg65/rJtDJZeEfP7rSCbPgBDlpmC4EDIACQMD3wDQ3N9j9CoIBO5AIPB9yPIbECKJSuUilMsXA4hMwPMTDY+BcCIiIpp1JCmDWOxS+P0P1S+wTAgBqOpfEAp9EgMDX0Kl8rapHiYRERHRLGEgkbgQivICAG8W+NAguBAKAAFJsmAYx8Pn+w6ESAHgSqOpYJqHIJO5G5HIh+D3PzjksQNRKHwKtdr4y6IoymbE42+Corzm3igRQoLPdz/C4Y8il/shDOOocb8O0XAYCCciIqJZxkI8fglU9S8A4Ga2SJL9mBAWotHrIEQS1eqZUzdMIiIiolnC7/8NVPXpYbcRwq6lXiq9B0AY1eqpkKTVSCbDAIpdGSe1ZpoHIZf7FRTlRajqX+o3KQ6s122Xxv38ktSPePwcyPKe+t+t+k/nJkkeicQFyGQegGmuGvfrEbXDQDgRERHNKn7/XfD5/tT2cUmyL8TC4Y+iWj0DEzG5JyIiItqXadq33VV47UiSHRA1jKNRqx0HAFAZlZpWTHN/mOb+E/68weC3IMu729aLt7PDKwgG/weFwpcn/PWJHPJUD4CIiIhoImnajfUlt+1JkoCqPgtVfaxLoyIiIiKavRRly7BB8EayvG2SR0PTjaZ9AyOVvpEkE5p2GyRpoDuDon0SA+FEREQ0qwzX9b5521cmeTREREREs58QEYjW/TBbbBue3MHQNFODouyslykcniRVIctvTP6QaJ/FQDgRERHNMoGOtxTCP4njICIiIto3VKtno5MQkxB+1GrHT/6AaBpRIETn4UchfJM4FtrXMRBOREREs0q1etqIpVEAQAgVtdoxXRgRERER0eym61fCDni2T/sVQkG5fAmESHVvYDQNyKjVjh9xfi4EYJqLYVn7dWlctC9iIJyIiIhmFV2/GiPVIBRCQaVyEYTo7c6giIiIiGYxIeZgYODrAKSW2b9CKDDNVSgWP979wdGU0/XrOihdKEHXrwNDlTSZ2J+XaMap1H92vvSfaDqQpH5o2vehKC/VM3GPry+h5FcRTSzLWopi8VOIRD4MIdBUj1AIBZa1AIXCJ6ZmgERENIsIAGUAPnBOQzONLL8KTfs+ZHknhIigWj0HtdpGAB0Uc26hUrkIltWDcPg/4PM97P5eiBB0/QqUSv8CIeITNHqaSarVs6Hrl0HTvteyVrgQMmq1Y6Hr7+7+4Gifwm9qohmhBE37LoLBr0FVXwIAGMZq6Pq7US5fDkCb2uERDUsgFPokQqHPADBgf/UIhEJfg2nOxcDAN1CrnTDFY+yEDlnuhxAhLuecAXT9+vqF2MegKNvc39uZ4OejUPhvCDFnCkdIREQzmSTtQTD4dQSD34Qs7wEAVKvHQNevQ7V6AcYaSCTqDh3R6HsRCPwIdvat/X4Nhb4AwzgQ+fx3YZqrxvTMtdoJyGZPgKJshqK8AiE0GMZhECI6ccOvk6Q8JCkHIRKT8vw0kSQUCl+CZa1AMPh5yHLWfUSIIHT9HSgWPwqA/XtocklCdNrXd2plMkUYxvDLnGnfoqoyksnwrH9vSFIa8fj5UNWn6n+3T1mn9pphHIZc7qe8s95gX3lvzBTh8L8hFPpcy8fsZZMycrk7u1KreSzvDUV5DqHQ5xEI/BCSVAUA1GqHQ9evR6XyVvBCd7qz4PM9CEV5FUIEUKttgmXNa7klPzuonVQqDEXhMt1GPE+olX3lc1RRXkAicTYkqR+SNHicQiiQJBPl8iUYGPgqgJH7Vewr9pX3xsxgIh5/M3y++zzvX4cQCoRIIJO5H5a1eNJHM5b3hs93P4LBz8PvvwuSJCCEhGr1dOj6/2EjzhmhAr//HsjyblhWArXayS1vZPBzg4Yz1vk5Z/RE01w0eg1U9RlIknCD4ADcv6vq44hGr5vCERK1J8svtw2CA6hPvi1EIh+EvbR4evH57kYyeTwCge+7QXAAUNXHEItdi0jk7zAdx02N7OY85fIVqFQubhsEJyIi6kwZ8fibIEnppiCiU/82ELitvhKOaPrx+38Bv/+elkFwwH4fS1IWodAnuzyyzgSDX0EicS78/t+518eSJOD3341E4hxo2leneIQ0sgCq1bNQLl+JavUCZvNTVzEQTjSNKcqzCAR+N2xTCUkyEQj8ArL8chdHRtSZYPCmEbuDS5IFVX0GqvrXLo2qM7K8E/H43wCoNZ2DzoVDMPgdaNoNUzA6IiIimgqBwE+hKNtHmJ8LBINfwGBvH6LpIxi8oYP5uQlNuw2SlO3OoDrk8/0BkciHAaDF/Nz+ezT6j/D5/tj1sRHRzMBAONE0pmk/hBAjl/IXQoGm/bALIyIaHVV9pIPu4HapH1V9ogsj6pym3QSg6lmJMZQQQCj0vwC4VI+IiGhfEAj8oF7abXiynIPff08XRkQ0Oqr6WEfzc0mqQlFe6MKIOqMoLyES+T9uidB2hFAQDH6xS6MiopmGgXCiaUyS9na4pew26SGiiREI3NZ2yahDkgBFeR2q+liXRkVERERTSVF2jTg/cHB+TjR+kjSAWOwypFKHQVFeGjZJxd7ehN//SwB6dwZIRDMKA+FE01jnDTAtWFZyUsdCNBaGccSISy8BewmxYRw6+QMaBVnOdLytJKUncSREREQ0XVhWasSM1MFtOT+n6ccwDutofi6EBMua34URDaeCePyiemDbTkLphCRZkKSBSRwXEc1UDIQTTWOVypsgScaI20mSiUrlwi6MiGh0dP0qjFQ2RAgZtdrBMIzDuzKmTllWD0SHfTCF6J3cwRAREdG0UKm8GZ00yhYihGr1pMkfENEo6fq7OiqNAsCtxz1VNO02qOrDHY/XIYQCIWKTNCoimskYCCeaxgzjCNRqw2fUCqGgWj0OpnlQF0dG1BnLWg5d/0Dbx+0amwoKhc8A6DDFo0sqlbdhpK9JISSY5rJpl81OREREk6NcfiuESI4wP5eh69cCiHRvYEQdqlbPRrV6xIjbSZKA3/9LyPKWLoyqlRo07SsY7TWCECoqlfMBaJMyKpr9FOVFBINfQij0aQQCtwEoTfWQaAKN3IWPiKZUPn8zEonTIcvN3emFkGGaS5DP3zhFoyMaWbH4bxBCQyj03wBqsL96BCTJgGXNw8DAVyDLOxGNvhOSVIBlLUC5fBkM47ApHbeuX4lg8DMA9La1QCVJoFT6e0y3IP7oVCHLOwDIsKwF4NSAiIhoOBHkcj9EPH4BgLJnfu6UTKnVTkSx+K9TND6ikSgwjHXw+R4Zsd42AMTjl6JaPRu6/nZY1rJJHpuFQOB2BINfhc/38Kj3tldzmtD19074yLqrBFl+A0CgPj+fydcaM4csb0M0+rfw+++vJ2zJ9WvWKEqlD0HX3w/+v5j5eLVLNM1Z1gJkMr9HMPglBIPfdOsWW1YPdP0a6Pp7IATrD9J0JqFU+jB0/V0IBG6Dqr4IIfyo1Y6HZfUiFrscivJGPbPKBKAiGPw6qtUTkc9/B0IkpmTUQvQhn/8R4vE3Q4jKkAtdBZJkolT6W5TLV07J+MZLkvYiFPoCNO1GyHIWAGBZvfXPlfeOokcBERHRvsUwjkQm8weEQp+Hpt0CSaoAAExzGXT9OpTL1wDwTe0giYYhywOwVz6OVHJEQFGeQzD4IoLBz6JcvhqFwn9jckJJBqLRq6FpP+mohvlQ9j4WCoUvwjA2TPzwOiRJ/dC070HTboUk7YUQPSiXL0G5/HYI0TPsvoqyGcHg56BpP4AkVQEAhrGy/rlyNRjCmzyyvB2JxCmQ5b0AUE+EsuqPDSAS+b+Q5b0oFj8+haOkiSAJ0WkF1KmVyRRhGJ1156Z9g6rKSCbD+9h7o1bP3AQsayH4RdjavvnemHkU5QUkkycAqLSs+yeEAsNYj2z21wD8E/KaY3lvyPIrCAa/Ck37LmS5CACoVk+Erl+PavVMzMSsAFl+DYnEGZDlnW1WmqxANvtrCNE3ZL9XoGm3QZbfgBBRVCrn1S80Zt6/QSv87KB2UqkwFIUVBRvxPKFW9s3P0RJkeRcAPzM3h7Fvvjemr3D4HxEMfqOjflSNhJBQLl+JQuHzEzYW572h6/8ETfuvjrLUm8cFVCoXQdffC8MYuezLZFHVhxCPvwWSVIC9AtbJUpchRAj5/G2o1Y5rs++fkEi8CUDV8//FWWlSrZ6KfP5WDL0uUtVHEAjcAUnKwbLmoFJ5K0xz1WQcXtd183MjGr0GgcBPRqxHn8ncz7KY08RY5+cMhNOMxckUtcP3xswQjV5Zn7QNP9nI57+JSuWtE/Ka43tvWJCkPIQIAghMyHimhkAicQJU9am2//ZCKKjVjkcud0f9NwVEo38LTftpPdvGnpBLkoFabR3y+e/CspZ2ZfSTiZ8d1A4D4c14nlAr/ByldvjemF5U9WEkk6eNef90+i8wzdUTNBYZyaQEIebWA8ijI4QCXb8OxeInJ2Q8YyXLryCVOgZ2yaTm97hdaiOATOYPTYFqScojlToIkjTQtiSj3Xvg/SgWP1p/vdcRi10Bn+8RCOEkyAlIkolK5WwMDNww4xuGdutzQ5L2oKdn/xGvS4VQUS7/DQqFL07aWKhzY52fc0ZPRERdJ0l7OgqCC6EgGLyhS6MaiVwv0zKTg+CAqv4ZPt/jw/7bS5IJv/8+KMoLAGqIxy9GIHCH+5gkGW6miqo+Xe9j8EY3hk9ERERE42QYR6JWO3zMJUg07VsTPKJfjzEIrsKy5qFU+sAEj2f0QqEvw17p2q63kAWghmDwy02PBQLfhyTl2+7r7K9pNwAoQZL2IpE4Har6eP0xo/6fPb/3+3+DePxCAJXxHdQ+wud7dMTrUsD+d/b5HujCiGgyMRBORERdpygvdTjZMKEoz3ZhRGMhoCgvQlUfhiy/PNWD6VggcHtD1kh7QigIBG5HIPAT+P1/GGZSb0KW99SboRIRERHR9Cchn78VlrUfhJAxmjoBkmRCVZ+e4PHs7ngMQsCdyxrGIchmf9tQzs+AojxZn5/vnOAxDseApt084vWNJBnQtO9haIBa037c0avIcgF+/+8RCv1PyxKHg69jQlX/Ak37QUfPS52XCJKk2iSOg7qBgXAiIpoCo/n6GX2myuQS0LTvIpncgFTqCCSTp6GnZz0SiePh998+1YMbkdMYs4MtIUlZBIM31JdytidJZn1SP/pMHiIiIiLqPsuah0zm9yiV/gWWNW+Ue090r6okpA7K6wshwbIWQtffi0zmXmSz98GyFgOoIBT6b/T0rEEqdRySydOQSq1BLHYhVPXhCR5rM0nKQZJKHW5bhixnhvwu3XFtdEnaDU27qYOkIhma9rWOnnNfZxhrOtrO7mF18CSPhiYbA+FERNR1hnFQvdb28Oxa1cd0YUSdEohE/gHR6HugKJs9j6jqU4jHr0Qo9J9TNLbOWFbfyBvZW8Ky+qCqTwy7TNMhSSUoyivjGxwRERFNS7K8DcHgFxEK/Qc07WuQpN1TPSSaAEIkUCp9COn088hkftXhPjJqtY0TPJKzIIQ24laSJFAs/guKxY/BMA6v/7aMePxChEIfhyzv9mzr99+HROJM+P13tH7CCdLJ2Ifb3rLmjph40rA3ZHlgxK0kyapn7rMm/0gsawWq1eNHLBUkSSbK5Wu6NCqaLAyEExHRFIigXL6so8mGrr+rS2MaWSDwE7dm+dCsDSdYHA5/HD7f77s+tk6Vy2/1dKJvz0Kl8pZJHw8RERFNX5KURSx2OVKpgxEO/wtCof9BJPKP6OlZjUjkPQD0qR4iTQgZhnEsarUjOqobXi5fOcGvH0OlcvWwwWAhFFhWLyqVizy/D4c/CZ/vjy0TN+ysaQux2NWTXColjFpt44j/dvZNhA31vkODyuW3dZR4YlkpGMaG8QyU2rCbkMpt34NCKKhWT0S1empXx0UTb9ID4V/+8pdxxRVXTPbLEBHRDFMs/jMsa0HbCaMQEsrlS1GrndTlkbUXDH5xxGwNu8HnV7o0otEzzUNQrW4adqIuhIJK5XxY1hIYxrqOMlSECME0V0zkUIloEnBuTkSdKyCROBt+/y8gSQKSZEGSavU/22XR4vGLAbBm7mwxMPA5AIERAroyQqFPQZZ3TOhr6/rHUKsdXa8B7n3M/p0PudwPATSuKtWhad8YocmkgF3D+9sTOt6hSqXrO6gRbkHXr2v6faVyEUxz3gjzc6BU+j8wzZWwrNiI4xFChmGsBfNfO2MYG5DL/RhCRAHArZ3v1KOvVk9DLncL+O85803q/8GbbroJn//85yfzJYiIaIYSohfZ7O9Qq22q/12GECqEkCBEALr+9xgY+AqADgoGdoEk7YXP98iI2RqSZMLv/zWAkZuBTpV8/tswzTX1f+vBf1/n74ZxOAoFu6O9rr97xGMWQkG5fDmA8GQOm4jGiXNzIhqNUOirUJRnh2nIZ8Hn+z007ftdHhlNFtM8BNnsb2Cads3kVkFpu+Hjd5BMboIsvzphry1JRcjyG7D71DQ/Lstl+P2/9fzO5/tjx2VCAoGfTsxA26hWL4CuvwNAu0A+oOuXt1lxGUQu91MIkWhKQHGC4+XyZdD19wMIoFy+qoPMfQvlcnPQndqr1U5Ef/+LyOe/gkrlAlSrZ6JcvhqZzAPI528DEJnqIdIEmOgOBwCAXbt24SMf+QgeeeQRLFu2bDJegoiIZgHLmodc7mdQlM3w+38NSSrCshagUrkAQsQhSf3QtJuhabdAlvfCspKoVC6Grl8JIeZ2daySNPIke3BbC/ZS4ek5WRKiB5nM3dC07yIY/BpU9WUAgGmuga6/G+XyZQACAIBK5UJUq9+uLzltvhC2l6nOQan0oW4eAhGNAufmRDR6JjTtBoxcX1iGpn0V5TJXmswWhrEOmcyDSKUOgixvbxmUtueEu5FKHQbDWIty+UqUy5dgPHPfYPBjUJStbUqc2D/D4U+iUjkXpnlw/fedN2ofzVx+bCQUCp+Daa5BMPi/UJTBjHnLmg9d/zvo+t+iXZKPaR6IdPphBINfRzD4TcjyXgghoVbbCF2/HtXque6+pdL7EQj8eY7dbwAARb1JREFUBLK8s+383DAOR7l88WQc6CwXRKVyGSqVy6Z6IDRJJiUQ/swzzyAej+OOO+7Al770JWzfvn0yXoaIaFJIUgaBwE+gKNsghIZq9VQYxhGYLpnJs5FproKur/L8TlX/hHj8zfUJroAkAZK0B6HQJxAKfQa53K2o1U7u2hiF6IUQSgcd2gEhwpj+2dFhlMvX1TNFKrDf3/4W2/mQy92GaPQ9CARuh72YzD4XJMmAYRyMfP67sKx5XRs5EY0O5+ZENFqyvAOK8saI29lZ4U8BqKL1PGKixvMaAoHb64kRCVSr58M0V07a6+3r7EDu8N8VdnDagKo+jkjkcYRC/41c7ucwzVXD7tdaHn7/90acZwuhIhj8JgqFzwGwA8ydEEKGZS0cw7hGS4KuXw9dfxdU9a+Q5f6Gut4j114XYg5KpY+gVPpnAGUAPrQK29kra+9CLHYFfL6/uuU77GsmE9XqWRgY+BqcxBYiGjQpgfCTTz4ZJ588scEJRWEdHvJy3hN8b9BQY39vWNC0j0HTPg+71qEKwEI4/EkYxiEoFm+EZa2e4NFSK7K8FbHYhQB0T1NKe8JtQYgK4vFLkM8/AMs6sOPnHd/nRhy12vnw+X4+bLNJu772FVDVkSe700dwhMej0PXvoFLZAr//B5DlnRAiXr8IPRyyLEGeBR/F/F6hdlplw80knJtTt/BzdPaQZTHyRg1UFRiu8urY3xsFhELvhd//Y9g34hUAFiKRj6JaPQOl0g0QomeUz0nDUZRHEQ7/O4To7PvP2UaWdyGROBe53F8BxEfxejKAhyBJ5Q5ey4DffzdU1XkfHQXTXApZfnWEsVqoVq9s2G+yyQA2wqont6tjiryNlFSzGIXCfVCUR+Hz/QyynIVlzUW1egksawWUmXQp0ga/U2g4Y52fT0ogfDLEYiNdpNO+iu8Namf0743rANwAwJn4Dzb+UdVnEI+fCuDPAJh9Mvm+DjtLufVyXHvJpIl4/MsAbhz1s4/9c+OfAfwc9oVYqwtECZLkg6Z9AJo23TPCx2Jt/T+bpk3dSCYTv1eIRsbzhIbD98dssBJADEC+g22XIpnsLBg9uvdGBcCbAfwJ9rxLoHFu6PffDb//rPrj0VE8Lw3vk2g9zx2eJJmQpDeQTP4YwN+Ncu9qx1sqSg3JZOM8+6MArhpuD0jSAoTDb0c4HBrluGaC4+v/2YKz8OOX3yk0kWZMIDyf12GaI9Uno32JosiIxYJ8b1CTsbw3FOWviMW+NswWJoTIo1b7AIrFWydmoNSGhUTiG8NmXdsMCHELstn/AtDZpHb8nxsHwOe7EeHwO+AsPXTYDWv8KBRug2EsAFAcw/PTVOL3CrUTjwchz4ZlDxOI5wm1ws/R2SUYvAqBwJeGLVchhAxdfxcqleHnPWN5b/j930Qo9EfP6kAvE0I8j3L5v1Euf7ij56ThSdJ2xOO/HubffHhCAKb5VQwMXN3xPvZ7o7NyKnbt61UoFBrfb2+Bpm1GMPhxCKG61xBOQ3gh5mJg4A5YlgDn5zMLv1NoOGOdn8+YQLhpWjAMvvGpGd8b1M5o3hvB4Nc9E6dWJMmEz/cLWNbrsKwFEzVMGkKSch03vpGkKixrNyxrv1G9xng+NwzjAlSrf4amfROBwA8hyzlYVg/K5b9BuXw1LGsRRm4sRdPZbPtekeVt8Pn+CsCEYRwE0+y8nBDZxNjiAbPabDtPaGLx/TE7FIvvhc/3A8jy3rYN+UxzGUqlt0OIzv5/j+a9EY1+tYOtLPj9N6BQ+AA6qcFMw/P5XhpzEBwAJElAll8bw/m/CrXaMVDVP7Vsljn4/CZ0/eqm5y8UPoxy+WQEgzfA7/8NJKkM01yMcvlqlMuXQYgEOD+fuWbjd4qiPA1VfRaAilrtyPo1JI3GWOfnMyYQTkQ0mVT1jx1kINslOVT1cVSrDIQ7FOU5BIM3IBD4GSSpCMuai3L5Cuj6VRCib9TPJ8Tolr7ZjSm7yzRXolj8JIrFT3b9tYk6pSgvIRz+J/j9d3kuamu1DSgUPg7DOHoKR0dERNOdZc1DNvtrxOMXQ1U31xvymbBLTRgwjHXI578PIWKT8OoVqOpzI24lSYCi7IIs72QgySXg8z0ITfsa/P57IUk1GMZKlMvXoFy+FMOtpBRi/A1PRzuXd5TL/4ZI5BwIIbUMxtvZ4GtRqZzXcn/D2ICBgQ1jem2ibvH5/oBw+F/g8z3q/k4ICdXqmSgUPgXLWjaFo9s3cI0nEREwbOZBs9l1N3o8NO0mJJNHQ9NuqmcL6ZDlVxEKfRyp1OFQ1UdHfpImflSrJ9RLjbQnhIxa7VA2SCJqQVFeRCJxMvz+3zVdTKrqI0gkzobPd88UjY6IiGYKy1qBTOYvyGZvR7l8GarV81AuX4lM5jfIZu+FZc2brFee5O1nK4Fw+MNIJM5GIHAnZDkPSdKhqk8jEnk/ksnjIcs72+5tGAfDsiJjf3Wholo9d0z7GsZxyOdvBhCAEJKb7elcExjGeuRyPwXgG/P4iKaS3/8bxOPnQVUf9/xekgT8/t8imTwRivLSlIxtX8KMcCIiALXaesjy9o6ywg3joC6MaPrz+e5BJPK+epBtcLms3b3ZAjCAePxNSKcfGXVmuK7/Lfz+3w+7jSRZ0PXrRz1uon1BNPpOSNJAy6XskmRBCCAWuwr9/S8CmKVdT4mIaILIqNVOQa12ShdfU4NpLoEsb63PLduzrAQsa353hjXNBYNfQChkl5RpnAM4N8UV5RXE4xchk/kDWpeSCaFcvhLB4FeHrQ3fjiQZ0PVrxzJ0AEC1eg76+5+Hpt0Cv/9XkKQiTHMpyuUrUKudhOmVy6nD7/81FGUnhIiiUjkdQsyd6kHRtFVENHo1AKvligf7fMsjGr0e2exdXR/dvmTSA+Gf+tSnJvsliIjGrVy+Fpr202G3EUJBrbaJy5XqQqH/hj0ZbT1Jdr7Mg8GbUCr9w6ieu1o9E6XSdQiFvgoh4LkAcrJDKpW3oVK5dExjJ5rNVPUx+HyPDbuNJFmQpCwCgZ+gUvmbLo2MpgPOzYloZpCg6+9GOPwRAO0LwQqhoFy+GswSBoAKQqHPDLuFJJlQ1Wfg99+NavWMltuUSv8Ev/93UJTNHQfDnXImhcInYJprRz1y73OloOvvha6/d1zPM3kshEKfRjD4v5DlAQghA7AQiSioVC5CofCZek1yokGa9qN6kkr7beyeZA9DUZ4e93lE7U2n22lERFOmVjselcq59YlMM/v3PhSLH+vuwKYpWX4Nfv+DHUyOLWjat8fwChKKxf/EwMD/NDXCtKz5KBY/gYGBrwAYIUWIaB/k890zYmkhwA4e+P0sj0JERNNTuXwlTHNV2+80IRRY1lyUSu/p8simJ7//d5DlzIjbCaEgEPjeMI/Hkc3+BpXKm91/eyHsObdlpVCpnATLinv2Mc0DkMt9dxoHryeKQCTyfoRC/wFZHgDgJBfYQcxA4CdIJM6EJOVHfCZF2Yxw+COIxd6KWOxtCAa/CEka+f8fzUx2ScKRQ7BCyPD77538Ae3DWBqFiAgAICGf/xai0fdB025tmHBLkCQDltWHfP67MIx1UzrK6WK42oKNJAmQ5TfG+CpSvanPO6Cqj0KW98KykjCMI9B6KScRAYAkldHZTSILklSZ7OEQERGNiRBRZLO/Qjx+GXy+P9WbdQo483M7+HrrmJqzz0ayvKNto8lGkmRCUV4bdhshkhgY+AYKhU/A778bklSCZS1AtXoqAD+ACny+v9RLlyyoZ6/O/gQVn+8+BIM3tX3c/rd9AaHQZ1EsfrTNVjVEIh9EMHhT/ZrTBCDB7/8lwuF/x8DA51CpXDbxg6cpZc/PO+llINe3pcnCQDgRkUvDwMDXUCx+GJp2MxRlG4QIolo9DdXq2eBH5iAhoqPYNjzOV5PrwW8i6oRpLgMwcr8DQKlvS0REND0J0Yds9rdQ1UcQCNwGWd4LIRKoVC5CrXYM9oXga6eEiIwYBLe3kyBErMPnnNOmhFoAtdpxoxzhzBcMfg1CKMOuipUkE5r2jXrN8F6Y5io0vk8jkQ9A077jbmuz/78JUUEsdj1yuRCq1Qsn6zBoCthzbhUjzdHtm3xLuzGkfRajOkREQ1jWcpRK/zbVw5jWTHM1THM/yPK2YeucCaGiUrmgewOjCVSDLL8OwIJlLQIQmOoBUYcqlQsQiXwQklQcdjtJMlAuX9GlUREREY2dYRwOwzh8qocxrVWrp0IIFZI00s1wgUrlvK6Mabbx+e7vqG66LOeRTJ4JADCMFdD1v0O5/A4oyosIBtuXjZQkux9SJPLPSKfPR/Mq2BIUZQeEUGFZi1s8TtNVufx2hEJfHnE7y4rx/JxkrBFORERjIEPX34ORs3BM6Po7uzEgmiCSlEMo9B/o6dkfPT3r0NOzHj09yxEO/zMkaddUD486Ekap9OFhtxBCRrl8aT1LiYiIiGY6IXpRqVw8bJ8QIWQIEUGlckkXRzZ7jHyToZmivIJo9P2IRN4DTbtxxD4ukgQoyvZ6TWmbLG9FJPL36O1dhlTqMPT0HIJU6iAEg58FoI96TNR9pnkgyuWL2vYkc5RK/wRA686g9lEMhBMR0Zjo+rtQqVxQX17pfcz5gi8UPg3TPHjInib8/jsRj1+Anp790NOzBLHYRfD7f4XO6qZ1TpIyUNWHoaoPQ5JyE/rcs5Ek7UUicTJCoc9Alvvd38vyAILBryCZ3ARZ3jKFI6RO6fr7UCp9AAA8F1x2fVWgWj0XAwOfn5KxERER0eQoFP4TpnlAy2Cr/TsV+fytLcoclqBp30YisQk9PQvR07Mc0eg7oap/mfAxyvIOqOpDUNVHADT2KilA025ENHoNotErEQp9HLI8fC3zbjOMlW7j0E455WqCwZvh9/+uo4xyIWSo6gsAAEV5Csnk8dC0myBJg0FvWd6BcPhjSCTOBlAY1ZhoagwMfAXVqr1SwJmT23+2z9di8R/ryWY0mSQhhoYvpqdMpgjDmNgACc1sqiojmQzzvUFNZvJ7Q5Zfg6Z9B4qyGYAPtdpxKJffAmC8dbYni4lg8GsIBr/kabpTqx2FUulDqFbPGLJ9CbHYZQgEfuepr+f8uVI5G/n8tzHeMhyyvBXh8CcQCPwIklSrv0YA1eqlCAQ+jkwmMa73hiTtgd//e0iSDtPcr14jceYvTYzFLoTff1/bCboQKkxzNTKZP2I21uScyZ8d7SjKcwgGvwmf70EABgxjHXT9WhjGUZiN/w8nSyoVhqIwf6TRbDpPaOLMxs9Rmhgz+b2hKE9B026pN6MMo1o9ux7Mmp6VZiVpAKHQZ6Bp34Qs24kgQsioVs9GqfRhGMY6z/ayvB3x+HlQlJdgN+qz6vvYZVaKxQ+hVPpXjHfeoKp/QSj0qXrzTTsMZVkJVCrXIhg8BEL8LeyArgKnKSpgQdevQ7H4CYw015bll+Hz/RmABdM8EIaxflzjbUXTbkQk8r5hS0O2YycPhSBJxRH3F0JGofAplMtXI5U6GLK8e5j5uYJy+VIUCl8Z/aCmuZn8udGegM/3R2jaN6GqTwFQUa0ej3L5Gpjm6qke3Iwy1vk5A+E0Y83OD0WaCDPzvWEiHP5nBINfhb1Yx5n8mRAigoGBr6JaPX9qhzgsC4ryLCSpAMuaB8ta2nKraPRqBAI/cSfYQ9nlGq5AofCFMY9EUV5AInE6JCnfNGG0A+69yOV+h2p1v1E/tyT1IxL5x/oxDD63aS5EsfgvM7rDu6K8iFSqs6ak2ewvZ2WDpJn52UHdwEB4M54n1Ao/R6mdmfjekKQ8otF3IBC4q569acEOFBswzYXI52+FYRza0XPJ8lao6pMAAMNYB8sa/Tx09MpQ1WcB1GCaSyHE3BbbGEgmj4GibB42U3lg4LMol68d80j8/l8jFvsbAKLF/FyCJAn351BCSCiX34lC4dMtn1tRXkAk8iH4/b/3/L5WOxjF4idRq20a87iblZBMboKivNxRZncr7Y5zqEzmASjKS4jF3tHBc6ro738BQvSNaUzT1Uz83KDuGev8nDN6IqJpIBL5BwSDX4Uk2ZNDSbLqPwFJKiIWuwJ+/2+nepjDkGGaa2EYR7cNgsvyKwgEftw2CA4AkmRB074LWd45xnEIxGKXtQyC289vAuhHOPz2UT+zJPUjkTilKQgO2Jk0sdj1CAY/N8ZxT71A4I4RaxYC9kQ7ELi9CyMiIiIimio1xGJvgd9v12mWJKM+P7drRMvyG4jHz66v4mxPUZ5DLHYRUqlDEI9fhnj8MqRSByMWezMU5blJPgYNhnEYDOOoNkFwwO//FVT1+WGDukIAodB/ARhb4FeS0ojFrgRgtpmfC8/PVo8Hgze0/LdWlGeRSJwMn+8PTY+p6jOIxy+A3//rMY27tRByuTthGAcBGCxpMbr00uHDcEIoqNUOg2GsQyDwsxFrStsMBAK/Gs0giPZZDIQTEU0xu3v4N4ad/AFAOPyPsDPFpwdJ2gWf7y74/b+FLG8fcXtN+z46/doJBH4wpjH5fPdDVV8cIUPDgKo+Wq9L2Llw+KNQlK1tJvD2z0jk36AoL4zqeacLScqis/8/gvXWiYiIaFYLBO6A3/+ntnNKO2FFRyj0ibbPoaqPI5k8GX7/vZ55viQJ+P33IJk8Gar6+ISOW5Zfht//a/h8v6vP7Yanad/tsHnjG/D5fj/sdq0oymZEIh8EoHeUBd2OEAo07aahv0U0+i5IUqnN/NwCYCEavRZAacyvPZRlzUM2ez+y2dtRqVyEavUYAP6O9y8W/y+A1sFz+/9FAAMD9upYSUoPm0Q0SOno/zcRMRBORDTlOuseLqCqr7TMdug2Wd6CaPRK9PSsQSLxZsTjb0EqdSBisUugKM8Ps992dFZbUIEs7xjT2Pz+X3gaj7QjhAq//xcdP68kZaFpt464BFIIFcHg1zt+3unEsuags0wfCZY1u5ZdEhERETXStBs6mJ+bCAR+Ckna2+JRC7HY2wGUh1mlWK5nSo+/5IOqPox4/Gz09KxHPH4xEokL0dOzEpHIeyBJe9rupyivdVziYzTzc0V5AfH4uUilDoem/XhMNbUbSZIJVX3a8ztVfQQ+35PDjt9ebZuHpv14fANoIqNWOwUDA99ELvdr6Prfjfh+EUJBtXoydP39yOdvgGXNdX/vZH0bxsHIZO6CaR4MALCsuR2t2JQkk/Nzog4xEE5ENMVUdfgJnEMIGYryTBdG1J6ivIhk8kQEAnd4xmxntvwWyeRJUNXHWu4rRKTDVxEYa3NQSeq0Y7o0im0Bn+/PkKRqB69vwOe7u+PnnU4qlYvQyYoDSTJQqVwy2meHJPUDGPnfkIiIiGiqqeozHc3PJclsWbLD57sXivLqCEFaE4qyBT7ffeMZKvz+3yCROAs+3x+HPH8VmnYLksmTIEm7W+4rRLTjsh5CdDY/HyxX8mBnT9zRawNDw1c+3+87ChIDyrj/jUei69dACK1tGRN7/BZKpQ8AACqVS5FOP4dc7gcoFv8visX/QCbze2Sz97tBcHu7izu8TtRQrZ4zylEX6/PzsZW8IZqpGAgnIppynXacFxipW/rkEojFrhqh/nYZsdjlaDWhqlbPcusqDscOtJ41phFa1gJ0Vj7GrG/bqVrHW3YSMJ+OLGsRKpW3DFuH0M5kOa7jxlA+3x8Ri/0Nenvnord3GXp75yEavbbtzRIiIiKaqSxI0p56wHU2NLUbTaikeVu//+5RrFIcexKFJOURi10Fu/5287+7JJmQ5e2IRt/Xcv9K5Vx0smJTCD9qtRM7GlM0en3bciVjJ6NWO9rzG0mqoLP/TxYkqfO5/FhY1iLkcj+BEMGm4Lz9PpBRKHxxSONOFdXqWdD190PX3wvDWO/ZT1Ufh6r+GZaVhBDt/x8JIUHX3wUhYh2MVMDvvx2JxOno65uP3t5l6OlZhHD4w5DlVzs+Xvr/27vz+MiqMv/j33NvrdnT3dDNIiPb2IDaoqyOLIKNgoqjMkIrCMMibjD+FBEBUQRRtAVlFAa0UUdkE5CRRRbF0UEZAR38/QYGB9ABdICGJklVart17z2/PypJd+hU5Sapyq1UPu/Xq19Jbt269+nmpHjqqXOeg4WMQjgAxKxa3Svikjcr399zHiKaWiJxvxKJ/4wws+VppVJ3b/ZYtXqAfH/nhn/X2uYwr57137NSOSpy0l2pvCfydYNgh0jnWesqCHaOfN12k89fomp1X1mrSQVxa42sNQqCVyiX+36ka2Wzl2lg4C1Kpe6YeGNmjK90+iYNDLxR6fS1Lfk7AACA+WNMXtnsV7VkyS5atmxHLVu2k5YseYW6ur68oPcUqVb3jriJeEZBsMtmx40pK1pLQCNjSjMPcEwtnyo27L9tTKBU6nY5ztObPVYuv09SeppCq6tyeY2sHZw2nkTid0om/6PJRXBJMiqXj510pJafRylwO5Fz+bnw/X01NPQ7FYunKQi2lrVJheGgyuVjNTT0a5XLx0S6juM8pYGBgzU4uL+6ui6WMXlJdiw/33jeeK7ueW+d6DveWKieno+qv/9YJRL3b3K/grLZKzQ4+PpJx4FORSEcAGJWrb5W0y1JqxWId488E7cVUqk7ZzCz5a4pHjHK5X4wtgRz8zcWtf54g8rlvqdobxw2FwQ7qVz+22l2V3fkeUfPaEZ4EOyiavV10+7abkygUun4yNdtP90aGfkXjY5+U77/yomjQbCjRke/rKGhn8napdNeJZn8V/X0fEqSNlsFUPs5VG/vh5RI/L6p0QMAgPljzAsaGDhI3d3nyXGemTjuOM+pq+sCDQwcKGOeizHC2bLy/ddG2BvGVbn8viln4gbBXylay4lAQfDyWUUp1fLzaKxSqZ9tftQuVS73z5Lcuvm57++mQuELke4SvV3JFBE2KMYXCucpDFdMOlapHC5re6e9bi0/j1aEnqsw3ErF4ll68cVH9cILG7Rhw5MaHb1YQbBrpOcb85wGBlZPrJ40xh/7o0l91q2VfH9P5XJXjk1SSU577Wz2UmUy3x+77uTVA7XNX4vq7383m26i41EIB4AYdXWt1cDAkWr0clxLJpMaHf3afIU1pdpslWgF6nozW4JgpYaGfqFK5e9k7caEzdqUyuU1Ghr6pcJwxznFmc9fpmp1v7HrbkzEN35/mIrFi2d83ULhHNVmY0z9b1D7sOI18rzDZnzt9pJSuXyMhofv1fPPv6jnn9+goaHfqVw+WVF7t2ezlzR8E1RL5I2y2cuaEjEAAJh/fX0nyHUflzHhpCJdrWgXynX/Z2zDyIXEV2/v8eru/mLD3tnWugrDFSoUzpjy8UrlKEWdEV4uHzWrSCXJmELD2eAbOTKmOOUjnvcWDQ/fIc87cFKeG4b9KpVO0fDwHRHbboy3CIw+oWXjJJtjFYY7jx1zJ94nhOGA8vmvqVT66BTP7lKx+Olpru+oVDp6zu8v5kt394VynPV120mO/57lct/T8PDdqlSOULTWmb6y2a83PKP2e5xTJnP1zIIGFpiojWkBAE2WTl+v7u7PS9r8U3mp9km/MVIQbK98/lub9Y2bb2H4MkWb2WIVBNs2uM72yuev0OjoF5VI/GGs5cbKSMsto+nWyMiPlErdpmz2CiUSD0mSfH8Ped4H1dNzhKSSZtq/slp9o/L576i39wOytjar2Zhasm5MIN9/jUZGblCUGRlzYUxervsHSaGC4K9l7UAL7zbzNMGYYaVSP5v2TVmtTcoNyue/qXh73wMAgJly3f9SKvXzhufUWnLcN5aLvXZe4pqr7u7PKZ2+SdLkGbjjxvPzanVf5fPfkrXLp7xOGK5QqXSSstnL6+ZEtd7OJ9W9RhRh+HJZe/+0+/AYEyoItqv7uO/vpVzuR3KcP8t1/0fWpuT7r5aUmVE8QbBjpD2Bah8ypFWpHC7PO1l9fQcrlxuVMb9SMnmvpKqC4BWqVN4uKV33OqXSR2RMTt3dX5rIyWvXT4ztO/TueZlMZMzzct0/SkrK93fVTP/dakaVyfwg0kqEbPbb8rx3Rr5yMvnvct0oqzOs0ulrVSp9OPK1gYWGQjgAxMKqq+tCWWvqJse1IqtUKn1Yvv+6eY5vc+Xye9TdfbamLyCHY/0GG7N2qarV1zclts0l5HnvkOe9Y/LRhKO5LIaqVN4lz9tPmcz3lU7/RFJRYbi9SqVjVa0ePKdrT8dxnlVX14XKZK6emHFvbUqVyt+pUPiUwvDlLbv3TBgzFHFmUm3WkDHFSMtaAQBA+0inb5pUeKzH2oTS6ZtUqbR/IdyY4YaF69o5ta+FwrkKw20aXq9QuECOM6xM5tqJwqy0aZH2SBUKF8wp5nL5mEgzeMNwiTzvkAjnbaswrD+hZTqVytsUhv1ynMb94Y2RXnzx1wqCncfyc0kyqlZfP8P3B0bF4pmqVN6jTOY7SiZ/JSmQ779a5fLxLX8P5boPq7v7i0qlbp2Y2BSGfSqXj1Ox+ElZ2x/5WonEY5H6xRsTKJl8cEZxGvNixPMkx3lhRtcGFhoK4QAQg9ou4I9FONMok/m+yuUTWx7TdKxdplLpZGWzlzaY2eKoUjmybYqyrWDtFiqVPq5S6ePzds/apjmrx5ZKbnzDaYyndPo6pVK3a3j4TgXBynmLqZ6ZzFC3Nilru1oXDAAAaAnHGVJtAsD0qwWNGWp5PM2QTv9YkjftedYmlMlcrdHR6TZ3Tyifv1yl0gnKZr+tROIBSbXezqXSifL9vTTbfXHGVauvl+f9jZLJf2/4oUSx+ClJqTndK5qMCoWz1Nt7et0zrHVULq9p6gbzQbBT5D7mzZJM/lr9/X8rqTppda/j5JTNflOp1F1jbWWWRLzizFarzkTUlbfWGoVh1HiBhYke4QAQA8d5NtJ5xlg5zv+2OJroCoXzVKkcKWnTnn4b+2973luUzzfuP4eZ6+v7eznO81O+waltoJNTX9971coEOiprB+V5b5x2oyRrE6pU3inaogAAsPDUimXR8o4oG223g1rOHa3f8qabgzZm5Pt7K5//loaGHtLQ0ENjLQ/31lyL4OPXz+Wulu/XZtxP3h+nlqsXi59QqfTBJtwrmnL5ZBUKn5G1Zsp4KpV3aXR0ob9fKKqvb40kr05+Hsh1H1NPzyciXzEIdpa19dvAjKttYPrqmQSranUfheEWkc6t9bcHOheFcACIgbU9Mzi3ndpG1Ga2DA//RJXK4QqCbRQEW8vzDtXw8M3K5a7R7HrioZ5E4iElkw807LdoTKBE4nElk7+Yx8jqKxZPnXaptBTQfxAAgAWqUjkiwv/rax/Yl8vvnoeI5q6Wc0cp7rttlZ9bO6jh4Ts1MnKVqtX9FQQrFAQvU7n8Xg0N/ZsKhc+qOUX3qIyKxU/qxRd/r1LpVFWre6ha3X0snl8on79S8zM7vXUymRvH2gHWHy/GBEqnb448AcraPpXLR047mcSYQKXSyTOKV0qqWDxl0maom9/fkbU9KpffO8NrAwsLrVEAIAbV6l4KwwE5znDD86x1x2bNthOjavVvVK3+TdyBLAqp1G0z6MF5q6rVN85TZPVVqwdrdPR89fScvVnstdlAgUZHvzExewkAACwsQfDXqlRWK5W6p26OYq2ravX1CoJXKbEAKg+VyqHq7j5z2vOMCVSpvG0eIpqJhDzvcHne4XEHMiEMX65C4dy4w2iJVOpW1eaVTvfBSaBU6i6Vy++PdN1i8VNKp2+TNDzl71Xtd2pvVSrv2PzJ0yiVTlUi8bAymetkrTOpiF/Lz1PK5X44g1YuwMLEjHAAiEVapdIHZG39l+HaJ/ZG5fLfz19YaDvGjCra/67t2LntoVQ6VcPDP5bnHTwx+8RaV553mIaH71K5fEzMEQIAgLnI57+lINhlrAXGxpmm1tZmlwbBzsrlvhdjhDMThjvI8w5pOCPXWldBsJU877B5jAztxphcw9ngGzkyJh/5umH4Mg0P36Ug2ElSrUBd+zPehvJQjYzcICk5i6gd5fOXK5e7Qr6/auKotVmVy8dqaOjeGW5UCixMC+BzWQDoTMXi6UomH1Qy+XPVipgbHxtPdvL5b89p53YsfGG4taJsRCWZsXPbR7V6oKrVAyWNynFyCsMBSWyOCQBAJ7B2iYaG7lY2e6Wy2cvluk9JksJwG5VKH1S5fHxbtRCJIp+/VIODb5LjPLXZjFxrXVnbpVzuOs2uEIlOEYbbRFqxaUyoIJhZfh4EO2to6H4lk/cqlbpFjpNXEGylSqUZG4w6qlSOUqVylIwZljElheFSLfRWNcBMUAgHgNikNDLyQ2Wz31A2e5lct9Y/zlojzztQxeLp8v19Y44RcatUjlB39znTnlfrwblmHiKajR6FYfS++AAAYKHoVql0ikqlj8qYEUmStf2a357UzWPtFhoa+rm6utYqk/muHGd07HhClcq7VSye3oRiZCeotfzIZNYpkfhP1Vqz7K9y+cRF0fquXH6vMpnrpz0vDHvleW+ZxR2MqtX9VK3uN4vnRmPtgKwdaNn1gXZFIRwAYpVUqfR/VCqdKtf9LxlTVBhu23YzexGfMNxK5fLRymS+X3cJZq3lyKEKgr+e5+gAAAAkyXRMUc3aJSoULlCh8BklEo9KChQEO9A7eYwxefX1HalU6t5Js6IzmWuUzV6lYvHDKhS+qIX6YUgUtVWPuyuR+L8NeuRLpdLHJGXnNTYAjdEjHADagqsgeKV8fy+K4NjM6Ohaed6bJWlS38rx76vVvZXPXx5LbAAAAJ0pK9/fXb6/B0XwTfT2Hqdk8j5JmlQEHv++q+tSZbMXxRLb/HE0MnK9gmDniZ7442obT0rl8t+rWPxEXAECqINCOAAAbS+tXO4ajYxco2p1f4Vhj6ztVrW6l3K572pk5JYF14MTAAAAC0si8Tul03dP2xu7q+urkorzE1RMrF2uoaFfaHT0m/L9V8naLoVhnzzvLRoevkWjo18TJTeg/dAaBQCABcGR571VnvfWuAMBAADAIpTJXCVrEzLGb3ie44wqnb5Vlcp75imyuGRVLh+jcvmYuAMBEBEfTwEAAAAAAKAhx3laUuMiuFRrD+K6T7U+IACYIQrhAAAAAAAAaMjaLkUrI4Wylk0iAbQfCuEAAAAAAABoyPMOkTHhtOcZE8rzDpmHiABgZiiEAwAAAAAAoKFK5V0KwwFZW7+UZK0rzztAQbDzPEYGANFQCAcAAAAAAMA0ssrlvi8pMWUx3FpXYbiF8vnL5j80AIggEXcAABYOY4aUTt8o131K1qbleW+S7+8lycQdGgAAALDoOM7TSqdvkuO8IGv7Vam8g5m4aKlq9QAND9+h7u5zlUr9YuK4tUlVKu9SoXCuwnDrGCMEgPoohAOIIFRX1/nq6rpEUlW1lw6r7u4LVa2+Svn8lQqCV8QcIwAAALBYjKq391Sl0zeqNinFlRSqu/vzqlRWK5+/QtYujTlGdCrf30MjI7fIcf6oROIPsjYh338tYw6YtUDGFGRtt2qv52gVCuEAptXT83FlMt+RMXbsSHXisUTiEQ0MvEnDwz9XEOwUT4AAAADAolFRf/87lUw+OJafW0kbNzBMpe7RwMChGh7+qaztiy1KdL4w3EGet0PcYQALViLxkLLZbyqdvknGVGVtQpXKu1QqfUi+/7q4w+tI9AgH0FAi8aCy2Ss3KYJPZkwgY0bV3f2ZeY4MAAAAWHwymR8ombxfxgRTPm5MINf9b2Wz9GkGgHaVTl+jgYEDlU7fKGNqkw2N8ZVO36SBgYOUyfxzzBF2JmaEA2gom10naxMyxq97jjGBUqmfyHH+l35waDnHeULJ5H9IsvL9V9OWBwAALCrZ7BURzgqVyXxbxeJpYpn9YmOVSDygbPbbSiQelCT5/l4qlU6U7+/Rovs9JNf9b0lJVav7Kgy3asF9gM6RSPxOvb0fkhTKmHDSY+O1l56eU+T7r5Dv7x1DhJ2LQjiAhhKJ+xoWwccZEyqReEieRyEcreG6j6in5wylUv866bjn7atC4QKWjgEAgEWgokTikWnPMkZy3efkOM8oDLedh7jQHnz19n5Emcw1kyYzue7/KJO5WqXSMRodvUTN+nAkmfypeno+o0Ti4Ylj1jryvLdpdPRLjD2gjmz2m5KczYrgk7nq6vpH5XIUwpuJ1igAGmr8wvxSMzkXiC6R+L0GBw9WMvlvmz2WTN6vgYE3K5G4L4bIOoMxG+S6j8mYF+IOBQAANDTTfJv8fDHp7j5D6fS1kjRpMtP495nMVU1raZlO36T+/nfLdf9r0nFjQqVSt2lw8EA5ztNNudfiY+U4/zuWn+fiDgZN5ymd/tG0Ew6N8ZVK3SqpOD9hLRIUwgE0VK2+RtZGWzzi+7u1OBosTla9vcdLKk/ZC7N2zFdf3/slTb96ARulUnepv/9tWrZsey1Z8jotW7aD+vvfplTqrrhDAwAAU8oqCF4ua820Z4ZhPy0qFhHHeUbZ7Lq6eztJkjFW2exlMub5Od3LmCH19p489v3mH7bU9pHaoJ6ef5jTfRaTZPLX6u09VsuWLdeyZQNaunSllix5nZYu3U69vccpkfh93CGiSYzJR1p1Xzs3lOMMtzagRYZCOICGyuUTp32RttaV571RYbj9PEWF+VVVOn29BgZWa9myFVq6dGv1979DqdRtmo9ZRsnkvUokHqu7IZRUSxBc9zmlUj9peTydIpv9uvr7j1Ay+atJx5PJX6m//whls1+LJzAAANBQqfSBac+x1lW5fIKkZOsDwrwzJqds9jINDu6pZcuWa+nSl6mv7whJ9YvgG1llMtfN6f6ZzA8kedMU3QOlUj+T4/xxTvfqfFbd3WdqYOAtSqdvljGlSf+uxoRKp2/WwMBBvNfpENb2yNpo5VhrjcKwr8URLS4UwgE0VK3up0rl7XVfqGvHkyoUPj+/gWFeGJPTwMBh6us7UYnEAzKmKMcZVTL5S/X3r1Ff3/skeS2NIZW6J9KqBGsTSqXuaWksnSKZ/IV6emrLYl/6AcP4zz095yiZ/Nf5Dg0AAEyjVDpOQfAKWTt1n2drXYXhChWLH53nyDAfHOePGhzcR93dZ8h1/1vGlOQ4I0ok/jNiW0tXrvunOcWQSv1MUYvuqdQv5nSvTpfNXqqurm9IUt0PFmr/Xavq63u/HOcv8xgdWiMtz3vrtO9xaxMOV0vqmZ+wFgkK4QCmYZTLXalK5UhZa2StO/an9qIdhltqePgW+f6qmONEK/T2njCx2/ymifV4sTSVul09PWe0OIqypOmX/9aS8UqLY+kM2ew36r55HmetO7aJCwAAaC89Gh6+TdXqXpJqkwE2zc99f1cND98ha5fFGSRaoqyBgcPlOM/IGPuSmcNRCtOSZGVtdo5xlGSipOdyZEx5jvfqZFV1da2NdGbt37uqTOY7LY0I86NY/GiEHuGBSiU+0Gy2aI1/ASxyaeXzl6tQOEOZzFVy3SdlbVaet1qed5h4KelMrvuw0uk7G55jjFUm8x0VCp+WtVu0JI4g2F5Re3/TnieKolKpu6Z9s1RbznqXapuzdM1LZAAAIBprt9DIyJ1KJH6rdPqHcpznZe2gKpV3qVrdV9EmEWChSad/JNd9ak7XMMaX571pTtcIgp1k7f0RCnnhWC6PqaRS98hxNkQ+v9Ym5YcqFs9uYVSYD76/r/L5L6u393RZ605apTv+8+joF1StHhhfkB2K6hWAyMJwexWLzdllHO0vk7lG1iYibORhlcncoFLpQy2Jo1L5O/X0nKnpW7BYlcvvbUkMncSY0cgzhmozjfKylkI4AADtyPdfJ99/XdxhYJ5kMlfJWidiC5TNWesqCP5K1eoBc4qjXH6/stl/nuZeUhgun3PRvZM5zjOzeM5ICyJBHMrlDyoIdlU2e4lSqbtljJW1Rp53oEqlU1StHhR3iB2JQjgANF2oVOqnSqdvlDEvytqlKpePGPsf2cLpSOU4/6tovf/cWSVxUVk7qGLxH9TV9ZW6SzCtNSqXT1AYbtOyODqFtf0RP+AYX2rdPw9RAQAAtFJZ6fSPlErdKWOKCsNtVS4fI9/fPe7AZsRx/jKnIriUUT7/Pc11xYDv76lKZbVSqXvqbmhvjMYmUVF2qsfamfV+rn240JpVuIhHtbq/qtX9ZcyIjBmStQOydiDusDoar0gA0ESO80f19x+hROLxiSVN1rrKZK6W779CIyM/VBi+PO4wI7G2T9GS5HDGSdxMFYtnyZi8urr+aVIRd/z7SmWNRke/1NIYOkdalco7lE7/S8NiuLUJVSrvkJSZv9AAAACaLJm8V319R8txXpS1jqRQUkLZ7LdVqbxJ+fx3x/Le9mdtr6xV3ckh9Z9Xm2VaKFygINilCZEY5XLfU3//0RMb22/Mz11JoQqFz6tcfn8T7tW5PO+NsjYlY6Zb+TrOqFw+uqUxIR61yUpMQJoPFMIBoEmMeV4DA2+R4zw/9nMw6avrPqGBgUM1NHSvrF0aW5xRed5hymavnPY8YwJVKm9tcTSOCoUvjy3DXKdk8t8lWVWrr1W5fAJLgmeoVPqo0ukf1X0jZa0khWzOAgAAFrRE4iH19/+txveb2TibuvZzKvVz9fW9RyMjt0lqvJF4O/C8dyiR+H+qFfPrs7ZLL774MyUSj0uSfH9VCybj9Ghk5EdKJn+pTOZKJRKPyNqUqtU3qlQ6XmG4Q5Pv13lqK4ePUibzg7oz6zeea2RtH4VwYI4ohANAk2Szl8lxnm+wPNCX4zyjbPZyFYtnznN0M+d5b1IQvFyO83Tdv5O1rqrVvRUEu81LTEHwSo2OXjwv9+pkvv865fOXq7f3g7LWTJoZbm1CklU+fxkfMAAAgAWtq+tcSUHddiK1zcF/rVTqDnleqyd2zEVZxoyoVDpCXV0Xylqv7p4v1joqlf5eYbibPK/VObpRtXrAnHuOL2aFwgVKJB5SIvFwg/dcjqzt1sjIjQtiQhXQzhZOs1oAaGuBstl1036Sb0yobPbbmm4WR3twNDJy7dgSzM1nyFjrKgxXKJ9fF0NsmKtK5UgNDf1S5fIaWZuWJFmbVrl8lIaGfqlK5aiYIwQAAJg9x3mqYQ/rcda6yma/NU9RzUwi8Vv19h6nZcu21rJlO2vp0lXy/ZWSTJ383JHv76VC4Zz5DxazYm2fhofvULH4fxSGA5scr30Nwx6VSv+goaHfyPf3iidIoIMwIxwAmsCYDXKcoUjnOs4LYxthNO/T/ETiQWUy/yzXfUxSRp53kMrl98naJXWfY8yIMpmrlMl8R677pKxNyfMOUal0snx/H0lSEOyqoaFfqLv7y0qnr5cxVUm15Zbl8vtUKHxK1m7ZtL8H5lcQvEqjo9/U6Og3JFUkpTXXzZMAAADagev+oe6s6U0ZE8h1H27y3QOlUj9VOn2tHOcZWduvSuVwVSrvkpSt+yzH+YsymXXKZK6W4zwnKZBkJv4exlglEv8pY0L5/k5y3ScmHgvDLVQqnaRi8WNij5eFpkfF4jkqFj+lROIRSZ6CYLux93LpuIMDOgqFcABoipn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       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-advanced/kernel-method_15_0.png"
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "def plot_svm(N=10, ax=None):\n",
-    "    X, y = make_blobs(n_samples=200, centers=2,\n",
-    "                      random_state=0, cluster_std=0.60)\n",
+    "    X, y = make_blobs(n_samples=200, centers=2, random_state=0, cluster_std=0.60)\n",
     "    X = X[:N]\n",
     "    y = y[:N]\n",
-    "    model = SVC(kernel='linear', C=1E10)\n",
+    "    model = SVC(kernel=\"linear\", C=1e10)\n",
     "    model.fit(X, y)\n",
-    "    \n",
+    "\n",
     "    ax = ax or plt.gca()\n",
-    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap=\"autumn\")\n",
     "    ax.set_xlim(-1, 4)\n",
     "    ax.set_ylim(-1, 6)\n",
     "    plot_svc_decision_function(model, ax)\n",
     "\n",
+    "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
     "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
     "for axi, N in zip(ax, [60, 120]):\n",
     "    plot_svm(N, axi)\n",
-    "    axi.set_title('N = {0}'.format(N))"
+    "    axi.set_title(\"N = {0}\".format(N))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d99e29ef",
    "metadata": {},
    "source": [
-    " One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, as can be seen from the comparison between the two figures, and increases the number of training points without affecting the classification results of the model.\n",
+    "One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, as can be seen from the comparison between the two figures, and increases the number of training points without affecting the classification results of the model.\n",
     "\n",
     "With the interactive component in IPython, you can interactively see this feature of the SVM model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "f09c5090",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a3bd290cd925471c8811c407f72e06be",
+       "model_id": "7c79b3d25d0740fa8a87d4b179f33082",
        "version_major": 2,
        "version_minor": 0
       },
@@ -380,31 +537,46 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_svm(N=10, ax=None)>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from ipywidgets import interact, fixed\n",
-    "interact(plot_svm, N=[10, 200], ax=fixed(None));"
+    "interact(plot_svm, N=[10, 200], ax=fixed(None))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "977db498",
    "metadata": {},
    "source": [
-    "## Beyond linear boundaries: Kernel SVM\n",
-    "\n",
-    "\n",
-    "```{figure} ../../images/svm/svm1.gif\n",
+    "## Beyond linear boundaries: Kernel SVM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm1.gif\n",
     "---\n",
     "name: Kernel SVM visualization\n",
     "width: 90%\n",
     "---\n",
     "Kernel SVM visualization (with a polynomial kernel). [source](https://www.youtube.com/embed/3liCbRZPrZA)\n",
-    "```\n",
-    "\n",
-    "\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Kernels is an \"easy algorithm\" that saves us from the tedious calculations in a high-dimensional space. If support vector machines and kernels are combined to project data into a high-dimensional space, making it possible to handle non-linear relationships with linear classifiers, the power of SVMs can be enormous.\n",
     "\n",
     "The following is the case for non-linearly separable data:"
@@ -412,38 +584,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "5f628eca",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-advanced/kernel-method_19_0.png"
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from sklearn.datasets import make_circles\n",
-    "X, y = make_circles(100, factor=.1, noise=.1)\n",
+    "X, y = make_circles(100, factor=0.1, noise=0.1)\n",
     "\n",
-    "clf = SVC(kernel='linear').fit(X, y)\n",
+    "clf = SVC(kernel=\"linear\").fit(X, y)\n",
     "\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
-    "plot_svc_decision_function(clf, plot_support=False);"
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap=\"autumn\")\n",
+    "plot_svc_decision_function(clf, plot_support=False)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "260b1634",
    "metadata": {},
    "source": [
     "It is possible to project the data into higher dimensions, so that a linear partition line is sufficient. As an example, we can use a simple projection to compute a radial basis function with the centre on the middle block as follows:"
@@ -451,17 +616,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "be09bd7a",
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "r = np.exp(-(X ** 2).sum(1))"
+    "r = np.exp(-(X**2).sum(1))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f41094be",
    "metadata": {},
    "source": [
     "This additional dimension of data can be visualised in a 3D diagram.:"
@@ -469,14 +632,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "0f3b2a3e",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6513f023a7404b9489170027a494f6aa",
+       "model_id": "a7f34a5b354847eaa98624c812298f27",
        "version_major": 2,
        "version_minor": 0
       },
@@ -486,109 +648,310 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_3D(elev=30, azim=30, X=array([[-8.02972148e-03,  8.26943642e-02],\n",
+       "       [-1.14274814e-01, -2.40752057e-01],\n",
+       "       [ 2.64096428e-03,  1.03536503e+00],\n",
+       "       [ 4.69565248e-02, -1.59581537e-01],\n",
+       "       [-8.39739514e-03, -9.63036634e-04],\n",
+       "       [-1.30621899e-02, -6.28704710e-02],\n",
+       "       [-4.97645389e-01,  3.80153657e-01],\n",
+       "       [ 1.87317239e-01,  9.88564520e-02],\n",
+       "       [ 8.65200814e-01, -4.10155017e-01],\n",
+       "       [ 4.46503496e-02,  1.32589042e-01],\n",
+       "       [-1.02863021e+00, -4.51696575e-01],\n",
+       "       [ 9.03197795e-04, -1.87697815e-01],\n",
+       "       [ 1.76191389e-01, -1.35598344e-01],\n",
+       "       [ 6.47147483e-02, -1.09292040e-01],\n",
+       "       [ 5.08341664e-01, -8.01299438e-01],\n",
+       "       [ 1.21459101e-01,  1.03069042e+00],\n",
+       "       [-5.95524596e-01,  6.58469210e-01],\n",
+       "       [ 1.15106772e-01,  5.33678890e-02],\n",
+       "       [-1.11216749e-01, -1.03253909e-01],\n",
+       "       [-8.83926975e-02, -9.30112426e-01],\n",
+       "       [ 8.41895920e-01, -8.65192865e-01],\n",
+       "       [ 1.51175704e-01,  8.48373951e-02],\n",
+       "       [-5.02421287e-01, -9.75834039e-01],\n",
+       "       [ 1.26138716e-03,  1.51762245e-01],\n",
+       "       [-1.41015814e-02, -5.13717822e-02],\n",
+       "       [ 2.31401383e-01,  1.50075864e-01],\n",
+       "       [ 7.66761224e-01,  6.81495673e-01],\n",
+       "       [ 3.34134811e-01,  1.00879645e+00],\n",
+       "       [-3.13941752e-01, -1.02907061e+00],\n",
+       "       [ 5.78586576e-02, -2.03710910e-01],\n",
+       "       [ 9.42502242e-01, -4.91990597e-01],\n",
+       "       [-4.66258698e-03,  1.33521512e-02],\n",
+       "       [-5.66729474e-02, -1.44444499e-01],\n",
+       "       [-6.35697423e-01,  6.94818160e-01],\n",
+       "       [-9.95908334e-01,  4.19831686e-02],\n",
+       "       [ 8.76417188e-01,  7.76109815e-01],\n",
+       "       [ 8.84911801e-01, -4.81645290e-01],\n",
+       "       [-7.99712167e-01, -5.80172794e-01],\n",
+       "       [ 1.69810107e-01, -8.31429792e-02],\n",
+       "       [ 8.04844536e-02, -1.52638291e-01],\n",
+       "       [-1.01639628e+00, -5.31793930e-01],\n",
+       "       [ 6.37775088e-01,  8.13550452e-01],\n",
+       "       [-1.83324434e-02,  2.02346310e-01],\n",
+       "       [-2.94858202e-02,  1.94084820e-01],\n",
+       "       [-1.04464667e-01, -1.19877860e-01],\n",
+       "       [-5.78374021e-01,  5.41797027e-01],\n",
+       "       [ 4.41043804e-01,  1.11743170e+00],\n",
+       "       [-1.28944944e-01,  1.65583010e-01],\n",
+       "       [-9.07888149e-02, -9.94952732e-02],\n",
+       "       [-5.88542170e-01, -6.62592420e-01],\n",
+       "       [ 1.23480143e-01,  3.06812889e-02],\n",
+       "       [-5.33431966e-03, -1.24838146e-01],\n",
+       "       [ 8.33791209e-02, -1.78803394e-01],\n",
+       "       [ 9.38855040e-01, -4.63595581e-01],\n",
+       "       [ 1.54991198e-02,  9.07202893e-01],\n",
+       "       [ 2.70973026e-01, -1.97154577e-01],\n",
+       "       [-7.18054351e-02, -7.88981080e-03],\n",
+       "       [-3.12698103e-01,  9.34143742e-01],\n",
+       "       [-3.35633118e-01,  8.81578485e-01],\n",
+       "       [ 6.99806600e-02, -2.35223496e-02],\n",
+       "       [ 5.93857849e-02,  3.31166119e-01],\n",
+       "       [-1.09262528e-01, -1.06275235e+00],\n",
+       "       [-3.01815659e-01, -6.21130919e-02],\n",
+       "       [ 9.01892755e-02,  4.01633907e-02],\n",
+       "       [ 1.13284505e+00,  4.59691103e-01],\n",
+       "       [-7.72703285e-01, -3.86447342e-01],\n",
+       "       [ 1.13226084e-01, -1.10544827e-01],\n",
+       "       [-5.09229275e-02,  1.74474845e-01],\n",
+       "       [-1.22821048e-01,  1.18027615e-02],\n",
+       "       [-9.63367050e-02,  1.60471062e-01],\n",
+       "       [ 4.88703445e-01, -9.82446880e-01],\n",
+       "       [-9.57849044e-01,  1.75026103e-01],\n",
+       "       [ 3.39755343e-01, -1.02733099e+00],\n",
+       "       [-4.85049953e-01, -7.30223867e-01],\n",
+       "       [-8.53223165e-01,  2.93783089e-01],\n",
+       "       [-8.91351063e-04, -5.10729403e-02],\n",
+       "       [ 7.59346490e-01,  3.30208870e-01],\n",
+       "       [-8.86276132e-01,  7.18525448e-03],\n",
+       "       [ 6.58562389e-02, -1.03946205e+00],\n",
+       "       [ 6.00627145e-01, -8.23246888e-01],\n",
+       "       [ 9.34089693e-01, -5.25041824e-03],\n",
+       "       [-2.37870021e-01,  9.13256262e-01],\n",
+       "       [ 9.95064146e-01, -2.11498586e-01],\n",
+       "       [ 3.37756424e-01, -8.35427907e-01],\n",
+       "       [-1.28247711e-01,  2.26049311e-01],\n",
+       "       [-1.04350322e-01, -3.09264680e-01],\n",
+       "       [-8.05583265e-01, -7.40233267e-01],\n",
+       "       [ 8.08466610e-01,  1.34929293e-01],\n",
+       "       [-1.13020992e+00,  5.84700254e-02],\n",
+       "       [ 1.30244028e-01,  1.26978570e-01],\n",
+       "       [-1.16355407e-01, -3.06333010e-01],\n",
+       "       [ 9.42622600e-01,  1.83493272e-01],\n",
+       "       [ 1.64918210e-01,  1.59469650e-01],\n",
+       "       [-1.11880972e-01,  8.47005329e-02],\n",
+       "       [ 6.29395733e-01,  8.00007825e-01],\n",
+       "       [ 6.55259464e-02, -9.87016302e-02],\n",
+       "       [-8.95416450e-01,  7.02821633e-01],\n",
+       "       [-2.93360592e-01,  4.77243482e-02],\n",
+       "       [-9.91724519e-02, -2.75952722e-02],\n",
+       "       [-1.11238037e-01, -8.70893388e-02]]), y=array([1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1,\n",
+       "       0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n",
+       "       1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
+       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
+       "       0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1], dtype=int64))>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from mpl_toolkits import mplot3d\n",
-    "\n",
     "def plot_3D(elev=30, azim=30, X=X, y=y):\n",
-    "    ax = plt.subplot(projection='3d')\n",
-    "    ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')\n",
+    "    ax = plt.subplot(projection=\"3d\")\n",
+    "    ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap=\"autumn\")\n",
     "    ax.view_init(elev=elev, azim=azim)\n",
-    "    ax.set_xlabel('x')\n",
-    "    ax.set_ylabel('y')\n",
-    "    ax.set_zlabel('r')\n",
+    "    ax.set_xlabel(\"x\")\n",
+    "    ax.set_ylabel(\"y\")\n",
+    "    ax.set_zlabel(\"r\")\n",
     "\n",
-    "interact(plot_3D, elev=[-90, 90], azip=(-180, 180),\n",
-    "         X=fixed(X), y=fixed(y));"
+    "\n",
+    "interact(plot_3D, elev=[-90, 90], azip=(-180, 180), X=fixed(X), y=fixed(y))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0be4fa2d",
    "metadata": {},
    "source": [
     "Next we describe how to select and adjust the radial basis function centres.\n",
     "This type of basis function transformation is known as a kernel transformation, and this form of transformation solves the problem of shifting the centre of the basis function to a certain extent.\n",
-    "However there is a disadvantage: there is the problem of computational overload as a new basis function needs to be computed each time. We can solve this problem with the help of the \"kernel trick\" applet, where we change the original linear kernel to a radial basis function kernel and then use the kernel model hyperparameters."
+    "However there is a disadvantage: there is the problem of computational overload as a new basis function needs to be computed each time. We can solve this problem with the help of the \"kernel trick\" applet, where we change the original linear kernel to a radial basis function kernel and then use the kernel model hyperparameters.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "62327b70",
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1000000.0)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "clf = SVC(kernel='rbf', C=1E6)\n",
-    "clf.fit(X, y);"
+    "clf = SVC(kernel=\"rbf\", C=1e6)\n",
+    "clf.fit(X, y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "7d36a34b",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<matplotlib.collections.PathCollection at 0x237e33da088>"
       ]
      },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-advanced/kernel-method_26_0.png"
-      }
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap=\"autumn\")\n",
     "plot_svc_decision_function(clf)\n",
-    "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
-    "            s=300, lw=1, facecolors='none');"
+    "plt.scatter(\n",
+    "    clf.support_vectors_[:, 0],\n",
+    "    clf.support_vectors_[:, 1],\n",
+    "    s=300,\n",
+    "    lw=1,\n",
+    "    facecolors=\"none\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7ddd95ea",
    "metadata": {},
    "source": [
-    "Currently, for linearly divisible data, we usually use a linear support vector machine. However, if the data is non-linear, such as a classification problem, we can transform it into a linearly divisible problem, thus improving the generalisation of the model. In this case, kernel tricks are used to transform linearly divisible problems into non-linearly divisible problems.\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Hinge loss\n",
-    "\n",
-    "An alternative to cross-entropy for binary classification problems is the hinge loss function. It is mainly studied for the Support Vector Machine (SVM) model. It is designed to perform binary classification of target values which in the set {-1, 1}\n",
-    "\n",
-    "## RBF Kernel\n",
-    "\n",
-    "<div class=\"yt-container\">\n",
-    "   <iframe src=\"https://www.youtube.com/embed/NYwVM6_EuxQ\" allowfullscreen></iframe>\n",
-    "</div>\n",
-    "\n",
-    "## Support Vector Regression (SVR)\n",
+    "Currently, for linearly divisible data, we usually use a linear support vector machine. However, if the data is non-linear, such as a classification problem, we can transform it into a linearly divisible problem, thus improving the generalisation of the model. In this case, kernel tricks are used to transform linearly divisible problems into non-linearly divisible problems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hinge loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An alternative to cross-entropy for binary classification problems is the hinge loss function. It is mainly studied for the Support Vector Machine (SVM) model. It is designed to perform binary classification of target values which in the set {-1, 1}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RBF Kernel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://www.youtube.com/embed/NYwVM6_EuxQ\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "video of RBF kernel. <a href=\"https://www.youtube.com/embed/NYwVM6_EuxQ\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
     "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://www.youtube.com/embed/NYwVM6_EuxQ\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "video of RBF kernel. <a href=\"https://www.youtube.com/embed/NYwVM6_EuxQ\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Support Vector Regression (SVR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "SVR regression (Squeeze-and-VectorRegression), also known as Squeeze Regression, is an optimisation problem. The basic idea is to find a regression plane such that the variables in all data sets are closest to a point on this plane, so that all points on the plane are correlated.\n",
     "Objective function:\n",
-    "In SVR regression, the objective function is to minimise the two-parametric number of weights while keeping the points in each training set as far away as possible from the support vector on one side of their own category.\n",
-    "\n",
-    "\n",
-    "```{figure} ../../images/svm/svr1.jpeg\n",
+    "In SVR regression, the objective function is to minimise the two-parametric number of weights while keeping the points in each training set as far away as possible from the support vector on one side of their own category."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg\n",
     "---\n",
     "name: support vector regression\n",
     "width: 90%\n",
     "---\n",
-    "An illustration of support vector regression\n",
-    "```\n",
-    "\n",
-    "\n",
-    "## SVM v.s. logistic regression\n",
-    "\n",
+    "An illustration of support vector regression\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SVM v.s. logistic regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Similarities: Both are linear classification algorithms\n",
     "Differences:\n",
     "1. Different loss functions\n",
@@ -599,68 +962,120 @@
     "4. Optimization methods: LR is generally based on gradient descent, SVM on SMO\n",
     "5. For non-linear separable problems, SVM is more scalable than LR\n",
     "\n",
-    "```{seealso}\n",
-    "https://www.geeksforgeeks.org/differentiate-between-support-vector-machine-and-logistic-regression/\n",
-    "```\n",
-    "\n",
-    "## SVR v.s. linear regression\n",
     "\n",
+    "https://www.geeksforgeeks.org/differentiate-between-support-vector-machine-and-logistic-regression/\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SVR v.s. linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "The problem of overfitting is a very tricky one in the field of statistics. In this case, many machine learning methods, such as least squares (OLSE), will perform poorly. Support vector machines (SVR), on the other hand, can minimise the overfitting problem. SVR allows for non-linear fitting when there is enough data for training.\n",
-    "A final issue to consider is OLSE linear regression. While linear regression is effective for most problems, it is not always effective for some special cases. For example, in OLSE linear regression, there is some bias in fitting the variables linearly because the model is not hyperplane. In contrast, SVR allows for non-linear fitting problems.\n",
-    "\n",
-    "## Support Vector Machine Summary\n",
-    "The basic model of SVM: is defined as a linear classifier with maximum interval on the feature space. Specifically, when linearly separable, it finds the optimal classification hyperplane for both classes of samples in the original space. When linearly indistinguishable, relaxation variables are added and samples from a low-dimensional input space are mapped to a higher-dimensional space via a non-linear mapping to make them linearly distinguishable, so that the optimal classification hyperplane can be found in that feature space.\n",
-    "\n",
+    "A final issue to consider is OLSE linear regression. While linear regression is effective for most problems, it is not always effective for some special cases. For example, in OLSE linear regression, there is some bias in fitting the variables linearly because the model is not hyperplane. In contrast, SVR allows for non-linear fitting problems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Support Vector Machine Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic model of SVM: is defined as a linear classifier with maximum interval on the feature space. Specifically, when linearly separable, it finds the optimal classification hyperplane for both classes of samples in the original space. When linearly indistinguishable, relaxation variables are added and samples from a low-dimensional input space are mapped to a higher-dimensional space via a non-linear mapping to make them linearly distinguishable, so that the optimal classification hyperplane can be found in that feature space."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Advantages of SVM:\n",
     "1) Solves machine learning in the small sample case.\n",
     "2) No increase in computational complexity when mapping to higher dimensional spaces as the dimensional catastrophe and non-linear differentiability are overcome by using the kernel function approach. (Since the final decision function of the support vector machine algorithm is determined by only a small number of support vectors, the computational complexity depends on the number of support vectors rather than the dimensionality of the entire sample space).\n",
     "Disadvantages of SVMs:\n",
     "1) Support vector machine algorithms are difficult to implement for large training samples. This is because support vector algorithms solve support vectors with the help of quadratic programming, which will be designed to compute matrices of order m, so a large amount of machine memory and computing time will be consumed when the order of the matrix is large.\n",
     "2) The classical SVM only gives algorithms for binary classification, while in data mining, it is generally necessary to solve multi-classification classification problems, and support vector machines are not ideal for solving multi-classification problems.\n",
-    "3) The common SVM theories nowadays use a fixed penalty factor C, but the losses caused by two kinds of errors in positive and negative samples are different.\n",
-    "\n",
-    "## [Optional] Let's dive into the math of SVM ...\n",
-    "\n",
-    "<div class=\"yt-container\">\n",
-    "   <iframe src=\"https://www.youtube.com/embed/05VABNfa1ds\" allowfullscreen></iframe>\n",
-    "</div>\n",
-    "\n",
-    "## Acknowledgement\n",
-    "\n",
-    "Some code comes from [jakevdp](https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html), for which the code part is licenced under MIT licence.\n",
-    "\n",
-    "<p style=\"text-align: center;\">\n",
-    "<iframe src=\"../html/svm-vis/demo1/demo.html\" width=\"105%\" height=\"650px;\"\n",
-    "style=\"border:none;\"></iframe>\n",
-    "A demo of SVM. <a href=\"https://cs.stanford.edu/~karpathy/svmjs/demo/\">[source]</a>\n",
-    "</p>\n",
+    "3) The common SVM theories nowadays use a fixed penalty factor C, but the losses caused by two kinds of errors in positive and negative samples are different."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## [Optional] Let's dive into the math of SVM ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://www.youtube.com/embed/05VABNfa1ds\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "math of SVM. <a href=\"https://www.youtube.com/embed/05VABNfa1ds\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
     "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
     "<p style=\"text-align: center;\">\n",
-    "<iframe src=\"../html/svm-vis/demo2/index.html\" width=\"105%\" height=\"600px;\"\n",
-    "style=\"border:none;\"></iframe>\n",
-    "A demo of SVM. <a href=\"http://macheads101.com/demos/svm-playground/\">[source]</a>\n",
+    "<iframe src=\"https://www.youtube.com/embed/05VABNfa1ds\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "math of SVM. <a href=\"https://www.youtube.com/embed/05VABNfa1ds\">[source]</a>\n",
     "</p>\n",
-    "\n",
-    "\n",
-    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Your turn! 🚀\n",
     "You can follow this [assignment](../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.ipynb) to practise Support Vector Machines with examples."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acknowledgement\n",
+    "\n",
+    "Thanks for [jakevdp](https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html), for which the code part is licenced under MIT licence."
+   ]
   }
  ],
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yWcBqBfJ5VANYfPNbMfe//xl6bZ3ZVRJRCchmFxGLxaAoEQwPD0HXdQCFu33cbjc0TTN2pq2urjGxUioHDCy0evk8vO89BMdPD0PQl3aMXL7mDMBx+Eeo6VEx86OfQa/ymlcnEZWEn/70JxgY6DO+bmlphSSF4fcH4XQ6zSuMyhIDC62a84ffh/Mnj570cSGfh1VR4P78P2PhDvaQItpKpqePQZYj2L17DzyewvqTYDCE2dk4RDEMUZTg9VabXCWVMwYWWjXXl/4PdIsVgpY/6fcIWh7ur34ZCx/9GGC3b2B1RLTRFhaSUFUFqipjcnISAOB2u7F373kAgFBIhChKXJdC64KBhVZH12F/7jenDCvLLNPHYO3vQ36ppwcRbR65XA59fTHIsoyhoQFoWuHysNVqRWenD42NTcb3Lq9PIVoPDCy0OroOaGfQ6XRpbQsRlb/lxbJAIbA88cRjxp4pzc3bIIphBAJBuN1us0qkLYCBhVbHYkE+EIS1JwrhuDevE9FdLuSXdqUkovI1MxOHosiIx+N405uuBgC4XC7s2LELdrsdoigZfX6Iio2BhVYt9d4PoPJv/+aU36NbrUjfdAvATZ+IylIqlUJPTxSKEsH4+JgxHo/HUVtbCCcXX3ypWeXRFsbAQquWvukWuL/ypcJZlvxr17LoViv0qios/F8f2fjiiGjNdF3H+PgYXnzxdxgY6Ed+aX5bLBa0t3dCkrjzLJmPgYVWr7ISM9/9IaoP3QT7C7+DbrMBuRyEpc3jtJZWzD70bWidPrMrJaLT0HUd+XweNlvhz0AymURvbwwA0NDQCFGUEAqJqKjwmFkmkYGBhc6I3tyMmcNPwP7sM3B+51uwTk7A0VCH+auuQerAnwA2/koRlbJEIrG0RX4EwaCICy7YB0EQ4PN14bzzzkcwGEJ9fYPZZRK9Bv+60JkTBGT3X4zs/oths1ngqPUgG08CuTO4i4iINkwmk0EsFoUsRzA6OmKM9/bGcMEF+wAUtsvft2+/WSUSnRYDCxHRJvbkk49DUWTjNmRBENDW1g5RDMPv95tcHdHqMbAQEW0Suq4jHp9GbW2dsbuspunI5XKora2DJIURCnEBLZUnBhYiojI3Pz8PVVWgKBFMTx/DddfdiKamZgDA3r3nYefOXWhsbOIW+VTWGFiIiMpQNruIWCwGVZUxNDRo7EZrs9kwPX3MCCzLe6cQlTsGFiKiMjM9fQyPPPJtZLNZY6ylpRWiKCEQCMHpdJpYHVFxMLAQEZW46eljmJubg8/XBQCoqamFw+FARUUFRDEMUZTg9VabWyRRkTGwEBGVoFRqwViXMjk5CY/Hg3e+8y9gsVhgsVjwtrfdgMrKSq5LoS2DgYWIqERks1n09/dBUSIYHByAttQh3WKxoKmpGZlMGm53BQCgqqrKzFKJNhwDCxFRifjtb5/FkSMvGl83NTVDFMMIBkNwu93mFUZUAhhYiIhMMDMTh6rK6OzsQnPzNgBAICCip6fH2C+Fd/gQvYqBhYhog6TTaUSjKhQlgvHxMQDA/HzSCCxNTU145zvfxXUpRCfAwEJEVES6rqOvrxeKEkF/fx/y+TyAwhb5HR0+486f5TEiOjEGFiKiInvmmf9EIjELAGhoaIQoSgiFRFRUeEyujKh8MLAQnUwyCedPfwzL5AQ0bzUWD74JekOD2VVRCUskElBVGYOD/Xj3u28FUDhrsnv3HszNzUGSwqiv5+8Q0VowsBD9sXweFf/4P+H+P1+AJZmEbrFA0DToNhvS19+E5P/8HPRK3lJKBZlMBrFYFIoiY2RkGABgsQjo7e1FQ0MrAOCcc/aYWSLRpsDAQnQ8XUfVbR+E85FvQ1jqzSIs7YUh5HJwffubsL3yMma+/2PAw9P5W9n09DSef/436OvrRS6XA1A4m9LW1o7t28Po7OzEwkLO5CqJNg8GFqLjOA4/Ctd3Hz7p40I+D9sffo+K+/4FCx/92AZWRmbTdR25XA52u90Yi0ZVAEBtbR0kKYxgUERVVRVsNgucTicDC9E6YmAhOo77S/dBt1ohLN3JcSKCpsH9wBex8NcfBWycQptdMjkPRSlskV9XV4crr7wKAFBXV4eLLno92tra0djYxDt8iIqM77ZEy3Qd9l89fcqwsswyOQFrXy/ywdAGFEYbLZtdRG9v4VbkoaFB6EuXB5PJeeRyOdiWguq5577OzDKJykI2m0VfXwyKIuPmm6+D1epa0/NseGBJJpO4+uqrMT4+ju985zs455xzNroEopNbRVgxLC4Wrw4yzW9/+2scOfICstmsMdbS0gpRlOD3B42wQkQnp+s6RkaGoSgyYrEoFpfeL7PZLFyuMgksX/jCF4yNk4hKiiAg39UNa1+vseD2ZHS7A1pHxwYVRsU0PT2NqqpK2O0OAIDNZkc2m0V1dTVEMQxRlOD1VptcJVF5iMfjUJQIFEXG/PycMe71VkMURbhca++JtaGBpaenB//2b/+Gj33sY/jUpz61kS9NtCrpd78Pnk/991N+j261In3djdCrvBtUFa23VGoB0agKWY5gcnICBw5cCUkKAwDC4TC2bduGbdtauC6FaBVSqZTRcmJiYtwYdzqdCASCCIUktLS0QhAEWCyWNb/OhgaWz3zmM7j55pvR3d29kS9LtGrpWw7B/eX7YRkaPOFaFt1ihe5yIfXXt5tQHZ2NXC5nbJE/ODgAbel2dYvFYuxCCwBudwXc7gqzyiQqC7lcDv39fVCUCAYG+lfMp46OTohiGF1d3et6CXXDAsvhw4ehKAruuecevPzyy+vynFbr2pMarY/lY7BpjkVtDeb+4zCqbnwbrJGjxh1DxuZxdbWY/8Z3IEhSya1Y33THYh1lMhk89NDXkE6njbGWlm3Grchu99pPU58Ij0Xp4LFYP7quY2xsDLIcQU+PumI+bdvWDEnajmAwhIqKkwf+szlpuSHvualUCnfffTduv/12VFZWrtvzer3r+yZDa7epjkWtBPzhJeAnP4Hw4IPA8DCE2lrg+uthueEGeNe4YGyjbKpjsUbxeBwjIyPYuXPn0ogHHR2tmJ2dxfbt27Fz507U1dUVvQ4ei9LBY7F2MzMzeOWVV3D06FHMzMwAKASPxsY6Yz7V19cXvY4NCSz33nsv6uvrcd11163r8yYSKeTz2ro+J50Zq9UCr9e9OY/F/jcU/ne8VB5IJc2p5zQ29bFYhXQ6baxLGRsbhcViQU1No9Fg8JJLDsDtdhvrUuLx4h3HrX4sSgmPxdpkMhljPo2OjhjjdrsdgUAQoiihra3dWJOy2vlUXe1e8zqWogeW4eFhPPDAA/j85z+PubnCiuGFhQXj/5PJJDxr3OI8n9eQy/EXsBTwWJSOrXQs8vk8Bgb6oSgR9Pf3GXcgCoKA1tZ2JJNpOByFT9YOhwv5vA7g1HeArW99W+dYlDoei9PL5/MYHOyHoijo71/ZcqK9vQOhkAS/32/cUadpMNaurNZpbsA8paIHlqGhIWSzWXzgAx94zWOHDh3Cnj178PDDJ98KnYjoZGT5KJ588gnj6/r6BoiihFBIhMezfpefiTYrXdcxOTkBRZERjapIpRaMx+rq6iGKYYRC4rou51irogeW7du34+tf//qKsaNHj+Kzn/0s7rrrLm4cR0SrkkgkoKoyqqtrEFzaYdjvD+J3v3sefn8AoiihoaHR5CqJysPc3BxUVYaiyIjHp43xiooKhEIiRDGM+vqGkrq1v+iBxev1Yt++fSd8bOfOncctiiMiWmlxcRGxWBSyHMHIyDAAoLl5mxFYXC4XbrnlUEm9qRKVqsJ86oGiFObTcssJm82Grq5uiGIYHR2dZ7VXSjGV2p2ZREQYHByALB9Fb29sxXX01tY2iGIYuq4bIYVhhejkNE3D0NAgVFVGLBZDLvdqy4nW1jZIUhjd3QE4nU4Tq1wdUwLLvn37IMuyGS9NRGXgpZeOoL+/DwBQW1u7dB1dQlVVlbmFEZWJY8emIMsRRKMKkslX7+Cpqakx5pPXW167dfMMCxGZJpmch6oqUFUFb3rTm4030J07dy31HpHQ2NjEsyhEq7CwkISqKlAUGVNTk8a4y+VCIBCCJG1HU1P5zicGFiLaUNnsInp7C1vkDw0NGtfRVVXG6153AQDA5+uGz8cWHkSnk81m0dcXg6LIGBwcMOaT1WqFz9cFUQyjs9MHq9VqcqVnj4GFiDZEMjmPX//6WcRiUWSzr15Hb2lphShK8PuDJlZHVD50XcfIyDAURUYsFsXi4qLx2LZtLRBFCYFACK4S35X7TDGwEFHRLC4uwuEobDLlcDiMsLJ8uUcUJVRX15hbJFGZiMfjUJQIFEXG/PycMV5V5YUkSQiFJNTU1JpYYXExsBDRukqlFowtvfP5PG688e0QBAF2uwOXXPIGVFdXY9u2lrK9jk60kVKpFKJRFYoSwcTEuDHudDoRCAQRCkloaWndEvOJgYWIztqpWs3PzSXg9VYDAMLh7WaWSVQWlueTqsro7+9bMZ86OjohimH4fF2w2+0mV7qxGFiI6KwcPfoyfvWrp5HJZIyxxsYmSFIYwWAIbvfJW80TUYGu6xgfH4OiRNDTE0U6nTYea2xsNLbI38rziYGFiM7I7OwMbDab0aunosKDTCaDyspKY3+Huro6k6skKg+JxCwURYaiRDA7O2uMV1ZWIhQqrPOqq6s3scLSwcBCRKeVTqfR06NCUWSMjY3i3HNfh4suej0AoKOjE3/6p29Da2vblriOTnS2MpmMMZ9GR0eMcbvdvtQXK4zW1raS3SLfLAwsRHRC+XweAwP9UBQZ/f29yOfzAApb4S8svNrR1WKxoK2t3awyicpCPp/H0NAAZLkwn45vOdHe3gFRDKO7uxt2u8PkSksXAwsRvYau6/j2t7+BeDxujNXXN0AUJYRConE5iIhOTtd1TE5OGLs5p1KvBv26unpjXUplJefTajCwEBHm5ubQ1xfDrl27IQjCUqPBdmQyGeM6ekNDo9llEpWF+fl5qKoMWY4gHp82xt3uCoRCojGfeAn1zDCwEG1RJ2o139DQiJaWVgDAvn37cckll/E6OtEqZLOLiMViUJQIhoeHjC3ybTYburq6IYphdHR0cj6dBQYWoi1kudW8okTQ29u7otX8H69DKYd280Rm0jQNw8NDUJQIYrHYivnU0tIKSQrD7w9yLq0TBhaiLWRsbBQ/+tEPjK9ra2sRCkll2WqeyCzT08cgyxGoqoxkMmmM19TULF1CDXM+FQEDC9EmlUzOQ1UVAMDevecBKHzqa2xsQnPzNohiuKxbzRNtpIWFJFRVgaLImJqaNMZdLhcCgRAkKYympmbOpyJiYCHaRLLZLBRFxtGjR41W8y6XG+ecswdWqxWCIOC6627kmyrRKmSzWfT390KWZQwNDRhb5FutVvh8XQiFJHR2+mCz8U/pRuB/ZaJNYGxsFLL8CkZGBpBIJKFphQV/y63mlxcAAmBYIToFXdcxMjIMVVXQ06NicXHReGz5zGQgEITb7Taxyq2JgYWoTOm6boSP3t4YIpGjcLsd8HqrEQwWbp2srq4xt0iiMhGPx6GqMhRFxtxcwhivqvJCFAu39tfU1JpYITGwEJWRVGoB0agKWY7gwgsvQmenDwAgSWHk81lceOF5cLurkc/rp3kmIkqlUohGVaiqjPHxMWPc4XAgEAghFBLZcqKEMLAQlbjlVvOKEsHAQL9xHV1RIkZgqaurxxvfeAC1tR7E40kADCxEJ5LL5ZZaThTm03LLCYvFgvb2TkiSBJ+vG3a73eRK6Y8xsBCVqFwuh6ef/iV6eqLIZDLGeGNjEyQpjGAwZGJ1ROVD13WMjo7i6NGj6OlRkU6njccaGxshioX5VFHhMbFKOh0GFqISkk6n4XK5ABTuRBgbG0Mmkzmu1XwYdXV1JldJVB4SiVn09KgYHIxhdHTCWIzu8XgQCkmQpDDq6upNrpJWi4GFyGTpdBqxWBSKIuPYsSkcOvQe2O12CIKAiy56PaxWK1vNE61SJpNBLBaFLEcwOjoCi0WA2+2AzWZHd7cfohjmfCpTDCxEJsjn8xgc7DdazS9fRxcEAePjY2hv7wAA+HxdJlZJVB7y+TyGhgaM+ZTL5QAU5lN7ewcuuOBcNDa2QhD4J6+c8egRbbCBgX489tjPkE6njLG6unpIUqHVvMfDVvNEp6PrOqamJqEoMlRVQSq1YDxWW1u3NJ8k1NR4jcXouZxmYsV0thhYiIpsbm4O2WzWWHtSU1ODdDqFiorlVvNh1Nc38NZJolWYn59f2iI/gunpY8a42708nyQ0NDRyPm1CDCxERbC4uIhYrAeKEsHIyDB8vi5cffU1AACvtxpve9v1aGpq5nV0olXIZhcRi8WgKBEMDw8ZOzfbbDZ0dXVDFCW0t3fCarWaXCkVEwML0TrRNA1DQ4NQlAh6e3tXtJrP5/PQNM0IKNu2tZhVJlFZ0DQNw8NDUFUZPT09K+ZTS0srJCkMvz8Ip9NpYpW0kRhYiNbJ4cM/Qn9/n/F1bW0tQiEJoZDEVvNEqzQ9fQyyHIGqykgmk8Z4TU3N0q39ErzeahMrJLMwsBCtQTJZuI4eDu8w9k3p6PBhfHwcwWAIohhGU1MTr6MTrcLCQhKqqkBVZUxOThrjLpcLgUAIkhRGU1Mz59MWx8BCtErZbBZ9fTEoiozBwQHoug6Hw4EdO3YBAMLh7dixYyevoxOtQjabRX9/L2RZxtDQgNFywmq1orPTB1EMo7PTB5uNf6aogL8JtHXk8xASs9CdLqCiYlU/stxqXlFkxGLRFa3mt21rQcVxz8PeI0SnVtgifwSKIqOnR10xn5qbt0EUwwgEgnC73SZWSaWKgYU2PcvIMNz33wvXg1+FZaltfPa885F6/4eQedv1wCnu1EmlFvAf//Hvxl0JXm+10Wq+urpmI8onKnszM3EoigxFkTG3NAcBoKrKa8ynmppaEyukcsDAQpua7aUjqP7zt0KYn4OwtJssANhe/B28f/k+pH96GHNf+CJgtRqt5mdnZ3DJJZcBACoqPAgGQ7Db7RDFMLZta+F1dKJVSKVS6OmJQlEiGB8fM8YdDgcCgRBEUUJLSyvnE60aAwttXskkqm9622vCCgAIy9fL//27GGluxpH9F2NgoB+apkEQBOzZcy6qqqoAAAcPvmnDSycqR/l8HgMD/VCUCPr7+4yWExaLBe3tnZAkCT5fNy+f0powsNCm5Xrk2xCmpnCiz2+TFgtetNuh2O1IffdhLDQ1A1ar0Wre4XBseL1E5UjXdUxMjEOWI+jpUZFOp43HludTMBhCRYXHxCppM2BgoU3L+Z1vAYIALK0/0QEjvIxbLPj9UiipWljATqsV/pvewVbzRKuUSMwu9fGRMTMzY4x7PB5jv5T6+gbzCqRNh4GFNi3L1CTSug7VbscrNhuCuRzOzxZ2ywzlchjOZhHOZtGRz2O+rh4ZhhWiU8pkMojFopDlCEZHR4xxm80Ov98PUQyjra2dLSeoKIoeWJ588kl88YtfRDQaxfz8PJqbm3Hw4EHcdtttxhoBovWUz+cxONiPp1xuDFRVYXn1SlYQjMDiBPCm405d60uNCYloJU3TMDg4AEWJoK+vF7lcDgAgCALa2tohimH4/X7Y7byMSsVV9MAyMzOD3bt349Zbb0VNTQ1UVcU999wDVVXxwAMPFPvlaYv51a+eRiRyFOl0CrZgCI6+GBryeezM5RDOZk/4M5q3GouXvnFjCyUqYbquY2pqcumSj4JUasF4rLa2DpIURjAo8kMnbaiiB5Zrr712xdf79u2Dw+HAJz7xCYyPj6O5ubnYJdAmtrCQXLGYL5FIIJ1OoaKiAqEbb8a+XzyOpuQCLFr+hD+vCwJS7/8QsLS9PtFWNj9faDmhKBFMTx8zxt3uCoRCIkIhEY2NbDlB5jBlDUtNTQ2AwtbMRGdqcXERsVgPVFXG8PAQbrrpFtTWFjadOvfc8xAOb0dHRycsFgts3/gOcMO10BcWVtzarFssgKZh8eprsPDRj5n1r0Jkumx2EbFYDKoqY2ho0Ngk0WazwefrhiRJaG/vZMsJMt2GBZZ8Po9cLodoNIrPf/7zOHDgANrb2zfq5anMLbeaV5QIYrHYilbzo6PDRmBpalp5xi533vmIP/403P/n83D/24MQFgqntvPhHUh94C+RvukdAN+IaYvRNG2p5UQEsVjPig+PLS2tEEUJgUAITqfTxCqJVhL05ThdZJdddhnGx8cBAJdeein++Z//eUUflrVIJFLI57X1KI/WyGq1wOt1F/VYTE1N4Yc//P5rWs1L0valVvPe1T3R4iKEY8cAlxN6TW3hludNZCOOBa1OqR6LY8eOQVEiUBQZ8/Pzxnh1dQ1EUYIkhVFdXW1iheuvVI/FVlVd7V7zXWQbFlgikcjS1udR3HvvvWhvb8dXvvIVnmak10gmk5idnUVraysAIJfL4b777oMgCJAkCTt27EBLC7fIJ1qNhYUFHD16FK+88gomJiaMcafTacyn1lZukU+lb8MCy/EikQiuvfZa/NM//ROuuuqqNT8PE7P51uvTSzabRV9fL2Q5gsHBAVRVeXHLLbcab6JTU5Oora1jwD0FfpIsHWYfi+X5pCiy0XICKGyR7/N1QZLC8Pm6YLNt/q24zD4WtNLZnGEx5bdVkiTY7XYMDAyc1fPk8xpyOf4CloK1HAtd15euo8uIxaIrWs07HE7Mzy8YbeZrauqh6+DxXgXOi9KxkcdC13WMjo5AUWT09Kgr5lNTU7OxRf7ynAK21nzivCgNZ3OKxJTAcuTIEWSzWS663eKeeeY/8fvfv2h8XVXlhSRJCIXYap5otWZm4lBVGbIsY24uYYxXVlZBksIIhSRjUTpROSt6YLntttuwa9cuSJIEl8uFSCSCL3/5y5AkCQcPHiz2y1OJKKxfUtHW1mb06+nq6kYk8gpbzROdoXQ6jWhUhaJEMD4+Zow7HA74/UGIooTW1jbOJ9pUih5Ydu/ejUcffRT3338/dF1HW1sbbrjhBrz3ve9lR9xNLpfLrWg1r2kadu/ei4svvhQA0NrahkOH3sNW80SrkM/nV8yn/NK+QoIgoKOjE6IooavLz/lEm1bRA8sHPvABfOADHyj2y1CJ0HUd4+NjUJQIenqir2k1X1//aoNBQRD45kp0CrquY2JiHIoiIxpVkU6njMcaGhohihKCwRA8nkoTqyTaGJt/iThtKF3X8ZOfPIqFpQ3allvNS1LYuBRERKeWSCSgqjIUJYKZmRlj3OPxIBgUIUlh1Nc3mFcgkQkYWGjNMpkMZLkHx46N4ZJLDgAo3Da5Y8dOzM3NQRTDaG1tY6t5olXIZDKIxaJQFBkjI8PGuM1mh9/vhyiG0dbWzvlEWxYDC52RfD6PoaEByLKM/v5eaFoebrcD3d0iWloKd31dcMFFJldJVB40TcPg4AAUJYK+vl7kcjkAhculra1tEMUw/P4A1/sRgYGFVimRmMVLL/3+Na3mGxsbcP75565Ym0JEJ6frOqamJqGqMlRVMS6fAkBtbR0kKYxgUERVVZWJVRKVHgYWOild143bIlOptLFnynKreVGUsG1bM+rqKhGPJ7kpE9EpzM/PQ1UVKEoE09PHjHGXy23Mp8bGJt6KTHQSDCy0wnKreUWJwOv14g1vKKxNaWpqwu7de9He3oH29g5ji3y+uRKdXDa7CFXtgaJEMDQ0iOVOKDabDT5fNyRJQnt7J1tOEK0CAwtB0zQMDw8ttZqPIZcrtJp3Op245JI3wGq1QhAEY/8UIjq5wrqUITz7bB9+//uXkcm8ukV+S0srRFFCIBCC0+k0sUqi8sPAssUdOfICjhx5Aclk0hirqamBKIYRCon85Ee0StPTx6AoMlRVxsJCEm63A9lsFtXV1RDFMERRgtdbbXaZRGWLgWWLWVhIwul0GUFkcTGDZDIJl8uFQCAESQqjqamZl3qIViGVWjDWpUxOThrjbrcLu3efg7a2bjQ0cF0K0XpgYNkCstks+vt7IcsyhoYGcOWVV8HvDwAAwuGdqK9vRGenb0u0mic6W7lcDn19vVCUCAYHB6BphcXmFosFPl8XQiEJgYAfjY3VXIxOtI74F2qT0nUdIyPDUFXlNa3mJybGjcBSVVXF2yeJTkPXdYyOjkBVZfT0RJHJZIzHmpqaIYphBIMhuN1uAIDNxs3diNYbA8smlE6n8Z3vfGtFq/mqKi8kSUIoJKGmhq3miVZjdnYGihKBoihIJGaN8crKKoiiBFEMo7aW84loIzCwbAKpVAqTkxPo7PQBAFwuF1wuFzKZNAKBEERRQktLK6+jE61COp1GT48KRZExNjZqjNvtdmM+tba2cT4RrUImk0FPj4q+vl686U1vPqvnYmApU7lczmg1PzDQD0EQcOjQe4xbJQ8e/BN4PJXshky0Cvl83phP/f19yOfzAAr7DHV0dEIUJXR1+TmfiFYhn89jcLAfiqKgv//VlhODg/1obDxnzc/LwFJGdF3H+PgYFEVGT4+KdDptPNbY2Ihkct4ILLzsQ3Rquq5jYmICihJBNKoinU4Zj9XXN0AUJYRCIjyeShOrJCofiUQCv//9i4hG1RUtXOrq6iGKhTtQzwYDSxl5+eU/4KmnfmF87fF4EApJkKQw6urYy4doNRKJBKLRwq3I8XjcGK+oqEAoJEEUJTQ0NJpYIVH50DTN6CCey2Xx0ktHABRauIiiCFEMo76+YV0uoTKwlKjlVvOVlVXo6OgEAHR3d+PXv34G3d2FVvOtrW1sNU+0CouLi4jFopDlCEZGho1xm82O7u5uiGIY7e0dnE9Eq1CYT4WWEx6PB1dc8ScACmdS9u49D62tbejo6Fz3+cTAUkLy+TyGhgYgy7Jx3a+jo9MILB5PJd71rvdyvxSiVdA0DUNDg1CUCHp7Y8Z1dEEQ0NraBlGU4PcH4XA4TK6UqPSdrIWLzWbHZZdljfVd+/dfXLQa+JevBExOTixt6a2suO5XW1uH9vaOFV2TGVaITm1qahKKEoGqKlhYOH4+1S61nJC49xDRGfj971/Eiy/+7iQtXKQNW4zOv34l4Jln/tM4Te12Vxit5hsaGnnrJNEqJJPzS1vkyzh2bMoYd7ncCIVEhEISmpq4RT7RaiwsJGG3O4wgks/njRYuwWDh75MZLVwYWDZQNruIWCwGVZVxxRVXwu2uAABs374TFRUVEEW2midarWx2Eb29hS3yh4YGoes6AMBqtcLn64YkSejo8HE+Ea1CNptFX18MiiJjcHAAV1xxJUIhCQAgioUNRzs7zZ1PDCxFpmkaRkaGl6779SCbLVz3U1UFu3fvBYClHTMlE6skKg/LLSdkOYJYLGrMJwBoaWlFKCQiEAjB5XKZWCVReVieT4oiIxaLrmjhMjk5YQQWj6cS3d3m397PwFIkqdQCXnzxBaiqvOK633Kr+a4uv4nVEZWX6enppXUpMubn541xr7faCPzV1TXmFUhUZrLZRXzrW99Y0cKlMJ/Ekm3hwsCyjo6/Hx0Q8NJLR5DP5+FyuRAIhCBJYVOu+xGVo1RqAdGoClmOYHJywhh3Op0IBIIQxTC2bWvhfCJahVQqhYmJMfh83QAAu92ByspKLC5mymY+MbCcpWw2i/7+XsiyjGx2EX/2Z9cBANxuNy64YB+qq2vQ2enj3T1Eq5DL5dDXV1iXMjg4AE3TAAAWiwWdnT6IYhg+XxfnE9Eq5HI59Pf3GS1cAODQofcYXcUPHDgIt7uibFpOcNavwXKr+eUt8o+/7jc3N2fcMnnuua8zq0SisqHrOsbGRqEoEfT0RJHJZIzHmpqaIYoSgsGQsUidiE7u1RYuhfl0ohYuy4HF6602q8w1YWA5Q6oq49e/fnbFdb+qKu9S3xHu70C0WrOzM1CUCBRFQSIxa4xXVlYt3dofRl1dnYkVEpUfWT6KJ554zPi6srLSaDlR7i1cGFhOI5VKQRAE464DQRAwN5eAw+GA3x9kq3miM5BOp9HTo0JRZIyNjRrjdrsdfn8QkhTmfCJapUwmg54eFRUVFcaNHD5fN5xOJ7q6ujddCxcGlhPI5XJGq/mBgX6cf/6FOO+88wEUfhmuvPJN8Pm6y+a6H5GZ8vn80nwqtJzI5/MACuG/o6MToiihq8vP+US0Cidq4dLS0moEFrfbjXe9672bcv8hBpYlhVbz45DlCHp61BXX/SYnJ41/ttvtCAZFM0okKhuF+TQBRYkgGlWRTqeMx+rrG5YuoYrweMzf24GoHExOTkCWC/Pp+BYudXX18Pm6V7Rw2YxhBWBgAVB4c/3ud7+1Iph4PB7jul99fYOJ1RGVj7m5OaiqDEWJIB6PG+MVFRXGfGpoaDSxQqLy9JvfPGvc6eN2Vxj7pWylFi5bMrBkMhkMDg4gGAwBKJyarqurRzw+A7/fD1EMo62tfdNc9yMqpkKr+ShkOWL0xAIKXVy7uwvX0dvbOzifiFZhuYWLokTwxjdeYdzIEQ7vgMPhgCiG0dHRuSXn05YJLJqmYXBwAIoSQV9f4bpfTU2N8Wlv3779uPTSN8BuZ6t5otPRNA1DQ4NQlAh6e3uNVvMA0NbWDlGU4PcH4XBwPhGdjqZpGB4eWmrhEjPmUzSqGNtjBAJBBAJBM8s03aYOLLquL7Wal6GqyorrfrW1dSvWqfBaOtHpFeZTBKqqYGHh+PlUa7Sa5639RKuTSqXw4ou/e00Ll5qaGohiGIFAyMTqSs+mDiwjI8P4wQ++Z3ztdlcgGAxBFCU0NrLVPNFqJJPzUFUFiiLj2LEpY9zlciMUKlxHb2rifCJajXw+byyKtVqt+MMfXkIul2ULl1XYNIFl+bqfrusIh7cDALZta0FlZRWam7dBkiS0t3du2tXTROspm11Eb28vVLXQal7XdQCFN1ifrxuSJKGjw9xW80Tl4vgWLqnUAq6//iYAgMPhwL59F6GqyovOTs6n0ynrwHL8dfRYrAfZbBaVlZWQpDAEQYDVasU73nErfwmIVmG51bwsRxCLRZHNvrouZdu2FoiihEAgZGyiSEQntzyfFEVGLBZd0cJlZiZudEPevXuvSRWWn7INLKlUCg8++FUkEnPGWHV1NUQxjFwuZ2xCxbBCdGrT09NLtyLLmJ9/dT4VWs0XbkWurq4xr0CiMhONKnj22V+9poWLJBVauCyHFTozZRtYMpkM5ufn4XQ6l9alhNHcvI3X/YhWIZVaQDSqQpYjmJycMMadTmfZtJonKhWpVGFjxOWmgjabzWjhEggU1k22tLRyPp2lsg0sdrsdV131ZrS1dbLVPNEq/HGreU3TAAAWiwWdnT6EQhK6uro5n4hW4Y9buOzdex4uvPAiAEBHhw9XXnkVfL4utpxYR0V/Z/rxj3+MH/zgB3j55ZeRSCTg8/lw66234rrrrjurtOnxeBAIBJHLaetYLdHmcnyr+WhURSaTMR5rbGyCJIURDIbgdleYWCVReTh+PvX0RFdsjTE9fcz4Z6vVamxMSuun6IHlq1/9Ktra2nDHHXegtrYWzzzzDD7xiU9gbGwMt912W7FfnmhLmp2dWdp/SMbs7KwxXllZaeyXUldXZ2KFROVF13U88si3MTExboxVVlYaLSfq6upNrG5rKHpguffee1e8Me7fvx8zMzP4yle+gv/yX/7LltxemKgY0uk0fv/7Hvz2ty9gePjVLfLtdjv8/iAkqdBqntfRiU4vk8mgv78PoZAIQRAgCAIaGhoQj0/D7w9AFAvziX/DNk7RA8uJPsVt374dDz/8MBYWFlBZyR1midYqn89jcLAfsixjcLAPDocVqdQiBEFAR0cnRLGwLoUtJ4hOL5/PY2hoALIso7+/0MLF6/Vi27YWAMAFF+zD619/CeeTSUxZXff888+jubn5rMOK1cpka7blY8BjsXF0XTdazRdaThTuULBYBDQ0NKOrq9BzhO0mzMN5UTpOdyyWW7jIcgSKsrKFS0NDPTQtD5ut8LNeL9tOnK2zOcG74YHlueeew6OPPoqPfexjZ/1cXq97HSqi9cBjUXyJRAJHjx7FK6+8gunpaWO8oaEW4XAYO3bsQFNTk4kV0h/jvCgdJzsWw8PD+I//eMT4ur6+ZsV84iXU0rGhgWVsbAy333479u3bh0OHDp318yUSKeTzvEvITFarBV6vm8eiSBYXFxGLRSHLEQwPDxtb5NtstqXr6JLRan75EySPhfk4L0rH8ccinU6jp6cHuVwOu3adAwBwu6vhdFagoaERkhRGR8erLVxmZhZO9dS0BtXV7jWv+9mwwJJIJPD+978fNTU1uOeee9ZloVI+r/G25hLBY7F+jm850dvba7SaB4DW1jZIUhh+fxAOh2Pp+2HsqQLwWJQSHgvzaZqGvr4+/OY3v0M02oNcLgu3uwKhUNgIJjfc8A7jb5Kug8esiJY+c63JhgSWdDqND37wg5ibm8O3vvUttp8nOoFjx6YgyxFEo8oJW82HQhK8Xq+JFRKVj+npacjyUfT0KNC0LFKpRWiajpqaGoRCEjTt1a7JvNOnPBQ9sORyOXzkIx9BLBbDQw89hObm5mK/JFHZWFhIQlUVKIqMqalJY9zlchstJ3gdnejMRSIv48iRF2GxCKit9SIQkBAMSmhqauZ8KlNFDyx33XUXnnjiCdxxxx2Yn5/Hiy++aDy2Y8cO47Q20VaRzWbR1xeDosgYHBww1qVYrVb4fF0QxTBbzROtUjabRX9/L2RZxp49e9He3gEAEMUwEokEtm/fjr17d2JuLsNLPWWu6IHl6aefBgDcfffdr3nsscceQ3t7e7FLIDLdqVrNNzdvgySFEQiE4HK5TKySqDzouo7R0REoioyeHtWYT263ywgsDQ2NuOqqt8Bmsyz1x8qc4hmpHBQ9sDz++OPFfgmikhWPx6EoESiKjPn5OWPc662GKIpsNU90BnK5HH73u+egKDLm5hLGeFWVF6JY2CKfNi+2ZSVaZ6lUCtGoCkWJrOg74nQ6EQgEIYphbNvWwuvoRKuQy+WMDuJWqxWxWBRzcwk4HA4EAiGIooSWllbOpy2AgYVoHeRyOfT39xmt5pdvM7ZYLEtb5IfR1dVtvPES0cnlcjkMDPRDUSIYGxvDLbccgt1uhyAIOP/8fQB0+HzdsNvtZpdKG4jvnkRrdHyr+WhURSbz6jXyxsYmSFIYwWAIbneFiVUSlQdd1zExMQ5ZjqCnR0U6nTYeGxkZhs/XBQAIBkMmVUhmY2AhOkOzszNQFBmqKmN2dtYYf7XVfPiETT+J6MSGh4fwy18+gZmZGWPM4/EszScJ9fUN5hVHJYOBhWgV0uk0YrEoFEXG6OiIMW632+H3ByGKElvNE61SJpNBJpMxNkL0eDyYmZmBzWaH3++HKIbR1tbO+UQrMLAQnUQ+n8fgYD9kWcbAQB9yuRwAQBAEtLd3QBTD6O7uZqt5olXI5/MYGhqAosjo6+tFZ6cPb3rTmwEANTW1uPrqt6CtrZ3ziU6KgYXoOLquY3JyYumSj4J0OmU8VldXv7RFvojKykoTqyQqD7quY2pq0phPqdSrzQQTiQQ0TTPOonR1+c0qk8oEAwsRgLm5OaiqDEWJIB6PG+MVFRUIhUSIYhj19Q28dZJoFXRdhyAI+PnPf4JoVDXGC00HRYRCIhob2XKCzgwDC21Zi4uLiMV6oCgRjIwMG1vk22w2dHV1QxQLreZ5HZ3o9LLZRcRiMXR1dRstV7Zta0FfXy98vm5IkoT29k62nKA1Y2ChLUXTNAwNDUJVZcRiMeRyWeOx1tY2SFIY3d0BOJ1OE6skKg+api21nIigp6cHuVwWl19+BcLhHQAASdoOUQxzPtG6YGChLeHYsSnIcgTRqIJkMmmM19TULK1LkYw7Fojo1Kanj0GWI1BVecV8qq6uhsXy6hkUNrel9cTAQpvWwkISqqpAUWRMTU0a4y6XC4FACJK0HU1NvI5OdCYWFpJ4+OFvGJdQC/Op0HKiuXkb5xMVDQMLbSrZbBZ9fTEoiozBwQHjTdVqtcLn64IohtHZ6eN1dKJVyGaz6O/vRTw+jQsuuAgAUFHhQUeHD1arxZhPbDlBG4G/ZVT2dF1fuo4uIxaLGq3mgcKiP1GUEAiE4HK5TKySqDzouo7R0REoioyeHhWLi4sQBAHbt+8ybud/85uv4ZkU2nAMLFS24vE4VFWGLEcwPz9njFdVeSFJEkIhCTU1tSZWSFQ+EolZyPJRyLKMubmEMV5ZWQVJCsNieTWgMKyQGRhYqKykUilEoyoUJYKJiXFjnK3mic7O8PAQnnvutwAK8+n4lhOcT1QKGFio5B3far6/vw+apgEALBYLOjo6IYph+HxdbDVPtAr5fN6YT21tHdi16xwAgN8fRF9fL4LBELq6/JxPVHIYWKgk6bqO8fGxpf0doitazTc2Nhpb5LvdFSZWSVQedF3HxMQ4ZDmCnh7VmE9zc3NGYHE6nbj66mvMLJPolBhYqKQkErNQlMIW+bOzs8Z4ZWWl0Wq+rq7exAqJyssLL/wOkcjLmJmZMcY8Ho8xn4jKBQMLmS6TyaCnR4WiyBgdHTHG7XY7/P4ARDGM1tY2bpFPtArZ7OKKjscTE2OYmZmBzWaH3++HKIbR1tbO+URlh4GFTLHcal6WZfT39yKXywEo3H3Q1tYOUQzD7/ez1TzRKmiahsHBAShKBH19fbjxxptRXV0DANi9ey98vm74/QHuPEtljYGFNoyu65icnICqKq9pNV9XV2+sS1ne64GITk7XdUxNTUJRZESjChYWXp1PfX292LPnXABAS0srWlpazSqTaN0wsFDRzc3NIRpVIMsRxOPTxvhyq3lRlNDQ0MhbJ4lWKR6P46c//TGmp48ZYy6X25hPjY1NJlZHVBwMLFQUy63mFSWC4eEhY4t8m82Grq5uiGIY7e0d3CKfaBWy2UXMzc0ZC84rKysxPz8Hm80Gn68boigubZfP+USbFwMLrRtN0zA8PARFiSAWiyGXyxqPtbS0QpLC8PuDbDVPtAqapi21nIggFutBZWUlbrrpFgiCALvdjquvvgb19Q2cT7RlMLDQWZucnMRvfvMCIpGjK1rN19TULK1LkeD1ek2skKh8TE8fg6LIUFUZ8/PzxrimaUilFlBR4QEAtLa2mVUikSkYWGhNFhaSUFUF0aiCZHIWqdQiNE1fajUfgiSF0dTUzHUpRGfgN795Fs8//1vja6fTiWAwBFEMo7l5G+cTbWkMLLRqy63mZVnG0NAANE2DxSLA43HB7w8gEBDZap5olXK5HPr6etHQ0GA06WxpaYXFYoHP14VQSILP18X5RLSEM4FOSdf1pevoMmKxKBYXF43Hmpu3Yfv27bjggr1IpzXkcpqJlRKVPl3XMTo6AlWV0dMTRSaTwZ49e/H6118KAGhra8ehQ+9mywmiE2BgoROKx+NQVRmKsrLVfFWVF6JY2NK7pqYWNpsFbrcb6XTyFM9GtLXNzs5AUSJQFAWJxPEtJ6qMNSlAoaEnwwrRiTGwkCGVSiEaVaGqMsbHx4xxh8OBQCAEUZTQ0tLK6+hEZ0DTNHzve981Nkq02+3GfGptbeN8IlolBpYtLpfLGa3mBwb6kc/nARQ+6bW3d0KSJPh83Ww1T7QK+XweAwP9GBrqx9ve9lYAhbkkihLi8WmIooSuLj/nE9EaMLBsQSdrNQ8AjY2NEMUwgsHQilPVRHRihfk0AUWJIBpVkU6nYLEIOP/8vaiqKmz0tn//xTyTQnSWGFi2kERi1tjf4USt5iUpbOykSbSVWCw9cLu/Dqv1KAA7stn9SKdvga7XnvRnFhaSOHr0FShKZMV8qqioQDi8HV6vF0sbPJdxWMnAZnsBgpCCpnUgnw+aXRBtYQwsm1wmk0EsFoUsRzA6OmKM22x2BAIBiGIYra1tbDVPW1QOlZV/A7f7S9B1K4A8AAEOxw/h8dyFubn/F5nMO0/4k4lEAr/5zbMACvPJ7/cjFJLQ3t4Bh8OGmhoP4vFyXYyeQkXFP8Lt/hIslhljNJu9EMnkx5HNXmFeabRlMbBsQvl8HkNDA1AUGX19vcjlcgAKn/La2tohimH4/X7Y7Ww1T1tbZeXfwOX6MgBAEPJLo4XTIrqegdf7XzAz40Q0egFUVYbbXYGLLy7cgtzcvA2SFEZrazv8/gAcjs0yn1KoqflT2Gy/hSCs3KrAZnsO1dV/jrm5e5HJvMOk+mirYmDZJI5vNa+qinFHAgDU1tZBkgpb5FdWVppYJVHpsFqjcLu/dMLHdB2YnLTg5ZftOHr0TkxNfRiAAKfTiX379sNms0EQBBw4cOXGFr0BPJ7PnjCsAIAgaNB1oKrqw8hmL4am+UyokLYqBpYyNz8/D1WVIcsRxOPTxrjbXYFQSEQoJKKxsamMr6ETFYfL9TXouvW4MysFf/iDDc8/78DU1HLn43lUVg6hu/sahELSJu+InILL9eUThpVlglAIdG73V5BM/t3GlUZbHgNLGcpmFxGLxaAoEQwPD0FfWtlns9nQ1dUNUZTQ3t65yd9Yic6O1foKgDwWFwGbDVhexhWPWzA1ZYXFoiMQyGHHDg2NjS1YXLzM1Ho3gt3+a1gsc6f9PkHIw+H4AQMLbSgGljKhaRqGh4eWWs3HkMtljcdaWlohSWH4/UG2midaBV3XMTioQ1VdUFUb3vzmFILBwpmWXbuyqK7WEQpl4XYDum5BMrk19k0RhIXTf5PxveW6oJjKVdEDS39/P7785S/jyJEjUFUVfr8fP/zhD4v9spvG9PQxyHIEqiojmXz1DaKmpgahUGGLfK+32sQKicrH9PQ0FCUCVVWQyVTAbrdDEIC+PpsRWGprddTWvvqBQBA0ZLP7zSp5Q2la26q+T9ct0LSOIldDtFLRA4uqqnjyySexZ88eaJpmXL6gk1tYSEJVFaiqjMnJSWPc5XIhEAhBksJoamrmuhSiVcpkMviP//h3TE5OGGNO5/nYs+cx7NiRQmvridds6LoVudwu5HKv26hSTZXL7UYuF4bVKkMQTvVerSGV+ouNKosIwAYElgMHDuDgwYMAgDvuuAN/+MMfiv2SZSmbzaK/vxeyLGNoaACaVngDtVqt6Oz0QRTD6Oz0sdU80SrkcjkcOzaF5uZtAACn0wld12GxWIz55PN1obKyBlVVf4UTfY4q7MviwPz8PRtbvKkEJJN/i+rqQyf9Dl23QtPakMn8+QbWRbQBgYUbkp2crusYGRmGqiro6VGxuLhoPNbcvA2iGEYgEITb7TaxSqLyoOs6xsZGoSgR9PREkc9reNe73mPsj/LGNx5AZWXlim7I6fRfQNfd8HjuhNU6vhRSdAiChnx+O+bmvoBcbq85/0ImWVz8M8zP3w2P5+MALMZdVLouANChaa2YmfkPAOwqTRurrD+uW63lGYZmZuKQ5QgURUYikTDGa2qqIYqFLfJrak6+JXgpWT4G5XosNpOteixmZ2cgyxHIsoxEYtYYr6qqQjKZQEVFEwCgpWXbCX8+n387EokbYLf/FBZLYWv+XO71yOdfB0DAWk5qlvuxyGZvQyJxAE7nF+Fw/AhACprWiUzmvVhcvAkWiwfl8lm03I/FZnM2KxnKOrB4veVz5iGVSkGWZbzyyisYHR01xqurKyFJErZv34729vayXZdSTsdis9tKx+IPf/gDfvKTnxhfe70eiKKIHTt2oKOj4wzn0w3rXl95H4sLlv5XYLEUbv/2lGlP1PI+FgSUeWBJJFLI50++wZHZcrkc+vv7IMsRDAz0I58vnFq1WCzo6OiEJIXR1dVttJqfmVn9LYWlwmq1wOt1l/yx2Ao2+7HI5/Po7++Dy+VCa2vhbhavtwGZTA7t7R2QpDC6u/0lMZ82+7EoJzwWpaW62r3mpSJlHVjyeQ25XGn9AhZazY9DliPo6VGRTqeNxxobGyGKYQSDIVRUvPoxpdT+HdaiFI/FVrWZjkVhPk1AUSKIRlWk0yl0dvrwlre0AADcbg9uvfU9cLlcxs+U0r/7ZjoW5Y7HojSczY3CZR1YSkkikYCqyq9pNe/xeIz9UurrG8wrkKiMJBIJRKMKFCWCeDxujFdUVKCurh66rhuXe44PK0S0eTGwnIVMJoNYLApZjmB0dMQYX241L4phtLW1804pojP0+OM/M+aUzWZHd3c3RDGM9vYOzieiLarogSWVSuHJJ58EAAwPD2N+fh6HDx8GAFx44YWoq6srdgnrStM0DA4OQFEi6OvrRS6XAwAIgoC2tnaIYhh+vx92+2ZpNU9UPJqmYWhoEKoq4/Wvv9S4hV+SwrBYLEvzKWDcmkxEW5egF3nr2aGhIVxxxRUnfOzrX/869u3bt+bnjseTG3JNUtd1TE1NQlFkqKqCVOrVxXy1tXWQpDBCIQmVlZVFr6XU2GwW1NZ6NuxY0MmV07EozKfCFvkLC4X5dMklb8A55+wGgBWXfMpROR2LzY7HorTU1XnWfIt50c+wtLe3Q5blYr9MUczPz0NVC9fRp6ePGeNudwVCIRGiKKGhobGs31iJNkomk8HRoy9DUWQcOzZljLtcbgSDIbS2thpjnFNE9Me4huWPZLOLiMViUFUZQ0ODRu8jm80Gn68bkiShvb0TVqvV5EqJSt/xZ0o0LY9f//pX0DQNVqvVmE8dHT7OJyI6LQYWFK6jj4wMQ1EiiMV6kM2+2qm1paUVoighEAjB6XSaWCVReVhuOSHLEaRSKbzlLW8FUDgzuXfveaisrEQgENpUd/cIwjSczu/Aah2ArjuRz/8JgBNfCieitdnSgWV6+tjSuhQZ8/Pzxnh1dTVEMQxRlOD1VptYIVH5mJ6eXrq1X8b8/JwxnkjMGvNo3779ZpVXJHl4PJ+B230PgCwKb6k6BOEfAeyCxfIAgLCpFRJtFlsusKRSC8a6lMnJSWPc5XIhEAhCFMNobt7Ga+hEq9TXF8Nzz/0Wk5MTxpjT6TTmU1WV18Tqiquy8r/B5XoAgrB870L2uEePoqrqIGZmnkA+HzKjPKJNZUsElmw2i/7+PihKBIODA9C0wkpxi8UCn68LohhGZ6cPtrV0OSPaYnK5HHRdM27dz2azmJycgMViQWenD6GQhK6ubpPmUx52+y9gtQ5C191YXLwcut5UlFey2Z6H2/3lU9YiCEl4PP8dicTDRamBaCvZtH+hdV3H6OgIFEVGT4+KxcVF47GmpmZji/zlfR+I6OR0XcfY2CgUJYKenijOPfd1OPfc1wEAurr8uOSSy5bmU4VpNbpcX0NFxf+A1TpmjOm6FZnM2zA//4/Q9fp1fr0vQ9dtEITcSb9HEPJwOH4Ci2UImta+rq9PtNVsusAyMxOHqsqQZRlzcwljvLKyytgvpba21sQKicrH7OwMFKWwLiWRmDXGh4YGjcBit9txzjl7zCoRAFBR8Tl4PJ95TZ8SQcjD6fwebLYXMDPz83UNLXb7r04ZVl6tQYfN9gIWF7dqYCmENofj5wDS0LQupNPvYICjM7YpAks6nUY0qkJVZYyNjRrjDocDfn8QoiihtbWN61KIVknXdfzoRz/A4OCAMWa32+H3ByFJYaNbcimwWl+Gx/MZAMCJprgg5GG19sHj+TTm5/+/dXzlM9lzs6j7c5Ysm+3X8HrfBat1BLq+/OdGR0XF/0A6/W7Mz/8jALuZJVIZKdvAous6YrEeHD16FP39fcjn8wAKG051dPggiiK6ul5tNU9EJ5fP5zE2Noq2tsKnXkEQ4HK5luZTJ0SxsC6lFFtOuN1fWtWlGZfr35BM3gVdX587/3K5c2G1DqzqLEsut2tdXrOc2GxHUFPzViwvRP7j/04u11cgCHOYm/sSAH6YpNMr28CSSCTw4x//CJpW+OTS0NAIUZQQDIbg8Wy9LfKJzpSu65icnDBaTqTTKdx00ztQV1e4bHLhhRdh//6LS34+ORw/X+WlmTRstueRzR5Yl9dNp98Hl+u7p/weXbcim70UmuZfl9csJx7PnQCyEIT8CR8XBB0u17eRSn0AudzaW7TQ1lG2gUXXdXg8Hvj9IUhSGPX1DWaXRFQW5ubmlvZLiSAejxvjFRUVmJtLGIGlfPYgyqz6OwVh9d97Otns65FOvw1O5/chCCfqUWMBYEcy+el1e81yYbH0wOF48rTfp+s2uN1fwtwcAwudXtkGFo/Hg0OH3g2NvayIVm10dATf//4jK1pOdHf7EQpJ6OjohMWytqZkZsrnRVgskyf9JL/ye4Pr+MoC5ua+CF2vgsv1IAoBpTBeOOPTjLm5B5HLmbsg2Qw220ur+j5ByMFme77I1dBmUbaBxW63w2JZNPZUIaKVNE3D0NAgstksAoHCH+qmpma4XG7U1tZCksLw+4NwOEpvXcqZSKffA4fjl6f8Hl23IJe7sAgbuDkwP/8vWFj4v+FyPQSrtR+67kY+fyUqK69HPp8BsBXfo85kTQrXr9DqlG1gIaITm5qawiuvvAJVlbGwsICqKi/8/gAEQYDVasU73nFr2YeU42Uyb0U2uwc22x9OeJZF1wUAApLJTxStBk3zYWHhb42vbTYLCm+v63cJqpzkcnuh68JxOwCfWGGNDy8H0eowsBBtAsnkPGKxKAYHYxgcHDEWo7tcbvh8XcjlssYdPpsprBTYMTv776iuvh52+/PQdSsEIb+0J4sAwIFE4gFks5eaXOfWoWk+LC4ehMPx+Ckv1QlCHun0+zawMipnDCxEm8Bzz/0GkcgrcLsdsFqt6OrqgiRJ6OjwwWq1ml1e0el6PWZmHoPd/jhcrgdhtfZC1yuwuPhmpNO3rPsut3R6yeT/hN1+OYDUSc98pdN/gVzuvI0vjsoSAwtRGdF1HSMjw1AUGTt27EJzczMAQBTDmJmJ44ILzkVTUztsts12FmU1LMhmDyKbPWh2IQQgn5cwM/MTeL1/AZtNXdo4TgCQB2BFKnUbkslPmlwllRMGFqIyEI/HoSgRKIqM+fk5AIDVajUCS0tLK6677gbU1noQjyeRy23FhZ5UavL5cxCPPwe7/Wk4HI8BSEHTfEinb+RZLzpjDCxEJSqXy+Ho0VegKBFMTIwb406nE4FAYYt8otInIJu9BNnsJWYXQmWOgYWohOi6bvS8slqtePHF5zE/Pw+LxbK0RX4YXV3dsNk4dYloa+G7HpHJdF3H+PgYFCWCkZFh3HjjO2CxWCAIAs4993xoWh6hkAi3u8LsUomITMPAQmSSRGIWshyBqsqYnZ01xoeGBtHZ6QMA7Np1jlnlERGVFAYWog02NjaKZ599BqOjI8aY3W6H3x+AKIbR2tpmYnVERKWJgYWoyPL5PBYXF+F2uwEU+veMjo5AEAS0t3dAFMPo7u42NnYjIqLXYmAhKgJd1zE5OQFFkaGqCnw+Hw4cuBIAUF/fgDe84XJ0dnahsrLS5EqJiMoDAwvROpqbm4OqylAUGfH4tDE+NjYKTdOMxbQ7duwysUoiovLDwEK0Tn7xi8cRibwCvdDEBjabDV1d3RDFMDo6OmGxWEyukIiofDGwEK2BpmkYHh7Ctm0tsNvtAICqqirouo7W1jZIUhjd3QE4nU6TKyUi2hwYWIjOwLFjU5DlCKJRBclkEldeeRWCwRAAYMeOXQiFJHi9XpOrJCLafBhYiE5jYSEJVVWgKDKmpiaNcZfLjcXFjPG12+027gQiIqL1xcBCdArJ5Dz+9V+/Bk0rNBO0Wq3w+bogimF0dvpgtVpNrpCIaGtgYCFaous6RkaGMT09jXPO2Q0A8Hgq0djYBEEQIIoSAoEQXC6XyZUSEW09DCy05cXjcShKBIoiY35+DlarFaGQaASTt771z4yFtUREZA4GFtqSUqkUolEVihLBxMS4Me5wOBAIhJDL5YwxhhUiIvMxsNCWpKoynn76KQCAxWJBR0cnRDEMn6+LAYWIqAQxsNCmpus6xsfHoCgRtLa2IRgUAQDBYAiqqiAUEhEMhlBR4TG5UiIiOhUGFtqUEolZKIoMRYlgdnYWQGGtynJgqajw4LrrbjSzRCIiOgMMLLSpRCKvIBI5itHREWPMbrfD7w9AFMMmVkZERGeDgYXKmq7rEATB+FpVFYyOjkAQBLS3d0AUw+ju7obd7jCxSiIiOlsMLFR2dF3H5OQEVFVBNKrg+utvgsdTCQDYvXsP2ts7EQqJqKysNLlSIiJaLxsSWHp6evCZz3wGL7zwAjweD6699lp85CMfgcPBT720enNzc4hGFchyBPH4tDHe0xPF7t17AQA+Xzd8vm6TKiQiomIpemCZnZ3Fu971LnR1deGee+7B+Pg47r77bqTTaXzyk58s9svTJjAzE8dTTz2J4eEh6LoOALDZbOjq6oYohtHe3mFyhUREVGxFDyzf/OY3kUwm8S//8i+oqakBAOTzedx111344Ac/iObm5mKXQGVG0zQsLCwYl3Tc7gqMjY1C13W0tLRCksLw+4NwOp0mV0pERBul6IHll7/8Jfbv32+EFQC4+uqr8alPfQpPP/00/vzP/7zYJVCZmJ4+BlmOQFVlVFR4cP31NwEAnE4nDhy4Eo2NjfB6q02ukoiIzFD0wBKLxXDdddetGPN6vWhsbEQsFjur57ZaLWf183T2lo/BWo/FwkISilJYlzI1NWmM67qGxcU0KioqAACSJJ59sZvc2R4LWj88FqWDx6K0HHdT5xkremBJJBLwer2vGa+urjY29Forr9d9Vj9P62ctx+LZZ5/Fr371K2iaBgDweFzw+/3YsWMHuru7YbPxJra14LwoHTwWpYPHovyV9V+ERCKFfF4zu4wtzWq1wOt1n/ZY6LqOkZFhVFfXGGtTbDY3ksk0mpu3QZLCCAZDcLsLbypzcxkAmY34V9g0VnssqPh4LEoHj0Vpqa52w2JZ29muogcWr9eLubm514zPzs6iuvrs1iPk8xpyOf4CloKTHYt4PA5VlaEoMubmEjj//AtxwQX7AABtbZ246aZbUFNTa3w/j+fZ47woHTwWpYPHojQs3ei5JkUPLH6//zVrVebm5jA5OQm/31/slycTpFIp9PREoSgRjI+PGeOFfXde/W212WwrwgoREdHJFD2wXHbZZbjvvvtWrGU5fPgwLBYLLr744mK/PG0wTdPwzW8+hHQ6BQCwWCzo6Oi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diff --git a/_sources/ml-advanced/kernel-method.md b/_sources/ml-advanced/kernel-method.md
deleted file mode 100644
index f487b1d70b..0000000000
--- a/_sources/ml-advanced/kernel-method.md
+++ /dev/null
@@ -1,348 +0,0 @@
----
-jupytext:
-  cell_metadata_filter: -all
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Kernel method
-
-SVMs are a powerful and flexible class of algorithms used for classification and regression. In this section, we will explore the intuition behind SVMs and their use in classification problems.
-To start with, let's understand the basic concept of SVMs. 
-Support Vector Machines (SVMs) are supervised learning algorithms that can be used for classification and regression tasks. SVMs try to find the best decision boundary that separates data points of different classes. The decision boundary is chosen such that it maximizes the margin between the data points of different classes.
-The margin is defined as the minimum distance between the decision boundary and the closest data points of each class. This makes SVMs very robust to outliers as the decision boundary is chosen based on the data that is closest to it.
-In classification problems, the goal is to find a decision boundary that separates the data into two or more classes. SVMs can be used for binary classification, where the classes are only two, or for multiclass classification, where there are more than two classes.
-
-## Motivating Support Vector Machines
-
-In this section we will consider differentiated classification: rather than modelling each category, we simply find a line or curve or flowform that separates these categories from each other.
-
-```{code-cell}
-%matplotlib inline
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy import stats
-
-# use seaborn plotting defaults
-import seaborn as sns
-sns.set()
-```
-
-```{code-cell}
-from sklearn.datasets import make_blobs
-X, y = make_blobs(n_samples=50, centers=2,
-                  random_state=0, cluster_std=0.60)
-plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');
-```
-
-A linear discriminant classifier may draw straight lines to separate two sets of data, but there may be multiple split lines that perfectly distinguish between the two categories. 
-
-This is shown in the diagram below:
-
-
-```{code-cell}
-xfit = np.linspace(-1, 3.5)
-plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
-plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)
-
-for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:
-    plt.plot(xfit, m * xfit + b, '-k')
-
-plt.xlim(-1, 3.5);
-```
-
-All three separators are good if we want to distinguish between these samples. But they have some significant differences. In the following, I will explain these separators with an example to show the difference between them. Now, suppose you want a new data point, which is marked with an "X" in this graph. So, which separator would you choose? This is a very interesting question! Because you can choose to give this new data point a different label. It's up to you!
-Obviously we can't simply "draw a line between classes", but we do need to think a bit deeper.
-
-
-
-## Support Vector Machines: Maximizing the _Margin_
-
-SVM proposes a new solution algorithm which finds the nearest straight line by applying an appropriate boundary fill to each line. The following is an example of an improvement to the SVM algorithm.
-
-```{code-cell}
-xfit = np.linspace(-1, 3.5)
-plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
-
-for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:
-    yfit = m * xfit + b
-    plt.plot(xfit, yfit, '-k')
-    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',
-                     color='#AAAAAA', alpha=0.4)
-
-plt.xlim(-1, 3.5);
-```
-In SVM, the best models are those that maximise this margin, and SVM is an optimal model that achieves the maximum margin and can effectively use the boundary information in the dataset to predict the distribution of new data points, thereby improving prediction accuracy. Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data.
-
-
-## Fitting a support vector machine
-
-
-
-```{figure} ../../images/svm/svm2d.png
----
-name: 'SVM for Linear classification : 2D case'
-width: 90%
----
-SVM for Linear classification : 2D case. [source](https://online.stat.psu.edu/stat555/node/102/)
-```
-
-```{figure} ../../images/svm/svm3d.gif
----
-name: 'SVM for Linear classification : 3D case'
-width: 90%
----
-SVM for Linear classification : 3D case. [source](https://www.youtube.com/watch?v=3liCbRZPrZA)
-```
-
-
-Now we will use a linear kernel and set the C parameter to a large size:
-
-
-```{code-cell}
-from sklearn.svm import SVC # "Support vector classifier"
-model = SVC(kernel='linear', C=1E10)
-model.fit(X, y);
-```
-
-Let's create a quick and easy function to draw the SVM decision boundary:
-
-```{code-cell}
-def plot_svc_decision_function(model, ax=None, plot_support=True):
-    """Plot the decision function for a 2D SVC"""
-    if ax is None:
-        ax = plt.gca()
-    xlim = ax.get_xlim()
-    ylim = ax.get_ylim()
-    
-    # create grid to evaluate model
-    x = np.linspace(xlim[0], xlim[1], 30)
-    y = np.linspace(ylim[0], ylim[1], 30)
-    Y, X = np.meshgrid(y, x)
-    xy = np.vstack([X.ravel(), Y.ravel()]).T
-    P = model.decision_function(xy).reshape(X.shape)
-    
-    # plot decision boundary and margins
-    ax.contour(X, Y, P, colors='k',
-               levels=[-1, 0, 1], alpha=0.5,
-               linestyles=['--', '-', '--'])
-    
-    # plot support vectors
-    if plot_support:
-        ax.scatter(model.support_vectors_[:, 0],
-                   model.support_vectors_[:, 1],
-                   s=300, linewidth=1, facecolors='none');
-    ax.set_xlim(xlim)
-    ax.set_ylim(ylim)
-```
-
-```{code-cell}
-plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
-plot_svc_decision_function(model);
-```
-
-This is a maximum expansion of the space between the two points. Note that some of the practice points just reach the boundary and are marked with black circles. Those points are important in the matching process and are called, support vectors, which is the name of this algorithm. In Skeeter-learning, the identification of these points is stored in the support_vectors_ property of the classifier at:
-
-```{code-cell}
-model.support_vectors_
-```
-
-One of the main points of this classification method is that just the space in which the support vector is located has a large effect on the matching result; on the correct side, no point away from the boundary can correct the match. This is due to the fact that during the fitting process those points do not affect the loss function used in the model. Theoretically, this is due to the fact that during the fitting process, those points do not affect the loss function used in the fitting process, and therefore, if they do not cross the boundary, then their location and number are irrelevant.
-
-In the dataset, the first 60 points and the first 120 points are each used as the training set, and we can use this dataset to train the model.
-
-```{code-cell}
-def plot_svm(N=10, ax=None):
-    X, y = make_blobs(n_samples=200, centers=2,
-                      random_state=0, cluster_std=0.60)
-    X = X[:N]
-    y = y[:N]
-    model = SVC(kernel='linear', C=1E10)
-    model.fit(X, y)
-    
-    ax = ax or plt.gca()
-    ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
-    ax.set_xlim(-1, 4)
-    ax.set_ylim(-1, 6)
-    plot_svc_decision_function(model, ax)
-
-fig, ax = plt.subplots(1, 2, figsize=(16, 6))
-fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)
-for axi, N in zip(ax, [60, 120]):
-    plot_svm(N, axi)
-    axi.set_title('N = {0}'.format(N))
-```
-
- One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, as can be seen from the comparison between the two figures, and increases the number of training points without affecting the classification results of the model.
-
-With the interactive component in IPython, you can interactively see this feature of the SVM model
-
-```{code-cell}
-from ipywidgets import interact, fixed
-interact(plot_svm, N=[10, 200], ax=fixed(None));
-```
-
-## Beyond linear boundaries: Kernel SVM
-
-
-```{figure} ../../images/svm/svm1.gif
----
-name: Kernel SVM visualization
-width: 90%
----
-Kernel SVM visualization (with a polynomial kernel). [source](https://www.youtube.com/embed/3liCbRZPrZA)
-```
-
-
-
-Kernels is an "easy algorithm" that saves us from the tedious calculations in a high-dimensional space. If support vector machines and kernels are combined to project data into a high-dimensional space, making it possible to handle non-linear relationships with linear classifiers, the power of SVMs can be enormous.
-
-The following is the case for non-linearly separable data:
-
-```{code-cell}
-from sklearn.datasets import make_circles
-X, y = make_circles(100, factor=.1, noise=.1)
-
-clf = SVC(kernel='linear').fit(X, y)
-
-plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
-plot_svc_decision_function(clf, plot_support=False);
-```
-
-It is possible to project the data into higher dimensions, so that a linear partition line is sufficient. As an example, we can use a simple projection to compute a radial basis function with the centre on the middle block as follows:
-
-```{code-cell}
-r = np.exp(-(X ** 2).sum(1))
-```
-
-This additional dimension of data can be visualised in a 3D diagram.:
-
-```{code-cell}
-from mpl_toolkits import mplot3d
-
-def plot_3D(elev=30, azim=30, X=X, y=y):
-    ax = plt.subplot(projection='3d')
-    ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')
-    ax.view_init(elev=elev, azim=azim)
-    ax.set_xlabel('x')
-    ax.set_ylabel('y')
-    ax.set_zlabel('r')
-
-interact(plot_3D, elev=[-90, 90], azip=(-180, 180),
-         X=fixed(X), y=fixed(y));
-```
-
-
-Next we describe how to select and adjust the radial basis function centres.
-This type of basis function transformation is known as a kernel transformation, and this form of transformation solves the problem of shifting the centre of the basis function to a certain extent.
-However there is a disadvantage: there is the problem of computational overload as a new basis function needs to be computed each time. We can solve this problem with the help of the "kernel trick" applet, where we change the original linear kernel to a radial basis function kernel and then use the kernel model hyperparameters.
-
-```{code-cell}
-clf = SVC(kernel='rbf', C=1E6)
-clf.fit(X, y);
-```
-
-```{code-cell}
-plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')
-plot_svc_decision_function(clf)
-plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
-            s=300, lw=1, facecolors='none');
-```
-
-
-Currently, for linearly divisible data, we usually use a linear support vector machine. However, if the data is non-linear, such as a classification problem, we can transform it into a linearly divisible problem, thus improving the generalisation of the model. In this case, kernel tricks are used to transform linearly divisible problems into non-linearly divisible problems.
-
-
-
-## Hinge loss
-
-An alternative to cross-entropy for binary classification problems is the hinge loss function. It is mainly studied for the Support Vector Machine (SVM) model. It is designed to perform binary classification of target values which in the set {-1, 1}
-
-## RBF Kernel
-
-<div class="yt-container">
-   <iframe src="https://www.youtube.com/embed/NYwVM6_EuxQ" allowfullscreen></iframe>
-</div>
-
-## Support Vector Regression (SVR)
-
-SVR regression (Squeeze-and-VectorRegression), also known as Squeeze Regression, is an optimisation problem. The basic idea is to find a regression plane such that the variables in all data sets are closest to a point on this plane, so that all points on the plane are correlated.
-Objective function:
-In SVR regression, the objective function is to minimise the two-parametric number of weights while keeping the points in each training set as far away as possible from the support vector on one side of their own category.
-
-
-```{figure} ../../images/svm/svr1.jpeg
----
-name: support vector regression
-width: 90%
----
-An illustration of support vector regression
-```
-
-
-## SVM v.s. logistic regression
-
-Similarities: Both are linear classification algorithms
-Differences:
-1. Different loss functions
-LR: based on the assumption of "given x and parameters, y obeys binomial distribution", derived from the maximum likelihood estimation
-SVM: standard representation of hinge loss ＋ L2 regularization, based on the principle of geometric interval maximization
-2. Support vector machines only consider local points near the interval boundary, whereas logistic regression considers the global (points far from the boundary line also play a role in determining the boundary line). Support vector machines do not cause changes in the separation hyperplane by changing the non-support vector samples
-3. SVM's loss function is self-regularising, which is why SVM is a structural risk minimisation algorithm!!! And LR must additionally add a regular term to the loss function !!! Structural risk minimisation, meaning seeking a balance between training error and model complexity to prevent overfitting.
-4. Optimization methods: LR is generally based on gradient descent, SVM on SMO
-5. For non-linear separable problems, SVM is more scalable than LR
-
-```{seealso}
-https://www.geeksforgeeks.org/differentiate-between-support-vector-machine-and-logistic-regression/
-```
-
-## SVR v.s. linear regression
-
-The problem of overfitting is a very tricky one in the field of statistics. In this case, many machine learning methods, such as least squares (OLSE), will perform poorly. Support vector machines (SVR), on the other hand, can minimise the overfitting problem. SVR allows for non-linear fitting when there is enough data for training.
-A final issue to consider is OLSE linear regression. While linear regression is effective for most problems, it is not always effective for some special cases. For example, in OLSE linear regression, there is some bias in fitting the variables linearly because the model is not hyperplane. In contrast, SVR allows for non-linear fitting problems.
-
-## Support Vector Machine Summary
-The basic model of SVM: is defined as a linear classifier with maximum interval on the feature space. Specifically, when linearly separable, it finds the optimal classification hyperplane for both classes of samples in the original space. When linearly indistinguishable, relaxation variables are added and samples from a low-dimensional input space are mapped to a higher-dimensional space via a non-linear mapping to make them linearly distinguishable, so that the optimal classification hyperplane can be found in that feature space.
-
-Advantages of SVM:
-1) Solves machine learning in the small sample case.
-2) No increase in computational complexity when mapping to higher dimensional spaces as the dimensional catastrophe and non-linear differentiability are overcome by using the kernel function approach. (Since the final decision function of the support vector machine algorithm is determined by only a small number of support vectors, the computational complexity depends on the number of support vectors rather than the dimensionality of the entire sample space).
-Disadvantages of SVMs:
-1) Support vector machine algorithms are difficult to implement for large training samples. This is because support vector algorithms solve support vectors with the help of quadratic programming, which will be designed to compute matrices of order m, so a large amount of machine memory and computing time will be consumed when the order of the matrix is large.
-2) The classical SVM only gives algorithms for binary classification, while in data mining, it is generally necessary to solve multi-classification classification problems, and support vector machines are not ideal for solving multi-classification problems.
-3) The common SVM theories nowadays use a fixed penalty factor C, but the losses caused by two kinds of errors in positive and negative samples are different.
-
-## [Optional] Let's dive into the math of SVM ...
-
-<div class="yt-container">
-   <iframe src="https://www.youtube.com/embed/05VABNfa1ds" allowfullscreen></iframe>
-</div>
-
-## Acknowledgement
-
-Some code comes from [jakevdp](https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html), for which the code part is licenced under MIT licence.
-
-<p style="text-align: center;">
-<iframe src="../html/svm-vis/demo1/demo.html" width="105%" height="650px;"
-style="border:none;"></iframe>
-A demo of SVM. <a href="https://cs.stanford.edu/~karpathy/svmjs/demo/">[source]</a>
-</p>
-
-<p style="text-align: center;">
-<iframe src="../html/svm-vis/demo2/index.html" width="105%" height="600px;"
-style="border:none;"></iframe>
-A demo of SVM. <a href="http://macheads101.com/demos/svm-playground/">[source]</a>
-</p>
-
-
-
-## Your turn! 🚀
-You can follow this [assignment](../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.ipynb) to practise Support Vector Machines with examples.
diff --git a/_sources/ml-advanced/unsupervised-learning-pca-and-clustering.ipynb b/_sources/ml-advanced/unsupervised-learning-pca-and-clustering.ipynb
new file mode 100644
index 0000000000..05f9df272f
--- /dev/null
+++ b/_sources/ml-advanced/unsupervised-learning-pca-and-clustering.ipynb
@@ -0,0 +1,1264 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies \n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython seaborn scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 16.5 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import sklearn.decomposition\n",
+    "from sklearn.cluster import (\n",
+    "    KMeans,\n",
+    "    AgglomerativeClustering,\n",
+    "    AffinityPropagation,\n",
+    "    SpectralClustering,\n",
+    ")\n",
+    "import sklearn\n",
+    "from sklearn import datasets\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from scipy.cluster import hierarchy\n",
+    "from scipy.spatial.distance import pdist\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.decomposition import PCA \n",
+    "\n",
+    "sns.set(style='white')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unsupervised learning: PCA and clustering "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this lesson, we will work with unsupervised learning methods such as Principal Component Analysis (PCA) and clustering. You will learn why and how we can reduce the dimensionality of the original data and what the main approaches are for grouping similar data points.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction\n",
+    "\n",
+    "The main feature of unsupervised learning algorithms, when compared to classification and regression methods, is that input data are unlabeled (i.e. no labels or classes given) and that the algorithm learns the structure of the data without any assistance. This creates two main differences. First, it allows us to process large amounts of data because the data does not need to be manually labeled. Second, it is difficult to evaluate the quality of an unsupervised algorithm due to the absence of an explicit goodness metric as used in supervised learning.  \n",
+    "\n",
+    "One of the most common tasks in unsupervised learning is dimensionality reduction. On one hand, dimensionality reduction may help with data visualization (e.g. t-SNA method) while, on the other hand, it may help deal with the multicollinearity of your data and prepare the data for a supervised learning method (e.g. decision trees).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Principal Component Analysis (PCA)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Intuition, theories, and application issues\n",
+    "\n",
+    "Principal Component Analysis is one of the easiest, most intuitive, and most frequently used methods for dimensionality reduction, projecting data onto its orthogonal feature subspace.\n",
+    "\n",
+    "\n",
+    "<img align=\"right\" src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp\" width=\"400\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "image. <a href=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "image. <a href=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More generally speaking, all observations can be considered as an ellipsoid in a subspace of an initial feature space, and the new basis set in this subspace is aligned with the ellipsoid axes. This assumption lets us remove highly correlated features since basis set vectors are orthogonal. \n",
+    "In the general case, the resulting ellipsoid dimensionality matches the initial space dimensionality, but the assumption that our data lies in a subspace with a smaller dimension allows us to cut off the \"excessive\" space with the new projection (subspace). We accomplish this in a 'greedy' fashion, sequentially selecting each of the ellipsoid axes by identifying where the dispersion is maximal.\n",
+    " \n",
+    "\n",
+    "> \"To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say 'fourteen' very loudly. Everyone does it.\" - Geoffrey Hinton\n",
+    "\n",
+    "\n",
+    "Let's take a look at the mathematical formulation of this process:\n",
+    "\n",
+    "In order to decrease the dimensionality of our data from $n$ to $k$ with $k \\leq n$, we sort our list of axes in order of decreasing dispersion and take the top-$k$ of them.\n",
+    "\n",
+    "We begin by computing the dispersion and the covariance of the initial features. This is usually done with the covariance matrix. According to the covariance definition, the covariance of two features is computed as follows: $$cov(X_i, X_j) = E[(X_i - \\mu_i) (X_j - \\mu_j)] = E[X_i X_j] - \\mu_i \\mu_j,$$ where $\\mu_i$ is the expected value of the $i$th feature. It is worth noting that the covariance is symmetric, and the covariance of a vector with itself is equal to its dispersion.\n",
+    "\n",
+    "Therefore the covariance matrix is symmetric with the dispersion of the corresponding features on the diagonal. Non-diagonal values are the covariances of the corresponding pair of features. In terms of matrices where $\\mathbf{X}$ is the matrix of observations, the covariance matrix is as follows:\n",
+    "\n",
+    "$$\\Sigma = E[(\\mathbf{X} - E[\\mathbf{X}]) (\\mathbf{X} - E[\\mathbf{X}])^{T}]$$\n",
+    "\n",
+    "Quick recap: matrices, as linear operators, have eigenvalues and eigenvectors. They are very convenient because they describe parts of our space that do not rotate and only stretch when we apply linear operators on them; eigenvectors remain in the same direction but are stretched by a corresponding eigenvalue. Formally, a matrix $M$ with eigenvector $w_i$ and eigenvalue $\\lambda_i$ satisfy this equation: $M w_i = \\lambda_i w_i$.\n",
+    "\n",
+    "The covariance matrix for a sample $\\mathbf{X}$ can be written as a product of $\\mathbf{X}^{T} \\mathbf{X}$. According to the [Rayleigh quotient](https://en.wikipedia.org/wiki/Rayleigh_quotient), the maximum variation of our sample lies along the eigenvector of this matrix and is consistent with the maximum eigenvalue. Therefore, the principal components we aim to retain from the data are just the eigenvectors corresponding to the top-$k$ largest eigenvalues of the matrix.\n",
+    "\n",
+    "The next steps are easier to digest. We multiply the matrix of our data $X$ by these components to get the projection of our data onto the orthogonal basis of the chosen components. If the number of components was smaller than the initial space dimensionality, remember that we will lose some information upon applying this transformation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples\n",
+    "### Fisher's iris dataset\n",
+    "\n",
+    "Let's start by uploading all of the essential modules and try out the iris example from the `scikit-learn` documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\anaconda3\\envs\\myconda1\\lib\\site-packages\\ipykernel_launcher.py:9: MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.\n",
+      "  if __name__ == '__main__':\n",
+      "d:\\anaconda3\\envs\\myconda1\\lib\\site-packages\\ipykernel_launcher.py:20: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.0, 0, ''),\n",
+       " Text(1.0, 0, ''),\n",
+       " Text(2.0, 0, ''),\n",
+       " Text(3.0, 0, ''),\n",
+       " Text(4.0, 0, ''),\n",
+       " Text(5.0, 0, ''),\n",
+       " Text(6.0, 0, ''),\n",
+       " Text(7.0, 0, ''),\n",
+       " Text(8.0, 0, '')]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 534,
+       "width": 539
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Loading the dataset\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "\n",
+    "# Let's create a beautiful 3d-plot\n",
+    "fig = plt.figure(1, figsize=(6, 5))\n",
+    "plt.clf()\n",
+    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
+    "\n",
+    "plt.cla()\n",
+    "\n",
+    "for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:\n",
+    "    ax.text3D(X[y == label, 0].mean(),\n",
+    "              X[y == label, 1].mean() + 1.5,\n",
+    "              X[y == label, 2].mean(), name,\n",
+    "              horizontalalignment='center',\n",
+    "              bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))\n",
+    "# Change the order of labels, so that they match\n",
+    "y_clr = np.choose(y, [1, 2, 0]).astype(np.float)\n",
+    "ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y_clr, \n",
+    "           cmap=plt.cm.nipy_spectral)\n",
+    "\n",
+    "ax.w_xaxis.set_ticklabels([])\n",
+    "ax.w_yaxis.set_ticklabels([])\n",
+    "ax.w_zaxis.set_ticklabels([])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see how PCA will improve the results of a simple model that is not able to correctly fit all of the training data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.88889\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train, test splits\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.3, stratify=y, random_state=42\n",
+    ")\n",
+    "\n",
+    "# Decision trees with depth = 2\n",
+    "clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
+    "clf.fit(X_train, y_train)\n",
+    "preds = clf.predict_proba(X_test)\n",
+    "print(\"Accuracy: {:.5f}\".format(accuracy_score(y_test, preds.argmax(axis=1))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try this again, but, this time, let's reduce the dimensionality to 2 dimensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x15efcfd6708>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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QvAAJZlmW1q5caKTRrivLqTUrFzI6DwAAACmv3++X9/ARI7W89UfU7/cbqQWYRvACjIPK6fnasKomrvDFleXUhlU1qpyeb25hAAAAwATp6/JK4bCZYuGw+rzdZmoBhhG8AOPk7juKtXX1fTEdO5pRkqutq+/T3XcUJ2BlsfEHQ2rrvKQP2j5RW+cl+YOhiV4SAAAAUshAjA11R6wXCBitB5iSMdELANJJ5fR8vfr0UjW3erWn7qzqmzsUDg/fgd1hSYsXTNPD985SVUVhUhwvsm1bTa1d2lN3VoebO4es3eGwtLiqVCuWzJSnoigp1gsAAIDk5XS5zNbLyTFaDzCF4AUYZ5ZlyVNZJE9lkfzBkLw9QQV6+4dck5OdocI8V1KNjG5p92n77ka1dV4e9vFw2Fbd8QuqO35B5SW5WrtyIceiAAAAMKKsokLJ4TBy3MhyOpVVWGBgVYB5BC/ABHK7MpMqXBnJe6cuasuuBgX7BiK6vq3zstbvPKgNq2qS6ngUAAAAkkeG263C2kXyHqqPu1ZBbY0y3G4DqwLMo8cLgFG1tPuiCl0GBfsGtGVXg1rafYlZGAAAAFJeyfJlRuqULn/ISB0gEQheAIzItm1t390YdegyKNg3oB27G2Xbw/exAQAAQHrL81TJXV4WVw33jHJNrZpvaEWAeQQvAEbU1No1Yk+XSH3UeVnNrV5DKwIAAMBkYlmW5qx5Uo4YG+06XC7N+eYTDHZAUiN4ATCivXUfGqmz59BZI3UAAAAw+UypmK1569dFHb44XC7NW79OUypmJ2hlgBkELwCG5Q+GVN/cYaRWfVOH/MGQkVoAAACYfPKr75Jny7MRHztyzyiXZ8uzyq++K8ErA+LHVCMAw+ryBRQOm+nNEg7b8vYEU2KCEwAAACbGlIrZqn55uy41n1DH3v3yHj4yZNS05XSqoLZGpcsf0tSq+RwvQsogeAEwrFgb6o4k0NtvtB4AAAAmH8uylOepUp6nSv1+v/q83RoIBOTMyVFWYQEjo5GSCF4ADMuV5TRaLyebv24AAAAQuQy3m6AFkwI9XgAMqyg/Rw6Hme2bToelwrzYOtUDAAAAQCojeAEwLLcrU4urSo3UqvWU0t8FAAAAQFpi7z+AEa1YMlN1xy/EXefhe2cZWA0AAKnDHwqo2+9TsL9XroxsFbjz5c7MmehlAWmt3+9XX5dXA8GgnC6XsooKOcqEcUHwAmBEnooilZfkqq3zcsw1ZpTkqqqi0OCqAABITrZt68TFD3Sg5W29c/6Ywvan01gclkM1t1frwcr7Nb94LtNYgHFi27Z6mprVuXe/vEcahkxJksOhwtpFKlm+THmeKv5cImEIXgCMyLIsrV25UOt3HoxpypEry6k1KxfyjxgAYNI7092mnUd26dyljmEfD9thHW5v1OH2RpVNLdXqRas0u6B8nFcJpJcrrWd0esfL8redG/6CcFjeQ/XyHqqXu7xMc9Y8qSkVs8d3kUgL9HgBMKrK6fnasKom6ilHriynNqyqUeX0/MQsDACAJHG886Q2vfXiiKHLjc5d6tCmt17U8c6TCV4ZkL58R4+pacPGkUOXG/jbzqlpw0b5jh5L8MqQjgheAIzp7juKtXX1fSovyY3o+hkludq6+j7dfUdxglcGAMDEOtPdpm11r6m3vzeq5/X292pb3Ws6092WoJUB6etK6xmd3Pq8wsFgVM8LB4M6ufV5XWk9k6CVIV0RvACISOX0fL369FJt+cYSLVkw7aZR006HpSV3TdOWbyzRK08vZacLAGDSs21bO4/sijp0GdTb36udDW/Itm3DKwPSl23bOr3j5ahDl0HhYFCnX3qFP5cwih4vACJmWZY8lUXyVBbJHwzJ2xNUoLdfOdkZKsxzMTIaAJBWTlz8IOLjRSM513NB7398WvOL5xpaFZDeepqaIz5eNBL/R2261HxCeZ4qQ6tCuiN4ARATtyuToAUAkNZ+1vJzI3UOtLxN8AIY0rnvgJE6Hfv2E7zAGI4aAQAAAFHyhwJqOH/USK2G9qPyhwJGagHprN/vl/fwESO1vPVH1O/3G6kFELwAAAAAUer2+xS2w0Zqhe2wugM+I7WAdNbX5ZXCZv5cKhxWn7fbTC2kPYIXAAAAIErBGBvqjlgvZLYekI4GYmyoO2K9ADvRYAY9XoBJwB8MqcsXULBvQK4sp4ryc+i/AgBAArkyss3WyzRbD0hHTpfLbL2cHKP1kL4IXoAUZdu2mlq7tKfurA43dyoc/nTkncNhaXFVqVYsmSlPRZEsyxqlEgAAiFaBO18Oy2HkuJHTcqggJz/+RQFpLquoUHI4jBw3spxOZRUWGFgVQPACpKSWdp+2725UW+flYR8Ph23VHb+guuMXVF6Sq7UrF6pyev74LhIAgEnMnZmjmturdbi9Me5a90yvljuTT9aBeGW43SqsXSTvofq4axXU1ijD7TawKoAeL0DKee/URa3feXDE0OVGbZ2XtX7nQb136mKCVwYAQHp5sPJ+I3WWVT5gpA4AqWT5MiN1Spc/ZKQOIBG8ACmlpd2nLbsaFOwbiOp5wb4BbdnVoJZ2X2IWBgBAGppfPFdlU0vjqlGWN0133jrH0IoA5Hmq5C4vi6uGe0a5plbNN7QigOAFSBm2bWv77saoQ5dBwb4B7djdKNu2x74YAACMybIsrV60StkxNtrNzsjW6prH6MUGGGRZluaseVKOGBvtOlwuzfnmE/y5hFEEL0CKaGrtivh40Ug+6rys5lavoRUBAIDZBeV6ZsnjUYcv2RnZembJ45pdUJ6glQHpa0rFbM1bvy7q8MXhcmne+nWaUjE7QStDuiJ4AVLE3roPjdTZc+iskToAAOCqBSXztHnpUxEfOyrLm6bNS5/SgpJ5CV4ZkL7yq++SZ8uzER87cs8ol2fLs8qvvivBK0M6YqoRkAL8wZDqmzuM1Kpv6pA/GJLblWmkHgAAuLrz5YWHNur9j0/rwOm31XD+6JBR007LoXumV2tZ5QO689Y5HGMAxsGUitmqfnm7LjWfUMfe/fIePjJk1LTldKqgtkalyx/S1Kr5/LlEwhC8ACmgyxdQOGymN0s4bMvbEyR4AQDAMMuyNL94ruYXz5U/FFB3wKdgqFeuzGwV5OQzMhqYAJZlKc9TpTxPlfr9fvV5uzUQCMiZk6OswgJGRmNcELwAKSDWhrojCfT2G60HAACGcmfmELQASSbD7SZowYQgeAGShD8YUpcvoGDfgFxZThXl51zbleLKchq9V042f/QBAAAAYDzw7guYQLZtq6m1S3vqzupwc+eQ40QOh6XFVaVasWSmKm7Pk8NhGTlu5HRYKsyLbbweAAAAACA6BC/ABGlp92n77sYRR0SHw7bqjl9Q3fELKi/JlWd2oY61dMV931pPKf1dAAAAAGCcELwAE+C9Uxe1ZVdDxL1b2jovqzPj343c++F7ZxmpAwAAAAAYm2OiFwCkm5Z2X1Shy6C+/rDinXA3oyRXVRWF8RUBAAAAAESM4AUYR7Zta/vuxpinFNlxtHhxZTm1ZuVCWfGmNwAAAACAiBG8AOOoqbVrxJ4uieTKcmrDqhpVTs8f93sDAAAAQDqjxwswjvbWfTju95xRkqs1KxcSugAAAADABCB4AcaJPxhSfXPHuNzLYUmLF0zTw/fOUlVFIceLAAAAAGCCELwA46TLF1A4HEeTlihkZjr1pc/NYZcLAAAAAEwwerwA4yTWhrqx6O0b0I7djbLj6cYLAACASa/f75e/7Zwuf3Ba/rZz6vf7J3pJwKTDjhdgnLiynON6v486L6u51StPZdG43hcAAADJzbZt9TQ1q3PvfnmPNEjh8KcPOhwqrF2kkuXLlOep4sg6YADBCzBOivJz5HBY43bcSJL2HDpL8AIAAIBrrrSe0ekdL8vfdm74C8JheQ/Vy3uoXu7yMs1Z86SmVMwe30UCkwxHjYBx4nZlanFV6bjes76pQ/5gaFzvCQAAgOTkO3pMTRs2jhy63MDfdk5NGzbKd/RYgleGQRz9mpzY8QKMoxVLZqru+IVxu184bMvbE5TblTlu9wQAAEDyudJ6Rie3Pq9wMBjV88LBoE5ufV6eLc+y8yVBOPo1+RG8AOPIU1Gk8pJctXVeHrd7Bnr7x+1eAAAASD62bev0jpejDl0GhYNBnX7pFVW/9CJv/A3j6Fd64KgRMI4sy9LalQvHtdFuTjb5KgAAQDrraWqO+HjRSPwftelS8wlDK4LE0a90QvACjLPK6fnasKpmXMIXp8NSYZ4r4fcBAABA8urcd8BInY59+43UQfxHv660nknQypAIBC/ABLj7jmJtXX2fyktyE3qfWk8p/V0AAADSWL/fL+/hI0ZqeeuP0OzVAFNHv2x7/KalIj4EL8AEqZyer1efXqot31iiJQumyeEwf1724XtnGa8JAACA1NHX5R3arDUe4bD6vN1maqUxjn6lH5o/ABPIsix5KovkqSySPxiStyeoQG+/XFlObX3jHbVfvBJz7RkluaqqKDS4WgAAAKSagRh3VYxYLxAwWi8dmTz6leepMlILicWOFyBJuF2ZKrstV3PLb1F5yVR968v/IeY+MK4sp9asXEjXeQAAgDTndJnt9+fMyTFaL91w9Cs9EbwASWpa0Wf0X75YpazM6P6YurKc2rCqRpXT8xOzMAAAAKSMrKJCyWHmbZ/ldCqrsMBIrXTF0a/0xFEjIInYtq2m1i7tqTurw82dCoeja5g1oyRXa1YuJHQBAACAJCnD7VZh7SJ5D9XHXaugtkYZbreBVaUvjn6lJ4IXIEm0tPu0fXej2jovR/U8p8NSradUD987S1UVhRwvAgBgkvOHAur2+xTs75UrI1sF7ny5Mzn+gZGVLF9mJHgpXf6QgdWkN45+paeUCV7C4bD+9//+3/rxj3+sU6dOye/369Zbb9XChQv1n/7Tf9I999wTV/0lS5aoq6sromsPHjyoW2+9Na77Add779RFbdnVoGDfQMTPyXBa+u3/q0KL5peq7LZcxkYDADCJ2batExc/0IGWt/XO+WMK258eVXBYDtXcXq0HK+/X/OK5fAiDm+R5quQuL4trko57RrmmVs03uKr0dO3ol4HjRhz9Sh0pEbxcvnxZv//7v6+GhoYhv37hwgVduHBBe/bs0apVq/SHf/iHMdW/ePFixKELYFpLuy/q0EWS+gds/a9/adH/+pcWORyWFleVasWSmfJUFPEDFwAAk8iZ7jbtPLJL5y51DPt42A7rcHujDrc3qmxqqVYvWqXZBeXjvEokM8uyNGfNk2rasFHhGI66OFwuzfnmE/yMaQBHv9JT0gcvtm1rzZo110KX++67TytXrlRRUZFOnjypv/zLv9T58+f1+uuvq6CgQF/72teivsfJkyevff29731PCxYsGPX6W265Jep7AMOxbVvbdzdGHbrcKBy2VXf8guqOX1B5Sa7W0ucFAIBJ4XjnSW2re029/b0RXX/uUoc2vfWinlnyuBaUzEvw6pBKplTM1rz163Ry6/NRhS8Ol0vz1q/TlIrZCVxdeuHoV/pJ+uDlpz/9qQ4ePChJeuSRR7R169Zrj1VXV2v58uX68pe/rJaWFr366qv6whe+oJKSkqju8f7771/7+jd+4zdUWFhoZvHAGJpau6Lu6TKWts7LWr/zoDasqtHddxQbrQ0AAMbPme62qEKXQb39vdpW95o2L32KnS8YIr/6Lnm2PKvTO16O6NiRe0a55nzzCUIXwzj6lX6Sfpz066+/LkmaMmWK/tt/+283PZ6fn6/NmzdLknp7e/X9738/6nsM7ngpLi4mdMG42lv3YULqBvsGtGVXg1rafQmpDwAAEsu2be08sivq0GVQb3+vdja8IduObkIiJr8pFbNV/fJ2Vf3xZhXeu/imUdOW06nCJYtV9cebVf3Si4QuCTB49MsRY6Ndjn6lnqTe8XLu3Llru1GWLl2q/Pz8Ya/77Gc/q1mzZuns2bPav3+/1q1bF9V9Bu9x5513xrVeIBr+YEj1zcOf1TYh2DegHbsb9crTS/lLGQCAFHPi4gcj9nSJ1LmeC3r/49OaXzzX0KowWViWpTxPlfI8Ver3+9Xn7dZAICBnTo6yCgvoGzIOOPqVXpJ6x8u//du/Xfu6trZ21GtramokSefPn1dbW1vE97h8+bLa29slEbxgfHX5AgqHE/sp1Eedl9Xc6k3oPQAAgHk/a/m5kToHWt42UgeTV4bbLXfZdOXOnSN32XRCl3E0ePTLXV4W0fXuGeXybHlW+dV3JXhlMC2pd7y0tLRc+3rmzJmjXltW9ulv1tOnT6u8PLLzrCdPnry2BXP27Nn6+7//e+3bt2/IyOqamhr97u/+rjweT/QvAhhBvA11I7Xn0Fl5KovG5V4AACB+/lBADeePGqnV0H5U/lBA7swcI/UAmDV49OtS8wl17N0v7+EjQ0ZNW06nCmprVLr8IU2tms9O9hSV1MFLZ2fnta+nTZs26rWlpaXDPm8s1zfW/e53v6srV64MefzChQv68Y9/rH/8x3/U7/3e7+lb3/qWHI6k3iiEFOHKco7LfeqbOuQPhuR2ZY7L/QAAQHy6/T6F7fDYF0YgbIfVHfARvABJjKNfk19SBy89PT3Xvv7MZz4z6rXu634zXr4c+ZSY60dJX7lyRUuXLtUXvvAF3X777fL5fPr5z3+uN998U319ffqrv/or2bYddQ8ZYDhF+TlyOKyEHzcKh215e4IELwCAlOQPBdTt9ynY3ytXRrYK3PmTPkQIxthQd8R6IbP1ACROhttN0DIJJXXw0tfXd+1r1xgdn69//PrnjWVwx4tlWXruuef0W7/1W0Mef+CBB/TFL35Rq1at0r//+7/rr//6r/Wbv/mbuvvuuyO+BzActytTi6tKVXf8QsLvFejtT/g9AAAwxbZtnbj4gQ60vK13zh8bsvvDYTlUc3u1Hqy8X/OL507KbfeujGyz9TLN1kPi9Pv96uvyaiAYlNPlUlZRIW/CgUkgqYMXp/PToxhj/aN6/ai8aI4CvfHGG2pra1MoFNI999wz7DULFizQunXrtGnTJknS3/zN3+iVV16J+B7ASFYsmTkuwUtOdlL/UQcA4Joz3W3aeWTXiBN9wnZYh9sbdbi9UWVTS7V60SrNLoist1+qKHDny2E5jBw3cloOFeTkx78oJIxt2+ppalbn3v3yHmkY0t9DDocKaxepZPky5XmqJmXQCDMI7ZJbUr8bu/74UDAYVFZW1ojX9vZ+uoVytOtuVFBQoIKCgjGv++3f/m1t2bJFvb29OnTokGzb5i8+xM1TUaTykly1dUZ+PC5aToelwrzRd4wBAJAMjnee1La619Qb4VGbc5c6tOmtF/XMkse1oGReglc3ftyZOaq5vVqH2xvjrnXP9OpJfzQrlV1pPaPTO16Wv+3c8BeEw/Ieqpf3UL3c5WWas+ZJxgjjGkK71JHUXWKv7+sSCARGvdbv91/7Oi8vz/hasrOzNXv21b/krly5okuXLhm/B9KPZVlau3JhQhvt1npK6e8CAEh6Z7rbogpdBvX292pb3Ws6092WoJVNjAcr7zdSZ1nlA0bqwDzf0WNq2rBx5NDlBv62c2rasFG+o8cSvDKkgiutZ3T0ybU6sfG78tYfHhq6SNdCuxMbv6ujT67VldYzE7NQSEry4OX222+/9nVHx/DbTYd7/LbbbkvIemLtIwOMpnJ6vjasqklY+PLwvbMSUhcAAFNs29bOI7uiDl0G9fb3amfDG0OOnqe6+cVzVTa1dOwLR1GWN0133jrH0Ipg0pXWMzq59XmFg8GonhcOBnVy6/O8iU5zhHapJ6mDlzlzPv2Hoq1t9E8xzp379DddZWVlRPW7urr09ttv60c/+pGamprGvL67u1vS1d4z+fn5Ed0DiMTddxRr6+r7VF6Sa7TujJJcVVUUGq0JAIBpJy5+MGJPl0id67mg9z8+bWhFE8+yLK1etErZMTbazc7I1uqaxzhekIRs29bpHS9HHboMCgeDOv3SK5MqaETkCO1SU1IHL9XV1df+sXj33XdHvbahoUGSVFpaqunTp0dU//3339fXvvY1rV+/Xn/7t3876rUXL168Fv7MmzdPmZkc3YBZldPz9erTS7XlG0u0ZME0ORzx/aDkynJqzcqF/MAFAEh6P2v5uZE6B1reNlInWcwuKNczSx6POnzJzsjWM0sen3RNhyeLnqbmiHcqjMT/UZsuNZ8wtCKkCkK71JXUwUtpaamqq6slSQcOHNCVK1eGve7dd9/V2bNnJUnLli2LuP7dd9+t7Oyr/5D9y7/8y6h9W15//fVrv0E///nPR3wPIBqWZclTWaQ/fOwe7X52uf77us/pa7/lUXZmdMeQXFlObVhVo8rp+YlZaIT8wZDaOi/pg7ZP1NZ5Sf5gaELXAwBIPv5QQA3njxqp1dB+VP7Q6H0BU82CknnavPSpiI8dleVN0+alT02qZsOTTee+A0bqdOzbb6QOUgehXepK6qlGkvSVr3xF7733nnw+nzZt2qRt27YNGRfd09NzbcxzZmamfvd3fzfi2rm5ufrCF76g//W//peuXLmi73znO/qzP/uzIWOspauhzxtvvCHpahj0O7/zOwZeGTA6tytTblemym7L1bxZBdq+uzGi6UczSnK1ZuXCCQtdbNtWU2uX9tSd1eHmToXD1496t7S4qlQrlsyUp6KI3TgAAHX7fUbGJktXR013B3yTborP7IJyvfDQRr3/8WkdOP22Gs4fHfI9c1oO3TO9WssqH9Cdt87h39ck1u/3y3v4iJFa3voj6vf7GRmcRkyGdnmeKiO1EJmkD14efvhh/ehHP9LBgwf1T//0T+rs7NSjjz6q2267TadOndJrr72m8+fPS5KeeOIJlZWVDXn+kSNH9Oijj0qSampqbjpS9NRTT+nQoUM6f/689u3bp/Pnz+vRRx/VjBkz5PV6tW/fPv3kJz+RbdtyuVx64YUXNGXKlPF58cCvVNyep6/9VpV2/+yUTpzpvulxS9L8ikKt/M075KmcuECjpd03akAUDtuqO35BdccvqLwkV2snMCACACSHYIwNdUesFzJbL1lYlqX5xXM1v3iu/KGAugM+BUO9cmVmqyAnf9KFTZNVX5f35ukzsQqH1eftJnhJE4R2qS3pgxdJeumll/T1r39d77zzjt59991h+72sWrVKX/va16KuXVBQoF27dukP/uAPdOrUKR0/flxPP/30Tdfdeuut2rZtmz772c/G9BqAWI0VZkiSLam51avX/r1pwsKM905d1JZdDQr2DUR0fVvnZa3feVAbVtXo7juKE7w6AECycsXYPHbEeplm6yUjd2YOQUuKGoixN8eI9QKT62gdRkZol9pSIniZMmWKvv/97+vHP/6xfvKTn+gXv/iFLl++rFtuuUV33323vvzlL6u2tjbm+uXl5fqHf/gH/eQnP9G+fft08uRJXbp0SVOmTNHMmTP167/+61q5ciU7XTDuUiXMaGn3RbXOQcG+AW3Z1aCtq+9j5wsApKkCd74clsPIcSOn5VBBTn78iwISxOlyma2XQwCXLgjtUltKBC+S5HA49Mgjj+iRRx6J6nmLFi3SqVOnxrwuKytLX/rSl/SlL30p1iUCRqVKmGHbtrbvbox6nYOCfQPasbtRrzy9lDPpAJCG3Jk5qrm9WofbG+Oudc/0anaCIKllFRVKDoeRnQuW06mswgIDq0IqILRLbUk91QhIV6bCjJFGxZmcNtTU2hVR09/RfNR5Wc2t3rhqAABS14OV9xups6zyASN1gETJcLtVWLvISK2C2hqOiqSRa6GdAYR24y9ldrwA6cRkmOGpLJKUuGlDe+s+jGudg/YcOnttrQCA9DK/eK7Kppbq3KWOmGuU5U3TnbfOMbiq0flDAXX7fQr298qVka0CNw1uE6Hf71dfl1cDwaCcLpeyigpTPmwoWb5M3kP1cdcpXf6QgdUgVQyGdiZ+7xDajT+CFyAJmQ4zEjVtyB8Mqb459h+Sr1ff1CF/MCS3K9NIPQBA6rAsS6sXrdKmt15UbwxTjrIzsrW65rGEH1m1bVsnLn6gAy1v653zx4b0pXFYDtXcXq0HK+/X/OK5HJ+Ng23b6mlqVufe/fIeaRh6LMfhUGHtIpUsX6Y8T1VKfp/zPFVyl5fJ33Yu5hruGeWaWjXf4KqQCgjtUhfBC5BkTIcZh5sv6M9+EPmxpWga9Hb5AkN2zsQjHLbl7QkSvABAmppdUK5nljyubXWvRRW+ZGdk65klj2t2QXkCVyed6W7TziO7RtyVE7bDOtzeqMPtjSqbWqrVi1YlfE2T0ZXWMzq94+WRQ4lwWN5D9fIeqpe7vExz1jypKRWzx3eRcbIsS3PWPKmmDRsVjqFhqsPl0pxvPpGSoRPiQ2iXuujxAiQZ02HGC38Xfa+YwQa9Le2+Ma8zKdDbb7QeACC1LCiZp81Ln1LZ1NKIri/Lm6bNS5/SgpJ5CV3X8c6T2vTWixEfhTp3qUOb3npRxztPJnRdk43v6DE1bdgY8ZtKf9s5NW3YKN/RYwlemXlTKmZr3vp1ckTZMNXhcmne+nUpFzbBjMHQLtrfN4MI7SYOwQuQZEyHGb0hMw16h2vI68pymlyqcrLZhAcA6W52QbleeGijNi1dq9rpC+Wwhv646rQcqi1bqE1L1+qFZd8el50u0e7CkaTe/l5tq3tNZ7rbErSyyeVK6xmd3Pp81DtAwsGgTm59XldazyRoZYmTX32XPFuelbu8LKLr3TPK5dnyrPKr70rwypDMCO1SE+9ygCRjOsyIx0edl/WPP2/VyQ+7h23Ie8+82+SwJBMbdJwOS4V5ZsfkAQBSk2VZml88V/OL515tYhvwKRjqlSszWwU549fE1rZt7TyyK6a+M9LV8GVnwxt6Ydm3+YR5FLZt6/SOl2M6diNdDV9Ov/SKql96MeW+z1MqZqv65e261HxCHXv3y3v4yJCeNpbTqYLaGpUuf0hTq+an3OtDYgyGdqMey7uOe0a55nzzCUKXCUTwAiSZovwcORyWseNG8frrn5wY9tfDYVtHTnQau0+tp5T+LgCAm7gzcyZsWtCJix/ENWlJks71XND7H5/W/OK5hlY1+fQ0NcfVs0KS/B+16VLzCeV5qgytavxYlqU8T5XyPFVXpzh5uzUQCMiZk6OswgKmz2BYhHapheAFSDJuV6YWV5Wq7viFiV7KuHr43lkTvQQAAIb4WcvPjdQ50PI2wcsoOvcdMFKnY9/+lAxerpfhdhO0IGKEdqmD4AVIQiuWzEyr4GVGSa6qKgonehkAAFzjDwXUcP6okVoN7UflDwUmbOdOMuv3+69+Um+At/6I+v3+Sfdms9/vV1+XVwPBoJwul7KKClP+NU7G1zTRCO2SG8ELkIQ8FUUqL8lVW+fliV5KwrmynFqzciHbHwEASaXb71PYDo99YQTCdljdAR/ByzD6urxDjkfEJRxWn7c7qjefyRoA2LatnqZmde7dL++RhqHfI4dDhbWLVLJ8mfI8VSnzM9RkfE1ApAhegCRkWZbWrlyo9TsPxjTlKCvTob6QoR9iEsiV5dSGVTWqnJ4/0UsBAGCIYIwNdUesFzJbb7IYiLGh7oj1AoExr0n2AOBK65nRm6aGw/Ieqpf3UL3c5WWas+bJpG+aOhlfExANxkkDSapyer7WP3aPMjOi+2PqynLq6S//Bzkcyf1JwYySXG1dfZ/uvqN4opcCAMBNXBnZZutlmq03WTijHIk7Zr2c0XcVXWk9o6NPrtWJjd+Vt/7wzbttfhUAnNj4XR19cu24j6n2HT2mpg0bI2427G87p6YNG+U7eizBK4vdZHxNQLQIXoAk1dLu01//9IRC/ZHvXBkMMxZ7pmlxVWkCVzc6y5IWVZXcFP44HZaW3DVNW76xRK88vZSdLgCApFXgzpfDMvOjstNyqCAn30itySarqFBymPk+W06nsgoLRnw82QOAK61ndHLr81GP1Q4Hgzq59flxD4kiMRlfExALjhoBSei9Uxe1ZVdDVMeMsjIc+v98Yf61MGMiG/TatvTYijv11MqF8vYEFejtV052hgrzXIyMBgCkBHdmjmpur9bh9sa4a90zvZr+LiPIcLtVWLtI3kP1cdcqqK0ZsT9LvAGAZ8uzCT36Ytu2Tu94Oer1DQoHgzr90iuqfunFpOmPYvI1DQQCSdmLB4gUwQuQZFrafVGHLpLU1x/W1l3vaOvq+1Q5PX/CG/QGevvldmUStAAAUtaDlfcbCV6WVT5gYDWTV8nyZUaCl9LlDw3766kQavQ0NUe8E2ck/o/adKn5RNKM1Db1mpo3bNSlX5xKul48QDQ4agQkEdu2tX13Y0wNdSUp2DegHbsbZdv2tQa9riyn4VVGJiebXBcAkNrmF89V2dT4ju6W5U3TnbfOMbSiySnPUyV3eVlcNdwzyjW1av6wj5kMNRKlc98BI3U69u03UscEU6/p0vsnk64XDxAtghcgiTS1dsW9Q+WjzstqbvVKutqgd8OqmnEPX5wOS4V5ZpvlAQAw3izL0upFq5QdY6Pd7Ixsra55jE/jx2BZluaseVKOGBvtOlwuzfnmEyN+n5M91Oj3++U9fMRILW/9EfX7/VHf3992Tpc/OC1/27monz9STVOvaSw040Uq4CNpIInsrfvQSJ09h87KU1kkSbr7jmJtXX2ftu9uHLdjR7WeUo4YAQAmhdkF5XpmyePaVveaeqMYMZ2dka1nljyu2QXlCVzd5DGlYrbmrV8XdR8Wh8uleevXjdh/JRGhhuneIn1d3pt3dMQqHFaft3vMNSZ6pLbR1xSB8erFA8SKHS9AkvAHQ6pv7jBSq76pQ/5g6Np/V07P16tPL9WWbyzRkgXTRpw29HtfGH6LbrQevneWkToAACSDBSXztHnpUxEfOyrLm6bNS5/SgpJ5CV7Z5JJffZc8W56N+NiRe0a5PFueVX71XSNek4hQw7SBGHvPjFgvEBj18fEYqW36NUVisBePbdvjfm9gLOx4AZJEly+gcNjMPxThsC1vT3DIrhPLsuSpLJKnskj+YGjYaUO2beufG9ri2hkzoyRXVRWFJl4GAABJY3ZBuV54aKPe//i0Dpx+Ww3njypsf/qG1Wk5dM/0ai2rfEB33jqH40UxmlIxW9Uvb9el5hPq2Lv/6m6V64IBy+lUQW2NSpc/pKlV88f8Po93qCFd3WUTzQQeZ4xHrEaslzPyBC3f0WNR7SoaPMYzb/26UQOum9Zg+DVFKtkaDAODCF6AJBFrQ92RBHr7R3xspGlDgw151+88GNN6XFlOrVm5kB82AQBx8YcC6vb7FOzvlSsjWwXu/KQYx2xZluYXz9X84rlX1xjwKRjqlSszWwU5ybHGycCyLOV5qpTnqboaYni7NRAIyJmTo6zCgqiO+oxXqBHP0Z2sokLJ4TCyM8dyOpVVWDDsY+M5Utvka4pWx779BC9IOgQvgGH+YEhdvoCCfQNyZTlVlJ8TUb8T0w1wY50qNNiQN9qR1q4spzasqlHl9PyY7gsASG+2bevExQ90oOVtvXP+2JDdJA7LoZrbq/Vg5f2aXzw3KQJ+d2YOQcs4yHC74+qpMh6hxpXWMzq94+WRJyf96uiO91C93OVlmrPmySEBRobbrcLaRUZGahfU1gz7/RrvkdomX1O0EtWLB4gHwQtggG3bamrt0p66szrc3DnkyJDDYWlxVak+d0+ZbrslR72h8LCBTFF+jhwOy8hxo3inCkXbkHdGSa7WrFxI6AIAiMmZ7jbtPLJL5y4N3+ssbId1uL1Rh9sbVTa1VKsXraJpLSKS6FDD1NGdkuXLjKyxdPlDw/66yZHake4mMfWaohZhg2FgPBG8AHFqafeNGlCEw7bqjl9Q3fELQ359MJBZsWSmPBVFcrsytbiq9KbrYmFiqtBgQ97mVq/21J1VfXPHkFDI6bBU6ynVw/fOUlVFYVJ8+ggASD3HO09GNTHo3KUObfrXP9Mz932d5rWISKJCDZNHd/I8VXKXl8UVjrhnlGtq1fCDEkyO1I40eDHxmmIVSS8eYDwRvABxeO/UxaiP5Ay6PpApzHNdDWAqC40EL6amCkXSkBcAgFid6W6LekyzJPUO9GnLz1/V7y38T/qNivsI/zGqRIQapo/uWJalOWueVNOGjTHVdLhcmvPNJ4b9szBRI7XjfU3xGK3BMDARGCcNxKil3Rdz6HIjb09Qf7v3F/ofP2qKu1aipgq5XZkquy1Xc8tvUdltuYQuAIC42LatnUd2RR26DArbYf3lv/29vrXvezrT3WZ4dZhMBgMAR4yNdocLNUwe3Rk0pWK25q1fF/U6HS6X5q1fN2Lj24kcqR3ra4rHaA2GgYlC8ALEwLZtbd/daHwSUbyYKgQASBUnLn4wYk+XaLRf7tSmt17U8c6TBlaFycp0qGHy6M718qvvkmfLs3KXl0X0fPeMcnm2PDvqqOeJGKl9vWhfU7xGajAMTCSCFyAGTa1dETWdHU9MFQIApJKftfzcWK3e/l5tq3uNnS8YlalQIxFHd673mdmzNOu//p6mzr9zxOdNnX+n5j/7XVW/9OKYI57Ha6T2aKZUzFb1y9tV9cebVXjv4quTpq5jOZ2jvt5ojNRgGJhI9HgBYrC37sOJXsIQTBUCAKQSfyighvNHjdbs7e/VzoY39MKyb7PzEyMaDAAuNZ9Qx979VwOU647hWE6nCmprVLr8IU2tmj/s76VEHN0Z3KEx5mjqX7l04n2d/cu/vmk09XDGY6R2RM+1LOV5qpTnqVK/368+b7cGAgE5c3KUVVggZ06Ojj65NmENhoGJRPACRMkfDKm+Of6t0fFiqhAAIFV1+30K24beuF7nXM8Fvf/xac0vnmu8NiaPsQKAsY6pJOrojqnR1DdK9EjtWGS43cPWSVSDYWCiEbwAUeryBYaMVR5Pa/7T3Sq7LZepQgCAlBaMsaFuJA60vE3wgoiNFACMJhFHd0yOph5OokZqmzbYiyfa78VYDYaBiUaPFyBKE9lQ952Tv2SqEAAg5bkyshNWu6H9qPyh6Jp/AtG4dnTHAMvpVGbBLUZGU9v2yB8MDo7Ujsd4HeNJRINhYKIRvABRcmU5J+ze9U0d8gdDE3Z/AABMKHDny2El5sfQsB1Wd8A35nX+UEDtPR1q8X6o9p4OwhpEbPDojgkFtTX699YzxkdT3ygRI7UTKZJmvIVLFqvqjzdH1GAYmGgcNQKiVJSfI4fDmpDjRuGwLW9PkN0uAICU5s7MUc3t1Trc3piQ+sHQ8EeZbNvWiYsf6EDL23rn/LEhfWYclkM1t1frwcr7Nb94Ln0iMCqTR3c69u4f+8IIdOzbrzxP1bCP9fv9cmRmasZXvqwP3/hb2X19EdedqGM88fbiAZIJwQsQJbcrU4urSlV3/MKE3D/Q2z8h9wUAwKQHK+9PWPDiyrz5KNOZ7jbtPLJL5y4N3yA/bId1uL1Rh9sbVTa1VKsXrdLsgvKErA+pb/DoTrwTeNyzZxkfTT0YSNi2rZ6mZnXu3S/vkYaYphq5Z5RrzjefmPAdJbH04gGSCcELEAV/MKQuX0CeysIJC15ysvljCwBIffOL56psaumIQUisnJZDBTn5Q37teOdJbat7Tb0RNvU9d6lDm956Uc8seVwLSuYZXR8mh8GjO/FO4Al5uxMymjrSsdTDiWSkNoDo8A4OGINt22pq7dKeurM63Nw5YRONpKsjpAvzzHbSBwBgIliWpdWLVmnTWy9GHIhE4p7p1XJn5lz77zPdbVGFLoN6+3u1re41bV76VMrvfPGHAur2+xTs75UrI1sF7vwh3yPExsQEnssfnDa6poFAIOqx1JLkyMrSjMe+ovy7FnCMB0gAghdgFC3tPm3f3ai2zssTvRRJUq2nlP4uAIBJY3ZBuZ5Z8nhMwchIllU+cO1r27a188iumGv39vdqZ8MbemHZt1PuU3/62YyPwQk8ke4uufHojunR1L1dXTr90qvRj6Xu69NHf/sDTZ33a4QuQAIQvAAjeO/URW3Z1TCh46Nv9PC9syZ6CQAAGLWgZJ42L31Krx7ZpfY4jx2V5U3TnbfOufbfJy5+EPdRpnM9F/T+x6c1v3huXHXGE/1sxtfgBJ5LzSfUsXf/1Z4t1x0fGu3ozrXR1AaOG1lOp9p+sDvusdTVL71IGAdj+v1+9XV5NRAMyulyKauoMC3DPYIXYBgt7b6kC11mlOSqqqJwopcBAIBxswvK9WcPbdT/r/Wg/rrx/ztkd0aksjOytbrmsSFvGH/W8nMj6zvQ8nbKBC/0s5kYsU7gGRxNbWJCUu6v3aFLJ96Pq8bgWOqRpiMBkRi1sbPDocLaRSpZvkx5nqq0CfkIXoAb2Lat7bsbkyp0ycp0aM3KhWnzFxMAIP1YlqXfrPyPum1KkZ4/+D/UNxD5uNvsjGw9s+TxIbs2/KGAGs4fNbK2hvaj8ocCSd8XhX42ySHaCTymRlObMtpYamAsYzZ2DoflPVQv76F6ucvLNGfNkxM+NWs8OCZ6AUCyaWrtMtrTJTPDobsqixRrZpKV4dC3v7pIldPzja0JAIBktaBknr73uW+pbGppRNeX5U3T5qVP3bRbo9vvi2nnzHDCdljdAZ+RWoliqp+NbU/cEIF0NTiaOh45ZdN16f2TRtYzOJYaiJbv6DE1bdgY8TQtf9s5NW3YKN/RYwle2cQjeAFusLfuQ6P1Qv1h+a70avezy/WHj94T1VSi0sLP6E+f+I+6+45io2sCACCZzS4o1wsPbdSmpWtVO32hHNbQH1mdlkO1ZQu1aelavbDs28Pu0gganJQkScGQmXr+UEDtPR1q8X6o9p4O+UMBI3VN9rORrvZl8Led0+UPTsvfdo434gk0OJraEWOjXYfLpfKV/7dkKjT71VhqIBpXWs9EPU1Lutpb6OTW53Wl9UyCVpYcOGoEXMcfDKm+Ob4fWobzUedlnTl/SUvumqZ7F5SqqaVLu392Ss1nvDdda0maX1Golb95hzyVRRwvAgCkJcuyNL94ruYXz706DjngUzDUK1dmtgpyxh6H7MrINroeV2bs9cZjwpCRfja2rfp/+ZGcHznpyzDO4h1N7TTcrHQgYCYQRHqwbVund7xMY+dRELwA1+nyBRQOJ2aL7Z5DZ68FKQvm3KoFc26VPxhS+8Ur+mX31U+Rbitwa3rxFEZGAwBwHXdmTtT9VQrc+XJYDiPHjZyWQwU5+TE9dzwmDJnoZ3Nrd0gP1l9SUc/HuvljIaVtX4bxFM9o6kiPdkTKmZPc/YyQXHqamuP+PTjZGzsTvADXSWRD3fqmDvmDoSGhituVqbnlt2hu+S03Xe8PhtTlCyjYNyBXllNF+TkEMgAARMidmaOa26t1uL0x7lr3TK+OqbHueE0YirefTXlHnx7+f3qU1R/Zh0+DfRnmrV+n/Oq7Yr4vbhbraGrTY6mzCgviroP00bnvgJE6k7mxM8ELcB1XljNhtcNhW96e4KjhiW3bamrt0p66szrc3Dlk943DYWlxValWLJkpTwVHkAAAGMsDMxcZCV6WVT4Q9XPGc8JQPP1sbu0ORRW6DBrsy+DZ8iw7XwyLZTS1ybHUBbU1UU1lQnrr9/uvBoQGDDZ2noy//whegOsU5efI4bASdtwo0Ns/4mMt7T5t39044kSlcNhW3fELqjt+QeUluVq7ciGTjgAAuMGN/VTiVZY3TXfeOifqNZiYMPTCsm9H9EFLzP1sbFsP1l+KOnQZlA59GSZaNKOpTY2lLl3+UNw1kD76urxGdlpJutbYeTIGL0w1Aq7jdmVqcVVk4ytjkZM9fNb53qmLWr/zYMRjrNs6L2v9zoN679RFk8sDACClnelu09P7n9X3/s8OHWl/L+7+LtkZ2Vpd81jUoYLpCUNjGexnE63pF0Mq6onvmPVgXwZMPBNjqd0zyjW1ar6hFSEdDMTYUHfEepO0sTPBC3CDFUtmJqSu02ENO0q6pd2nLbsaou4vE+wb0JZdDWpp9xlaIQAAqet450lteuvFuAOPQdkZ2XpmyeNRN7qVDE0YknSg5e2IrhvsZxOtBR+YeYPTsW+/kTrJJtVGapsYSz3nm0+wewlRccb4+23EepO0sTNHjYAbeCqKVF6SG/Huk0jVekpv6u9i27a2726MualvsG9AO3Y36pWnl/KPJAAgbcXaT2UkZXnTtLrmsZhCFxMThgY1tB+VPxSIqLHvg5X3R9XPJisUVkW7me/XZOrLYNu2epqa1bl3f0qO1I53LDX9ehAtGjtHhuAFuIFlWVq7cqHW7zxodMrRw/fOuunXmlq74g54Puq8rOZWrzyVRXHVAQAgFcXbT2WQQ5Zqyu7WssoHdOetc2J+Ux3vhKHrhe2wugO+iIKX+cVzVTa1NOIdP1P8YTlMtbSbJH0ZrrSeGX2Uc4qM1I5nLDUQLRo7R4ajRsAwKqfna8OqGmNTjmaU5KqqovCmX99b96GR+nsOnTVSBwCAVGOin4okPfMfv6Gn7v2vml88N66dDPFMGBq2XiiyepZlafWiVcqOsNFuZsjsIIFU78vgO3pMTRs2RhRUSJ+O1PYdjb+BcyIMjqWu+uPNKrx38dUdCdexnE4VLlmsqj/erOqXXiR0QVxKli8zUmcyN3ZmxwswgrvvKNbW1feNOmkoEq4sp9asXHjTD3H+YEj1zWbOodc3dcgfDI06qhoAgMnIVD+Vtz88rP8wzRN3nZgnDI1ULzPyerMLyvXMkscjOnYVyjR7TCaV+zJcaT0T9dEcKflHascylhqIxWBj50iDy+FM9sbO7HgBRlE5PV+vPr1UW76xRPNnRX/e0JXl1IZVNcOOfe7yBYyNrQ6HbXl7zHYUBwDAFH8ooPaeDrV4P1R7T4f8ITO7IxLRTyVesU4YGo7TcqggJz+q5ywomafNS59S2dTRpzRecTsUNpS9pHJfBtu2dXrHy1GHLoMGR2rbttkdRKZluN1yl01X7tw5cpdNJ3SBUTR2Hhs7XoAxWJYlT2WRnvuD/6jm1i69/OZRdXT9+5jPm1GSqzUrFw4bukgy2j9GkgK9/UbrAQAQD9u2deLiBzrQ8rbeOX9sSN8Th+VQze3VerDy/riO9kxUP5XRDE4YiqbR7UjumV4d03pmF5TrhYc26v2PT+vA6bfVcP7okO+T03Jo4eyFyl7YrtC/Nce9zlTuy9DT1BzXp/TSpyO18zxVhlYFpB4aO4+O4AWIQlVFkV77w19Xc6tXe+rOqr65Y8iuFafDUq2nVA/fO0tVFYWj/iBpqn/MoJzs6P44+4MhdfkCCvYNyJXlVFF+DkeVAABGnOlu084ju0bsvRK2wzrc3qjD7Y0qm1qq1YtWxTRBaKL6qYwl2glDI1lW+UDMz7UsS/OL52p+8Vz5QwF1B3wKhnrlysxWQU6+3Jk58k1p0gkDwct49WXo9/vV1+XVQDAop8ulrKLCuAOfzn0HjKytY99+ghekPRo7j4zgBYjS4A4YT2WR/MGQvD1BBXr7lZOdocI8V8ThRVF+jhwOy8hxI6fDUmHe2Fv7bNtWU2uX9tSd1eHmziH3djgsLa4q1YolM+WpKJrUW/0AAIlzvPNkVKOdz13q0Ka3XtQzSx7XgpJ5Ud1rIvupjCbaCUPDKcubpjtvnWNkPe7MnGF3zqRCX4ZEjnfu9/vlPXzEyDrHGqmdiNAISEaDjZ0vNZ9Qx979V/+MXffn1nI6VVBbo9LlD2lq1fy0ec9B8ALEwe3KjHmXiNuVqcVVpao7fiHuddR6SsdcR0u7b9RGweGwrbrjF1R3/ILKS3K1dpRjUgAADOdMd1tUocug3v5ebat7TZuXPhXVzpfBfiomjhvF0k9lJIMThja99WJMY66zM7K1uuaxhL8hGezL0LRhY0w9ThLdlyHR4537urxDg5x4DDNSO5GhEZDMaOx8M5rrAhNoxZKZRuo8fO+sUR9/79RFrd95MOLpTG2dl7V+50G9d+qiieUBANKAbdvaeWRXTEGDdDV82dnwRlRNSgf7qZgQaz+VkQxOGIp0vPOg7IxsPbPk8ZiOXsVisC9DtE0xE92XYTzGOw/E2FB3xHrXjdS+0npGR59cqxMbvytv/eGbA55fhUYnNn5XR59cqyutZ4yuBUgWNHa+iuAFmECeiiKVl+TGVWNGSa6qKgpHfLyl3actuxqibuYb7BvQll0Namn3xbU+AEB6OHHxg7iO1kjSuZ4Lev/j01E958HK++O656B4+qmMJNIJQ4PK8qZp89Knoj5yFa/Bvgzu8rKIrnfPKJdny7PKr74rIeuJd7xzpCGGM8YJLCPW+9VI7fEIjQCkFoIXYAJZlqW1KxfG3GjXleXUmpULR9yeatu2tu9ujHmCUrBvQDt2Nyb9iEQAwMT7WcvPjdQ50PJ2VNcP9lOJh8l+KjcanDC0aela1U5feNOoaaflUG3ZQm1aulYvLPv2uO10udFgX4aqP96swnsXS46h67ScThUuWayqP96s6pdeTNhOl/Ec75xVVHjT64zV4Ejt8QqNAKQWerwAE6xyer42rKrRn+xqUG8UAYkry6kNq2pG7cPS1NoV8fGikXzUeVnNrV55KoviqgMAmLz8oYAazh81Uquh/aj8oUDEx35SoZ9KJBOGkkEy9GUYz/HOGW63CmsXyXuoPq77SVdHajtzcoyERtUvvUjPF2CSYccLMIFs29bxlo+1//CH6gtFHrrMKMnV1tX36e47ike9bm/dh3Gu8Ko9h84aqQMAmJy6/T4jDW6lq6OmuwO+qJ6TKv1UpKt9aaZPLVVl4UxNn1qaNKHLjSaqL4PJ8c6RKFm+zMj9Spc/ZDQ0AjC5sOMFmCBjTRm6kcNhabGnVA/fO0tVFYVjfhLiD4ZU3xzfWftB9U0d8gdDMU9wAgBMbsEYG+qOWC8Ufb3Bfio7j+yKqNdMWd40ra55bMKO9uBm4zneeZDJkdqnnv+zmGtcr2Pf/jF36wBILQQvwAR479TFqBveZjotLVs0I+IjP12+gMJhM71ZwmFb3p4gwQsAYFiuKHeajFkvM7Z6g/1U3v/4tA6cflsN548O2YnjtBy6Z3q1llU+oDtvncNxjiST6PHOwzE1UnsgEBj30AhA6iB4AcZZrFOGekNhbdnVoK2r7xu1r8ugWBvqjiTQ22+0HgAgdflDAXX7fQr298qVka2cTJcclsPIcSOn5VBBTn7Mz0+Vfiq4WSLHO49mcKR2tE1xrx+p7W87N+6hEYDUQfACjCNTU4ZeeXrpmJ/SxTopaSQ52fx1AQDpzLZtnbj4gQ60vK13zh8bErI4LIfyXVOj7s0ynHumVxsLR9yZOQQtKSRR450jMThS+/SOlyM6duSeUa4533zi2nSniQqNAKQG3kkB42g8pwwV5efI4bCMHDdyOiwV5pn9YQgAkDrOdLeN2jslloa4I1lW+YCROkg918Y7G9g5MjjeORpTKmar6rk/UffhI7r41ttXm9xeN5bacjpVUFuj0uUPaWrV/CEfgk1kaAQg+RG8AOPI5JShsYIXtytTi6tKVXf8Qtz3q/WU0t8FANLU8c6T2lb3WkyjmqNVljdNd946J+H3QXIyPd450qM6tm2rp6lZnXv3y3ukYWjw43Aor2q+bv2/HlBBbY0yP/OZYWtMdGgEILkRvCBh/MGQunwBBfsG5Mpyqig/J63fvI/3lCF/MKTPzis2Erw8fO+suGsAAFLPme62cQtdsjOytbrmMRreprmS5cuMBC+lyx+K6LorrWdGP14UDqvneJN6jjfJ/eMyzVnz5LXjRdebqNAIQGogeIFRtm2rqbVLe+rO6nBz55BjLg6HpcVVpVqxZKY8FUVp94PVeEwZGu37H6sZJbmqqiiMuw4AILXYtq2dR3aNW+jyzJLHGe0Mo+Odx+I7eiyqhrr+tnNq2rBR89avU371XTc9Pt6hEYDUQfACY1rafdq+u3HEHibhsK264xdUd/yCyktytXblwoim80wWiZ4yNNb3PxauLKfWrFyYdiEZAEA6cfGDEXu6mFSWN02rax4jdIEkc+Odx/rZ5UrrmainGElSOBjUya3Py7Pl2Zt2voxnaAQgtTgmegGYHN47dVHrdx6M+E1/W+dlrd95UO+dupjglSWPRE4Zivb7HwlXllMbVtWkVTgGAPjUz1p+bqROQU6+HNbQHzmdlkO1ZQu1aelavbDs24QuGGJKxWzNXfukHFlZUT3v+vHOo7FtW6d3vBxTsCNdDV9Ov/SKbHvozuLB0MgRY6PdSEMjAKmHHS+IW0u7T1t2NUS9oyPYN6Atuxq0dfV9afHmPlFThmL9/o9mRkmu1qTZjiQAwKf8oYAazh81UssXvKSdn/9jBfqDCoZ65crMVkFOPmOecZPBJrcXfvJP+uTdfxsyUWgsN453Hk1PU3Ncu1Ikyf9Rmy41n1Cep2rIr0+pmK1569dFvZsm0tAIQGoieEFcbNvW9t2NMb/pD/YNaMfuRr3y9NJJn+4nYspQvN//6zkdlmo9pXr43lmqqiic9P8/AAAj6/b7FLbjn84iXR01HegPavrUUiP1MDldbmnVqT/dpt6LH0f8nNHGO4+mc9+BWJc5RMe+/TcFL5KUX32XPFueHb1p73WiCY0ApCaCF8SlqbUr7uMtH3VeVnOrd8zxyKnq+ulOpqcMmfj+S9Ka/3S3FjMyGgDwK0HDDXWDocQ36EXq6ti7X2f+4q+i2uFiZWXpjm+tVWFtTVT36vf75T18JNolDstbf0T9fv+wE4imVMxW9cvbdan5hDr27r96z+tGTccaGgFITQQviMveug+N1Nlz6OykCl4SMV1o0PVThkx9/985+Uv9+j2crwcAXOXKyDZbL9NsPUweHXv368xrfxn18+y+Pn2w/aVhm9yOpq/LOyQAiUs4rD5v94ijny3LUp6nSnmeKvX7/erzdmsgEJAzJ0dZhQWMjAbSCMELYuYPhlTfbGbaQX1Th/zB0KTYcZGI6UKDrp8yxPcfAJAoBe6rDXFNHDdyWg4V5OTHvyhMOpdbWq/udInRYJPb6pdejHjHyECMDXVHrBcIRHRdhttN0AKkMaYaIWZdvoCxnRzhsC1vj9l/CCdCIqYLDbpxytBEfP/9wZDaOi/pg7ZP1NZ5Sf5gyMj9AQDJxZ2Zo5rbq43Uumd6NY10cRPbtnXqT7dFdbxoOINNbiPljHHi0Ij1cvi9DWBs7HhBzExO0ZGkQG+/0XrjLRHThQYNN2VovL7/ox2bcjgsLa4q1YolM+WpKOJ8MgBMIg9W3q/D7Y1x11lW+YCB1WCy6WlqjqqR7mhGanI7nKyiQsnhMHLcyHI6lVVYEHcdAJMfwQti5spyGq2Xk526vx1NThcaNNaUofH4/o91bCoctlV3/ILqjl9QeUmu1jKCGgAmjfnFc1U2tVTnLsV+rLUsb5ruvHWOwVVhsuj4pz3Gao3W5PZGGW63CmsXyXuoPu77FtTWcHwIQERS950uJlxRfo4cDsvIcRenw1Jhntmtn+PJ5HShsttylZOdocI816g9VxL9/X/v1MWodvC0dV7W+p0HtWFVje6+ozjuNQEAJpZlWVq9aJU2vfWiemOYcpSdka3VNY+xGxI36ff71f3Ov5krOEaT2xuVLF9mJHgpXf5Q3DUApAd6vCBmblemFleVGqlVm+KjjE1OF5pbfovKbssd8/uRyO9/rMemgn0D2rKrQS3tPiPrAgBMrNkF5XpmyePKjnLKUXZGtp5Z8rhmFzAxDzczOlnoVyJtcitJeZ4qucvL4rqfe0a5plbNj6sGgPRB8IK4rFgy00idh++dZaTOREjEdKFIJeL7H++xqWDfgHbsbpQdZ7M8AEByWFAyT5uXPqWyqZGF/WV507R56VNaUDIvwStDMur3++VvO6fLH5yWv+2c+v3+m64xPVlIiq7JrWVZmrPmSTlibLTrcLk055tPsJsLQMQ4aoS4eCqKVF6SG9cxmxkluaqqKDS4qvGViOlCke7+ScT338SxqY86L6u51StPZVFcdQAAyWF2QbleeGij3v/4tA6cflsN548OGTXttBy6Z3q1llU+oDtvnZP0b0j9oYC6/T4F+3vlyshWgTufyUtxsG1bPU3N6ty7X94jDUN3szgcKqxdpJLly5TnqZJlWcYnC8nhiLrJ7ZSK2Zq3fp1Obn1e4SiCIIfLpXnr12lKxexoVwkgjRG8IC6WZWntyoVav/NgTDskXFlOrVm5MGl+QPMHQ+ryBRTsG5Ary6mi/JwxQ5CJnO6UiO+/qWNTew6dJXgBgEnEsizNL56r+cVzrwYXAZ+CoV65MrNVkJP8wYVt2zpx8QMdaHlb75w/NiQ4clgO1dxerQcr79f84rlJ83NJKrjSekand7wsf9u54S8Ih+U9VC/voXq5y8s0Z82TcpWWGJssJEm33PMfYmpym199lzxbnh19/ddxzyjXnG8+QegCIGoEL4hb5fR8bVhVE3VPEFeWUxtW1Uz4FJx4xyVP9HQnk9//RBybSuXePQCA4bkzc5I+aLneme427Tyya8QJTWE7rMPtjTrc3qiyqaVavWgV/Wki4Dt6LKodI/62c2rasFHz1q8zNllIkm7/f30+5udOqZit6pe361LzCXXs3S/v4SNDAiHL6VRBbY1Klz+kqVXzCeUAxITgBUbcfUextq6+b9TRw9ebUZKrNUkwetjEuORkmO5k6vs/kcemAABIhOOdJ7Wt7rWIJzOdu9ShTW+9qGeWPE6fmlFcaT0T9TEdSQoHgzq59XnN+upjRoKX7NuK425ya1mW8jxVyvNUqd/vV5+3WwOBgJw5OcoqLGBkNIC4EbzAmMrp+Xr16aVqbvVqT91Z1Td3DHkT73RYqvWU6uF7Z6mqonDCPzEwNS55cLpQ3fELca8pnulOJr7/E3lsCgAA0850t0UVugzq7e/VtrrXtHnpU+x8GYZt2zq94+WoQ5dB4WBQF/bsVU55mQIRHPEZkWXpjnVPG/2ZMsPtJmgBYBzBC4yyLEueyiJ5KovkD4bk7Qkq0NuvnOwMFea5kmb3Q7zjkreuvm/IbpEVS2YaCV7ine4U7/d/oo9NAQBgim3b2nlkV9Shy6De/l7tbHhDLyz79oR/WJRsepqaI+qJMppA2zlV/P7jOvs3b8Qc4Mz+2n9RbmVFXOsAgPHAOGkkjNuVqbLbcjW3/BaV3ZabNKFLIsYlD04Xiofp6U6xfP8Hj02ZEOuxKQAATDhx8YMRe7pE6lzPBb3/8WlDK5o8OvcdMFLHd+y45q1fF/1YZ8vS7Mf/q0pXPGRkHQCQaAQvSDsmxyUPGpwuFOuOkWSZ7jR4bMqEeI5NAQAQr5+1/NxInQMtbxupM1n0+/1XG9Aa4K0/oilz58iz5Vm5y8siek7mLfnybHuO0AVASiF4QdoxOS75eoPThaINX5JlutOgFUtmGqkT77EpAABi5Q8F1HD+qJFaDe1H5Q8FjNSaDPq6vMbGQCscVp+3W1MqZqvym08ou/jWMZ8S+sSnD7a9qK66Q+r3+82sAwASjOAFaSUR45KvNzhdKNJjRzNKcrV19X1DmvVOtGQ8NgUAQDS6/T6FbTPhQNgOqzvgM1JrMhiIsR/LiPUCAfmOHlPzH31HvRc/jug5vb/8pU49/2c68p8f1S/+9AX5jjcNOQIOAMmGzpdIK+MxLjnVpjvdaPDY1PqdB2Pqg5Msx6YAAOkrGGND3RHrhczWS2XOaPuxjKG3q0unX3o1tga7ti3voXp5D9XLXV6mOWue1JSK2UbXBwAmELwgrYzXuORUme40ksFjU9FOfkq2Y1MAgPTkysg2Wy/TbL1UllVUKDkcRo4bWU6n2n6wO+apRtfzt51T04aNmrd+nfKr74q7HgCYxFEjpJWJGJecrNOdxjIZjk0BANJTgTtfDsvMj7lOy6GCnHwjtSaDDLdbhbWLjNTK/bU7FGg/b6SWJIWDQZ3c+ryutJ4xVhMATCB4QVphXHJ0Bo9NbfnGEi1ZMO2m753TYWnJXdO05RtL9MrTS9npAgBICu7MHNXcXm2k1j3Tq+XOzDFSa7IoWb5sopcwonAwqNMvvULPFwBJhaNGSCuD45Lrjl+Iu1a6jEtO9WNTAIDI+UMBdft9Cvb3ypWRrQJ3fsqGDg9W3q/D7Y1x11lW+YCB1UwueZ4qucvL5G87F3ONnLLpuvT+SYOr+pT/ozZdaj6hPE9VQuoDQLQIXpB2ViyZaSR4ScS4ZH8wpC5fQMG+AbmynCrKz0mqYMPtykyq9QAA4mfbtk5c/EAHWt7WO+ePDZkG5LAcqrm9Wg9W3q/5xXNTqnH6/OK5KptaqnOXYp9mWJY3TXfeOsfgqiYHy7I0Z82TatqwMab+LA6XS+Ur/2+dev7PErC6qzr27Sd4AZA0CF6QdgbHJbd1Xo65hslxybZtq6m1S3vqzupwc+eQCUgOh6XFVaVasWSmPBVFKfUDLwAg+Z3pbtPOI7tGDCfCdliH2xt1uL1RZVNLtXrRKs0uKB/nVcbGsiytXrRKm956Ub0xTDnKzsjW6prH+Ld3BFMqZmve+nU6ufX5qMIXh8uleevXyel2J3B1krf+iPr9fmUk+D4AEAl6vCDtDI5LjrXRrslxyS3tPv3BC2/pj/78kA4d77hp1HU4bKvu+AX90Z8f0h+88JZa2n1x3xMAAEk63nlSm956MeIdIecudWjTWy/qeGdijockwuyCcj2z5HFlRznlKDsjW88seTxlQqaJkl99lzxbnpW7vCyi690zyuXZ8qzyq+8yPpb6JuGw+rzdib0HAEQoZYKXcDisH/7wh/rKV76impoaVVVVaenSpfrWt76ld955x8g93n//fa1bt05Lly5VVVWVamtrtXLlSv3gBz9QX1+fkXsgOQyOS442fDE5Lvm9Uxe1fufBiHfetHVe1vqdB/XeqYtx3xsAkN7OdLdpW91rUe8E6e3v1ba613Smuy1BKzNvQck8bV76lMqmlkZ0fVneNG1e+pQWlMxL8MomhykVs1X98nZV/fFmFd67+Oqo6etYTqcKlyxW1R9vVvVLL2pKxWxJ142lTqCBQCCh9QEgUilx1Ojy5cv6/d//fTU0NAz59QsXLujChQvas2ePVq1apT/8wz+M+R6vv/66tm3bpoGBgWu/9sknn+iTTz5RY2Oj3nzzTb322msqKSmJ+R5ILoPjkrfvbowo/JhRkqs1KxcaCV1a2n3asqtBwb6BsS++TrBvQFt2NWjr6vuYIAQAiIlt29p5ZFdMx2+kq+HLzoY39MKyb6fMMZzZBeV64aGNev/j0zpw+m01nD86pJeN03LonunVWlb5gO68dU7KvK5kYVmW8jxVyvNUqd/vV5+3WwOBgJw5OcoqLBj2uM/gWGrvofqErcuZk5qNoQFMPkkfvNi2rTVr1lwLXe677z6tXLlSRUVFOnnypP7yL/9S58+f1+uvv66CggJ97Wtfi/oeP/3pT/Xcc89JkoqLi/X1r39d8+fPV3d3t95880299dZb+sUvfqGvf/3r+p//838qOzu67apIXoPjkptbvdpTd1b1zUOP+zgdlmo9pXr43lmqqig08oOYbdvavrsx6tBlULBvQDt2N+qVp5fygyEAIGonLn4QV8NZSTrXc0Hvf3xa84vnGlpV4lmWpfnFczW/eO7V6U0Bn4KhXrkys1WQk7rTm5JNhtsdcV+VkuXLEha8WE6nsgoLElIbAKKV9MHLT3/6Ux08eFCS9Mgjj2jr1q3XHquurtby5cv15S9/WS0tLXr11Vf1hS98IapdKVeuXNGf/MmfSLoauvzDP/yDbrvttmuPf+5zn9Of/dmf6S/+4i908uRJ/d3f/Z1+7/d+z9CrQzIY73HJTa1dcTX2laSPOi+rudUrT2WRoVUBANLFz1p+bqTOgZa3Uyp4uZ47M4egJQmYGEs9koLamrRurNvv96uvy6uBYFBOl0tZRYVp/f0AJlrS93h5/fXXJUlTpkzRf/tv/+2mx/Pz87V582ZJUm9vr77//e9HVf9HP/qRPvnkE0nSk08+OSR0GbRmzRrNmjXr2nrC4fBN12BycLsyVXZbruaW36Ky23ITMjp5b92HRursOXTWSB0AQPrwhwJqOH/USK2G9qPyh+ihgdgNjqV2JKDRbunyh4zXTHa2bct3vEm/eG6bjnz5Mb33xBodf+YP9d4Ta3Tky4/pF3/6gnzHm2Tb9tjFABiV1MHLuXPn9P7770uSli5dqvz8/GGv++xnP3stGNm/f39U9zhw4IAkKTMzUw8//PCw1zidTj3yyCOSpI8//ljvvvtuVPcABvmDIdU3x7e9e1B9U4f8wZCRWgCA9NDt9w3pbRKPsB1Wd8BnpBbS1+BYapPhi3tGuaZWzTdWLxVcaT2jo0+u1YmN35W3/rB04wfF4bC8h+p1YuN3dfTJtbrSemZiFgqkqaQOXv7t3/7t2te1tbWjXltTUyNJOn/+vNraIuu039/fr2PHjkmS7rrrLrlH2X53zz33XPv60KFDEdUHbtTlC9w0MjpW4bAtb0/QSC0AQHoIxthQd8R6IbP1kJ6iHUs9GofLpTnffCKt+uD5jh5T04aNER/Z8redU9OGjfIdPZbglQEYlNTBS0tLy7WvZ86cOeq1ZWWf/kV9+vTpiOp/9NFHCoVCEdUvLy8fdl1ANGJtqDuSQG+/0XoAgMnNlWF2QIArk4EDMOPmsdTRBycOl0vz1q+7NrI6HVxpPaOTW59XOBjdh3HhYFAntz7PzhdgnCR1c93Ozs5rX0+bNm3Ua0tLS4d93mh++ctfDvv84RQWFiorK0t9fX0R1wdu5MpyGq2Xk53Uf4QBAEmmwJ0vh+UwctzIaTlUkJMf/6KAX7lxLLXv6DF9uOv76v3lxTGf655RrjnffCKtQhfbtnV6x8tRhy6DwsGgTr/0iqpfejGtdggBEyGp37X19PRc+/ozn/nMqNdef0zo8uXIJsb4fL5rX0+ZMmXM691ut/r6+iKuD9yoKD9HDodl5LiR02GpMM98MzoAwOTlzsxRze3VOtzeGHete6ZXMxkICZPhdqvo3sUqXFyrS80n1LF3v7yHjwzpXWI5nSqorVHp8oc0tWp+2oUHPU3NcU+E8n/UpkvNJ5TnqTK0KgDDSergpa+v79rXrjEabl3/+PXPi7R+dvbYW2UHr4m0PnAjtytTi6tKVXf8Qty1aj2lCZm65A+G1OULKNg3IFeWU0X5OQm5DwBgYjxYeb+R4GVZ5QMGVjOUPxRQt9+nYH+vXBnZKnDnE+6kuRt3wfR5uzUQCMiZk6OswoK0HpHcue+AkTod+/YTvAAJltTBi9P56bGMsRLs68eiORyRta6Jpv7190i3NB1mrVgy00jw8vC9swys5irbttXU2qU9dWd1uLlzyI4ch8PS4qpSrVgyU56KIn7/A0CKm188V2VTS3XuUuxT9srypunOW+cYWY9t2zpx8QMdaHlb75w/NuQYlMNyqOb2aj1Yeb/mF8/l36A0l+F2T6qgpd/vV1+XVwPBoJwul7KKCiN+ff1+/9UdQAZ464+o3++fVN9bINkkdfBy/fGhYDCorKysEa/t7f20q/5o141WfyyDO10irQ8Mx1NRpPKSXLV1xn5kbUZJrqoqCo2sp6Xdp+27G0dcTzhsq+74BdUdv6DyklytXblQldPzjdwbADD+LMvS6kWrtOmtF9Ubw5Sj7Ixsra55zEgIcqa7TTuP7BoxBArbYR1ub9Th9kaVTS3V6kWrNLugfNhrgVRg27Z6mprVuXe/vEcaho59djhUWLtIJcuXKc9TNeqfsb4u780jo2MVDqvP203wAiRQUk81ur6vSyAQGPVav99/7eu8vDzj9a+/R35+fkT1geFYlqW1KxfG3GjXleXUmpULjfzA+96pi1q/82DEIVBb52Wt33lQ750au8kdACB5zS4o1zNLHld2lFOOsjOy9cySx42EH8c7T2rTWy9GvPPm3KUObXrrRR3vPBn3vYGJcKX1jI4+uVYnNn5X3vrDNwcn4bC8h+p1YuN3dfTJtaNOHBqIsaHuiPUieC8EIHZJHbzcfvvt177u6Bj9H+XrH7/tttuM1/d6vdd2vBQXF0dUHxhJ5fR8bVhVE3X44spyasOqGiM7TlrafdqyqyHqEdfBvgFt2dWglnZf3GsAAEycBSXztHnpUyqbOvpkx0FledO0eelTWlAyL+57n+lu07a616LecdPb36ttda/pTHdb3GsAxpPv6DE1bdgYcTNcf9s5NW3YKN/RY8M+7hyj/2W0nDn0UgISKamDlzlzPj073NY2+j+w5859+pdYZWVlRPWnT59+7bjR9c8fzvX3v35dQKzuvqNYW1ffp/KS3Iiun1GSq62r79Pdd8Qf/Nm2re27G6MOXQYF+wa0Y3fjkN5KAIDUM7ugXC88tFGblq5V7fSFclhDfzR0Wg7Vli3UpqVr9cKybxvZ6WLbtnYe2RXTMSfpaviys+EN/g1CyrjSekYntz4f9djncDCok1ufH3bnS1ZRoRRhX8uxWE6nsgoLjNQCMLyk7vFSXV0ty7Jk27beffddfeELXxjx2oaGBklSaWmppk+fHlF9y7J01113qb6+XkePHlUoFFJm5vDTW955551rX3/2s5+N4lUAI6ucnq9Xn16q5lav9tSdVX1zx5DGtk6HpVpPqR6+d5aqKgqNNRVsau2Kq8eMJH3UeVnNrV55KouMrAkAMDEsy9L84rmaXzz36lShgE/BUK9cmdkqyDE/VejExQ/iauwrSed6Luj9j09rfvFcQ6sCEsO2bZ3e8XLUocugcDCo0y+9ouqXXhzyc2CG263C2kXyHqqPe40FtTX0dwESLKmDl9LSUlVXV+u9997TgQMHtG7dOk2ZMuWm6959912dPXtWkrRs2bKo7rF8+XLV19fL7/dr7969+uIXv3jTNQMDA/rhD38oSSosLCR4gVGWZclTWSRPZZH8wZC8PUEFevuVk52hwjxXQkY576370EidPYfOErwAwCTizsxJ+Pjmn7X83EidAy1vE7wg6fU0NUd8vGgk/o/adKn5xE0jn0uWLzMSvJQufyjuGgBGl9RHjSTpK1/5iiTJ5/Np06ZNCt/QhKqnp0ebNm2SJGVmZup3f/d3o6q/YsUKFRVdfeO4bds2tbe333TNSy+9pA8//FCS9Oijj464KwaIl9uVqbLbcjW3/BaV3ZabkNDFHwypvjm+TxoH1Td1yB8MGakFAOnIHwqovadDLd4P1d7TIX9ocje49IcCajh/1Eithvajk/77hdTXue+AkTod+/bf9Gt5niq5y8viquueUa6pVfPjqgFgbEm940WSHn74Yf3oRz/SwYMH9U//9E/q7OzUo48+qttuu02nTp3Sa6+9pvPnz0uSnnjiCZWVDf3L58iRI3r00UclSTU1Nfrbv/3bIY/n5uZq/fr1+ta3vqWPP/5YX/rSl/T444+rurpaPT09evPNN/Uv//IvkqRf+7Vf01e/+tVxeNVA4nT5AkOOM8UjHLbl7QkmJCACgMnKtm2duPiBDrS8rXfOH1PY/vRDJYflUM3t1Xqw8n7NL55r7Ihpsuj2+4a83niE7bC6A76E79ABYtXv98t7+IiRWt76I+r3+4ccCbIsS3PWPKmmDRtjOsrkcLk055tPTLq/Z4BklPTBi3R1x8nXv/51vfPOO3r33Xf17rvv3nTNqlWr9LWvfS2m+p///Of18ccfa9u2bfrkk0/03HPP3XTN3Llz9Rd/8RfKzo5u7CKQbGJtqDuSQG+/0XoAMJmd6W7TziO7RuxxErbDOtzeqMPtjSqbWqrVi1YZaWibLIIxNtQdsV7IbD3ApL4u780jo2MVDqvP231TL5YpFbM1b/26qJv3OlwuzVu/TlMqZptZH4BRJf1RI0maMmWKvv/972vr1q1avHixbrnlFmVkZOjWW2/Vgw8+qDfeeEPr16+PK6396le/qh/+8Id65JFHdPvttysrK0uf+cxndNddd2n9+vX64Q9/GPGYaiCZRTvCeiw52SmR3wLAhDveeVKb3nox4say5y51aNNbL+p458kEr2z8uDLMfoDlyuQDMSSvgRgb6o5YLzD80br86rvk2fJsxMeO3DPK5dnyrPKr7zK5PACjSJl3TA6HQ4888ogeeeSRqJ63aNEinTp1KqJr582bp61bt8ayPCBlFOXnyOGwjBw3cjosFea5DKwKACa3M91t2lb3WtQjlHv7e7Wt7jVtXvrUpNj5UuDOl8NyGDlu5LQcKsjJj7uOPxRQt9+nYH+vXBnZKnCbn+SE9OR0mf0ZyZkz8u/LKRWzVf3ydl1qPqGOvfuvHnG6breN5XSqoLZGpcsf0tSq+RwvAsZZygQvAMxwuzK1uKpUdccvxF2r1lNKfxcAGINt29p5ZFfUocug3v5e7Wx4Qy8s+3bKv1lyZ+ao5vZqHW5vjLvWPdOrYw5I0rnPDsZPVlGh5HAYOW5kOZ3KKiwY/RrLUp6nSnmeKvX7/erzdmsgEJAzJ0dZhQWMjAYmEMELEAF/MKQuX0DBvgG5spwqys9J6cBhxZKZRoKXh++dZWA1ADC5nbj4QcTHi0ZyrueC3v/49KQYn/xg5f1GgpdllQ/E9Lx077OD8ZPhdquwdpGRkc8FtTVRBScZbjdBC5BECF6AEdi2rabWLu2pO6vDzZ1DjuY4HJYWV5VqxZKZ8lQUpdynYZ6KIpWX5Kqt83LMNWaU5KqqotDgqgBgcvpZy8+N1DnQ8vakCF7mF89V2dTSuMKosrxpuvPWOVE/73jnyaiOfA322XlmyeNaUDIv6vsBJcuXGQleSpc/ZGA1ACZKSjTXBcZbS7tPf/DCW/qjPz+kQ8c7buqHEg7bqjt+QX/054f0By+8pZZ238QsNEaWZWntyoUxN9p1ZTm1ZuXClAucAGC8+UMBNZw/aqRWQ/tR+UPDN9dMJZZlafWiVcqOsdFudka2Vtc8FvW/QfH22TnT3RbV8wBJyvNURdz0diTuGeWaWjXf0IoATASCF+AG7526qPU7D0a8G6St87LW7zyo905dTPDKzKqcnq8Nq2qiDl9cWU5tWFWjyun5iVkYAEwi3X6fkUay0tUjMN0B36jX+EMBtfd0qMX7odp7OpI2qJldUK5nljwedfiSnZGtZ5Y8HvXRH1N9dmw7/sb0SC+WZWnOmifliLHRrsPl0pxvPsGHXUCK46gRcJ2Wdp+27GpQsG8gqucF+wa0ZVeDtq6+L6UCibvvKNbW1fdp++7GiIKmGSW5WrNyYUq9RgCYSMEY3+iPWC90c71UbRS7oGSeNi99atR+K9cry5um1TWPxdRvhT47mEhTKmZr3vp1Orn1eYWjGDHtcLk0b/06TamYncDVARgPBC/Ar9i2re27G6MOXQYF+wa0Y3ejXnl6aVL9YDuWyun5evXppWpu9WpP3VnVNw89WuV0WKr1lOrhe2epqqIwpV4bAEw0V4zHaUaslzm0Xqo3ip1dUK4XHtqo9z8+rQOn31bD+aNDgiOn5dA906u1rPIB3XnrnJj/DaLPDiZafvVd8mx5Vqd3vCx/27kxr3fPKNecbz5B6AJMEgQvwK80tXbF1WxWkj7qvKzmVq88lUUjXpOME5Isy5KnskieyiL5gyF5e4IK9PYrJztDhXmuCV8fAKSqAne+HJbDyHEjp+VQQU7+tf+eLI1iLcvS/OK5ml88V/5QQN0Bn4KhXrkys1WQkx/zyOhBieizE+ma+v1+9XV5NRAMyulyKauokEkzaWxKxWxVv7xdl5pPqGPvfnkPHxkyatpyOlVQW6PS5Q9patV8PuwCJhGCF+BX9tZ9aKTOnkNnbwpeUmlCktuVSdACAIa4M3NUc3u1kfHJ90yvvvaGP95GsZuXPpVUO18GuTNz4g5abpSIPjujrdG2bfU0Natz7355jzQMeWMth0OFtYtUsnyZ8jxVE/5vPsafZVnK81Qpz1N1NZjzdmsgEJAzJ0dZhQUEc8AkRfAC6OoulPrm+M5+D6pv6pA/GLoWXrS0+0btoTI4Ianu+AWVFrn15P/7blVVjLxjBgCQWh6svN9I8LKs8gFJ5hrFvrDs22nxxn88+uwMutJ6ZvSjJOGwvIfq5T1UL3d5measeZKjJGksw+0maAHSBFONAEldvsBNI6NjFQ7b8vZcbZwW7YSkji6/1v/3Ov3hq/+Pjrd8zPQEAJgE5hfPVdnU0rhqlOVN0523zpFktlFsqotkilOi++wM8h09pqYNGyPq3yFJ/rZzatqwUb6jx0wuDwCQhNjxAkgxN9QdSaC3P+YJSZJ04my3/ujPD6m8JFdrmSIEACnNsiytXrRKm956MaZdKtkZ2Vpd89i13Snp3ig22ilOieyzM+hK65moJ9ZIUjgY1Mmtz8uz5Vl2vgDAJMaOF0CSK8tpvF48E5IGtXVe1vqdB/XeqYuGVgYAmAizC8r1zJLHlR3l7ovsjGw9s+Txa/1YEtEoNpWc6W7T0/uf1ff+zw4daX/vpjBlcIrT9/7PDj29/1md6W671mfHhOv77AyybVund7wcdegyKBwM6vRLr7DLFQAmMYIXQFJRfo4cDjPn3J0OS53d/rgnJA0K9g1oy64GtbT7jNQDAEyMBSXztHnpUxEfOyrLm6bNS58aMoEoEY1iU8XxzpPa9NaLER+zGpzidLzzpB6svN/IGgb77Fyvp6k54uNFI/F/1KZLzSfiqgEASF4EL4CuTvJZXBXf+ftBtZ5S/es78f0AdqNg34B27G7k0zAASHGzC8r1wkMbtWnpWtVOXyiHNfRHMaflUG3ZQm1aulYvLPv2TZOHxrNRbDKJd4pTTobLaJ+d63XuOxBX3UEd+/aP+ni/3y9/2zld/uC0/G3n1O/3G7kvACDx6PGCtOcPhtTlC2hBZZHqjl+Iu96vf7ZMf/x6g4GVDfVR52U1t3pvGlUtffoagn0DcmU5VZSfw0hoAEhSlmVpfvFczS+eK38ooO6AT8FQr1yZ2SrIyR91VPF4NYpNJiamOP33d76v31/0mL771nYjfXYG9fv98h4+EtO6buStP6J+v3/IlBtGUwPA5EDwgrRk27aaWru0p+6sDjd3GptoNKMkV8W3uI3Vu9GeQ2evBS+jvQaHw9LiqlKtWDJTnooifhgDgCTlzswZNWi50Xg0ik02pqY4Bft79cySx6PeOXNjn53r9XV5h4Yh8QiH1eftvha8MJoaACYPgheknZZ2n7bvbjTWg2WQK8upNSsXqjdkdkLS9eqbOuQPhnSh699HfQ3hsK264xdUd/wCk5EAYBIZbBR7uL0x7lrDNYpNRianOD1173/V5qVPaeeRXRGFOWV507S65rFhQxdJGoixoe5IBgJXmx37jh6LakrS4GjqeevXKb/6LqNrAgDEjx4vSCvvnbqo9TsPJiR02bCqRpXT841PSLpeOGzr4NHzUb0GJiMBwOSSyEaxySYRU5zi7bMz5FqXy8jartXLyYl7NPWV1jNG1wQAiB87XpA2Wtp92rKrIe4RzzeaUZKrNdftKBmckJSo40av/e8m9fVHt615cDLS1tX3sfMFAFLc/OK5KptaGtfxm5EaxSaKPxRQt9+nYH+vXBnZKnCP3stmUCKmOLkzc+Lqs3O9rKJCyeEwctzIcjqVWXCLmv/wj+IeTV390oscMwaAJELwgrRg27a27240Fro4HZZqPaV6+N5ZqqooHPLDzeCEJBONeocTbegyaHAy0itPL+WHMQBIYZZlafWiVdr01otGG8WaZtu2Tlz8QAda3tY7548NCVAclkM1t1frwcr7Nb947ohrGY8pTqP12en3+9XX5dVAMCiny6WsosIhzW8z3G4V1i6S91B93GsrqK3Rv7eeMTaaOs9TFfeaAABmELwgLTS1dhk5XvSNRxbIU1mkwjzXqFODViyZmbDgJR6jTUYCAKSO2QXlxhvFmnSmu23UPiphO6zD7Y063N6osqmlWr1o1bBrmogpTtFOEipZvsxI8FK6/CF17B19pHSkOvbtJ3gBgCRCjxekhb11Hxqpc7y1S2W35Y45qtlTUaTyklwj9zRtz6GzE70EAIABC0rmafPSp1Q2tTSi68vypmnz0qe0oGReQtd1vPOkNr31YsRHoc5d6tCmt17U8c6TNz02OMXJhEimOF1pPaOjT67ViY3flbf+8M1HiH41SejExu/q6JNrdaX1jPI8VXKXl8W1NveMcrlnzzI+mhoAkBwIXjDp+YMh1TfHN4Zy0OBUobFYlqVvPLJAWZmJa7Qbq0hfAwAg+ZlsFGvCme62qHfhSFJvf6+21b2mM91tQ359cIqTCWNNcfIdPaamDRsjPuozOEmo59hxzVnzpBwxNtp1uFya880nFPJ2Gx9NDQBIDhw1wqTX5QsYa3QbDtvy9gRH3PFi27aaWru0p+6sDjd3JqzBbjzGeg0AgNRiqlFsvGzb1s4ju2LqOyNdDV92NryhF5Z9+1rPF38ooLtL5xsZnz3aFKd4Jwl5tjyreevX6eSWP1W4N/LX73C5NG/9Ok2pmK3LH5yO6t5jGRxNPVmN1X8HAJIJwQsmPdNTjAK9/cP+eku7T9t3NxofVS1JWZkO9YUMfQqmkV8DACC1jdYoNtFOXPwgrklLknSu54JOXPxAkoZtyhur0aY42bat0ztejmuS0C+e26bPzJ6lcF9fxM9zzyjXnG8+oSkVsyUlZjT1ZBNt/x0ASBYEL5j0XFlmj/vkZN/8x+a9UxcTMqpaurr+//LFKr36v44ZqzncawAAIB4/a/m5kTrbDv4PBfpjC0GGM9YUp56m5rgnCfVevKjeixfHvtDhUOHiRSpd/pCmVs0fsibTo6mzCgvirpNMrrSe0ekdL4/8/+pX/Xe8h+rlLi/TnDVPXgu1AGCi0eMFk15Rfo4cDjOfejgdlgrzhn4i1dLuS1joMqMkV1tX36f/WH17Ql8DAADx8IcCajh/1Egt06HLWFOcOvcdMHa/sViZmSp58DeH3ZExOJrahILamkl17CbW/ju+o+Y+tAKAeBC8YNJzuzK1uCqyiQ9jqfWUDumNYtu2tu9uNBq6OByWaqtKtOUbS/TK00tVOT0/oa8BAIB4dft9Ro4EmRTJFKd+v9/YJKFI2L29Orn1eV1pPTPs4yXLlxm5T+nyh4zUSQbx9t8Z6XsNAOOJ4AVpYcWSmUbqPHzvrCH/3dTaZaSny/WfeYXDthre/6X21J1VU2uXbPtqg95EvQYAAOIVjLGhrmnRTnHq6/KamyQUoXAwqNMvvXLt3/frmRpNPbVqflw1koWJ/jsjfa8BYDzR6AFpwVNRpPKS3LhCkhkluaqqKBzya3vrPoxzZVf9/9m787io6vUP4J8zwzKMbA2IIIvKZgoYWiKpV7PMJW1xya7XSq8tdqNM/WU3La+ZpS1mVnrvtU0tTVtupZaK1zJNXMgQFTUFUVkEZQkQZwZmmPP7g8sEsjjLGWbh8369er2GmXO+55mBZObh+zzPtW8HDAYRaccuIu3YRXRRKTFtbG/0je1sk+dARERkLYWbp71DwJNJDyPpOiOjr1Vn4Qd6a6kv5KEq6wT8EuKbxqPRIPzPk5C94j2zGvU2aBhN7SqNZaXov9Paa01E1J6YeKEOQRAEzJ7cD/NW7bOoLEjhIcesyf2avJFRa3U4kGXd9AZTXCpX4/VPDkMmAAnRgSgqvQqd3vy/zrX0HIiIiKSgUvpDJsjsWm6UUZSF23rcatY5Uk8SMkfR9h3wS4hve1KPGRqPpnYVUvXfaXit28Lx1ERkS0y8UIcRHeaP+dOSzG6Eq/CQY/60JESH+Te5v7RCA4Oh/bauGkTgaHYpAEAQAHN2zbb2HIiIqP2pdRqUqyug1ddA4eYJldLfbiOgpaJ090JSaCIOFmTYLYb0gkyodRqzXkspJwmZq+zAIVSeOIncf79v9a6Oa0dTuwIp+++UHTgEvVrdLJHC8dRE1F6YeKEOpW/PICxNGYy3N2aYVLLTLdgHsyb3azFhYYspRqYSRdOTL209ByIiah+iKOLE5TNIzdmDXwqPNtkZIhNkSApNxIjoIYgLinXaD3gjoofYNfFiEA0o11SYlXhpmCRUtv+ADSNrhcGAEy8thmhBSRFQPzJalZzU4mhqR2PJbhJJ++8YDKgtK29yTXPGUytCghH9dAr84npLEw8RdThMvFCHEx3mj5XPDkPW2TJ8n3YOB7KKmuxckcsEJCeEYMzAHoiPCmj1jYzCQ95eIbdIFAEPNxl6dVfheG6ZRc+BiIhsL7c8D6sOrUV+VcvlqQbRgIMFGThYkIFw3xCkDJh23aawjiguKBbhviGtPs/2oNWZ3+Q3ePRI+yReAIuSLoKHB2Jnz4R/4k0OXQpj7W4Sqfvv1Gk0xtsVmUfNmpSkLSpG1vwF8O3dC+GTH+AOGCIyGxMv1CEJgoCE6EAkRAdCrdWhrFILTY0eXp5uCPBTmDRuOdDfCzKZ0K7lRteq1RtQUV2Dz14ehfKqGrOfAxER2dax4lN4M201akyc+pNfVYSFu5dj7qAZbY5BdkSCICBlwDQs3L3c5OcrNYW7+U1+GyYJWVvu017E2lrkb/oCAbcm2zuUVpmzm0QZEY6YWTOblUlJ3X9H7uVljM2S8dQAUHXyFE4seKnVmImIWsNx0tThKRXuCO/ig9iIGxDexceYsFBrdcgrrsKZvN+RV1wFtVbX7Lxb40PsEXITF4qvILewqsXnQERE9pNbnmdW0qVBjb4Gb6atRm55no0is51IVQTmDpoBTztMOZILMqi8/M0+TxAExMyaCZkdG+2aq2FSjyOqyDyK4/MXmJzIUufl4/j8BajIPNrkfmP/HQkIcjk8AlRWj6du0FrMREStYeKFqBFRFHEspwRL16Vj8oLtSHlzN/7vnb1IeXM3Ji/YjtfW/YJjOSUQ/9dc5a5B3e0b8P98v/+cvUMgIqJGRFHEqkNrLd75UaOvwar0dcbfN86kT3AvLBo2B+G+pv1xwstNmoRHfzNHSTfmHRWJXvOec6rkS9H2HfYOoRlLd5MYtFqcWvoGqs/mGu9r6L8jBVVyEtyUSknGUzdoKWYiotYw8UL0PzkFFXhq2W688K/92H+sqFkJkcEgIu3YRbzwr/148o0fse9oITzd5QgJtH999YHjRc125BARkf2cuHzG6l4n+ZUXcbIkW6KI2lekKgLLRi3AwmGzkRzWDzKh6VtOuSBDcng/LBw2G88OmiHJNUdGD7XqfP/Em5CwZDGUEeGSxGNrDZN6HIW1u0kMWi2y33mvSbIxePRISWILGT0KgHTjqRu0FDMRUUvY44UIwJHTl80aM11wuRqvf3LYxlGZzmAQUVapZYkREZGD2JmzV5J1UnP2IC4oVpK12psgCIgLikVcUGz9CG1NBbS6GijcPaHy+mOEtiiKVjflDffrit6dY6yO2TsqEonvvo2qrBMo2rajfpxxo6awglwO1YAkVOfkoOZyidXXs0oLk3rsSYrdJA0lVH4J8QCk6b+j7BYB3/g4ScdTN3ZtzERELWHihTq8nIIKs5IujkpTo7d3CEREBECt0yC9MFOStdILMqHWaSwuoXEUSnevVp+DtU15Pd08kZI0VbIpM4IgwC8hHn4J8fVjkMvKUafRQO7lBY8AFdyUSlSfzcXx+Qus7hVircaTeuxNqt0kRdt3GJMYDf13LH2tZQoFYp55GoIgSDue+hqNY26LJWO1icg1MPFCHZooinh7Y4bTJ10AwMuT/zsTETmCcnUFDKI0H/AMogHlmgqnT7xcT0NTXnObEXu6eWLuoBk2G7/tplS2+MG4oSeMuf1MZB4eMFgwQro1DZN67E3K3SQNJVQNr7vFr7VCgV7znjNOHpJ6PHVj18bcmLVjtYnINbDHC3Vox8+WIq/4ir3DsJpcJiDAz3kaAhIRuTKtxKOUtTr7jGZub+Y25Q3364pFw+bYbey2uT1hlN0i0PulBZJP6nEEku4m+V8JVWOWvNYJSxbDP/Em431Sj6duooWYgfpmw5kzZ+PEgpdQduBg89fof2O1Tyx4CZkzZ7NRL5EL45/IqUPblnbe3iFIIjkhhP1diMgpqHUalKsroNXXQOHmCZXS3+V2cygkHqWscG//0cz20tCU92RJNlKz9yC9MLPJ7iG5IEP/sESMjB6K3p1j7L5DwKSeMMlJCBk9Cr7xcRAEAQHJA1C2/4DV126Y1OMIpN5N0lIJlSWvdWPG8dQ2Kje6NuaKzKNm7dJpGFHda95zTRJGROQamHihDkut1eFAlnUTJxzFmIE97B0CEVGrRFHEictnkJqzB78UHm3yQVomyJAUmogR0UMQFxRr9w/SUlAp/SETZJKUG8kFGVRe/tYH5URMbcrrKEzpCdNY8OiRkiReGib1OAKpd5O0VkJl7mvdWMN4aile++vFbO1Y7YQli40lUkTkGph4oQ6rtELTbGS0M+oW7IP4qAB7h0FE1KLc8jysOrS21Yk1BtGAgwUZOFiQgXDfEKQMmGazfh3tRenuhaTQRBwsyLB6rf5hiQ6XaGhPbTXldUSt9YRpTMpJPY5Cyt0kppZQmfJaX0uqpNe1Gscs1VjtxHeWu0QimojqsccLdViu0FBX4SHHrMn9+IuZiBzSseJTWLh7ucljgvOrirBw93IcKz5l48ikp9ZpUFBZhJyy8yioLMLQHgMkWXdk9FBJ1iHH0TCpR2bhLpHGk3ocRcNuEinYsoSqIekltcYxSzlWm4hcB3e8UIel8JDbOwSrKDzkmD8tCdFh/vYOhYiomdzyPLMn1ABAjb4Gb6atxqJhcxx+50tbJVQCBHi5KaDRW977ItyvK3p3jpEiVHIwUk3qcSTOUEJl7Xjq1jSO2RZjtYnI+XHHC3VYgf5ekMkc469FyfHBeP7h/ggOMO0vPN2CfbA0ZTD69gyycWREROYTRRGrDq01O+nSoEZfg1Xp6yCKjlsOmlueh2d3LMbLP63AoYIjzfq5iBCtSrp4unkiJWmqQ+1qIGlJManHkUixm6Q9Sqgakl6W7ji6VuOYbTFWm4hcA3e8UIelVLjj1vgQpB27aO9QkH7yEmZP7oeBfYYj62wZvk87hwNZRU160MhlApITQjBmYA/ERwXwzTgROawTl8+YXF7UmvzKizhZko24oFiJopLOseJTFu3mMZWnmyfmDprh8Dt+yHrWTupxJNbuJmnPEqqGpFf2inetKgu6NmZbjNV2lMlVRGQdJl6oQ7trUHeHSLwYDCLKKrUI7+KDhOhAJEQHQq3VoaxSC02NHl6ebgjwU3BkNBE5hZ05eyVZJzVnj8MlXiwtoTJVuF9XpCRNZdKlA7l2Uo+m4CK0l4oBCPDsEgRlWKjTfPh2phKqxkmvC59twpWT5vWWainm9hirTUTOiYkX6tASogIREeyDvOIr9g4Fmhp9k6+VCncmWojI6ah1GqQXZkqyVnpBJtQ6jcNMtbG2hAoAvNwUqKmrbVKaJBdk6B+WiJHRQ9G7c4xD72og2xBFEZXHs1C8bQfKDqU33TUhkyEgeQCCR4+EX0K8w/98mLubRNktAjHPPG2XvjUNSa8+S19B5YmTyHlvFbRFxdc9r7WY22usNhE5HyZeqEMTBAGzJ/fDvFX77D7lyMuT/zsSkfMrV1c063diKYNoQLmmwmESL1KUUGn0Wjz/pycR5B0Ira4GCndPqLz8AdS/dmfLL0Dh5gmV0t9hnjfZVvXZ3LaTFAYDyvYfQNn+A1BGhCNm1kyHbK7bmDOWUPnF9Ua/f620KmZ7jNUmIufAT3rU4UWH+WP+tCQsWZtut+SLXCYgwE/av5IQEdmDVuISHK3ONiU9lpCqhOqn8wcxZ+BjxqlInx/f2mwqkkyQISk0ESOihyAuKNYhPpiS9Coyj5pVlqPOy8fx+QvQa95zDttkt8G1JVS1ZeWo02gg9/KCR4DKIcunrI25Yay2FNOdbDlWm4jaHxMvRAD69gzC0pTBeHtjhl3KjpITQlhWREQuQeHmKe167tKuZympS6hOXs7GR79ubHUHjUE04GBBBg4WZCDcNwQpA6ax74uLqT6ba3YvFAAwaLU4tfQNJCxZ7PA7Xxq4KZVOl0SwNGZnGKtNRO2P46SJ/ic6zB8rnx2GJX8bhEF9urbrqOkxA3u027WIiGxJpfSHTJDm7YVckBnLcOxN6hKqJXvfM7lsKb+qCAt3L8exYvOaf5LjEkUR2SvetWj6D1CffMl+5z2HHrneUTnLWG0ial/c8ULUiCAILU4VKvldjfU7fkPB5WrJr9kt2AfxUQGSr0tEZA9Kdy8khSbiYEGG1Wv1D0t0mD4nUpdQ1dbpzDq+Rl+DN9NWY9GwOdz54gIqj2dZNcYYANQX8lCVdQJ+CfESRUVScKax2kTUfrjjhagVSoU7wrv4IDbiBgy6KRT/fO52yXfDKDzkmDW5H3+5EpFLGRE9RJJ1RkYPlWQdKUhdQmWJGn0NVqWv4y4HF1C8PVWSdYq275BkHZJWw1htmZlTjuwxVpuI2gd3vBCZqLXdMF6ebigsuYK3NmSY1ZxX4SHH/GlJiA7zt13QRER2EBcUi3DfEKsmAIX7dUXvzjESRnV9ap0G5eoKaPU1zSYLNZRQSVVuZKn8yos4WZKNuKBYu8ZBltOr1fUTcyRQduAQ9Gq10/VP6Qicaaw2EdkeEy9EFlAq3Js0ww3v4oOFj3rg3S8yUVR69brndwv2wazJ/Zh0ISKXJAgCUgZMw8Ldy1FjQYmOp5snUpKmtstuwIbJQqk5e647WUiqEiprpebsYeLFidWWlkkybhgAYDCgtqyciRcH5YxjtYnINph4IbKCKIo4frYU36edw8GsYhgMrW//lssEJCeEYMzAHoiPCuAvVyJyaZGqCMwdNANvpq02K/ni6eaJuYNmtEsfk9zyPKw6tNbkyUKjYoY5ROIlvSATap3GYfrfkHnqLGyo2+p6Go2k65G0nHGsNhFJj4kXIgvlFFSYPH46JFCJmZP6Ij4qsB0iIyJyDH2Ce2HRsDltJjcaC/fripSkqe2SdDlWfMqspFB+VRHWZX6FzkoVStTlNo6ubQbRgHJNBRMvTuLaEjYvN2n/8CL34s+Bs3DGsdpEJA0mXogscOT0ZSxZm25yT5eiUjUWfXgQ86cloW/PIBtHR0TkOCJVEVg2agFOlmQjNXsP0gszm5TzyAUZ+oclYmT0UPTuHNMuuwFzy/PM3okDALV1taiouQIPubvZU4mkptVJO2WJpNVWCZunHpghCBAkaJIsyOXwCFBZvQ4REdkWEy9EZsopqDAr6dJAW1uHJWvTsTRlMHu7EFGHIggC4oJiERcUW//Xf00FtLoaKNw9ofLyb9edG6IoYtWhtRb1ngEAXZ0OnZUqVNVeNWsNd7k7dBImaxTu9p+yRC27XglbjRuQE+aBmHzrk2eq5CTuoCAicgIcJ01kBlEU8fZG86YXNaatrcOKjRkcBUpEHZbS3QthviGIDuiOMN+Qdi+XOXH5jFXTlgCgRF2O4T0GwcvNtFGx4X5d8cKQpyETpHnbJRdkUHn5S7IWSetY8Sks3L38uj9jx2Kl+bkPGT1KknWIiMi2uOOFXIZaq0NphQba2jooPOQI9PdqMnlICsfPlprU06UtF4qvIOtsGRKi2e+FiKi97czZK8k632f/eN1jvNwUePCm8RgeNRiCIEg2Fal/WCL7uzggc0rYCoLcUeonR2ClZX/IAerHD/vGx1l8PhERtR8mXsiptTVVSCYTcGt8CO4a1B0JUYGS9A3Ylnbe6jUA4Pv955h4ISJqZ2qdBumFme12PY1ei0+O/gddvAPRJ7gXRkQPkSTxMjJ6qATRkZTMLmETBOy81RcTd1XAQ2/+LliZQoGYZ57mhEQiIifBxAs5retNFTIYRKQdu4i0YxcREeyD2ZP7WdVbRa3V4UCWddvTGxw4XgS1Vif5jhwiImpdubqiSZPT9lCjr8Hr+/6Fx2/+CyJviECoTxcUXrlk8Xrhfl3Ru3OMhBGSFCwpYStRueP7P/lhzM+VZiVfZAoFes17Dt5RkeaGSUREdsLECzklc6cK5RVfwbxV+6yaKlRaoWmyo8YaBoOIskotEy9ERO1Ia2FDXWvp6nRYlb4OACATBMgEmUUJIE83T6QkTW23XQ7XjkFWKdu3EbIzsbSELS/EA18N98eIA1UmlR0pu0Ug5pmnmXQhInIyTLyQ07HXVCFLG+q2RlOjl3Q9IiJqm8LN/pOADKIIwPwkvqebJ+YOmoFIVYT0QTXS1hhkmSBDUmgiRkQPQVxQLMtc/sfaErYSlTs23KVC6GUdbsrWIKZABxj+eN0FuRyq5CSEjB4F3/g4vu5ERE6IiRdyKlJNFXrv2WFmv3FReMgtumZrvDz5vx8RUXtSKf0t3m1iT+F+XZGSNNXmSZfrjUE2iAYcLMjAwYIMhPuGIGXANJvH5AwkKWETBBR28UBhFw8su20uAms9UKfRQO7lBY8AFUdGExE5OX7yI6diz6lCgf5ekMkEScqN5DIBAX6mjSElIiJpKN29JJssJCWZIIMoihAb7YSRCzL0D0vEyOih6N05xua7HI4VnzJ5Ig8A5FcVYeHu5Zg7aAb6BPeyaWyOTuoStlo3GZRdwiRdk4iI7IuJF3Iq9pwqpFS449b4EKQdu2j19ZMTQtjfhYicjiv0/JBqspCUDKIBYb7BmD3wMdToa6Fw94TKq/1eW3PGIDdWo6/Bm2mrsWjYHJN2vrjCz09LpC5hU7jbvySOiIikxcQLOQ1HmCp016DukiRexgzsYfUaRETtwdV6fsQFxSLcN8TsCTS2VlBVjKqaasQFxbbrdc0eg3yNGn0NVqWvw7KRL7b4/Xe1n5+WSFnCJhdkUHn5W72OXq1GbWkZ6rRayBUKeAQGsFyJiMiOmHghp+EIU4USogIREexjVblTt2AfxEcFWHw+EVF7ccWeH4IgIGXANCzcvdziZIOtpObsaffEiyVjkK+VX3kRJ0uym8Xuij8/LZGyhK1/WKLFu4BEUUTl8SwUb9uBskPpTRr0QiZDQPIABI8eCb+EeKdNchEROSuZvQMgMpUjTBUSBAGzJ/ezuNGuwkOOWZP78Q0PETm8Y8WnsHD3cpM/lDf0/DhWfMrGkVkvUhWBuYNmwNMBphw1ll6QCbVO067XtHQM8rVSc/Y0+dqVf35aMiJ6iCTrjIweatF51WdzkTlzNk4seAllBw42TboAgMGAsv0HcGLBS8icORvVZ3MliJaIiEzFxAs5DUeZKhQd5o/505LMjkfhIcf8aUkWjbImImpP1vb8yC3Ps1Fk0ukT3AuLhs1BuG+IvUMxMogGlGsq2u161o5Bbqxx0qgj/Pxcq6GEzRrhfl3Ru3OM2edVZB7F8fkLoM7LN+l4dV4+js9fgIrMo2Zfi4iILMPECzmNhqlCUrB2qlDfnkFYmjIYEcE+Jh3fLdgHS1MGo2/PIIuvSUTUHqTq+SGK0pSG2lKkKgLLRi3AwmGzkRzWDzKh6dsiuSBDcng/PHzThHaLSatrv/InScYg/09D0qgj/fw01lDCZukuKk83T6QkTTV7R2z12VycWvoGDFqtWecZtFqcWvoGd74QEbUT9nghp+FoU4Wiw/yx8tlhyDpbhu/TzuFAVlGTHjRymYDkhBCMGdgD8VEBLC8iIqdgy54fjkgQBMQFxSIuKLZ+6o6mAlpdTZPJQqIoYve5/e3SkLc9J9pIPQZZq6vpcD8/jTWUsJm728fTzRNzB80wu7+NKIrIXvGu2UmXBgatFtnvvIfEd5bzPQoRkY0x8UJOxdGmCgmCgIToQCREB0Kt1aGsUgtNjR5enm4I8FNwZDQROR0pe3442wdnpbtXi41N26shr1QTbUxlizHIW377ryRrOePPD/BHCVtbTYUbC/fripSkqRY1Fa48nmVyeVFr1BfyUJV1An4J8VatQ0REbWPihZyKI08VUircmWghIqdmi54flk5ocTSW7mYwhzUTbSwh9RhkLzcFf37wRwnbyZJspGbvQXphZpPXWC7I0D8sESOjh6J35xiLd5sUb0+VJN6i7TuYeCEisjEmXsipNEwVmrdqn0VTjjhViIiodbbo+eGMH5xbY+5uBnNZOtHGUlKPQdbotPz5+R9TStisoVerUXbwkCSxlh04BL1aDTelUpL1iIioOTbXJafDqUJERLZhi54friZSFYE3R76IhxMnSloWZOlEG2tJOQaZPz8tU7p7Icw3BNEB3RHmGyJJMqm2tKz5yGhLGQyoLSuXZi0iImoREy/klDhViIhIerbo+eFqcsvzMDf1FXyS+ZVko58tnWgjBSnHIPPnp/3UWdhQt9X1NBpJ1yMioqZYakROi1OFiIikJXXPj/ZsFNsejhWfkrzHi6UTbaRibePgxkkj/vy0H7lCIe16Xs5Z0kVE5CyYeCGnxqlCRETSkbrnh7P252hJbnme5EkXaybaSKnHDeGY2PsubDy+2aykybVJI/78WEet06BcXQGtvgYKN0+olK33gvEIDABkMknKjQS5HB4BKqvXISKi1jHxQi6DU4WIiKw3InqIJB+c27tRrC2JoohVh9ZKknSRaqKNVHLL8yxqFtxa0og/P+YRRREnLp9Bas4e/FJ4tEniSybIkBSaiBHRQxAXFNvkZ8VNqURA8gCU7T9gdQyq5CQ21iUisjEmXoiIiMiooeeHNVN77NUo1lZOXD4jyRSjJ5MeRpID7eSwpHRKJsjwlz734e6ew1tMGvHnx3TXS3oZRAMOFmTgYEEGwn1DkDJgWpNEV/DokZIkXkJGj7J6DSIiahub6xIREZFRQ88PTwsbpdqzUayt7MzZK8k6GUVZDpN0sbR0yiAa8OWJ73Hu9/wWH+fPj2mOFZ/Cwt3LTU5Q5VcVYeHu5ThWfMp4n19CPJQR4VbFoewWAd/4OKvWICKi62PihYiIiJqIVEVg7qAZZn94tnejWFtQ6zRIL8yUZK30gkyodfafHmNt6VSNvgar0tdBFMUWH+fPT9ssTXrV6GvwZtpq5JbnAahPcsXMmgmZhY12ZQoFYp552uWTXEREjoCJFyIiImqmT3AvLBo2x+RRw+F+XbFo2Bz0Ce5l48jaV7m6QpIpPUD9bhGpRlBbQ4rSqfzKizhZkt3q4/z5aZnUSS/vqEj0mvec2ckXmUKBXvOeg3dUpEVxEBGRedjjhYiIiFoUqYrAslELcLIkG6nZe5BemNkkCeFojWJtQSvhFCMA0OqkXc8SUpVOpebsQVxQbKuP8+enOSmTXg2vvX/iTUhYshjZK96FOq/lErDGlN0iEPPM00y6EBG1IyZeiIiIqFWCICAuKBZxQbH14241FdDqaqBw94TKq/Vxt65CYWGvklbXc5d2PXPZonSqrZ+Bjv7zcy1bJb28oyKR+O7bqMo6gaJtO1B28FCTUdOCXA5VchJCRo+Cb3xch0hyERE5EiZeiIiIyCRKd68O90FZpfSHTJBJUm4kF2RQeflbH5QVbFE6ZerPREf8+WnM1kkvQRDglxAPv4R46NVq1JaVo06jgdzLCx4BKo6MJiKyIyZeiIiIiFqhdPdCUmgiDhZkWL1WfwcYJe2KpVPOoj2TXm5KJRMtREQOhM11iYiIiNowInqIJOuMjB4qyTrWcLXSKWfCpBcRUcfFxAsRERFRG+KCYk2eztOacL+u6N05RqKILNdQOiUFRyidciZMehERdVxMvBARERG1QRAEpAyYBk8LPzh7unkiJWmqQzQ0bSidkoIjlE45Eya9iIg6LiZeiIiIiK4jUhWBuYNmmJ188XTzxNxBMxCpirBRZOZzpdIpZ8KkFxFRx8XECxEREZEJ+gT3wqJhc0wuOwr364pFw+agT3AvG0dmHlcqnXI2THoREXVMnGpEREREZKJIVQSWjVqAkyXZSM3eg/TCzCaTauSCDP3DEjEyeih6d45xiPKiazWUTi3cvRw1FjR8daTSKWfTkPTKryqyeA0mvYiInA8TL0RERERmEAQBcUGxiAuKhVqnQbmmAlpdDRTunlB5+Tt8CYhap4GH3B1/SbgX6499A12dzuRzHbF0ypkw6UVE1DEx8UIuR63VobRCA21tHRQecgT6e0GpcLd3WERE5IKU7l4On2gBAFEUceLyGaTm7MEvhUeb7NIxVbhfV6QkTWXSxUoN/YLeTFttVvKFSS8iIufFxAu5BFEUcfxsKb5PO4eDWcUwGETjYzKZgFvjQ3DXoO5IiArkX4mIiKhDyS3Pw6pDay0qb3GG0iln1NAvyNTvC5NeRETOzWkSL7W1tdi4cSO2bduGnJwc6HQ6BAUFITk5GQ8++CBuvPFGq9ZXq9W4+eabYTBc/y9AHh4eOH78uFXXI+nkFFTg7Y0ZyCu+0uLjBoOItGMXkXbsIiKCfTB7cj9Eh/m3b5BERER2cKz4lNk7Kzzk7pjSZxwSgm90itIpZ+UK/YKIiMg0TpF4uXTpEh599FGcOXOmyf35+fnIz8/HN998g7lz52LatGkWX+P06dMmJV3IsRw5fRlL1qZDW1tn0vF5xVcwb9U+zJ+WhL49g2wcHRERkf3klueZnXQBgNo6HT47vhmLAqOYdLExZ+8X1Ba9Wo3a0jLUabWQKxTwCAyAm1Jp77CIiOzC4RMvNTU1eOyxx4xJl7Fjx+Lee++Ft7c3MjMzsXr1alRUVGDp0qUIDAzE2LFjLbrOyZMnjbf//e9/Izg4uNVj+RcHx5BTUGFW0qWBtrYOS9amY2nKYO58ISIiu1LrNChXV0Crr4HCzRMqpTQftkVRxKpDay1q4AoANfoarEpfh2UjX+T7nnbiLP2C2iKKIiqPZ6F42w6UHUoHGv9RUyZDQPIABI8eCb+EeP5cEVGH4vCJl48//hinT58GADz11FN4+umnjY/169cPI0aMwJ///GeUlJRg6dKluP3226G0IJt+6tQpAIBSqcTQoUMhk8mkeQJkE6Io4u2NGWYnXRpoa+uwYmMG3nt2GH/xExFRu2qr0a1MkCEpNBEjoocgLijW4t9RJy6fsWpkMQDkV17EyZJsxAXFWrUOdQzVZ3ORveJdqPPyWz7AYEDZ/gMo238AyohwxMyaCe+oyPYNkojIThw6u6DT6bB+/XoAQGhoKJ544olmx4SFhWHu3LkAgNLSUnzzzTcWXathx0vPnj2ZdJGAWqtDXnEVzuT9jrziKqi1po+qNMXxs6Wt9nQx1YXiK8g6WyZRRERERNeXW56HZ3csxss/rcChgiPNpgsZRAMOFmTg5Z9W4Nkdi5FbnmfRdXbm7JUiXKTm7JFkHXJtFZlHcXz+gtaTLtdQ5+Xj+PwFqMg8auPIiIgcg0PveDl8+DBKS0sBAHfffTfc3VseCTxmzBi89NJLUKvV2LFjB6ZMmWLWdfR6PbKzswEAvXv3ti7oDqw9JwttSztvZbT1vt9/DgnRgZKsRURE1BZzG93mVxVh4e7lmDtoBvoE9zL5OmqdBumFmRZG2VR6QSbUOo3Tl8CQ7VSfzcWppW/AoNWadZ5Bq8WppW8gYcli7nwhIpfn0Fs7fv31V+Pt5OTkVo9zc3NDv379AABHjhxBTY159cw5OTmora0FwMSLpXIKKvDUst144V/7sf9YUZOkC/DHZKEX/rUfTy3bjZyCCouvpdbqcCDLuu3TDQ4cL5J8Nw4REdG1LG10W6OvwZtpq83a+VKurmi2k8ZSBtGAck2FJGuR6xFFEdkr3jU76dLAoNUi+533IIri9Q8mInJiDp14OXv2rPF2jx492jw2PDwcQH150rlz58y6TkN/F6C+dOmDDz7AAw88gH79+qFPnz648847sXDhQrPX7SiOnL6Meav2mVz60zBZ6MjpyxZdr7RC0yyxYymDQURZpWVvFoiIiEwhVaNbUz+cai28Tqvr6aRdj1xH5fEsk8uLWqO+kIeqrBMSRURE5JgcOvFSXFwMoH5HS1BQ26N/Q0JCjLcvXbpk1nUaTzRKSUnBsmXLkJmZiatXr6KmpgZ5eXnYtGkTxo4di08++cSstV2dtZOFLNn5YmlD3dZoavSSrkdERNSYlI1uTaFw87TqWs3Wc5d2PXIdxdtTJVmnaPsOSdYhInJUDp14qaysBAAoFIrrNrxtPMmoqqrKrOs03vGiVqtxzz33YNWqVfjyyy+xatUq3HfffZDJZNDr9Xj11VexYcMGs9Z3VVJNFjJ3e6nCQ27R9Vrj5enQrY6IiMjJtXejW5XSHzJBmrd4ckEGlZe/JGuRa9Gr1Sg7eEiStcoOHIJerZZkLSIiR2SzT5y7du1CSkqKRef+8MMPCAsLM/ZdUSgU1z2n8TEN55lCFEVj4sXT0xP//ve/MXDgwCbHDB8+HKNGjcJTTz0FvV6PpUuX4rbbbkNoaKjJ13FFUk4WMqfBbaC/F2QyQZJyI7lMQIDf9X++iIiILGGPRrdKdy8khSbiYEGG1dfsH5bIxrrUotrSMsAgTS8hGAyoLSuHW6M/pBIRuRKH3vEil9fvbDBlAk7jXRPmjIMWBAHbtm3Dhg0bsH79+mZJlwbDhg3D9OnTAdT3kfn0009NvoarknKykDmUCnfcGh9y/QNNkJwQAqWi5WlZRERE1rJXo9sR0UMkuebI6KGSrEOORa9WQ52XjytnsqHOy7dot0mdhQ11W11Po5F0PSIiR2KzHS89evTAE088YdG5vr6+AP4oH9Ka8A9740lGHh4eZl2vS5cu6NKly3WP+/Of/4z3338fAJCWlmbWNVyNLSYLmZMAuWtQd6Qdu2j1tccMbLtpMxERkTXs1eg2LigW4b4hVvWWCffrit6dYyw+X2pqnQbl6gpo9TVQuHlCpfTnbhwziKKIyuNZKN62A2WH0pvuVpHJEJA8AMGjR8IvId6kP3rKTdiRbg65F7+XROS6bJZ4iYqKwuzZs61ao1OnTgDqEy+iKLb5S0DdKFPv5+dn1XVbExoaCl9fX1RVVeHiRes/9DszW0wWMifxkhAViIhgH6tKnboF+yA+KsDi84mIiK7HXo1uBUFAyoBpWLh7uUXTlDzdPJGSNNWkD+C2JIoiTlw+g9ScPfil8GiT3UMyQYak0ESMiB6CuKBYu8fqyKrP5iJ7xbutTyAyGFC2/wDK9h+AMiIcMbNmwjsqss01PQIDAJlMknIjQS6HR4DK6nWIiByVQ5caNfRQ0el0KC0tbfPYoqI//qJjyu4VSzX0kjGnj4wrsvdkIUEQMHtyP4sb7So85Jg1uR/fpBERkaTUOg0KKouQU3YeBZVF8HJX2K3RbaQqAnMHzYCnmckfTzdPzB00A5GqCDMjlFZueR6e3bEYL/+0AocKjjQr2TKIBhwsyMDLP63AszsWI7c8z06ROraKzKM4Pn+ByWOf1Xn5OD5/ASoyj7Z5nJtSiYDkAVKECFVyEvu7EJFLc+hxLjExf2xvzcvLQ+fOnVs9Nj+//peJu7s7unfvbvI1CgsLcfr0aZSXl+Pmm29Gjx6tl57U1dWhoqICABAYaHozWFfkCJOFosP8MX9aktnjrBUecsyfloToMH+zr0lERHSt6+3K8Ff4mtybpS2WNLrtE9wLi4bNwapDa00qOwr364qUpKl2T7ocKz6FN9NWm7xbJ7+qCAt3L8fcQTPQJ7iXjaNzHtVnc3Fq6RswmNmPxaDV4tTSN5CwZHGbO1+CR49E2f4D1oaJkNGjrF6DiMiROfSOl8TEROPtw4cPt3qcTqdDRkZ95/6bbroJ7u6ml6zs3bsXf/vb3/DCCy9gy5YtbR57/Phx406XhIQEk6/hihomC0nBmslCfXsGYWnKYEQE+5h0fLdgHyxNGYy+PYMsuh4REVFjpuzKkCLpAlje6DZSFYFloxZg4bDZSA7r12wHjlyQITm8HxYOm41lI1+0e9IltzzPrKRLgxp9Dd5MW82dL/8jiiKyV7xrdtKlgUGrRfY77zUZYHEtv4R4KCPCLQ0RAKDsFgHf+Dir1iAicnQOvePl5ptvRnBwMIqLi/HNN9/g0UcfNU46auz777839ngZOXKkWdcYMOCPLZJbt25FSkoK3Nxaflk+/vhj4+2xY8eadR1X0zBZSIoGt9ZOFooO88fKZ4ch62wZvk87hwNZRU36z8hlApITQjBmYA/ERwWwvIiIiCRh7q4Ma1jb6FYQBMQFxSIuKLa+Sa2mAlpdDRTunlB5OU6TWlEUserQWotf0xp9DValr8OykS92+N/3lcezTC4vao36Qh6qsk7ALyG+xccFQUDMrJk4Pn+BRQkemUKBmGee7vDfKyJyfQ6deBEEAVOmTMFbb72Fc+fOYcWKFfi///u/JscUFBRg2bJlAIAbbrgB48ePN+sakZGRGDhwIPbv34/8/Hy89dZb+Pvf/97suLVr1yI1NRUAEBcXhzvuuMPCZ+U6HGmykCAISIgOREJ0INRaHcoqtdDU6OHl6YYAPwVHRhMRkaQs3ZVhCakb3SrdvRwm0XKtE5fPWDWJCQDyKy/iZEk24oJiJYrKORVvT5VknaLtO1pNvACAd1Qkes17zuySJplCgV7znrtuE18iIlfg0KVGAPDXv/4VsbH1vzjff/99/O1vf8Pu3btx5MgRrF27FhMnTkRJSQkA4MUXX4S3t3ezNb7++mv07NkTPXv2xPPPP9/s8X/84x/GSUgff/wxHnnkEezcuRPHjh3Dzp078cQTT2Dp0qUA6pM7b775Zos7bzqahslC1rDFZCGlwh3hXXwQG3EDwrv4MOlCRESSsnZXhjkcpdFte9mZs1eSdVJz9kiyjrPSq9UoO3hIkrXKDhyCvtH00Jb4J96EhCWLTS47UnaLQMKSxfBPvEmKEImIHJ5D73gB6pvlfvjhh3jkkUeQnZ2NH3/8ET/++GOTY+RyOZ577jmLy3969OiBjz76CM888wwKCwuxb98+7Nu3r9lx3bt3xzvvvIOoqCiLruNqGiYLzVu1z6IpR5wsREREzkiKXRmmcJRGt+1FrdMgvTBTkrXSCzKh1mkcdmePqbSlZbh6Nhf6K1fg5uODTlGRUARe/w9WtaVlkox5BgAYDKgtK7/u1CHvqEgkvvs2qrJOoGjbjvrET6MYBLkcquQkhIweBd/4OL7/I6IOxeETL0D9eOivv/4amzZtwrZt25Cbmwu1Wo3AwED0798f06ZNQ1ycdU25EhISsHXrVnz11VfYtWsXzpw5g+rqavj7+yMqKgojR47E/fffDw8PD4melWtwhclCaq0OpRUaaGvroPCQI9Dfi7tkiIioVVLtylB5+aNCW9WkIa9ckKF/WCJGRg9F784xHerDabm6ollzYks1NDV2xsSLwWBA0ZbvcHHzVtSWlzd73CNAha733I2Qe8ZCJmt583qdhQ11W1On0Zh0nCAI8EuIh19CPPRqNWrLylGn0UDu5QWPABVHRhNRh+UUiRcA8PDwwMMPP4yHH37Y7HPHjx9vUu+XTp06YerUqZg6daolIXZYDZOF3t6YgbziK9c9vluwD2ZN7mfXpIsoijh+thTfp53DwaziJs14ZTIBt8aH4K5B3ZEQFdih3vQSEbkatU6DcnUFtPoaKNw8oVJa10hWyl0ZFdoqrBr7CjR6rUM2um1vWolLt7Q625eCSe3ynr3IeW8VRJ2+1WNqy8pxfs06XFi/AdFPpyBo6JBmx8gVlk2LbI3cy/yfSTelkokWIqL/cZrECzk2Z5oslFNQ0WaSyGAQkXbsItKOXUREsA9m2zlJRERE5hFFEScun0Fqzh78Uni0yS4KmSBDUmgiRkQPQVxQrNm/j6TelaHRaxHmGyLJes5O4eYp7Xru0q5nawX/+RoXPtlg8vGiTo/s5e+gtrQUYROa/oHRIzAAkMkkKTcS5HJ4BKisXoeIqCNj4oUk4wyThY6cvmxWWVRe8RXMW7UP86cloW/PIBtHR0RE1sotz8OqQ2tb7cFiEA04WJCBgwUZCPcNQcqAaWb1UOGuDNtRKf0hE2SSJLbkggwqL3/rg2onl/fsNSvp0tiFTzbAIzCwyc4XN6USAckDULb/gNWxqZKTuHOFiMhKDj/ViJyTI04WyimoMLsXDQBoa+uwZG06cgoqbBMYERFJ4ljxKSzcvdzkxrf5VUVYuHs5jhWfMvkaHX1Xhi0p3b2QFJooyVr9wxKdpmTLYDAg571VVq2R894/Ybhmd0vw6JFWrdkgZPQoSdYhIurImHihDkEURby9McOi6UtAffJlxcYMiKJ4/YOJiKjd5Zbn4c201WaPeK7R1+DNtNXILc8z6fiGXRlScLZdGe1hRHTzfiWWGBk9VJJ12kPRlu/a7OliClGnQ9HW75vc55cQb/J459You0XAN966ARZERMTEC3UQx8+WmtT4ty0Xiq8g62yZRBEREZFURFHEqkNrzU66NKjR12BV+jqTkusddVdGe4kLikW4lT1vwv26onfnGIkisr2LW7ZKs87mpusIgoCYWTMhs7DRrkyhQMwzT3PIABGRBJh4oQ5hW9p5Sdb5fv85SdYhIiLpnLh8xuTyotbkV17EyZJsk47tiLsy2osgCEgZMA2eFpZ0ebp5IiVpqs2SBXq1Guq8fFw5kw11Xj70arVV62lLy1Bb1nxktCVqy8qgLW36ByLvqEj0mvec2ckXmUKBXvOeg3dUpCSxERF1dGyuSy5PrdXhQJZ1b8gbHDheBLVW5xA9a4iIqN7OnL2SrJOaswdxQbHXPa5hV4Y1yR5n25XRniJVEZg7aIbZpWOebp6YO2iGWc2STSGKIiqPZ6F42w6UHUpvOilIJkNA8gAEjx4Jv4R4sxM+V8/mShqr+tw5KAIDmtznn3gTEpYsRvaKd6HOy7/uGspuEYh55mkmXYiIJMTEC7m80gpNk9HW1jAYRJRVapl4ISJyEGqdBumFmZKslV6QCbVOc93yn4ZdGQt3L7eovMnWuzJcQZ/gXlg0bE6bE6oaC/fripSkqZInXarP5radsDAYULb/AMr2H4AyIhwxs2aalbDQX7GuDPpausqqFu/3jopE4rtvoyrrBIq27UDZwUNNEkiCXA5VchJCRo+Cb3wcfzaJiCTGxAu5PEsb6rZGU2NdAzwiIpJOubpCkvHDQP2o6XJNhUl9VxxtV4YrilRFYNmoBThZko3U7D1IL8xs8r2WCzL0D0vEyOih6N05RvJkQUXmUZxa+gYMWq1Jx6vz8nF8/gL0mvcc/BNvMukcNx8fa0Jsxt3Pt9XHBEGAX0I8/BLioVerUVtWjjqNBnIvL3gEqDgymojIhph4IZen8JBLup6XJ/+3ISJyFFoLG+q2up7O9PUcZVeGKxMEAXFBsYgLioVap0G5pgJaXQ0U7p5QefnbrDlx9dlcs5IuDQxaLU4tfQMJSxabtPOlk8TlPMoePUw6zk2pZKKFiKgd8RMkubxAfy/IZIIk5UZymYAAP8umAxARkfQUFjZhbXU9d/PWs/eujI5E6e7VLlOgRFFE9op3zU66NDBotch+5z0kvrP8ut9vRWAAPAJUkjTY9QgIaNbfhYiIHAMTL+TylAp33BofgrRjF61eKzkhhP1diIgciErpD5kgk6TcSC7IoPLyN/s8e+3KINuoPJ5lUhPatqgv5KEq6wT8EuKve2zXe+7G+TXrrLoeAHS9926r1yAiItvgOGnqEO4a1F2SdcYMNG0LLxERtQ+luxeSQhMlWat/WKLVSRKluxfCfEMQHdAdYb4hTLo4oeLtqZKsU7R9h0nHhdwzFoK7dX8LFdzdEXL3GKvWICIi22HihTqEhKhARARb18CuW7AP4qO4hZeIyNGMiB4iyTojo4dKsg45L71aXT/xRwJlBw5Br1Zf9ziZTIbop1Osulb0009CJuPbeiIiR8V/oalDEAQBsyf3s7jRrsJDjlmT+7E2n4jIAcUFxSLcN8SqNcL9uqJ35xiJIiJnVVta1mTMslUMBpN7twQNHYJuD0+x6DLdHp6CoKHSJB+JiMg2mHihDiM6zB/zpyWZnXxReMgxf1oSosP8bRMYERFZRRAEpAyYBk8LG+16unkiJWkqk+uEOgsb6ra6nkZj8rFhE8YjZs4zJpcdCe7uiJnzDMImjLc0PCIiaidMvFCH0rdnEJamDDa57KhbsA+WpgxG355BNo6MiIisEamKwNxBM8xOvni6eWLuoBkc8UwAALlC2smFci/zevwEDR2C5C82ovv0afAIULV4jEdAALpPn4bkLz7jThciIifBqUbU4USH+WPls8OQdbYM36edw4GsoiajpuUyAckJIRgzsAfiowL4F1AiIifRJ7gXFg2bg1WH1iK/qui6x4f7dUVK0lQmXcjIIzAAkMkkKTcS5PJWkydtkclkCL33boTeeze0pWVQnzsHXWUV3P18oezRgyOjiYicEBMv1CEJgoCE6EAkRAdCrdWhrFILTY0eXp5uCPBTcGQ0EZGTilRFYNmoBThZko3U7D1IL8xsMmpaLsjQPywRI6OHonfnGCbXqQk3pRIByQNQtv+A1WupkpPgplRatYYiMICJFiIiF8DEC3V4SoU7Ey1ERC5EEATEBcUiLigWap0G5ZoKaHU1ULh7QuXlzxHP1Kbg0SMlSbyEjB4lQTREROQKmHghIiIil6V092KihczilxAPZUQ41Hn5Fq+h7BYB3/g4CaMiIiJnxua6RERERE5GrdOgoLIIOWXnUVBZBLXO9Ok51DZBEBAzayZkFjbalSkUiHnmaZaxERGREXe8EBERETkBURRx4vIZpObswS+FR5v0rpEJMiSFJmJE9BDEBcXyQ7+VvKMi0Wveczi19A0YzBgxLVMo0Gvec/COirRhdERE5GyYeCEiIiJycLnleW1OazKIBhwsyMDBggyE+4YgZcA0Tmuykn/iTUhYshjZK941qexI2S0CMc88zaQLERE1w8QLERER2Zxap0G5ugJafQ0Ubp5QKR2/ya2jxHys+BTeTFuNGn2NScfnVxVh4e7lmDtoBvoE97JxdK7NOyoSie++jaqsEyjatgNlBw81GTUtyOVQJSchZPQo+MbHcacRERG1iIkXIiIisglnLI1xtJhzy/PMSro0qNHX4M201Vg0bA53vlhJEAT4JcTDLyEeerUatWXlqNNoIPfygkeAyuqR0URE5PqYeCEiIiLJOWNpjKPFLIoiVh1aa3bSpUGNvgar0tdh2cgXHSax5ezclEomWoiIyGycakRERESSOlZ8Cgt3L281gXGthtKYY8WnbBxZ6xwx5hOXz5gcT2vyKy/iZEm2RBERERGRJZh4ISIiIslYWxqTW55no8ha56gx78zZK8k6qTl7JFmHiIiILMPECxEREUlCqtIYURQljqx1jhqzWqdBemGmJGulF2RCrdNIshYRERGZj4kXIiIikoStSmPUOg0KKouQU3YeBZVFkiYRHLWcp1xd0aSxrzUMogHlmgpJ1iIiIiLzsbkuERERSULK0pjenWPaZbqQlDHHBcVKshYAaC3cgdPqejpp1yMiIiLTMfFCREREVpO2NOYI5mxfhMIrl1p8XKrpQrYo51G6e0mynsLNU5J1jOu5S7seERERmY6lRkRERGQ1aUtjxFaTLteyZrqQI5fzqJT+kAnSvE2TCzKovPwlWYuIiIjMx8QLERERWU3q0hhzWDpdyJHLeZTuXkgKTZRkrf5hiZLtxCEiIiLzMfFCREREVpO6NMZcNfoavL3/A+RXXDS5+a6jl/OMiB4iyTojo4dKsg4RERFZhj1eiIiIyGoNpTFSle5Y4tLVUvxf6mKTm+9KGbMtynnigmIR7hti1dSlcL+u6N05RsKoiIiIyFzc8UJERERWk7I0xloNzXdf/mkFnt2xuNUSJEcv5xEEASkDpsHTwp05nm6eSEmaatXUJyIiIrIeEy9EREQkCalKY6R0vea7jl7OE6mKwNxBM8xOvni6eWLuoBkWTXsiIiIiaTHxQkRERJJoKI1xNG0135UiZluX8/QJ7oVFw+aYHGe4X1csGjYHfYJ72SwmIiIiMh0TL0RERCQJa0tjbKlGX4NV6esgimKT+52lnCdSFYFloxZg4bDZSA7r12zUtFyQITm8HxYOm41lI1/kThciIiIHwua6REREJJmG0pg301ajxo4jpluSX3kRJ0uyERcU2+R+S2Nu73IeQRAQFxSLuKBYqHUalGsqoNXVQOHuCZWXP0dGExEROSjueCEiIiJJmVsaE+YbDAHt0wA2NWdPi/c7WzmP0t0LYb4hiA7ojjDfECZdiIiIHBh3vBARUYej1mlQrq6AVl8DhZsnVEruFpBaQ2nMyZJspGbvQXphZpOxzXJBhv5hiRgZPRS9O8fg7f0f4mBBhs3jSi/IhFqnafH7bW7MnBZEREREpmDihYiIOgRRFHHi8hmk5uzBL4VHm3yglgkyJIUmYkT0EMQFxfIDtUTMKY0ZET2kXRIvBtGAck1Fq4k2lvMQERGR1Jh4ISIil5dbnodVh9Yiv6qoxccNogEHCzJwsCAD4b4hSBkwjc1JJaZ092ozadEwXai175GUtDrT+rhcL2YiU+jVatSWlqFOq4VcoYBHYADclEp7h0VERO2IiRciInJpx4pPmdU0Nb+qCAt3L8fcQTM4jrcdNUwXWrh7uc2b8ircHW/qErkWURRReTwLxdt2oOxQOmD4Y4cdZDIEJA9A8OiR8EuI5w47IqIOgM11iYjIZeWW51k0XadGX4M301YjtzzPRpFRSxqmC9lyHLVckEHl5W+z9Ymqz+Yic+ZsnFjwEsoOHGyadAEAgwFl+w/gxIKXkDlzNqrP5tonUCIiajdMvBARkUsSRRGrDq21ePdEjb4Gq9LXQRRFiSOjtpg7Xchc/cMSWT5ENlOReRTH5y+AOi/fpOPVefk4Pn8BKjKP2jgyIiKyJyZeiIjIJZ24fMbqfiH5lRdxsiRboojIVA3ThRYOm43ksH6QCdK9XRkZPVSytYgaqz6bi1NL34BBqzXrPINWi1OvvobKEydtFBkREdkbe7wQEZFL2pmzV5J1UnP2IC4oVpK1yHTXThcqU/+O13/+Jy5fLbN4zXC/rujdOUbCKInqiaKI7BXvmp10aWCorUXW/AVQ3ZqMkLtGsfcLEZGL4Y4XIiJyOWqdBumFmZKslV6QCbVOI8laZBmluxfC/bpizsDHLe7/4unmiZSkqfwwSzZReTzL5PKitpQfOMjeL0RELoiJFyIicjnl6goYRMP1DzSBQTSgXFMhyVpkHUub73q6eWLuoBkcEU42U7w9VdL12PuFiMi1MPFCREQuRyvxOGKtzrbjjcl05jbfDffrikXD5nA0ONmMXq1G2cFDkq9r0Gpxaukb3PlCROQC2OOFiIhcjkLiccQKd9uNNybzNTTfPVmSjdTsPUgvzGyyw0kuyNA/LBEjo4eid+cYlheRTdWWljUfGS0Rg1aL7HfeQ+I7y/lzTETkxJh4ISIil6NS+kMmyCQpN5ILMqi8/K0PiiR1bfPdck0FtLoaKNw9ofLy58hoajd1FjbUNZX6Qh6qsk7ALyHeptchIiLbYakRERG5HKW7F5JCEyVZq39YIj/EOziluxfCfEMQHdAdYb4h/H5Ru5IrFDa/RtH2HTa/BhER2Q4TL0RE5JJGRA+RZJ2R0UMlWYeIXJNHYAAgs+1b6rIDh6BXq216DSIish0mXoiIyCXFBcWa3IC1NeF+XdG7c4xEERGRK3JTKhGQPMC2FzEYUFtWbttrEBGRzTDxQkRELkkQBKQMmGb26OEGnm6eSEmayoaWRHRdwaNH2vwadRqNza9BRES2wcQLERG5rEhVBOYOmmF28sXTzRNzB81ApCrCRpERkSvxS4iHMiLcpteQe7F3ERGRs+JUIyIicml9gnth0bA5WHVoLfKriq57fLhfV6QkTWXShYhMJggCYmbNxPH5C2CwwZQjQS6HR4BK8nVdlcFgQHV1NaqqqlBbW4u6ujp7h0RENiaXy+Hh4QFfX194e3tDZuPeW+Zi4oWIiFxepCoCy0YtwMmSbKRm70F6YWaTUdNyQYb+YYkYGT0UvTvHsLyImlDrNChXV0Crr4HCzRMqJcdVU3PeUZHoNe85nFr6huTJF1VyEtyUSknXdFVXrlxBYWEhRFG0dyhE1I70ej1qampw5coVCIKA0NBQ+Pj42DssIyZeiIioQxAEAXFBsYgLiq3/IK2pgFZXA4W7J1Re/CBNTYmiiBOXzyA1Zw9+KTzaJFEnE2RICk3EiOghiAuKZaKOjPwTb0LCksXIXvEu1Hn5kq0bMnqUZGu5spaSLoIgQC6X2zEqImoPdXV1xv/3RVFEYWGhQyVfmHghIqIOR+nuxUQLtSq3PK/N0jSDaMDBggwcLMhAuG8IUgZMY2kaGXlHRSLx3bdRlXUCRdu2o2z/QavWU3aLgG98nETRuS6DwdAk6eLt7Q2VSgWlUsnkKFEHIIoi1Go1ysvLUV1dbUy+xMbGOkTZkf0jICIiInIQx4pPYeHu5Sb1AwKA/KoiLNy9HMeKT9k4MnImgiDALyEeN/59LuKXLIbg4WHROjKFAjHPPM3EgQkaPmgB9UmXsLAwdOrUia8dUQchCAI6deqEsLAweHt7A6hPxlRXV9s5snpMvBARERGhfqfLm2mrUaOvMeu8Gn0N3kxbjdzyPBtFRs7ML643er/wPGQKhVnnyRQK9Jr3HLyjIm0UmWupqqoy3lapVEy4EHVQgiBApfqjGXnjfxvsiYkXIiIi6vBEUcSqQ2vNTro0qNHXYFX6Ojb0pBY19H4xdeS0slsEEpYshn/iTTaOzHXU1tYCqP/QpWQjYqIOrXGJYcO/DfbGHi9ERETU4Z24fMbk8qLW5FdexMmSbMQFxUoUFbmSpr1fdqDs4CHA8EfTZkEuhyo5CSGjR8E3Po47NszUMDJaLpfztSPq4Bqaauv1eocZJ8/ECxEREXV4O3P2SrJOas4eJl6oVQ29X/wS4qFXq1FbVo46jQZyLy94BKg4MpqIyEUx8UJEREQdmlqnQXphpiRrpRdkQq3TcGoWXZebUslECxFRB8EeL0RERNShlasrYBAN1z/QBAbRgHJNhSRrERERkWtg4oWIiIg6NK2FDXVbXU8n7XpERETk3Jh4ISIiog5N4eYp7Xru0q5HREREzo2JFyIiIurQVEp/yARp3hLJBRlUXv6SrEVERESugc11iYiIqENTunshKTQRBwsyrF6rf1giG+sSEV2jvLwc3333Hfbu3Yvc3FyUlZVBJpNBpVKhW7duGDx4MEaPHo2QkBCbx6LX65Gfn48ePXrY/FpEDbjjhYiIiDq8EdFDJFlnZPRQSdYhItej1uqQV1yFM3m/I6+4Cmqtzt4htYsNGzZgxIgRePXVV/Hzzz+jsLAQWq0WarUaBQUFSEtLw+uvv46RI0firbfegl6vt1kshw4dwr333ovvvvvOZtcgagl3vBAREVGHFxcUi3DfEORXFVm8RrhfV/TuHCNhVETk7ERRxPGzpfg+7RwOZhXDYBCNj8lkAm6ND8Fdg7ojISoQgiDYMVLbWLt2LZYuXQoA6Ny5MyZMmICbbroJAQEBAICSkhKkp6fj66+/xpUrV/D++++juLgYb7zxhuSvR1FRER5++GFJ1yQyFRMvRERE1OEJgoCUAdOwcPdy1Fgw5cjTzRMpSVNd8oMTEVkmp6ACb2/MQF7xlRYfNxhEpB27iLRjFxER7IPZk/shOsy/fYO0oYsXL2LZsmUAgPj4eKxZswa+vr7Njhs+fDgeeeQRTJ06FefOncOWLVtw++23Y/To0ZLGU1dXJ+l6ROZgqRERERERgEhVBOYOmgFPM6ccebp5Yu6gGYhURdgoMiJyNkdOX8a8VftaTbpcK6/4Cuat2ocjpy/bOLL28+WXX0Knqy+nWrhwYYtJlwZdunTBm2++afx63bp1No+PqD0x8UJERET0P32Ce2HRsDkI9zWtwWO4X1csGjYHfYJ72TgyInIWOQUVWLI2Hdpa83ZYaGvrsGRtOnIKKmwTWDvLzs423o6MjLzu8QkJCYiNjQUAnD592mZxEdkDS42IiIiIGolURWDZqAU4WZKN1Ow9SC/MhEE0GB+XCzL0D0vEyOih6N05huVFRGQkiiLe3phhdtKlgba2Dis2ZuC9Z4e51L8thw8fxm233Xbd455//nlUVVVBpVLBYDBAJmu+T+CXX37BV199hcOHD6OkpARubm7o2rUrkpOT8dBDD6Fbt25Nji8oKMAdd9zR5L6VK1di5cqVAIBPPvkEAwYMaPL4pUuXsHHjRuzbtw8XLlyARqOBv78/4uPjMWrUKNx9992Qy+WtPo/ffvsNmzZtQnp6Oi5evAiDwQB/f3/07t0bd9xxB+699154eHi0en5xcTG+/PJLpKen49y5c6isrIRcLoefnx969+6NkSNHYuzYsXBz48d5Z8HvFBEREdE1BEFAXFAs4oJiodZpUK6pgFZXA4W7J1Re/hwZTUQtOn621OTyotZcKL6CrLNlSIgOlCgq+4iLi8N///tfAMCLL76I1157DYMHD27znEGDBrX6WE1NDV544QVs3bq12f3Z2dnIzs7GZ599hlmzZuHxxx+3OO4vvvgCS5YsgUajaXJ/SUkJdu/ejd27d+Ojjz7CypUrmyV5gPqGwq+//joMBkOT+y9duoRLly5h9+7d+PDDD/Hhhx8iPDy8xfOXLVtmLNNqTKPRoLi4GD/++CM+//xzfPjhh+jUqZPFz5XaDxMvRERERG1Qunsx0UJEJtmWdl6Sdb7ff87pEy+TJk3Chx9+iOrqapSUlOCRRx5B9+7dcccddyA5ORn9+vWDt7e3SWsZDAakpKTg559/BgD069cP999/P6KioqDT6XDs2DGsX78ehYWFeOuttyCKImbMmAEACAoKwrfffovLly8bEzIPPPAAJk+eDACIiPijP9emTZuwcOFCAICHhwceeOABDB06FL6+vjh//jy++OILHD58GGfOnMFf/vIX/Oc//0FwcLDx/F9//RWvvfYaRFFEWFgYHnnkEcTGxsLd3R0FBQXGXTDnz5/H3LlzsWnTpibPc+vWrU2mQD344IPo3bs3/Pz8cPnyZfzyyy/4/PPPodVqkZGRgdWrV2POnDmWfHuonTHxQkREREREZCW1VocDWZaPpG/swPEiqLU6KBXukqxnDwEBAXj33Xfx5JNPQqvVAgDOnz+Pjz76CB999BHkcjl69uyJAQMGYPDgwRgwYADc3Vt+vp999pkx6fLEE09g1qxZTUqxbrnlFkyaNAmPPvoojhw5ghUrVuDOO+9EZGQkPDw80KtXL/j4+BiP79y5M3r1atqbq6ioCK+++ioAwNvbG2vWrEGfPn2Mj990002499578cYbb+Cjjz5CaWkpXnjhBXz00UfGY7766iuIogi5XI5PPvkEoaGhTc4fPXo0Hn/8cfz88884cuQIfvvtN9x4443GY95++20AgI+PDzZs2NBsR82dd96JUaNGYcqUKTAYDNi+fTsTL06CzXWJiIiIiIisVFqhgcEgSrKWwSCirFIryVr2NGjQIHzzzTfNeqgA9eOdT548iTVr1uCRRx7Bn/70J3zwwQfNSmwMBoMxudGzZ89mSZcG3t7exsSJwWDAp59+alas69atQ21tLQDg73//e5OkS2Nz585FYmIiAGDfvn04efKk8bGSkhIAgFKpRFBQULNzZTIZnnrqKUyZMgV///vfm5QJFRYWolOnTvD29sa4ceNaLGMC6nf7hIWFAahPFpFzYOKFiIiIiIjISpY21G2NpkYv6Xr2EhkZiU8++QTbtm3DzJkz0a9fvxZ3tvz+++9YtmwZJkyYgPLycuP9Z86cwcWLFwEAAwcObLPpcFRUlDEpceDAAbPi3Lt3L4D6pMl9993X6nGCIOAvf/mL8euGnTgAEB0dDQC4cuUKZs6c2WSyU4PExET84x//wPTp05v0eAkNDcXWrVvx66+/Yt68eW3G2rlzZwCATqdr1kuGHBNLjYiIiIiIiKyk8Gh9yo0lvDxd66NaVFQUUlJSkJKSAo1GgyNHjuDQoUPYv38/jh8/DlGs3y10+vRpPProo/jqq68gk8lw4sQJ4xpr1qzBmjVrTLpefn6+ybHp9Xrk5uYCAHr37t3mxCGgftdJgzNnzhhvT5kyBV999RWuXLmCH3/8ET/++CNCQ0MxcOBADBw4EMnJyVCpVNeNp2Ga09WrV1FQUID8/HycP38ep0+fRkZGBgoKCozHtjb9iRyLa/3fTEREREREZAeB/l6QyQRJyo3kMgEBfgoJonJMXl5exmTE7NmzkZ+fj5UrV+Lbb78FAJw4cQI7duzAXXfdhd9//92ia+j1elRXV5vUwLeystKY+AkICLju8Y2PqaioMN4ODw/HmjVr8MILL+D06dMA6kuIvvzyS3z55ZeQyWS46aabMG7cOIwfP77FnT8XLlzA2rVrsWfPHhQWFrZ4fZlMxp0uToaJFyIiIiIiIispFe64NT4EaccuWr1WckKIUzfWra2tRUlJCcrKyhAVFXXdkcfh4eF4/fXX4e/vj7Vr1wKoL+G56667UFf3RwnXs88+e92R1I15eZk2kc7cJEbj468tfUpISMDmzZvx66+/YteuXfj555+Rk5NjPO/IkSM4cuQINm7ciDVr1uCGG24wnrtlyxbMnz+/SZ8bb29vREZGIjo6GvHx8RgwYAAWLVqE9PR0s2Im+2LihagFaq0OpRUaaGvroPCQI9Dfy6l/+RERERGR7d01qLskiZcxA3tIEI39rFy5EqtXrzbevvPOO006b/r06cbEy6VLlwAAfn5+xscFQWg2jUgKja9RVlZ23eNLS0uNt/39/Zs9LggCbrnlFtxyyy14/vnnUVJSgvT0dOzduxc7d+6EWq3GqVOn8Oabb2LJkiUAgJycHGPSRalUIiUlBXfeeSciIiKaJXeuXr1q4TMle2Hiheh/RFHE8bOl+D7tHA5mFTfZJiqTCbg1PgR3DeqOhKjANpt6EREREVHHlBAViIhgH+QVX7F4jW7BPoiPun65iyPr3r278faePXtMTrwolUrj7S5dugAAYmNjjfcdPHgQjz76aJtrrF69Gn5+foiIiMDAgQNNuq6HhwciIyORm5uLkydPora2ts0+L5mZmcbbkZGRxttqtRrnz5+Hh4eHsdEuUN8Md8yYMRgzZgxmzJiBcePGQavV4qeffjIes2nTJuNOlxdffBETJkxo8dqiKKK4uNik50WOg114iADkFFTgqWW78cK/9mP/saJmtbkGg4i0Yxfxwr/246llu5FTUGGfQImIiIjIYQmCgNmT+1ncaFfhIcesyf2c/o98t912m7F/yebNm439Tq7nxx9/NN5uGEGdkJBg3FWSlpZmLNtpyd69e7F8+XIsXLjQuOOmwfUa0P7pT38CUJ882bx5c6vHiaKITZs2Gb9uKH26cuUKbrnlFowbNw4vv/xyq+dHRkYapxnV1NQY7z937pzxdkJCQqvn79u3r8munMalWOS4mHihDu/I6cuYt2qfyX+ZyCu+gnmr9uHI6cs2joyIiIiInE10mD/mT0syO/mi8JBj/rQkRIf52yawdqRSqTB9+nQA9f1epk+fbhzX3Jpff/0Vr776KgAgLCwMY8aMAQC4u7vjoYceAlDfI2X27NktlgNdvnwZL730kvHrqVOnNnm88Q4WtVrd7PyHHnrImCx64403mkxTauztt9/GkSNHAABJSUno06cPAMDHxwf9+/cHAKSnp2PXrl0tnn/ixAnjBKXGCZbGvV4a74Rp7LfffsP8+fOb3FdbW9viseRYWGpEHVpOQQWWrE2Htta8TLG2tg5L1qZjacpgl/jlSERERETS6dszCEtTBuPtjRkm/XGvW7APZk3u51LvK2fNmoW8vDxs374dpaWleOyxx9C3b1/ceeediI6Oxg033ICrV6/iwoUL+Omnn/DTTz9BFEX4+Pjgn//8Z5OJP48//jj27t2Lo0eP4syZM7j77rsxdepU3Hzzzairq8Px48exZs0aY++V0aNH4/bbb28Sj7+/P9zd3aHT6fDdd99h4MCB8PX1RXh4OFQqFcLDw/H8889j8eLFqKqqwuTJk/HAAw9g6NCh8PX1xYULF/DFF18Ym9recMMNWLZsWZNrzJw5E+np6TAYDJg1axbuuece3HbbbejSpQsqKipw5MgRbNiwAXV1dZDL5fjb3/5mPPeuu+7C1q1bAQDvvPMOLl26hCFDhsDf3x/FxcX46aef8N133zVLtFy5cgU+Pj7SfePIJgSxYW4WOazDhw9jypQpxq83bNiAW265xY4RuQZRFPHUst1W1+C+9+wwp98OSkREROTMsrOzodfr4ebmhpiYGHuHYySKIrLOluH7tHM4kNW0nF0uE5CcEIIxA3sgPirAJd9P6nQ6rFmzBv/6179a3GVyrb59++Lll19u0telQVVVFZ599lns2bOnzTXGjBmD1157rcUeLTNnzkRqamqT+xYvXoxJkyYZv/7ss8+wdOnSNneSJCQkYPny5YiIiGj22H/+8x+89NJLbZ6vVCqxcOFC3HfffU3uf+mll7Bx48ZWzwPqd9nEx8fj448/BgC8//77GDp0aJvndETW/Jtgi8/f3PFCHdbxs6VWJV0A4ELxFWSdLUNCdKBEURERERGRqxAEAQnRgUiIDoRaq0NZpRaaGj28PN0Q4Kdw+amZ7u7uePzxxzFx4kT88MMP2LdvH7Kzs1FRUYGqqip06tQJnTt3Rt++fTFixAgMHjy41QSUr68v3n//faSlpeHbb7/FkSNHUFJSAoPBgM6dOyMxMRETJ05ss6HukiVLEBAQgB9++AHl5eXw8fFBRUVFk2P+8pe/4I477sD69euRlpaG/Px8aLVadOnSBT179sR9992HYcOGwc2t5Y/SEyZMwC233IJNmzbh0KFDyMvLg1qtho+PD8LCwjBkyBD8+c9/NjYPbuyll17CoEGD8MUXXyArKwuVlZXw8PBA586d0atXL9xzzz24/fbbce7cOWPi5fvvv2fixQlwx4sT4I4X23ht3S+SjPsbdFNXPP9wfwkiIiIiIiJLOOqOFyKyD0fb8cLmutQhqbU6HMgqkmStA8eLoNbqJFmLiIiIiIiIXAsTL9QhlVZomo2MtpTBIKKsUivJWkRERERERORamHihDsncKUbXo6nRS7oeERERERERuQYmXqhDUnjIJV3Py5N9qomIiIiIiKg5Jl6oQwr094JMJs3IPrlMQICfQpK1iIiIiIiIyLUw8UIdklLhjlvjQyRZKzkhxOVHARIREREREZFlnDrx8sgjj6Bnz5748ssvJVvz0KFDmDlzJgYPHoz4+HgMHjwYf/3rX7F582Zw8rZruWtQd0nWGTOwhyTrEBERERERketx2sYUa9euxb59+yRbTxRFvPbaa1i7dm2T+0tKSlBSUoL9+/fjP//5D/75z3/C29tbsuuS/SREBSIi2Ad5xVcsXqNbsA/iowIkjIqIiIiIiIhciVPuePnyyy/x2muvSbrm6tWrjUmXHj16YOnSpfjiiy+wYsUK3HzzzQDqd8PMmjVL0uuS/QiCgNmT+1ncaFfhIcesyf0gCNL0iiEiIiIiIiLX41SJF71ejzfeeAMvvviipGU/+fn5WLlyJQAgNjYWX331FcaPH4+bbroJo0ePxvr16zF27FgAwM8//4wdO3ZIdm2yr+gwf8yflmR28kXhIcf8aUmIDvO3TWBE5HTUOg0KKouQU3YeBZVFUOs09g6JiIiIiByA05QaZWVl4ZVXXsGRI0cAAHK5HHV1dZKsvX79euh0OgDAvHnzmpUSyWQyLFq0CPv27UNFRQU+/PBDjBo1SpJrk/317RmEpSmD8fbGDJPKjroF+2DW5H5MuhARRFHEictnkJqzB78UHoVBNBgfkwky9AtJQP/QPgjzDYGXuwIqpT+U7l52jJiIiIiI2ptTJF7eeustfPDBB8ZdLjfffDOGDRuGZcuWSbJ+amoqACA4OBgDBw5s8Rhvb2+MHDkSn3/+OY4fP46CggKEhYVJcn2yv+gwf6x8dhiyzpbh+7RzOJBVBIPhj11VcpmA5IQQjBnYA/FRASwvIiLkludh1aG1yK8qavFxg2jA4YtHcfjiUeN9MkGGpNBEjIgegrigWP5bQkRERNQBOEXiJTMzE6IoolOnTpg5cyYefvhhfPvtt5KsffHiRRQV1b9pHjBgQJvHJiUl4fPPPwcAHDhwAPfff78kMZBjEAQBCdGBSIgOhFqrQ1mlFpoaPbw83RDgp+DIaCIyOlZ8Cm+mrUaNvsas8wyiAQcLMnCwIAPhviFIGTANkaoIG0VJRERERI7AKRIvPj4+mD59Oh577DGoVCpJ187JyTHe7t69e5vHRkT88ea48XnkepQKdyZaiKhFueV5FiVdrpVfVYSFu5dj7qAZ6BPcS6LoiIiIiMjROEXiZeXKlZDJbNMH+NKlS8bbXbt2bfPY4OBg4+3i4mKbxENERI5LFEWsOrTW6qRLgxp9Dd5MW41Fw+Zw5wsRERGRi3KKqUa2SroAQEVFhfF2p06d2jxWqVQab1+5cv0mrERE5FpOXD7Tak8XS9Xoa7AqfZ2k0/qIiIiIyHHYbMfLrl27kJKSYtG5P/zwQ7s1rq2trTXe9vT0bPNYhULR4nlERNQx7MzZa5N18ysv4mRJNuKCYm2yPhERERHZj1PseLEluVxuvG3OdAlOoiAi6ljUOg3SCzNttn5qzh6brU1ERERE9mOzHS89evTAE088YdG5vr6+EkfTusblQzU1bdfsa7Va420PDw+bxURERI6nXF0Bg2iw2frpBZlQ6zRQunvZ7BpERERE1P5slniJiorC7NmzbbW8ZBr3dVGr1W0e2/hxf39/W4VEREQOSCtRQ93WGEQDyjUVTLwQERERuZgOX2oUGhpqvH29SUWNHw8KCrJZTERE5HgUbm33AZOCVmfb5A4REZGtzZw5Ez179kTPnj2xdu1as89Xq9Xo27cvevbsieTkZKfprfnQQw8Zn7der7d3OE289957xtj2799v73A6pA6feImN/aORYV5eXpvHNn48JibGZjEREZHjUSn9IRNs+2tT4W775A4REdmHWqdBQWURcsrOo6CyCGqdxt4h2cTEiRONt7ds2WL2+Tt37jRWGtx7771s8UAuwWalRs5CpVIhIiICeXl5+OWXX9o8Nj093Xj7lltusXVoRETkQJTuXkgKTcTBggybrC8XZFB5+dtkbSIisg9RFHHi8hmk5uzBL4VHm/QKkwkyJIUmYkT0EMQFxbrM8I7BgwcjODgYxcXFOHHiBHJzcxEZGWny+d98843x9v3332+LEInaXYff8QIAo0ePBgCcP38ehw8fbvGY6upq7NixAwDQu3dvREREtFt8RETkGEZED7HZ2v3DEtnfhYjIheSW5+HZHYvx8k8rcKjgSLMG7QbRgIMFGXj5pxV4dsdi5Ja3vfveWchkMowbN8749datW00+t6ioyPjH7sTERERHR0sen618+umnOH36NE6fPg03tw6/v4GuwcQLgEmTJkGhUAAAFi5ciIqKiiaPGwwGvPTSS6isrAQATJ8+vb1DJCIiBxAXFItw3xCbrD0yeqhN1iUiovZ3rPgUFu5ejvyqIpOOz68qwsLdy3Gs+JSNI2sfEyZMMO7gMSfxsnnzZhgM9QmqxiVLRM6uQyRevv76a2Mzoeeff77Z42FhYUhJSQEA5OTkYPz48fj888+RmZmJ1NRUPPTQQ8Z/MAYPHoyxY8e2a/xEROQYBEFAyoBp8JS40W64X1f07szeYUREriC3PA9vpq1GjZnT8Gr0NXgzbbVL7HwJDw9HUlISACA/Px+ZmZkmnbd582YAgFKpxF133WWr8IjaHfdA/c/jjz+O0tJSrFu3DoWFhfjHP/7R7JikpCS88847LlN/SURE5otURWDuoBkWvaluiaebJ1KSpvJ3CxGRCxBFEasOrbX490ONvgar0tdh2cgXnf73woQJE3Do0CEA9bteEhMT2zz+6NGjyM3NBQDcdddd6NSpU5PHS0tLsX79euzduxcFBQVQq9VQqVRITEzE3XffjeHDh7f4mh06dAgPP/wwAGDDhg0wGAxYtmwZfvvtNygUCkRGRuLFF19EfHw8gPqBKhs3bsSBAwdw4cIF6HQ6+Pv7IzY2FkOGDMH999/fLDagfqpRQ5nUiRMnWiw3qq2txa5du7BlyxacOXMGly9fhpeXF6KionDnnXdi8uTJUCqVrb5G6enp+Oqrr5CRkYHLly9DLpejS5cuSEpKwuTJk9GrV682X+O2iKKI3bt349tvv8WxY8dQVlYGDw8PdO3aFQMHDsSUKVNabbfx/PPPG3vz7Ny5E926dWvxuPfeew8rV64EAKxZswYDBw5s9phcLsfJkyexceNGrFmzBhcvXoS/vz969eqFFStWtPjaOwMmXhqZP38+hg8fjs8++wwZGRkoLy+Hl5cXbrzxRtx7770YP348ZLIOsUmIiIja0Ce4FxYNm4NVh9aavI28JZ5unpg7aAYiVewbRkTkCk5cPmPV7wUAyK+8iJMl2YgLir3+wQ5s5MiRWLx4Ma5cuYJt27Zh3rx5bfY+aaup7tatW/GPf/zDOO2owaVLl5CamorU1FQMGDAAK1asgEqlavUahw8fxsqVK6HT6QAANTU1OHXqlDFRsG3bNvz9739vNsK6pKQEJSUlSEtLwwcffIDVq1cbEzWmys/Px8yZM3Hy5Mkm9+t0Ohw5cgRHjhzBhg0b8MEHHyAqKqrJMdXV1Zg/fz5SU1ObrXvu3DmcO3cOX3zxBaZMmXLd17klly5dwrPPPttkmAxQnyg6c+YMzpw5g/Xr1+Ppp5/GE088Ydballi9ejWWL19u/LqkpARhYWFOm3QBnDjxMn78eIwfP17yY5OSkozb4oiIiFoTqYrAslELcLIkG6nZe5BemNmscWJbwv26IiVpKpMuREQuZGfOXknWSc3Z4/SJF4VCgTFjxmDTpk0oLy9HWloahg5tuZ9ZbW0ttm/fDgCIiYlpsjtm69atePbZZwEAAQEB+Mtf/oJbbrkFSqUS+fn52Lx5M/bs2YNDhw5h+vTp2LhxI7y8Wm5W/+6778LT0xOzZ89G3759kZeXh5KSEvj4+CA/Px/PP/88amtroVKp8OijjyIhIQEKhQLFxcXYsmUL/vvf/6K0tBQzZ87Ejh07TB51XVFRgcmTJ6OkpAQAkJycjIkTJyIiIgIlJSX46quvsHv3bhQWFuKxxx7Dli1b4O3tbXxt/va3vxmTIuHh4Zg6dSp69+4Ng8GAjIwMrF27FuXl5Vi/fj0qKyuxbNkyk+ICgMrKSkyfPh05OTkAgF69emHKlCmIjo6GVqvFgQMH8Omnn0KtVuPtt9+GRqPB7NmzTV7fXHV1dVixYgXCw8Mxc+ZMhIaG4tixYwgPD7fZNduD0yZeiIiI7E0QBMQFxSIuKBZqnQblmgpoarUovFKM9MKjyLh4vEkyRi7I0D8sESOjh6J35xin30ZORER/UOs0SC/MlGSt9IJMqHUap592N3HiRGzatAlAfQKltcTL7t27jQNOGjfVLS0tNbaA6NmzJ9auXdtkR0ufPn0wZswYfPzxx3j99ddx6tQp/Pvf/241MVBXV4clS5YYp9r269fP+NjmzZtRU1NfIrZy5UrcfPPNTa4zYsQI/OMf/8Dnn3+OwsJC7N27F8OHDzfpdXj99deNSZfHH38c//d//9fk8eHDh+OVV17Bp59+isLCQqxfv964s2TdunXGpMvAgQPxz3/+s0liqX///pgwYQL++te/4syZM9i6dSuGDRuGMWPGmBTb22+/bUy63HPPPXjttdcgl8uNj996660YP348Hn74YVy6dAmrV6/Gbbfdhr59+5q0viU8PDzwySefoGvXrgDQ5HvhrJh4ISIikoDS3cv4BjkmsAdu63GrMRmj1dVA4e4JlZe/07+JJiKilpWrK8za+dgWg2hAuabC6X9nJCQkoGfPnjh9+jR++OEHqNXqFnuYfPvttwAAd3d33HPPPcb7169fbywvWrJkSatlRNOnT0dqaioyMzOxYcMGPP300y2W23Tu3BmjRo1qcY2GxAgAdO/evcVjHn30UQD1w1lM3YFRXV1tHNRy4403tpoU+r//+z988803qK6uxt69e/HEE0/AYDDg448/BgB4e3vjrbfeanE3T2BgIN5++23cfffdMBgMeP/9901KvPz+++/46quvANTvpHnllVeaJF0adO/eHa+88goee+wxiKKIDz74AP/85z9Nev6WuP32241JF1fBhiVEREQ2onT3QphvCKIDuiPMN8Tp30ATEVHrtBI0XG+ynk7a9exlwoQJAAC1Wo1du3Y1e7y8vBw///wzgPqdH42TKz/99BMAwN/f/7o9Vf70pz8BAK5cuYKsrKwWj+nTp0+ru02jo6ONt5966ikcPXoUoig2OSYiIgIvv/wyHn/8cfTs2bPNeBr8/PPPxp4y48aNa7VnqJeXF7744gukpaXhs88+AwBkZWWhvLwcADB69Og2+9dER0djwIABAIDffvsNly5dum5sBw4cMMY2YcIEeHq2PrVxyJAhxl44jc+zhZtuuslma9sLEy9ERERERERWUri1/qHVovXcpV3PXu655x64u7sDgHHnR2Nbt241fohvXGak1+tx+vRpAPU9Unr27Nnmf++9957x3Ly8lkdyh4SEtBrnfffdZ3w8IyMDkyZNwp/+9CfMnTsXX3/9tUmJjJacO3fOeDsuLq7NY6OiohAYGGj8+syZM8bb15sKBaBJ+U92dvZ1j298jCnrNxyjVqtRUFBw3eMt1db3yVkx8UJERERERGQlldIfMkGaj1dyQQaVl78ka9nbDTfcgDvuuAMAsH//fuMOjgabN28GAISGhjYZL1xVVQWDwbLSrcrKyhbvb2hY2xIfHx+sW7cO/fv3N95XUlKCLVu2YN68eRgyZAjGjRuHDz/8sNl0pbaUlZUZb99www0mnwfUlwI1aJyQaU3jYxp65ki5fkBAgFnrW6qt75OzYo8XIiIiIiIiKyndvZAUmoiDBRlWr9U/LNGlylMnTpyIHTt2QK/XY9u2bXjwwQcB1O+4OHHiBID6SbSNy3D0er3xdr9+/YxNdk3RpUsXi+Ls1q0b1q9fj5MnTyI1NRU///wzTp06ZUwAnTx5EidPnsRnn32GdevWmdTnxZqSnGtLna6ncaKqtZKm1o43Nx5T1rf0eq44fICJFyIiIiIiIgmMiB4iSeJlZHTL03+c1aBBgxASEoKioiJs3brVmHj55ptvANR/iB8/fnyTc/z9/Y23q6qq0KtXr3aLt3fv3ujduzdmz56NyspK/PLLL/j555+xY8cOVFRUoLCwEC+++CLWrVt33bUaPw9zd4k0Pre0tPS6xzc+pvG5rWm8A6e0tBQxMTFtHt+4AXHj9RsnStpKFl25cuW6MbkqlhoRERERERFJIC4oFuG+1vWnCPfrit6d2/4A7GxkMhnGjRsHAMjMzMTFixchiiK2bdsGoD4xc+0UGw8PD+N0obNnz+Ly5cttXmPPnj34+OOPkZqa2qycyRS1tbU4c+aMcQdOAz8/PwwfPhyLFi3C999/j86dOwMADh48CI1Gc911GzftPXnyZJvHLlq0CCNGjMD06dNRVVXVpIFvZmbmda915MgR4+3IyMjrHm/u+kePHgUAeHp6Nvl+NZ4gpdVqWz2/uLj4utdwVUy8EBERERERSUAQBKQMmAZPCxvterp5IiVpqkuWWowfP974vHbt2oWjR4+iqKgIQNOmuo01TCoSRRGffPJJq2vrdDosXLgQr7/+Op555pk2P/y3RBRFDB06FHfffTdmzpzZ6nGBgYFNGuTW1tZed+0BAwYYRzS31Fy4gV6vxw8//IALFy4gNzcXvr6+iIuLM04y2r59e5OeLNfKyclBeno6gPomvcHBwdeNLTk52dj4+D//+U+bz2fv3r3Iz883PqeG8wDA19fXeLu1prsajQa//PLLdWNyVUy8EBERERERSSRSFYG5g2aYnXzxdPPE3EEzEKmKsFFk9hUeHm4cd/zf//4XO3bsAACoVCpj891rPfzww8bdFB9//DF++OGHFo9bvHixMYlz5513Nts9cz2CIGDYsGEA6hMHrSV5CgsLkZFRX0oWEREBPz+/667dpUsXjBo1CgBw7NgxvP/++y0et2LFCuPkpEmTJgGo30ny8MMPAwCqq6vx7LPPtphUKi8vx5w5c4xlPo899th14wLqX/v77rsPAJCfn4+FCxeirq6u2XF5eXlYsGABgPrX6pFHHmnyeOMysE8//bTZGgaDAa+++qpNG/I6OvZ4ISIiIiIiklCf4F5YNGwOVh1ai/yqouseH+7XFSlJU1026dJgwoQJOHjwIH799VfjmOX77ruvye6JxiIiIvDcc89hyZIlqKurw1NPPYV77rkHo0aNwg033ICCggJs2rTJuJPC398f8+fPtyi2J554Atu3b4darcaSJUtw+PBhjBo1Cl27dsXVq1dx8uRJrFu3DlVVVQCAlJQUk9eeN28e0tPTUVJSgrfeeguZmZm477770KVLFxQWFmLz5s346aefANSXJk2fPt147qOPPoo9e/bgyJEj2LdvH+655x48/PDD6N27NwwGAzIyMrBu3Tpjf5cxY8YYy7pM8dxzzyE9PR0XLlzA119/jdOnT+Mvf/kLoqOjodVqceDAAaxfvx7V1dUAgMcffxzJyclN1rjtttvg7++PiooKHDx4EI888ggefPBBBAYG4ty5c9i0aRMyMzPRrVs3XLhwweTYXAkTL0RERERERBKLVEVg2agFOFmSjdTsPUgvzIRB/GOqi1yQoX9YIkZGD0XvzjEuWV50rZEjR2Lx4sWoqqoyNmptrcyowdSpUyGXy/Haa69Bp9Ph22+/xbffftvsuNDQUPzzn/9ESIhlPXYiIiLwzjvvYPbs2aiurkZqaipSU1ObHefu7o6ZM2cad4qYonPnzvjkk0/w5JNP4ty5c/jhhx9a3L0THx+Pf/3rX1AoFE2u9+GHH+LZZ5/F7t27ceHCBSxevLjZuYIgYPr06Zg9e7bJcQH1ZUKffvopnnnmGRw5cgQnTpzACy+80Ow4d3d3zJkzB3/961+bPebt7Y3XXnsNzzzzDGpqanDgwAEcOHCgyTHJyclISUnBQw89ZFZ8roKJFyIiIiIiIhsQBAFxQbGIC4qFWqdBuaYCWl0NFO6eUHn5u9TIaFN4enpi7Nix+OyzzwAAffv2RVRU1HXPe/DBBzF8+HBs2LABaWlpKCgowNWrV+Ht7Y3Y2FgMHz4c999/P5RKpVXxDRkyBNu3b8fGjRuxf/9+nD9/HtXV1VAqlQgJCcGgQYMwadIk9OjRw+y1IyMjsWXLFnzzzTdITU3FqVOnUFVVBW9vb9x4440YO3Ysxo0b16RRbQNvb2/8+9//xv79+/H1118jIyMDpaWl8PDwQNeuXXHrrbdi4sSJ151K1JouXbpg48aN2LlzJ7777jscO3YMZWVl6NSpE0JDQzF06FBMmDABYWFhra4xbNgwbNu2DWvWrMHPP/+MoqIiKJVKxMbG4r777sP48eORlZVlUXyuQBDNHQ5O7e7w4cOYMmWK8esNGzbglltusWNERERERESOIzs7G3q9Hm5ubhZ/+CQi12HNvwm2+PzN5rpERERERERERDbCxAsRERERERERkY0w8UJEREREREREZCNMvBARERERERER2QgTL0RERERERERENsLECxERERERERGRjTDxQkRERERERERkI0y8EBERERERERHZCBMvREREREREREQ2wsQLEREREREREZGNMPFCRERERERERGQjTLwQEREREREREdkIEy9ERERERERERDbiZu8AiIiIiEg6erUataVlqNNqIVco4BEYADel0t5hERERdVhMvBARERE5OVEUUXk8C8XbdqDsUDpgMPzxoEyGgOQBCB49En4J8RAEwX6BEhERdUBMvBARERE5seqzuche8S7UefktH2AwoGz/AZTtPwBlRDhiZs2Ed1Rk+wZJRETUgbHHCxEREZGTqsg8iuPzF7SedLmGOi8fx+cvQEXmURtHRkRERA2YeCEiIiJyQtVnc3Fq6RswaLVmnWfQanFq6RuoPptro8iIiIioMSZeiIiIiJyMKIrIXvGu2UmXBgatFtnvvAdRFCWOjIiIiK7FxAsRERGRk6k8nmVyeVFr1BfyUJV1QqKIiIiIqDVMvBARERE5meLtqZKsU7R9hyTrEBE1mDlzJnr27ImePXti7dq1Zp+vVqvRt29f9OzZE8nJyTh37pxxvWeffVb6gM3QEMfkyZMlX7ugoMBhnidJj4kXIiIiIieiV6tRdvCQJGuVHTgEvVotyVpE1Da9Wg11Xj6unMmGOi/fZf/fmzhxovH2li1bzD5/586dUP/vtbn33nvh7u4uWWxE9sJx0kREREROpLa0DDAYpFnMYEBtWTnclEpp1iOiJkRRROXxLBRv24GyQ+lN/9+VyRCQPADBo0fCLyEegiDYL1AJDR48GMHBwSguLsaJEyeQm5uLyEjTR9h/8803xtv333+/LUIkandMvBARERE5kToLG+q2up5GI+l6RFSv+mwusle823o/JoMBZfsPoGz/ASgjwhEzaya8o0xPUDgqmUyGcePG4V//+hcAYOvWrXjmmWdMOreoqAjp6ekAgMTERERHRwMATp8+bZtgzWTLOMLCwhzmeZL0WGpERERE5ETkCoW063l5SboeEQEVmUdxfP4Ck5tgq/PycXz+AlRkHrVxZO1jwoQJxh08W7duNfm8zZs3w/C/XUGNS5aInB0TL0REREROxCMwAJBJ8xZOkMvhEaCSZC0iqld9Nhenlr5h9rh3g1aLU0vfQPXZXBtF1n7Cw8ORlJQEAMjPz0dmZqZJ523evBkAoFQqcdddd9kqPKJ2x8QLERERkRNxUyoRkDxAkrVUyUns70IkIVEUkb3iXbOTLg0MWi2y33kPoihKHFn7mzBhgvG2Kbtejh49itzc+qTTXXfdhU6dOgFoe9rPoUOHjI8dPnwY6enpmDRpEvr06YOkpCT8+c9/RlZWVpNzzp07h4ULF2LEiBGIj49H//79MWXKFPznP/+BKIp4//33jWteq7WpRo1jbEgefffdd5g+fToGDhyI+Ph43HbbbXjuueeaxdPSGm1NNbp06RL+9a9/YeLEibj11lsRHx+PIUOG4Omnn8aePXvafI2Li4vx3nvv4aGHHsLgwYORkJCAxMREDB06FH/729/w7bffQq/Xt7kGWYY9XoiIiIicTPDokSjbf8DqdUJGj5IgGiJqUHk8y+TyotaoL+ShKusE/BLiJYrKPkaOHInFixfjypUr2LZtG+bNmwc3t9Y/flrbVPfw4cNYuXIldDodAKCmpganTp1Ct27djMds27YNzz33nPEYANDpdDh8+DAOHz6M7777DomJiWZfu7Ha2lo88cQT2L17d5P7i4qKsHnzZmzZsgUvvvgiHnzwQbPX3rx5MxYuXAjNNb25Ll26hJ07d2Lnzp0YO3YsXnvttWbToNauXYtly5Y1ee4NNBoNiouL8eOPP+Lzzz/Hhx9+aEx8kTSYeCEiIiJyMn4J8VBGhFv1AU/ZLQK+8XESRkVExdtTJVmnaPsOp0+8KBQKjBkzBps2bUJ5eTnS0tIwdOjQFo+tra3F9u3bAQAxMTEWJT/effddeHp6Yvbs2ejbty/y8vJQUlICHx8fAMDu3bsxZ84ciKIIT09PPPjggxgyZAjc3d3x66+/4qOPPsL+/ftx+PBhi58zALzzzjsoKSnBjTfeiIceeggxMTGoqKjA119/jR07dkAURSxduhSDBw9G9+7dTV5369ateO655wAAnp6eeOCBBzBkyBB06tQJv/32Gz788EMUFhbiu+++Q0BAAObPn9/k3KVLlwIAsa9R1AAAIHpJREFUOnfujAcffBC9e/eGn58fLl++jF9++QWff/45tFotMjIysHr1asyZM8eq14GaYuKFiIiIyMkIgoCYWTNxfP4Ci0oaZAoFYp552mXG1xI5Ar1ajbKDhyRZq+zAIejVaqcvBZw4cSI2bdoEoP7Df2uJl927d6OiosJ4jiXq6uqwZMkSjB49GgDQr18/42NarRaLFy+GKIrw8vLCunXrcNNNNxkfv/nmm3H33XfjwQcfREFBgUXXb1BSUoLhw4djxYoVTXadDB06FAsWLMAXX3wBvV6PzZs3mzzt6ffff8crr7wCoL7/zSeffIKEhATj4/369cOoUaMwadIk5OfnY8OGDZg6dSpCQ0MBAG+//TYAwMfHBxs2bGiyCwgA7rzzTowaNQpTpkyBwWDA9u3bmXiRGHu8EBERETkh76hI9Jr3HGRmTjmSKRToNe85lxhbS+RIakvLgP9N5LGawYDasnJp1rKjhIQEY6+UH374AWq1usXjvv32WwCAu7s77rnnHouu1blzZ4wa1XL55M6dO1FYWAgAePLJJ5skXRqEhIRgyZIlFl27Mblcjn/84x/NSn0AYNKkScbbZ86cMXnN1NRUY2Jq5syZTZIuDVQqFZ5++mkA9Umo/fv3AwAKCwvRqVMneHt7Y9y4cc2SLg369euHsLAwAPVlUSQtJl6IiIiInJR/4k1IWLIYyohwk45XdotAwpLF8E9s/qGDiKxTZ2FD3VbXu6aPh7NqaLKrVquxa9euZo+Xl5fj559/BgAMHz4cKpVlk9b69OnT6i6+H3/8EUD9bsG2dtQMGDAAUVFRFl2/QUxMDLp06dLiYxEREcbbrSWhWtI4/vvuu6/V40aPHo3NmzcjMzPT2CcnNDQUW7duxa+//op58+a1eZ3OnTsDqO97Y5AqiUgAWGpERETk9NQ6DcrVFdDqa6Bw84RK6Q+lu5e9w6J24h0VicR330ZV1gkUbdtRX+rQ6A2zIJdDlZyEkNGj4Bsfx/IiIhuRm7n77LrrebnGv+P33HMP3nzzTeh0OmzdurXZjpatW7caG75aWmYE1O9Yac2pU6cA1CchrpfYuemmm3D27FmL42jYNdISZaPSMXOmB507dw5Affw33HBDq8d5eHjgxhtvbPVxmax+38XVq1dRUFCA/Px8nD9/HqdPn0ZGRkaTMiuDwWA8nqzHxAsREZETEkURJy6fQWrOHvxSeBQG8Y8P2jJBhqTQRIyIHoK4oFh+0O4ABEGAX0I8/BLioVerUVtWjjqNBnIvL3gEqJy+TwSRM/AIDABkMknKjQS5HB4Blu38cDQ33HAD7rjjDuzYsQP79+9HeXl5k+RHw/jl0NBQDBw40OLreHt7t/pYSUkJAJi0myYgIMDiGADAq42EmaW/j0tLSwGgzaTL9Vy4cAFr167Fnj17jGVX15LJZNzpYiNMvBARETmZ3PI8rDq0FvlVLddgG0QDDhZk4GBBBsJ9Q5AyYBoiVREtHkuux02pZKKFyA7clEoEJA+QZNS7KjnJpf4/njhxInbs2AG9Xo9t27YZRylnZ2fjxIkTAIDx48fbbIdFw44aZ00qmLM7piVbtmzB/Pnzm4yS9vb2RmRkJKKjoxEfH48BAwZg0aJFSE9PtzZcagETL0RERE7kWPEpvJm2GjX6GpOOz68qwsLdyzF30Az0Ce5l4+iIiDq24NEjJUm8hIxuuUmssxo0aBBCQkJQVFSErVu3GhMv33zzDYD6nRbjx4+32fVvuOEGXLp0Cb///vt1jzXlmPbm5+eHkpISY4Ndc+Tk5BiTLkqlEikpKbjzzjsRERHRbAfO1atXJYqYrsWiLSIiIieRW55nVtKlQY2+Bm+mrUZueZ6NIiMiIgDwS4g3udl1a5TdIuAbHydRRI5BJpNh3LhxAIDMzExcvHgRoihi27ZtAOoTM127drXZ9Xv37g2gfsJPeXnb06KysrJsFoelYmJiAAAFBQWoqqpq9bja2loMGTIE48aNw3vvvQcA2LRpk3Gny4svvohHH30U3bp1a5Z0EUURxcXFNnoGxMQLERGRExBFEasOrTU76dKgRl+DVenrIIqixJEREVEDQRAQM2um2WPeG8gUCsQ887RL9uYaP3688Xnt2rULR48eNY4ttqaprinuuOMO4+2GnjItOXHiBH777TebxmKJht43oihi69atrR538OBBXLp0CSdPnjSWVTU05gXQ4hjqBvv27UNZWZnx67q6OmvDpkaYeCEiInICJy6fabWni6nyKy/iZEm2RBEREVFLvKMi0Wvec2YnX2QKBXrNew7eUZE2isy+wsPDMWDAAADAf//7X+zYsQNAfcPbxokRWxg7dqxxVPLKlSuNU44a+/333/H888/bNA5LTZw40TgR6d13321x6lJ1dTVeffVVAICbm5uxdKtxQ96ffvqpxfV/++03zJ8/v8l9tbW1UoRO/8MeL0RERE5gZ85eSdZJzdmDuKBYSdYiIqKW+SfehIQli5G94l2o8/Kve7yyWwRinnnaZZMuDSZMmICDBw/i119/Ne7EuO++++Du7m7T63p5eWHRokVISUlBdXU1Jk+ejIceegiDBw+Gh4cHjh07ho8//thhS21uuOEG/OMf/8Dzzz+PiooK3H///XjooYdw6623Qi6X47fffsNHH31k3EH05JNPIjy8vuTtrrvuMu6Seeedd3Dp0iUMGTIE/v7+KC4uxk8//YTvvvuuWaLlypUr8PHxad8n6sKYeCEiInJwap0G6YWZkqyVXpAJtU4DpXvr4y6JiMh63lGRSHz3bVRlnUDRth0oO3ioyahpQS6HKjkJIaNHwTc+ziXLi641cuRILF68GFVVVcYRz7YuM2pwxx134OWXX8bLL78MjUaD999/H++//36TY0aMGIHff/8dv/zyCzw8PNolLlONGzcOtbW1eOWVV3D16lX8+9//xr///e8mxwiCgMceewwpKSnG+26//XZMnjwZGzduhF6vx/r167F+/fpm6yclJSE+Ph4ff/wxgPqJU7bsu9PRMPFCRETk4MrVFTCI0ozANIgGlGsqmHghImoHgiDALyEefgnx0KvVqC0rR51GA7mXFzwCVC41MtoUnp6eGDt2LD777DMAQN++fREVFdVu1580aRJuvvlmfPLJJ9i/fz8uXboEDw8P9OrVC5MmTcLdd9+Nhx56CACMpT2O5IEHHsCf/vQnfPrpp9i3bx8KCwtRW1uLoKAg9O/fH1OmTEGfPn2anffSSy9h0KBB+OKLL5CVlYXKykp4eHigc+fO6NWrF+655x7cfvvtOHfunDHx8v3332Po0KHt/RRdliCyy57DO3z4MKZMmWL8esOGDbjlllvsGBEREbWnnLLzmL/rdcnWWzL874gO6C7ZekRE9padnQ29Xg83NzfjBBgiS4wdOxbZ2dmIjY1ts5EtOTZr/k2wxedv7nghIiJycAo3T2nXc5d2PSIiIkf2/fffY8eOHYiIiMC0adOMjXavVVRUZOw9c+ONN7ZniOTimHghIiJycCqlP2SCTJJyI7kgg8rL3/qgiIiInIRCocDOnTsBAHK5HHPmzGl2jF6vx6uvvgq9Xg+gvh8NkVSYeCEiInJwSncvJIUm4mBBhtVr9Q9LZH8XIiLqUAYPHozQ0FAUFhbi/fffx4ULFzBq1CiEhIRAo9Hg/Pnz+OKLL3Dy5EkA9Y14hw8fbueoyZUw8UJEROQERkQPkSTxMjKajfKIiKhj8fT0xD//+U888cQTKCoqwo4dO7Bjx44Wjx0zZgxefvnldo6QXB0TL0RERE4gLigW4b4hyK8qsniNcL+u6N2ZTSeJiKjjufHGG7F9+3Z89dVX+OGHH3DmzBlUVlZCqVSic+fO6Nu3L+655x4MGDDA3qGSC2LihYiIyAkIgoCUAdOwcPdy1OhrzD7f080TKUlTIQiCDaIjIiJyfF5eXnjooYeMI6OJ2ovM3gEQERGRaSJVEZg7aAY8zZxy5OnmibmDZiBSFWGjyIiIiIioNUy8EBEROZE+wb2waNgchPuGmHR8uF9XLBo2B32Ce9k4MiIiIiJqCUuNiIiInEykKgLLRi3AyZJspGbvQXphZpNR03JBhv5hiRgZPRS9O8ewvIiIiIjIjph4ISIickKCICAuKBZxQbFQ6zQo11RAq6uBwt0TKi9/jowmIiIichBMvBARETk5pbsXEy1EREREDoo9XoiIiIiIiIiIbISJFyIiIiIicmpyuRwAUFdXB1EU7RwNEdmTKIqoq6sD8Me/DfbGxAsRERERETk1Dw8PAPUfuNRqtZ2jISJ7UqvVxgRsw78N9sbECxEREREROTVfX1/j7fLycu56IeqgRFFEeXm58evG/zbYExMvRERERETk1Ly9vSEIAgCguroaBQUFuHr1KhMwRB2EKIq4evUqCgoKUF1dDaB+AqS3t7edI6vHqUZEREREROTUZDIZQkNDUVhYCFEUUV1djerqagiC4DA9HojIdq7t7yQIAkJDQyGTOcZeEyZeiIiIiIjI6fn4+DRJvgD1fwXX6/V2joyI2lND0sXHx8feoRgx8UJERERERC7Bx8cHsbGxqK6uRlVVFWpra43TTYjIdcnlcnh4eMDX1xfe3t4Os9OlARMvRERERETkMmQyGXx9fR2mqSYRkWOlgYiIiIiIiIiIXAgTL0RERERERERENsLECxERERERERGRjTDxQkRERERERERkI0y8EBERERERERHZCBMvREREREREREQ2wsQLEREREREREZGNMPFCRERERERERGQjTLwQEREREREREdkIEy9ERERERERERDbCxAsRERERERERkY242TsAuj61Wt3k69OnT9spEiIiIiIiIiLXde3n7Ws/j1uCiRcnkJ+f3+Trl19+2U6REBEREREREXUc134etwRLjYiIiIiIiIiIbISJFyIiIiIiIiIiG2GpkRO4/fbbm3wdEREBLy8vO0VDRERERERE5Jo0Gg3y8vKMX1/7edwSgiiKotWrEBERERERERFRMyw1IiIiIiIiIiKyESZeiIiIiIiIiIhshIkXIiIiIiIiIiIbYeKFiIiIiIiIiMhGmHghIiIiIiIiIrIRJl6IiIiIiIiIiGyEiRciIiIiIiIiIhth4oWIiIiIiIiIyEaYeCEiIiIiIiIishEmXoiIiIiIiIiIbISJFyIiIiIiIiIiG2HihYiIiIiIiIjIRph4ISIiIiIiIiKyESZeiIiIiIiIiIhshIkXIiIiIiIiIiIbYeKFiIiIiIiIiMhGmHghIiIiIiIiIrIRJl6IiIiIiIiIiGyEiRciIiIiIiIiIhth4oWIiIiIiIiIyEaYeCEiIiIiIiIishEmXoiIiIiIiIiIbMTN3gEQ2dq5c+fw6aefYv/+/SgqKoKbmxu6du2K2267DZMmTUJ4eLi9QyQbys/Px2effYaDBw8iPz8fWq0Wfn5+uPHGGzF69Gjce++9cHd3t3eY1M4eeeQR7Nu3D6+88gruv/9+e4dDVjAYDPjmm2/w7bff4vTp01Cr1ejcuTP69euHP//5z+jfv7+9Q6R2tmDBAnzxxRd44oknMHv2bHuHQzZWWlqKjRs3Yt++fTh37hzUajW8vb0RExODO+64A5MmTYJSqbR3mGQjly5dwqeffoo9e/agoKAAANClSxcMHjwY999/P3r27GnnCMkeqqqqMGbMGFy+fBl33303li1bZu+QIIiiKNo7CCJb2bRpE1599VXU1ta2+LiXlxfmzZuHBx54oJ0jo/awceNGLFmypNXvPwDceOONWLVqFcLCwtoxMrKntWvXYunSpQDAxIuTu3LlCp588kmkp6e3+LggCJg2bRqef/75do6M7OW///0vnnrqKQBg4qUD2LVrF55//nlcuXKl1WNCQ0OxatUq9OrVqx0jo/awa9cu/P3vf0d1dXWLj7u5ueGJJ57A008/3c6Rkb3NnTsXW7ZsAQCHSbxwxwu5rB07dmDhwoUAAE9PT0ydOhVJSUlwc3PDkSNH8OGHH+Lq1atYuHAh/Pz8MGrUKDtHTFLasmULXnrpJQCAUqnEgw8+iIEDB6JTp044f/48Nm7ciIyMDPz222945JFH8J///Afe3t72DZps7ssvv8Rrr71m7zBIAqIoYtasWcaky+DBgzF58mQEBgbi1KlT+OCDD1BYWIg1a9ZApVLh8ccft3PEZGt79uxhoqUDSU9Px6xZs6DT6eDu7o5Jkybhtttug7+/P4qKivDNN99g9+7dKCwsxPTp0/H1118jJCTE3mGTRI4cOWL8/svlckyaNAlDhgyBt7c3Tp48iQ8++AClpaVYuXIlOnXqhOnTp9s7ZGonu3btMiZdHAl3vJBLqq2txe23346SkhK4u7vjs88+Q58+fZocc/bsWUyYMAEajQYhISHYtWsX3NyYi3QFV69exfDhw1FeXg5fX1989tlniImJaXKMwWDAwoUL8cUXXwAAZsyYgTlz5tgjXGoHer0ey5cvx0cffdTkfu54cV5btmzB3LlzAQDjx4837mJqUFFRgSlTpiAnJweenp7YuXMngoOD7REqtYO1a9di2bJl0Ol0xvu448V1iaKIsWPHIicnB+7u7vjoo48wYMCAZsetWrUK7777LgBg7NixeOutt9o7VLKRcePG4eTJkwDqv8/Dhw9v8nhZWRnuvfdelJSUQKlU4qeffoKfn589QqV2VF5ejrvvvhulpaXG+xxlxwub65JL2r17N0pKSgAADz30ULOkCwBERUVh4sSJAICioiJkZma2Z4hkQ7t370Z5eTkA4Mknn2yWdAEAmUyGF154ASqVCgDw7bfftmeI1I6ysrLw4IMPGpMucrnczhGRFNasWQMA8Pb2xt///vdmj/v7+2PRokUAgJqaGnzyySftGh+1j/Pnz+OJJ57A0qVLjX/5JteXmZmJnJwcAMCf//znFpMuQP17gNjY/2/v/mOqquM/jr9gQy1JRTGhwkBJa5mB+YMpphhqqTVLrbX8ATYIZxCV/RjhUJbiSiuHi7mpf5A6GUVMjaLJFOegBLNNm1AqeUFSMFCugUJxv38wzuCLoF+/nHvg8nxsbJ977+fsvrd7L+fc1z3n/RkjSfrxxx9VX1/vtBphntOnTxuhy9y5czuELpI0bNgwvf7665Kk+vp6HTlyxJklwiLr16/XlStXjOP7noTgBS7Jw8NDM2bMkK+v7y3/GbcaNWqUMf7rr7+cURqcoKioyBg/88wznc4bMGCAJk6cKKmlOVttba3ptcG5tmzZosWLF+vkyZOSpKeeeopfwF1AeXm5cdAdFhamIUOG3HLexIkTFRAQIKnl8lO4lj179mjBggU6fPiwJCkwMNAI2+Da7nQ/7+bmpmnTpklqORv6/PnzptcG8zU2Nio8PFwjR47U7NmzO53HcX7fkpOTox9++EHu7u5KTEy0upwOuK4CLmnWrFmaNWvWbeddvHjRGN9///1mlgQnmj59ujw9PVVVVXXbSwvaXm3ZVRNe9E6//vqrHA6HBg4cqLi4OC1fvpyzm1zAiRMnjHFISEiXcydPnqyysjJdvHhRNptNI0eONLs8OMmpU6fU1NSkfv36KTIyUqtXr+bs1T5i/PjxiomJ0eXLl41wtTNt9/M3b940uzQ4wYQJEzRhwoTbzuM4v++4cuWKkpOTJUkRERF68sknLa6oI4IX9FkXLlxQZmamJMnX1/eO/oGjdwgPD+/yTKdWTU1N+uWXXyS1NGAeNmyY2aXBye677z6tXLlSUVFRPfK0U9yd1ksMJMnf37/LuX5+fsb4jz/+IHhxIf3799eSJUu0atUqPfjgg1aXAycKCQm5beja6ueffzbGvE/6jpqaGu3atUtSyyILYWFhFlcEMyUlJam2tlYBAQGKj483Wk70JAQv6DMcDocaGhpks9mUk5OjvXv3ym63y8PDQxs2bJCHh4fVJcLJMjMz9ffff0uSpk6dSnNlF7Rt2za5u3NVrau5dOmSMX7ggQe6nNt2FZO226H3S0pK4vONLuXn5+vMmTOSpDFjxtBg28XdvHlTFRUVysvLU3p6uqqrq+Xm5qa1a9fKy8vL6vJgkuzsbB06dEju7u5KSUlR//79rS7plviWgT4jNzdXb731Vrv7AgMD9fHHHys4ONiiqmCV8+fPt1vdoLUBG1wLX8pc07Vr14zxwIEDu5x77733GmO73W5aTXA+Pt/oSk1NjZKSkozb7Odd26lTp4xFM1r5+Pho3bp1nO3iwi5fvqwNGzZIarnEqCd/pyN4QY9z6NAhrV69+q62zcvL00MPPXTLxyorKzvcZ7PZtHv3bg0dOlQPP/zwXT0nupdZr39bVVVViomJ0fXr1yVJCxcu1KRJk+7qOdH9nPEeQO/Wth/TgAEDupzb9nH6OAF9wz///KNVq1YZDVUnT56sF154weKqYKZbHedXV1crIyNDw4cP17hx4yyoCmZLTExUXV2d/P39O/zA3tMQvKDPCA4O1s6dOzVo0CBVVFTom2++0bFjx3Tw4EEVFBRox44devzxx60uEya7dOmSIiMjdeHCBUnSo48+qnXr1llbFID/k7ZLBru5uXU5t21jTc6QAFyf3W5XdHS00WjZx8dHn332GZ9/F+fv76/t27dr6NChqqqq0nfffaecnBwdPnxYP/30k1JTUzV9+nSry0Q3yszM1NGjR41LjG73Q4zVCF7Q4wQEBCgmJuauth00aFCnj7U99Wz8+PGaN2+eUlNTtW3bNtXU1Oidd97R999/z47ZYma9/pJ07tw5RUVFGV3uAwICtGPHDt1zzz139Xwwh5nvAbiGtpcP3bhxQ/369et0bttVTLqaB6D3q6qqUnR0tNHXxdvbW7t27dLw4cMtrgxmGzt2rMaOHWvcDg8PV2hoqBISEtTQ0KA1a9YoLy9Pnp6eFlaJ7lJZWalNmzZJklasWNErFkkheEGPM3r0aL399ttOea7Y2FgdOXJEp0+f1p9//qnjx4/fcZd8mMOs17+wsFBxcXGqq6uT1NJkj4OxnsmZ/wPQO7Xt69LQ0NBl4FZfX2+MBw8ebGpdAKxTUlKiN954w2ii7ePjo127dmn06NEWVwarLFq0SPn5+crNzdXVq1eVm5urRYsWWV0W/p8cDoc++ugjXb9+Xf7+/oqPj7e6pDvCT/vo8+bMmWOMS0pKLKwEZsnMzFRUVJQRugQHB2v37t2ELkAv1XZJ2NYeDp1p+/iIESNMqwmAdfLz8/Xqq68aocuoUaO0d+9eQhe0O85vPRMKvVtGRoYKCgokScuXL1dZWZnOnDnT7u/cuXPG/Lq6OuN+K5eZ5owXuKTa2lqVl5erpqZGM2fO7HLukCFDjDGNF11PWlqavvjiC+P27NmztXnz5h5/HSiAzj3yyCPG2GazKSgoqNO55eXlxjgwMNDMsgBY4Ntvv1ViYqL+/fdfSdKECROUlpbW7vgOrsVut8tms6miokJz5szpstdX2/dBU1OTE6qD2Vr7N0lScnLybefn5+crPz9fkvTmm28qNjbWrNK6RPACl7R69WqdOHFC7u7uKiws7HLna7PZjLGPj48TqoOzbNu2TampqcbtFStW6MMPP6SPD9DLBQUFyc3NTQ6HQ8XFxV2uVnL8+HFJkq+vLyteAS4mKytLCQkJRhPt5557Tp988gn9nFxccnKy9u/fL0nKzs7WY4891ulcjvPRU/DtAy6pdWng5uZmZWVldTrvxo0bOnDggKSW1S6mTp3qlPpgvgMHDrQLXdasWaOEhARCF8AF+Pr6Gme55ObmGkvD/2/FxcUqKyuTJM2dO9dZ5QFwgqKiIiUmJhqhy9KlS/X5558TuvQBrcf5kvT11193Oq+5ubnd46GhoabWBefYtGmTSktLu/zLy8sz5j///PPG/Vad7SIRvMBFLV682NjxfvnllyotLe0wp7GxUe+//74uX75sbOPt7e3UOmGOysrKdktEx8XFKSoqyrqCAHS7ZcuWSZKuXr2qpKQkNTc3t3v82rVrSkpKkiR5eHho6dKlTq8RgDmuX7+u9957T//995+kliaqa9euve3y8nAN8+bNk5eXl6SWfh+FhYUd5jgcDm3cuFG//fabJGnatGl64oknnFon0BaXGsEl+fn56d1331VKSorsdrsWL16sZcuWacqUKRo8eLBKSkr01Vdf6ezZs5JalqD74IMPLK4a3SUtLc34BXzMmDEKCwu7o4ZqI0eObLdaCoCea/78+crKytKxY8d08OBBXbp0ScuXL9eIESNUWlqq7du3G0vHx8bGys/Pz+KKAXSX3bt3G42zhw8frpdffvmO9vO+vr70fnEBnp6eWr9+veLj49XU1KSVK1dqyZIlmjFjhry9vVVWVqZ9+/bp5MmTklouMdq4caPFVaOvI3iBy4qIiJDD4dCWLVvU2NionTt3aufOnR3mhYaGavPmzfL09LSgSnS3hoYGZWdnG7d///13vfjii3e0bXp6uqZMmWJSZQC629atWxUTE6OioiIVFxeruLi4w5yIiAhFR0dbUB0As+zbt88YV1dX65VXXrmj7VJSUvTSSy+ZVRacaO7cufr000+1du1a1dfXKyMjQxkZGR3mjRs3Tlu3bqW/CyxH8AKXFhkZqZkzZ2rPnj0qKChQZWWlmpub5e3traCgIC1cuFBPP/201WWiG509e5bVqYA+wtPTU+np6crOztb+/ftVUlIiu90uLy8vBQcH67XXXlNISIjVZQLoRjU1NbddRh59w4IFCzRp0iTt2bNHR48elc1mU2Njo7y8vDR+/HjNnz9fzz77LP390CO4OVo7UgEAAAAAAKBbEf8BAAAAAACYhOAFAAAAAADAJAQvAAAAAAAAJiF4AQAAAAAAMAnBCwAAAAAAgEkIXgAAAAAAAExC8AIAAAAAAGASghcAAAAAAACTELwAAAAAAACYhOAFAAAAAADAJAQvAAAAAAAAJiF4AQAAAAAAMAnBCwAAAAAAgEkIXgAAAAAAAExC8AIAAAAAAGASghcAAAAAAACTELwAAAAAAACYhOAFAAAAAADAJAQvAAAAAAAAJiF4AQAAAAAAMAnBCwAAAAAAgEkIXgAAAAAAAExC8AIAAAAAAGASghcAAAAAAACTELwAAAAAAACY5H8AGw+BxK11JBgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 421,
+       "width": 559
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Using PCA from sklearn PCA\n",
+    "pca = PCA(n_components=2)\n",
+    "X_centered = X - X.mean(axis=0)\n",
+    "pca.fit(X_centered)\n",
+    "X_pca = pca.transform(X_centered)\n",
+    "\n",
+    "# Plotting the results of PCA\n",
+    "plt.plot(X_pca[y == 0, 0], X_pca[y == 0, 1], \"bo\", label=\"Setosa\")\n",
+    "plt.plot(X_pca[y == 1, 0], X_pca[y == 1, 1], \"go\", label=\"Versicolour\")\n",
+    "plt.plot(X_pca[y == 2, 0], X_pca[y == 2, 1], \"ro\", label=\"Virginica\")\n",
+    "plt.legend(loc=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.91111\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test-train split and apply PCA\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_pca, y, test_size=0.3, stratify=y, random_state=42\n",
+    ")\n",
+    "\n",
+    "clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
+    "clf.fit(X_train, y_train)\n",
+    "preds = clf.predict_proba(X_test)\n",
+    "print(\"Accuracy: {:.5f}\".format(accuracy_score(y_test, preds.argmax(axis=1))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The accuracy did not increase significantly in this case, but, with other datasets with a high number of dimensions, PCA can drastically improve the accuracy of decision trees and other ensemble methods.\n",
+    "\n",
+    "Now let's check out the percent of variance that can be explained by each of the selected components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 component: 92.46% of initial variance\n",
+      "0.361 x sepal length (cm) + -0.085 x sepal width (cm) + 0.857 x petal length (cm) + 0.358 x petal width (cm)\n",
+      "2 component: 5.31% of initial variance\n",
+      "0.657 x sepal length (cm) + 0.730 x sepal width (cm) + -0.173 x petal length (cm) + -0.075 x petal width (cm)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, component in enumerate(pca.components_):\n",
+    "    print(\n",
+    "        \"{} component: {}% of initial variance\".format(\n",
+    "            i + 1, round(100 * pca.explained_variance_ratio_[i], 2)\n",
+    "        )\n",
+    "    )\n",
+    "    print(\n",
+    "        \" + \".join(\n",
+    "            \"%.3f x %s\" % (value, name)\n",
+    "            for value, name in zip(component, iris.feature_names)\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the handwritten numbers dataset that we used before in the [3rd lesson](https://habrahabr.ru/company/ods/blog/322534/#derevya-resheniy-i-metod-blizhayshih-sosedey-v-zadache-raspoznavaniya-rukopisnyh-cifr-mnist)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X = digits.data\n",
+    "y = digits.target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by visualizing our data. Fetch the first 10 numbers. The numbers are represented by 8 x 8 matrixes with the color intensity for each pixel. Every matrix is flattened into a vector of 64 numbers, so we get the feature version of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 509,
+       "width": 1277
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# f, axes = plt.subplots(5, 2, sharey=True, figsize=(16,6))\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "for i in range(10):\n",
+    "    plt.subplot(2, 5, i + 1)\n",
+    "    plt.imshow(X[i, :].reshape([8, 8]), cmap=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our data has 64 dimensions, but we are going to reduce it to only 2 and see that, even with just 2 dimensions, we can clearly see that digits separate into clusters.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Projecting 64-dimensional data to 2D\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'MNIST. PCA projection')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 837,
+       "width": 909
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca = PCA(n_components=2)\n",
+    "X_reduced = pca.fit_transform(X)\n",
+    "\n",
+    "print(\"Projecting %d-dimensional data to 2D\" % X.shape[1])\n",
+    "\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "plt.scatter(\n",
+    "    X_reduced[:, 0],\n",
+    "    X_reduced[:, 1],\n",
+    "    c=y,\n",
+    "    edgecolor=\"none\",\n",
+    "    alpha=0.7,\n",
+    "    s=40,\n",
+    "    cmap=plt.cm.get_cmap(\"nipy_spectral\", 10),\n",
+    ")\n",
+    "plt.colorbar()\n",
+    "plt.title(\"MNIST. PCA projection\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Indeed, with t-SNE, the picture looks better since PCA has a linear constraint while t-SNE does not. However, even with such a small dataset, the t-SNE algorithm takes significantly more time to complete than PCA."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\anaconda3\\envs\\myconda1\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
+      "  FutureWarning,\n",
+      "d:\\anaconda3\\envs\\myconda1\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
+      "  FutureWarning,\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'MNIST. t-SNE projection')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ee0+LFi3S9u3bFQqF5PV6NXr0aM2ZM0eHHnporysNU2Wz2fS9731Pjz32mGw2W58qEt1ut1UNmZaWJqkt4AUAAACA3iBsBAAAAADstIqKii7PpbpnYiwWUzQatca3D+pcLpfsdru8Xq9isZjsdrsikYgMw1A8HreCScMwrPMd24pWV1erf//+8nq98vv9ne5vGIZCoZDC4bBKSkrkcrlks9nk9XpVWFiovLw8zZs3T/F4XE8//bTeeOONTvM0NTVp5cqVWrlypQoLC/XTn/5U3/3ud1N6/q58/vnnWrx4sWpraxWJROTz+TRx4kQdccQRevbZZ9WvXz9VVVX1el6PxyNJys7O7vW+lgAAAABgImwEAAAAAOy0UCjU5bnc3Fw5HI6U2nzG43Hrc/sALD8/X5K0YcOGbqvv2u9B2LGq0DAMVVdXa/jw4SouLk64VyAQUDgctioEzYpGwzDU2tqqzZs3KxAIqKysTO+8846WL1/e47NUV1frjjvuUHV1tU455ZQex3f05ptv6tVXX9W2bds6nVuxYoWeeeYZZWVlKScnp1ML2p54PB7rGQsLC63jWVlZvV4nAAAAgP9t7NkIAAAAANhpXq+3y3N2u90KC3ti7iEoSXl5eZLaAsTCwkJVVFR0W0FpMkNGs+KxfYvR2tpaeb1ejRo1yrpXa2urQqGQNc7hcCSsQ5JycnI0ZMgQXXvttXr++edTehbTk08+qbfeeivl8fF4XHfddZfuv//+pEGjKRwOq76+XvF4XD6fr9Oau+J2u5Weni6p7ffmdrvV1NQkl8tFhSMAAACAXiNsBAAAAAD0WTgc1ubNm9Xa2qrW1lZFIpGk4/r379/j/oUej0dO538b8GRnZ8vj8Sg3N1fhcFilpaU97k0Yj8dlGIY1LhwOq6GhQa2trYpGo4pGowoEAsrJydGBBx6oWCzWac1me1GprX3r4MGDNXr0aPn9ftXW1qqqqkrbt2/vdh0dPfroowoGgymNfeCBB/Tuu++mPHdBQYGGDh3aY1WiuTej1+u12s1GIhGtWbNGxcXF2rJliy688EKdf/75ev3115O2mgUAAACAjmijCgAAAADotW3btun+++/Xa6+9prq6OhmGoZaWFsXjcWVnZ2v48OEqKCiwxqelpWnEiBHatGlTl3MWFBQkBJI2m00TJkyQzWbrMWg0w7P2Y9pXOEYiEUUiETkcDrW0tMjn8ykej8vhcCgzM1PhcFjRaFQ2m01ZWVnyeDzKz89Xbm6u1W60/b6I27dvV2FhYUI42h2/36/33ntPs2fP7nbcypUr9eabb6Y0Z3u5ubk68cQTNX/+fNXV1VnvwmazyeFwyO12W1WL4XBY4XBYXq/XClrtdrv1+yorK9Pf//53Pfnkk7r00kt12GGH9Xo9AAAAAP53UNkIAAAAAEhZPB7X5ZdfrsMPP1yPPvqoamtrrYDP4/EoHo+roaFBn332mT788MOE6rj8/HyNHDkyaYWjzWZLCCclafTo0XrmmWd0yCGHqL6+3jpuGIbi8XjCXx3bpTocDus+5nipbS/H0tJSNTc3W/scOhwOpaenKzs7W9OnT9fkyZM1duxY5eXlWUFjJBJJWEM8HldNTU2v3t2CBQt6HDN//vxezdnetm3bdM899+hb3/qWhg0bptzcXGVnZysjI8MKGs33kpGRYT2bzWbTiBEj5HK5EuYLBAK66aabtGTJkj6vCQAAAMD+j8pGAAAAAEBKDMPQL3/5S7355ptJqwydTqecTqei0aiktmq+5cuXa9q0acrIyJDUFjj6fD5VVVVZFXiSNHDgQCvsGj58uObOnauZM2fK7XbroIMOUm5uriorKxWLxazg0FyTyQwX7Xa7HA5HQrVjPB63wjXDMFRSUqJoNGodc7lcGj16tHw+X9Jnb2lp6fTM1dXVGjBgQI/tYU2bN29Wc3OzMjMzk56vrq7WihUrUpqrK+Xl5brxxht15513qrW1VYFAQLFYTA6HQ+FwWFu2bEkIFW02m4YNG2btj9mRYRi65557NHDgQB144IE7tTYAAAAA+yfCRgAAAABASq666qoug0apLbjyer1qbW21QsRIJKKVK1fqyCOPtFqOpqena/jw4Ro6dKjq6+t1wAEH6Nhjj1VGRoYmTJig8ePHJ8xbUVGhcDgsm83WbbBntg1tX7HndDqtfRyltlDRbrcrFAopEokoNzdXhYWFKigo6LYlqvk87ZlzmFWDqWhpaekybFy+fHmPe1L25OOPP9YFF1yg/v3761//+pdWrFhhzfn1119bn812sQMHDuxxr8doNKoXXnhBV1111U6tDQAAAMD+ibARAAAAANCjpqYmPfXUUz2GYXa7XRkZGfL7/dZ+gKFQSJs2bdKYMWMSxqalpemCCy7QmWee2WWI2NLSoueff14tLS1yuVzKzMy0qgzNELHjtWYlX/tKR7N1aP/+/eX1etXS0qKamhqNHz++U/vQZLpaXzQa7VXY2N29mpqaUp6nuzkMw9DYsWN19dVXq7q6Wm+++aY+++wzrV27Vjk5OUpLS1NBQYHS09NTnvfTTz9VdXW1CgsLd3qNAAAAAPYvhI0AAAAAgB49+OCDCgQCKY212Wzy+XyKxWIKh8MKh8OqqKiwwsbCwkLNmTNHxx13nLKzs7ud6/bbb08I4VwulxwOR5dBoySrfWr7SkVzTSNGjJDNZlNDQ4Pq6+tVU1OjQYMG9fhMXYWEZhVle8FgUNXV1dqxY4fVUtbpdCovLy9hD8vdoeP7KCws1FlnnaWWlhaNGzeuz/MahqFFixbpzDPP3NklAgAAANjPEDYCAAAAAHr0r3/9q9fXOBwOpaenKy0tTYZh6OSTT9YJJ5ygzMzMlPY53LRpk1auXNmp7WhGRoaam5u7vbbjPo1ut1tjxoyx7mtWI7a0tKT0LFlZWfJ4PAqFQtYxm82WEEKGQiGVlpZa1YXtRaNRBQIBXXjhhTr44IN1wQUXqKCgoNM9dlZX77asrGyn594VcwAAAADY/xA2AgAAAAC6FYlEVFFRsdNzPPTQQ/rwww8VjUaVkZGhgw46SHPmzNHAgQOTXrNgwQJJbSFcWlqagsGgpLZqwszMTDU0NHR7TzNstNvtGj9+vNLS0qxzPp9PXq836V6MXSkoKNC2bdusn/v16yeHwyFJ8vv9KikpsVrHSm3tXM19Hc1qy8bGRm3cuFGrV6/W3XffrWHDhlnjDzvsMD388MOKx+Mpr6mjI444IunxXVFRuburMgEAAADsmzr3ewEAAAAAoB2/39+rUM5kGIYCgYCampoUCAS0fft2VVRUqKamRps3b9bLL7+s8847T9dcc422bt2acG04HNb7778vqa2CsONegXa7XR6PJ2FvxmQ8Ho/69++v/Pz8TucKCwuTtkHtSkFBgRUumteba123bp0VNMbjcbW0tKi5uVnhcFiGYcjhcMjhcCgWi6m2tlYff/yx5syZo1WrViXMP23atJTXk8zcuXOTHvd6vTs1766aAwAAAMD+h8pGAAAAANiPVFRUaOHChVq/fr2am5uVkZGhMWPGaO7cuRowYECf5kxPT5fNZuvUGrQ7hmGotbXV2rNQUkJQ137cqlWrVFJSoj/96U+aOHGiJKmhocGqZJTagrja2tqE6jqzatFut8swjIT12Ww2q81p++pBwzAUjUYVj8eVk5PTqypCl8ulUaNGad26dfL5fFZ71/LycoXDYUlt1YwtLS0Ja7Hb7fL5fJ3mq6ur0znnnKMnnnjCeu558+Zp+fLlKa+pvQkTJqioqEhffvmlGhoaFIvFlJGRofHjx6uoqEhr1qzp07ymoqKinboeAAAAwP6JsBEAAAAA9gNr1qzRk08+qXfffVfV1dUJews6HA7dddddmjlzpn71q19p/PjxvZrb7XYrJyenx7alpmRBo6SkgZuptbVVN9xwg2699VYNHz48Yf3mM4wZM0bFxcVWCOl2u62QzwwXOxowYICysrIUCoVUXV2tmpqahHVNmTJFNTU1ysnJSdh/sSvZ2dk65JBDrHtFo1HV1dVJaqtobG1t7RQ0ZmRkdFlBWVlZqWuvvVZ33nmnhg4dqqlTp2ru3LlWC9lUeTweDRs2TGeffbZqa2s7nZs4caL8fn+fqxPtdrtmzZrVp2sBAAAA7N9oowoAAAAA+7hFixbp5z//uZ544gmVlZV1CupisZiqqqr0zDPP6Ec/+pHeeOONXt9j3rx5KY+NRCKdgkan06khQ4YoFApZLUYDgUBCMOf3+/Xggw9Kaqum7Mjtdmv8+PHKyclRNBq1WrvG4/FOVZdmyJeTk6ONGzfqiy++UEVFRcK6vF6vPB6P0tLS9NVXX6msrKzHZxsyZIgeffRRPfTQQ/r+97+vlpYWqzoyGAxan+12u9LS0pSZmdltq9ZYLKatW7fq6aefto6dd955Ou6443pciykSiai5uVkLFizoFDRKUigU0sqVK1VaWqotW7akPG9706ZNU0FBQZ+uBQAAALB/o7IRAAAAAPZhH3zwgS699FLV1NSkNL6+vl4XXXSR0tLSdOyxx6Z8n0suuUTPPvtsSns3dgw7DcNQRkaGiouL1dLSknDO4/GosLBQBQUFcjqd+uqrr7RlyxYNGTJE/fr1U319vTU2Go2qtrZWwWBQdrtd0WhUhmFYa7LZbHK73UpPT5fT6VQ4HNaXX35p7Ztos9nkcDjk8XjkcrmstrKZmZmaMGGCampqVFFRoYEDB3Z6puHDh2v27NnKzc3V/Pnz1draKqfTqf79+ysQCCgSiWjLli1KS0uTw+FIqUrS1NTUpGXLlqm+vl79+vWT3W7XxRdfrHHjxunVV1/tMiB0u90aPHiwSkpK5Ha7e7zP4MGDtW7dOsViMY0cOTLl9TmdTp188smSpKqqKjU2NkqSsrKy+tyaFwAAAMD+g7ARAAAAAPZRra2t+t3vfpdy0Gjy+/264IILtHLlSmVkZKR0TVFRkY4++mgtXry423HtKw6l/1YdmnsZdhQKhVRWVqby8nIVFRWpsLBQCxYs0G9+8xvNmjVLzz33nLXmdevWWW1TzcrBtLQ0BQIBK+CMx+NqaWmx9phs31rV3K8xGo3K6/UmVEO6XC4NGjRIhmFo8uTJOvTQQxUMBuX1ejVkyBCVlpbq1VdfVUVFRcL6165dq5aWFjkcDjkcDjmdvf9jtvnO3nrrLf3kJz+xjh933HE67rjjtGbNGi1evFi1tbWKRCLy+XyaNGmSRo0apauuuiqloFFqawFbVFSkLVu2yOv1phQU2u12/eY3v1Fpaan+9re/adOmTQnnhw8frrlz52rGjBlKS0vr3YMDAAAA2C8QNgIAAADAPur1119PqfVnMjt27NCDDz6o3//+9ylf8+ijj+rYY4/Vhg0buhzTvk1pPB5XPB5XZmamHA5Ht3PH43GVlpYqGo1qxYoVkqTZs2frhRdeUEtLi4qLizu1ZjWZIVcoFFIsFrNamTocjqT7OJqVjZs3b1YoFNKQIUOsczabTWvWrNGll14qr9erHTt26JZbbtGmTZsUj8cVDocVj8dlt9vl8XisULOxsVGRSMQKQM2xsVgsoarS7XZ3aqtqs9kUj8f1zjvvqH///pLaqgYnTZpk7bc4ceLETs9x1113KRKJdPteTbFYTM3NzXI4HMrPz9e2bdtUWFjYbYtXr9er2bNn67HHHksaFEuyQsjHH39cv/3tb3XYYYeltB4AAAAA+w/CRgAAAADYBxmGoccee8wK1vriueee61XYmJ6eroULF+rkk0/W6tWru1yX+b+GYSgzM1Mejyfle2zbtk3Z2dmSpLy8PM2ZM0d//vOfuwwapbawLj09XbFYrFNVpRmm2Ww2uVwuud3uhOrD7du3y+VyWSFfY2Ojqqur9b3vfU8FBQVau3at/H6/HA6HotFoQjjncrlkGEbC78Cssuy4h6TUtrdiMBiUy+Wy2q3G43G1trZq9erV8ng8qq6utsb7fD7NnDlTc+fO1eDBgxPmam5u1gcffNDj+wwEAqqurlZdXV2nILi8vFy5ublyu90J1ZFFRUWaN2+eXC6X7rvvvpS+Y62trbrpppt0ySWXaObMmT2OBwAAALD/IGwEAAAAgH1QaWmp1q5du1NzbNu2TZ9++qmmTZuW8jVZWVlasGCBXnrpJT388MP68ssvO+3j6Ha7raq/7irnuluXaezYsfL5fJ32gezIbI9qtjI11+RyueRyueR0Ortcy7Zt25SWlqYtW7YoGAxKklpaWtTc3Gy1LjU5nU6lp6fL4XAoEokoEomotbVVUluAZ97X6XQmraqUZF3ncrmstqhmANlea2urXnvtNb3++us666yz9OMf/9g6995771ktZbtSXl6u8vLypOfsdrv8fr+GDBmicDis73//+zriiCOUnZ2toUOHqqSkRFdccUWvwmzDMHTfffdpwIABmjBhQsrXAQAAANi3ETYCAAAAwD6osrKyxwCuJ4Zh6Ouvv+5V2Ci1tSc9+eSTNXHiRD3//PNatWqV/H6/fD6f0tPTVVtbqzVr1vS56jIcDmvNmjWaOHGiFixYoFGjRmnr1q2qqqpKWjFoXiPJql40A0CpLVgzqx5tNlunfRWDwaDWrFkjl8uVcKyurq7TM0SjUbW0tCgjI8MKNu12u9Ve1QwY4/F4t61j4/G4/H6/FYRK6lQBahiGduzYoerqav32t7/VbbfdptGjR1sBYfvKzY62bt2qysrKLu9vPqPNZpPH49Hrr7+uwYMHa968eZKkf/3rX91WkwaDQdXW1lrrcDgcysjIUF5enp577jndcMMN3d4bAAAAwP6DsBEAAAAA9kFmBd7OCgQCvb7mnXfe0euvv65NmzZZx9LS0hSLxVRXV6dVq1YpGo1a1Y29lZeXp4ULFyo3N9eq3iwqKtLAgQNVU1Oj6urqhKo+u90uu92urKws2e12RSIRq71pNBpVOBxOWIfdbrdah5qhn91uTwgbm5ubrb0WOzIMQ62trcrIyLDmMoNf8xozCOzqerMCsn2YmZ+fb32ura1VeXl5QqBcWloqp9OpxsZGbd68WQ0NDRowYIAGDRqUMH9tbW2PQWPHe0vSQw89pKKiIvXv39/aN7OjxsZGVVZWqqmpqVPwW1NTo7KyMm3btk2nnXYa1Y0AAADA/wjCRgAAAADYB+Xl5e2SeXJzc1MeG4vFdM8992jx4sVdjvF4PHI4HGppaVE4HLYCuVTZbDYVFBRoy5YtCfsXSm1Vi4MGDdKgQYMUiUQUi8XkcDjU3NysDRs2WOPC4XC3VXnxeFzBYNCq7DPDPzMojEQiVkvW7uYIhUJKT0+X9N/qyfa6CivNcWYYGQ6HlZ2draysLEltbV23b9+e9L4VFRXq16+ftYfktm3bFAwGNWLECOteFRUVXa67vY7PZxiGXnrpJY0cOTJpBen27dtVXl7eZXWp1Fb5WVlZqXPPPVdPP/20hg8fntJaAAAAAOy7er95BgAAAABgjxs/frwyMjJ2ao709HRNnjw55fH3339/t0GjyVxXPB5XS0tLt+FUR/369ZPL5ZLf7+92T0Jzj0OXy5VQoRcMBnvcy9BkGIbVBtT8WfpvS9aeqjLD4bA1h1ld2X6eZM9tGIYMw7DGm2Fj//79ZbPZVFVV1WXQKLXt49ja2iqv12vNt23bNi1btkyff/65Pv30U9XU1CgUCvX43s052lu5cqW+/PLLTscrKiq0bdu2lH+XNTU1+tOf/pRShSUAAACAfRthIwAAAADsg5xOp773ve/t1Bzjx4/XuHHjUhq7dOlSvf322ymN9Xq9SktLk/TfvQlTkZ6ermHDhlmffT5fSteZoWAkErGqFaX/BntdBWRmyGhWNZrHIpFISu1fDcNQMBi09mp0OBw9VnGa4xwOh3UPh8Oh7OxsRaNRlZWV9XjfmpoaZWZmKhgMqrGxUcFgUM3NzfL7/WptbVU0GlUgEFBTU5P8fn+Xz19YWJj0mczWtSa/369t27b1uK72YrGYGhsbdccdd/TqOgAAAAD7HsJGAAAAANhHXXLJJb1qUdqew+HQL3/5y5THv/HGGymPTU9PV1pamjwej6S2ELDj/oAdeb1ejR07Vk5n224fQ4cO1YgRI6w5umOOCQaDCfshmmKxmGKxmOLxeELw1v6zGRia15rr6En7e9lsNjmdTjmdTjkcDrndbnk8Hnk8HqWlpSkzM1Mulyvhd+bxeOT1ehWJRFRbW9vje5KklpYWFRcXdzoeCoUS1mNWXTY3N3eaNz093Wrb2lHH/UCrq6t7VZ0qyXrG4uJibdy4sVfXAgAAANi3EDYCAAAAwD5q7NixOv7441OqwmvPZrNp8uTJmjt3bkrjy8rKkrbW7Ep+fr7sdrtVneh0OhUMBhUIBOT3++X3+xUIBBSNRuV2uzVkyBCNGzdObrfbmmP27Nny+Xw66qijeryf2bY1FAopGo0mDezi8bgVOnYVnJlVkE6nUy6XK6VnTTaX3W5XRkaGBg0aJK/Xa4WvDofDGu9yuZSRkWHt+WgYRqc9KpMJh8OqrKzUjh07rPDUnDMSiSRdTzweV2trq3XOZrNpyJAhXd6jfUVpLBZTXV1dj+vqyHwuSZo/f36vrwcAAACw7yBsBAAAAIB92D333KPp06enHDjabDaNGTNGf/vb35Lu2ddRNBrV22+/rWg0mnJ1m9PpVL9+/ST9N4yLRqOd1mjuXdi+pagkDR48WFOmTJEkHX/88T3er6WlRc3NzZ3WZ7PZkt4zWSBpt9sVDofl8Xjk8/lSqqg075FMVlaWxo0bpylTpig/P19ut9tqn+pyueR2uxOqJ82WrF0JhUJqampSc3OzwuGwQqGQtbdk+8rNrsRiMYVCIUlSUVGRcnNzuxw7adIk67kaGho6VYqmon2L1g8++KDX1wMAAADYd6TWFwYAAAAAsFdKT0/XM888o4suukgffPCBmpqako6z2WzKzMzUuHHjdNddd2nkyJFdzhmJRPThhx9qwYIFKi4uVmlpqaqrq+VwOJSXl6f+/fsnVK4lU1hYqLKyMivgstvt1j6O7QWDQW3ZskUNDQ0aNWqUnE6nTj31VCvsGjlypGbMmKElS5YkvU8oFNL69eu7rDC02+1JqxljsZh1DzOUdDgcGjt2rIqLi61gsKegzeFwKB6PJwR9NptNhYWF2r59u6qrq61Q0DwXiUQUiURkt9vl8XiUnZ3dZdtWsyrRXIdhGAkBp7nuWCzWZaBrCofDGjdunAoKCrp9pm9961uSpNWrVysajXY7NpnMzMyE70cwGFQwGEz6+wcAAACw7yNsBAAAAIB9nM/n0yOPPKJPP/1Uzz77rBYvXqxAIKBYLCaHw6H09HRNnTpVp5xyimbMmNFtULhkyRI98sgjamxs7HQuFoupurpa1dXVys7O1siRI7tsN1pdXS2n02mFjT1VRTY1NWn9+vX6f//v/2nGjBkJ5y6++GI1NTVp5cqVna7bvHmzduzYYQV6HfdhNKsn2/9sMoM7c0/FvLw8ZWVlKTMzU83NzfJ6vWppael27W63W3a7XYFAwDqWmZmpuro6tba2Jh1vVjDG43EFAgF5vd6k9zAMQy0tLVaQaa7f4XAkjDP3ijSfxwwIzetsNpv1Durq6pSdnZ3Qsrbj+mbOnKmRI0fqiy++6PVejTabTYMGDep0PJW9KAEAAADsmwgbAQAAAGA/YLPZdOihh+rQQw9VZWWlNmzYIL/fL4/HowEDBmjs2LE9zvHyyy/r0Ucf7XQ8WdVdY2Ojvv766057LUpSbW2tamtrrX0JW1tbe2zzarPZlJuba4ViHe9/1VVX6eGHH9abb75phWlbtmxRWVlZQiCWrLIvFovJbrdbgZw53m63KzMz0wrvzOcsKChQc3OzHA6HvF6v/H5/0tDN6XTK4XDIbrcrGAxa+z2mpaUlDRrNe7pcLkUiEWu90WhUW7Zs6TTW7/cnhHRmcNrTuzQDyY5j4/G4Kisr1dzcrAEDBmjo0KGdrv3Od76jzMxMTZ48Wb/+9a913XXXdXuvjoqKipSdnZ1wzAy8AQAAAOyfCBsBAAAAYD8zYMAADRgwoFfX/Oc//9Fjjz2W9Fxubq62b9/e6XgoFFJJSYnGjx+fUG1XWVlpfXY6ncrKylJGRoYMw5Df70+Yw+12q6CgQAUFBXK73Xrrrbf0k5/8pFOA6XA4dN555+m0007TokWLdPfdd2vr1q1WCOhwOKwwzjAMqwrQPG+GdmZ7VI/HI7fbLZ/PZ63J3MOyX79+2r59u4LBoBWYBgKBTi1FzX0dbTabfD6fIpGIvF5vQtvUZNLS0qw9MNPT02Wz2dTa2prQtjUej1uBpClZVWP7c/F43KpijMfjScea76SiokKRSCShna7P59PJJ59s/TxnzhyFQiGdf/75PbaTtdlsGj58eNIWrYccckjKe4oCAAAA2PcQNgIAAADAfiQWi+njjz/WW2+9pa1bt8rv98vr9aqoqEhz5szR9OnTO4VQ8Xhc//znP7tsmenz+eTz+ZJW6wUCAdXW1qp///6SpJaWlk6Bot1u16hRo+TxeBQIBBSJRKwqQK/XmxBENTc364MPPtDMmTOTriU7O1sDBw5Ubm6uBg4cqLq6OtlsNsXjcWu/SnMfQ/PZpP+2THW5XPL5fAlr69evn6qrq+X3+7Vu3TpJbYGg3++X3W6Xw+FQRkaGYrGYwuGwIpGI0tLS5PP55HK5lJ+fr/z8fNntdn322Wc97nPocDjk8/lkGEZCG9r2gV6ywNLlcnX5OzKrN81nSqX9aW1trdLS0jRo0CB5PB798Y9/VEFBgerr62W325WRkaETTjhBn332mebPn6/q6mqrLa7J4/GooKBAubm5amho0JdffqlwOGyFnT6fT6eeemqPawEAAACw7yJsBAAAAID9xBtvvKEXXnhB9fX1CceDwaDq6+u1evVq5eXl6ZRTTtHcuXOt859++qlqamq6nbt///7atGlT0nPV1dVW2FhXV9fpfHZ2tlUFmJ6e3mNLzffff7/LsFGSXnvtNUmyKvmkzu1JTR3bsnas0GtubpbH45HL5er03iRZFYdpaWlWW9XBgwd32pdwypQpOvLII3XzzTdr+/btam5u7nL9WVlZGjRokGw2mzZu3Jiw36QZFIZCISsgtdvt8nq9CoVCnZ7PfA9SYgtZM1g1KyjbH2+voqJCEyZM0Ny5c/X8888n7NPocDh02GGHadKkSVq1apUGDhyo1tZWa06XyyWPx6OysjJ99dVXVjVpJBJRKBRSLBZTfX29fve73+mGG27QmWeeqd/85jfKz8/v8t0AAAAA2PcQNgIAAADAfuChhx7SG2+80eO4uro6PfDAA9q+fbvOOeccSdKCBQt6vC4vL0/19fXasWNHp3OBQEBNTU3KysrqFIa5XC4NGzYstYf4/zU0NHR5bvPmzVq7dq2kzkGix+NJGsa1Z7YndTqdam1ttSosJ06cqEgkourqajU0NFjBn9keNRKJqH///ho4cKBVGelwODR9+nTNnTtXU6ZM0Z///GdlZ2crOztbgUBA1dXVCgQCisfjVmBYUFCQELZOmTJFO3bsUHV1terr62UYhlpaWhQOh62g0W63KxaLJQ1TzWdKtpejGY5GIhGFw2FFo1GrPa3NZlNWVpZ8Pp/Ky8v19NNPd5o3Fovpo48+0kcffaQdO3bI4/EkVIVGo1GtW7dOLS0tktra6pp7V5r3MFvT7tixQ/fff78WLlyoH/3oR7rggguUlpbW7e8KAAAAwL6BsBEAAAAA9nHPPPNMSkFje6+++qqysrJ0yimnqKSkpMfxNptNo0aN0vr169XY2NjpfEtLi7KyshKq6Fwulw444AA1NDSovr7eCsqcTqdyc3NVUFCQ0EbU1N3+gIsXL7Y+m9WSJqfT2WW71/bC4bBCoZAVvo0aNcqquMzKylI4HFZjY6PVDtXlciknJ0dFRUX62c9+plgsJq/XqxEjRqhfv37WvO3fS3p6ekohq7nfoxkYZmRkaMyYMVq1apVVsRiPxxUIBGSz2ZK2RzUDyY7MfRxjsZhsNpvS0tLUr18/+Xw+FRYWqrW1VRs3blRDQ4MmTZrU5Rqbm5vl9/u1ZcsWZWZmWoHqhg0brKAxEAgktFg1g0an87//2sEwDG3atEmvvPKKysvLdeONN1phJAAAAIB9F2EjAAAAAOzDqqur9dxzz/Xp2meeeUYzZ87stMdiV+x2u8aMGaOKigpVV1cn7CtoBoROp1M2m005OTmSpHXr1lmhmSkUCqm1tVXl5eXKzc1VUVGRVXEnSRkZGV2uoba21vqcn5+vioqKhPPmnoxmRWEyZuhpt9s1duxYZWdnJ5x3u90qKCjodN327du1ceNG/fSnP1Vra6uKi4u1Zs0aOZ1O5eXldXm/7oRCIRUXFysUCslms8npdKpfv37KycmR3++39j9sHxyaVZc2m83ar1KS9bMkqxVr+wDQ6XSqqalJTU1NKi8vVygUsvbRNFu2mmKxmGpra63qTFNra6t27NihUCikcDhsVZN2vE96enqnvUHNdW3dulXZ2dm6+eabdf3113eqyAQAAACwbyFsBAAAAIB92Jtvvpm02i0VsVhMb731llwuV0Jw2B2bzaZBgwZp4MCBamhoUF1dncLhsAYMGKBJkybp0EMP1XvvvaeNGzcmhFRSW9tNs6LQDLcaGxtVXV2tSZMmKTMzU5J08MEHdwq/pLagqqysTLW1tTIMQw6HI2klo8vlsp7JbB/acZ6MjAxlZGRowIABvXpn//73v1VbW6uPPvooIWCTpMrKSkltIWj7ir6uxGIxrVu3rlNQJ0k5OTmKx+NWGGi2JzX3dDSrGc0g0gx7zYAvFotZwa/5TtpXgvr9fkWjUYXDYfl8Pmu81Fb5uW7duqQhtBmG1tfXKxgMyu/3KxaLyeFwyOl0yuPxJK2ybC8YDKqpqUmrV6/WihUrNG3atB7fFQAAAIC9F2EjAAAAAOxjqqurtXbtWjU1NemJJ55QJBJRVlZWn+Z66623VFhYqG3btvXqOpvNpn79+lltRC+99FLNmDFDTU1NevrppxOCxkgkkrTS0DAMa4/CZcuWKScnR06nU9FoVM8884y8Xq+mT5+uo446Sps3b9bChQu1bNmyhOpGM8D0eDydAj632y23223dxxwfDoflcrk0YMCAlKvqDMPQli1bVF1drZqaGuXl5XUa43A4tHnzZm3fvl2jRo3q8XdSW1vbKZA1K0ILCwtVX19vrbdjgNdVq9mOlY/Sf6s9TfF43ApgDcNQa2urQqGQnE6nIpGIiouLFQwGE8aHw2HrPW7ZssVqWWuGx2lpaUlb4nalurpa2dnZmj9/PmEjAAAAsI8jbAQAAACAfYBhGFq5cqXmz5+vlStXWiHd119/Lalt/8LCwkIVFBSkVFVnqq+v1+GHH97rsLE9n8+nI444QpL0yiuvyOv1qqGhQVJblVx3bVrNqjzDMFRbW2sFdGYI9vLLL+u2225TWlqaRowYoaysrISw0QwnW1paOoVqpvbtRaPRqOx2uxwOh5qamrRjxw45HA5lZmYqPz8/aetPSdq4caPq6+sltb2zZGFjv379VFZWpmg0qpKSEo0ZM6ZTi9b2qqurE352uVxWeJuVlSWn05l0f0ypLdh0uVyKRqMJwWPHKlen0ym73a5gMCin0ymn09mpItNms2nz5s2aOHGiNm3aZAWNZpBrtp01maGx0+m0Pvv9fmVmZvZY1WhqamqSJK1atUqVlZW9rjAFAAAAsPcgbAQAAACAvVwwGNQtt9yilStXJhxvHzKFQiGVlZWpoqIiaVVdPB5PCN3amzp1qt5+++2UW6l2dMwxx8jj8SgajWrRokUaMGCAGhoa1NjYmHLQaP5st9u1fv16HXjggWpsbNT69eut4HHt2rUaNWqUFTCa0tLSJLW9p9bW1qSBoyRrb0FzrWYgKrUFiGVlZcrLy9OgQYOsvQhra2tVUVFhhX52u13p6elJ53c4HMrPz1dlZaUMw9CGDRs0YcIEa33tNTc3d6pqLCgoSPj9eDyeblvkGoah7OxsNTY2Wq1pO46PRqMJ78rhcFjv3LyX2+2W3+9XTU2N9ZzBYDChurHjfaW271QkEpHNZpPD4VA4HE76rMm0/72XlJQQNgIAAAD7MMJGAAAAANiLhcNhXXXVVSouLu50Lllw2L6qLhaLqbq6Ws3NzVaw43K5lJ+fr8LCQmsPv6ysLB177LFasGBBl+sIhUKqqalRKBRSPB639kscNGiQ5s2bJ0n66KOP1NjYKIfDoTFjxmjp0qVdztc+aGwfNjY1NSkQCFhVfe3Ds1gspo0bNyo7O1t1dXUJ85ltPEOhkBUothcMBhWPx+X1ejudM8XjcdXU1Ki2tlYZGRlqbW1VPB7vtI6amhoVFxdr4MCBnSoXBw0apMbGRgUCAev9FxUVKRKJqKGhwQr+mpqaFI/HrUpA812aIpGI/H5/0j0p278P89kDgYBVgWiz2az32vE7EovFrDU4HA7ZbDbrfWzZssV6V10Fje21/73FYjGFQqGUw8b2wXdXzwcAAABg30DYCAAAAAB7sQceeCBp0Cipyz3ywuGwVqxYIZ/P16mtZSQSUUVFhSorK5Wbm6sRI0aoX79+Ouecc7R161atWbMmYXxTU5MqKys7BW6SVFdXJ5/Pp1deeUU//vGPEyov/X6/XC5XQrjVPviKxWKd9nC02WyKxWKy2WzatGmTHA6H0tLS5Ha7rTFmpZ7H4+nUDtThcMjr9cpms2nYsGFW4NbQ0KB4PC632y2v12u18EwmGo2qtbVVO3bsUGZmplUx2HGdTU1Nam5u1pAhQzRw4EDrnNPp1NixY1VSUqJAIKCKigqFQiHt2LEjYR4z0HO5XMrOztaYMWMSflc1NTUyDEMul0sZGRlWeNmeOZ9ZmdgxXDRD4WTMgNB8X+Z78vl8PQaN5vj29zIMQ9FoVJFIJKW9G9v/TrsKfwEAAADsG1LbTAEAAAAA8I2rr6/XkiVLujyfnp7eqWVoKBRSa2urYrFYt21RDcNQfX29KioqrKrAa665RtOmTbPGVFZWqqSkpFNQJrW1Ex01apQyMjL05ptv6tJLL9XmzZsVjUZVUVGhL7/80lqHeT8zYOwYNLavcjPHRaNRhcNhNTc3d6p8a2pq0siRIxMCq47PFo/H1b9/f2VkZKi5uVkul0ujR49WQUFBl+8kFouptbXVCilbW1uTvkMzTDMMQ2VlZaqqqko473a7deCBB8rtdqupqUlVVVVJ26HabDbZ7XZFIhFt27YtYYx570AgoHA4LKfTaQWvdrvdutbn82nw4MFKT0/vFCx31YLVfNdmaGiONQyjU4Db1fXt5zCZv9v2IpGIgsGgAoGAgsGg9T7b73lZWFjY4z0BAAAA7L2obAQAAACAvdSiRYs6hTcd9e/fX5s2bZLUFuy03wcwlT300tPTdf311+vmm29WWlqarrrqKq1YsUJ33323Pv30006BlcvlUkFBgQoLCxPCvqamJi1fvlxNTU2y2WwJ1XHtw6n2QWPHyjhTPB5PCMH8fr8ikYjS09OtezY1NWn8+PEqLS1NWnVZU1OjAQMGqLq6WhkZGRo+fLi8Xq/i8bhcLpfVcrQ9M2g0JXv3NputU+Xe1q1blZOTk1ChV1FRoXA4rKysLGVmZsrhcCgajcpms8npdMrpdKq2tjZhvfF4XIMHD1ZVVZXKy8uTBp02m01ut1sej0der1cTJkxQWVmZtW9i+70Qu2KGkg6HQ5FIxKomNQwj6Xvpag3mfo3t72dWgoZCIYXD4U7Vq1JbVWd+fr6CwaBGjBihiRMn9nhPAAAAAHsvwkYAAAAA2EstWrSoxzH9+vVTWVlZp6BRagvtumtr6Xa7lZubq3Xr1un999/XcccdJ5vNpsGDB6u5uVmTJ09Wc3OzotGo7Ha73G63srKyOlXQSVJDQ4Oqq6sVjUaVmZmZNPAyKw7bB4mpikajVujo8/nU3NyswYMHa8yYMQoGg6qurlZ9fb0VlsXjcX3nO9/p9Px2u10FBQXavn17wvyRSCRpMBaLxRJakbrd7k77IBqGoerqag0dOlRSW3vZyspKSW3BXEZGhoqKihKuMfeCbB/uVVVVqbKyUi6Xq8t30z7IKywstPaZNKsdnU6n4vF40mcxg1KPx2PtRyn9N5ROtsdjMi6XS2lpaYpEIrLb7Qn3ikQiam5uTnp/k8PhUG1trRoaGjRr1qyU7gkAAIA9I/Pee5Wbnr6nl7FXy+zw57D/RbRRBQAAAIC9UCwWU01NTY/j7Ha7Ro8enXQPREldhj5mG1QzOJw/f751buHChYrH4/J4PMrPz9eAAQNUWFionJycpEFjIBDQxo0bO+3RmOpa2utYKWcy23yaeyq2D+nS0tJUVFSkgw46SNOmTdO0adM0ZcoUHXzwwUmD1oEDB8rr9SYc66rlbPu1mHtIJmNWJkrq1FY12b6JZuhpMp/LDIy72muxvbq6OlVUVCgajcrpdFqhnd1ul9PptNrs+nw+ZWRkKCsrS16vVw6HI6EKMxwOW9WKqfB4PHI4HEpPT7fauZq6+h6azH01pbbf9XvvvafVq1endF8AAAAAeyfCRgAAAADYC3W332JHGRkZ8vl8SSvEkgV3TqdTY8eOVUZGhnVs48aNKikpUTgc1rvvvturtVZWVioej8vpdMrhcCgUCiXdPzCVsFFSl5Vu5vVmMNed9PR0NTc3Jz3ncDg0duxYK/Tqrn2ouRaHw9HlOzbXZO4v2dLSknCuY7BpKiwstEJFv99vvaNIJNJj8Od2u2UYhrZu3Wqts2Ow6vF45HK55HK5EsJI85x5DzPIzcnJ6faeUtt7dTqd1hzp6elyOBwpVSe2f4dmSG6323XzzTd3+bsCAAAAsPcjbAQAAACAvVBaWlqv2ktGIhFlZmbK4/EkXNf+s9PpVGZmprKyslRVVaWNGzeqrKzMqqb7+uuvtXr16pSCH3NvwGg0qvr6euu4uZdfx7AslZapPT2vGYpJUigU6nY/y9GjR3d7T5fLpXHjxmnAgAFJqzVNZvvYjIyMbsdJbb+D9vswSm3vo6sQz+12a9SoUZ1auIbDYdnt9i7b35pVi1JbyGkGpe2rFR0OhxUKdsXr9VrXuN1uFRQUdPs7SE9PT7iHec9k37v2zCpI8x2aYXdmZqaktqD1nXfe6XatAAAAAPZe7NkIAAAAAHshm82mMWPGqKSkpMexhmEoFovJbrcrPT3d2k8vFoupsLBQaWlp8vv9CgQCSYPEiooKZWZmavXq1fL5fF3ew9yXsbm52QryYrGYFS7a7XZ5PB5Fo1G5XC4Fg8Eun62n8LGn4NHpdKqurk6FhYVJz8+dO1d+v7/bORwOh4qKipSfn69Vq1YpHA5b62q/v6Hb7U650rTjuMLCwm6fJTs7W16v16pslP4bzHo8nk4Vly6XK+F3ZLPZFAqF5HK5rPaooVCoy3avHZkB4umnn67Vq1dry5YtCe/NbK/q8Xi6DFvNikWfzye73S7DMBQMBq09JNtXQhYUFKigoKBTkLpw4UKdcMIJ7N8IAAAA7IMIGwEAAABgLzVnzpyUwkabzZYQ4JkBkc/n0/Dhw1VSUtJl8Gdqbm7WG2+8ocbGxk7n6urqVFZWljRwC4fDCoVCCgaDcrlc8nq98vl8ys7OVigUSjmk6xgy2e32bvebdLlcXYaN/fr102GHHabGxkY5nc4u95A0ud1u669kc+Xk5GjTpk09PoPT6UxYs9vt7jIMNQWDQasqNRwOJwSeZngYDAbldDoTWp+2v6ff71c8HrfC5uzs7JTfu91u1+zZs/WXv/xFzc3NOuecc7Ry5UrV1dUlbc2aTEZGhtU6dvz48crIyJDf71dra6ui0ajsdrvS0tKUlZXVZZhYUVGh4uJijRs3LqV1AwAAANh7EDYCAAAAwF7qO9/5jh555JGU2pqaoVR7eXl5Ki4uVigUSul+brdbn376qVpbWzV06FBJbfsxlpWVdVmJ2P54JBJRS0uLMjIyNHr0aOXk5Fj3703FmsPhsCrkkt3XbOXZ1T6LZ511lhwOhxVw/ec//1E4HLb2lczIyFBhYaGysrIk/be9bLL3bI4LhUIqLy/vcs3mHHV1ddbPY8eO7bGVqRkKmoGcx+OxgsWmpiY5nU55vV4Fg0H5/X6Fw+GEYNRsS2oYhmw2m4qKitS/f39VV1ersrKy2+rSrKwsDRkyRL/61a8kSZmZmTrnnHPk9/tVW1urbdu2dRta2u125efnq3///vryyy/l8/msfUC9Xm+Xe1V2xXx3AAAAAHonEAjoX//6l95++22tX79efr9fWVlZGj9+vH74wx9q7ty51n7xuwNhIwAAAADspdxut84880w98MADPY7Nz8/Xtm3brJ99Pp+amppSDhqdTqdyc3NlGIY2bdqk7OxsRSIRlZWVKR6PdxkWdjxutlW12Wzq37+/+vXrp6+//lo7duxQIBBIaBWarHLRDBrNz+bekJKsNq1m2JgsiDz99NN19NFH6+GHH9Zbb72lmpqahLag4XBY9fX1qq+vV3p6uoYOHaqcnBwVFhZ2ChvT09OtQHLw4MFyOp3W++goLy9PDodDGRkZCgQCGjVqVEqtTNs/g2EYCgQCstvtisViCUGly+VSJBJRKBSS3+9XMBiUz+ezWpjm5eWpqKjICiELCwtVWFioxsZG1dTUKBQKyTAMa40FBQVKS0uzqkBNM2bM0HvvvacvvvhCeXl5VuvclpYW63vg8XiUn59vtUMNBoOy2+0aNmxYj8/bna7CYwAAAABdKy0t1a9//Wtt3rw54XhdXZ0++OADffDBB3r++ed13333KTs7e7esgbARAAAAAPZic+fOVW1trV544YVuxxUUFKi8vFyGYSgtLU3Dhg3T2rVrU76PGZZJUk5OjjZu3KjGxsaEyjaHw2FV1Zkho3mNWYVoGIbC4bBKSko0ZMgQZWRkaNKkSaqtrVVpaal27NghqS2UNOcwDEN2u112uz0hvLTZbAmBo8fjSaiWa/9f5nq9Xv3sZz/TzJkzdc011+iLL76Q1Fat179/f1VVVXV65kAgoPXr12v48OHKy8tL2JvRbrdr5MiRCeP79++v/Px81dbWqqqqKqFqcMCAATriiCN0zDHH6Pbbb++xba3JDBQNw5Df71ckErECzvbMoNXtdicEsFlZWYpGo3I4HCouLrZC44KCAjmdTmVnZ3f5LxRsNpvOPvvshFDT6XTqj3/8o6677jqtXbtW/fr1U79+/aw1JgudvV6vRo0aZVU19tXOXg8AAAD8r/H7/TrnnHNUVlYmSZo2bZpOO+00DRw4UKWlpXr00Ue1YcMGffLJJ7rwwgv1xBNP7JZ90gkbAQAAAGAvd9ZZZyk3N1dPPvmkAoFA0jEul0t5eXmKRCIaOXKkqqqqumx92pHdbrf2Fty+fbvq6+vV0NAgKbFyMRaLKRAIKBgMyu12Ky0tTTabTbFYzKr2s9lsikaj2r59u5qampSenq6CggKrynHlypUKh8OKRqNW1aXX67UqIk3xeLzT3oUDBw5Uc3Ozda/s7GwNHz5cc+fO1YwZM+TxePTnP//ZChpNw4YNUzweV01NTadnNwxDpaWlcjqdGjFihNatWye73a7Ro0fL5/N1Gu9wONS/f3/1799f4XBYsVhMP/rRj3TWWWdZ42fMmKGFCxem9O69Xq9cLpeampoUiUTkcrmsys5kzAA2EAhYbWvz8vJ04IEHat26dWppaVFLS4vKy8vVr18/DR06NOm+i2bQeNRRR3U65/P5dOONN+qf//yn3n77bSs4TfYvJcaPH69zzjlHd955p/UvOPrC7XbrwAMP7PP1AAAAwP+ip556yvr/4T/4wQ/0l7/8xfr/7d/61rf0/e9/X+edd56WLl2qTz75RG+99ZZmz569y9dB2AgAAAAA+4Dvf//7Ou6447RkyRItWLBApaWl1rn09HTNmDFDM2fO1IMPPqj169envP+dzWbTiBEjlJaWpk2bNqm2ttY63lX7VMMwFAqFrH0Q23M4HNb5SCSiHTt2qKKiQi6XS0OHDtWwYcOsVqSxWEySrHajsVhMfr9f0WjUWoPNZrP2M2xsbFRGRoby8/OVk5Ojv/3tbzrggAOsey9fvlzLli1TQ0OD6uvrrXnMaj+v19upItF8ni1btmjq1Kn6/ve/r7KysqQBXUdut1s//vGP9bOf/Szh+CmnnKLly5ervr6+xzlsNpvy8vJUW1srm82m9PT0bsfH43GrpanJ3OfxwAMP1JYtW1RbW6t4PK7a2lq1tLRo7NixVutZqa3l7tlnn61vf/vb3T7bueeeqzPPPFNLlizR4sWLVV9fr0gkooyMDE2ePFlz587V8OHDJbVV4D700EM9Pm9Xvv3tbyszM7PP1wMAAAD/i95//33r8xVXXNHpz29ut1uXX365TjjhBEnSu+++S9gIAAAAAP/L0tLSNGfOHM2ZM0eBQECtra1yuVzKzMy0quFuuOEG3XTTTVqxYkWP85mtQvv166eysjIraDQ5nc6k+xNKssJCs/2p2Qq1fYtPMyyU2vbj27p1qzIyMuR0OmUYhtxut9UKNR6Pq7W11Qo4zevsdrt8Pp/1fGbl3vTp0xPanIbDYd16661avXp10r3/6uvr5XK5VFBQIJ/Pp/r6eqsy0el0KiMjQ7/5zW80Z84clZSU6KmnntJ7772nqqoqtba2Ws/q8XhUUFCggw8+WD/5yU909NFHd7pXfn6+rrnmGl199dVqbGxM6fcgKeE5kzEMw3pH7YXDYev9jxgxQkOGDFFNTY1qa2sVCoVUUlKiqVOnavLkyZo3b56mTZvW7X3a83q9mjdvnubNm9ftuGOOOUaPP/54yu1jO5o7d26frgMAAAD+l5l/hsvKylJeXl7SMSNGjLA+J+v2siuk9qcLAAAAAMBeJT09Xfn5+crOzk4Ijnw+n66//nqNHj26y0oxp9OpAQMGaOLEierXr5/C4bAqKys7jXO73fJ6vZ2CKcMwrKrEeDwuu90ul8uVEDQmE41GFQgEFI1GZbPZNGvWLOXn5yet1jOrGTMyMhL2ZjTX7/f79fjjj0uSmpubdckll2jp0qVJg0ZTJBLR9u3btX37dhUVFWncuHGaOHGiDjzwQA0ZMkRLly6V1NZKdtu2bUpLS1N6enrCX16vV16vV3V1ddqwYYP1HjoaOXKkbrvtNk2YMKHL9Zji8bhGjRqVsHdiMsFgsNP90tPTFYlEEkJNl8ulQYMGafLkyZo2bZomTZqk2bNn6/rrr9f06dNTDhp7w+v16he/+EWfrj322GM1duzYXbwiAAAAYP9nbofR1NTUZZC4adMm6/OAAQN2yzqobAQAAACA/YzD4dCIESOUnZ0tv9+v5uZmxWIx2Ww2eTwe5eTkJARO1dXVXYaEbrdbbrfb2mMxHo8rEokktOeJRCIyDKPTHB2rFKW2Kjyfz6dhw4YpLS1NL774on784x9b1XnmnoRdtTF1Op0aPXq00tPT9eKLL2rSpEl69tlntWrVqpT3qGxtbdW6des0bty4hPfw+eef61//+peeeuopSW2VpEVFRUnnCAaDeuWVV7RlyxZdddVVSdc7cOBA3XLLLSotLdWCBQv04YcfqqWlRYZhyOfzafr06ZozZ47+8Ic/KB6Py+FwJA1927+79tLS0qz2qIFAQDk5OUmvs9lsWrJkiX7+859bLWt3h7lz56qpqUlPP/10ytccdthhuvDCC3fbmgAAAID92cyZM/XJJ59Iku644w7dcsstCedjsZhuv/126+fd1VGEsBEAAAAA9kOHHHKIFi1aZFXidSUejyf9L2DNakWT0+m02qqaVXRmuNe+bWr7wM+sgHQ4HFaI6Ha7NXjwYBUUFGjHjh2aP3++DjjgADmdTtXX13fZttVmsykzM1PDhg1L2NfwL3/5i/x+f5cVhl1pbW1VeXm5hg4dah2rrq7WP//5zx4rDNv77LPP9Ne//lV/+MMfuhwzfPhwnX/++Tr//PMVj8dlGIZVrdl+38uioiKlpaVp+/btnYJFM4yV/lv16Xa7rfNdvTeT3+/XkiVLNGfOnJSfrS9OO+00FRQU6KmnnurUlrc9n8+nefPm6cwzz0y6LygAAACAnv3kJz/Ru+++q08++UQvv/yyKioqdMopp2jgwIEqKyvT448/rq+++kpS2/9XP+qoo3bLOggbAQAAAGA/NG/ePC1atKjHcaFQKGnr0aysrC7Hd9cu1QyOzHDR5PV6rXCsoaFBAwcOlCS9+OKLKigo0MiRIzV06FDV1NSovr7eqpZ0Op3Kzs5WYWFhQsgotYWZS5cu1fjx4/sUWNXU1Gjw4MGy2+2Kx+MqKyvrcp+T7nz44YeaM2eO7Ha7SktLFQgElJ6erqKiIk2aNClhbR1bmJr7VpphaWFhoRXE1tTUKBAIKBaLKRQKyeVyye12J62i7NhqNpkVK1bs9rBRavuvq2fMmKFPPvlECxYs0MaNG+X3++XxeDRo0CDNmjVL3/3ud3drlSUAAACwp5SUlPT52kGDBmnQoEEpj/d4PPrHP/6hRx55RI899piWLVumZcuWJYwpKCjQ5Zdfrh/84Ad9XldPCBsBAAAAYD80cuRIHXjggSouLu52XDQaTXp82LBh2rp1a6dQMRgMdqpeTKZj+9RAICCXyyWbzZZQtVdZWSm73a7CwkJrr8FU/3Dd0NCgcDishoaGhCq/VEWjUdXX1ys/P191dXWSOoeBPYnFYqqtrdW5556rgoKCTucHDx6sOXPmaObMmSopKdHWrVsTwsiDDz5Yw4cP18aNG61rbDabcnNzlZubax0rLi5WU1NTl+voGMQm09zcbK25ublZwWBQXq9XmZmZu7y60G6367DDDtNhhx22S+cFAAAA9nbXX399n6+98MILddFFF/Xqmg0bNmjt2rXy+/1Jz9fW1mrhwoWaMGGCDjjggD6vrTuEjQAAAACwnzr//PP1hz/8QYFAoMsxycK13NxcFRYWqqWlxQrhpLZwrqd2nVJbWNZxXsMwFA6H5fF4EgLKaDSqlpYWFRYWpvJICRoaGiS17RlZUFAgp9PZZXja3Rz5+fmqqqrqdVVjIBDQunXrFAqFZLPZlJOT06nqcMuWLbrhhht02WWXaejQoZ1a2vbv318DBw5UNBrttn1rd2FgWlpal5WoHdf78MMP691331VLS4t1vLCwULNnz9asWbOUnZ3d4zwAAAAA9g7vvfeeLrnkEgWDQfXr108XX3yxZs6cqZycHG3fvl2vvfaa/vGPf2jx4sVatWqVHnnkEU2cOHGXr6N3/8kmAAAAAGCfMWLECF199dXy+XxdjjGrDU3Z2dk64IADZLPZNGLEiIRwLBQKpXRfc4/Gjszr24dqhmH0OiA0me1fDcOQ3W5Xfn5+r+eIRqOKxWLy+/29CjyDwaCKi4utZzIMw6ocNPn9fq1Zs0ZlZWVqbGxUcXFxp+C3qqpKq1b9f+zdaZhcdZn//8+pvbqq931J0tkXgQQIBJTFEJYsqMA4jIpIAAcVB8QZFWQUjAyCojgIjsuoiD9nEDdQSMLuEDBA2MKapdPZe9+X2pfzf5B/Hbu6qro7RUIW36/r8rL6nO8551vVeWD56fu+X9XmzZtz/iWypDGDyKqqqjHDyGQyqW3btumJJ57Q7373O/X29qbNuOzs7NSvfvUrrVy5Uvfdd1/OalUAAAAAh4/Ozk598YtfVDgcVmlpqR544AF9/OMfV1VVlVwulxobG3XttdfqZz/7mZxOp/r7+3XNNddM+Hvd/qCyEQAAAAAOU4ODg3riiSf0zjvvKBAIyOl0qrq6WkuWLNHcuXMndI9jjjlG3/nOd3TvvffqlVdeyQiSXC6XioqKrLCtrq7OCq5sNpvmzJmjpqYmDQ4OKhaLWfMNR0u1Tc0VNEr7Qq9EIpFWhZcK0YaGhpRIJGQYhlwu14Tago6+R1VVlTo6OvY7LIvH4youLp7wM03TVFNTU8asy5GhaSgU0ubNm9OOxeNxbd26Vccdd1zGHMfy8nJt3rxZc+fOzbqP8vLytCrTFLfbPWbImkgk9Prrr2tgYEAul8uqBjUMQ4WFhaqqqlJpaakMw1A8Htfvf/97dXZ26ktf+tIBb60KAAAA/D246aabNHv27Lyu3Z95jQ899JD1B4vXXnutJk+enHXdokWLdMkll+iXv/ylWltb9dRTT2n58uV57S+XIypsbGpq0m9+8xutX79e7e3tSiaTqq+v1+mnn66VK1eqtrY257XJZFIPPvigHnroIW3ZskXBYFCVlZU64YQT9LGPfUwnnXTSe/hOAAAAACC3trY23X///XruuecyAi1JeuyxxzR16lRdeOGFWrx48bj3mzx5sm6++WZ1dHRo7dq1euONNzQ8PCyn06ny8nItX75cjzzySNaWqg6HQ3PmzFF7e7s2bdqkeDwuwzDSAr2RIeN4AVUymbQqCEOhkJLJpDo7O9Xf3y9pX5AXi8VkGIa8Xq8KCgrkcrlUXl6uoqKitPunWpaWlJRI2tdOdOrUqdq+ffu4n8nI91ddXb1fsxr7+/uztqZN3cM0TW3dujVrxWYkElFfX5/KysrSjtfW1iocDqupqUnHHntsxudYUlIit9ud9lfIDodDs2bNyln1GAgE9MorrygcDstms2VUlA4ODmpwcFAul0tTp061WqiuW7dOFRUVuvzyyyf4iQAAAABImT17thYuXHjQn/PGG29Yr5csWTLm2nPOOUe//OUvJUkbN278+w0b/+u//ks//OEPM76sNTc3q7m5Wb///e9155136swzz8y4dmhoSFdffbU2bNiQdry1tVWtra1avXq1Vq5cqRtuuOGgvgcAAAAAGM+mTZt0yy23ZLTkHG3Hjh268847tXXrVl111VUTqkKrrq7WypUrM46bpqk33nhDe/fuzXpdah6h3+9XIpFQKBRSNBpNOz/RKjiv1yuPx6Pdu3ervb1diURCDodDyWRSkUhE0WjUCjIDgYBsNpt8Pp+6u7vl8XhUXV1ttQ0tLS1VLBZLqwRMVfnt2LEjo8LRNE0lEgnruM1m06xZs/Ttb39b11xzTdp7GktnZ2fW4y6XS9K+OZBjtSbq7OzMCBulfW1v9+7dq4GBAStAHamqqkp79uyRtO9znDlzpjweT9ZnDA0N6e2331Y4HLb2lut3FI1GtXXrVk2fPt3a15///Gd95CMfybpPAAAAAIfeyDEMhYWFY64dOZ9+vO+a+TgiwsZ77rlHd999tySptLRUV1xxhY4//njF43E9+uij+u1vf6vh4WFde+21+uMf/6jp06db15qmqeuuu84KGk877TR9/OMfV0VFhTZt2qT//u//VktLi+69916VlZXpqquuOiTvEQAAAAB27dqlVatWKRAITPiaRx55RC6XK68qtHg8rueee05r165VS0uLXnvtNSWTSXk8HlVVVam8vNyq1ktVxdntdvn9fg0NDSmZTMrn8ykYDGZtrTqaYRiqq6vTjh071N3dLUlWYNjR0ZH1HslkUsPDw/L5fAqHw9q1a5eGhoY0bdo0lZaWZv1SXVFRoYKCAnV0dKinp0exWEyRSESxWCwtaPR6vaqsrNRrr72mU045RevWrRv3PUSjUQ0ODmYcd7vd1l46OjrGvMfg4KAikYjcbnfGuYaGBs2cOVMnnniiHn/8cfX29lrnamtrVVhYaLV9zRUepiokU9WXDocj67NGMk1T27dvl8vlkt/vVzwe1+OPP66PfexjY14HAAAA4NAoLS21Xu/evVtz5szJuXbkd5SRweOBctiHjZs2bdKPfvQjSVJ9fb3uu+8+TZo0yTp/6qmnat68ebrpppsUDod111136Qc/+IF1/uGHH9Zzzz0nSbrooot02223WecWLFigZcuW6ZJLLtG2bdt0zz336MMf/rBqamreo3cHAAAAAH9z55137lfQmPLHP/5Rp5xyyoTnOEr7vis98MADGhgYsI7NmTNHW7duVSAQ0I4dO7Rnzx7V1NSorq5OTqdTHo/HqpRLfbGNx+Nyu91Z24qOZBiGFWKlgkZpXzC4e/fuMecsmqapQCCgwsJC2Ww2K4C77LLL1NDQoAceeCDjmoKCAjU2NiqRSKitrc2aBZlq+epwOFRXV6fOzk79+Mc/lrTvL3zH+4vgkZWXI1VWVsowDEUikQn9pXCusDGRSOj555/X7NmzdcYZZygSiaihoUHHHHOMysvL5Xa79Z3vfEcvvfRSznu3tbUpGo0qFovJ4XDI5/NNqPI0mUyqpaXFmi/z6KOP6uKLL05rMRsOh7Vp0yYNDw/LbrertLRUc+bMYb4jAAAA8B47+eST9cgjj0iS/vSnP40ZNj788MPW64MxVvCwDxt/8IMfWDNB/vM//zMtaEz5p3/6J/3617/W1q1b9fTTTyscDlutZO69915Jkt/v1/XXX59xbUlJiVatWqVLLrlEkUhEv/rVr/SVr3zl4L4pAAAAABhl8+bN+zVrcLTVq1dPOGz86U9/mvZlM8Xr9ep973ufurq61NXVpXA4rL179yoUCmn69OmqqqpSR0eHKisrVVlZKZvNpl27dqmnp0eJRCJnG1KHwyGv16uqqqq0FqT19fUaHBxUPB6Xz+dTIBDIGTqapqloNGp910smk1qwYIHOOussbdy4UVu2bMlY39zcrN7e3qyhXkFBgerr69OOtba2qra2VkVFRTk/u2zVl3a7XZWVlZI04Vaso+8TCoXU2dmp7u5uJRIJ/eY3v5Hf77fOT5o0ScuWLdM555yjr33ta3r00Ue1Zs0a7dq1K+0+iURCPT091mc+VvvUbAYHB63v1D09Pert7VVFRYX27Nmj1atX6y9/+UtauyZpX3vepUuX6txzzx3zswMAAABw4KxYsUL/+Z//qd7eXt133306+eSTtXjx4ox1Dz/8sP7whz9IkqZNm6YPfOADB3wvtvGXHDp9fX169tlnJUnnnXeejjvuuJxrr7zySl188cW64oorrC8+e/bs0TvvvCNJWrx4cdaZF5K0cOFCTZ06VdK+v9wEAAAAgPfK8PCwdu3apfvuu0+hUGhC7Uiz+etf/5pWpZjLb3/726xBY4rD4VBtba2OPfZYzZ49W5MnT5bH41FDQ4O+973v6aSTTrIqHe12u6ZNm6YFCxZo5syZ8vv91vxGm81mtRb1+/2y2+1yuVzWH5M2NDSooqJCXV1dSiQSstls8vv9cjqdOfcWjUbldDqtVqOPP/64XC6XbrrpJs2aNSttbUtLS1oL0pEKCgo0e/bstIo9ad/MxN27d485b9Fut6f9bBiGZsyYYe17rArNkVJtaSWpt7dXb7/9tjo6OpRIJCRlhpF79uzRT3/6U335y19Wb2+vli9frnvuuUe33XabPvShD+nMM8/UGWecocbGRs2aNUuNjY1yu937XXFommZaIBwIBHTffffp6quv1urVqzOCRmlfS6b77rtPV1xxhV588cX9eh4AAACA/Pj9ft16662y2WxKJBL63Oc+p3/7t3/Tk08+qTfeeENPPvmk/u3f/k1f/vKXZZqmPB6Pbr/99rTvIgfKYV3ZuH79esViMUnS+eefP+baCy64QBdccEHasVdeecV6fcopp4x5/cknn6wdO3aopaVFu3fv1uTJk/PbNAAAAABMwJtvvqk1a9bohRdeUDwe1yuvvKJEIiGn06nKykpVVVXJ5XJN+H7xeFwbNmzQOeeck3NNX1+f7r///gndzzAMFRcXq7i4WJK0d+9eTZ06VZ/61Kf0s5/9LG2t0+lUfX29VanY2dmpvr6+tOCtoqJCoVBIlZWVKioq0tDQkF566aW08Mpms8nlcsntdisejysej6fNWHS5XJo0aZIqKiokSU1NTWpqatLMmTP1rW99S7/97W/12GOPqbe3N+vcRIfDocrKStXX12cEjZLkcrk0c+ZMxWKxnDMOvV6vHA6H4vG4bDabZsyYYX1GUmYYOVoymZRhGEokEopEIgoEAmpubs4IKXPdZ+fOnfrqV7+q7373uyouLtYxxxyjY445xjp/xx13aM+ePerr6xtzH2MZ2cr3f//3f7V+/foJXReJRHTrrbfqK1/5ik477bS8nw8AAABgYs466yz94Ac/0A033KDh4WE98sgjVmvVkSorK/X9739f8+fPPyj7OKzDxs2bN1uvR1Y1JpNJdXV1KRAIqLq6Wj6fL+v127Zts143NjaO+ayR7VmbmpoIGwEAAAAcFN3d3frWt76lpqYm61gymbQq2mKxmFpbW7Vnzx55vV4VFRXJZrPJ6XSqrKxMLpdLoVDIqgZ0u91WKDk4ODjmsx977DHF4/G8975mzRp95jOfUX9/v37/+99nXVNUVKSioiJFo1ENDg4qFotp1qxZuvLKK3X33Xdr06ZNam5ulqSMCsJkMmnNhHS73WltRFNGX/PXv/5VM2fOlNvt1qWXXqqPf/zjuvPOO7Vjxw7rj1cdDodKS0tVVlaWNWQcye12q7S0VBdffLEef/xxtba2pp232Wyqr69XJBJRVVWV1dY1ZWQYmZJqARuNRpVIJOR2u7VlyxYlk0kFg0G5XC45nU6rCjHVAjWX9vZ23XnnnVq1alXGuVR4myssnYjUv8Xe3l4999xz435mI5mmqTvvvFP19fVWByEAAAAAB88555yjE088Uffff7/WrVunHTt2WDPvZ86cqbPOOksXX3xxziztQDisw8bUl2+n06mqqip1d3fr7rvv1tq1a632QHa7XSeddJKuueYaLVy4MO369vZ263VdXd2Yz6qtrc16HQAAAAAcKJ2dnbr++uvV3d2d9bxpmgoGg4pEIkomk+rr61NnZ6c8Ho8cDoe2bdumZDJphVPSvgrEoqIiVVVVjdmCNZlM6rHHHntX+3/66ad12WWX6bLLLlN1dbX+93//N2cFncvlUkNDg84//3xdeumlCoVCuuqqqzQ8PJz2fnNJfQajvxCngrCU0QGrw+FQW1ubZsyYsb9vzxIMBlVZWakf//jHev3117V161YFg0G53W7V1NRo6tSpuvbaa7Pu32azqaKiwvpeGYvFFAwG09amwuFoNGpVcBqGoYKCAquydbyA79VXX9WePXvS/nBW+lvIWFBQYM3B3F82m02maVqB9v6KxWJ66KGH9MUvfnG/rwUAAACw/8rKyvT5z39en//85w/J8w/rsLG/v1/Svr6zGzdu1Gc/+1nrWEoikdALL7ygF198UV/5yld0xRVXWOdGzisZL7EtKCiwXg8NDb37zQMAAADACNFoVN/4xjeyBo2maSocDisQCGQEhrFYTLFYzJqFaLfbrTafbrdbpmmqp6dHPT09+vGPf6zTTz9dNTU1Gc9obW3NGXJOVDAYVHNzs973vvdp6dKlOvvss/X8889rzZo1am5uVigUktvtVm1trZYuXaqFCxdqYGBAb775pn70ox8pFArt1/NisZhCoVBald/o9qLZAr+9e/eOed9wOGwFfE6nM2u72tbWVhmGoQULFmjBggUZ588///ycsy/Ly8vV2tqqSCSicDicNjfR5XJZ7yEajaa9j0AgIJ/Pp6qqqjH3n5KqNB1p5B/SVlVVaceOHTmvN00z60xHt9ut/v7+tO/J++u5557Tpz/9aRUWFuZ9DwAAAABHhsM6bEz9BWYkEtFnP/tZDQwM6NJLL9XHPvYxTZ48Wb29vVq7dq1+8IMfKBgM6tvf/rZqamq0fPlySelf3Ea3thlt5PmR1wEAAADAgfDMM89oz549GcdjsZg2b96sUCiUtTIxFaalgiHTNGWz2RQKhRQKhdIqz958802dfvrp+upXv6qLL744rQ1pPhVu2YysTHQ4HDr99NN1+umnp+0xNY/yZz/7meLxuAYGBrRlyxYFAgEZhiG32y273Z416BotEonI7XZb73N0AJYtzBrdalXaN9Oyq6tLnZ2dGecLCwtVXV2t0tJSa0/jBaP//M//rP7+fj377LPWsaGhIXV0dKivr0/BYNC6h2EY1szJVHAai8Wy/r4TiYTi8fiE2qA+/fTTGWHj2WefrT/84Q8yTTMt9JT2/X4ikYii0Wjas+12u1wul1wulwzDUGVlpaTx/2h3LNFoVE8//bQ+8pGP5H0PAAAAAEeG/e+H8h5KfTELBoPq7+/XLbfcoq997WuaMWOGXC6XampqdPnll+vee++1Wgjdfvvt1hepkX/xOt6X2JF/DZtPmxgAAAAAGMvq1aszjiUSCW3ZskW9vb1Zr8lWtZdMJhWPx5VIJJRIJKzQKBUUDQ4OatWqVfrUpz6ldevWWdelvjO9W2Pdp7m5WZdffrmuvvpqrV271pq/2NnZaV0bjUY1NDSkQCAw4T2l/iDU5XKppKQk7dwpp5ySsX50INnR0aGNGzdqz549WYPIoaEhbdu2Ta+//roVyo5X1WcYhr785S/r4osvls1m09tvv61Nmzapt7fX+r2lvluapinTNBWLxaznZwsaXS6XPB6P2traxnx2SjAYtGY0ptTX12v+/PnW82fNmiW73a5QKKTBwUGFw+GMZycSCet8MpnUvHnzDsi8xdHzLgEAAAAcnQ7rVG1kteGpp56qf/zHf8y6bsGCBfroRz8qad+XyPXr10tK/3KY+pKby8gvnNla6AAAAABAvpqamtTc3JxxvK2tTYFAQNFo1GqTOpaR4WPq9ciwMSUcDqutrU3f/e539eijj0raN8NjIpWE40lVvaUkk0mtX79eX/rSl7RkyRKtWbNGW7du1aZNm7Rx40Zt3bpVPT09kvaFjakALtUediJSYWNlZWXae2hsbNS8efMUj8e1detWvfLKK9q4caPKy8utNS0tLdq1a9eY8yxHPmfz5s0aGhrStGnTxl1vGIbOO+88FRYWqry83KpaTFUnOp1OeTweOZ1Oq5IzHA5nBISGYcjj8VjfYfv6+ibccSfbZ/gP//AP1ufk8Xjk8XgUi8XGnJEp7fs35XA4NHny5Al9XuMZ73s4AAAAgKPDYd1GdWTLn3PPPXfMtWeddZbuv/9+SdLGjRu1ePHitJYvoVBIRUVFOa8f+WWvuLg43y0DAAAAQIbt27dnHDNNU11dXWkhkN1uVyKRGDcUGnkP0zSVTCYVDAZls9msGYQdHR2qqKjQj370I9XW1mr+/Pk64YQT9Morr+T9PmbMmKFJkyZZP7e3t+ub3/ymdu/erXfeeScjXEomk+rq6rKqGH0+n9xut9XFJpFITOi5yWRSdrs9Y5bhaaedpl//+td6/PHH1dfXZx3v6elRW1ubPB6Penp69itkTSQS2rNnjyZPnjzu2tQczoGBAdXU1KimpkamaWrHjh2y2WzWc03TVDQaVTQaVSKRUDQatQJIt9ud8Qevpmmqu7tbdXV1CofDGhwcVCKRsFrQFhcXW6FttlanCxYs0MqVK3Xvvfdqx44dCgQCKioqUjQaVSQSyfq5OxwO1dbWas6cOVq/fr2Gh4fTvpNnMzw8rGg0KtM0Zbfb5ff75XD87f9meDczHwEAAAAcOQ7rsHHkX8zW1NSMubaurs56nfqSWV9fbx1ra2tTdXV1zutHtqkZax0AAAAA7K9s8/96e3sVi8XSKtgMw7ACx/2pLEsFjqkWq+FwWOFw2AqMHnjgAc2fP1/Lly9/V2Hj8uXLrddtbW36yle+ov7+fvX39+ecCZkKTmOxmLWfZDKZNkfQZrONGbAahqHGxsa0tqter1f/8z//k/W60tJS7d69W+3t7TJNUy6Xy2o7O7LFqcvlktvtzggjS0pK9PDDD+vTn/70mJ9HtjmcqerFkfdMhYQTmcOY0tPTo6GhIQ0ODma8R4fDocrKSn3gAx9IC/dGuuiii9Td3a2bbrrJOpaay5j695WasWm329XQ0KD6+npr352dndb6keLxuNra2tTV1aVIJGLNo5T2faZlZWWqrq6Wz+ebUHUoAAAAgCPfYd1Gdfbs2dbrgYGBMdeO/IKeqmCcOXOmdWz37t1jXj/yC+KMGTP2a58AAAAAMJaRIyJS+vv7JWXO7kuFPxOVrWrPNE1FIhFt2bJF8Xhcb775pvbs2aOTTjop7Y8y90dxcbHOOOMMSfu+f918883We0jNZBxvf/F4XMFgUF6v1/pMDMOQw+GwAqvRbDab/H5/WgWfzWbT8PBwzoDSZrOppKRE8XjcCjlHtxFNJpNW1eDIMNjtdquqqkpPPvlk1vmOI6XmcMZiMbW2tmr79u1qampSX1+fIpHIuBWquYLCQCCgrq4uDQwMZL1HKvB788039eyzz+a8fygU0rHHHquampq0Z9ntdjmdTnm9XtXX1+u4445TQ0ND2u+qsrIy7fcaj8e1fft2rVu3Tps2bVJ3d7cVhgYCAcViMSUSCXV3d+vtt99WS0uL3v/+94/5/gEAAAAcHQ7rsHHBggXW6/H++rapqcl63dDQYF2f+rL08ssvj3n9hg0bJEm1tbXW9QAAAABwIGT7jhGPx8e8JjXD8d3MWQyFQmpqalIymdSjjz4qwzD07//+7+O2xxzN5XLp61//ulWZt27dOqs7TCQS0eDgYM5rR4eI0WhUyWRSHo9HRUVF8ng8SiaT8vv9crlc1vtNtV0tKiqyKuzKyso0f/58xePxcQPZnp4eq82stO/zzhbcpYLZ4eFhORwOzZo1Sw6HQ4FAYMwgr6mpSW+88Ya2bdumjRs3au/everu7raCxlAopMHBQQWDwZxVqtmOp4LR8X7vTqdThYWFuuOOO7Ru3bqs91m3bp08Ho8mT56sBQsWaObMmZo6daqmTp2qmTNnasGCBWpsbMza7rSwsFChUEimaaqjo0Mvv/yytm3blvXfbSwWUyAQ0PDwsNWiNZFI6Pvf//4Bmf0IAAAA4PB2WIeNp556qtVKde3ateru7s659sEHH5S07y80zzrrLEn7gsNUYPnYY49peHg467Uvv/yyduzYIUk677zzDtT2AQAAAECSdMwxx+T8o8ZsodK7CRhHGxoaUnd3t9XtZdKkSfrWt76l8vLyCV1fWFioW265Ja3zTKqiT5IVSOVit9szKvhSnWlsNps8Ho/8fr/mzJmjhQsXatGiRZo3b57q6upUWloqv9+vhQsX6sYbb9TPf/5zdXZ25qyCTOno6FBfX1/azETTNBWLxRSPx7POxTRNM63iUpJ27tyZ8xl/+tOf9M4776i3tzfjXqn9pWY1Dg0NKRaLZdwjmUyquLjY+jkYDCoajSoejysSiWhgYMCqHBwZ8hmGocmTJ1vtZ7///e9ntHPdvn17Wgcgm82m0tJSVVZWqrKyUqWlpeMGthUVFdq1a5d27NihoaGhMddK+wLG1Pfu6upqvfjii/p//+//jXsdAAAAgCPbYR022u12XXnllZL2/VXml770paxzQO677z49//zzkqRzzjlHVVVV1rlLL71U0r4WRTfffHPGX1UODAzo5ptvlrTvL0M/+clPHpT3AgAAAODv27Jly9J+Ts0fHDmHcKT9CRxzhW+pMKmzszOtVejUqVN111136ZOf/KQqKiqyXltcXKyLL75Yd999t+bNm2cdb25u1rZt26yfU5VsYxk9929kCCbte68ul0t+v19FRUVqaGjQ7NmzNW/ePM2bN0933XWXPvCBD+jNN99UU1OTurq61N7erq6urowQLB6Pq6mpyWrrOVpqvmUqdHQ4HPL5fPL7/VZb0JRgMJj1/bz44ov67W9/mzNkHf1+TdPMCAxTGhsbVVhYqGAwqGAwaAWhhmFYe021gh0aGlI8HtekSZPSwuJ4PK5HHnkk7b65Zmjuj9TnF41Gx20JO9LIOY6rV6/OOrMUAAAAwNEj+4CIw8hll12m//u//9MLL7yg559/XhdeeKEuu+wyzZ07V0NDQ/rzn/9sfakqKyvTTTfdlHb9ihUr9Mc//lHPPfecHnnkEbW3t+tTn/qUqqurtWXLFv3kJz9RS0uLJOmaa67RpEmT3vP3CAAAAODot2TJEj344INWx5bS0lJ1d3fL5XIpHA5nrLfZbOMGeWO1WnU4HFbYGAwGMzq9FBcX65/+6Z/0j//4j3rppZe0c+dOa55iQ0ODTjnllKwzBUdX0E1kvqTT6ZTNZrP++DOZTFqB2sj3m83ChQtVV1enxx57TKtWrVJzc3PGGq/Xq6qqKpWXl+vtt9/OCAlHVjjabLa0Z5mmKYfDYa3p7Oy0qg29Xm/GswKBgL773e+OGQanPvvRv79gMKiioqK0Y6nqxJFBpM1myzmLM7Xf0f7yl79o5cqV1p5HB577yzRN7d27VzNnzlRbW9uEZlDabDb5fD6ZpqnOzk7V19crFArpL3/5i5YvX/6u9gMAAADg8HXYh402m00/+clPdP311+vRRx/Vrl279M1vfjNjXWNjo374wx9mbQV011136bOf/axeeuklvfzyy1nnN65cuVJXXXXVQXkPAAAAAODz+XTzzTfrhhtuUCAQUGlpqdxutyKRiJxOZ0abzVTYlKpwyyVXSDc6bMo1I9Jms2nRokVatGjRhN7H6GDU6/WOu0fDMOTz+TQ8PGytGxk22u32rOFYY2OjLr30Un3hC19QS0uLWltbs94/FApp165dam5utoLGbHsa+ZmmJBIJBQIB+Xw+GYah/v5+RaNRuVyurK1vn3zySYXD4bR2q9l4PJ6M6sJkMmndW9r3O9q+fbuGh4dlGIbsdrsViKYC2dR+HQ6H3G63HA6HduzYIYfDoZKSkrTP4LnnntM555wjSWkdf/LR399vzWK02+0qKipSNBpVNBrNCFGdTqdcLldalW5XV5fq6upkGIYee+wxwkYAAADgKHZYt1FN8Xg8uuuuu3Tvvffq/PPPV11dnVwul8rLy3XCCSfopptu0h//+EfNmDEj6/V+v1+/+tWvdNttt+nUU09VaWmpHA6HKisrde655+q+++7TV7/61QM6FwUAAAAARmtsbNR3vvMdKwhK/XcqsBvJMAw5nU7Z7faccx1HtqscKVt4N3369APyHkZX+7nd7oxqvWzsdrv8fr+135HvqaKiIuN9HHPMMfrXf/1XrVq1yupGM1alp2maGh4etlp+jjfXcaR4PG6Fsan7eDweffCDH8x4xtq1ayXtm2U5VuDodDqznh/ZQragoEADAwOS9gWRqd956vdqt9tlt9ut8DEUCmlwcFDBYFA7d+7MCFM7Ozut15MmTdKsWbMm/BmM1tnZqdLSUutzMQxDbrdbhYWFKioqsv67uLhYPp8vox1wNBpVX1+fJOUMiQEAAAAcHQ77ysaR3v/+9+v9739/XtfabDZddNFFuuiiiw7wrgAAAABg4iZPnqwf//jH1qiHP//5zxocHJTP57Mq4VKVYg6HQ4FAwJrjN1KqEm60VCvLkYqKilRZWXlA9j9lypSMY1VVVVZoNha73a7CwkJJ+8K6VGvXVOhqGIZOOukkLV++XMcff7y++MUvqre3N+36XFJtPk3TVCKRsELaic4ajEajVmAWj8e1ePHijGB1x44dVvBpGIaqqqq0e/funPf0eDwyDCNtZmE8HlcymZTdbk+rZk3tc7zWuaZpKhKJqKenRz09PWkzN0fPwly+fLm2bt065v1S1Yqp9qypPQeDQU2fPj3r73WiQW4wGFRZWZkikciE1gMAAAA4Mh1RYSMAAAAAHA2cTqcWL16sxYsX67Of/axuv/12bdmyRbFYTD09PWntRn0+n6LRqDXvUJJV8Taa3W6Xz+dLC4MMw1BdXV1ay813o7GxUXPnztWmTZusYyUlJWlh6VgMw1BDQ4Pq6uoUCoU0a9Ys/dM//ZMKCgrU2NhohaKvvfaatm/fnnZtQUFBWnA30sigLRU6Zpt7maujTSwWs1rE9vf3y2az6c0339Sxxx5rrenv70+7pqKiQu3t7Rkh30hut1tOp9MK9VLtUUtKStTX1yePx6Py8nINDQ2NGzSOlEgktGXLFpWVlVm/79Eh8+mnn65f//rX1pzQFNM01dfXp46ODg0NDaWdc7lcqqqqktPpVFFRUcasz/2Rej8FBQV53wMAAADA4Y+wEQAAAAAOoZkzZ+onP/mJHn74Ya1Zs0bt7e0Kh8Pq7OzUwMCA4vG4CgsLFYlEZLfbM2YmSvvCy9Q8v5EMw9CUKVNUXFysM84444Dtefny5Wlho2EYmjlzpjZt2jRuFZthGFaguGDBAt16661yu90Z61avXp1xrLKyUj09PRnHk8mk1YY0Vc2YTCblcDis16lnj67KS503TVPBYNCqDF29erVWr16tSZMmadmyZTrnnHPSAl9JcjgcmjVrljZv3pxzJqa0rxLQ4/HI7XYrkUjozDPP1Pvf/36tXr1aBQUF2r17d9ZgdDzBYFBtbW2qr6+XJM2dOzftvMvl0s0336zrr7/emmUZCATU1NSUMyCNRqPq7OxUIpFQW1tbRoC5P1KB+LRp0xSNRvXyyy+rp6dHyWRSfr9f8+fPT6vMBAAAAHBkImwEAAAAgEPM4XDowgsv1AUXXKBXX31Vf/3rXzU4OChpX7vRY489Vnfffbfi8bh6enq0bds2JRIJKzzLVq1ns9k0depUlZeXa+HChVar0gPhtNNO0x/+8Aft3LnTOuZyuTR37lxt27ZtzGq4VNXcokWL9KUvfSlr0DgwMKCXXnop43hRUZG8Xm/O6kZJVtiY+k+qnWrq8xop1XI1VUVqGIbKyspUV1dnrdmzZ49++tOf6vHHH9cll1yS8byCggLNmTNHTU1NEwpaq6qq9L3vfU8vvPCCfD6fEomEuru75Xa709qqToRpmurs7FRdXZ0mT56cVoWZ0tjYqNtuu02rVq3S7t27tWXLFivUHFltmeL1ejV9+nR1dHRoz549qqmpkdvtzqsVqsfjUTgcViKR0GWXXZbx78Jms+mkk07SihUrtGDBgpxVpwAAAAAOb4SNAAAAAHCYMAxDJ554ok488cSMc6+//rqefvpplZeXq6SkRN3d3ers7MwI3jwej6qqqlRRUWFVOn74wx8+oPt0OBy6+eab9ZWvfEVdXV3W8VTgODg4qM7OTvX396fNTCwvL9fKlSv1oQ99SDNnzsx5/87OzowqwpT6+npt27Yt57U2m8261jRNK5C12WxyuVyKxWJWEJmqRkyt8fl8qq+vzxp67dy5Uz/72c/k9/szQrOCggIde+yx6u3tVWdnZ9ZQraysTFVVVZo9e7amTJmil19+WZLU09OjRCIhh8ORtveJSCaT6u/v14YNG9Tf369PfvKTqqys1JIlS3TWWWdZ7UunTZumVatW6dJLL7XmgKZmXKbY7XZrTmhzc7NsNpvi8bja29tVVFS032Gjw+GwWr263e6sn2kymdSLL76oF198UWeccYa++MUvZlTnAgAAADj88b/iAQAAAOAIcNVVV6m5uVm7du2S3W5XdXW1qqurFQqFrIo4p9Mpj8eTFux89KMf1YIFCw74fioqKvTd735X//Ef/6GmpibruGEYKi4uVnFxsaLRqCKRiJLJpM444wx9+ctfVnFx8bj3HmsGYllZmRoaGrR3717rWKq6MxUupl6P3JPP57OCrFgspuHhYevn1OflcrnGbOvZ0dEhp9OZ9ZzNZlNFRYUqKioUCoWs9+1wOFRQUGA9a9myZTIMQ+Xl5ZKkvr4+6x5er3dCcy9TbWNT7yUej6u4uFgDAwMaGBjQtm3bdN999+m8887TypUr5XA49PTTT6uoqEhut1t2u10ej8f6vOx2e0bIl0gkFAwGrUrS/a1udLvd2rVrl6ZOnTqhisV169YpHA7r3//93zNa3QIAAAA4vBE2AgAAAMARwOfz6ZZbbtE3v/nNtMo+r9crr9eb9ZqPfvSj+tSnPnXQ9lRWVqY777xTb7zxhtasWaMXXnghbe5gWVmZlixZouXLl6e1Jh3PeHMC6+rq5HA4tHv3bit0czqdVkhpt9vT2syODPskZW2parfbVVNTo127dikej8swDDkcDpWWlqq0tNRanwoDXS5Xzv3l+p243W6dffbZkqRTTz1VXq83bdaj0+nM2iZ2ZHA6Mmi02+1W1eTogC4cDutPf/qTtm/frhtuuEFPPPGEtm/frsHBwZyB6UiGYcjlcikQCMgwDE2fPl2tra0TavVqmqaGhobU0NBgzeeciA0bNuj+++/P2q4WAAAAwOGLsBEAAAAAjhClpaW67bbbtHbtWq1du1ZtbW0ZawzD0PHHH68PfehDWrhw4Xuyr+OOO07HHXechoaG1NXVpUgkooKCAtXW1o4Zyo2clThSXV2dCgsLNTQ0lPPaqqoqlZWVqaurS52dnUokElbYaBiGPB6P/H5/1mtHV04mk0l5vV61t7dnrO3p6ZHL5VJlZaX1fiZNmqQ9e/bk3Fsun/vc56wg1ePxaPHixXrllVfS1rjdbtlsNoVCISUSCSWTybQZlCM5HA4VFhbKbrfnfOabb76pL3zhC9qzZ496e3v3a79ut1vJZFLBYFD9/f2aN2+empqaFAwGc15jGIYKCwtVVlam6urq/XqeJK1evVr/+I//OOa/GwAAAACHF8JGAAAAADiCeDweXXjhhbrgggv02muvaePGjVZL0LKyMp155pmqra3Neb1pmgoGg3K5XBOqcNsfhYWFKiwsHHPNzp07tWbNGq1fv16Dg4OSJL/fr0WLFmnFihWaMWOGXC6Xzj77bD344INj3svhcKi2tla1tbUKhUJqamqyKvFqamo0efJka7ZlKiCLxWJWZaBhGEomk9a8wlyi0ahaWlo0ODio6dOn65133lFNTY1efvllGYYhp9OpsrIylZSUZG0ZahiGrrzySi1ZsiTt+Pnnn68f/OAHWd9XauZhrqAxNd8xEAhYLVlzee2119TT0zPmGmlfxWc8Hk+bdenxeBSJRDQwMCC3261jjjlGQ0ND6ujoUF9fX9q+CgsL9elPf1p//etfrWPxeFzRaFSmacput+ec35gyNDSkdevWWRWgAAAAAA5/hI0AAAAAcAQyDEMnnHCCTjjhhHHXRqNRPfvss1qzZo2ampqsgKi0tFTnnHOOli1bNuaswgOhra1NP/jBD/TWW29lnBsaGtKTTz6pJ598UrNnz9Y111yjZcuW6aGHHsoI2XLxer2aN2+e3nnnHYXDYVVVVclms6mqqkpVVVUaGhpSIBBQb2+vksmkDMOwZhD6/f5x5wqapqnOzk51dHSooKBAPp9P1dXVam1t1eDgoFUBWVVVpZqaGqut6YwZM/SJT3xCJ510UsY9J02apJUrV+rOO+9Me04gEEhr5Srtq75MtagdeTwej6u/v1+xWCxneByJRNTX15e1Pa1pmorH44pEImktXVNS7VT9fr+WLl2q//u//5O0L1hMJBKKxWKqqanRsmXL9JGPfESPP/64/vrXv2pwcFCdnZ0ZgaTT6VRlZaWqqqpyBrxPPPEEYSMAAABwBCFsBAAAAICj2Nq1a/XLX/5SO3fu1NDQkOLxuGw2m1wul4aHh/Xb3/5Wv//973XaaafpmmuukcfjOeB72L59u2666SYNDAyMu3bTpk1auXKlpk2bpo6ODu3evVt2u10+n0+VlZU5qwelfRWBc+fOVSgUUkFBQdq5kVWXw8PDCofDMk1Tfr9/zDakkqwKwlTYl6oqLCsrU1lZmdU+NhQKqbu7Ww6HQx/96Ef1kY98RLNmzcp5397eXtXW1srhcKi3t1emaaYFiiPf58iWsyP3m9pLU1OT5s2bl/U5kUhEsVhMiUQi7dqRwWYupmkqEomora1NCxYs0OWXX66tW7dqeHjYquicOXOmtX779u16++23rdmWo8ViMbW2tqqtrU3V1dWaPHlyxprOzs6c+wEAAABw+CFsBAAAAICj1I9//GP913/9l3p6erIGSh0dHVaF3rp169TS0qJbb701awVcvrq7u7Vq1aoJBY2tra1qb29XPB7X1q1bNXfuXJWUlKi3t1f9/f3q7++X2+1WfX19zkrMU045RTfccIM2bNig1atX65133kk7n2rlGY/Hx23pKWUGjdK+StFU5aKUvX3s4OCgpk2bptdee00vvfSSBgcHZbPZVFxcrPnz5+upp57S888/r0QioYqKCkUiEYVCIcViMStYtNlsstlsMgzDakM68rnSvrmK0r4Atb+/XyUlJRnvYWQVZCpsnEjQOJJhGLrtttt0ww036LTTTsu6prOzU7/73e9yBo0jmaap9vZ2xWIxTZ8+Pe3c6JmaAAAAAA5vhI0AAAAAcBT65S9/qVtvvXXc4CYQCGj79u0KBAIyTVO33nqrbrnllnGr/SbqgQceUG9v75hrTNPU9u3b0+YKxuNxtbS0aMaMGdqzZ486OjqUTCYViUS0fft2RSIR1dfXW+sdDofOO+88/fM//7PsdrvOOOMMnXHGGdq1a5eam5sVCATk8XjkdDp14403qrm5eUL7D4fDaUGjtC8EHBgYUGlpadZrksmkHn30Ub300ksZrU3D4bBWrVolh8Oh6upqlZeXq76+XkNDQ4pGo3I4HNZMyVTo6PF4FA6HM57jcDissFHaF/ZlCxsLCwutwDIlFApNOGiU9oWapmnqzjvv1NSpU9M+e2lfQPiNb3xDoVBowveUpJ6eHrndbjU0NFjHDmTYDQAAAODgI2wEAAAAgKPM3r17tWrVqv2qEOvo6LBad7744ot6//vf/673EQwGrRl/Y9m1a1da0JjS19enWCymSZMmqba2Vl1dXers7FQkElFLS4ucTqfmzZunZcuW6dxzz80atE2ZMkVTpkxJOzYyoBuLaZqKxWIZx91ut3p7e7OGjfF4XE1NTRoaGlJnZ6eOO+64tHNbt25VJBKxQtOBgQFNnTpVM2bMsNqHjq5ejEQiMk0zrQrT4XBkhHIDAwOKRCIZ78/tdqfNR0wmk1nfVy42m01er1c2m02xWEyrV6/WVVddlbbmmWee0Z49e+T3+yd835T29nbV1NRYFZizZ8/e73sAAAAAOHRs4y8BAAAAABxJrr/+egWDwf2+rr29XQMDA1qzZs0B2cdTTz2VtSJvpGAwmHNGn2ma6urqkrQvXKutrdX8+fO1cOFCHX/88aqvr9cPf/hDXXzxxVmDxlxGV+XlEo1G06oBpX3Bm9PpzFoVmEwmtXXrVg0NDUnaV8U4ODhonW9ra8v4PHp6erR9+3aZpqmCggJ5PJ6MsNE0zbTWqh6PR36/P6MFrGmaWSsLS0pK5PV6rSrLbO9rLG63W+Xl5dbPTz/9dMb7SP2bKS0tTQs2JyKZTKq7u9v6efny5ft1PQAAAIBDi7ARAAAAAI4iLS0teuGFF/K+vrOzU2+88YZaWlre9V7efvvtcdd0dHSMeT4V3I2UCvxisZieeuqp/d5XTU3NhMLJbNV/Xq835/qWlhYNDw+nHUu1kE0mk1ZwOlpvb6+6urpkGIY8Ho+Kiork8/nkcrnkdDrldDplt9tVUFCgoqIieTyenHuIx+Pq6+vTli1b9Oqrr+qll17S66+/Lkmqrq6WYRj7VfHqdrvldrtVVVVlHQsEAlq/fr31c1NTk7Zt2yZp32zHysrKCd8/JRU4T5s2TXPnzt3v6wEAAAAcOoSNAAAAAHAUefjhhzMCr5FM01QikVA8HlcikbDmA6b09/crEolo8+bN73ovY+1DkhKJRNb2qSONN1dw7dq1+72v4uJizZgxQ4WFhWOuG139N7I6MNXyMyVXmJjaf29v75jvZfTn4HQ6VVBQIJ/PJ5/PJ7/fP241YjQa1fbt29XU1KSBgQHF43GZpql4PK6SkhL19PQomUxmzKDMxePxyOv1qqioKCNkHVmNumPHjrRz1dXVE25VmxIOh2UYhlauXLlf1wEAAAA49AgbAQAAAOAosm7duqzHk8mkQqGQBgcHNTQ0pOHhYQ0NDWlwcFCBQMCq4jNNU319fero6NDOnTu1ffv2tBaX+2O8dprDw8MZYedoo1uKjrZnz55xA8vRTj31VNlsNs2ePVvV1dXjPsNms8nn86UFaKPnNfb09IwZJg4MDGQcM01T0WhU4XBYAwMDCoVCOe9ht9tlt9tz3j8cDisYDGb9PA3D0KRJkzR58mTrZ5vNZt0vmUwqHo9b/zFN06podLlcmj59esY9R1ZHjm7d6nA4NGvWrP1qp2oYhq688kodf/zxE74GAAAAwOHBMf4SAAAAAMCRIls1YTAYHLN1ZiwWUywWk91ul8vl0q5du3TXXXfp/vvvt9Y0NjZq+fLlWrx48ZhtPEeqqKgY8/x4VYvS+IGltK+t58iZguM544wz9Itf/EJDQ0OaMmWK6uvr1d3dra6uLkWjUSWTSdntdut9pqoZR+5pdNiYapc6WqoCcmQ1YTKZVCQSyZidaBiGIpGIbDabFfSNnMuYqm6MRCJpz4hEIgqHw3I4HFmD0ylTpsjr9crr9co0Tb355ptKJpPWvQ3DsILH1LFIJKJ4PK7i4mINDQ1ZFa+macput+uVV17RihUrVF5envXfg9fr1bx589Tc3Jy1Fe5ILpdLjY2NOu+888ZcBwAAAODwRNgIAAAAAEcRm80mh8NhBXkjqxbHkgqxIpGIEolERsi3detWPffccxoeHta0adNUXV2toqIinXrqqVq6dGnWsG/JkiVjtjkdGaTlMl5gKWW2NB2Py+XS2WefrQcffNC6vqamRjU1NWnr2tvbtXv37ozrKysrM/ae6zNOhZKp9bFYTMFgMGtLVLvdrng8blWhRqNR+Xw+61q73a4ZM2Zo69atVjWhaZoKh8OSlNG61DAMTZ48OW3eYk1NjTo6OtTW1qZEIiGHw5HxXmw2mxV0trS0aO/evfL7/WlB5uuvv64rr7xSJ598sk488cSs793lcmnu3LkKBALq7OxUb2+vFboahqGioiJVVVWppKREFRUVEw6xAQAAABxeCBsBAAAA4Cji9/tVUVGh9vZ2hUKhCQeNiUTCCsBisZg1zzAajWr37t3q6+uzzr/++uuaMmWKqqqq1NzcrP/+7//WrFmzdNFFF2natGmaNWuWJGn27NmaPn26mpubsz53vKpFj8ejoqKiMdfYbDYVFxeP+x5H+/jHP66NGzdmzBscqaKiQnv37k1rTer3+1VbW5uxNlt46PF4rL05nU7FYjEFAoGczzMMw1qXarEaiURkt9tlGIaCwaBcLpfq6+sViUTU2dmpwcFBq9owVYFps9lUWlqq6upq+f3+tGckEgklEgnZbDarotHpdMo0TRmGIYfDIafTqVAoZIWY0r6K2VTgWFBQoMLCQiUSCT3//PN67bXX5Ha7MyouU3w+n6ZOnaqpU6da/85S7yll6dKlOT8XAAAAAIc3wkYAAAAAOIosWrTIqlzLFf6MNDpolPYFY319ffL5fNqyZUtGC1bTNNXc3Kzu7m5rTuCmTZu0du1azZ49W7NmzdKyZcu0ZMkSfeQjH9Gdd96Z9dl+v18ejyct1Bqpurp63OrHRYsWyefzjfs+R/N6vfrGN76hb3zjGzkDR4fDofLycnV1dUnaF5rNnDkza6vSbNWVIysKi4qK1NTUNOaeUmFjOBxOCzhTFYgul0t9fX3q6+uTx+PR9OnTtXnzZtlsNtXU1MjpdMrtdqusrCyj9WtKe3u7YrGYbDabksmkTNOUx+NJ+5zD4XDGv51kMqlwOKyCgoK095Va39PTI7fbnRFujpZt7qTD4dC555475nUAAAAADl+Z35AAAAAAAEesFStWyOPxTLi1aCpwSrHZbPJ4PGptbdXWrVuzznoMBoMaGhpSW1tbWigWjUa1detWbd++XT/96U915ZVXqrKyUsuXL8/5/NHBVUp5ebmqq6vH3f9Y9x5PWVmZvv3tb+vCCy+0KjlHmzRpkoqKilRbW6u5c+fmDPFGV1cWFRWl7T8YDGYNKUdKVS/abLa0tamqw5HPDofD2rRpk2w2m0466SRNnz5dkydPVnV1dc49mqaprq4uGYZhtSw1TTNtdmYikcgZ/kajUXk8nqwtc0tKStTa2jrm+8vl7LPPVllZWV7XAgAAADj0CBsBAAAA4Cgybdo0zZ49WzabLWsV2Wij23/6fD7ZbDb19vYqGAxmrA8EAlYAmWrJOVIkElFLS4ukfa03b7rpJp166qn6yEc+kvX5FRUVGfusrKzUtGnTxt375MmTNX/+/HHXjcXr9eqKK67QL3/5S1133XVatGiR5s6dq7lz5+qUU07RV77yFT3zzDM69dRTxwwLR85xLCoq0syZM62fUyHf6JmKIxmGoUgkYgWLdrtdDofDeubIykPDMFRcXKzJkycrmUxm/A5y6e/vt353LpdLXq/X2l/KWNWwNptNJSUlWf9d2e12NTQ0ZA2nxzJ//nx95jOf2a9rAAAAABxeaKMKAAAAAEeZK6+8Uvfff7/8fr8CgUBa5dpII6saDcOQ2+2W2+2WaZqKxWJyOBxpwVI4HM6YARmJRFRQUJB2rKenR5MmTZLdblcsFtPtt9+uu+++WyeffLJWr16tF1980QrIHA6HZsyYoaamJhUVFamqqkolJSXjvsfCwkLdeOON47ZZnSiXy6UlS5ZoyZIlWc/fcccd+s1vfqMnn3wy69xFp9OphoYGmaap2tratH0NDAwoFovJ5XIpHo9POJBLhY5ut1ter1ezZs2SzWazfk/hcFi7du1SS0uLZsyYMe79+vv70352u91pFZOp33s2TqdTXq93zJmTBQUFmjNnjnp7e9XZ2Tnufk4//XRdd911E67CBQAAAHB44n/RAwAAAMBRpqKiQjNnzlRTU5P8fr9isZgikUhG6JiqorPZbHK5XPL5fGkVdiMr3kzTzFr1lq2qLpFIqLu722ojGggEtGbNGl122WU67rjj1NvbqxdeeEEDAwMyTVOFhYVyuVz62c9+lrOF50jl5eX6xje+ofr6+v39aPLm9/v16U9/WpdeeqmeeeYZbdiwQUNDQ1a13+mnn67p06fr+uuvV09PT9q1I8PFVDA78pjNZlMikcganDqdTmsmpd/vTwvmnE6nDMNQb2+v9u7dmxYsOxwOlZWVqby83KqOzPa7crlcmjNnjuLxuHbu3Jn2OzcMQy6XSy6XywqdcwXXKVu3btVvf/tbPf/881qzZo02bdqU8bzTTz9dK1as0MyZM8e8FwAAAIAjA2EjAAAAABxlvF6vCgsLNXfuXLW0tKivr09Op9Nqe5oKlKLRqEzTlNvtlsvlssKuVKA0MvxKrR0t2zFJGhwcTJtZ+MQTT+gTn/iEnE6nysrKss5aPPbYY/Xwww/r6aefztrCtaysTOedd55WrFiRMSPxveJ2u3Xuuefq3HPPzXp+1apVuummm9Tb22sdG/0ZFRQUyOl0WqGuw+FIm30p7QsLU0FfrvukrhkYGFA4HLbmMKYMDAxoz549qqioUH19vVW5mAqSDcOQ3+9XUVGRJCkWi6VVNmYLP8erJE0mk4pEIvrgBz+oD37wg9q7d686OjoUjUbl8/k0derUnPMxAQAAAByZCBsBAAAA4Cjj8/lUXl6unp4ezZgxQ9FoVF1dXRoaGlI8HrcqGcPhsAKBQEaAlAq1RrZQzdX6M1f4NLoCbmBgQC+88IJOP/30nPuuq6vTZz7zGV122WV69tln1draqnA4rIKCAk2fPl2LFi3KOi8wHA7rueeeU0tLiyKRiLxe75jrD6YpU6boe9/7nn7+85/rhRdeUDwel91uVzweT6ssdDqdmjZtmioqKrRlyxbreKqtaa65iCmRSERbtmyxwsNccxvj8bhaW1vV1tamaDSqUCiUsaa5uVlVVVXW88eSark6lpH3aGhoUENDw7jXAAAAADhyETYCAAAAwFHGMAydd955+t///V9J+1pXZms52tramrWCUNoXbI0Mt0ZX3qWkWnRm28NoLS0t4+5dkjwej84555xx17W1tenhhx/WU0899Z5VQgYCAfX29ioSicjn86mqqiojGKyoqND111+vtrY23XXXXVq9erW1P5vNZrU/HRwclMfjkcfjyRkWpvj9fuuzjsVi2rJli8LhsDVXM1eFaSwWUzAYlGmastlsVkWj9Ldgs6enRz09PfL5fGnnsykrKxtzn3a73Wr7CgAAAODvA2EjAAAAAByFzjvvPD3wwANjhliVlZVqaWnJCKpsNlvabEApd7tUt9ud9Xi2CriJzGOcqJdfflnf/va3x7xnb2+v7r//fj3xxBO6+eab1djYOOH79/b2KhAIyOFwqLS0VM3NzVqzZo3Wr1+fVrVZVlamc889V0uXLlV5ebl1vL29XatWrVJLS4saGxsVjUbV39+f9oxwOKzdu3crFAqlzUXMJlV5KO0LbUe+b5/Pl3WeZiwWUyAQsH4eHRinZnQmk0lFo1Fr5mOqsnJka11pX2vXke8xm0WLFuUMoAEAAAAcnQgbAQAAAOAolKrqW7NmTc41TqdTpaWlafMFJVkVbiMZhpFxzG63Z4SSKdlCqYKCgoluf0wbN27UrbfemtGqNZfu7m7deOONuuOOO7JWeKYEAgE99dRTWrt2rfbu3StpX/vY7du3y+l0qqqqKmPeYG9vr37zm9/od7/7nS644AJddtll6u7u1vXXX5/2uVZXV2eEjSMNDw/L7/dnDRxTcy6lfW1Re3p60s7bbDbV1dUpGAxaoWMikUgLGlNS4aHf77fe88g5jam5njabTeFwWE6nUx6PRzabTZWVlRlBYjKZVG9vr0KhkBKJhE4++WQ99dRTOv3009PmTQIAAAA4ehE2AgAAAMBR6p//+Z/V3t6uV199Neea2tpa9fX1WUGi0+nUnDlztHXr1rRqudTcwZFyVTW63e6sbUunT5+ez9tIEwgEdNttt004aEwZGhrSt771Ld1zzz1Z24Q+/PDD+tWvfpVWMRiNRrVp0ybrc0i1Gp0xY0bGe08kEvrDH/6gnp4e7dq1KyPALS4uVmlpqfr6+jKe7XK5FIlEFAgEVFhYmLG/yZMnWyFfT09P1mrV2tpaeTwedXZ2qqurK+tzDMOQy+VSUVGRAoFA1tazNpvNChtN01Q0GlUsFlNlZWVaUBuNRtXR0aGuri7rd+H1erVhwwZt2LBBP/vZz7RkyRJ9+MMfTqvKBAAAAHD0obcJAAAAABylHA6Hvv71r2vx4sU51/h8Pk2dOlWGYcjr9WrevHnyer2qrq5OWzc6XPN4PDkr16qrqzMCs5qaGp1wwgl5vpO/efLJJ3POmRzP7t279frrr2cc//Wvf62f/vSnaUFjMpnUli1bMtqTBgIBvfPOOznbt/7pT3/Ss88+m/Xc9OnTVVRUlHE8NR8zmUymVRkahqHJkyenVYlmCxF9Pp/8fr8cDofq6ur0vve9T263Wx6Px/rvgoICFRcXy+v1yuFw5AxrDcOQzWZLa7maCppDoZCkfcHtW2+9pba2Nus+drs9LUweHh7Wn/70J1177bV64403sj4LAAAAwNGBsBEAAAAAjmIOh0P/+q//qrvvvlvLli2Tx+NJO28Yhs477zzdcsstOuGEE6xQsbq6WqWlpdY6p9NpVdd5PJ6M+6SUlpZmBJWStHTp0qwVhfvDNM0x28JOxOrVq9N+fvzxx/XAAw9krOvp6bHCtdFisZi2bNmSNbDr7OxUe3t71upDm82m2bNnq6amJqNdaurzjEajkvaFu9OnT1dNTU3aumzPrK2tzdi73W6Xx+OR1+tNC4aTyaR6enpUUFCggoKCrG1bbTab9R+v1yu/369kMqnm5mYNDw9r69ataftwOByaNWtW1ja5gUBAq1at0ubNmzPOAQAAADg60EYVAAAAAP4ONDY26uqrr9bll1+u3bt3KxgMyu12q6qqShUVFZKkiy++WE888YTWrl2rzs5OTZ8+Xdu3b1dvb68cDodqa2s1NDSUMbcvpaysTNOmTcsIFaurq3Xeeee96/ewdetWtba2vqt7bNiwQYFAQD6fT4lEQv/zP/+TdV1nZ+eY94lEIurq6koL+mKxmPr7+2Waprq6ujKCQulv1Yr19fXq7u5WV1eXIpGINRMzFotpypQpWQPbbGpra615jiP3lks0GrVa5rpcLrlcLiUSibTjqSrXGTNmqL29XcPDw5KkUCikd955x7qXzWZTWVmZ6urqcobPqWf+x3/8h37xi18wxxEAAAA4ChE2AgAAAMDfEa/Xq9mzZ2c9V1xcrI9+9KP6h3/4B23ZskX9/f1KJBLatGmT3n77bW3btk179uxRW1tb2nV+v19VVVUqLy/PCBqLi4v1jW98Q36//13vvaen513fI5lMqr+/Xz6fT88//3zGbEVpXzVeIBAY916dnZ1pYePIwK6zszMjbIzFYtYah8OhqqqqjFCxtbV1zODO6XRar+vr69PmKI58j7lEo1E5HOn/V4DdbpfX60075nA4VFZWprKyMgUCAXV3d6uvr0/BYFCVlZXy+/0qLy/X4OCg2trarDmPLpdLFRUVGe9hYGBA69at09lnn51zbwAAAACOTISNAAAAAIA0hmFozpw51s8f+MAHJEnNzc1av369nn32WW3YsEGGYai0tFQ+ny/rfaZOnaobb7wxa4XfRI2stss1Z3B/pe6zdu3arOcnOhMyEoloYGBAxcXFkv62V0kKh8NKJpOy2Wzq6+tTZ2enBgcH09a4XC5VVVWpsrLSChHr6up0xRVXqLW1Vf/3f/+XMRuypqbGqkjN1rZUUtbWqKn3nUwm0wLLXEbew+fzyefzKRgMKhaLadKkSerq6tJbb72V9XfS1tamoqIi1dTUWJ+NJK1Zs4awEQAAADgKETYCAAAAACZk+vTpmj59ui699FINDw/rySef1Nq1a9Nam9rtdp1yyilavny5jjvuuLyes3PnTq1evVrr16/X0NCQpH3Vk/X19QoGg/J6vYpEIlY1ndPpzKjWG0sqHG1ubs56Ptu8xVwCgYAVqKUCulQ71DfffFO9vb2Kx+MyDEMOh0Mul8vaazQa1d69e9XS0qL6+nrV1dVJkmbOnKkLL7xQl19+uV599VUNDg5KkoqKivS+971Pn/vc5zIqLxOJhBKJhOx2e87w1zRN2Wy2CX1Wo+8RiUQ0NDSkZDKpd955Z8zg1zRNDQwMaGBgQA0NDdb7ampqst4rAAAAgKMHYSMAAAAAYL/5/X5dcMEF+shHPqKenh4NDw/L6XSqtLQ0Z8XdeFpbW3XXXXelzQVMGRoa0muvvaZXX31V8XhcHo/Hmh1pGIZKSkpUXV2toqKiMZ/R0NBgzajMVcGYayZlNiODSdM0FQwGFQ6HZZqmAoGA1VbWNE1Fo1FFo1HZ7XZ5PB6rwtA0Te3du1exWEyzZs3S1KlTJUkFBQU67bTTMp55zjnn6KGHHlI8HldXV5e6urrSKiCdTqdisZjsdnvaezFNU263O6PVbTZVVVVpP8diMSWTSQ0PD8vv90/4M9q7d68kWYFjT08PYSMAAABwlCFsBAAAAADkzTAMVVRUWAFevrZt26abbrrJqmQcraWlRa2trYrH44pGo4rFYvL7/bLb7TJNU319ferr61NBQYFmzpwpt9ud9T7Lly+3Xrvd7ow2pZLGnJk4ms1mU39/vzo6OtTa2mq1TzVNU8lkUoZhyDAM2Ww2K+RLJBIKBAIqKCiQy+Wy7tXR0aFTTjlFPp9P4XBY4XBYXq8347184hOf0G9/+1u98cYbWeczxmIx6xkul8sKfwsLC7O+39G8Xm9GaJtMJhUMBq33tD9aWlpUXFwsn893wFrhAgAAADh8EDYCAAAAAA6pzs5OrVq1KmfQuHPnTnV2dkraFxBGo1GZpqnh4WEVFhamVdkFg0G98847mjt3bkZo6PF4dNZZZ1k/19XVafv27RnPKyoqktfrVSgUGnPfpmmqp6dHe/futeYZ2my2tEDONE0reBxdaRgMBmUYRlqF42uvvaaVK1eqp6fHWldTU6OlS5fq3HPPld/v1z333CPDMOT1ejPaqaakPqdoNKpkMqm6ujpNmzZNb7755phtYg3DUENDQ8bxWCymeDwuu92+32GjaZrq6OjQtGnT5Pf79+taAAAAAIe/ifeGAQAAAADgIPjNb36j/v7+rOfa2tqsoFHaNxcxVQ1ommbWSr1YLKatW7dmhGqXX3552izC8847L+eeKisrx9xz6tnhcFjxeFyxWEySxgziEolERiViKBSSaZoKhUIKhULavXu3tm7dmramvb1dv/z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El6Ze2nb9PeEe7vfvZ8pMoVB0624uTFzIMnfZXB+SOEpTplbJarFsDjaTt/mWdRpfE+0YDPrQ9IzQhhh1+Pd6e9Vu3Q1A3uTJk4+rYBuFNmSP2YOrXLIq2/F4o2aUHcEO3jfxPq7quYpnJJ4xtwc7zVA0NO/2qdPtjfYe1fZCCCGEEEIIcSK79957CYLa6ItXvvKVM677mte8hte85jXH7VwkbBRCCCGEEOIEkFRJPtb7MT4z9Rnuq97Xdp36bEbf+kREcw4a68q23BTg7I/2Ny231nKvfy8byht43H+8Zfv/UP/BOd45vDL9Sp6TfM68ji2O3JgZo2iLBLZ1fqI6dJveQrdu+v2+9UmpFJGNKNoiCZWIA0uDISAgTXN1o8FQtEUslsAGVKiQUu2/BRsSUrVVHOvw8amPc23vtax0W9v4zCajMi33GUw8k7SxfaunPJIqGYeodQ7OvI8rhBBCCCGEEE8XTz11+MvKjVWNxhiGh4cpFossXryYbLbzF0aPFQkbhRBCCCGEOEEkVZIP9HyA7eF2bijfwJ2VO6nYSrx8sbMYgAkzQcmWjugYjYFV1Vbjn0Mb8tn8Z7mzcmfHba21POI/wiP+I1ycvpg/z/05jpJA53jp1t1MmkmGoiGgNTiEWtim0TPONmysbqyrUnvuG9voWmzbasKKrTQdu2qrLWFjPQQ0GB4PHielU2RVlv+b/7/8/YK/n8OjbbbKXYVGYw7dyrbcNmyNWwXbKq5yyagMGo2Dw0K9cN7HFUIIIYQQQoiniy1btgDgeR4DAwOMjIzwhS98gRtvvJHJyUkAHMfhggsu4O1vfzvnn3/+cTsXCRuFEEIIIYQ4wZzsnsxfdP0Fb86+mQPmACVTIq3SDOgB3jT2Ju6u3j1jNVsnGo2PT8XWKtPq8/mstVw7de2sMyMb3VK+hchGvKP7HfM6BzF3FyYv5MvBl9u2T22k0Vhsx8BxethYr1DMqmxT9Z9CgYXGzqwWi2/9pv3V70uoBBVbiUPGxuPVA8BvFL/BpJnkytyV86qG7dE99Ok+hswQBVOYU0vV0IbkbZ6cztGv++nSXXM+nhBCCCGEEEIcK5s2bTribZctW8ayZXMbXzIxMQFALpfjkUce4S1veUt8X10URdx///088MADvPe97+VP/uRPjvjcZiJhoxBCCCGEECeotE5zsj656b5uVZuv5yq3baXXTOpVbMNmmJXOSk51TwXgB+UfzCtorLutchune6fzivQr5r2tmN0lqUv4Yv6L8e8zBcwODgbTdnnjfQpFaEMyKlNry9tAo1GqeQZkY/VrI9/6BAQdW7s2HvsR/xE+PvVxrsxdySvTM88RqVvprmTAGWBntHNesxstlpIpsdBdyAp3xZy3E0IIIYQQQohj5Zprrjnibf/iL/6Ct7/97XNat1gsAlCtVnnLW97C5OQkf/iHf8gVV1zBSSedxNjYGDfeeCOf//znKZVKfPKTn2TJkiVcdtllR3x+nejZVxFCCCGEEEKcKBY4C1igF5AgMa/t6nPtoBYgLXIWcUHiAqy1XF++/ojP5wflHxzxtmJmi5xFcSAMtISD02l0U9DX+HN9+Wp3Ncvd5W33pZWOw+y6TtWSPn7boNFTHlo1/5lZsAWstfxz4Z+5q3LXjI+h7mWplzFhJkiRankcM1EoMirDkBni0tSlc95OCCGEEEIIIZ5uyuUyAKVSiYmJCT760Y/ywQ9+kFNPPZVEIsGSJUt405vexJe//GU8r/Y34Cc+8Qmq1fZfKj0aEjYKIYQQQgjxS7Y33MsT/hM87j/OznAn1s69HapvfU5xT2GxszhunzlbO1VPeWTV4YHwkY24JHUJWml+5v+MwWjwiB/L7nA3T/hPHPH282WtZWe4kyf8J3gyeJID0YFf2rF/FV6VfhUJVQuWZwuY661SFQoHB7fhllM5VjorWems7Li9g8MqZ1XTfe1em51mOwIkSbbcF9ko3tc/Ff6ppS1rOymVwmJxVe3c3WlNeernUL9ZLC4uXaoLRzlUbIUVjlQ2CiGEEEIIIX59pVKp+OfnPe95/O7v/m7b9c4991xe97rXATA4OMi9986/s9FspI2qEEIIIYQQvwQVW+H2yu3cWL6RneHOpmVLnCVckr6Ei1MXzzpnLqMytXl5JEioRK1q7FDYWK9sq98cHJIqGYdVdT26h0vTtaqvO6p3YKxhwk7gWx+LxcGhS3fFMx1nc3v1ds5KnDXHK3FkJs0kN5dv5ubKzQxFQ03L1npruTR1Kb+V+q2Wx/p0t95bz3p3PZvDzVSp4uF1bJ+rUHHg2DiLUaNxlctiZ3HLskYL9UL6dB/7zX7Kthzvc7pOQaOLi6ta/8RsrHTMmzx3V+/mpamXtt1H3V2Vu+hTfQwyCApyKkdgA0q2REAQv+YbX+9KKSIiNJolegm3VG7hiuwVMx5HCCGEEEIIIY61q666ivXr1x/RtnOd1wi1WY11F1988YzrvuQlL+Gb3/wmAI888ggXXXTREZ1fJxI2CiGEEEIIcZw95D/EtVPXUjTFtssPRgf5SuErfKP4Dd6SewsvRg2KFAABAABJREFUT7+87XoTZoIngyd5PHgciyWlat9iLNtyXOFosSgUadIkdWuVmYfHn+X+jG7dzWA0yI8rP+bR4FFCwuYVo9p8yAFngAVqQcssv0Zj0dgcr8SR+WH5h/xr4V8Jbdh2+ZZgC1uCLXyl+BXe1/0+npF4xnE9n1+mZ3jPYI27BheX/dF+hqIhJplsG/hpNEmS+PhN1a4JlWDAGaBX9wK14G46D4+lzlKUUqxz17Ex2IiPX3veG4obG19j04+dUZm2j2F6AHxD+YZZw8bHgsfI6iwnuyezLdxG1VYp2RJAS5VjXWADAgIWqUX06T4eDR7lCiRsFEIIIYQQQvxyrV+/nvPPP/+4H6e/vz/+ecmSJTOu2xhijo+PH/NzkTaqQgghhBBCHEf3VO/hmslrOgaNjXzr8/n85/nv0n+3LBuKhnjP+Huo2EpTkJRSKbIqi9NwUyjKlFvaVWZUhnMS5/C/sv+Ln1R+wp+P/Tmbgk2tQeMhU3aKreFWNoeb41aY7QS0r7Q7Fr5V/BZfyn+pY9DYaMpMcdXkVTzkP3TczueXTSnFpelL8ZTHKncVz0w8k9Pc00irNC4uHh4JlSCncnTrbtI6TVZlm8LApc7Spvaovbq3KQB0cVnnrotneiZVktO900mrdNwW1WAID93qbUsjorgSNqdyGGUo2zJFW6Roi7UqRBuwSC9qekybg82z/nso2tryPt1Hn+qLKy1nk1IpQkK2RlvJR/k5bSOEEEIIIYQQT0eN1ZOTk5Mzruv7hz8f6O7uPubnImGjEEIIIYQQx8nWYCufmfoMxrZvO9nJvxX/jQerD8a/F02RqyevZjAaJKVSdOvmPwwSKlELmlS6qUVmyZaIbESf7uM07zTO8s7i5amX86j/KJ/Kfwrf+jiqfUvNRpN2kk3hpo6Po12l3LFwd+Vuvl78+ry2CW3IJyY/wZ5wz3E5p1+Fl6deTo/uAcBRDivcFTw38VxOcU+hR/eQUZmm9qX1OYcplWKJs4S17tqm8FGhGNADQO25O907nYRO4OPHFZNJleQM5wwW6AVNwWJdYyVtSEje5imYAlVbrVUY2gDf+lRshW3hNvZF+5qqMQu20PaxhjZkNBqlZGpBZT7KM2bH6Fa117du8yesQpFUSbpVd1ztO2Em2BZtO9JLLoQQQgghhBAnvHPPPTf++aGHZv7S7ZYtW+KfV6w49vPtpY2qEEIIIYQQx8l/lf+r43y9mVhr+VbpWzw7+WwAbqjc0BSerXBWsNFsbAp/6oFLUiXjEAhqYdKp7qkAZHWWFyRfwP+Z+j9YW1verbqZZOZvQEItHNoV7WKNu6Zl2TmJc+b9GOfim6VvHtF2FVvh++Xv85ddf3mMz+iXa9JMMmlqz81fdf0Vn5z6JFVbBYgrHVfYFQxFQwzb4bj6M6mSrHBWcH7qfN6UfRO3VW7jfv9+iqaIUoqcyvH7qd/nR+UfsTvazS+CXzQFgTmVo1/3M27HmbJTZFWWoi1isRgM+tBNUZuRGFGrenVwWsLApEriW5990T7GzTjrvfV4eC1zI3eFu9hQ3sCPKz+mYivsinYxGA3Gc0QTKkGS2us7slF8vgqFtpqA2jzH+utaoXgqeIqJaIJep/fYPzlCCCGEEEII8Sv2vOc9j/7+foaHh7nxxht5xzvewaJFi9qu+/3vfx8Ax3F4yUtecszPRcJGIYQQQgghjoOxaIz7q/cf8fb1OYSnuqdyU/mmpmU5leMU9xS2hduaAse6yEa1uX3WUqLEkzzJEmcJH+r5EA/5DzUFoP1Of0vVWSejZpQVdgWe8uL70jrNRcljO1ge4DH/saOqTryrchdvzr6ZrM4ew7M6/kIbck/1HjaUN7Ax2Ni0bMAZYFe4iyRJtNKUTIlBM8iYGYsDv/o+hu0wy/QysjrLO7rfARBXpo6YET459UkCAiZN6/zHgi0wFA4R2ICMypBQCSIiqrZKihQ+tfY7EVHTthER6tANakFjY7vWki2xKdjEM7xnxNW5eZPnM1OfaWl9u0gv4kB4IJ7TWLEVkipJSqVwVK1dsLWWCpX4tT7dpJnk1SOv5vcyv8cfZf+ILt019ydCCCGEEEIIIU5wjuPw5je/mU984hMUCgXe85738I//+I9ks81/B//7v/879913HwAvf/nLGRgYOObnImGjEEIIIYQQx8GPKj+a05zBmdxYuZHnJZ7HUDTUsqxP9+F4DtuD7fHMRN/6VG21KXgCGDNjrHJX8YmpT7Ar3MUivSgOgVxc+nQfI2Zk1vMxGIbNMMucw4PlL0peRFqnj+ZhtnVT5abZV5pB1Va5vXI7r8q86hidUS3E3R/tp2ALeHgsdBayQC84Zvt/zH+Ma6euZcJMtF0+FA2RUAmKtkg5KrMz2tm0XKHo0T0sdhbTo3q4s3ond1bv5PXZ1/MH2T9AK83ecC/vn3g/E2aCJEmWOcvYHe2mbMsoVFxxWJ/3WbRF0qRrbUx1mkkzSWgPz25suUZEODgkSODiEtgAjY7b9ZZsCa00CZVg0kzyvon3sTfc27KfrMqilaaepVtsPK80rdIYayjaYstrvU6j8ZTHvmgfN5Vv4vHgcT7a81H6nf65PBVCCCGEEEII8bTwxje+kTvuuIP777+f++67j9e+9rW88Y1v5PTTTyefz/ODH/yAH/7whwD09fVx1VVXHZfzkLBRCCGEEEKI42BXtOuo97E73E2X6lyN1aN6ODdxLmNmjC3hlrgKrM5THgkS5HSOrMpyIDrA3mgvQ9EQ69x1ZHQGgOXucib9yTi0nMmIGYnDxkXOIl6fef1RPMLOdoY7j3ofx+I5gFqV6k2Vm7i5cjNj0Vh8v1KK87zzuCx9Gc9OPBul1Ax7mdn91fv55NQnZw2oHRwmzAQHo4Msc5bVgjcMDg5ZnaVqqoxFYwwxhMXi4vLF/BcpmzKvz76eD09+mKFoiCEzxHA0HD/nHh5VqlRsJQ7wFAoPD41mlbuKPt1HwRTYEm7hgDnQ9vzqLXwDgqYKWsc6JFUSD4+SKTEZTXLN1DVtg8a6rMoywUTTfVVbBQshYcegEWpVlXA4NN0X7uOqyau4tvdacvr4zBgVQgghhBBCiF82rTVf+tKX+Ju/+Rtuuukmdu3axTXXXNOy3urVq/nHf/xHFi5ceFzOQ8JGIYQQQgghjoOyLR+TfRRtccZ1FIq8zaOozeIzmFqFmjo8Py+ytVCmHrz4+DwZPEm/0x9XqKVUirIp16bxKd3xePV99Dl9XNNzDX1O31E/znaO1fU7Wt8ofoPvlL4TX8NG1loe9h/mYf9hlrnL+GD3B1nprpz3MbYF27h26to5VcKOmTEORLWg72B0kNO808iSZdgMsznY3PYxj5gRPjL5ETaUNzAcDbMj2tES1DnKIUMGg2HKTsVBZUZlahWR0V76dB85nWO1s5phMxxXNtqGGxDPdWwUEVGypVpLVhvxxcIX2RxsnvGxZlSGpErGcyrrypRb9t/IU14cNtZbxwLsDffyjdI3uDJ35YzHFUIIIYQQQoink1Qqxec+9znuvfde/uu//ouHH36YkZERurq6WLVqFa985St5zWte09Je9ViSsFEIIYQQQojjIK2OvrVoWqWbZt61czA6yM5wJ4ENmuc32sOhS4LaPgyG0IZUqRLYgHyYb5ppqJWmbMtoq0mQiFtfNrJYnuk9k7d3v51FTvvB88dCvWJvxIwwYSYIbIBC4apa29c+3Tdj4FTfx9G4Ln8dN5RvmNO6+8P9vHfivfyf3v/DGnfNvI7z7dK34xB3NvWgEWrP575wH45yGDNjM2wFAQG3VG5BocjoTMdrF1ILPBWKiIiCLZAjR4UKU3aKbtVN3ubJqAwlW2oJGoG2c0Tr+0ypFBvDjYyURjjJOWnGc3Zw4uewHjhabBxytnsMnvLIqsOv6emv4dsrt/NH2T8ipVIzHlsIIYQQQgghnm4uvPBCLrzwwl/JsSVsFEIIIYQQ4jiYLUiZi5XOSoqmyMHoYFwtllIpenQPFsv2cDt7wj0d258GttbKMlIRAQFFU6RgC4eXEzRVoWk0WZXFYAhsgIsbL6uHfKvcVXxkwUfifTzmP8bNlZvZG+6lQoUUKda4a7g0fSnrvfVH9Ljrj/nn/s9bW2VamDAT7GY3/U4/y53lHYOzo3kO/qf0P3MOGusKpsBHJj/C5xd8nm7dPadtRqIRHvAfmPP+GytdrbUcsAdIqdSswWvVVKlSxcGhaIrkdA5Fa9vX6UGhwVCmTJYsQ9EQ3W43IWEcgudtvu3sxunqry2NJiBgW7iNZXoZrur8J2lO58DUQmNPeVRtlaqtxoFj42N2lUuSJJ7ymvehmlumFk2ROyp3cEn6klnPWQghhBBCCCHE3EjYKIQQQgghxHHw8tTL+Wbpm23bb84mtCEjZoT7/PsYj8bZE+1pCoESJAgICAnnNGcR4Of+z9u26azaaksFoEaTVEkUivXu+qYZd+d55wFwZ+VOvl36NnvCPS373B5u57bKbZzincIbMm/gguQFczpHqIVvV01exWg0OuNMvpCQA9EBpswU6731uNP+tEmoBC9JvWTOx20U2IDvlL5zRNuORqPcVL6J38v+3pzWv7lyc1Orz5mMmJGm3wMCfOtjsSRIoJSqtcFtEzz6+HFIFxFRtmUyKjOn4wY2iKsc4XAg6eLi4MT3tato1GgSKhG/nqD2+o5sxLAZZqmztONxF6gFJEjg4+Pi4ioXi42rQJMqiUbjKa9j2DrgDLTc96D/oISNQgghhBBCCHEMzfz1VyGEEEIIIcQR6XP6eG7iufPermzL/CL8BaNmlKIpklAJFugFTetM2AnGzThTZqpjy8o6hcK1LpNmkoqtxOFQXWDbh5UGQ8mW+FnwMx7xH+Gx4DGeDJ4kq7L8S+Ff+PTUp9sGjY22Bdv46NRH+c/if7Iz3MnGYCM7w52UTftZigVT4KrJq9gT7qFbd8+pDWrRFtkcbG6prntR8kVNIel83F29mykzdUTbAtxUuWnOAeL2cPuc99s4uzC0IQVbICCgYisUbZGCKTBlpmr3Nzyv1to4lK6/Xlra7h7SKbTz8ePgvB7s1vfpNNzqYWdSJcmqLN26m5RKNVVRWiwKxZAZwtrOr1+lFP1Of/OdlvgYKZWKA8d2sirbUtkIkDf5jscUQgghhBBCCDF/UtkohBBCCCHEcfK6zOt40H+wY6A3XdVWeSp4ipCQk9zDLUCX6CWMm/G4Mq1e2WUwWCwOTtuWmFCr8AsIsNbG7S8bqy3bts205aZzLlAgQa067dulb7Mv2scivYiVzsq2cx3rSrbEYDTIX47/Jaud1fGMx5RK8aLUi7g8fTknuyfH63+t+LU4wLRYFjuL2RnunPW6FWyBg9FBljnL4v3/dua3Z92uk5vKNx3xtgDD0TA/83/Gs5PPnnXdki3Neb/157tkS/jWjys/pz+HoQ0JCXGsQ1Zn21aI1isEkyrZdL+nPJRVLfv0rR+/xnp1L4NmsCn8rC+z2FqrVHu48japkk0ht0LFbVGn7BQ9qqfjY16sFzNqRqnYylwuUdMxVrorOy4TQgghhBBCCHHsSGWjEEIIIYQQx8mp3qm8q+tdaDX7225rLVvCLQQErHRWNlUz5nQunj/YGPBALdzp1G7UxSVpk3E4CWCsaalurItsRMEUWsJR3/qEhGRUhm3hNqq2yqAZZGO4sWnf8X6I2Bxu5ongCYajYQyGXdGuuI1rxVa4pXwLfzX2V3xs8mNUbIWKrXBr5VaGzBBPBE/wM/9n7Ax3UrEVpswUFVuZsYpzyAxhsbjK5a+7/7pj0DQXO8IdR7xtXT0ktdZSMAXyJt+2ii+lUnPep0ZTNEUCG8xpTmJE7flsfH00Bm2dQvD6PMZGFhsHk92qmyTJ+BwstSA7JIzvq7dr9a1P3uTJm3x8PEc58THKtn2Va52rXNa6a0lQW7/+b0mjO4aGCsUadw3dqv3czF7dO+MxhRBCCCGEEELMj1Q2CiGEEEIIcRy9IPUCkirJp/OfpmQ6V7FN2VqgtsZdQ7/ub1m+2FmMtZbHw8fj+xqryQymqZ2kpzwyZIiImlp6hoR06S6KpojBxPswGIq22BRi1efwVajgGpcD9kBcYaasoqzKBGHA2e7ZcYVjRMRTwVMUbbHp/CMiRs0oi53FTfc/UH2AD0x8AA+P+6r3tQSn9RacZVumYiskVbJte1Xf+lRtlY/3fpxzEud0vM6zsdbOu4qunS3BFq6euJoN5Q2MmlFCwlq1nbOS12Rew5XZK+l2ulnuLOdn/GxO+yzaIiGtczdnqtSrV6pqdNPzXV/WTlIlW0JtqLUGhkPtTXU/g2YwDhUbz6Xd+UREFG2RNGmWOksp2RIVWyGyERNmgoqtYKgF4RmVoUt3xdumVZozvDPYHm4nsAE+fttAtH7uq93VM1ZLviD5go7LhBBCCCGEEELMn4SNQgghhBBCHGcXJC/gK95X+EbpG2wob2BfuA9H1UKVtEoz4AyQ0zmyOhvPw2un1+klF+WoUiWwQUtwpNG1akaVxMVFKdW2ms5i6dJdlGwpDijLttwSNNZDJAcHT3lNAWK9DedwNMzjPM453jkoFNvCbS1BY92QGWoJGwFuLd/K/mg/rmr/2JMqiYeHjx+3D22cxZdRGQb0AC9LveyogkaoBWkplTriwLFkSmwJt/Cw/zARUUs15pZwC9dOXcsX81/kiswV/GH2D/mf0v/Mut/prW0bdZpZWFcPjTsFge32l1GZphavCtUUgi/Wi3mCJ1qCxk5Vs3VVWyWncqRVmm3hNvaYPWhTO39rbfwcKxQJlSClUuRUjgFngHXuOsqmzCPhIyRUIr62CkWP7mFAD8xatdire7kweeGs10AIIYQQQgghxNxJ2CiEEEIIIcRx5Fufu6t3s6G8gS3BFgC6dFc8z+4U9xRek3oNXyx8ccagEWptTh3lkCGDxRIQULIlIiIUiqzK4imvaZt2rUettShVW3+du45xM85UONW0TUTUNAsytGHHSriRaIRtbGOJu4QJM9Hx/OvViY2tQ/dH+xkyQ5RtmYzKdAyrtNKkSJEkSURERmU4yTkJT3lxpWOFo69IBFjproyfq0ZFU2TYDFO11VoVnnLIqiz9up+ESpA3eTaGG5kyU6RVuuW5aNqXLfLl4pfZHG7mZPdktofbZzyn4WgYpRQeXlPoOJcAUaEwGJIkmyojZ9quHubV25x26+6mMDhUYdzKtB5kzjQ7FGohZlZn2RXtwsHBt34tFEdRoYJv/abXa2hDKrZCqEOmwikSJFjmLOPy9OVMmImmmaVz9Yr0KzqG2kIIIYQQQgghjoz8lSWEEEIIIcRxciA6wEcmP8K+cF/T/Y1tQPeH+/l0/tNsD7ezzl3XsT0kELcqhVoFXoIEChVXEirVGvTMVPXWrbvp1b2UbIluXZtvFxJSMIVZg89GBsOgGaQQFGZdN7Qh9TwqtCH7o/3xsqqtklGZlm2stXGwBLWQrGRLKKWaruVcqvbm4hWpVzSFjWNmjIPRQQp22uOzMMEE+6P9dKkuJs0keZsHmDForDMY7q3ey7neuaDoGJoZaxgxIwCkSMUtWaHzNo3q6yZVkiTJ+HHMdo6NFbJr3bVNy4bNMEmVrAXXhwLHmWZq1qsULZYpM0VKpejTfRhryJPvWLVpMBRMgZzO4eMzbIZ5c/LN7I5286j/aLxeRMS4GT8cBOPQpbvoUofbsZ7unc7vZX5v5oslhBBCCCGEEGLeJGwUQgghhBDiOBiMBnnv+HtnrPSrs1hKtsTGcCOnu6d3DBwTJHBwmlpXesojQaJWFWYt0/O2erVZYxDkKAcXl9XOagAmzWS8zLd+y3EdHLTSzJAlERAwYkbi0LKTxkBw2AzH1ZJaaQIbYJU9PEfSmrit5vSqSsc6bAu2cZZ3Vlyp1q1mPvZcvTj1Yr5c/DJFU2RvtLcpEG3HYjloDlKxFRSq7UzJTiIitkfbuTh1MTvCHW3XCQ/doPbcZclSsIU5t0UFSJKkZEvx6yAiiucyJkjUnt82FjuLOcs7i88t+Bz3+PcwFA1RMiX+s/yfnOaexu5od/x69K1PYIP4uVIolFJx4F211fga+daPq2onookZz91gKJoiK9wVnOqeyvXl6/nD7B+iUNxXvY9BM8hoNNoy75PoUItdZ4AXJV/EVT1XzRjmCyGEEEIIIYQ4MjMP9xBCCCGEEELMm7GGj0x+hGEzzLgZZ8SMMGpG46q36erVaVVbZWu4teN+tdIs0ota7k9Ta9nZrrJRKdUUsHjKI0mS9d56tNLsj/Yzbscp2RIlW4oDqEYJlZh1LqA5dOtUodZ4fKhVKw6ZocP342GxcVDlW5+8zVOxlbbtWyMihswQPw9+zrgZB+D5yefPeOy5Sqokr0m/hv3R/lmDRjgUih4KROvtZ+djwkxwIDrA/87976bq1brpIZqnPNa762edTwiH2+g2VkPWA9GkSuJbnyk7VQsi7eHK0YV6Iad7p3OyezJvyr2JJe4SfifzO7y1661clrmMft1Pv9PPUmdpfKyESpDVWXI6R0IlMNSuS9mWKdsyJVuKg9OAID52l+4irdIdr5unPNI6zVK9NF7nO6Xv8NzEc8nbPFNmqjVobLh2oQ0pm3LbIF0IIYQQQgghxNGTykYhhBBCCCGOoW3BNj4x+Ql+UPlBrdUnChc3DtmSKsmAHqDf6Y9blSZVkoSqVScWbIEpM9WxQnDAGWDQDDbdp5QiZ3Msd5Y3zRSsB00eHlWqKBT9uhYQ7Y/2M2EmanP5TJmIKA7LFLVqtPpMvgSJWoWa1R3nNtaPVbXVju05cyoXB58BQVOw6eHFFZhVW41nBc7Gtz5bw608M/FMzk+cP6dt5uJ5iedRsXObARlwuJrPwaFsy3Nqo1pnsWwNt5LVWf6l71+4qXITt5RviUPUesDm4dHv1EK+JElW2pU8HjzOmB1rCXk95YGtnVtdSqXwrR8HovUAUqEIbUikIk51TmWBswCP2vn/r+z/4pL0JU37zpvDoXmP7qlVJ5qJuIqxYAqEhE2vwfrjrP9XodgSbiGhEnFlqqe8+LE6OGg0jnLioHvIDMX/LvZH+/nI1EdYrBfTr/qZslOMmbG4AtTFZYFeQI/qQSnFrmgXH5j8ANf2XkuXPtxaVQghhBBCCCHE0ZOwUQghhBBCiGPgjsod/E/pf7i5cjO7wl1x6AFQpYq2moRKYLHsifawP9rPWm8t3ao7DgH3RbXZjo2hynRplaZP9zFmxpru73P6yKgMXXRRoULRFONlChXPzOvVvWwPtzeFQBpNRNQUBtXDxy7VFVdMJlWyYwhY30enMBJgQA/EP0+vRKvPoIxsNOegsX6uFsuoGWVTuInTvNPmvO1MbqjcwMnuyTiRw2A0OOO6ISEWG7esrVd4zidwLJoiD/kPcVHqIv4g+wdckbmCjcFGpuwUoQm5Nn8t0NyGVivNmd6ZbAm3kLf5ppmWVVulTPN11ErXQl2rWoJUi6VgCzwZPMnJ9mTOTJzJH2f/mIvTF7eca9VWGYwGGTJDlG0Zay0hIVVTja/FbCIiypSp2AqOdVrawSoUnvLQVsetgcfNOBERgQ3YHm4nqZIs1otRStGjeujRPTMec1+4j8/lP8cHez446/kJIYQQQgghhJg7CRuFEEIIIYQ4CpGN+EL+C/yo8iO2hFsYi8aagsY6g6FiKwQ2IKdzRERsCjaxzltHj+qh3+lnf7Qfi2XcjBPaMK74mm6NsyaugoxsRJUqYRSyI9zRFPZpNEmVxKPWhhILTwZPxi006zzltW1/qtFUbZWESsSBZcVW2oZJCRJNVXTTeXj06b7493azBtOkqTC3asLG/XTrbvp1P/9Z+k8+1POheW3fTsVW+HH1xwCsclYxoAcYMkOMRqMtz21O5XBxMdY0Bag+flwdOBchIQVTiH93lcszEs+If99tdvOd4ndatnOVy3p3PfvNfoaioVrYZ21LmOjiEtmo1kZXJeMWp9PnYdbb4S7Ty9q2pX3Ef4RPTH2CXdGu+D6lFGnStZmbh27xsobWrdNfN7bh5uI2vSYsFt/6VKniWCduL7s13Epka8F4xVYICOZ1nR/0H+RAdKCp/asQQgghhBBCiKMjYaMQQgghhBBH4Qv5L3Bb5Tb2RnuZMBMzVvZBraKrYArkdA6ArcFWzkqcRZJk3N603kq0U9joKId1zjp+Hv6cvM3jKpeiLbaEOQZD2ZYJVcgKtYID9gAZlaFqq1gsKZUCarP2GqsJG9uoGgwlWyKrsigUGZWhaIvN54ODpzw8PCLbOjtPoznVPRWtDs999PDi/dd16S6stQQ26DiDb7pu3c1ady0KxU/9nzIUDTHgDMy+4Qwe8R+hbA5fj7RKs8pZxUpnZW3uoA3RSpOgVi26OdzMhJlo2kdgA9rkqR3VW4Z2cmnqUr5b+i7Gtr6+tNKscFawTC9jzIyxO9pdqwik9ly6ym2Zu6nRpFQqfg3UW5tCrXJxU7iJv534W56VeBabw80UbZFRM8rD/sMsUAtIqVRToGmswad1JqJC4eA0BYvT1Wd1Ns5srIeellrVpGNr7VRHw1GKFOPwe6a2ve1Ya7mxfCN/kvuTOW8jhBBCCCGEEGJmevZVhBBCCCGEEO3cWbmT2yq3ERExFA0BtA1TpouI4qAmIorbdK5wVtDv9APMGFpaa9lldmGxLHGWxLMfp/OUR07lyKos26Jt+NYnoRJ06S5cXJIqSVZlSakUXaqLBAncQ7fGYCqwh2cSesojq7JNx6lXSSZVsiUgdXA41T21ZU6eo5ymSkeohWYKRU7nSKlU0zlMp9H06B7O8s6KQyprLTdXbu64zVxNmsmOx8ypHL26l27VHQd1SZJxq9lGswXPjZIqyQK9oOPyRc4iLkld0nE51K7fImcRnvLilr0hIWVbZspMUbCFthWs0FxpOm7HeSp4im+VvsXn85/nEf8RHvMf49byrQxFQ2wKN1G2ZXx7OFws2VJTG9f6rR4wtqtkbdR4raJDt8Z/S/XfIxXF4WTFVtgWbGsbcs7k9srt81pfCCGEEEIIIcTMpLJRCCGEEEKII3R9+XoARsxIXIk3W6hS51uflEqhUIxEI6xwVqDRrHHWkCTZEs41OmAOMG7GWeosjdtJJlUSY00c0Gil47DO2EMtM/HpUl1odBwKnumdGa+zMdzIgehA25CsaqukVRqoBY712ZAKFbexdHHJqmxtRiWaPt3HUmdpvN10A3qAETMC1Koa61V7ChVX3QU2ICDA2kNBlqodz1Meq93VLdd7T7hnDlf/2FqkF7E32jtjG9m57ONFyRfNuM6VuSsZNsP8tPrTtssNhp3hTg5GBw+/DhqqR0MbEhKiba2qMaESLfsICSmaIqEKSagEQ2aIxXoxg9FgU7WptZayLRMRkVZpqlQ7nvf0qsV26qFkvaKx0zrTVaiwKdjEGd4Zsx6jbtJMztimWAghhBBCCCHE/EhloxBCCCGEEEdge7idTcEmAIaj4fh+jZ4xcKwHKhERRVPEtz5lW2ZPtCcObNZ6a/n6oq/ztq63cbJ7ctx+VCnFUmcpXbqL8xLnsdxZzkhUC+sUCkc5uMrFVc2ViSFhHOZU7eFQqGqrjJvx2nkrzXp3Pd26u+15T6+Ic5TDSc5JPNd7LivdlSzSizjZPZk/7/pzzk2cy7neuZzsntwxaATI6Rw5lSOt0qx117YNmTzlkVEZsjpLVmfJqEzcsnWRXtSyfmM72CPV6Rp0ktVZenVv20rMkJDABvFz0E5apTkzcSZnJ86e8TiOcvjb7r/lFelXtFRSGgybwk0MRUPxcVzctoFivTXu9LmO9ddkY7vTiq0wYSYYNaNN62ql45a8RVucuRJ3WnVjp38fEdGs+6lX2dYDTIWibMvsjnZ33K6dThWeQgghhBBCCCHmT77KKYQQQgghxBG4u3J3/HNjgKeVxsNrajFZDxgbQ5zGFpcazfZgO4PRIL26l9elX0eX6uKS9CVckr4kPkaCBHdU7+Dvp/4eqFVUhoSznmtjgNNYUQkwbIbj9p2ucjnHPYeHw4cpmuYAqfHnnMoxoAdY5NTCvmUsQynFh7o/xAXJC3ireSsfm/wYTwVPzXpul6UvY8JMMGEmZmyb2kijWeeta7v+TOHmXJ3rnUtap5vmNs5mqbOUUTNKxVbi57pxjmN9hqKnvFq72YY/xZbqpbwy/co5HcdRDn/R9Rf8TuZ3uLF8Iz+q/Ii8ybMt3Ebe5NFoEipBkiSOciiYQsd9VWwlXh+gZEptA9F90b62MzQ95ZGhdYZnu300vk47ha6d5jnWGQwVKk3tWqfMFEmVZCQa4STnpDlVN2ql4xa4QgghhGjvQOkgg+UhqpFPxk2zOncSXYnOnTeEEEL8ZpOwUQghhBBCiCNQrwiE1tl8CRLxHLl6FWNdu0DFYGpVjRYw8ID/AO+eeDfv7X4vgQ0o2RIZlWHAGeCWyi3xdkdSxVevDosDJltqWp7USc5xz2FjsJEKlaaKvCV6CQv1QrK6eWajUoq35t7KBckLar+jeHbi2YyaUZ4MnmQoGsLFZaFeSJ/uw9Uuz0k8h8vSl3GOdw4jZoSrJ69mZ7iTvM3PeP4uLuu8dS1zI+tOck+a9zWZLq3TXJS8iBvKN8x5my7VRTfd5Mm3nVFoMLUZhrZ2/V1Vazm7RC/h1ZlXc2nq0nmd41JnKX+S+xP+IPsH/KjyI66dvJYVzgpc6/JY+Fj8mpttbmTZlkmoBCFh0+u0McidqUVqQiUw1sSP+2hMv26N+5t+PeszIQ2Gsi1TtVX2hHtY7a6e9ThneWe1nbEphBBC/KYLTcj9Qz9lw55beGLsyaZlCSfBCxY/l8tWXsz63rW/ojMUQghxopKwUQghhBBCiCPQGOI4OE2VW65y8fCo2uqsQWOjwAa1KkhruLV8K/9d+m/WeevimYhpnWZXsIsu3UVapdtWm7XTLviqi2zrPjI6w5mJM9kabo3DSAenbZDXrbt5a+6tvCD1AkajUf69+O/8pPqTuE1lv+6nV/fW5vvZCKssFycv5s+6/iwOPPudfv5+wd/zreK3uHry6pYAFCCpkvTrfvqd/vh6tDxOpXhF6hVzuibTTZpJ7qnew7gZj1t0lm151krJkJD94X52h7spUuy4nm24OTiENsRgeF36dby7+91HHH4lVIKH/YfJ6VztDlWb/zhoBue0vcXiW7/p9avRTZWXswWWrnI5ypwRoGPQOH0dhWqpajUYdkQ7WOQsIqdyMx7n8vTlR3+yQgghxK+Z/cUDfOTnn2R/8UDb5X7kc/v+u7h9/108f8lzeddZbyPhtLZrF0II8ZtJwkYhhBBCCCGOQBzuUAvnpsxU0/KkTVLmcOXhbEGjQuHiUrRFHgoeits8bg22crp3OgBlU5vtGEURfbqvY+g2nTvtbX/juTiqfdvJlEpxlncWeZNnyAy1BJaneadxWfoyXpB8AZ7y2B3u5sOTH45nSDby8PCUR30XN1VuYle0iw/3fDiukkyqJG/MvZFfBL/gp/5PKdkSkY3QaFI6RY/qmfVxXpC4gH6nf9b1Gm0MNvLD8g+5t3ovU9EUQ2aICTNBQBC3RO3X/axx1zQFjyVb4mB0kOFomEk7GVeA1oOwxpa5jeotdRfqhZzsnExIWLs2R2g4Guan/k+b7htwBuKwUaNnDQurttq0TkIlmsJPDw8fH2stPj4RUdNjrQfWCnVU1Y0zbdt4vIzKtK3qjWzE5mAzZyfObnnN1y10FvKcxHOO+ByFEEKIX0f7ivt574MfZsqfmn1l4J6D9zPpT3HNsz6Ap4/8fYwQQohfHxI2CiGEEEIIcQTOT5zP9aXrAVisF7eEjb7ycayDOXSbiUKRJElIrdotshFJlUShyNs8U3aKbtUN1MLByEaMmTEcHKy1s1bFOcrBtW5cvdYYHM5Wudelu+jSXVzdczVr3DWEhORUjozOxOuMRCNcNXkVo9HojPtqtDHYyMemPsY1Pdc0hW1XZK/gqfApcnbm6rTpXOXyu5nfndc23yp+i68Xv07Zltu2cE2qJBVbYW+0l0EzyFK9lDXuGvI2z45wBxZLwRbiGY0KhYMTX9964NgYfLm4JFQCi6VChesr19M71ctZ3lk8P/n8lha1s9kYbMTY5tdXWqXpUT1M2kk85RHamed6NlbIKhQJmqsUelQPu8yuuPK2UX32aP3n483BIUGCgKDtvNKQkJFohCXOkpZlSimuzF3ZMWAXQgghfhNVoypXP/yJOQeNdU+MPckXn/wX/uqstx6nMxNCCPF0omdfRQghhBBCCDHded55LHWWAtCre+OWoHC4NeX08Gm6eqVWlixW2TiUrM9VrBuKhuKfM+pwyBcRUaEyp/NNqmT8s8PhsKVfz14JuMJdwbOSz6LP6WPAGWgKGgH+rfhv8woa657wn2iZi/jMxDO5MnflvPajlOIvu/6S07zT5rzNN4vf5OvFr1MwBTYGGzvOikypFFmVxVjDAXOAnwc/Z2uwtRay2TCu8tPolue6sd1nTufo1t24yqVsy0yaSbaH29kf7edfC//KF/Jf4I9G/4jPTX2O7eH2OT+Ogi20vf8U9xRSKlVrccrsQWB9eUZl0Orwn4kuLmN2jNCGbfcx14Cx3v60k/q1c3FbWqSqhpvBUKRIVmVbqhfr+2/XQlYpxVtyb+HC5IVzOl8hhBDiN8WdB+7hYGlu7deh1kkgMAGRNdy+/y6Gy61dLYQQQvzmkbBRCCGEEEKII6CU4tL0pbWfUaxwVsTLfOs3rVuvemtUn4vn4eEqtylcnL6PcTNOQG35gB5oOZd2cxenc3HxlIdGx5WECZVggV4w83bK5W25t3VcPm7Gua9636zH7+TGyo1Y2xxYvTL9St7V/a6mALeTtE7zge4PcFHqojkf8+f+z/lG8RtUbIXN4ea2FXKNPOXRpbtIkKBgClSpkiaNVpokSZxDt05hmsFQNEUKtlBrD3uokrD+HI+ZWpjnW58fVX7EO8ff2RLCdjy3Nq10q1Q5YA7gG5+SqR0vPHSrh6PTaTRZlW2qMo1sRMVWiIg6PheNVbszhYkzcXDigLF+bo0BY+M5AoQ2pEKFrMrWWr4eWqd+7lVbZcoers5Y4izh/d3v57L0ZUd0fkIIIcSvsw17bp51ncAE7C8d5NGxJ3ho5FF+Pvo4D408ws9HH+Oan39y3lWRQgghfv1IG1UhhBBCCCGO0OXpy7mveh8bg40s0ouoOlX2RfuaApjGYKceitQrHh0ccjpHxbZWJ07fR9VW8ZRHr+4lqZJUbRWoVSz61m+qVmxHKUXGZtBax+e0VC+dMSDylMd7ut/DWYmzOq5zc/nmWdt0zmRfuI9Hg0c5N3Fu0/0XpS7i/MT5/KjyI24s38iB6EDT8pXuSi5LX8ZLki9pqbSczf+U/qd27GjfrEFjo3pQl1Zpcqr2vHnKi5+LmVSp4tjmQNJgCG2Iq1x868dViMYarstfBzBrQNY4o9Ji2RnuZMSM1J5jBTly+NanwOF2rwbTVImZUikG9ACTdvLwvqxtChkTKoFv/ZaWwNPDwcaWrHXTqz2nh53TKxfbmR48+tYnpVNkyGAwBAR06+641atCcUHyAi5PX84zvWfO2mpYCCGE+E20ZXIb26d2dlxurWV3cS8HSoNUogqhjcBaUAqNwjcJvrfzevYVD3D5SRfzpnV/0NQhQQghxG8OCRuFEEIIIYQ4QgmV4EM9H+LDkx9mS7CF5c5yPOXxVPBUx23qQaOHVwvJLC1VjdDanrIxhFnhrGBbuA2oVXslVAJlFVbN3NIyqZKc5p7GsB3GxWWxs7jjuuu99bw592ZO906fcZ8P+g/OuHwuHvQfbAkboTYv8rWZ1/Ka9GvYHe0mb2qtTnt0DyvdlS3r+9bnrupd3F65nZFohICAnMrxjMQzuCx1GSvcFRyMDvJw8DCBDRgzY/M6T9/6cYvcYTuMVXZObUTrIaXBtITC9e3bhXRfKnyJde46TvVO7bjvs72zWeQsYigaYku4hUkz2bRcKUVSJfGsR97m43Oph3fdqpvF7mIW6UVMBoe3zeosJVuKf69XPhZsIT7nTkH69AByOo1uGypqdMewcnrlaP15SKokWmmW6qWc6h6+Tn+a+1NenXl1x+smhBBCCNhb3NdxmbGGX4w/xXBlpBYyNrIWA4RhmXJYYevUDr6/cwMHS0O875x34miZjyyEEL9pJGwUQgghhBDiKHTpLv6u9+/49+K/c1vlNgYYoOyW2RPuoWqrTZVz9dapDg5KqVpFo20/9256SNMYUi3UC6k6VfZGe+P9rvJWsTfc27FSz8VlrbuWtJPmPZn38LuZ3+XOyp3cVrmNETOCb32yOsuZ3plcnr6ck92T5/T46wHg0ZhtH0opVrmrOi4Pbcg3it/gpspNLfsaYYSd4U6uL13P2Ymz6df9WGsZNsNznjcIEBLGAZlvfRzlENmoZb5gO+3CuenL2lWmGmu4vnw97/Te2XHfWmkuSV3CNZPXtASN09frppsqVaq2GrdWnbST9Nt+DKY2mxJTq3I0k5QoNe3DUQ45laNoihhMS4vT+vWph4xJknHI2rheUiVrrWVtsema1PcxPcTs1KK2HjYmVIKT3JOalmV1tuO1EEIIIURNJercnWHjxCYOlGef5WixHCgfpBpVsFj+6al/421n/NmxPE0hhBBPAxI2CiGEEEIIcZSSKsmVuSv5o+wf8ePKj/lm8ZtNwc+oGcVaS0iIpfbfep5iDt0UCo2OQ5V6S02ohTAplWo65jJnGa5y2RPuISIiRYpzE+cyakYZjAabqtLSKs253rm8OvNqLk1fyhJnCQAXpy/m4vTFR/XYj3RO37HaR9VW+djkx3jEf2TWdR/zH2NvtJecyjFl5jdbyNjDgVn9+QoISJOeebuG8Mweuk1vK+rgkFTJttv/pPoT/tT8KV26q+MxzvLOYtyMz/oYIhUR2ailRW/JlhiLxjjDO4NJM0lWZdkd7W67DwcnblfqW5+QMH7d1m/60C2t0rjKbXrc9cdesRVcass8vLjiMkGCiChu9TrTa8NgSKgE6931JGieKbnWXTvr9RBCCCF+06Wc9u8/hsrD7CsdnPN+FIoJf4rtUzu5ee9tvGbV5SzPLjtWpymEEOJpQMJGIYQQQgghjpGUSnFp+lJekXoFbx57MyPRCKNmlHEzTpX23xyvt52s3+pVXI3hyUJnYdvKtwE9wMLEQkbMCMvd5eRNnn7dT7/up0qVte5aLkxcyDMTz+QU75R4/t6x1Kt7W+YpHsk+joSxhk9NfWpOQWOdb322Rltxj/JPIa00rnXRSuPhtW2FC83tb6dTKFzlslAvxFHt24351ufu6t0zzm68x7+HU9xT2BJu6VitWbGVeDZoYyiYUznSKs1qdzVQq4DdFm5rqVyczlMenvLoo48yZaytvX4LttDy2Du1UwXw8MjpXPz7Yr2YJEk2R5vbzoisUyg85XGGe0bL6/rMxJkzVsIKIYQQomZNV+v/X1pr2TK1fc770Orw/8+PVccZr05ww55b+bPT3njMzlMIIcSJT8JGIYQQQgghjrF6a8sv5L/A9nA7CRIdw8ZGFktERIJEU2XjYt15tqKDw3JnOV/q+xK+9SnYAi4uvbr3l9JK8gXJF7Ax2HjU+zgSP6n+hAer85sZ6SgHg2HKTrVUi85kejUiQFZlCQlJkiSgfdg403485aFQDOiBGbcZiUY6LqvaKrdXbqdX97LWXcu2cFvL3MPGoLHOwSGrsmilGTNjnGRPwlO14G8pSxkLxvCU1zG8zKkcS5wldOkuHvEfieeFutbt2Mq3Uf06NAaFCsViZzEeHvvMPlIqhW99AoK4srQeMiZUAhe3bYB+WapzMCuEEEKIw1Z3reK03nU8NbEZYw3j1QkOlAeZCg63pddolAJrW79EpZUmoZvfTw2Vh7lt/5380dorSHaonBRCCPHrR8JGIYQQQgghjoMXJV/E+yfeX6tWVE7H6rd6hVn9w5vpM+76dB8ZlZnxWBcmL4zbbC5k4VGfe8VWuKNyB3dU72A0GiUkJKuynJ04m8tTl7PcXR6v+9LUS/mP4n9QtbOHqe2s9dayzlt3RNveULlh3tt0q24GGazNLLRhU6g7E1e5cevb+ja9qpdxOw5qbiHb9Oq+JMna86tnfn59/I7LDkYHKZpi7Xx0L2d5ZzEUDTFshgkP3RqDRgeHhEqQIIFStfOxWMq2jKc8oBaiusplgV5ATuUo2ELcLjWpkizSi8iqw0H2gDPAYFSb6ZRUSUIbtn28jZxDt8awsFt1xwHwUmcpe6O98bm221W7MH29t54Lkxd2PK4QQgghml209IX8aN8dDFdGCExIPsi3tI+3tnmecrzMGnzlo5UioWv/nz7pTzFWGWdvcT+ndK/55T0QIYQQv1ISNgohhBBCCHEc3O/fzynuKTwVPoVvfTIqQ9EW2wZSjR/a1NulBjagT/dxsnvyrMc6VpVcgQ34avGr3FK5JQ6w6kYYYVe4i+tL13N24mz+NPenrHHXkNVZLkpdxE3lm47omJenLz+i7XaFu/iF/4t5b9ere0moRG3mIP6c26kqFAmVwLd+PF9xwBmgy3axI9pBVmUp2EJLVWEjjY5/TqgEPbqHNc7sH8LlVK7jssbZnFAL+/qdfgyGETNCwRSwWDSaBAlSKhWHjI0az9tVLjmVY9yMsyqxiiUsmfH8TnJOwrc+42a8VnVI7frOFORmdIYMGXxbC1ITJOJWrlCbSepbnyEz1HEf0ytCV7gr+FDPh+YcIAshhBC/6UYqo1y/+0Ym/EkCU3uPWv8v0LbDQeP8aa00xhpKYZlAh2TdNBbFZDBFMSi2bCuEEOLXl559FSGEEEIIIcR8WGu5sXwjKZXiDPcMulU3CkVWZ+PqsUb16kYXNw6ktNKs99Y3BVTtnJU4izMTZx71OZdNmasmr+L7pe+3BI3TPeY/xt9M/E08K/GPs398RDPyXph6IS9JvuRITpeH/IeOaDuFol/34ylvxnmK7dRbd2o0GZUhp3P0O/2scFaglCKnci3hZf3DuPpzDLX2qcv1cta76zvOaqwz1lA2Zb5V/Bb/Xvh3vlv6Lo/5j8XLG1vBlkyJTcEmHg8eZ9AMxpWN9bmgVapM2SkqttJUoQC0zAQd0ANYLMPR8KzXRaE41T2VAWcAhSJNmj7d17GyMauynOGdwVJnKVC7ruu99XGIW7faXc0KZ0XbfwMJlWCBXhD/fn7yfK7tvZYe3TPr+QohhBAC8n6eD/7sY3EForGGqSBPZKN4lvhMtFJN72MCE1AMywCEJpQWqkII8RtGvvIphBBCCCHEMbY13MqB6ABQC0VO806jZEsMRUOMmlECFVC1VXzr4+CQVEm01ZQp46hDv6OJiGYMG5e7y/lA9weO+nwjG/F3U3/HE/4Tc96mbMp8fPLjfKL3E5zincI1Pddw9eTV7Ah3zGn7F6RewDu73tm2ym4uCqZwRNsBLHYWM2bG4qq6uUqQoM/pY8pOsUQfrvZb5iwjqZLsjfairCK0IT4+gQ3iFrkODhrNIr2Ite7aWedpVmyFoWiIMmW+V/5ey/IV7gouS13G85LPw1Uuo9EoW8OtTRWK7R6fxVKxFSIVkbEZlFIoVEvQ16f72BPtYdSMssxZNuu1UShWO6tZopcwZIZY6axkJBph0AxSsiUUil7dy2JnMd2qG6hVRKZVmhSptrMX69d2QA8wYkYYMkNxS9gVzopaVW3yIi5PX85Kd+Ws5yiEEEKIw/5l03+wr7ifseo4O/K7UGrmFuiNFAqnTSeBwARUoypaafpTi471KQshhDiBSdgohBBCCCHEMTZuxlvuy6gMq93VrGZ1/I1xZRWBCohshKMclFVsi7ZRsLUgLbBB20pIgNO90/lgzwfjWY1H447qHfzc//m8t6vYCtcVruPTCz5Nn9PHJ3s/yffK3+OWyi2MRWNtt1ntruby9OW8IvWKIw4aoVb5eaRcXNZ569gcbCat0oyZ9ufadDw0p7qn0qN70OiWb/sv1AvpU31M2kmGzBBFUyQgQCmFi0u/7mexXoyn2z+fjcbNONvCbRhMU2tRqM1NCgnZGe7knwv/zI2VG1mul/NA9YGWFq4zVSQENqCkSmRshh7d0xI2aqVZ7a5mRzS38LgupVK8o+sdvCH7BvZH+ymaInvCPTwRPkHJlghsQE7nONs7m4tSF5E3ed438T6Gos7tUl3lssRZwhJnCaENeW3mtfxe9vdqlaTSMlUIIYSYtyl/irsP3sdwZZSd+V1YQKHp8nJENsQ3rXPG58o3Pmf0rqcvtYBd+d3cceAnjPsThCYi52U5b+HZPLv/WUf1PlAIIcSJR/4yE0IIIYQQ4hibrT1n3HJKHWpfWf+sRcHp6vQ4sJpOK80FiQu4LH0Z53nnHbMPaTaUNxzxtpuCTWwPt3OyezJpneYN2TdwReYK7vfv52fVn5G3eRwcenUvL0q+6Ji0fAWaWmgeiSRJXpB8AWvdtXyn9B1GzEjHdetBcU7leGX6lfxZ9s/4Tvk7/KD8A/ImH6+nlKJX9dKrewE4yT2JN2bfSFIluXryakLbOq9zugkzwdZwKxZLVmVZpBcRETES1Sr7yrYcr+visifaw6SZrFVRTgtgZ2t/FtiAUIUMOANtly/QCw5VLjhEtvMsykaXpi/lj7N/jFIqDkrPTJzJJVzSdv2Uk+LTCz7Np6c+3dQetp2szvIn2T/h4vTFczoXIYQQQrR3y74fM1odi4PGRo5yUYQzvo8wGCIToZVueT9qrGFFdhnve/BqfjG+sWXbDbtvZiDdzytWvJT/76RLSbmplnWEEEI8/UjYKIQQQgghRAdFU+Sn/k+ZMBNYLN26m2clnhWHSZ10qSOvNmwMrD7V+ymqVCnZEhmVYYWzgkXOsW1JtSXYwpZgy1HtY0N5A2/venv8u6Mcnp98Ps9PPh+ohVoKdUyr0J6ffD7/UviXOQV4nbw89XLemHsjb+l6C/+Q/wd+UP4BQ9EQFouDQ4/uYbGzmC7dxfmJ87k8fTnPSjwLgCuyV/Dbmd/mJ9WfcGvlVg5GB6naKhmVYZ23jstSl3FW4qz4WO/oegefzX92xvP1rc+2cBsWG+9nMBpkf7S/pWoRICRkb7iXvM2j0XSprqb2Z7O1QrNYfOtTNEWmzFSthS9J+nRfHIif7J3Mu7vezdeKX2Nj0PqBYd0SZwm/nfltLk1fOuMx21mgF/Dx3o+zI9zBDeUbuKNyR9wuFeBk92RemX4lL0q9qKUCUwghhBDzd8/g/ewrHegYJ872hSWAiAhra++ZGgNHRzn8984NnNy9uuO2Q+VhvrrlW9wzeD8fPu999KWO7ktkQgghfvUkbBRCCCGEEGKabcE2NlQ2cFflLqq22rTMVS4XJi/kstRlHav01nnr6NbdTJmpIz6Hle5KTk+cPut6xhoe9h9mX7SPiq2QVmnWuGt4RuIZczrOw/7Ds65T/8CpU3j1kP9Qy31PBk+yobyBB6sPxsFRRmd4fvL5vDL9Sk52T57T+XXSq3t5XvJ53F25+4i2V0rFwdhiZzEf7f0oH+39KPvCfTxQfYC8zaNQdOtuLkxe2Lb6L6ESvCT1El6Sesmsx/ut1G/Ro3v4UuFL7A33tl1n2AxjMCzUC1ntrmZPtGfG9qKRjSjYAtGh26SZJK3ScSDnKa8ptKuzDbeAgL1mb9Ns0N3RbhbqhSx2FnOedx5nJ87mU4lPsT3czo3lG9kSbqFkSiRVkmXOMi5OX8wzvWcedaXtGncNb+t6G2/JvYWCLcQtV1NKKh6EEEKIY2lfcT95v/386/l8kctia+MADgWOxhqSToZwjh0Rtk/t5IMPfYxrn30NWW/medZCCCFObBI2CiGEEEII0eDbxW/zteLXOi4Pbchdlbu4q3IXr8q8ij/L/llLyOIpj5enXs5/lf7riM/jsvRlMy6fNJPcXL6Zmyo3MRwNtyxf6a7k0tSlvDT1UjI603E/eZtve3/BFhiMBpkwE3FVnYvLAmcBi/ViMurwPgvm8IdVW4OtfC7/OXaGO1v2WTIlbi3fyq3lWznNO413dr2TZe6yGR/nTF6VftURh43PTjy7bYC43F3Ob7u/fcTnNJNzE+dyXd91POY/xobyBh4LHqNoi7XrqhYwZac41zkXD4/90f6OQaNvfaq2SkREOK3NWdEWqdgKSZUkqZI4OE1VkRYb/+5Qq14MbNBUMRgRMWSGGDEj/EnuT+L7T3ZP5m1dbzum16QdRzn0qJ7jfhwhhBDiN9Xe4v6291eiKpGJUKg5VTdCc+CYdlJ4jgdz3BZgT2Ev/2/Tv/OOs/58ztsIIYQ48ejZVxFCCCGEEOI3w9eKX5sxaJzu+tL1fKHwhbbLLklfcsSVXimV4iXJztVym4PNvG3sbXy1+NW2QSPAnnAP/1z4Z94x/g72h+0/UILDgVNdyZZ4IniCJ4MnGTWjTUFVSMhwNMwTwRNsDDZSoVY1V2+5+Zj/GO+feH/boHG6p4KneM/Ee9gWbJt13U5O907nf2X/17y3W+ws5i+6/uKIj3u0zk6czft73s83F32T/1n0P3yv/3u8vfvtdKtuPDwiIvZH7Z+zsi1TsqX4eWn8INBgUCiSKklERN7k8ZQXL7dYDAYHBxc3rlTt9GFiWqX5Vulb3Fe971g9dCGEEEKcAKpR0OZeSzWqxrPEZ2vHXqdQaKXJuhnSbhoAV8+vvuWuA/cy5R95RxAhhBC/ehI2CiGEEEIIATxQfYBvF7897+1uLd/KjeUbW+5f4izhd9K/w4HoABuDjTwePM4TwRNsCjYxHA0TzdBe6q1db+1Yjbgt2MbfTvwtk2ZyTud3IDrA+ybe17FKboE+PCNnyk6xMdhIyZZm3W/e5nnSf5KSLbFAL2BXuIuPT328bdvOjvswea6evLpjYDoXv5/9fa7IXjHn9Ve4K/h478dnnbv5y1IPpBuvwUg0gsG0rFu25Za2vu1ERGRVNm5B2qf7yKosLm6tzdkcPjzUaFa5q7DW8umpTzMajc7jUQkhhBDiRLYo1ddyn298LLaptbqa5V2Dq13Sbpqcl6Vqqlhb+wJTj9c9r/MJTMAt+348r22EEEKcWCRsFEIIIYQQv5Z2hDv4cuHL/P3U33Pt1LVcl7+OB6oPxB+CTPe90veO+Fj/Xf7vpv2Om3E+O/VZflD+ASEheZuPK9Im7SQ7oh08GjzK7nB3U+iolOLPcn/WcQagb32umbpmXoFe/Xw+PvnxtstekHwBWmnKtszWYGtTJeNsQkI2BZs4zzuPrxa/SsnMHlJON2Em+E7pO/PertEbsm/g6t6reWai89zAhc5CXpp6KW/IvIHd4W42BhvnNZPoeAs4XGEwaAZbl9ugbdDY7iPAwAYYTPxhYbfuJquyHZ/b6fvQaE51TyWnckDtdXdT5aa5PxghhBBCnNBO613b8g6iGvkAaDX9nYGKb433JHWSHq+brJvBWEtkDZGN8LTLgmTvvM/prgP3zHsbIYQQJw6Z2SiEEEIIIX6t3FW5i+vL1/NU8FTLshvKNzDgDPCK1Cv4/zL/HymVAmBnuJMngyeP+Jj7w/08EjzCeYnz2Bfu46rJq+JKwpXOStIqzf5of1NIGBJy0Bwkb/Osc9ex3lvPG7Jv4NnJZ3c8zp2VOxmLxo7oHLeH23nUf5RzEuc03b/IWcSzE8/mq8WvEjL/8C0k5EB4gJ+HPwcgshFjZowRM0JAgMXi4NCtuhlwBuJr3uiOyh28KfumGWdLzuZZiWfxrMSzOBAd4MeVH9eObwM8PCq2wvZwO7dVbuO2ym3xNj26h4tTF3Np+lL6nf4jPvaxUA/2IqKWMDkkpGiLTWGhRscf/LVrgxra2izHkJBCVCBBAqi1TJ3+EWJjq9WcynGSe1J8PnW3VG7h9ZnX4yr5E1IIIYR4ulvXcyq9iR7G/cOdMiJb76qgUEpjbWuXhfp7CEc5JByPwASENsQ3AZGJKIZF+o4gaAQY9yeOaDshhBAnBvlLUQghhBBC/Fow1vDFwhe5uXzzjOsNRUN8tfhV7qnew4d7Pkyf08ePKj866uPfWrmV1e7qpqCxbpFexCK9iCk7xVA0RMVWiIhwcEirNMvcZVy74FoSKjHjMW6o3HBU53hD+YaWsBHghckX8oV8+9mTs1mgF3BD9Qb66OOgOciwGW4bWpZsiUEzSLfqZqWzsilYrNgKt1dv55XpVx7ROTRa6izl97O/D8BD/kN8aupTHSsuJ80k/1n6T75b/i5/mP1Dfjfzu0d9/CN1hncGWml868f3+danaquEh26N6vMZp1cZKBQREUVbbJrJ6OPHAWU9cFQoEipBWqXp1t0M6AGyKtv2/MaiMR4NHuVZiWcdh0cvhBBCiF+mS1e8nP/ZuYHCRInABDDti0uO0lhr236hSaEw1lANq3Coo4SxpjYb2lom/TyPjj1Bf2oRyzJL0GpujfVMh+4jQgghnh4kbBRCCCGEEE8bTwZPsi3YRsmWSKkUy5xlPCvxLLTSXFe4btagsdH2cDtXTV7FJ3s/2XGe4XwMRUN8pfCVGffVrbrpdltn2AxHw/yg/ANel3ldx213hbvYGmw9qnN8wH+AvMnTpbua7t8T7WGps5T90f557S+lUqx2VrM53Mweu6ftnMFGFsuknaQQFljrrqVbH74WTwZPHpOwse7e6r18aupTM87GjM/LWv6j8B8UTZE/zv3xMTuH+ahXmN5dvRuohbP14LHdB331+xuXKRQhYVOYWNfYVrWxKuFc71x6dM+czvFIq2qFEEIIcWI5uXs1Zy88i8gaNk9uxTfBtDUUjnaITNTyXsNiawGiavzCU21ZykmhlCIwIftLB8kHBdZ2n4yrZ/8IusvLzbqOEEKIE5eEjUIIIYQQ4oRWsRVur9zODeUb2BXualm+yFnEGmcN9/r34uG12UNnO8Od/GP+H5kwE01hzJEomAJ3h3cf8fY3lm/kd9K/03Hm4FwC0ZIpxWGTi0tGZZr2V29xOj1s/EXwC1Y4K7BYDkQH5nS+GZVhnbsORzmMmTECgo5VcdNFRGwJt3CaexpZnY3P/VjZGe7kM1OfmVPQ2Oi/Sv/FSnclL0299Jidy3xcnr6c+6r3UbGVpgrHTmHjdI1hb7tZjtO5uBw0B+nW3XNav3GupBBCCCGe3l5/8m+zeXIrp/euZ29xH/mg0DxLHIWrHSJrsNaiVK2iEWh6z6yAhE4S2gBXO03HyAcFtk7tYF3PKbNWOJ6/6Lxj9+CEEEL80knYKIQQQgghTliD0SBXT17N3nBvx3VGohHurtxN2ZZZ561rmTXXTsmWGDJDjEajPOQ/RI/qYdyOk1VZBpwBFuqF8w4eB80goW0/89BgGDNjTJkpIqK4feUivYiMqrUTHYqG+Kn/044zGzsFPaENGTWjDJkhyrbctCypkvTrfvp1fzyXrzHEqiuaIlCbL9mluhiMBpmyU21Drvo+F+vFOMphxIwc3ufseVUsImJXtIsz9BlA89zAo/X90vfbPs65+E7pO7wk+ZKOoe/xdI53DkucJXMOFzuZXtXYTkqlSKkUk2aSYTPMgB6Ydb9z+bclhBBCiF+tHfmd3LrvDgbLQ/iRT8bNcHrvOl667LfoShz+wtkF/c/kTevewL9t+hqndK8h6aTYnt9JaIJD70Vq7ycyjoerXaaCPMaauGW7sgpXOaScFEknQSkst53tPBXkGaqMsCTd+b2GUopLV77seFwOIYQQvyQSNgohhBBCiBPSSDTC+ybex0g0MuN6ZVsmb/MAPBU8xene6R0r7AICtoXbmDJTTffX5+EVbZEd4Q72sIeV7kr6df+czzewQUvAExJyIDrAcNR+juHB6CA5lWOxs5iFeiEP+g92DBvbPaZxM872cDsR7Sv4qrbK3mgv+6J9rHJWMeAMkNOtgVFj0Nere+nVvVRshVEzim99LBYHh27dTa/qbQrihqKhIw7mCrZA0RTJ6iz9ztyv9UzyJs9Pqj854u33h/t5JHiE8xK//G/XK6XoUl0s1AubWtrOFhxqdFzVqA/dmvbbED66uCRVsuk5H4qGZg0bHeVwpnfmvB6PEEIIIX557ht8kO/tvJ6nJja3LLt38AG+uvXbvHDJ83j9yb/N0swSAF67+lWknTRfeurLLM0McLA8iLHJpm1DG1IIi4QmwFFOU4WiBcpRhUpUpSfRjW2Yu2ixRDbCWsve4n56vG7SbqrtuZ/TdxbLs8uOwVUQQgjxqyJhoxBCCCGEOCF9YuoTswaNAKNmNP7ZYNgcbOacxDktgUuVKk8FT1G11ZZ9hDbEwYlDu5CQHeEOQidkqbN01nNQSrFIL2o63woVNgebqdjKjNsWbIFCWKDgFFpC0EanuqeSVMn4/EfNKNvD7XOqgrNYdkY7yehM21Cp3+lnU7Cp6b6USrHcWT7jfou2SMEWSKokgT2yFptDZog1eg0vSx2bb7PfXrn9iKsa624q3/QrCRu3BdvYGm7lDPcMyrbMhJmI5y+2o1Atr3ONpkf1EB661bdPkQJF24rdki2Rt3m6VFfLsrrnJJ7DQmfh0T1AIYQQQhwXX93yLb6z/fszruNHPrftu5MHhx/mQ+f+NacvWA/AJStfxnMGzueWvbeTD/6ZzZPbDm9jfKpRFY3CUW7HL5gpwNgIg8UaS2CCpjmQxbDEw6OPsTC1gIFUPwuSvfG7m5yX48rT/vhoHr4QQogTwJEPpRFCCCGEEOI4eTJ4siX86mR6yBUQMGKaQ8p6CNkuaAQIVUhCJSiYWuA3aSaZMlNsCjexJ9oz6zlckLigqfIwIGBTsGnWoLHRYDTIQ/5DHZdndZYXpV4E1OZDzjVobDRlpviZ/7OW+4806Js0k0CtWu5IW2xO2knWe+s52T35iLafbne0+6j3MZfn/HioV2RqpTnPO48BPUBapXFx48CxXqXoHLo1VjXW79dKk1AJMipDVmXJqAzP8J5BggRQm91ZsRVKtkTJlijbclMlZTuXpS87jo9cCCGEEEfq29u+N2vQ2Cjv57n64U+wM394FvqCZC+vP+W3+fHlP+T3T3kd63pOYVlmMQ4OObf2Hq9z0KjIuFm00lSiKoWg0BQ01oUmYMrPs3VqO0+MP0k18sl5Oa46772szK2Y56MWQghxopGwUQghhBBCnHA2lDcc1fZD0VDT78PRcMs8w7qKrTAWjVE0RSIiTO072RgMvvXZFGziieAJpmz7qsOUSvEH2T+gR/fE9+0Md3YMNmeyJdjCw/7DHZdfnrocgAPmwLyDRoWi3+nnW6VvtSx7pvdMljhL5neyHG4/m1M5VrhH9iFRaENem3ktoQ35SeUn/O3E3/L6kdfzmuHXcMXIFbx7/N3cUr5lztdzPgFvJ51eK8fbpJ2Mf3aVy5nemSzUC+nSXXTRFQeM9ZCxseKxHjS20626yeosvbqXgimQt3kqtoJvfXzrU7VVDkQHeCx4jIPRwTi8rHth6oWckzjn+DxoIYQQQhyx7VM7+drWb897u1JY4jOP/0PL/a52+fRzPsbvrnkNI9UxHO2glMJ0eN+plSbnZnG1phAUCU2IUgpXOy2dGRr3UQkrDFaG+Ouz/zKusBRCCPH0Jm1UhRBCCCHECaVsytxbvXfO67cLWEq2RNEW42rDIXM4fIyICGwQh4kGg4uLVposWYq22BTkGQyTZpJNwSbWuGtYpBfFyxIqwft73s8adw0vTL6Qx/zHqFJlwkzE+w8J4/k1WmkSJJrm5TVaoBfww/IPeWbimW2Xn+KdwnMSz+Gn/k/nfH3qljpL8fDYGmxlc7CZdd66eJlSildnXs2X8l+a1z7rHyItcZbQp/so6mJLVWkn9faeK92VTEQTvGr4VWwNt1IwBQwGhSKhEvTrfn4R/IJ/K/4br06/misyV8w4IzKjMvN6DMdrH3Vj0Rhbwi0UbZEECQacgaZr38jY5pBPoxnQA1SjKgUKtdlHh1r91sNGFzf+N9Cu3aqLy0nOSewIdzBiRvCUR2hb54dCLajdHe1m3Iyz1luLi8uzEs/inV3vPJpLIIQQQojjZMOem49425353Twx9iRn9Z3RdL+jHc7vP4913acyVBlmtDLWsq2rHBI6gac9lFKUwhKhrc8Qr3Vh6E50EUQBvvExWFztkHHT9CS6GUgtIukk+eenvsIXLvwUnm7/3lgIIcTTh4SNQgghhBDihDJmxjqGIe306l4OmoMt9/vWJ6uyTNkpyrYcV3DVw5p6FSPUgq+Cqc0ezKkcJVuK16vvy1MeO8IduK5Lr+5lhbuCd3S9g/Ve7dvYL069mC8Xv8x2fzsFW2g7wzCyEQEB2mqSKklSJeNlaZWmW3fzM/9nDEVDDDitsxUBVrur6dE9TJiJOV0fgyFFiqqtsiXcgoPD30/9Pf93wf8lrdPxeq9Mv5LNwWZ+XPkxAEVTZMJOxM+FpzwW6AWk1eFtPDyW6FrQCLDGXYOOdEtlaf0a15+D+nVPqRTbgm28ceyNLZV6FkvVVtkb7WVftI9FziLyJs+eaA/v6XoPWrVv0rLaXT2n6zKTY7GPx/zHuL50Pff792OtbQpIV7mruCx9GRclL2p6Drp1d/zzuBlnZ7iTgNrrKKdzpG2aoi3GFaX1lqoaHQfkjdfQxWWdu45BM8iwGQYgqZIoFBVbaapgbJzlmLd5tofb+evuv+ZN2TfhqPYVk0IIIYT41SkGRe48cM9R7eOGPbe2hI31+zNumtW5k1iZWc4T409SCEuAQivV9N7AWNPSNjUwtS/bJZwECafWxn1xup9VuZVN6+0r7ucnB+/jomUvOqrHIYQQ4ldPwkYhhBBCCHFCqYcrc5VTORIkKNsySqk4NKkHKXmTp2iLcfhXrw5rDFosloCA0IYkVZIu3UVoQ3x8AhvE4Q7UqiY/3/N5zkue13QeKZXiNPc0ri9d3xRUtmMwlG2ZiCiuolusF9fOxVruqd7DazOvbbvtPrOPte5a9kR7GIqGWlpe1oWE+NaPZ/iNmtF42a2VW3nj2Bu5KHkRv5P5nTjY/KvcX7Ez3MmG8gYKttCyz73RXrpVN4udxSzQC3hT7k3cVL6pKeha7aymT/cxGA0yYSaw2DjsrVczJlSCJMnazEBKaFsLGpMqSUqlWo5rsbVWuKaMxdKlunhr11vbPu6LkhfxlcJXjqqd6qWpS49425FohLePv517q/dStdX4MadVmgE9wEK9kF3hLq7LX8c3i9/kqp6rWOutBeDZiWfz/dL3GYlG2BHtaGmV6yiHbtUdt0A1mPi1XA8ePeWhUCzQC1jhrKBoi3HQWJdQCTy82msEP37du7gkVZJ+3c9CvRCDkaBRCCGEOEE9NPII1Wj+bfsb3Tf0YMuXoiIT8djYE/HvjnZYnFlMtbC37T6qpsNMdBPGQSNAX3JB2/Vu2HOrhI1CCPFrQGY2CiGEEEKIE0q99elsKrbC7nA3jwSPkLd58jbPlJli0kzGQZ7Fsi/aF7dNDQ7dpgd0BkN46Fa2ZUqmhKtcMipDj+6hW3XzTPeZnO+dzzJnGVVaP1TZFmzjcf/xtq0sO/GtT8mWWKAX0K/74/tnqlqs2AoKxUnOSZybOJeTnJNIqVR83Pp/jTWkVbqperIuIqJsytxQvoF3jb+LTcEmqrbK3+X/jh3hDla4K1igF7R9LHmbZ8yMcU7iHP537n/znORzWtbpVt2sdddytnc2Hl4cJGZUhm7dTUZlCG1IhUp8DIulYiuUbKnjYy/YAlvDrWwob2BnuLPtOhmd4cWpF3fcx2xWuas4K3HWvLeLbMR1+et4/uDzub1yOxVbicNCi6VkS+yMdvJI8Aj7o/1A7Xn+wMQH2BxsBuCsxFlkVKZt0NgopVJ0626yKounPFzcWjCIwyp3Fed453CqeyoplWIwGmy7D6VqwWRWZVnmLOM5iefwzMQzOdM7kwFnAEc5/Kjyo2MyA1MIIYR4OqpGVQ6UDrIzv4vB0hChmXvnjV+GSb/9PPH5CE1IMSw23VcKS/EIgLr+1MIOXSUsftT+i4KN72UybpouL9d2vacmNrMrv3t+Jy6EEOKEI5WNQgghhBDihLJIL2LAGWjbihNqIdrOaCejZjT+ECNBolaZSBi369wR7mAveynbMiFhxwrAOttwK1PGtS4JVfs2tqMcXH34rfOG8gaem3xu0/afy3+OgICcypG3+VmrGxuPu0AtYNgOUzW1Srj7qvdxXuI8zvHOaZlP2NjG1MVlibOEJc6S2rXBMBKNsDPaGZ97O41zLifNJB+a+BDLneVsDbcCtbCw2+3Gx2fCNLdS7dW9eHg86j/KP+T/gVelX8X91fvbHmdftI+AoG21YpFiS+tUqAWw9UrAdibNJBNmgg3lDbyt621t13lt+rXcVb2LkukcXLajlOL3s78/r20AQhvyscmP8d3yd2dtbxsRsTfaS9VWWe2spkKFayav4bq+6+jSXQQEMwaNjTzl4eGBqlXc5lSOVc6qeHnRFNtWqE632Fnc9v6yKXN75XYuS182p/MRQgghfh3szO9iw55buOPAT6iEh790053o5uXLX8xlKy9mIN0/wx5+Oeb6fmHW/UwLFtuFio5yWJTqY6jcPJvbHHrvPJuB1KIZl+8t7mdV10lzOFshhBAnKqlsFEIIIYQQJxSlFJem27exNNawOdzMiBlp+mBDKUVGZXAPfZeu3kpy1IxStMVZg8ZGFovBULSHv+U9vTrw0eDRpg9mNgYb2RHuACChE+RUjpRKNc3Bm87BIUkSLDwRPsHOcCcHogMcjA7ykP8QH5r4EG8Zfwv/XfpvyqYcb9cYJk3n47Mr2jXrY5we5G0Lt/H98vdb1kuQYEAPsMxZxjJnGf26vxZuHfKjyo8YNsO8LvO6lm1LtsSIGWm5H4hD1U7Xp3GuYztDZog7Knd0DBOXucv4QPcHZgxc23lj9o1cmLxwXtsAfD7/ee7372ckav942xk2w+wz+4BagHpL5Ra2BlvxrU+P7pn3OXSrblIqRWQPh9xjZmzW7RbqhSzSnT8AvKt617zPRQghhHg6KoUlrnn4k7z93vdy054fNQWNAFP+FP+14wf86d1v5x9+8c+/8krHbq8r/tlYQyEsMulPkQ8KLTMUO9FKk5tWcZhxMzja4UBpkB35XWyb2sHO/G6STpKUM61jxgw5Yz20XJDooX+WsLFylO1ghRBC/OpJZaMQQgghhDjhvDz1cr5R/EY8Z7FuR7SDKdu+ZZRWmixZKlRIqRS+9edU0diOxRISUrG1fTW2OIXaN8CLtkhO1T6cuaF8Q7ysV/UypIZIkSJJkpCwqVpNoUiQwGAOtwy1NIWTvaoXgP3hfv618K/cWrmVq3uupt/p5+Wpl/PN0jebQqW64Wi45dvl9UrP0IbxspzKMW7G4+MMRUP4+EzZKbpV97yu1Q9KP+CzCz4LwHdL343vHzLtK1OBeAbm9OpPfegGtQrHdhWRAFNmigk7weZwM+cmzm27zjmJc/h478f5u6m/YyyaOXRLqARX5q7kFelXzLheO1uCLfy48mOGoqF5VxgciA4woAdIqAQ3lW9ib7gXheJU91S2hFuYMnNrj5ZQCda763GU01S1Otv80z7dxxp3zYzrjJvxOZ2DEEII8XRWDIq8/6cfYUd+9i9tWWu5ee9tjFRG+eB5f93U/aKTg6VBbtl3O/uKB6hGVdJumlO61/Dy5S+mJzH/LxkBPGvRuUQ2Ym9xPyOVsab3hgroTnQxkB6gN9HTscn/s/uf1dRFY9vUDr6383p2TO3iYLn1vZxSitCGtXdsStNpxwqFq1x6Ez2c0r2mpVPHdGm3/Xs+IYQQTx8SNgohhBBCiBNOj+7h9zK/x9eLX4/vK5kSo2Z0xu2UUqzQK1jtrOZh/2GmOLpZNmVbJqMybSu/XHX4rfQD/gPxzwN6IA7alFJ4h26N6rMaG4U2JKESdKkuMjoD1OYzDppBHgkeYUN5A8/wnkGv7mU4GmZvtJfI1uZSOsohq7JNlWwGQ8VWCGxzW04Hh4ItsCXcUpujSAYfH6iFjt3u/MLG7eF2NoWbeGPujZzhncEPyj/gYf9hRqPDz5WxBp9a4GkwVDhcKdDYRjUiwmBQKKq22jFstFgKpkDBzNwi9DTvNP6171+5t3ovG8obeDJ4smn5MncZl6Uu46Wpl5LT7ecIzWZDeQNQq1ScL4tl2Ayz3FnOweggeZMHas/Renc9+6J9DEfDHUNDjaZP97HCXUGCWhXnRamL6FW9fKv0LUbMSFyhWw+5XeWSVmkWO4sZ0AOznmO7UFsIIYT4dWKt5f888vdzChobPTTyCP+08d/4izOvxFjDeHWCUlgi5aToTfbgaY8nxp7kuzv+h4dHH21pV/qTg/fx9a3f4cLFz+H31rxmXm1EQxPy5c1fZ1dhD8Pl0ZYwzwKTfp5JP0/KSbGu55TWqkTg8pMujn++88BP+OwT1xGakEWphW3DRmtr7zsDE5BQ3qGviSnMtC9cZd0Mq7pWsjjVP2vQqJRiTVfnzh1CCCGeHiRsFEIIIYQQJ6QrslcwbsbjqsGZKuXqenQPa9w1aDQBARodz2GEw8HWXCrQLJaIiG7V3RQsQq0KsR6EhTZsanOa0Rm6VBd5m2+736aKxmn3Aww4A1SosDPc2VLZ9lP/pzg4VGyFoi3iKIeMyqCtpmAKtftwSKhEx1akjQFe1VYZs7WAMquyjJtxDGbG9q/t/LjyY07zTuOC5AVckLyA28q38Z6J91A1tf37+Gg05tCtUbvnxmJrMzht2HLt60Ib4imv7bJGrnJ5UepFvCj1IkaiESbMBBERXaqLpc7SWT8Am0nBFLi7ejcGg2/9I9rHcDTMMr0MpRRjZixu2atQrHBWsNxZzpgZY9SMxsGxq1x6VA/9Tn/cOhhqr6E7K3cSEpK3+aZl9WuaIEG/7m+p1u3kSENYIYQQ4uni0bHHeWzsiSPa9oe7b0KjuW/4QcYq43Hr0IyboT+1iM1TW0nqzm3dQxNy14F7eHD4If7m7Hdwfv95sx5zyp/iLfe8k3sPPkgxLDIVFFDU5iomdAJPe03vbypRhY0Tmzi9d31T4Lg8u4xz+p4BwL2DD/CZx/8hDkQzbpouL0c+aP1il0KR0Am00izNDOBqh0l/Kl6WdBKc2/cM0m77+dvTndN3FkszS+a0rhBCiBOXhI1CCCGEEOKE9daut7LEWcI3it+YsapRo+l3+jnJOQmFwlpLhUocbKmOzaNmptFxxVijF6deHP/cbt+r3dVsDDbG7UIbVW3nmTQL9AJSpNjob2ypZiuaIgEBXaoLV7lkyFC0RQq2QFZn49AuJKRqq00tSevSKt0S0BlriIgoUiSrskRE8w4bp88G7NJdLNFL2GQ2YbEkVZKCKczY0tZim66lpdaqtlt1t73GWukZZw22s8hZxCJnftvMZGtYm7F4JK1663x8qlRJkYo/oGykUCzUC1moF3bch8EwFA2xOdyMwZBRtcrYwAYENsBTHkmVxMXFYNgd7WbKTnGqe+qsz/WzEs864scmhBBCPB1s2H3LvLex1rItv4N9pYPcP/wzkvrQl4WUojfRTUInuKP8EwAWJHs5uWs1Tpv/n6+rhBX+zyOf4aPn/y1nLji97TqRifiXTf/BdRv/lQOlg4BCK4ekk6Aa+YQ2IozKqKhCykmSbAgWAxOyaXILZy04A0dpEk6Cd5715yilmPQnm4LGulO6V/PkxCb8qH2HBWMNI5Uxzuw9ncfGnoi/yrema9Wcg0aAy1ZePPtKQgghTnjz+xRBCCGEEEKIX7LXZl7LR3o/wknuSWRVNg6eFIqUSrHSWcm5iXNZ5ayKl4WEtCtenG+I5uAQqdY2kpenLz+8zqHqwrItUzAFyrZMggRr3bVNlWVweH5iO12qi5XOSjaHm1uDRluM76u3PPWUR1bVQsaiKcaBV0QUV2U2Vg1mVCaummunHkzNd+5g/ZiNNJot4Za4hWfFVprW6fQ8TD92vRVsO6ucVZzinTLvcz2W6o+vcU7ikai3Kp1rtWGjsi3zmP8Yvwh+QcVWmq6ti1ur8rVBXPlav8YTZoKt4dYZn2+tNJemLp33OQkhhBBPF6OVMR4cfmhe21Qjn/uGf8r2/C6qUZVKWKX+xtNay2hlnE2TW5kK8oQ2ZLw6wcaJTbO2Jg9MwKce+xyhaf6yWiks8f0d1/PcH7yMDz30cZ6a2MyEP8VUkKcSVUnqJEnn8JfjLJZyVKEclpv2U418RqujpNwUf3vuu1nfuxaAW/b+GD9qfX+a0AlO61nXtO9216IUluhP96OA1bmV9KcWYqyhGlUphWUqUZXItv9i1tqeU3jOwPkzXhchhBBPD1LZKIQQQgghTniRjZpaP9bn+nWqWDSYWvvNaTlKvdrPYFoCsnY8vJYw5qzEWax2VwOwK9zFhvIGtoRb2Bfti9dxcOjTfax2VjNqR5kwE1gsoQ1b9qfRJFWSM70z2Wf2tQSNoQ0J7OH7fOuTVrVvi3vKo5tufPy4/Wjj/g2GnMrh4bWtmoPmysyqrbYEpHORVdmm33dEO+I2su0C1pkqTRsrHBWq6fHWJVSCP8z+4bzP81irV73Ww9x27XHnov7cvCr9Kr5a/OqctyvbMhuDjeRtPn49N1biKlWb01gPbAMbULTFuDXqhJlgKBpisbO47f4vSFxwTCtBhRBCiBPNzsJuTIcgrJ1KVOVnIz+n1BDkWSzGWvSh1qW+qb3vMdZQCIpk3QylELZMbee0nrUz7n+sMs59Qw/ywiUXYq3lW9v/i+/tvJ5fjG1ktDqOH/nxOz1jDZWoQiWqkHQSpJ0UvvHjYK9qfHSk4wpHRe09y6ee/RHWdK2unbu13LT3Rx3PJ+UkOXPB6QyWhxiujLStchypjvGmdW9gb3E/mya2sKewj/3lg5TCMsZGWECj6PJyrM6dRH+69t5iWXYpV5333o7vUYUQQjy9SNgohBBCCCFOeI1zBmH2CsX63MJ2RVsKFVeitQscG4MuV7lN4dtCZyHv7no3VVvlM1Of4YbyDZRtmYqtULEVlFVopfHx2R/tZ5BBFjoLOcs9i1E7yoHoQBz8aKXxDt0WOYtwlMNo1Noqtkpz21WLxVobz+JRSpEkiUK1/bBMo2f8EMdVLqEN6w+eiq3EbTjn6tzEuU2/P1B9IJ5b2a5aUqNnDXvrz3E9rEyowyHaSmclL0u9bF7neDwMOANNP+8Md857HwqFh8daby2vy7yOGys3MhKNzLqdwcRVsPUw11OtoXKSJAFBfL1DQkq2FD/Hg2awbdjYo3t4c+7N83osvu/j+z7GGLTWJJNJPG/2uZpCCCHEr8r06r+ZhDbkyYmnmoLGOttQ2Ti9SrAUlsh5Wab8PFNBnm6va8bj3LDnVl6w+Hl89okvcvv+u8gHBUar40AtQGynGvl42qXLyxGaCN/4GGsITMjCVB/dXhcDqUUknMShSsyapyY2M1QenvF8XOWwPLOUZZmljFcnKIZFQltru590PBalFvGW097E17Z9hx/uvonB8hCRNVhrMdT+W6+2HKqMkHASvHjJC/jUBR+hJ9kz47GFEEI8fUjYKIQQQgghTniLncU4ypm1/VSdq1yyKssUUwQcDrvqFZEajYODQnUMverr9OjahyBLnaVc3XM1BsPvDP8OjwaP4lsfay0BASVbIiKqBY7ouPKyHJXJmzxnu2fjOA572NP28Q1Hwy2z/wymqapxJhaLVRZlm6sGq7baMqexUYIEFWoBaJIk42acjDP3sLFLd/HC5Avj37cF29gUbGLAGSAf5lsqNeFw4Nvp2ltsU6AcEMQVey4u1/ZeS1rPfRbQ8bLKXcWp3qlsDbayUC9kD3vmVDHbaIFegKtcLk9fjlaa16Rfw78U/mXW7UbMCFVbbQpzk7S2yVVKkSVL0Rbjc/OtT0ql0GgqtsKUnaJbdcfb9OgePtzzYZY6S2c9D2stT008xcjkCKpae+2nVIou1YVSinQ6TU9PD9lsNg7IhRBCiBNF41zD2RwsDZEPim2X1f8fLrIRZtqXrCxQDivkvByD5eFZw8Ynxp7knzb+G7fvvwuAoXL9S0h2xirMwISUwjIZN4OrD3/k25dcwJL04S9I7S7u5fQF6wEY9ydmPJdGCuhL9tKX7G263wIfe+TT3LL3dkphmbSTphAWiGzU9IUzBSilMdZw18F7+O0f/SGfee7HOHfh2XM+ByGEECcuCRuFEEIIIcQJr1t389zEc7mnes+c1i+YQtyydHpb0fp/66Gji4s5dKtT1NpPZnSGU91TuSR9CZenL2fYDPO6odfFLVONNXGIUw8X6/MS64FaYANG7AgPBA9wsntyy7kuc5aRUzn22/0ty+oVh9NbpJZtGRe3qdqvk5BwxuX1CsvIRnjKm3X96V6WelnTeTwePA5An+5jr9pLwRTabufg1NqO0f5Ds8ZWq9ZaULUQ+dqea3l+6vnzOsfj6bLUZXw++DwODsucZeyJWsPkmQzoAda4a+LA9tWZV7Mj3MFtldtm3G4oGgKIw+i0StdaB7ehlSZHjjLlOJysB44AY2aMbqcbpRQXJC7gT3N/OmvQ6Fufuwp3MXJgBBO0PocplWLAGWBRaRHlcplkMsnSpUtxXfkTVAghxIljWWbJnNazWIbKw21nG9ZDtPp67YQ2IrIRE9UJfBOQ0J2/COabgO/v+iFJnSAwAeP++KF9z843AUkb4jS8JxgqD7M41R9/6aexmnMuX+QzWMaq4wxXRqiEVYyNcLRDxs2wONXPhD/JzqldDFdHMTaiFJYAhaudpnNWDf8bWcP9Qz/j7fe+l4+f/yFetPTEeW8nhBDiyMhfekIIIYQQ4mnhsvRlcwobR80o28PtGGuaZjQ2hVcNN6fhVl8voRIs1Uv549wf8/7u96OVZne4m78c+8umoLFgC3FY1litV993RBRXUBZtkX3hPhpOg6XOUlY4K4D2H/aEhERELUGoj49vfSq2QkIlSKpkHHYmVZKqbW292mlOYtVW42/JF0wBD48BPdAyJ7Gdpc5SXpd5XdN9RVuMz3Odu44HzAMdPx2rt7OdHjhOP1etNCudlXys92Nckr5k1vP6Zfqt1G/xtdLXGIvGWOospUo1DgJnk1VZ1npr+XDPh5sC27/q+isyOsP1pevbble0xXg+pMWSUZlZg2elFBkytaARn9CGJHUSi6VLdfG7md/lkvQlTa1hOxmJRvjE2Cc4Y+QMUuZwi2MHB2UVRhkqtsLucDcH1AHWueugCvv27WPFihU4jjOXyyOEEEIcd8uzyzir7wyeGHtyxvXGDoWE7cJET3vxe5eZAkHf+DhOmlJYIpHo3D50uDLCgkQvaCgGJYyt7XWu/QGqkU+m4cs9lahKYAISTu29Qto9/B6vy8vNuK/9pYMcLA8RmuYvo0WRwY8mGauMkw8KpJwkCSdBISw1VHbONKW79qW6pya28OnHv0BfcgFn9Z0xx0cohBDiRCRhoxBCCCGEOG4mogmuK1zHT6o/oWALuLj0636uyFzBpZlLSaq5t646O3E26731bAo2dVxn3IyzPdxeC9cOzTKsB2kK1RJqWSwhIS4uCkVKpUirND2qh1elX8V7ut+DPtTu6WOTH4tn8llrKdpi25CsHlo2Bo71uY8FCiRtkl7dyxJnCQv0gsPbKtX0CVV9DuT0YzS2FzXUQh3f+uR0Lm5hCbQEjo2MNZRsiSpVDCY+Px+fQTPIpD/JQr2Q5c5y+nRf230MOAN8pPcjdOvupvsbZ1ymVZrFejH7on1tKxgVqqmytLF9aj1A9fB4cfLFfHHhF+cUhP2yJVSCD/d8mL8Z/xsqtsJqZzUJEuyP9nes2oRa+9pXpV/FNb3XxK1665RSXJm7kouSF7GhvIG7q3fHcxmh9tqoz/ocY6xlrudM6v8ukirJGd4ZeHg8N/lc/ij3R3Pafiwa42/G/4YLRi8gZVK41iVrsmSiTNNrMyKi6BQpOSWeCp/idO90MkGGgwcPsnz58jmfrxBCCHG8Xbby5bOGjYWgUOu00EZjK1Y9Q7wWmhCcztWEkY0oBCX2FveT0AkUh7tc1Kj4felMgkOhaGPU13jMVbmV8c+n9awj62UpTmsPa4Ht+Z2MVsZmPFZ9NmQ5qsQ/z0c5KjNYGuLLm7/OZ5778XltK4QQ4sQiYaMQQgghhDjmdge7+eDkB7mzeicVW2lZfmf1TpZMLeH1mdfzzu53zjl0/GDPB/nr8b/mYHSwZVlowzhorEupVFxZWJ+pWA+1puvW3Wg0WZXl4vTFfLj3w/F5Peg/yN5wL6NmFKjNEOw0m68eOAJNAZqjHDSale5KFuqFLZWD9ZmEcDhonP598Hr14nQGQ8EU6NW9GAxplcbBoWIrLR82BTZgyk7FVZz1oBVqlWkuLr71ORAdYMyMcZJzEqvd1YfXUQ7PSTyHt3a9lV7dC8CEmeCW8i38/+y9d5xkVZ3+/z43VewcZnpyZMhDDoqggugMurrooq5xZQ2ru37dXeMqIrquCua4wfAz7BrWBRWJIgICkjNMZFJPh+ncXfmGc35/VNXtqq7q7uphgEHOu1863Tece+6taur0ec7nee5172WLt4XHvMewhU270U6X2cWUmsJVbrGKso4AZ2MTEREMYRCoYjXoams1EVEUZj/U8qFDKjQ+7j5Ob9BLTuWIiRirrFUcaR950O2tsdbwb63/xqcnP82EnGCJuYRus5vhYJghOVQl/BoYrLHWcHnr5fPawa631/MB+wNcLC/mUe9RUjKFgcEWbws35G7AEAYZmZlTWJ6L8mvRZMydHVXJ56c+TyQXoSVooc1vIybrV8CamDQHzTQFTWTMDNvZzvH28eRyOfL5PNFotO55Go1Go9E805zZfRpLE0voy9Ra2pfxVVC3tNA2LEwxXbFvCjO01Z9JeZshqiv8s0GOodwwo/kxPOmT8bPsTu0ttWfgSrdkuyqIGA65oHZsXX0dCGRQldtYtnld1bSCI1uPCLdHrSgvXXI2V++9rqqNfeneeYVGUBRCW9ni94YwMIQx51mVeNJnIHeA7ZM72Tm5i3UttZEDGo1Go3luoMVGjUaj0Wg0Gs0h5d7Cvbx59M1MyIlZj5FI+oN+vpr6Kve59/GDjh80JHi0Gq1c3nY5n578NDu9nVX7RuRIjQAYF3GOcI5gNBhlf7CftEqHGXczCVRAj9XDu5Pv5uLkxWGFIMA1uWuqqgwrq8zmolzpVRYxAVpESyg0TqkpsjIbtuspD0MYoUBbmQNZbmc2QyqJDHMQFQpHODjCoUk0YQubvMqTUzlSKgVQJTKWsbFJGkkCFeDi4imP3qCXqIhyauRUzoqcxSuir6DD7ACKdpo/yPyAOwt3hivvpZK4yiWnckzJKUzMomWnKFbT+fhIJUMR1BRmKM5CMZdxibkkzAzsMrs41Tm1oec9k/3+fq7OXc31uesZCUYYV+NkVIZm0UyP1UOEaZF7tbWazbHNvDj64qrXvlHW2+v5Vvu3uDF/I9flrmMoGKLH7KHH7CmK0ypgubWcV8dezctjL1/QNZqMJl4QeUH4c0RE+F3+d0Dxd2IqmFpwf2HaxvZ05/SGjt/ibWGLt4XzcufR6XXiqPkzQwWCZJDEVCaj5ihdZheTk5NabNRoNBrNYYNlWFx20sf40D2XMF6YqHuMUcr6FkwbUZjCIG7Fq44TQuAYDgVZbyFQcdwVLVVCKhR7UvsYzo+W9hfHcBFj+vO1ELhk/Rx58iTsBI7pkA/y8+Y3VoqdQgjskvC4efn5NcduWnZeldiY9XMcyA3PcwWQajp7W6pi36UKMISgcdNXSHsZ3MDl2t4beX/Lexo+T6PRaDSHF1ps1Gg0Go1Go9EcMna4O3jj6BtJyVRDxysUtxdu552j7+RHnT+aN3cOoM1o40utX+Ju926uzV3Lw97DSCkZktM5ec1GM91GN21GGwJBj9lDt9HNiBxhMBhkUk3iKx+JxBAGCRKcEjmFK7uurBGBxoIxHvYeDoXMQAX4VOfWzIdP6VoYeHiMBqPkyNEfVK+gz6t8KIaWhcqy2GhiVtlUzsTExMMjKZKkVTrcvtJaSVzEyakc97r3ztlG+fmbwiRGLBRFFYo3xt/I2dGzw2P3+nu5dPJSRoPRqjZMYdJhdISvR1D6yss8SSNZtFmdZf7Jx8dXPhmZYZfahS1sXh57+YJWyAP0+/1cMXUFtxRuYTgYJq/yVba3gwyyO9hNp9HJBnsDDg67/d18K/UtrspdxadaPhWKnQuh2WjmdfHX8drYa3nAe4B+v5+cyhE34qw2V3OMc8yC26zHGmt61X+n0cn+YP+clq31iIhI0dbY7OI057SGzrk2dy2JIMGRuSMbEhorickY6UKarngX6XSazs5Ond2o0Wg0msOGRfFurjjtM3zqgc+zP9NXsz9iRkqinYMrXSxhkrATdReBRcz6YqMhDJrsBDEzigJ2TO5iOD9CQboE0q9KOlQoHNPBMixMYRAoSdpLk7QTRMwI+aBxV4N2pxVTmPTEF/OSnhfV7F+eXMZFa/6SX+y6CoCh/PxCYxFFxIhU2acqisJjUXBstBWFrwK2TGxv+ByNRqPRHH5osVGj0Wg0Go1Gc8h41/i7GhYayygUtxRu4ceZH3Nx8uKGzjGEwZmRMzkzciYDwQB/yv+JK1JXYGISE7G6VWOmMFlkLmKRuQipJD5+KOCJ0oTIlJwialafOypHUWo6S3Chok6Z8nm7/d0EBBxrHUtgBFUVmbawKahCcWV4qVLRwiIgmFMkBELL14iI4OLiKpcmo4m4KK64HwgG5rTbLFdCQlFQrawS7Qv6+HX216HYOBKM8MnJTzIW1LfXWmQuYlgOh6vqoyIaiohJI1lzfEEVQotVW9hMqklQxSrQ67LXscffw6tir+Kc6DlzPgOAHd4O3jf+Ph51H0Uii/lHKl1jZ+Ypj4FggAk5wcnOyeFz6vf7+cj4R/hi2xcP2rpVCMHJzsmc7Jx8UOfPxzJrGcc5x/Go+yiWsOgwOhiWjU4MFukyugB4RfQV4ft/LvIqzx2FO1ifX09UHlxVohM4ZIIMCTOB7/tabNRoNBrNYcWieDffeMHl3Hngbq7t/R2Pj28J93VG2+nL9tMV7WCsMF7MKfTzCIrj0qrcRmEQNaPkZ9idRgyb7ljx83frxDb6sgPIGTmQcStG1s+RC/LkgzyO6eAYNrmggAIyXpakkyRQEk9WunUolFLI0njHDVwCFeCUrtkaaeFTJ32UqFX/M/wt699Axs/ym73XNmCfWlw3tiyxhL3p/biy2vFDKrmghWLlkICMn5n3WI1Go9EcvmixUaPRaDQajUZzSHjIfYgt3pb5D6yDQvGj9I94R+IdDQkflfSYPayyV9FpdDZ8jiGMqozEMhNyokZgKlcxRkV01gyeRpBKklVZkkYyFA5XWitZqpYyKkcZlsP4yg9XyJf/LduaZlV21rYrhcIpOcWR9pHsC/ax2loNFKsLB4KBsO8z78ERDlGiYa7iTDvarMry0+xPOSVyCn8V/yt+kPnBrEIjUMxBNFexO9gdbksYiTBfsCyMKhQZmQmfsYlJnKLo1yJaWGetwxAG27xtbPO28ZD7EH/f9PdV2UiVDAaDvG/sfTziPVI0IlOKjMrM+ZrlVI773Ps4I3JG+J4Yl+N8ZvIzfL3t6wt+Pz5TXBC7gEfdRwFYZi5jSk01nN0oEHSZXRxpH8lr4q9p6JyxYAxPeawqrDrIHhfxfA9MUOrgfo80Go1Gc+h5fHwLfxz8E+OFCSbcCfal+7ANi1anhagZZUliMS9fei5HtW14trv6tGMZFmf3vJCze17I/kwffZkBCkEBU5h85bFvc/vgXWT8LIGqHisZfoaIESFuxUpiYwRQYQWigSBpJ2mPtNGb6WN/pr9mdBI3YziGg2f4eNJDUbRRNYURjkElCjfwSFhxckGOQlBAKhnamELxc95TPl7g40mflJfmr9f8FUsSc7s2vOeod+BKj8fGtxAEs0cGxK0YS+M9tDjN7M8M1Owv9kPRqJWqKQwsw8YxFuaaoNFoNJrDCy02ajQajUaj0WgOCV9Pff2ghTiAJ/0nuatwF2dGz1zwuWXrpqdKvarFctaiJSzajXYGg8EwF1EiqzNxSnk+9aoQ8yqPKaatUMuCmSWssOKyP+in1+8lRy7MhczIDM1GM0BdwdERTliVB0VxdI21hm+2f5PbCrdxc/5mdvu7w8zJyj6X++srn0kmw231mJSTfGHqC9ySv4X9wf55Ky27zOLK/T3BnjCfMWkkkUiiIkpe5UnLdChsmpgkRIKYEaPb6GaRsahG6LspfxNCCN7f9P661/xG6hs86j0a3p+H11Alal7l2eJuYaOzMdy2x9/Dg96DnOScNO/5zwZnOGew3l7PDm8HtrDZYG1gu789zPuci8XmYo6xj+GTLZ9syLoYwMUlKqN0+o2L+vVQvkI5CsNYmDWuRqPRaA4tSilu2P97rum9gT2pfaS9DPuz/Uy50w4VjmHTFetkcayb3/fdyprmVbxu9at50eIXzNFyLb70+dPQPTw5tZusnyNqRlgS7+HsnhfUZB4eTixLLGVZYikj+VHed+eHuG3wTibdKQwhkKp6AZpUklyQIx/kSdoJomaUqBnFECaFoEDUjLChZT3D+VH2pPZVjZgtYRI1o1ilXMWI4VRVLQZKYgqjJCiCK12ipkPEiOAFHsGMsU65qjBqOhzVuoGuaCff3vJd+rIDXLzhLXMupDqh/ViObzuGSXeK4fwI+aBAoAJMwyRhxemKdtJkT7tULIp1kfYzNWPxRqVGS5i0RVqxDSus+tRoNBrNcxMtNmo0Go1Go9FonjJKKe4o3PGU2vDwuDJ35UGJjU1G01O6dtiOqG1nqbmUdrOdsWCMTtHJXrV31sxGhSIgCPMZDYwqYdLCwscnRgwbu+75QgjixLEpWqr6+LjKxREOBgYuLp7ysLBwhIMtptuJiAhdRhdXtF5Bi9nCkfaRvDXxVt4+8nYe5MFQeCv3rbxK3sMLJ8xmy4aUSDzlcZ97H8NymKPto+veQyVdZhcJkWBIDjEqR0M72BXmCgaDQXIiR5QoCZGgWTTTYXbQLJrnnAT7Xe53nGCfUJUfCUUx9Le531aJi2XBthGG5TAeXtU9XZu79rAVG01hcmnLpXxk4iP0+X1ERZSjrKPYH+xnTI7VVKeW6TF7eH/y/bwp+aawwrQR4iJOm99GQBCKxweDoQwMw8Cy9J+iGo1G82zhSY8vPfpN7hi8C4DRwhi7Untrqs5d6dGXGWCiMMkRLWvZNbWHyx/+GntS+3jL+jfMe53xwgS/3Xc9N/bdzERhsmb/97b/mBf3nMWrV25mWWLpobm5Q8x4YYK/ufW93DvyIAJBzIySC/KYhkkgg5qFdgpF2ksDiqgZwzFs1jWv4gunXsYDow/zvW0/RiqJJUxMYeKYTo1jQzGn0ayqngyUJGpEKchiJaMbeORlAYkqni8UxZdPETEjRMwIKxLLaLanx7a/3nsNpmHwN0e8edb7NYWJEILWSAutkZZ5n8/iWDe9mb5iXw9izaFjOnRHiyLjeUvnt8t/utk2sYNdqT3k/BxRK8rSeA/Htx972DpdaDQazeGE/gtPo9FoNBqNRvOUyahMQxVV8zEqRw/qvHXWulAQPFiWWktZYi6p2W4Kk5dHX85P0j+hX/YDRbFuroq5suhYFnzKwoyrilmKCDggD2ALm3E5jqtcFIq8ypNXeSIigi1sbGEXV5MLkx6zaH1lYdEkmsioTJhzaAqTpEjSKlpJmAlazOnJobsLd/OE/wQmZl2BqCweVf5cvseZ96RQTMgJCqrADm8HR9tHz/tc40acVcYqlqllxXvFZbm1HB+fZeayuhmO83F17uoasfE32d8wHEznFgYqmFUUrkdAwE5vJ+vt9VilP5Pude9lUk7SYsw/2fZs0GK0cEXrFXx+6vM84j6CLWxWW6tZrpYzKkcZl+P4FK15HeHwytgr+WzLZ4kYjYuMZTqNTjpFJ1JIckaOuDy4ShRDGMTjcV3ZqNFoNM8SSim+/Oi3QqFxwp1k19SeOXWijJ9l2+ROjmo5AtMw+cWuq4hZMV63+tWznrNrag+XPfh5xvLjsx6T9/Nc33sTf+j/Ix86/v2c3n3Kwd7W08bH7r2M+0YeDIXYcjZjLshjGSZKKQJV7XShgLSXpclKcsHKV/CV0/+NuB3HVwFrm1YzXpiokShd6YU5i+V9gQqKLhRCAIIAnyY7SUG6FKQbVhMqFFJJlFLYhh06afRlB+jPDtDitNAd66TFbubK3VdzYsfxnNBxfN37bY+0Lej5xKwY3dFOejN9GMII+9SINGcIg6SdoNVpoclpWnDF7KGiEBT4Q/8f+cXuq3hsbEv43B3DpjXSwrLEUjYtP4/zlryYJufQLHDUaDSaP0e02KjRaDQajUajecoEBAdd6VRJI5aX9SgLgj/N/PSgr705unnWVctnO2fzEe8jTKrJee1DoToTsfxcTKZXrXvK43HvcYCqDMdABaHgaAubmIiFq907jI4qu9Qk9UW6U5zpibpt3ja+mvpqWMVYr5/1rG/rCY4CgSlMfFUU8NIqTUql6laD1sMSVmitGhGRBWVszmSrt5Vd/i7WWGvCbdfmr62q5pvL0lfV+QLYF+wjpVI0G810G920GW0MB8OHrdgIxarez7Z+lh3eDq7NXctthdsAQmvedrOd86Pn84roK+gwOw76OoYwODVyKjlyZIzMQYmN5erc5ubmg+6HRqPRaOZHKcXOyV3cPXwvY4UJLMNiVXIFR7dtYMfkLm4f/BMAEsXu1N6GCtKyfo7ebB+rkisA+NGOn/LCRafTE19cc+z+TB8fv+8zpQq/+SkEBT738Je55MQPc3LnCY3e5tPOjsknuWXgduSMis+IGcEyLNzAxZUuQhSdLCjZnAqK1YlHtR3Jv7/wK+H48preG2pGXoWgQD4o1B23GMIgkAFSgSEEnoSYVbRZFQqiZoRCUKAgXQwhiFuJUAwtoygKyhPuJB2RNlY3reTqfdfPKjYe2XoEPfHFDGQHG35O65vXkvYzjObHi+4ewmA+udFA0GwnWde0BiEEr1z+cmxjbseMp4PB7AHec/s/8vj4FqbqvF+ttMn+TD+7pvbwq73X8MkTP8La5tXPeD81Go3muYAWGzUajUaj0Wg0T5mESGAJ66DskyrpMA5eDHlF9BX8IvuLKsupRomICOdGz63Zvs/fx9W5q/l19tdMqSkKqgAQ2o+Wv6+k3mTRzKrCAoXw+5RM0WQ0YWBgChNLFa1WPeXhK5+kkcTExFVuldg4G5ujm8Pvf579Ob7yiYt4WCVZyWxWm0BoBRvegzBpES2kmM5yGgqGaLIWvsJ7t7+7YZFyNm7L38aa5LTYOBpUV8XWszUr51XOJrKWxe4pOcWUnMIRDk94T7DOXveU+vpMsN5ez/+z/x/vVu9mXI6TV3kSIkGH0VFjz3awnJU8i5sO3IRruOSNPFEZXdD5Dg62bROLxQ5JfzQajUZTzZQ7xf/tuZofbv8fnpzajVuR+xc1o3THOvGlT8yK0h5pY6wwjicbdwEYzY+xPL4Us1TRd23vjVy84a1Vxyil+LeHvjyr0BgoiRACY8b4KZABn3/4K3z/Rd88bKrHfrHrKiYKE3X3mcIkZsWIEsWTHqokNBqAZdgYwmDH1JP0ZwdYmlhC3s+za2oPhjAQQqCUIhfkKASzW74LBJZhIlWpcpEAX/rI0jOUqjiySVoJHNOZd+HfaGGcQAUYwmAoN1w3I1EIwabl5/H9bT9p+DmZhskJ7cfxyNgTDOWHKxbvTYuvleKjIQxa7CY2tKwnaSc4rftk3rD2tQ1f71DRm+7jVTe+noHsgVmP8VXAgdwwQ/kRMn6Gj917GV847VOsblr1zHVUo9FoniNo7xqN5jlCoKYYl79kn/9+dvtvYbf/Jvb672Y4+E9ctf/Z7p5Go9FonudYwuJE+8Sn1IZAcGH8woM+v91s5+LExQd17nub3kvCSFRtuy1/G/9v/P9xfe56eoNeoiIaZtwZGNjYVZmMUCtwGRhYWDOstWpFsIzMhD9X5ugpFGmZRpa+5mOVtYpjnGOAohB4n3sfAO1GOwlRfX+zCW6z7U+KJG1GW1Wm4VzZgHNRtth6KozLGbZsYuaP0xskEh+/SmysR0BQZQfsKpdvpb7F/e79T6mvUkkGg0Ge9J6k1+8lLRur9DgYoiJKj9nDams13Wb3IRMaAbqiXbTH2wEYM8dwReOZmDY2EStCe3u7tlDVaDSap4Gb+m7hNb97Ex+/99NsmdheJTQC5IOi2LVlYhtbJ3awZWI7g3OILPUIlGSkMG1Z//v+23BniGUPjz1Kb3p6jkIB4+4E2yZ3cO/Ig9w/8hD3DT/IA6OPsC+zn3wwvQAr7+e5qf+WBfXp6cINXH6995p519EV7TYdImaEqBnBMSOlyj7I+Xm+v70o2qX96bFe3IySDwpzCo2VVzCEgWVYGBg4hoNjOnTFOjENk2a7iYgZadhhZMKdYn+mnzsO3DXrMecteTExa2ELgyzD4sSO4zhn8QtYGu/Blz6e9Kv+BUhYcdY0reK49mNodpo4u+eFfHTjP4bP7JnCDVz+8qY3zSk0VqKUYldqL73p/Vz2wBdq3vcajUaj0ZWNGs1hT6AyjMj/Ii1vQ+IBLoqAounEBJ7qZ1JeTUwcT5f5bhyx4tnuskaj0Wiep7wr+S7uKNxxUOITwGprNS+MvPAp9eFV8VeRURn+O/PfDR0vhOCdiXfy0uhLq7b/Mf9Hvpj6IkopPLxQ2IqJGAbGdFZiyRp1NtvUshgpkdjYYX7eTAICPOVhCxsLC1vYeMoL287KbLFydA4c4fD3TX8f/nxD/oYwX0ggWGQuYkpNhe02QvkeDQyWGMU8yzajjalgKuybq1xiYmETUl1mVzG78ikwU3xtM6ozhsrVpGWRscxcAqtAhGJjVESxSl+fn/w8V7RdwSpr1YL6OCknuSF3A9fnr2fAHwjthiMiwpmRM9kc28xGZ+OC2ny2ObPjTO4u3M1wMMyINUJb0EZMzv36m8Kk2W4m4kRobW19Zjqq0Wg0zyOu3nsdVzzydbZPPRl+9tfDkx6KYgajVBJXuiTt5IKs8McK4ywqVcSl3BRPTGytsuS8Zt+N4ffj7gR70724Qe3Yw5c+g9khDmSHaHFaWNO8EktYXNd7E69Z+cpZre2fKSbdKaa81PwHzsP2iZ0A2Mb0OK490kbfAmxKywghcKVLs92EJcyqsaFUEl8FKKUQpWMtYdV9jkP5YYZzI7Nep8lp4iMbP8BnHrycQDY2rlfAcGGUNqeFY9uOoifWza7UXlJeGoUqLsAzLOxS1edZi8/glStewUkdG5+V1/ozD15Ob7pvweftTu8jbsW5bfBOzlv64kPfMY1Go3kOo8VGjeYwxlfj9AeXUFA78dQwPkMoKifmTCw6sEU3OfUI+4MP02NcQsw45lnrs0aj0Wiev7wk+hJWWCvY7e8+qPPfknjLIenHGxJvYJm5jJ9nf84ef8+sx6231/PG+Bs5NXJq1fYDwQG+kvpKOFmXlukqgSoiIkREBE95FFQBAyOsnBOIUJyDongVIYKLiyh9zYarXGxhI4QgruJkRTYUBufLxIyKKB9t+Sgb7A3htpn3vshcxIHgAONqvOFszPJ9txlt9Fg9AHQanewP9oei8kJzNtdYxWyeJ70nF3TeTJqMaou1l0dezi35W8KfhRAYymhYaIRpoTiv8piY9Jg9GMIgr/L8b/Z/+VDzhxru388yP+OnmZ8yFAxxQB4go6YrGgSCHf4Ors9dz0ZnI59o+QTdZne4f1JOcr97P1NyCoGgxWjhVOfUmurbZ4OmpiaOiB2Bk3MYDAYZs8YwlUkySBKX8SrrXYnEsAw6nU4s0yIWixGNLsx6VaPRaDRzc//IQ3xry3fZObV7TqERihmNZbJBrvivnyNhNZ7Dmw/yDOdHCVSAKQweGn2Uje3HIYQg42W4Z7joBjCUH2Fvat+8lYHlTMEnxrdxZOsRDGQH2TqxnaPaNsxz5tOLK12CQ+DEkAuKi5iSVpKoFSXv54tWsqElf32r0dkwKNqwjruTQFG0LUgXT9YKugKBYzpEDKeqctCXAdsm5x6Hndx5Ah/b+E9c8cjXKZSqTwMVECiJKUzMivYU0J8doNVpwTZs8kGBbJAnYSdwjAiecjEwaHKaaHGaabGb8KXP8e3HPCtCY9pLc9Xeaw7qXKUUB3JDXNt7oxYbNRqNZgZabNRoDlOkytMffIq0vB2PQeqHYAX4DOGrIUxaibCGAfkZlokrcMTyZ7rLGo1Go3meYwmLz7V+jotHL64SVhrhJOck3pN4zyHry1nRszgrehZPeE9wfe569gf7yaoscRFnlbWKTdFNrLfX1z33utx1VdV/s1Vq2sLGFkVLUYlkSk6hUCRIFPN4EOGEEKooYM0lGHp4YU5iWXAsiAIFVcAWNqNylCazWmATQrDR3sjbE29njbWGKTlFRmWIEKmyZoWijeVR9lE85j3GpJycV3gr02K0cLxzfCgimcKk0+jkgCzaTpWrOxvllbFXMigHn7LYeLJzctXPb0u8jc+nPs+knAy3LVQIrbyXgirQZU5nGd1ZuJMJOUGr0TpvO99JfYcfZ37MXn8vPrVZWArFmBxjTI6xL9hHn9/H19u/zpSa4re533Jn4U58VX1eREQ4O3o2r4y9kjXWmpo2nymEEPT09BDsD1gcLGZEjjAUDDEpJplUk5iYODi0m+10m91EjKItsG3bLF68+Fnrt0aj0fy58vMnr2QoN3xQmdVSBXjSQyo5j42lwpUebuAiUVU5j/+59YfcNXQfm5afxzGtRyKVZNydbEhorCQfFNg+uZOjWzcwVhif/4SnmYQVxzwE6VMJuyjkmobJi3vO4rremxjKD2MKg3xQqHHHKGc61hszGsIgYcXJeFkERSFzpl1uJQpFISjgBgViVgzHcMJ9O6d2zdv307tP4RMnfpCvPPptbhm4vcryNmpG6I51srZ5DVEjgic9fOmzfXInk+5UzWsvCRgvTJDxMwSxgIdGH+Vrj/07Hzz+H+btx6Hmt/tuYCQ/Ov+BJSQSN3AJSpWjGT/L9b03sWnZebxs2Uvnb0Cj0WieJ2ixUaM5TJmQV5OSv8OnsQFQwAR5tYUoRzESfJcl1mVPcw81Go1Go6nlJdGXcHnr5Xxk4iOkVWO5dMfZx/HLzl8+LTluR9tHc7R9dMPHe8rjd/nfVW0zGphoKmcQCgSBCHCEU7VfqLmrGssopcJF7UIIokSJEGGZuYyAgOXWcjzlkTASHGsfy6boJlqNVm7K38QVqSvo86ftoHb5uzAw6Da6iRvFia64iHO8fTxbva2MqbHipMksU4ECQVIkOcU5pUZQXGYuI63SFFSh5l7n4kXRF3Fe9DxG5Si/zP7yoLMbe8weTrJPqtpmGiZ/EfsLfpz5MQA+fmjbVc5qnIuZlaemMIsTt6VNvvK5MXcjFyUumrOdX2V/xQ/SP2BfsK8hQTersvyp8CdePfzqYsbiLOJtQRX4Xe53/C73O96ceDOvT7x+3rafLhzHYdmyZfT397PIX8QicxFKKQICDIyaCetIJEJPTw+meejyIzUajUYDu1N7eGJ865yWmJXMHItIpTCEwpUuUbN+5blCkvYyYZXfzCxgW1j0Zfr57tYfETEdMn6O3vT+BQmNZbJ+juH8aM2Cm2eDFqeFxfFFYQXhwSCE4JjWo8KfNy97Gd9+4ruM5seK1YxCVFWjKhSBChAKhDBK1YPTr1nMjHJEyzoeH9/KpDfZcOWlovhsMcExi+M2x7TnPMcNXL75xH9yy8DtKKU4pvVIRgtj5INSlIAwsQ2HicIkQ/lhklaCnVO75u2TG3j0pvvIeBkUigtXvYo1zasauo9DxQ37fz9vFTAUxfhcUKitGlUB/dkDfPrBy/nNvuv4y1Wv5KVLznmaeqvRaDTPHbTYqNEchiilGJbfbFhoLCPJkVfbEVh4agBb9DxNPdRoNBqNZnZel3gdi8xFfGbyM2zxt8yazddsNPPq2Kv5QssXMI3DQ4QoW1dW0oiYVikq1cswLAuH5TzARtop02V2sdRaii1svt3+7ap9v8z+kp9lfkZBFWrOs4XNQDDAkByiRbSw1lqLJSyiIsoJzglMqSke9x5nUk5WVQCWrV/jRpweo6eu+GUKkyOsI0A0JsYCnB09m39s+keEEHSanZzmnMZdhbsaOncmm2Kb6tpufar5U9ySv4XeoDd835X7N1+WqFXxp5EtbKJEGZEjVdalu/y5qwBc5fKd1HcaFhrLpEjxuPc4AQHLzGXzHv+TzE/w8Hhz4s0NX+NQ4zgOK1asIJ1OMzk5SaFQqHqGALFYjJaWFhKJxLOevaXRaDR/jlzf+3smvak5q9sqsQ0LVxY/H8uLbJSSFIL6YqNCkfIyVYuDKrMHbcMiaU9/TqbcNI+OPY5C1YiSjTKUHyZpJw/q3EOJEIK3rHs9H7/vMwclnAK0Oa28dvVfAMWFaT/bdSUZLxu2Z2CAoGbxlQKUkhA+R4ElTDa2H0PMioJQ+NJHzFmNWks2yGEKk5ZIM1EzMutxeT/PJ+//N7ZMbAu3mYZJd6yr5th96f30ZQdwpUvMjNKIFSzAWGECI7WXa3pv4B+OefeC7uOpMpSfX5z3pU/Gz846npNIpFLsSe3jK49+m51Tu3nnhrfp8Y5Go3lec+iXj2s0mqdMWt5CXm0/qHMlaXzGmZTXHeJeaTQajUbTOC+KvogbF93I1Z1X89r4a1llrWKRuYgl5hKOt4/nMy2f4dGeR/li2xcPG6ERYEROTz5IJRmVo4z4IxRkgYzMkJf5utV4lZUCClWzWjohEkRFdF7hcmZFWKvRyipzVc01AP4j9R/8MP1DsirLlJpiXI4zqSbJqWIGU5fZFZ4zqSZ5wn+iyh62WTRznH0cbUYb7UZ7+L8Oo4OkkcTAoMuonVQq4xgO/9n+n7wt+baqvMGZrLHW8P6m9/PBpg9iiekJyouTF9NitMz5POqxwd7ABbEL6u5Lmkmu7LqSZeayUEAN7WznmPyysMJJ14iIECeOEIIC1SLufPbAtxVuY7u/fUFCY0EVcJWLRNIb9DZ83s8zPz9osfZQYRgGzc3NLF++nGXLlrFo0SK6urpYtGgRK1asYOnSpSSTST3xptFoNE8T+zL7KQT1F3XVwxJW1VjDEEZx3FLKDpxJxs/UjHsqrTi7op1V7Tmmg698Mn6mbnuN4AYuXtCYePp08/q1F9IaaT3o80/tOpG1zasB+M+t/x93Hriblcnl4TMTQmAKM3xd6lWeAiTtBCd1bCRpJ5FKkvcLBy3mFmSB7mgXCWv2HOjLH/laldA4G1JJRgqjZP0shcCtsllthJH8GL/acw0Zb2HxC0+V+Zw1AhWQ8TNzj+eUwqr4G+bqvdfxox0/PVRd1Gg0muckz6nKxpGREX76059y++23s3v3brLZLMlkkvXr13Puuedy0UUXEY/PHmotpeSqq67iV7/6Fdu2bSObzdLV1cVJJ53EG97wBk499dRn8G40mtkZUz+HBWYMVeKrA2TVg4euQxqNRqPRHCQnRE7g25Fvz3/gYYKvfFzlMhgM0h/0k1VZfHwkMqyMM5SBoxxixDCFiUtRKCpbdQpE+G+ZDrODA8EB4iKOgUFBFWomMIzSV/n7bqOb5ebyUKipFOauzF7JL7K/YEgOMRqM1lTtJUSCReYimoymsFIzr/Js97dzlHVUOMmVFEnajXbGZW02UpNoCu1X67EpuokV1gpWWCt4bey13Ofexz3uPaRVGgODVqOVsyNns8HeUPf8xeZiPtXyKT41+amqnMW5WGuv5ZKWS+YUbVdYK/i/jv/jrKGzGFJDBAQVE6m1GBiYmERFFBu7atJ05mTUzIrVmfw4/WOyKtvQvUDRzaKyKjUjMw3nQgJclb2KMyJnNHy9p5NoNEo0Wt+CT6PRaDRPD/kgX6qAawwhBBHDIRcUnRYMIQhUcZyhqK5JC1SAL6vHF7ZhVwll3dHOmmsYwkCqYsZjpTDZKO2RNvamezlj0bM/R5e0k7xzw1u54pGvL1g6bYu08r6j3wnAQHaQa3tvBCBmxUjaCTIV1rRCCEzMumMVgWBN0ypaI8Vx4FhhHCEEMStG1s8taIFTGVMY5Pwc3936IyzDpCPSzosWn0lrpJVHRh/j3uEHGmonHxTIB4XwPvJBAcd0Gna9ANif6Wdvupej245c8H0cLO2RVhzTxp1F1M762XmfqiEM4lb1uPCXu3/NGd2nsqG1fi68RqPR/LnznBEbb7rpJj760Y+SSqWqto+Pj3PPPfdwzz338KMf/YhvfetbHHXUUTXnp1Ip3vve93LPPfdUbe/v76e/v59rrrmGt7/97Xz0ox99Wu9Do5kPpdRTFgoDUvhq6BD1SKPRaDSa5w8pleJh92EmVbW1aGXun0SSL32ZygwnVMr7ANIqTYQIDg7NZjOLzcUMBUMoFFFRzGH08CioQigUOsIhJmJ0GV10Gp1VVYAAZ0XOAoqC1GcmP0N/0D/rfWRUhl3+LgQCiQz7mFEZxuQYneb05OBaay3b/G2k5PQ4WyBYYi6Ztf0zI2fy7uS05ZUQglMjp3JqZGETg+vsdXyx7Yt8L/097nHvmXWleVREOTd6Lm9Pvp2omF3QGglG+Gn2p9yav5W4EadJNpEmjUf1ZJKgOKlXmdM4UyCG2lyquZ4JFG14Z1Kuci23X1nlVxayy0gkQ3KoYbHxCe8J9vh7WGWtauh4jUaj0fx5ETNjC3aIcAwHT3r4KgAEjmEjlayp/y/MqFIzhCjZZBZZmVge5v9VUrZfz/sFLNvCEIJGrTUBFsW6SpWRhwf/fPw/sCfdyy92XdXwOU1OkktP/Aind58CwLW9N4bjAduwaHdaEQjcwMWVbpXoWEYgiJgOjuGQD6Zt+Ifzo7Q6LXjKQwhRsmVtRHBUCAwCFfDY+BZsw+bXe68J935/+094waLTGcgeaPg+AxXgzqisdWex5J2NSXeS3kzfMyo2Htt2NFsmttOXGajZ50u/oSzMjmhbzVgd4JreG7TYqNFonrc8J8TGe+65hw984AN4nodt21x00UW8+MUvprW1lYGBAa666ir+8Ic/0NfXxzve8Q6uvPJKenqms+qUUnzgAx8IhcazzjqLN77xjXR2drJlyxb+67/+i76+Pn7wgx/Q3t7Ou971rmfrVjUaJCnkPBZd86MIOHwG5xqNRqPRPBfY7+/n++nvM6Em6k7amJg1VXI+PmbpqyxIlgW+nMoRiIDjzOOIEKHFaGFCTgDFySQHZ7pCT8FGeyMRo35+jhCCzbHNSCX5u7G/m1NorEShkEqihAon/4bkUJXYaGCwwdrAHn8Po3IUhWKFuaKuxaklLC6IXcA7Eu+osXw9WBabi/l4y8cZCUa4Pn8997n3kZIpTExajVbOiZ7DSyMvJWbMXVW429/NpyY/xVgwBkCcOP2iHxTY2CgUPn54vEJVCcV5lUcKSUzFwsm+hJi2GBNCcH70/Fmv7ymPlJoWbKWSFChapFa+nwxlEBFFIdql1vqubIPbKDflb+Jvk3+7oHM0Go1G8+fByuRyktbC8g2FEMStOBk/S6ACbMMmaSdRSlaILAqvIgfSEIKElShZfcLy5DK6Y9VVjZ70GM6PkvbTSCXxlEfgBRgIbMMmYkbmtf5cFOsibsUPqiLy6eQbL7icrmgH/7n1/yvZ1hbrQGcKqUIIFsW6+PTJ/8KrVxYt393A5ff9t1W11x3rYsKdImJGiJgRfOUTyKA0XhCYwihlYxbbHs2PsTy+FNMwKQQFliWWUJAufZkBmuwEuSCPJ31mQ5R6rJAESlRlbeYrzr1m3w1sm9zB0vgSeuKL5n0uBqLqfVK+32IeZGMCswJ2Tu3i5Zzb0PGHgk3Lz+PWgdvpzw7WRB8U5Py2xKYwWJFcXnff7YN38c4Nb6PJaTokfdVoNJrnEoe92KiU4rLLLguFxu9973ucfvrp4f7jjz+el7/85XzrW9/i61//OmNjY3zxi1/kS1/6UnjM1Vdfze233w7AhRdeyOc+97lw3wknnMCmTZt405vexM6dO/nmN7/JX/zFX7B48eJn7iY1mgokOQT2U27HYO4JOY1Go9Foni+4yuVB90HG5TgSSVIk2ehsrBHT/mXiX9jmbcMSVlW2YZly9l+lYAUQEIT7KrMBy9acT/pPcpR1FIvMRaHYOJMl1pJZhUaAk+yTWGwu5ofpH/LHwh8XdP+WsEIRVKFIqzRZma2ySDUwWGOtYRWrOMo6it3BbiaCCQxh4ODQbXWzKbqJ82PnN1x1t1A6zU7enHgzb068ecHnHggOcMnEJVV2rB5e1etYrmIsC3/lisKyCAvF94oQghixmszKjfZGllizVzbawi5WvypFjhyuqj9ZVRai8+TD1+WpMBRoNwuNRqN5vvKKZefy233X02w3MeWl5j+hhCEMklaCQpAnbsVYkVhKwo7Tnx1krDBBIGVoq2obNlEziikMmp0mFscW0eI0h20ppejLDtCfHcCXQalKUhQXJSmFEgJXerjSwzYs4la87mdfR6SNFYllAHTFau1Zn03G8uOsb1nLCxadwYMjDzOYO4ArPQQCx7RJWknWtazhzWsv4sLVr6Iz2hGe25ftJ+VWvzatTgtRMxpWLFrCwjJnn6INlCQX5EgaSSzDoj3aXsxLzI9RCAokrEQxy1EWcINCSUAToW2ur6qr9ZrtJg7khhnOj5D1pxc5edIjH+TJ+XkCFbAsMbejg23aNcvzJAqpVEmIbYyDzZ88WDa2H8eqppUcyA3Tnx2s2KNqxNN6tEfaSc6SeelJj/tGHuQlS84+RL3VaDSa5w6Hvdj40EMPsXPnTgDe8IY3VAmNlbz3ve/l+uuvZ/v27dx4441ks9kwv/EHP/gBAMlkko985CM157a2tnLZZZfxpje9iUKhwI9+9CM+/OEPP013pNHMjSCCRQcejVUszEZcnHyIeqTRaDQazXOTgWCA63LXcVP+piqLUChO6pwVOYvNsc0cZR/Fw+7D3OveS0CAg1NjuwnF6reysFj+uUxAgIGBjU1MxFCosGoxp3LsCfawzlrHYnMxg8FgVbtdRhdLjaWz3keH2cHfN/09GZnh19lfz5oJqFBhFV3ZstPGxhTFqsvF5mJsbIbkEBmVIc602NhtdnOKcwo5leMu9y5c5eIYDgEBcRHnhc4LeVH0RU+b0AjFvMyMyhCogCajCVs0vvjqm6lvVgmNOZVjVI5iC7tKcDQxq8TissVs5aRnQRWIEKHD7Aj7IITgtfHXztuPOHFGVW2GZj0q30+VfXBYWDXHbKKmRqPRaP78Wdm0gmPajmKsML4gsRGKn22LE4s5oeN43rbuDfxp6B4m3EnyQZ696f08Pr6FqBktio1GhM5oBzGr2h5TKcWWye0cyA2F1XFSSQJV/Bw0DTusqgPwpE/KS9NkJxAYJVFMEbNiCAR7M70k7SRHtRzxFJ/MoUEpxfe3/5ir911PUMqvPLJ1PRta1pH2M7iBiyhVIb5kydm8ef1FxK3qvOuMV3/cdkTLWp6Y2IY/R0ViJb4KiJgRTuk8kXyQxxAmG1rWsXVyOzk/Rz5wS0JZtWV7NsihlMIQBoYwiBg2Y4UJ0n69fqmSsJlny8Q2FIrlidnHqLbhYBtWTVXlQnIkm+0m2iNtDR9/KBBCcPGGtzCQHcSVHiP5UYCGeh2zohzbXhvfVclCfxc1Go3mz4XDXmy89957w+/PPXf2knohBC984QvZvn07ruuya9cujj32WHp7e3niiScAeMlLXkJra2vd80855RRWr17N7t27uf7667XYqHnWMGnCMrowZTMBUwfVhsCkxdh8iHum0Wg0Gs3hh698puQUPj4JkSBhFFcZ35C7ge+kvxNOdtU775b8LdySv4XNsc30+X2My3GgWKFmY9dUN5Yr4epNoCgUFhZHWEewzFrG/mA/B4IDoeg0LscpqAIrzBXFXL5gCAuLxebiOXMAu81uLmu5jE6zk99kf1NXaAxUQIECnvJq+lbOlYyICKPBKBudjSw2F7M5tpkzI2cCxazIqzJXcW3u2qpzrdJXXuX5de7X/Cb/G14aeSnva3rfgoTA+XjcfZxr89dyZ+FOfFWcrBJCcLx9PBfELuB05/Q5LVv3+/t5yH2oattwMBzmY/rKD59LOauxUgwMCLBm/Fnk41e9Ln+b+FtOcE6Y8z6UUljCakhorKSywtLAoM1Y2IRbZYWqRqPRaJ5/vGHthTwxvpUhe5gpL93weUIIlsQX8zfr/5pXLD+PzSumrcKHcyO847b3zXm+J30eHH2YCbd63kIIgVAl204lUSVbUMMwkSpAKUXWy5Gw44BCCEE+KJCvyIh8/10f4bSuk3n1ys0c0za3sPN0oZTi8ke+xu2Df6rZJ4SgyU5SaUp13/ADfPSeT/G5Uy8lYU9XvUXM+s4VUTPCka3r2T65EzeYv5quyW7iMyd/nN/suy7sU9SM0BFpZ0fhSXxZb/yhSq8BYcVpQbokrNnGcaLiTHhyajcRI1JjmVt5dNyKMznzPbAA14buWCetTq11/9PNqV0n8b6j38k3n/hPomaEgexgKcd0dhJWnFM6T8Cuk9V4sOxL9zKQPUAhKBCzYqxtWk179JkVXzWaRvhGCmLjz3YvDm9yep3B4S82Hn/88bznPe/hwIEDrF69es5jK322C4XiIOX+++8Pt51xxhlznn/aaaexe/du+vr62LdvHytWrHgKPddoDg4hTJrFubj0HrTYGBEbiBvHH+KeaTQajUbzzOIql3vdexkJRvDwSIokx9nHsdRayg5vB9fkruGPhT9WVXattdfSITq4y70rzOOrR17lcXGRSvLTzE/Z5e+qEuoSIkGadCh+lavQ5iIgYFgOEw2iLDOX0WP2MCJHGA6GyakcQ3KI1dZqzouex1JjKfuCfez2d9dtK2kkOTd6Lq+Lvy6sJrw+f33NPRVUYd6Mv4CArMriKpcxOUaH0cEqaxUnOCeQkzk+Pvlxdng75mwDimPt3+d/z7Ac5lMtn3rKguN+fz9XTF3BLn9X3Ws97D7Mw+7DdJqdvL/p/ZzonFi3nWvz1SJpoAJG5AhQFPASRoKMzISvb/kZll/PykpQKE6QOcIhQjFb6r3J93J+bPasxjIPeQ/RZDRVWbXOR/nYcoVlRESq8jQb4Sjr2ZmE1Wg0Gs3hwQkdx/N3R1/MNx7/D7ZObCfjz5/9K4A1TSv52w1v5WVLX8Idg3dx9/B9TJXsPss2qUqpqiq5Mp70eXx8S43IVGxbIISBUrJ0rkCiQEma7CakUmT8DJ70sGdkM5YzDwMZ8KcD93DX0L2844g385pVr1z4g2mQSXeS3/Xdws39tzGaH8NXPkk7Qc7PM5wbCbMNG2F3ai+ffehLfPaUS8Ln1hntQAhRkw0IEDdjHNt2FEO5EYbzI6U8yGpsw6Y71slXzvg3NrSuJxfk+c3e63Cly1hhnLHCGHEzhjKLeYmudMNRiCzZqVqlRVu+8jEQWLPYls5c3OVKj93pvUTNCM2zZBB2R7tIe5lwgZ+Auu+ZekTNCN3RLs7oPqWh4w815y97KS1OM/+x9QcsinUxkh/jsfEtVYsVi9WgDl3RTtY1r8Yy5p9Kb7bnzmv0pMftg3/imt4b2TZRPQY3DZPTu05h8/KXsbHjuIO7MY1Go3mWOOzFxjPOOGNekbDM3XffHX6/dGmxzL9swQqwatWqOc9fvnw63HfHjh1abNQ8azQbm5mQv0GqRXgcWNC5BkkWGf94SPrhql58NQ5ITNGEw2rEHJUFGo1Go9EcCgaDQa7NXVvX/tTDY1JOFivARFvNZMZDhYfY6m/FxGS5tbwqc08iGZNjDAVDpNX0yn+pJONqHBubpJEMxaikSJIjR0EV5hUay0KVh8euYBcuLkvMJSwyFrHIWARAq9HKjzt/XHXeDm8Hfyj8oUpQPd4+nnOi54Q2rFDKQwr6MIVZrLrEa0horMTH50n/SdqcNnrMHgC+MPWFhoTGSh5xH+EbqW/wT83/tKDzKtnp7eSSyUtIy/krMEaCES6bvIx/avonzo7W5t/8qVBdcZBTuSqrVAuLpJEkIzNhFWHZtrQs9JXFRgODhEhgY3Nu7Fzemnhrw+LfNblr6DQ6iYs4GZVp6BwDo6pidrG5uKbKci6iIspLoy9t+HiNRqPR/HmyafnLiFsxvvrod9g2uYOxwvisy15MYXJc+1G87+h3kfEzvOOP72MsX1uu0p8dZDg/Qne0k0Wx7iohaufUk6S89BzXMErjqelxmlSStJtGieL3hcCtERtXN60kZsbCn5VSfG/bjzGFyatWbmr0cTRE3s/zH1t/wK0Dd9Tk9A26aR4efxylFHErxsrkciJmhJH8KFk/h1QBhjCJWVG6op04xvQCrEfHHueB0Yc5ufMEANoirZzSeSL3Dj9Qtx+WsFgSX8yS+GIm3Emyfo5ABZil9ludFja2H0er08IPtv+E3/XdQl+mn7SfJuVlKtoxiZgRYlYsXPSU8tLIUlbjtBgoyAZ5ElatM4IpTExhVoltbuDSnx2YVWxcHO/iQG6ItJ9GKoVt2A1VNlqGxREta3lRz5k0V2SAPtOc3n0Kp3WdzD3D93Nt741k/SzD+REURaG3M9JOd6yL6CwVqjNxTIdTOusvkAPYn+njsge+wGC2/lxfIAPuPHA3dx64mxM7j+djG/+JmBWre6xGo9Ecbhz2YmOj3HrrrWzZsgWAI444gsWLFwMwODidibNkydzBxj09PeH3ledpNM80jlhC0ngBqUACAo/G3o8mTSTEGTQb86++nw2p8qTULUzKa3FVdbWFJTppFi+n2Xg5ltC2DhqNRqM59NyWv42vpr5aY2EKUKDAFm9LWMnYIlpYZ63DrFidPSgHUSh8fHb7u/FMjyXmErIqy3Z/e918u3Jlm4vLlJwiIiLERPGP+piI4Su/SryqZLbJlP3BfhycKqFqUk0ilayarFtvr2e9vX7e5+LhhZNFXWYXvX7vgoTGMnmVZ0yOsdHeyOPu49zv3j//SXX4Q/4PXBS/iGXWsgWfWxYPGxEaywQq4Cupr9BhdHCMc0zVvsqsRqCuMGxi0mw04+NTUEXLWVH6MjBoNppZai4lIRJYwqJJNPGy6MsaFhpHghHude9FIFhtrWabv63ue3gm5esrFCYmS4y5/16ZydnRs0PrYI1Go9E8vzmn5yxO6TyRm/pv4crdv+WRsccYyY/iloS0uBXjxM7jedeGt3Ny5wl8/uGvsH1y56ztdUU76csO0JvpZ9JNsb55DaZhMuWmSHkZCnKuzGBBk52gIAtVeX4F6WIaJgKBr4JQVBNCsKZpJR2R9rqt/de2H3Js+1Gsblp1MI+mhpSb4hP3/yu7pvbU3T+cHwkrEVNeivtHHsIxnNqqtgL0ZwZojbSyJL44FPCu2XdDKDYCbF5+/qxiYyWtTktdS9EVyaW8985/xi1VPi5LLOHhsceqjvFVgO9nsQyLhBUvLqoq3YNSKrRRFcLAk17NmLRMxHTIVlTHKhRTXpqcn6sresXMGK2RYp8zfoaIOX/2dMR0OKJ5HVEzygXLXz7v8fOxZXwbdxy4mwl3EoWixWnmtK6TOKGjMccvIQSnd5/C6d2n8FerX8OH7/lkcYx2EIvtX7T4TJpmEWb3Z/r48D2XknIb81p8cOQRPn7fZ/i3Uz5JdEZeqkaj0RyO/FmIjWNjY1x66aXhzxdffHH4/eTk9ORDIjH3H+Lx+PSqnlRKm+xqnh3Kg8Fu4wN4ahChBIZqxudAyVa1du2gQQyLbhyxiiXWv2KIgxuEZOVDDMovIFX9yT9fjTCm/ptx+XM6zL+h1fiLg7qORqPRaDT1uDV/K19KfamuzVRAwDZvW5VYOKkm2eZv40jrSAxh4CqXCTlRdd7+YD8SWZWfOJOy6FSucCuoAhJJQiRwlUtAEApCczFTeNwX7KOgCoyr8VCs/Puxv+fM6Jlsim5akF2mI5xwpXm30c1OZp8cnK+PEklapWvsRxfKtflreVfyXQs+78rclTWvUyP4yudHmR/xBecLcx4312p6CwtLWCCmszcFgh6zh+Xm8qpjKytL52OHvyMUg5eYS8ipHL1Bb11xeyYmRbE8JmILsqZtMVq4KH5Rw8drNBqN5s+fhJ3g1Ssv4C9WbGZ/po+Ul8aXATErwrrmtQghKAQFPnbvZeyYfHLOtmzDoie2iP7sIFNeih1TuziiZS1D+eFQvJrrXMuwsAwTT/oUpIsXFLOlpZJVC8UWx7rnrRxTSnHNvhv5+2MWNu54bOwJ/jR0L09O7WLrxA7SfoZWp4Xdqb3k/Bxd0c6aij2lFMP5UaBod5n1syiKlqJJu7goqep4YLwwwYQ7GQqm9408yFBumO5Y0WHj5M4TWNm0gr2pfQvqPxRtPH+774aqbU12EtuwQiG5El/6pL0MTXaC8vyRpCg0loVeAFe6RM3auSPHsMmLfMmCdXq8NJQfYWVyec3xUKxIfcLfRovdRGesk6HcMBm/Nmc8bsXojnbSEW3HFCZvWncRG1rnX3RXD6UUN/bdzDX7bmB3am/N/qv3XsfSxBI2L38Zm5efP6/96d7UPq7pvZF7hu9n2+ROsn4O27Boj7TRHe1qSESForBcDzdwufT+zzUsNJbZMfkkX3v83/nIxg8s6DyNRqN5NnjOi42ZTIa/+7u/Y2BgACjmLv7FX0wLIK47/Qd+NDq3AFO5v/I8jeapoJSPpIBBfFbf+oLaw6S8hoy6k0AVBx6GSBLnRCQeCLBoRao8PmMoPCjGrGPSgimasUQnPealRMSqg+pnRt7NYPB51CyVG1X3hM9I8F9IlaHdfONBXU+j0Wg0mkp6/V6+mvpqXaERYDgYJq/yNdvTKs2+YB+rrFWMyJEaQVCh2O5tJ2kkZ722IQwMVbSzlEhMTDzlkScfZjbOh0CE9pcKRU7lcJVLVmVD0crAoDfopTfTyy+zv+Q05zTelXwXXWbXXE2HrLJW8aT3ZEPWVLPhCIc20cZV2au4s3DnQbcDcHP+Zt6WeBsR0ZitFBQzJm/O3wwUMzkzqpjxYwiDCJF5q/Se8J5gj7+HVdaqcFuz0cxoMBr+3KhIWPkcbapFPiEEHUZHQ+0AZFX1hNoaaw2mMBkIBsJKypnvTROTiIgQEREWmYsYCobmtestkzASfLLlkywyFzXcR41Go9E8fxBCsDxZ333gp0/+cl6hscyyxBJc6TGSH2XKS9GXGWC8VD02G5YwSVhxlFLFCjqK4mIggtDGPGoWc5FXNa2gJ9bYZ9ktA7fzN0e8icQ8GYpSSa7ffxPX9v6OR0YfY3+2n4w3/TntSpdCUMAxHEbyY8StGD3xxXRGi1WVnvRwpYcnvRrBLOfnaJolj08pxa6pPRgtJm0lQbMsNgoh+OSJH+af7/44E4XJuufXwzIsCkGhSpwFGCuMYxsOcUuQ83M1r0egArJ+LsyKFAgMw6ga+7jSqys2giBhJUh7mapM65xfOw4vEzEczl58JgLBlJeiK9pBxsuQC/KlcZ5JzIxW5V9etOYvecPa1zb8LCpxA5crHvk6dw3dO+dxfZl+/mvrD7l76H7+5YR/qvve6U3v51tPfJfHx7eE21Yml7N1YjuFwGV/up/dqb1EzSid0XYiZoQ2p5V4nSrPi9b8JUe0rKvbl1sH7mAoN7zAOy1yx4G76M8MsCTRM//BGo1G8yzynBYbU6kU73rXu3jooYcAWLx4MV/+8pcxjOkyd9Oc/kCeL6C4cnKrsg3N8wulFFl1Hyl1M74aRuFhkCQmji3Zh9a39ajEVyNMyuuYUjcRqLHSVkFUHEmLsYmkOAshbFzVz1DwNfLqiZo2pEqR5jYURaHRFovJsx2H6sFgRKylxbiApDgbYwGTfZUU1F4GgysaEhorGZP/gy2W0mTU5idpNBqNRrMQfpP7zZzC3pAcmnXfiBxhmVpGQRVq9hVUAR8fT3lzVo1FiZImXTVZUyluzifwCQSOcIpVgzId5vC5yg3FryYxPTklleSuwl1s97fz6ZZPs9JaOWf7AJuim/im903G5BgxYgSlr0YxMTnGOgZb2Fybv7ZhIXU2MjLDYDDYUN/L3JK7hX6/nyE5xIScqJkcS4gE3UY37UZ7zcRamWtz1/LepveGP5/hnME1uWvCn6MiSpNoIqUaWzkuELSarVXbNtobF1R5GqF6DCYQrDJX0WV0cSA4wKgcxcML/94whEFMxOgyuugyu3BwaDfaWWotZSwYq3eJkDXWGj7Y/EGWW/WrCzQajUajmQ03cLmx7w9A0XZzJD/CSH4MTxYXxZjCpNluYlGsOxRT1jStJGI6DGQPcCA/hCrZcZZRSoWf57Zh4ZgOOT+PK6sX2vjSD8UrhSpWP4rGpyULQYG7hu7j3KXnzHpM3s/zhUe+yn3DDzKUH2ZvqrdGFi0EBQIlyQX50OJ1V2oP+SDPssQSAhWgUFVWomUCJQmUjzmzulEpfOXjSpcHRx6mM9rBlx/9JpuWv4xNy17GonixevPy0z7Npfd/joHs/FE53bEuNrSs54+DtYvDyhWNjmFj21ZJIHXxK7IWPenTbCcJlCQfFGrGfWqOylRTmCTtBBk/g2VYKCQFWSAfFLANq2aMtrZ5NZee9FF60/v52a7/4+6h+0vXb8KZURF4XPsxvGblBZzWffK8z6AeSikuf+Rr3D10X8PnPDL2GP/60Bf59Mn/gl2Rr7l1YjufeuDzZLzqrO24GaM71sXOqd1hnmc+KDDlTZG0kvSJAZrsJItiXbQ5rQghePXKC3jL+jfM2odrem+Ydd98KKW4tvd3/O2Rbz3oNjQajeaZ4DkrNg4NDfGud70rzGns7Ozk+9//Pl1d1SvDK61R8/k8jjP7SudCYXqCaq7jNH+eKKWYVFczIX+Dr2qDmnPqEcbkz0kaZ9BuvAVHLK05Rqo8Q/IbpOXtwMyBmyKvtpAPtjAivkeTOI8pdSNynokwAQRMoPBYZH60ZH+WwSCKLXqIiDUHf9MlJuSVKGonaBthXP5Mi40ajUajeUpkZZZb8rfMuv9AcIBxOY5UMpykMoWJIxwMihWJw3I4FPgqKdtYusqdW2wUUbIqWzUpVm6vvAq/PDlWD4vixEtKpqr6UZn12G1215w3Foxx6eSlfLH1i/OKWy+OvpgfZH6A67sIIUiSJKMys+ZJViIQrLXWhhWeo8Eo0YO0Xa9kZkXfnMfKLF9Of5lt/rZZj8moDLuD3fTJPo4wjyBuxGuO2eZVn39B7IIqsRGKzzrlNyY2NhvNRGcs5toc29zQuWVmqzBMiARrrDWsYAUpmcLHD4XpJtFUNVnbYXTwX+3/xRZvC9fmruVu926C0qShLWzOipzF5thmjrSPXFDfNBqNRqMp88fBPzFZmGRfZj8j+bEaK1SfgOFglOH8KEk7wcrkchJWnKXxHhbHutmb7mWsMI4vA5RS4eeUIcpZgD75oBDm3RUzAkWVIKlQ5IMCgQywjPoLi2Zj3J2o2eZJjz8O3sn1vb/nhv2/Zzg/iq98sn6OiOGUrlH8vA2UT1Bxz77yyfhZElac/uwgpjBoj7ThBe6sY75C4BK3Sm4WShUrJWUhtB0FmHSn6MsO8H+7f8OVe67m5M4TeNv6N7KqaSVfP/ML3DJwO9f03sCeOraqK5LL2bz8ZZy16Azec8c/zfIkpq8lhMAxHRzTqXrOgQwwDJOsl8NTXpjZaAgBiHnCAYoLo5rsJgSQ8tJkvByPBI8jhKDNaaE72sXp3adw/rKXIJXiXx+8IswAjVpRMn6W/twgS+KLObrtKDa2H8O5S86ZteK2UX677/oFCY1lHht7gl/suoo3rSta0Pdl+vn0g5fXCI1SSXal9jBWmCBmRhBQEuNBKsWUl8IxHbJ+lqH8MMsSS/nCqZ/iVSs3zXrtJ6d28+TU7gX3uZLf99/K24/463ntYDUajebZ5Dn5X6itW7fy7ne/m8HB4kqgxYsX8/3vf5+1a9fWHFuZ05jL5Whubp613Wx2erKkpaU2kFnz54tSPgfkV0jL2+Y5MiAt7yCrHqbHvISYOHp6j0rTH3ycgto17/U8NUS//AQW3dgsAQKKg0UTMUsAtVQZhuU3WG5+CVscOuuEQKVK4ujB4apesvIR4kZjwdsajUaj0czkjsIdVVWEKZXCUx5TaorxYJyUStVULXrKI6/yGBg4wmF/sJ8YMTxVXH0sEEglQ+FvPkFOCEFMxchSLZ5JJAbFz2YTs247BsUqtXLW40yUUkSNKK2ite61R4NRfpL5CR9o/sCcfYyICK+Lv47PuJ8J+5wggY+Pixve+8y+RUSEpEhWVcLNlz/ZKDFRayFVj7zK84nJT7DPbyyryFUuW/2tHGkdWSM4zhQ4l1vLOd45nkfcR8JtbaKNiIjUrXadSbdRLQL3mD2c7pzeUD/LrLfXs8paxR5/T939FhZtRtucbZwTPQdHOGx0NrLR2Vhy28giEHVFV41Go9FoKhkvTDBemCBQAUk7weLYohqHr5v7b2PL5PYqW9HZSHsZtkxsZ13zalqdFkxh0h3tKgqKRp6CLFCQBcpCnlQyFB8VRSFSlMJf6qFQ7M/002w3zepmMJNATlfuKaX4392/4td7r2XKnWIgO8hgbgiFYsqdQlEUiQxhEDOj2IaNJ2sdIXxVzJSMmhH2Z/ppdVrrZiGGx5eqIZVSZINcWPlWiStdIqUMSqUU9w0/yOPjW/mXE/6JEzqO5xXLz+MVy89j68R29qT2kQtyxMwYK5LLOLqtuKjotoE7SHvpun2YWVlZRghwA4+CdJFKkpP56TFxSWSVqigAW3M8czcokAvyxKwojhGhyW6ixWmiI9JOoCSmYWKZFiOFUb637cekZwh2BoKuaAddFC3pd04+SbOdpDPauEV9PZRSXNN740Gff/3+m3j9mguxDIsf7fhZTX6iUoonU7sZL1ndGsIkbsWRKiDjZ4u2wCVROWI4OIbNaH6Mj917GVsndvCaVRewvqV2brov03/QfS6T9tJMuSnao3OPJzUajebZ5DknNt5666184AMfCIXBNWvW8N3vfpelS2urzICq7QMDAyxaNLsXfDn3EZjzOM2fH0Pymw0IjdNIlWYg+DRLzS8QEStRKmAw+GxDQiOAp/qRFCiwE5e9iIpfRUPFsejGogMxY/AnVYox+T8sMv+54b7OR0r9HsVTyyidUtcTR4uNGo1Gozk4huQQHh7DwTDDcpiCKpBX+VCAlBQrGssVhgoV5itC0So1rdJhlWPlceXzGiFuxImoSJX9ZqUoV6+60cAgThxHOEzK+hk8AsEKc8Wclv5/LPyRi+XFNBn1c4DKvC7+On6b+y3X5a4rti0EdulLIvGZtigzMLCEhY3NUfZRVZN9HUYHOXKzZmQ2gi1suozG8ia/lvoaO7wdDb8WUBSItwfbOU4cVzUJWS8j8u+b/p4Pjn+QKTkFFFfjrzfXs8XfMqfVbKfRWSUCJo0kl7RcgjHL4q+52BTbxHdS31nweWUuiF1Q9bMQgoSYO5dKo9FoNM9vAhlw9/B9XLPvRh4Ze6xq39LEEjYtP4/zlryYhJ3Akx439t3ckNBYRirJzqndHNm6nqSVQAAHckNEzSgRM0JBFucSFAqpaj9vy6Jj5ee4KP3PMYp2q7tT+1jXvLqh/pQz/6SSfOnRb3LbwB3F6yjFUH4EAE+6VUuqpJJk/CwxM4qqsygMwJUFIoYDQjCQHcQQJtS5n/I9AeRmERqn77KanJ/jsw99ic+dcinrWooOVUe2HsGRrUfUbaF8P/VodpogM3OrIuNX90kqSdJOELh+OG5WqGJVakmEnDnmyQcF8kEeS5g4xrTr2/LEsqqcwlyQ54b9NyOAI1vWE6uTYVjJAyMP8/H7PsO/nfJJotbBuWs8PPboUxLuJgqT3Hngbo5tO5q7h2urIw/khkKhsYxUAWk/W3pWJoYoju1jVpTy6zycH+V/d/+KO4bu4r1H/S3nL3tpVRuF4KnNuZXJB7PnZmo0Gs3hwHMqmPCqq67ive99byg0nnTSSfz0pz+dVWgEWL9+ffj9vn1zr6Tu7e0Nv1+3rn6gr+bPj4y8l5T8/YLPkyrDcPCNYhvqT+TUY/OcUTqPAi57kUyhKCDJzLBsy+KyhxwP49Wxc03LOwhU44Hi8+GqxioMnu42NBqNRvP8Zau3lYfdh9kf7KegCqHYWElZSPPw8JkxYVISH8uWlOUsw/L28vGNCGvH28ezxlqDiVk3p9HCCkVHE5MECWJGDFfVt9syMFhlrZq3qs1VLjflb5q3fwAfbPogS8wlNf0zRLHKMyIiOMLBEhZxEedo5+gay9TTIqdxon1iQ9ebjbMiZ5Ew5hfDBoIB7igUJwPrCYVz4SqXEVk92VbPsrTH7OHTLZ+m1WgNt8WNOEdaR+JQPx6hw+hgtTU9udlhdvC51s8ddBbiS6Mvpd2cP9u7Hic4J7DGeurW+BqNRqN5/rBzchfvvP39fO6hL9cIjVCspvru1h/xtlv/jut7b+L3fbcymp87F7geUkn2pntRSrErtTfM4DOEgVPKvyta3denLDiWRS0hjOK5pXbGC+OhaDkXQghO7Cgucv73Ld8PhUaACXcyFHRmE3ZyQb6qMrL6HlWYaThSGMOZw3ofRCkjcfbqx4jphBWQleT9PF9//N/naHsadw6BKmHFQ+G1SK3QWMYSJgk7gTFj3CgQpP1MlQDrSpd8kMcUBgk7QVlMS9qJKqFRItk+uRNf+njSZ8vkdsYLE0x5KTJ+ltneDTsmn+RrDd5/PW4bqM2vXHAbg3dy/f6bat4LSikO5IertkkkaS9TYzesUDWv/4HcEH7g843H/4Ob+2+t2hdboLg64U6yZWI79488zH0jD/Hw2OPsmHqSrRPbn9JCQY1Go3m6ec6IjVdeeSUf+9jH8P3ih/WmTZv44Q9/SGtr65znnXDCCeEq8vvum9vT+5577gGgp6eHZcuemoe45rnDpLxm/oNmIa+2kVc7G25D4ZNTD82oJFQoageECh+Xvbiqd8Z2jyn1u4Pu80wktaHnT1cbSim2eFv4U+FP3FG4g0fcR8IsLY1Go9E8P7k9fzvX5a4LxUOJJKfqf66oiq/yz5UEBKEAVxYay8dIJFmyc/6BnhRJEkaCddY6Xhh5ISvNlZiYYaWkgRHaYS42FtMm2ogaxcmD2exVl5vL62Y11uMB94GGjjvWOZazImdxgnMCS82lOKJaTBMI2ow2NtgbONY+lgi1At/m2OYF5xLWa6MRrstdFz73TrOzrog7F8OyeuLn/Oj5dY9ba6/lS21f4tzoueEzSRgJjrGPYZm5LBQ6oyLKSmsla621CASLzEW8Lfk2vtH2DVZZqxbUt0qiIsonmz+54CzMpdZSPtz84YO+rkaj0WiefzwxvpWP3XcZw7nZq9/KFIIC33riv/j64/9x0HlvGS/LQO4AuSBPiz3twhCzYpjCDLMC631BacxWGoKZwiBhJSrGbDR0Hxvbj2VJoocdk09yXW/1nMhYYRwojvcCVb96ESiJcfXHgmXxyFc+CLBneVaGELhziKOVImw9dqf2smV89vzqMgl7bgv17uh01rcrvbpCoyj9v23YJOxk+MzLWZpSSXJ+eYGfohAUcAybZNWxsCS+uKrd0fwYhaCAJz3SfpqR/BiPjj/B1okdPD6+lYdGH6U301dX+L3jwF0MZAfnvf96TLhPfeH9hDvJo+NP1GyfdKdq+pv1ZhdOZ4rJ+aDAlFd0R/nG4//JYHa6eGBN06o5HU7K9GUHuOPAXdwz/AC9mT5GC2OMFcY5kBtiMDfEpx+4nL/94z/w233Xz9uWRqPRPBs8J2xU7733Xj7xiU+EkxRvfvOb+cQnPtHQf6h7eno44YQTePDBB7nhhhv48Ic/TDKZrDnuvvvuY/fuYljvy1/+8kN7A5rDFk8NklWNTezNxljw37NWNUpyJSFOghK49CGptSxRFGDGqnuFj8KnwC4ClcKiC4s2hLDIycdoM173lPpdxqCxrKW525h7EDwpJ7khdwM35G9gKBiq2tdkNHFe9Dw2xTbRYx66LEqNRqPRHP7s8ffw5dSXq6wi62XsGRg1Yl69KsKZVquV203MYs6jyBOr89knECwzpxebRUWUI+0jWWmupEAhXO1uCYsmUcwW8vEZDoYZkSNhjqBAYAmLCBFsYVdVzs1HpX3rfGyKbWJPag9LzaUsMZdQUIVQbHWEgzXHMH+NtYaj7KNQSrHaWs1uf3fD1y1zvHM8R9pHznucVLKqYjNChBajhQk50fC1sipLWqZJGkm6zC5OdU6d9dhus5sPNH+Ai+XF3JS/iUe8R8jIDLawaRftLLYWEyVKnjxxEWeFtYKT7JMa+ruiEdbaa/lc6+f49OSnGZfj8x6/3l7PJ1s+Oa99rkaj0Wg0ZYZyw/zrQ18k7zduqZjy0myZ2E5HZCF5b8XqLVd6KCV5fHwrhjBocZpospOkvHTJ6cGY1Z602Mr0wi/LsGiqk9E4UhhlWWLJnL25YEVxru7aOpl9XkWO4lwUBTaFUedzX5VESgNBgCRuxkmrdI14aQprVrFRIEhacSiNB2fjmt4bOaptw5x9Pbp17nFWe6SNkcIYU25q1ipIs0IwjZoRDATZIIslrND1w5c+EdMhYcVRpXuoZHlyGa1OS9W2vuwAU166quLPlS4RI4IQAl/6DGQPMJg9wOL4IpYnph3plFJc23sjF29465z3V4+ZFYYHQ6ACCl7t3xujhbGa4/xZrHSh/t8io4UxWpxmfOlzbe+NvGPDWwBYkujh+PZjeXj00bptudLjkbHHGCtMzH49pdgyuZ2B3CCD2QPsSe3jfUe/85CNYTUajeZQcNiLjel0mg996EMEQfE/8K997Wu55JJLFtTGW97yFh588EEmJia49NJLueKKKzCM6aLOyclJLr30UgBs2+bNb37zobsBzWFNRt3LbKvaGiWlbsFkWsBWKALG8NQQksrMJ6/KMrV6ABdUrPjzSuLj9KDGow9JGpd9WKodm+pVZU8FR6w8BG2smHXf7fnb+UrqK7NWMKZkiquyV/Gr3K/46/hf84bEG55yfzQajUbz3OBX2V/hKY9mo5mYiJFTubqfF/X+mJ8NicTEDG1UgbAqEYq2nFGiVX+YCwSrzdU0G8017f1t09/yy+wv64qgFhY9Zg89Zg/b/e01Alq70V5TdTgXcwmEMzkveh43529mm7cNgWi4ms4SFu9MvhMoTrh9suWTfGjiQ4wE81cUlFlqLeWjzR9t6NiUSpGS1SJqj9nDpJxc0OuaV3mSJHl17NUN5Sk2GU38Zfwv+Uv+suFrHCrW2ev4Tvt3+H3+91ybv5Y+v6/mmKPto7kgdgEviLxgzslIjUaj0Whm8qu915ByG1+gBDBVOj7jZUMxCIpjgdo8ZUU+KFAIpi3ipZThoqZ96f3FbUohhCBQAeUZjrk+2w0EG9uOoTfbXyMKeoFXErrqc+ai0zi96xTSXprbBmutNBsfUxSXpM2FIQxMYaFQJKwEWT9bJTrNNg4xhEHSimMIk4jpVNmOzuSe4fvn7en6lrWsb1nLjsknZ73e+qY1PDaxhQm3vigWMarHoVErwrLEEkYKYyXhrlh92h5pI+vnquapDCFYmVxBV7Sjqo3h/CjDudGaZ162orUrLGgVMJA9gCd91jRNzz39vv823r7+TZhGteg8Hwn7qWdZJ60Eab8m8LLGFrUQ1I79q6l9t1ZWl97UfytvXvf60C74guXn1xUbXenx0Oij81ZtlvMzJ9wptkxuByBuxUJBU6PRaA4HDvu/bH/yk58wMDAAQFdXFxdddBFbtmyZ97yenp7QYvWCCy7gyiuv5Pbbb+e3v/0tg4ODvPWtb2XRokVs27aN//iP/6CvrzgJ8A//8A8sX35wOS2a5x6BmnrKbUiVxhBJBMU8xoLaXtdWVFIIvwOJKq0BLA9QJC6KLJUiY3Ff0Vk/oICBgUsvE2oE27+UVuMviYmNT2klU5N4KaP8aIa168JoEZvqbv99/vd8LfW1hjzllVL8d+a/yakcf5P8m4Pui0aj0WieG6Rkij8W/hj+3G10s9PfWXeyqJzH2MhEkgo/YYuCY739Lm5oLergsMpaVZX1VyZhJHhN/DWssdbw2anPzrmaeqaoGBdxVpmr5u1vJfPlOs683iUtl3DJxCUNVyZawuKDzR/kWOfYcFun2ckVrVdw2eRl7PH3zNvGBnsDl7Rc0nAlXj2Rtkk0sdxczr6g8cxnieSl0Zfy6virGz7n2SRhJPiL+F/wqtireNx7nIFggLzKEzfirLXWPiW7Vo1Go9E8fykEBW7uv23B5xUzBl3SXhoqxEYAsySOOYaNAjJeJhTXlCrmLZbt7k1hhKMxSUAgp8dG5UzrehhCYAqTA/kRVidXsCe9D6kqXShAIRF10p5O7DyeDx73Dwgh2DKxvaqKL5BBsbrPS5HxsiBK+ZAYs8+TCFG64owMw/B4QWe0neH8KEbJ8tVXPoXALQmN1ePRyudXbrOrwuK0Hjk/hy/9eW1tNy8/n69NfmfW/aZh0ua0MlGYxJVuVc8MUbRPLZOw4qxILiNpJUjaSfam9zHhThKoYi6hQmEKi6SdYGm8h65oR9X5AJ7yeTK1e9YxeaACbGotZEfyo0TNSGjHmnJTpLwUrZHWOe9/Jqd2nliV1XkwnNp1Eo+MPc5OdlVtn3lP9WxpK6lfHTv9fcpN8cDow5zRXXTkOL37FDa0rmfbxI6Ka8L2yR1MuXPPTUYMp0rkznhZdqX2cNWe3/LinhexpnnVnOdrNBrNM8VhLzb+7Gc/C78fHh7m9a9/fUPnfe5zn+PCCy8Mf/7a177Ge97zHu69917uu+++uvmNb3/723nXu9711DuteZ5R/MBXuOTVlrqinSKAmiwnVdpuQLh/5oBNUax6FICPLP3KSiTD8t+ZUjcQExtpMTbRJF6GWbKhy6ttTMmb8NQgigKGSBBlA83Gy7FE9USmKZpIGi8iJX9/UHfviBXEjGOrtknl8pD7S74y9VU8/JKxSrKqAnQ2rsxeyQprBedGzz2o/mg0Go3mucEt+Vuqqhg7jU56RS8ZVbvSuLJCcbbJjcrJrbLgaGGFFqzlbVCsbuwyuug2u2kTbbNORl0Yu5CoiHJa5DQ+3vxxLp+6vK54BsUqxrJVeJNoYr21vsYibD7OiZyzoONbjBa+0PoF/j3979xWuC20eq3HKmsV706+u0poLNNpdvLVtq9yl3sX1+au5RH3kar9QghOdk5mc3QzpzinLGiRU0zUX9W/2FyMIQz2+nsbEpFfFHkRH2j6QMPXPVwQQnCscyzHUvvcNRqNRqNZKLcN3EnGqx0r1SNQASP5MYbzIwxkD+ArDxDYwsYURmgPGqiArJ8jR760iLrkulQSGis/p8uCh1SyWNlYkZVdOdaCouwmhIEhjHB71s+Q9XNsaFlPb6aPdOle6lVYJuwEm5e/jDetvSisgEt7aQDcwKU/N8hofoxASTzpFcdBqthvHx+hBEbp+pUIBBHTqcnns0oVeRHTYVl8CaOFsbB60xY2LU4za5pW0Zfp50B+uOicIYya8Z4Qgq7Y3GKjEKIhp4Zzel7I9ftvqhKoZhKogJgVI6qiuNINbW8TVoKI6dBkJ+mOdpG0E6S8NI+OP0G+VLWXtJJIJTENk0D5KCWQUjJaGKM10lIjHA7nRvCD2cebcy00H8wVLVWN0nshF+RpnfcJVHPW4jP57rYfM1lRBZgP8gzlRhh3J0KB0BIWrZEWFkW7iFVUmEbMCOcuOYfOaAd3D1XPC1sVr6NqYHRaL5dzZqXm/nQ/k62TJK0kpmFyyYkf4qP3XMb+TLHgZcKdYMKdmjUXEsA2bKJmrYPJWGGCXJDnmt4b+Idj3j1PbzUajeaZ4bAWG8fGxsKqxqdKMpnkRz/6Eb/61a/4zW9+w9atW0mlUrS1tXHiiSfypje9iTPOOOOQXEvz3MESrYegjXYA8mr7rNWBqqqywoAwz0CW/ldeWVd75vS/04N3Ucp3LKg9GCQYCb7HhPgtzWITGfVHCurJmmay3Me4/DkJ4wzajTdWWZ+2Ga8lLe9AUZv5UBwsBiiCUiWmWTXJ2G68MfzeU0NMymuYUr/jf7IPklXVnvcGcSzRjUVn3RWLZf43+79abNRoNJo/c/qD/qqfTWGy1FjKhJwIV8/D9OTVXMy2it4WNgmRCDNpDGGE+Y3z5Q2eFz2PixIXhT+fFjmN77R/h+ty13Fj/kYmZbXVUbNoZqm5lKiI0ibaGppAqqTdbOfMyJkLOgcgZsT4x+Z/5G/k33Bj7kZuLtzMaDCKj09CJNjobOSC2AUcbR89ZzumMHlh5IW8MPJC+v1++oI+cipHTMRYaa2k2+xecN8AkiLJInMRB4IDNfu6jW6a7WaG5BAjwUhNLqeBQbvRTrfZzT82/6POpNFoNBrN855dqT3zHuNKj/7sAKP5MfJBgYyfDcVBULjKrSuUBCogUAGmMBGIKqFxumqxOHchZ4iQ08cUxxSI8uhM1Bwzkh9laWIJR7duIONnGcoNEzEjtDgtOKZNV7ST85a8mHN6XhhaUJYxhUnWz7J98skq20vHsMmRL1XnGfil/pXvYWalY8SIYGCQC/Jhv8rPpDvaiW3arEquYFdqLwBJO8ERLWuxhEWL08L4HJaXq5IrsOexSE/ayYbGirZhc8mJH+Jf7v0M+9K9cx4rhCBiRoiaEZYnl7E4Vj12m3An2Tm1q6qiVJQqTh3TRioTXxbnrnJ+ni3j2ziiZR1NdnHRuAKG8sNzj8fm2OfLgNH8WGjLmrDic95PPSzD4mVLX8wvd/+aQuCyJ72XKTdV85eCqzyGciMM5UZospOsSq4gZkU5p+eFJOwEp3edQke0ndH89JxVs9PMxDwVhmVMYWLWeY2b7SbSXoYD+WHGCuMcyA3xwx3/gxCCkzo2snn5+Xzh1E9xxaNf56HRRzmQG541bxOKFY1RMzrrMx/KDXPrwB2844g3HxKLWY1Go3mqHNZiY3t7O9u2bTtk7RmGwYUXXlhV8ah5fpMQZzDMf0Edm7VGaRGvZFJdiyQ7x1EVgzlEqVJRztg/c/BQb2LVBxxE+Kur8NQQEbGKrLyPKa4nytEYIjJLL3zS8nay6gEWG/9C3NgIgCOWs9j8CIPBZ1GliT6pXHyG8BlGMT2IF0SwVBc2XXSYf0PSOAuAjLyXweByFHkmpMvD7njN9SVZXLUHnyGi4ohQNJ1Jn9/Hw+7DbHQ21t2v0Wg0muc+eVW7wCVmxEiKJDlyeKq+ddHM6sZ6QqMjHCIiEmYgGhgsMZcwrsbJqRwKhVSy7iSPIQwujF3IWxNvrdnXZXbx1uRb+evEX3Ovey9DwRAeHgmR4Cj7KLZ4W/h26tsNP4NKNkU3LbgSspJWo5WLEhdVCaQHyxJrCUusJU+5HShOYm2KbeL/S/9/dfdHRZQV5gqWmcuYlJN4pTGHiUmL0YKFxfHO8Sy1lh6S/mg0Go1G81wmF9RGtlTt9/Nsn9pJIXDxZUDWL85TzBwvSVRpIVa5MpHQLj5QAUKI6cXOQmCUkg6lkjXVjmXC5WFKlqrEaoVGoyQEjubH6I51krDirG5ayb+c8M+cuei0ee9fKsm2yZ14cmZ1ncAxbQqBixACoabHi1JJEMWxRXh0SZizDLN0jlGqNhShBWpntIOOaDsj+VE6oh1hRV5bpIW9GVFTxSeA5cmlNRmH9XjRosYXmLU4LVx+2mV844n/5M4Dd9dct9KK1TFtlieW0RGpdrTK+Fl2Tu2uEhqr2hAWhmGQltNVs4GS7JjaxdGtG4iaESbcSdzAw5xDJDXnWFQOMJwfoSvaQVeskyZ7bkv+vJ/njgN305ftpxC4xK0Y65rXcOGqV3HzwB+5ue/WmpzFeqS8NFsmt3Fm92m8ZV3RLc80TF654hV847H/YCg/UszllD4pL12qfLVRparWekTN+nNZB3LD5IPpv3HKY3ulFPePPMT9Iw/RHevi/x3zHjYvP5933Pb3pczTaQwEjungzLBOrcdIfpTlyWU8Ov5EaNeq0Wg0zyaHtdio0TzdWKKDhHE6GTkdMK6UJGCUgFRJeDMwsDHpDG1KlQoIGC/mLBoevhxB4VeIgPNRWd1Yj7kqOKrP8xkFZeAzCECBHUTVMXOuNpMqy0DwGZaKzxEV6wFIGKfQw2UMBp8jpx4ptlv3D4gCHn0YwkGSQilJVj3IQPBZyqLtnYURgjnuQZIlr7YSFUch6vj5A1yfv16LjRqNRvNnTNyoXc3cLJpxhIOBQUCAi1tltToTAwMTs2rVvYVFQtSu7I2KKMdZxzEhJxiRI5iGWTVZ02q0cn7sfDZFN9Fpzm19ZQmrbhXiSnMlj3qP8sf8H+ucNTsnOCfwuvjrFnTOc4nzo+fzP5n/mfe1nC2z8oLYBU9X1zQajUajeU4RMeovLIaitei2yR2hAFOQ+fCv8rJNaWX+dFFQBFtYCMPAl364qEsphSnMkhhZmltQMqwYnAuJxJdgGdWCo2NOZxqOFYpiY3G7g0Jxx+BdJO0EG1rWE7VqbSMBrtt/06wCTNSM4kl/2hZU+tP5kkqG1Y22YYfiqyks2iMJ1resJeWmeNnSl7CqaSVJO8Hx7cdwdNuR3D/yED998pehlalt2LQ7rYwWphdYJ+0ES+KLaXVa5nw2ZTYvf1lDx5VJ2Ak+uvEfGcoNc13v7/h9/62MFyYA6Ii0kfGzdEc7aYu04UmX0cJYUTTGwDFsDuSG5sweb4u04hh2aGtbxpc+/dlB1jStJOcXhW5DmFjCqrHvFxSfbZjzqYrmoEIILGFiCCNs4xXLzpt1zmogO8jV+67n5v7b6loGN9lJ9qZ753W8qPz7wBQmUsnQPvZPB+7h9323ciA/xHhhukrVFAau9PB9v5gZr2rtbiOmg204VWJkoIKqHFRK991cR1Adyg1z6QOf45UrXs7yxBJG82NFVxdVPKcs8DdCoCRu4JJy0w0dr9FoNE83WmzUPO9pEReQ4U6U8vAYwGckrO4rEwAeBxAqgsBGkUfhY9LMlLyOAruRZCitQcLAoXJQLTBmDMfnzyaqjwEIFF4o0ilcXPaVrlkU8iRTmMw9yFUUOBB8iRXmd8KBTFQciSNW4LIbqXKle5pGEMEObVBtxuTPmZQ3UlB7gAAhLCzaGAxqq1VmIslTULuIig119/f7/XW3azQajebPg7XW2ppthjDoNDoZlIOYwiRGjJiIMSknq6xVy3abouKrTFTUn5xKGEUBstVo5bTIaVzScgmjwSguLgmRYIm5BGsey6v5EELwT03/hIHBrflbGzrnlMgpfKT5I0/52oczTUYTr4m/hl9kfrHgczfYGzjdOf1p6JVGo9FoNM89liVmdx7Yk+4NhUapZE31X2hsOkPH8JSPpaywQq4sFIlQaJyWbRpFofBlUCU4VgqlBekxmBtiKD/CsngPn3voy+G+uBXnpUvOZvPyl7E8uWz6/lJ7eWJ8K13RTvZnaucLBIKklSDtZ4qCo7CKOYSl/RKJiUnEmK5Ki5oRjmhZR9JO8PET/pmXLDm7pt2TO0/g5M4T2DW1h+v2/469qV5anGbuGrqXmBWjK9q5IEvQY9uPZmXTivkPBDJehpv6b+GWgdsZzY/hq4CkleD07lM4f+lLWZZYQtSM8o93/QsPjDzEzqknmShMVs04SSVJ+2kcI0LEtGsibUSpmtMSJr3pPvwZlXZjhXFWJJdVVeBFTAffr35/2cKiIF1c6dYVNm3DwjEimMLk/KUvqXu/9w4/wOWPfI28P/uc0mPjWziQG8Y2bFqdFqa8VEVVrk8hcPGkF/qHOaZDs9PEaGGMH+/8Oeub1/D97T9BKcXa5jXsmtrDWEk4jppRfOkjURiIUFwv54lahkkgAyaDybB9IQRSKZqsZFU/25yWGhvgMr70+eH2nxKUfs9MzIX8elVRFNcXFt+g0Wg0Txd/vrMaGk2DxI3jScpzGFJfR1GY9TiFR8AkoBBEMGnGEauRFIp2IMSRZFDkCHAxSISDuKIwWFnNWGmb2qjwaJQyE4sC47TYWCi1PY3H0LxiI4Cn+siph4mLEwAYkl8mr57AogtLdCHJhxaqAhNBcQAdMI6vhgiYQpIDFAYxUOCyl6ySKGTY39kImESSK547g5ya2x5Go9FoNM9tzoqcxXeN75KSqart3WY3B+SBqlXzURGt+lwor7o3Znz+GRjYorZivkk0ERPTnzWbY5tpNVppNVoP0d1MYwmLf276Z051TuW3ud+y1dta97g11houiF3AedHzFpzv+FxjQk5wpnMmj7mPcb97PxEiDd3zMmsZl7Rc8pTsZTUajUaj+XPipUvO5oc7fkohqJ67KAQFJityBF1Z6yZQzq426qganvRCMbLs/FAU7IqfwQKgVB050/axHpUWpoYwcQw7/Owv21W6QYEVyeW0z7D8zPpZfrvven6773ouXP0q3r7+TQghuKb3RgC6Yp0MZAcJ6ghahjBospPk/Bye9EBYxQo7JZFKYouiqGoIQXukjVVNK3jR4hdw4apXcUTLujnvaU3zKt539DvDn/975y/42ZP/N++zqKQ10sI/HvveeY8rBAW+t+3H3Nx/W81rnXJTDGQHub73Jo5q3cDb1r+RCXeCbZM767clXaRS5IM8hSBP3EpgV1ivtjktYV7lolgXfdnBqvOlkozkR6vGY7ZhYQkzFCaVkrh4EMxua+pJv1g9KwTxOuLsAyMP8dmHvkggZ39/BSpgpJSz6EmPjJ/h6NYNjBbG2JPaR6H0vjeFiWXYREwHgWDKTTHlptg2uZP1TWtojRTnywwE65pXM1Zo5UB+mJSbJmEnyHgZpDAQpfeNEMWKzpnvOQVIGYAQoaAbNSMIIeiOds16H8VnpjiQGyrJ+QePIQxaGqyo1Wg0mqcbLTZqnvf4apScehyTVnwO1D1G4VVV+Sk8DJIYRPApB4rbGCRK2Y0BkjQmTZRFRYFTqogshrIXtxsU6yZV1dVqEVAh3E1nKKpSlWO1lUrABEr5iAaqJCbVNcQ5gbzaTlreUbXPIApMV4goJK7aXbJYLW9zKSY+REriqsIWOSQugmipjdnxOYDDqprt9ez1NBqNRvPngyMczouex1XZq6q2R0WUZeYyeoPecJuNTY4csvSlSl8+fmnBT/Frts+ObrM7/L7JaOJFkRc9PTdVQgjBOdFzOCd6Drv8XdxRuIMpOYVE0iyaOT1yOkfaRz6tfXi2UUpxn3sf1+Su4QHvgXDickSOMCbH6DA66Da6cUT9Fd8nOifyoeYP0WTMneej0Wg0Gs3ziYSd4OzFL+B3fX+o2j6UH6maSZhNELQNG8ewQzvJMuUs60q3CKUUpmGglEKWWjcwwrFYPWZma0uliJgWcau46KsoEGVxDJu1zatrhMaZXLn7atJehn845t3cceDu4j0Ii7XNq9kx+eQssydFMUuhcEtVbsWMSsmqppWc1Hk8x7Qdxdqm1bxs6Utoj87dh9l407qL8KXPL3f/uqHjO6MdfOqkj9Idm1uESntpPnn/v7Fj8sl523xiYiuv/f1b6Yp00Oq0MFEhOJcJKipcFZDxMySsePG9YNqsSC4P9y+J95AL8oyVLFqn+5ShI9pesUWQsOOkvExov9sIcSuOL30+9cDn+fTJ/xJW06bcFJ9/+KtzCo0Ao/mxqve2J322T+1EKUXEjBAxZ7cZVigmC5M8Fmzh2Lajqixv2yNttEfayAV5JtxJ8n6e0cIYaS9DLsgXKxBLNryV2IYVVhAroCALSAJWJJbR7Mw9ho1ZUdJemqSdIFXHLrYRLMOkK9bJxvZjD+p8jUajOdRosVHzvGdIfouAESJiJSZJPDU4Q1iUJQERisNWB4MIAeMETFZV7xUFxyYUBRQukiwGRds2A4cgrJysDUuv/lnN+NmoM3hTJbvXmceW97kNZUhm5D0oI2BSXjPncQooqCcJGK/Y5lOu1lQUEKUKxWWmyT2AIo+EOQVHX43iiFU129dYa+btu0aj0Wie27w69mr+kP8DE3KianuP2YNE0hf0oVTRMFwiCZieXAhX35c+qQUCT3k1dqQJkaBNTE8i/VX8r2YVuJ4O1lhrnnefaf1+P5+Z+gz7/f01+5aby1lsLmY4GGaLv4UOo4OlxlKEEERFlBdHX8zm2GZWW6ufhZ5rNBqNRnP485pVF3Dr4B24wXT14vgMcUjVUeGUUlhGMTsvakTwlBdWaonS/rKYIhBYhkXUjJLxs2EbQggMZVSNyWZStrgvipfgGOWYGYUnPRJWnJV1Khpn48b9N7M0sYSUO+2G0eq0sK5lDU9O7Zk1i1AgQgHKMW2OaFnHuza8ndevvbCh6zbC2474a45sPYJf7b2Gx8aeqHtMwk5w3pIXc+GqV80rbHrS418f/GJDQiPAvvR+xgsTTLpTHNG8tiiouVNVx9QTZLN+lo5oOxta1odVjVB8fdc2rcYU+xjOTy8yD1RAq9OCY9q4Qdn9yqDZTpLyMg1Vu0ZMJ6wefXTscf5r6w/5u6MvBuB3fX8I8xznYsqrdkSRFKsu42Y8FC5nww1cFOAGHjundnNU6xGh/a1Sigl3kklvikAGYWVii9NCb3o/+aAQuqoIIXAMG8dw8JVfY1fcKBEzQrPTXJUhv1A6Ix1sWnbevPeu0Wg0zxT6v0aa5zWeGiQr7wt/tujAEh0EZPDVCAqXgImSZakV5iJOn3+AiFhLpUWqwEAQQxEFfGwWl0S/oihZYAcBaaYrGssDi3r/FjMSZrMjVeE1a3+Vy/vmR+KpAdLy9jmP8umvEhpnXkPhoogiEJzuOPw2l8dFlao5zdD2tbafAQEpIChZr1oYxNkc3dxg/zUajUbzXKXD7OCTLZ/kkslLyMjqFb1LzaXEiLHV30pe5cMKxnLejkAQEITbBYKCKiCRJERxoU9URDnCOiK07doc28xfxv/yGb/P5xP7/H18dOKjNfa4ldjYLDGX0GP24ONzqnMqf9f0d7Qarc+oEKzRaDQazXORFcnlfOj49/P5h78SVoL5M/MZS96MShXdIMoZjAXpUpBuuGjLFEapYsvEVxLHdDCFQaAkljCLVqQzMIQRtltZxRhmQlIUY5J2EkMYtDotrG9Zy970PoZyIyUhp3NB93zVnqtrllm3Oa0c03YkB3JDpYq3ejmBNt2xTrpjXdjCmrUi86lwevcpnN59CvvSvdzUdwsj+TFc6ZKw4hzbdjRn97xgzoq7Sn7fdyuPj29p6FhfBaEgKJVkf7afo1qOYCB3gKHccJjfWW+Zu23YtDmtWMKkPzvAaGEcX/ooFJawimJu8yom3CnGCuMYovjqdke72J/pJ2I6dEe7Sna9wwQqoBAUwqzEMgYCx3RwDAfHtKuqI3/X9wfevO4iknaS6/bf1NA9zxQ1C0EBqRQF6c4vNpYsVsu/D/szfaxrXsOB3DDD+REKQbX1sEIx5U5hCJOEFSdAkjDjVdWNnu8VbXpLd20JEwODodwIi2LdJO3EnH1aFO1if7afmBklF8yeU1kPASxJLOYVy85b0HkajUbzdKLFRs3zmkl5HfXWeZkkMEUCpSQ5Hsagvi1bwCQKH4sOfIar9hWHYjYKhSOKIe7F6ox+YKrudWtRzP5rWq7oKFqXFq1Vi9asRcGz8V9vl30lO9TZeqHw1NAs/av8XgImMUNwkmNzl1sezBXqio2KApI8GXVPVVurzSRR8X3y6m1ExYaG70Oj0Wg0zz3W2+u5vPVy/m3q3+jz+6r2pVUaS1g00VSsblQSS1j4+JiYWMIiJ3NVq+s95ZEly2JjMeusdVjCwhIWr4+/njck3vBM397zirRM86nJT80pNFYiENjY3Ofex22F23hd/HVPcw81Go1Go/nz4IzuU7nkxA9z+cNfI1tReVjGwEQpj0AFoQOEUiqsAixXHpb/jjeFQdJOYAoTT5hk/CymMJF1FjELIcL2Kh2YymKPAOJ2fDqjUflIFTBaytorVsgtbHHRRGGSnJ8L7VjLxMwoq5IrWJ5YymhhnEJQIFABZkkgaou0VvVxPmvLp8KK5HLeseEtT6mNa0u5lI0wkh+pqupMexmyQY4l8cX0xBYx4U4yWhjDVz55P48QAktYOKaDUoreTB8jhbGayjqfgMHcECJXfF4bmtfzwsVncN7Sc8gHBb695bsoWRSaHx57DChmJJata8tCdPE9VlxAD7A4tqgqL9STHjf23cxRrRsYzNaPNJpJteOXCqssiyKnYjY7V6lkVRUvFN9Tj49vrbEULlOuhAxUQKACbGHR5rSSkzmyfo58UJiueBQGhhAlK9WioP/o+BNsaFk3ZwVvi9PMlJ+iM9LB1ontYQZmI3RE2/ngce+nK7Yw4V6j0WieTrTYqHlek1ZzV/MFTFXkI9ZDETCOJbrx1XDdIwJGUGoFQgg8+kqinkV1ZWN5kF+JCFtQpQqO6T0OCr/UVrkycDoLUuAQkJk3L7GIMa/uGTBeV4ws5jFUMv3TudEID3keeVW2e5Wl+yw+NckUqvQMKoVIAbwiFjAqv8e4/BmtxmtYZn4BIepXRmo0Go3muc8KawXfafsOD3gPcG3uWu517yUv8xyQxYmHJqOJbrObTqMTAwNPeQzLYYbkEJZh4SufAgV85WMLGweHFdYKFluLeUX0Fbw89nJajdZn9yafB9yYv5HhoP54aD5+mf0lr4q9iohobOW/RqPRaDTPd07uPIHvn/1Nbu6/jQ/fcyljhWknIssw8X1/VgFGoUoiSrG6EYqCjTCKgpQhihVp+VmqrQyMqsrGyrxHUxhVtvYKxWihWHloGxbLE0sP6n7L4mU9TGHOWy0phODUzpMO6trPBFvGt7E7tbfh4yttTssM5YZZ3bQSIQRtkVbaIq10R7vYOrkjPEYqSdrPIFVRZLaN+sKvQjGSH2MgO0TMirE7vZeYGWVt0xoeG3+CrJersRAViJpcQyjmVS6JL67ZfmPfH+bNsKzEqeirWxIYw/5W2ADXu5ewj6XK3LSfJS8LxMxY3XMK5UpIVYxs8KVPb3Y/CStOxs8WhV4xbRs8s4Y07aXZMbWLntgilieW1u2bEIIzuk5FCIFUkh1TTzZky9rsNPGF0y7j3KXnzHusRqPRPJNosVHzvCZQteHZlSjqr3CqOkZ5mCKBRTs+Y3XaCABJoPJ49Jfs3pyS7ahCEAFMFOVBvKAozMmwhaIwaZV+UoCPxC0NZcqiZeU1XXI8QEEliXE0pmhhNhLG6RiifuVmGV+NzLJn5n9CpgdPi0yTixNx/jOdxUMhcTGIovAJmKy4P1F19uviMY6yi8KiJMOY/B98NcYq63sIUd9OVqPRaDTPfYQQnOyczMnOyWRkhu9lvofMFisZ7RnV8bYo2XAaPXh4BARFK1Ul8PGRSF4afSkfa/7YnBNTmkOHUorrctcd9PkZmeHW/K2cHzv/EPZKo9FoNJo/bxJ2glet3MRQfpj/3vm/eNLDkx6PjW+ZtcprJoGSKIrWmmW7+ma7GZhZSTaNEAITM7S1NCvGWzMtQy1hkfcLWIbF+ua1RBu0FJ1Jk5PEC+ZaDD43p3addFhXgT04+siCji/UqcibmdcIRWGqbNOplCJTEhoBAqXqBt64QYG8LLuKmDwy/hhHt24gakYZyg2jUOxO78WVbpUAOBMBLI4vmlVgHsweWNBr2hFtZyhfnJ+aaR081yr6ynexY9jkgjzB/8/ee8dJctR3/++qDpM2x8t3uqiThLIQQWCUkYQNmGiMgcfZxtg/Y5tgG4TBBvMYjHmMH2MwDjwEG7BNlACBkIQkQEKgeNLlfHu3eXdyh6rfH90zO7Mzszu7d0J3Ur3vddJud1V1de/tXW996vP56hARhXM1oFAorWJHZE2CigriupGVyODo+0ehkPGviqiogVDFLlEhWj6DdR1reMu5v8kHHvoIjrQ5XhxlvDTZtA6mAC4aOJ+/vuwvuKD/vJb3azAYDE8VZvXF8Ayn/mVEUSRgioAJAqYXcTXW44qNWLSK5FAEzMWQRgIjSFwsOrFII+mI40+jKNT585zbNaiqAmZltPmI2CmpyJLnx3j6WMt5d4sbccTwgvfWKmI1mq9Td6SWbY7DWzoz9MhKTUtNyGzN3KFyr0kheEMmzQsS83/w0Mzqb3IsvHnBORoMBoPh6UNCJLivfB8pkWoQGmsRQuAKl5RIkRAJXOmSlmk6ZAcPeg9S0kurfWJYPj/1f8rx8PhJjXFraflipcFgMBgMz2RuXHsdHU6GLreLw/lj+CrAWsJmXa01vvKj+Hq3g82dZ9HjduPIRd7DpEPKTlZFSVc6DeJTb6KH1ZlVnNOzbdEadguRtJJs7Nqw7P43rT29NzTlgvzijWpQulFcayZQQST4AZRVeV5ty8YximGRQliqCpIJK0GgQg7n58odpK0UK1JDLcVoW9qsTA9zft95CzpZlVYk7XYSuSI6nY5qlG5DNlgLV2N0Llp3kkJiYeHHrkXVQqCsRA7Pf55KKwI1d0zUiOwqFh3rxonHHymcYNZrXmbg2tVX0uV28YHLbuajz/0gb9jyS1w6eCEbOtYymOynL9HDyvQwL1l3Pd+64b+59cX/ZYRGg8Fw2mKcjYZnNJboxNdjhEzi61EUubrzCg/wESQQ2GgUc07CuD6iqNQlkCTENjx9gIAJal99oiDRuYgLgY3NcJ1zMnI7ChRFBCquxVj7oqKqLSvXm/u8tt38HyhCSjwO2sKdJyo6YjUpcQFCCFLiPIr60abPSTep0zB3Ly4aH4FTM6c5Ntg2N3d18pif4vvlPE8EYV3vNZbDFQmXS12XRMuXQ82k+gzD6k+wZWuXpsFgMBieHoyEI8yohdMHFqOkS+wP9nOue+4pmpVhIQ4EB06LMQwGg8FgeCayMr2Ci/rP59tHvsds7G6b7z5cCEtIymGZhJXg4oELeMs5v8mXD3yDWw7fxsHcoQZRRgqBKxMkLBdBFAGZspLVeHutNba0GUwO8IWr/o3bjn6Prxz8xkndY8pK8gfn/g5vv/9mSsHSNpRdu/pKLh648KSu/2RjLzHJyRJynnDYWnDrsNMkZIKp8lQsgEX1FOeLhaWwRDmc22yetBJVwXm6PIMXetV6m7awqudWpofpcbvRaCxhkbJTdfUZW+FIh2f1noNruXhh803u81mRGmJf9mDd6JaQTdejKggEjozquPvaj/80a7RWVYeoFAJbOggEoQoavm+iMkJxzziytVKXsiL8Kq1q6qHWM1oaa6gZ2p/s4+L+C6qfb+/dxvbebfzW2W9irDRBKSyRtlOsSA0vSZQ1GAyGpwojNhqe0STYwqy+vWVcqsBCUYhFx+YRIr4eBWFj0x8LjhtxWE2gRwkYQ5CMRUyFwMFmEFsMIkng62N4HKUiTAocJDZRTGo5npeiEq0qyRBJl3OOy+iFxyISQVu9YIV47MXWHUiRifslGLb+qPoy2i1vohg2FxsjobU5kcjoVN2azbCE4CK3g/PcaWaVTU6D1pCWKfpley9MiiJj6h9ZKd/RVnuDwWAwnLnkdG7xRm2Q10vbIW5YPkVdPOkxAh0Q6KCuzpPBYDAYDIb2+N3tv85/7P1vPDW3XlARPRYSHKWIoh810ON2Uw7LnN2zlb997vN58zm/wR/+6B3ce+J+ymEZIaKajhWRSRDFdA4lB+lN9DSMfePa61jXsYZQB+yZ3UcpLMe1Ai0ydpqh1GDVqbYYm7rOYmPXBt590dv4qwc/TN5v7z3v51ZewZvP+Y222j6VNHt+C5FxMg1uuVonqtaaKW+a0eIYs34OL/RQ6KpjUWnwhY8tbSxhobSiVBPNmrKSdZG4GhgtjbMms6p6/QrHi6OsSA3jyKW9w23p3kSn28kLVjyX7x69s60+A8l+sn6Ow/k5wXmhKNdqv0Q/haBINsgS6hClFVIIijV1SQVFHOkQLCLQV+KGAaSwUHou0rUiOApRLzpOeTOUQ498kOdEaYycn2ddZjUv/84v05/s45pVL+LFa69hINlPp9tJp9sqOc1gMBhOX8xP8oZnLJ4+TE7/aMG6jAJZ42YkLq1u15y30ZTx9H58jpIU2xA6iaaEJInDanrky5H0MKu/Ath1gqUjViF1Bp/jcbxopXi7g4WDJlOdXxSd6qIaYkghcjcmEAs4EDVlfEZJcBZSpFkh/5Sk2FI9nxHPwRGr8JtErlp0Nrg+a7EZQiAJaR4LUZmBpkSnFHQCgiSSpe3MmlJfYCVGbDQYDIanOwmxvFo+83HF4gsPhlNDUpz8bmtb2EZoNBgMBoNhmeSCPP2JXg7lDtc5EatiolYoravurOicwBE2ruXiSpdy6KG15njhBEOpQTZ1n8UXrvp33vnjv+DhiceY9maqtfJsadOX6CFpNX8HWJ1ZxXBqkN+6+//jaGGEWT9XV2cvHxQYLY3T4WRYk17V4Pqazw1rrgHgWX3n8jfPfi//b89/8qPRH1fFs/kMp4Z46fobecm6Fy8YsXm68MIVz+ffd3+eUC3uRAUYTg02iI39iT4gqiO4b3Y/U95cUkigQyxhIZEoFFprAh2S9XOkrCQajQBcyyUhXWQTp+WsNwux2FiJNC0ERbTWjJXGWZVesaR7vnHttfH/r2tbbATY0LGOUIdVh2PFbdmK/kQf6zNruG/8J3ihX/0ekPM262ugHHoEOqhzMi70p0cgsMScg7hSAskWVl2UcTks88DEQ1UH60Cyj+HUEAATpUn+c99/88X9X+aKFc/l9875DVJtivAGg8FwOmF+mjc8I1G6zLHwPbEnsLOFSKYJGwQ2jSZExDuYat18ihIF/QCCJBUxUOCQ03cTMkWop7AZwhJddSNaohuLbpQuEjCOphy/5FlIMtgMoPDI8wM0OSrCJ/EV5tyMCpBxIEbjy7bGJ2CcXvFaBqw34oq1deeFsFlp3czR8E8IdX1RcVsM4evjNMvzFyRIsAWBjcchAsaZL4ZGomzlhwqJJIlg6QvAPsdQKkAucbecwWAwGM4s+mU/UsiWi0ftMiSHTtGMDIux1lq7eKNFWGOtOQUzMRgMBoPhmcm+2QO4lkuH00EpLOErv/oTfOS0spBCo3UUt+lIh4R0saVFRU4phSUUus7tlbSTvOfid/DuB97PvtkDbc1lVXolGzvX86+7PgtEqxaDyX5GCica2ub8PDtn93BWxzoGkv3Nx8us5ML+86ufr+1Yw59e+EeMlyb41pHv8tjUE+SDPI50GEwOcPWqn+OSgQvPCJGxQn+yj2cPXsIPTtzXVvsetwfXcvDCyMkqRPSMlVbsmd3LzDwhsiqcxfG6STuJVRMBqtF0uV0t6zACDY6/weQAB3OHARgrjbMyvaKN8NSIbreb5w8/B4Ct3Zu5dvWV3Hb0e231FUKwuWsjuaCA1opABY01HIFOt5Ph5CA9bjcHc4fr7i36nmhMBqvUXYyeV7TCZglrQbfjfAexRte5LcthmWJYImklsC2L4dQg6zKN771KK+4auYcj+aP85SV/btyNBoPhjMOs2BuekeT0nQR6FACXDZR4vEYMi1AUiIS9SNCbE9CieooSF0EUUaHx4/YaKGERCYquWBsLgZKACTyOIbSDpAOBROBii34sepEihUvjQp3WAR67gYDoFb11gfYoqlUgSMdzV7FwKQCLBBvJyMsahMYKrljFaut/cyy8mUDP/RAgSWDRTch0XXtJmgRbkbFzJMEGXL2agDECxlGUAY3DCsAhZHyR+S+GJmASF7N4bDAYDE9numU3l7mX8aPyj5Y9xnZnO6vsVadwVoaFuNS9lAFrgPFwfNljvDj14lM4I4PBYDAYnlkUwkLkXBOStJ1Go/Fil5bWFaFJ4konjrtsLguFKiRjp+uOdbvdfPCyv+Aze77Ad47d0TLCNGknedHKK7CExTcOfavu3HBqiPHSBL4KGvpprdmfPYgtbHoS3Q3nf3nTq5oKhwPJfn5586ubzuVM5OfXvbhtsVEAazKrqwJwf6IXRzoczh9tEBrnYwlJ0nKp/BkIdUDWL+FIZ8GUiflfg4FkH6OlMYpBVOuxFBZJWe058l6/+dXYNRvJ33zOb5APCtx7ov33/9/Z/qv84MR9lMMyk+VpgjjO1BY2PW43qbjO4VhpnNHSOJaM6kz6ym9aV3E+mrgepBDN9t7XUXEQVzZLVtyWnvKq4n2HnYmiY52OBcfaN3uAv3rww/zlpX9e94wMBoPhdMf8jWV4RjKtvk7ILBoPBNh6GJ/jzMWlqrq6iFR3NFUI0EgUJUCgqa1TpFB4JMXm2JVYItDj1ZhUTQlNCUE6KjytpyLRkSEcsbJup5XWijJ7CMkjcBoE0eZoNMVY0JwfeyEpqJ9Q1D+lpHeiKCBwcRimU15Lp3gRrljNOusfyOo7mFW3UNb7AHDFGoo6C4RIOnAYwqKvYSeYEA4Oq3CIFngtBlhn/x3Hww8xrva3Mf+FEFUh12AwGAxPb25M3XhSYuNNqZtO4WwMiyGF5Prk9Xw2/9ll9U+KJFclrjrFszIYDAaD4ZlDQiZIWAns2IUlECSsBAmWFk/vWi5rO1Y3HE/aSX797DfwK1tew50j93DPiR8y481Gjjink2cPXsLVq3+OE8VRfv/etzeOKx22dm/miendTWtIamB/7iAXuOfVCUFv2PJLvHDl85d0D2cqz+o7l1/a9Eo+v/dLbbUfSPThZTwmy1Osz6wl1IqxYvONXzJea5JCxvUWa9eeov+XwzK23Xqp2JX1m8ctYbGtazOPz+yK4kdVSMMyVBNevfHlvHjtNfVjSYt3XPCHfH7vl/jqoVsXrMk5lBrk9ZtfzZWrXsh/7P0vPrvnC6xMD7dsf7wwOncPVuTmlULWRarWIoiEeYmII4lFXazq/PjVuX4CW1j0JXo5q3MDvvLZl91P2k7hSJtV6RWLCo0VHpt6nHtP/OgZ82ffYDA8PTBio+EZRaAnmQj/nWn1P5HQWEPFARgJjeX4JSKkXmQU1c91LOpVHIfELxtR/GkShxWE5CnrXWh8BG5N/UUfKCFIxZ97+BxB6RwJsTl2Q0JARaSM4krrBdCF0CiKWHTUHPEps48p9Z/znI0FQqYphTuZEP9Kt7iJPvnLdMvr6ZbXU9K78fURFEU8dZQp9T9Ns/ubYYk+VlvvwxLdJGrqQy4Xi04sOVcPwtejTIb/QZGHCMkhSZEUWxkUv4Utm8evGAwGg+HM4CLnIjbYGzgQHFhy3yFriOcnzA/mP2tuSN3A14tfZ0bNLN54Hr+Q/gVS0tSmMRgMBoNhuazOrMQSkv5kHyeKY4u2D3VIqEO01ggBUlikrRQvXPE8ut1Gd2GFhJXgujVXcd2a5puEbjl8W8u+GTvN9t6t7JnZRyksN5z3VcBUeZr+ZB9JO8mvbf2VBlHq6c7rNr8KYEHBMdABE6UpPOUxmBzg51Y+j8endzNVnmwZ9+lKlxBFxk63jEr1VYBCtRTTKjUh68a1XM7p2cbe2f2Lxtam7BRv2PJaXrKueZqFEILXbX4VrzjrF7hr5F6+dfS7HCscpxSUSNtpNnedxY1rr+OywYur13rtpldgS5tP7/581cFby6yXrYsFXpVewXR5Gg0krSS+8mu+DwR+6KOEoiLGynitMKp7Wvm4+f0JBBknTYgzinUAAQAASURBVMpO0ZfoYaI8hSVsLBGNttD3VTO+cfjbRmw0GAxnFEZsNDxjyKl7ORF+GF+PNAiNQL2jEJ+52FQx73fUIqIiRoZxe5tI6Cvg6+P4HKu6ESOxcO5lWuOhSdZdN2Sast4XC44QEO2+kqTjcfIsmt1QJajWl1SUY7ejhVggEkPpPFP6C5TZz0r5ToRwSIotJCtCoYRO/SLGwo9VHY+tSIkLGbb+AFsMANAnX83x8H1V8XQ5dMqoeHhBPcqo+jty6s6G8Wa5lTH+kYx4Piutt5GWFy/7egaDwWB46hBC8K7ud/FHU3/EtJpuu19GZnh397sXjIAyPDl0y25u7r6ZP53+U0q6tHiHmOcnns/r069/EmdmMBgMBsPTn/N6z2FVZiXFoLSA2KjxlE859Jq6CwFsaZP1ssuqF1cICtwxcveCbdJWivP7zmXam2G0OBa7I+cohiV+8+w3cdWqF8YOvGcer9v8Ks7r3c5XD93K/WM/qUZz5oMCJ4qjTJancKXLUGoQRzrsntmHRDBemoy3yEduPKAqQA+lBjmQPUSuiWNQ1qhnXuiTtBrdsLaMHHvNcKTD2T1beddFb+NHYz/mzpF7KNeIyes713Hj2mu5auULSdrJpmPUkrASXLvmSq5dc+WibQFeedZLubj/Ar5+6JvcdfzeumuPlycQQtDrdjOUGqTL6WT3zF6mvBkEoq62IkSuxVpxUiBwLAcXh7xfiF23Yl4fgWu5uJaLRFZjiEdLc9+HXW5nNda1XXZMPcGB7EE2dK5fUj+DwWB4qjCrMIYzllDnyervkFV3EDAJBEg6SIsL6ZY34Yp11bZZdRcnwg8RvXKplmNWiMRIAdgN+72il2AV/557gZs7G0WlFnkUgY0kesmIajQm0ZSqbTUeYl6kScgkIRMI7cZ1IC2SYgu+HkUxuwR3Y+U+LDRFBA4CiWTxHxgK6n5O8HessP6k4VxSbGGt/VFK+glm1C3k9X0oXQDAEt10iBfQLW/EFfXFrqVI0iWvZ0p9se351yKwGJJ/wFT4FY6Gb2uoH1mLxiOnv8ee4G5WWX/JgPWmZV3TYDAYDE8tQ9YQH+z5IO+ZeQ8j4cii7fusPt7T/R7W2+YH8qeKLc4WPtj7Qd47814mwolF29+QuoHf7vjtRXfCGwwGg8FgWBghBDesuYZP5f8fA8k+xkuTdec1ipyfJ9St10R63W6+e/QOfjz2U9510Z+wrWdpCUWPTO6gFLS34ShtpehwMgghCZSPQJJx0qxIDZ0RQuPh3BG+deR2jhVGKIVl0naKTV1ncd3qq+hPNjoAl8r5/edxfv95jBXHufP4Pdw5cjd3jtyDIx22dm2iy60vMSOFxFMeGkjbac7q3EDCcrDE3LrWcHKwhdhoYQlJqFXLNbPB5ABSSLJ+jmJYQmmFJSzSdipyrPZs49lDl/DsoUv4ne2/xqyXxVc+GSdNR5vRoSfDxq4N/P55v82vbfsV7ht7gBlvFqUV/7nvf5j2ZuoiYNd2rCE3nW9aP9SVLqWwjEKhtUJpjdYKRzqRoCidaqSqQCCFbKiBOpQcBCDn5RqOLZUd0zuN2GgwGM4YjNhoOONQusS4+hRZ9b06pyBAyCwz+hgz6haS4lwGrd8EBKPhR6jIhI11DOuJBLq5Go26mmwv4iNBfK5ZrIQiClnQcTsVR1BEL1aSZCxRlmqu1bhjzNejWHQgsEmILUjSOGIIXx9botjoV12cggSSTF20al1brQiZiiNkFVPhF0iwlV7rpU3bJ8XZJK2zq32JYyUWYkj+ATPq66i6GpftkRaX4+tDHAnfiiK3eAei+z8avhOBTb9lHBMGg8FwJrLKXsVHez/K7eXbuaV4C4eCQw1tVloruSF1A9cmr6VDPvmLGYaF2Whv5JN9n+Tu8t18o/gNdvo7686nZZorE1dyY+pG1tnrWoxiMBgMBoNhqVyz+kX894GvsUGvw1M+s14WiLY6Z/181SHXjA4nw9buzQDMeDP82Y/fxwcuu5kt3Zvavn7WX/xn9Zyf53jxBFPeTEPs5ayf5URxlL995B9409bXsbZjTYtRnjoeGH+QL+3/Cg9PPMpEeZJ8UCDUCktIklaCz+z5AlcMP4fXbPxFNnZtOOnrDaYGSNspDmQPsb5j7YJtVfw8C0GRfdn9nNOzrW57fMZJk7KSTHnTCASWsLCkFdX3lAkKYbFpFGmHk8EWNg9P7qAUNorJGSfNi1a+AC/04pqINn3J5i7IJ5uMk+HKVS+sfv79Ez+gEBTq2iStBFu7NrFrdm+D4KgI0SiCmuMhCqkVjrQpBEWkiOqhJq1Gp2KXEzkYQx1WHbv9iV56lhihWiHvFxZvZDAYDKcJRmw0nFGEOsux8N2U9Z5F25b0YxwJ3k5CbKhGmQKx03Cu9mItGhULYbUxqapaAnpOhGwlqlWck3NipCZAUag6HCVJdBxtGo3fiCJPQmzGZhBJsjpvm0E8ii37NRJScWiChcCmpHfGz0MgcJF0oikTMl73nACOhu8gp2+nS95Ip7gSKdwm14gKZ7dDUm5hlfV+joZvbxpl2wpXbGCN9SH2Bb/YttA4h+Jo+Gd0ip/DlQu/mBsMBoPh9CQlU9yUuombUjfxmPcYB8IDFHWRpEiyxlrDBc4Fxhl3muEIhyuTV3Jl8kqOBkcZU2P42icjM2y0N5IUS4uRMhgMBoPBsDgdTgc3X/wO3nn/X7C1axMHcoeZKE2QCwoLC412mssGLsISc5uzy2GZv/jJB/nkCz5Kym6vrrJc5H1svDTB/tyhpoJWBaU1d5/4AY9M7eCdF/whFw1c0Na1fxZ8+cDX+cfH/4WRwnHGS5NNo2gP54+xP3uQO0fu4S8ueSeXD116UtfcN3uAf3r8X9tqawsLP96gXgxKHMgeYkPnOk4UxzicP0rWzxGqMNoWr1XVmZewXFzpVmsT1pK2U5TDMofzR1te1w99bj92J/tzB3nPxe9gINm//Bs+xaStdNPjGSfD9p5tHMkfrQrfvvIpBAW0pupeBECDFgpP+QgEgQrxVZ5SEDlaXcuNn6VgTWYVQBy3GrmFz+pcv+yfVVzLWbyRwWAwnCa0pxAYDKcBSnuMhO9rS2ic65NjUn0exVxMhBRJrCZRoooCITNAY4xCpUXz6NRm7epfnKP6jHMvoQIHiw4sunAYxqIXi24s+nBZS5oLcVldFRorJMQmbNqP49Cx0zJ6SYruL2QGRR5FjoATlHiYMrsIaYzSCJmipJ9gLPwYR8O3Eeiptq/din7rdayxPoiknUgUQVKcwyb7f8iqO/E5saxrakqMqo8tq6/BYDAYTi/Odc/lptRNvDL9Sl6SegkXuhcaofE0Z7W9mgvdC7kscRnnOOcYodFgMBgMhieRTV1n8deXvYeBVD8bO9ezqWsjFpF7rZbIzeayJr2Sy4cuw5GNosaMN8P3Rr7f9rUXcm9NlqfYnz24oNAYzQtsYVMMivzlgx/i8amdC7b/WXHr4dv46KMf59GpxzlRHGtZ81JrzURpkgcnH+GPf/TnPDL52Eld9+uHvrmgUFxL57y40uPFUX409gCPTT1RjRUVIhIYBQKFItAB+aDAtDdDoANCpVA6JG2nWJkephSUm8aNVpBCsrlrI1JIDmYP8fb7bmaydPJrR6eKc3q3tTyXtBJs7trIBb3n0uv2UA7LCCGxpIUt5/w5tXUahRBY0kIKET+7PFk/h0azsXMDHXH870Cyn+09W6vPZrkMJgeW3ddgMBh+1hix0XDGkNW3UdKPL6lPwAQan7Kuj1yzGar7XJGviU+FxjqMtf+PerSmEtcq5h1tdPIJErhiPUmxhaTYRlJsxhErEcJGNPn2FEhS4gJshhe4/vzZRm7LirNy7qgfi7CayL1ZrBNlK61CZgEo670cDd9JqLOLXnsx+qzXscW5hT75OmwGmC/eClyS4mxWyfex2f4qDiuY1J+mmRu1XWbU11C6fTelwWAwGAwGg8FgMBgMZyIbuzbwiSs+ylvO/S1saZGyk3Q5nXQ5nXTaHXS5XWzoWMtF/edzTu/ZWAuIIbccvq3t657fdx7dTQRHXwXsyx5o6yf6nkR3VZzxQo+/fvgjdZGWTwVjxXH+9pF/YOfM7rbnorVm98xe3nbfu5c9/7yf587j97Tdfig1VxfQC8vk/ByzXnbOoYcm1CGBCqo1B6vzjdukrVSUgyVdxkuTqAXWv2xpsbVrU1VgAxgtjvGBh/627Tk/2Vy/5upFxT5HOuSDPB1OB11OJxk7VRW9K45FiGo3BiogVGHV/VgRgtNWknN7t3P50KW888K38qkXfIxf3fb6k9oU2e128+zBS5bd32AwGH7WGLHRcMYwo25Zcp9KbURFFsVczrlFb7WOoqK4pDqIc1ReuHTT35qgrrB2vZgZIcnEBac9lC6j9dwLaEI0L8QukKTFBaS4IHYHNr64RPGoKSJXoxPXaax9iVR1z2PuuN8gONY6Mn19lBPqI03ntVSS4mzW2h9hq30Xg+L3yXA5Sc4lzaX0iFezRn6IAevXsUQnJXZS1vtP6noBE8yq9n9IMhgMBoPBYDAYDAaD4UwlYSV40corGEj2c27v2Wzt3sSWro1s79nKxf3ns6V7E11uY+rTfA5mD7Frpr2EKVvaXLv6RQ3Hx0rj1XqCizGUHKz7fLI0xQ9G72ur75PFrYdvY+fMHsI2HYYVNPDgxKPcdvR7y7ruPSd+hBe2v2k6bafocDIEKiAfFgl1WOOK1AQqbHBJirpfoIVmRXoYT3lMe9M1QuUctrAYTg1ybs/ZTf8MPTG967RxpA4k+xcV7Ka8aTxVWRfU5P1ClGkmBGk7hSNdlFYEOkTX/AKQwsLGAgTT3jR/ftGf8Lzhy7GkxQ1rrj0psfHa1S+qc1gaDAbD6Y4RGw1nBEX1KN48d2I71Ip9vh6tfiyEIMkWBAk05Ra9F3shqAiLrfpqIIxFx4oAWSs+hmg8CvyEIg9S5KHoY/0ovh7FEWtIifNbXt0Rw3SI55PmUhzWIOlG0hlHsvYhSWPRVVOjsnbmjcLn3Dl/nguzvm9B/RhPt87qbxetQ6bV1zim/owCPwChsUQGISQeuxhR7+VQ+FtMqy8TqPHq16n2pW6peBw86XkbDAaDwWAwGAwGg8FwJjDtzeCFHhk7TW+ih/5kHz2JbtwmkakLMVoca7vtDWuvxZJztR+11oyVxtvqm7SSdLtdDceX4q481QQq4PP7/oty2GrtaGGUVnziiX9bVt+J8uSS+6zLrKGsyiitqitRAKFWC66lRGKjpBSWmShPgY7qHbrSYUVqiMFkPytSQ5zVuY4L+p/F+o61JKxEy/G+cfjbS577k8Uvb37VgnVHR4tzfz495aOqiWXR8/OVH4mKwq7/LW2kkPg6oBAUuWvkXj76yD9WxxpMDfD84ecsa84JK8ENa69dVl+DwWB4qjBio+GMoKB/uqx+grkX3KgeY8054WDRB9h1PSLaERJbIan/1tIwT3BU5FDkCckxP5JVUcDjIFPqP7DE4tnstuglJc6hQ1xOh3guGXEZSbENi14EjT9ARFLdwi/JqkZsbBxDM6tuXXReC46vS4yEf8F4+An8BYRLX48wHn6K4+HfoMjF9Saj39HHxTrn5WI0i7I1GAwGg8FgMBgMBoPh6YinTs3PwOUluOuGUoO8eftvVD/PB4W2+ltCsrn7rKbnHp3cwVR5uu05nEp2TD3BvtmTS1p6YPxBZsozizecx3LiV6WQOMKpq42p0QvWfRQILGEhhMBXPqWgxFQ5cjWWwjJdTicbOtaxrmMNg8mBBWN3K9xz4ocUgsZErZ8llWewoXM9f3rhW5uKo6EKmfXnygVVRGWBwJZ2Xb1KIUTd7+p10AQ6QAMff+Jf+N6xu6rn3nLub7Khc92S5i2F5E/O//26WFyDwWA4EzBebMMZgSK3rH5RzGiFRlEqZBKLDhQ+mnzcZr6QWCsuLuZ2FERCY7N2YexmLEBcQ3F+XccKFl2AJqdux2aQgPZ3EULkTkywDg8ImL+DMGDx2ocBmhBJEovGeguz+rsM8OtLmlN1bjpgJPwrivrBRduGOo/HfkJmUZTqxGNi0TQSTu34eS78wmvTt6w5GwwGg8FgMBgMBoPBcKaRttKnZJyMs7Rxrl1zJb72+afH/xVfLV62xpY2W7s2kbZau89mvBl6Ez1Lmsep4HjhBDPe7EmNUQ49Hhh/iKtWv3BJ/WprIbZLxUVqCYtQR+tgCwmNUkgksiqeaa3JBTlCFWKpaNn4wclH6Ev0MpQcYCDZ31a0Z6ACpsrTpO2l/xksBSUemnyUGW8WjabL6eTC/mct6E6E6M/It47czneP3clocYxABaTsFOf0bOPGtdfx/kvfzUce/b8cyc9teg9qyhmFOiTUCktIElaCQlBse84VYXPKm+Efdvwzzxu+nISVIG2nef+l7+Z9P/0bHp9ePFo2YSX4k/N/n8uHLm372gaDwXC6YMRGwxlCJDJpHcYCk0JgIUggFthRVXH3RTUZ50eJVsYK0RSrPaJ2tcJk5duklUg3XzCMwic0knrXokaRRyARpBC4Ledti+Hqx74eJSE34On2IkBdsYFQzxKKSWw93CA26gWKe9e387FZ01TAUzqH0iWkSLY1VoWS3sOU+gJZdTsCC0kGIZr/NRTqWUrsBkJE/DWIvu7Nvt4Bilw0Xp0gOYckQ0Y+e0nzNRgMBsNTSxAElMtltNYIIXBdF8dZWuyXwWAwGAwGwzOV3kQPK9LDHC+cWPYYlrTY0rVpyf1uXHsd6zvW8tFHP86e2X1NV1MsIelP9rEytYKE1XqNBGi75uOpJhvkl1nIpZ4Zf+nOxgv6nrXkPoWgSKgVQghsYWMJiaf8eJ2qEg8aOfPmi4xhXJNQ6Pp1Lk/5FMMSh/JHOVI4xqr0SlalVyw6l6U4YgEO545wy+HbuP3YXQ2uyJSd4sqVL+CmddexrmNt/fxCj48//i98b+T7DW7QfFDgW0dv5/N7v4Qtbc7u2cZgcoBiWCLrzxkbKoKsLSyEkBSDIlqrBdccm6G1Zsab4a6Re7l2zZUAdLqdvP+yd/P94/dyy+HbeGJ6V0O/LreL61ZfyQ1rrzWORoPBcMZixEbDGYGiSFnvI2CSWgFPYGPrAWyGmgpfQkhsPYjPsSZxoGE10pSaFy6wqpGnFeEQqBEP57scawXKufaibpxKe4Ugg6R1rr3NEDY9NfcgCMkyJN/KrP4GJd18J5Qt+ukSL6ZH/gIHwl8DDZbIIHXHsp2hthhqeS6KJF1cbFS6SFbfzoz6BmV9iKJ+uCbGVWLrfhyGkCJT06dAORYaK0T1NUu0Tn9WKPJIOpoKkmlxAUlx9qLzNRgMBsNTi9aaYrHIzMwM+Xy+4XwqlaK7u5tMJlMXX2QwGAwGg8FgqEcIwYvXXM2/7frcsse4fPBS+pPLSwk6t3c7v3/ub3Egd4jx0gTlsBw7xyw6nDT9if62IjkBOp2OZc3hZMksw5nXjKS1tM3aABu7NrCtZws7p3e33Ufp+sSuhHQJtQIx576b/w5dKzTWHK1rE+oQW9gorTmSP4avfNbPE/3mk17EiVjLf+3/Cv+++/N18a+1FIMitxz+Nrcc/ja/vPnVvHbTKwDI+3lu/skHGp6RRnM0P8JYabwuCvWe4z9gXccahlNDWEiGU0O41m68sIyvQgIdonVAqKJYVKFV5P4UgoXSzoSAUAdoDbN+ji/u/3JVbITIvXvlqhdy5aoXsm/2AI9P7yQfFHCkzXBqiMsGL8ZZYi1Vg8FgON0wYqPhtCbUWY6Hf01e30/ABPNfdjQBPsfxOYGtB3BZ37DryGGIgHFs0T9vdAtFoWHMyrlI6Kp1ybWu1RhJjEkESTRe7KTUNeNUxlwYm0Fcsb7heKgnkdJhjfUhynofWXUHIVNoAiQdpMVFZMTlCFG5xtwLUILNlNhRU6+wvYVZm4EFRdH6iNrmFNVjjKi/Quko/z5kel69SEXAGAFj2Hoo/voJPI401GKUpAkpRbvsWt6DQlNGUP9CK7Dpl7+66HwNBoPB8NQShiHHjx+nWGwdWVQsFikWi7iuy8qVK43T0WAwGAwGg2EBrlt9FZ/b+yW8JbrMKty09rqTuv62ni0MJgdIyIWdiwuxoXMdg6mBk5rHcrmo/3xsaTc45pRWKK2qaxSRINVcOO1wMqxMDzc9txg3rr12SWKjFFakfBE5R6W0qstSzTbqNRUaBaAbBcnapZgTxTFc6ba8r75kL4PJ9r5mn9vzRT6/90tttQX47J4vUA7LvH7za/jAQx9peD6hDtk9u49ZL9vQVwOHckewhMW0N8NkeZpiUMS13Lm1Jj33NDTR81FaYMuK2aB2PI3WmrxfrJ7aN3uAA9lDdDmdvHzDS3jO0GVYcm5NcGPXBjZ2bWj7fg0Gg+FMwYiNhtOWUM9wJHw7vj6KJIFFNyHTLVprAsbQeCT0ljrBUQiXpD4Xi87YGRf30B60iBSNxMNMLBqq6tH514yOCQROVXwT2PMcjQBhjbDZeE1JBkcMY9P6RWxG3UqHvIKE2EjC2tiyHYBFN4ropUoKl6Q+mxK70HHdw8UiQCRprBp35XyS4uwaYbM5BfUwI+F74mcYEerJlu0DRtEEOHoNIY3xIlHARwpFiYWE20jsTdYIkoIO8Xy65PULztdgMBgMTy1hGHL06FE8r72FMM/zOHr0KKtXrzaCo8FgMBgMBkMLOt1OXn3Wy/nMnv9cct/LBi/m/P7zTur6jnS4dvWL+K/9X132GDeepOB5MgynhzinZysPT+4ANL4KKIdlvHm1KAUC13JJSLehpuH6jrWc3b11Wdd/4Yrn892jd/Hw5KNttU9ZSax4vSZlpxbYrB2h41+1SARC1gunzYTKkcJxhlID1evVcv3qq+sEtlb8aPTHSxIaK3xp/1fI+jkemnik7rhGs6eF0FhBac2O6Z0krQQgsKVN3s+TcTJxJlkjGk2gwjrBMRIhI+dj5THPuSDhoclH2Dmzm5XpFbz74rexJrN6yfdpMBgMZxJLC542GH5GaK0YCd+Hr+eKNjti8Tz4kBk8DsVj+AR6El+PkpSbGLB+A4d11bYBoy3qJgokaSQpLLqQpAE7fkGb/3JlxW0y80YQcW3GSCBzWYdFdzxuJORZ9GIzTFKcQ0JsJNR5ivpRCvohivoRSnoXAZPVl76Sbsx0b0WHeH7d51IkSXEODmuQpGi+z0AgcJF0InCxReuYlG5544LX9/UYx9X764RGiCThhQiZpMwuWjlIJRkki0Wn6JrrCtLiUtbZ/4QUy99FaTAYDIYnnxMnTrQtNFYIgoCRkZGWcUsGg8FgMBgMBnjNpl/khrXXLqnPOb1n87bz/+CUXP/GtdctO/4+bae5cuULTsk8lsuvbv0VNJpZL8usn6UUlgl1WPc70AHFoMisnyXrZatrOY60edXGl5G0lx6jClH85p9e+Fa2dm9uq/1gagBXOnS5ndjCbum2rFCpVViLEJLUvNjXZuMEOmSi1Lip3JIWL15zTVvz/dL+r7TVrhn/trsxHnisNMHMAkIjRDUoS2G5Gq9qCxuEoBSWFowyrQiOoQ7xlR9H1oKsWV53pUtl7bAi9I4UjvO2+27mYPbQku7PYDAYzjSM2Gg4LSno+xtqE1p04bBq0b4+I5T0ExR4kDJ7CBilrPcyEt5MiUcRpBGk8BmPXYghmiD+Hb0MRh9HLw0CF4sOJF2xOGkTfesIBAnEAi47SZok25l70XCxGSIptpIUW7BFP74+TFE/QsAJFAU0ZRRFQqYp6z0U9UP4HENRqi6mBnqSkt5NUT+Opw+hdX3kaLd8MfO/vYWwccUqUlxAgk1x7Gsi/j0nrIqqgNr8RdgSXXSIKxb8Gsyor6N0Y52tViJiLSETDbvqarEZIMGWJjU4awmw6KJHvoKN9uexRe+i1zUYDAbDU0e5XKZQKCyrr+d5TWs7GgwGg8FgMBjm+N1zfp03bn0dqUXq6FnS4prVL+J9l/zZsgWy+QylBnnDll9acj8hBG8+59dP2TyWy5UrX4Af+pSVV41ObUYlctPTPlkvC2g2dm7gJWtPLmkp42R4/2Xv5sVrr8G1Ft5IfU7v2Vy/5irWpKP1M4HAbSGgad3E1SgElrBIWomq8OZIu05Qq2W0NN5w7GXrb6Ivufg6zL7ZAzwx3f7G+lqKYYnDuaPk/PqfA0aLY4v29VS0wbEcRwsLIUhbKQIV4EinqRu04gBVqGrsrKY2ZjUSbRPx10cIUXW4+srnRGGUP/rRn7N/9uCy7tdgMBjOBEyMquG0ZEZ9o+lxV6wBDT7Hmp6PxDoPHw9JCkESSZqifrimBuATcZjpLAACB01Y8zKh4yhOD7DjcazYqWjH5yOXY+R6bMSiC5shLHqj3Xt67qXMEpELMmCKst5LqyjXChoPTx9BUSKn7mFWf4uifoha4c4SfXSJ6+iWL8YW/dhigIx8Dnl1b8N4QghcvQ5NqWlcKYAtWtcS6Je/ihAL7PTSPln9nabnRBt/5WgU4CFa1IsUWCTEWbhswNdH8TiMokgl1lbgkBGXc5b9bzhi5aLXMxgMBsNTz8xM83+PltK/o2Mx57vBYDAYDAbDM5tXnvVSblp7Hd8b+T63Hr6NAzVOq75kL9evvpoXr7mmLaFoOdcuBAW+uO/LbbUXQvDb23+VF658/uKNn0S01vzuvX+EEFENxLCJE3A+Sit87eMIh18/+w2s7Vhz0vNIWAnefM5v8IbNr+U7x+7gu8fuYrw0ga98OpwM5/Wew41rr+Xc3u38eOynvOuBv2S8NIGnfFzLbYh9BZqKpjIWGkGQsVPkfBW79ZpTCIporavO1StXvYA3bnld07aPTu7gzuP3MFWeRmnFI5OPMVGepC/Ri0CgtGK8PMmsN0ugo3U6RzoMJPvocjrrxsr6kXtxrDROh5OJj+UoBK1rvwMEKiCMN+wHei59y5Y2aTuDRpGwXMIgrJETWyMQVcGx1v3Z43Qz62UZLY0x62WrY7z69jdxycCF3Lj2Wq5e9XNknEzzgQ0Gg+EMxIiNhtMOX49Q0A+2PO+KNVh04usTsVgW/ZOtyFfjMzUhkl4U0wQ1dRor56K2ZebiUSv1F+cToMghySCwESTQ+AgsJB24rMJmIK4jGAIWkhRSzIubiIUzgY1FHyHZtoTGuTkrPA5xKPwtEqKxXmOoJ5nS/8GU+iJ98tX0Wa9jUP4WZb2HQI82tBdCkNCbKbETRa7unM0gNs1/sOi33kiXvHrBueb0Dwj1bNNzlughWKBuY4VI6G0uNlqiB4h354k1uKyJX/1CiKNrO+QLjNBoMBgMZwhKKbLZhaOOFqNYLOL7vqndaDAYDAaDwbAIKTvFjWuv48a11xGogEJQIGklF3XMnQresOWXWJtZzRf3f4XDuSMt223t3szrNr+KSwYufNLntBj/c+Br3HviRzjSJWNnKAQFwgXcjRCtV0hhgRA8e+CSUzqfTreTl2/4eV6+4edbtrl08CJ+e/uv8tFHP87Omd2gwBYWwbxUrPlYwiJlJWvERcFQaoCM3cF4eaJl6YJQh6TtNL+44ed53aZX1UXmaq355pHv8PVD3+JQ7nBdv50ze5jxZjkgDmHF82sW6zpemiBpJRlODTKcGgQgUNG9VNyJANPe4hsYfTW/3I+umg8caeNKhw4nw/7sITxVXlRorFARGnN+nrSdZNqfYcqbbugzWhznaP4Yn3zi3/n07v/gN85+I9evWXidzWAwGM4UjNhoOO0o6wMsFrdp0Y0lulGUCfQEAaNAMY4GjdyHIZMN0QdR4EGeSOQTQIiOXYu1Lxg09MrHtQxtBG58ncjpKEUqroO40Hx7EdjYDACSst5P+0JjiCKHIEnAOLbuxxLdLVqHTKrPEzLDoPU7rLb+kmPhzfh6pKGlEBZJfTYeBwkYBzQ2A7hiQ5NxJY5YRVbdzoz6OoIkjlhFt7ietHh23YtkbZ3NxufQh+DQgrUbo69F8/Nzz3D+cUHtX2fN2hgMBoPh9CQIglNSc9HzPCM2GgwGg8FgMCwBW9p0uV0/02teueqFXLnqhTw88SjfOno7R/PHKIVl0naKDR3ruHHtdWzubtxk/VTxT0/8W3WFKmElkEJSCksEKkSh6t5jhRBIIeMYUhetFV89dAtn9279mc/7petvIm2l+fAjf88T07vRaLJ+rqmYB1HdwrSdImHNbfx2LYet3VtwpcOazEpGS+OMlcbxwjnBLmklePmGn+eywYvpdruY9WfpdqM1q0AFfPiRj3H38R80vabWCqVDZv0sSissIck4maaRraWwxMHcYbJ+jk1dG6rinqpZWwtU67WmCmreemPDKqCAzV0b6bQ7eGDioSY9Ks3m6jJKIavzCVTArJ+j1+1pev1SWGLWz9LldFIOy3zssU+Q9XO88qyXLjp3g8FgON0xYqPhtEPPcyIuhCSBI1YQ6ONVwS8SFLNIOmBePcXIzVh5ERHxxwqwqoKVwIqddbquJ2gSYgtoTZm9QCQitoMQEpd1SLpQzLZ9jxqfkCyVlxuNR4knSHIuFq3j4mbULThiFT3ypayx/oYp9UVm9XdRut7FKIQkwVlkeB6OGCRgFF+PAiquVdlFSBalS/jU7zr09VEK3I8tBumVr6Bb3gQQuzxbPAckNoP4jFDxa0IQR6dG56k6TRux6I/bLEznIu5Lg8FgMJw+KNXe5pvFOBWCpcFgMBgMBoPhZ8P5/edxfv95T/U0FmT/7EEennys7pgjHRzpEOqAcuhV6/dVRCdXutVah4EO+erBW3nzOb/xlMRlXrvmSp49dDHfOnw7n937BXZMPcGx4nH8MIjqD8bCoyUshIBSWEahSEiXDqeDrd2bq/UeHemwOr2S1emVKK0oqTKTpSlyfp6vHbqVrx26FYgE10sGLuSGNddyx8jdLYXGCjk/X5XzAh0yU55FCll1jkoRRakmpIsUFpPlKUSWqphniZp1P9HMQNAaGZdIasZMkKXb7SIfFFBaVWt1VuY1JzLOjVGJU7WxFjA0QDEo1cXC/vuuz7EiNcQVK567pPkbDAbD6YYRGw2nHa1q9VUIyaIpoVEILJQu1jnh5oTC+a5GHZ+rIImExtrFyQBBGkkyFsAqLxESsLDIIISLxiPUMw1xqa2QIsUK8S5G1Ufx9KG4RqTVsr3GR1GKY2Er8aBRVGjIDEX9KBYdOGJ1NfI01DkCRlHk0IQcDt5KTt5Ll7yefvlG+ngDOX0nBf0gSucQIop07ZRXkhLn1F9fB8zoWxkPPwnoOufifAI9xlj4ccr6IIPydxZ1edpiCE8fjh2m9QvDmjA+EqIoIklCdbdYElesXnBsgKQ4l4RYv2g7g8FgMJweSLn4JpKf5TgGg8FgMBgMBgPAVw5+o6UT0BI2aXvxZdWR4glOFMfY+BTV5ut2u3n1ppfzqo0vY+/sfm499G0+vfc/OFEcpRCUqmIaVMQyRagVjrRbrgUdL45ytDCC1ppV6RV157TW/Hjsp3zzyHcZyR9nc9dGUnbztbNCUKQi4YUqrAp5ElkVEZXWlEOPcuhFdRWtFBOlKbqdbmxpk7HT1fEcsfjXo1YAbBYd7AiHsvKYKc/gSIe0naIYFGPnosZXAQKBLRuvpbWO5i0EXujVuUQhipvVWpMP8pTCrrg2ZsRn9nzBiI0Gg+GMx4iNhtMOVzQWztaEBIzj61E09cWew7jmoMRF4MTuxcbdSZFwVxNvgUDHLrpIuJRxOy+OSZ0vBmp8PYYrVuOwgn7rDWTVXSidI2CCgImqmCmwsOjCYghLZLAZZlx9HK0LVVdfXHUQQaLOrReJjKXqZ9F9yPkzQVGgrPcQ0o+iGIt39W1y+i5K4Q4mxKfoFjfRK19Ll7y2+YOvIafvZjz8xKLtaplVt2KRITlPuKyfk0dZ74rvVxIJqfVEXxELTZmQIBJ4SZMU2+KI3IXpkSZ6wmAwGM4kHMdBSnnSDkfXffLrDBkMBoPBYDAYnjmMlsZPegwv9CiGxcUbPskIIXhk6jG+fex7rEgNsyI1TFl5HModYbQ4BkQuwUoc6IyXZcfUTrb3bK26GwH2zu7naGEET3loHbkjjxdH6XI6GEoN0hNHqJ4ojlIMSzw+s5Ozu7eStus3pnuhh6/8BqERonhVRCX5ao5ABeRUjoyTYaw0xlCyn6HkXBmdvmQvxwrHF3wOjrTxVLR2N1ebco6+ZC+T5anqbFzpYtkWZVWu1nuU80RYgcCVTlQ/Mj7lKZ+ElUBrjac8POURxqLu0fwI46VJ0naKoeQA/ck+juaP8dDEI1zQ/6wF528wGAynM0ZsNJx2uGIdSbGdkn4cAEWBkt41z5VYS0gUyRkAAo1CkmwSV9Asu91ivuAVXaf5rquQWWA1nfIqBuRvorViii8S6FFUjQgaiZgWPo8hdQdK55AijaQTSRpFgUjkLKMpI3Dj4+Wq0Kir7kzZ5F50/N8yZfYgSSNofEny9Ri2GCDUs0zqz1NmHyvkOxAL7PZSusSY+njL8wsxpb7EWusfsMUQgR6dN+OQkt4ZPyeJRUcsrM6PrAVJGvDRccRqSpyNbHJ/8+mRr6BDmp1gBoPBcCYhhKCrq4vp6ellj5HJZLDb2FluMBgMBoPBYDC0i+bkY/oVqkFoeyq4a+Qe/nXXZ+uOJaTLho61ZP1c03qH5bDMrpk9nNOzDYXmkckdjNUIsK50kEKitGLam2XamyVhuQwlB8n7BQACFbJrZg/n9W6vcwOOlsajckFxPGktmsjROF/Ug6jIUD7II4TgmtUvYrQ4XhUB01aKTidD1s839KtgCzuuqzlXZ7GCFILBZD9H8yN1xy1pkZZplNbM+rOx+Bqt1FnSwhFONIea6WoUvvIpBMWG+7NkZG4oBEUO5A5zOH+MjZ3rufXId4zYaDAYls2dd97Jb/7mb7bV9oorruBTn/rUKZ+DyZsynJZ0yxuBitD4+AJCYz2aEAgROE3Pzid+NZh3dCFnRUiHvIJ+8SaOhu8kq7+DTS8p8SzS4kJS4jxS4lkkxfZqfUjFLCWeINSz8RUdJJ0IklDjpgzJVV2bAid2PVpNhMaoRcXdGM242PT+dHy+Ql79iFH10QXuD7L6DpRu/WK2GLP6VrrE9Q3HPX20TpCNwmlTWHQhSMWuRQuBg00fCc7G5SwEdoNw2Yxe+RoGrDcte94Gg8FgeOro6uo6qf7d3d2naCYGg8FgMBgMBkPE6vTKBcvKtEPCSrAiNXyKZrQ8lFb82+7PNa1x7kiHLV0bmwp7EAlix4uj7JjayXhponrcFhYpq1FELYceu2f3Vt2DELn85rtEp8rTlMISoukGe1rG10bnNF7osa17C2991pvrvkZDqaGW/SDa6NhhZ0jXxK9W6Ev0Ygu7pcgshWAoOUDaTpO2U6TsFK50EUIQ6nojQ6gU+aDQKDQKiT3PABDqkD2z+xatb2kwGAwL8cQTTzzVUzDORsPpSYe4ggnxWWbVN2MBcSGi4M3azxUlLDriGoAB0X60oC4uda61ABLxKJWo1caaj4IEKfEshsQfcky9i7LePe+8i8BFoyjpR+KxIjQBZXaTZHs1nlWQRJMAQjQqFlQtJBkEsqZmYyPRDIt1RxQecl69S91EOM2qO+gUV5GWFzUde0bd0vR4u2T17ayVH2NWfJNAR1EcIbN4HGTORSpiQdUmEh0TEM/dZQOOiF4OtdaE9MeO0kYENhn5PLrlS0iJ7Sc1b4PBYDA8dbiuS2dnJ9lsdsl9U6kUqdRTv1vcYDAYDAaDwfD04tLBi+hze5goTy17jAv7n0XqKXY23j/2E8aKrSNhO50Ozu7eyu7ZvfgNDkfN7tm9WMKqCmeOtElb6TqRT2mFil2KgQqi2oaOwIkjWMdK46xMDVf75IMCxbCE1prKr1qab7yvnItEUoHgihXPRWnF3z36j/jKpz/Ry0xyhvHSZNO+GTvN5q6z2Du7n1wwt0E/aSVZ17EWoFovcj6udNjatYkdM7saxNDa+VeexXxRMRqjeWqXBh6afIzHp3ayvXdby3s3GAyGVuzYsQOAgYEB/vmf/3nBth0dHU/KHIzYaDgtEcKmS1zDLLcu3harRlSzIBbuIoFq7h//SLRUcdv6eFKJE9d7VGh8LHpikVIicbHpR9JNRl5Oltso6R0t5xMwHrsa69GEeBzCop+A0XjuArARQEiJKIB1sV1zIq43Of/FpgzzxMZW5uUZ/Q3SNIqNgZ7E0/sXuf7CKF3A5wgr5c0cCP8Xnt5PwHRDrc0oQrbyMqaIXq0sFB6BHsVhBRZ92KIXm1765f9CCAels4DEEj1kxHOwRc9JzddgMBgMpwdDQ0MEQUCx2H5NG9d1WbFixUnvODcYDAaDwWAwGObz7MFL2Nq9mR+M3r/sMd68/ddP4YyWxy2Hv71omw4nw/m95zJenmC0OE4xjEr8eMqjHHpIIXGkgytdnDgOVWuNrwO8sExQ4+wLdYjSiqyfo9vpwpIW5dBjxp9FIDiUP8KMN1sn2EXrXDV1G9ForRve86WQpK0koQ75yqFbSFgJUnaS12z6RcaLE9x1/F7O6lwPUCc4VuojDiT7kUKypXsTu2b2kA+KpOwk27q3YMciY7fbxUjhRN11XemwrXszKTvFitRQQ23I2rU8pVXTn0+kkLhW6xJBAsF/7vtv3nPJO1u2MRgMhlY8/nhUku6cc85h+/anxpRjxEbDaUte/5Ak51Bm17z4zXoiN6EP2EiSKKaJX0uqLsIIyZyopYmERwuBXY1dFUhsBkmK5ruIOsVVTKrPLDjvhSI/Q2ZJMFQVGytUhFCg6lCMRNRm9+u0iJVVDc5N2aL2ZF7dTyDHscVA3XFFruXcl0KgJ5nhVoS2iJ51vUNz7n5r79CKXy4L+BQIGEPSiatX44iVlNnDCvm2UzI/g8FgMJx+CCFYtWoVo6OjbTkc0+k0w8PDWFbznccGg8FgMBgMBsPJIIXklza9kh3TTzDjLT2BY0vXRq5e/aJTP7El8sT07sUbEdUSHE4NMZwaIufnKIce+7IHEbYk1CFJa26NKdQh+aCwYNxpqENmgywJ6ZKyUhwvjpL1svGqXKtSR5UVPU2oQywshBDYwsaxbEIVko/rIO6c3l0npHa6nVy56gqGkkN4yuOnEw/z6NQO8n6BTqfexeNIh+cOPZuElWDGn8UL59bZupxOklYyinkVgj63h7WZ1VWhcHV6JaWwzGSN47VS/7Hi1LSEhdYaVV37EtjCwgs9XMttajRIWQl+MvEQxwsnWJF+aqN3DQbDmUUul+PQoUNAJDY+VRix0XBaUtYHKOs9SJEgqc8jZIqA0SZxmgKbYUKmAUVI7cufrgtDjYSs+ZGrIVC/q8gWzfPdbTGA1Bk8fajlvENy1TqKrdtksehsmOvcR5FDMRJAKwJpLS60FAXrX9acFvcCioL+KV3i2nnHT37BVmvNlP4PfH0cKZIk9XkUeBDFNBo/dow2exmtCMSVl0ufkGnK+ChdJqUvPOm5GQwGg+H0RgjB8PAwfX19zMzMMDs7i1Kq7nxnZyfd3d0kEvPd/AaDwWAwGAwGw6nlpnXX8a2j3+V7x75PIWg/gaPL7eTvnvuBJ3Fm7aG1phi2P+8KHU4HmhwajSNtgrDeuZjz861rGyJjkS1y+XnKJ9ABZVWuCpZSCMLm3REIpJCRm1JEka1KKPJ+fQ3ESkRrhayX5RuHvk2H08GfX/THvHbTKwB4YnoXd4zczVR5mlCHdDgZLhm4kOcOPRtb2pSCEneM3M33j/+AaW8mikCVNodyRxhM9jdcRwjBps4NONJmtDiGJopHLYUlQlSUXqbC6lwFAktKAh0ShCGlsIQjHZJWsipSAgymBtBa860j3+WNW1+3+BfJYDAYYp544olqXd6nytUIRmw0nKYEei6uQAiBTR82fShdiuM3QwQ2khRCOAR6kiKPEImHFsR1GhtrL0qoqwEp4lqOIQILgYtFb9M5dcubyKsf4OsRQqZj0UwgsKO4T/pRYmGhESBkhiTbKLGjRU3GWiHOqYsfFaRaOh7nE82rf4F5NAqWFt001sBcGgGjKJ3DojOahxBInQQyBE1rL1a+PpHbtF7wVCjyBBynqB9Z9pwMBoPBcGbhOA4DAwP09/cTBAFKRVFEtm0jZfOIcIPBYDAYDAaD4VTT4XTwV5e+i7fd927uG32Aaa/ZukY9A8k+PvKcD3Dp4MU/gxkujBBR3cRa5167ZP3KupFAVjaGa00+KLQUGivXFFpUo1A1Gk9Fa2gVsdESFj7z60POYQkJCAId4mmPclCuz8YSFokWkaQ5P8fND3yAD1x2M1u6N3F2z1bO7tna8lpJO8mL117Di9deUz1WDsu88/6/YPfM3pb3uL5jLcOpIUaLY4yXJ7ADG09V1vmizfRCzD07qmeieFpf+WScDLawcS2HHrcHgAO51iYHg8FgaEalXiPAueee+5TNw6zWGE5LVJOahwBSJLFEN7bowxJdCBHtLrJEFxWRKnLGNa/dFEWMyupnUXRnxU0ocMW65lEG4gLK6jBj6uN4HCYki6KIokDILB4HKPIQgT6x4AtXRBA5NjkbUa2xOP/Fw0dRiOelmBNIy4Rk49qSze+wgs2KukjVxpaNLkZLdJAWy38Z1loTMouMhca5a9koSkT3URGAm32dGouCR4JjibLeQ6Anlj03g8FgMJx5CCFwHIdEIoHrukZoNBgMBoPBYDD8zFmZXsFHn/NBXrXx5Zzbu53BuO5fLZaQDKUGedHKK/j0i/6J69Zc9RTNtpHhVKvUq4UJa+owpmKR0FPegtGpFaqxojW/Qj3n9rOEhWxRd13GQiPxCJGjsZ6EdHFk6/qH5bDMXz74NwSqtaC5EAkrwc0Xv50NnesWbJe0EqzrWMOFfc9idWYVtrTr4mCVVpGjUQUEKiCMP/ZVgKd8psszTHvTSGTVObsUB63BYDDAXL3Gzs5OwjDkAx/4ADfddBPnn38+F198MS9/+cv52Mc+xuzs4htmTgbjbDSclkjSS2of1fdz4yT0IgI7dh42H70iONZGdjqsxqavoXVKXICnjxLyUFxrsDmagDAWCCWZBYS+6JpSpEjpc/A5TsBYLC5GEaOKfE3LBJqgOleBqtY81HGdwwirek2bfhyxquVco9atHZyF8IEF+7ZCMYslMg0Sok0vHgdpHp9aSzM3Kmg8LDqZUd+k3/rlZc2tOkftkdN3UtAPocgjsLDpp1NeSVKcfVJjGwwGg8FgMBgMBoPBYHj60Zfs5S8v/XMO547wjcPf5rtH72TKmyLUClc6PHvwEn5h/Y1cOnARooWItlQCFTDrZSmrMhk7TafTuayxr139Iv5l52eW3K8iGNrCYlV6BYfyR/FUrUNSo7Su/j/aUi4jZyNRVKrWkeAm4tWrcuiRtBK40qWsPHSNAAlzEaoVVFz3UGpZvXdHOjjSYSDRuIZXy2RpiruP/4AXrXrBku8doNvt5oPP/gs+8cS/cdfIvfiqWTpZRH+yj8end2EhCVqsRypUNUisvj4llMIyj009TqeTYXvPtmXN12AwPHOpOBt93+clL3kJvj/391W5XGbHjh3s2LGDz3zmM/z93/89l1122ZMyDyM2Gk5LEmLzIoJhPYEeB0DionHQ+Ah8opjUyouQQOAgceOxfTQemjCOY639dhCkxPl0iBcwqf6TkPH4aH1O+3yiiNMQRR5JR1OXZO0YQji4rMXRqymxB4/98TxdQCJwEcjqmHNiXaWWY1gVHCUJKjUsI4dma6RIkxHN/1JJi0txxEp8PbLgvTYjZAaHlc2uyJyQuBjN2kgUHrP62/Tp18z7WrU5Nz3DlPoSs/o7KN0YITujbiEhNtItf4EueXWT/lmy+nv4+hiKEpI0CbGJDvECpGi9m85gMBgMBoPBYDAYDAbD04O1HWv47e2/ym9v/1W80CPUISk7dUqvcSh3mG8c+jbfG/k+xRqX28r0Cm5Yew3XrHoRnW7nAiPUc82qF/GZPV9YcpSqG9cq7E/2MZga4EDuMKFWkYCIQmvVJHcrEtREzS+FrsaieioSG21pYQkL5Fx9w6i2oVW3llZxUVbOO9ImbaVIOym62ngGtxy+bdliI0DaTvP/nfe7/K+tv8xtR+/gjpG7mSxPEaiADifDRf3nc+3qK/mtu/8/8kEBRzqEOoxdnHPMT/Gq3q+IQlZDHWIJi6yf5+4TP+BYfoRVmWbrawaD4Uxh586dy+67atUqVq1a2EhUwfM89u6NIp9LpRKdnZ286U1v4vLLL6erq4v9+/fzX//1X9x3331MTU3xa7/2a3zuc5/jvPPOW/b8WmHERsNpiS16yMjnklPfb6v9nKBYeaFxcTgLh2EUJQImCZgCPKLcdAuLbmyGEKRRTJPmAjrkFUjRSUY8G1es5ljwXor6obi2YiSWVeo7NiM6bhGJgGUEycZ7a1JHUQiJ1Hacgp+MhcP6cS260Hio+B6IfZxRNKmLwxocsQLZ5Jrz6RRXIUV9u6geZogkzQrrHRwN34HSS4luEKTlZXX1NiuEzMYCbzsvto1io8AlZIZQT+JzDJeFYyzm4+ljjIQ34+vjC7Yrqb0U1F8wJv6BpDiXyD3qEzKDr06AaHS2jotP0SWuoUf+ArYYWNK8DAaDwWAwGAwGg8FgMJyZuC1qBi6XnJ/jQw//PQ+MP9j0/EjhOP+y8zN8Zs8XeNVZL+O1m17R1ridbifXrHoRtxz+9pLm05fo5XD+GMOpQSxh0eV0Mu3NVONVFyojVIlOlXECV8WtGImHUaJVQroUw1IcPdq4GlQZA6LyCikriStdhBAMJ9uLhn18eicnCqMMp5cXJVuh2+3mlWe9lFee9dKGc3/14IfYObMHiP5MlJWHLQRK10fHzidyf8YibOiRslMIIG2lec9P/poPX/6XSxKVDQbD6cV73/veZff9vd/7Pd7ylre01Xb37t1VJ+OGDRv41Kc+xZo1a6rnL7jgAl72spfx4Q9/mE984hOUy2Xe9ra38fWvf/2Ul6oxYqPhtKVb3EiOdsXG+f9wC2wGEcLGogOLDhKxQKW1boidsBgmI59Nv/UGAPLqPk4EH2VS/z/mR39qSigUggSSxhdLQQJNIXZNJua5GyU2g43z1z4BE7EbsrmQGY3tYuES7SHLVWNdXc4iITa07FePpFveCEBZ72NGfZ2s+j6aUvV8Wl5Mr3gVU/p/UGQXHVFgM2T9IVl1OwGNYmMUA+sCRdpzN9aPLUlWXa5hHDHbLoGe5Fj45wR6rGUbrTUBowSciGpL6p2U9G4s0UVZ7wMUAhtb9+OwqlorFEDpLNP6f8jq21lp3UxSbFnS/AwGg8FgMBgMBoPBYDA8s5nxZnjn/e/lcO7Iom290OOze77ARHmSN5/zG22N/+tnv4FD+SM8Ormj7nioQ8ZLk0yUJ/GUh9ZgS4sup4vh9CCv3vgy9szsA6K6lJX1t4WExlo0GltEdQxdy0VpRdpOAYIut5NSUKKsPAQCjcYLPQIdxO5JjRIKiSAlkySsaGP+QLKPoVT7m70ny1MnLTa2Yrw0wdcO3orWc7UobWER6BApxIL1LRWaQIUIISiGJRzLoS/RS8pOMlI4zq1HvsOrN778SZm3wWB4+nD22Wdz2223ceTIEdatW1cnNNby1re+lfvvv5+f/vSn7N27lzvuuIOrrjq19YVPrXRpMJxCUvI8MvLyttpGAp1GowCFzXDLaMtW+faSDrTWjIX/xEj4PnL6dprVGBQkiJyLhTjaVM8bJ4pAjfrWx8Da9DWNAPUZAzQua0iILSz2rSmQsTAZiamKfNsS3qD1OwhcjgRv43DwB8yq22qERgBFQf2YCfVpojjZC5Ei02IeDp3yRayxP0ynfCFSdLRoF/1XYFc/a4dIaMzUjEE8RvuMqf+7iNCoKLMXj4OR0Bjjc5SSfoLKnwFNgM8JiuxA6VLDOKGe4Vj4Ljx9aEnzMxgMBoPBYDAYDAaDwfDMJVQh7/vp37QlNNbyzcPf4T/3/ndbbR3pcPNFb+eywYuBSOg6mDvMgxOPcDB3mJyfxwt9fOVTDEqMlcYo+kW80KcUlqNj5Yk49rO9dR2BwJY2lbBVL/RJWSnO7d3Oeb3bObt7Cxf0P4uh5EC1fcJKkLEzdDgdJK1ELEJG9Q4DFTCYHOCsjvVLek6Bbq9E03K49fBteDW1HLXWWMIm1CG+8uN7b/4LovtSWhHqkJyfp+AXGCuNo7Xmm0e+UxUxDQaDoRWWZbFu3Tqe97zntRQaIdJEXvOa11Q/v/fee0/5XIyz0XBaMyz/hGP6Zkr6sabnNQEBY4Tk0RSAyP3nMwpa4YghJOm2rpWS5zOuPs6MugWI6g82Q2AjSKIpofGr9RlrkXTEzkO/WqNRksal+QtRwBgWvThiTVwM+xx8PULAJK1qGDoMYNGLx6HYbTmLRdcCdygYsH6DlDiHI+EfE+rpRZ4IKGYp6UcZlG8BGVDWe1EUECRxxCq6xFVYorvaPiMuJcedTUZyqAioUZxqJAq3ureoXmWiLk42eo6iaQxtK3w9Sl7d1/K81hqP/YRMzj8T36fdIG5qypTYSUpvR8wTtJXOMxK+n3XWP56ygvAGg8FgMBgMBoPBYDAYnr78YPQ+dk7vXlbfL+7/Mi9Zdz0Zp/km8VqSdpJ3XfQ27jp+D++8/72MFscaVmUc6TCY7GcoOYBruRzKHcZTHsfyx5FIKtpXxYnYChkLjZWt45W2oQ7rxEoBnNW5nt5ED6PFMaa9WXzlU1YevvKr7ZWORLtZf5aR4gkGUwM4TTbzNyNjL/5sloPWmm8fvb26tOWFHiVVRmm16PNpRlImCXTI/uwhZrxZNJr7xh7g8qFLn4TZGwyGJ5t3v/vdbNu2bVl9263XuFS2b99e/fjo0aOnfHwjNhpOa6RIsMp6L2PqH8mqeqehp4/hc4wo4lLGpZUT8W+Fz1E8fRiLNA6rsUVfHOXZiCs2EOrJqtAIVGM7m86LZCyVldAEKEp1tRIrzkPw4/YZkmxFiMaIVK0VFt24Yn31hUuSJiE24bKOgHGULqLj+5Qijc1AVQSzdAc+JxZw/Flk5OX0yJfhsJIj4VvbEhprn8OY+hir7L+iS163YNsOcQXj4p8Jdb1Qa4v+uJajDQSIWFDUdbUnK/fe0TSe1hb9pMWF2KKv7bnPqm+yUGxryCQBEw3HFWWiV2G/+txr0ZQpc4gkmxv6+vooBf0TMuKStudpMBgMBoPBYDAYDAaD4ZnDVHma24/dyfHiKP994GuMFcfJ2Gn6Er3V+obtUA7LfPfYnfzC+hvbah/qkG8e+S79iV46+tJk/VwU+YnAlS5dbmfD9TN2mhl/BikkUgiCWHFs5XAUQmALi7l1HonWqromprRquEaP202n08Hj07uYKpexhYVlSUoaEpZL2k7H9+txJH+M48UTbO7aSJezcE3DvmQv6zpaO31OhkJQYLo8gy1timGJcliunpNCInT7gqMtbJL23Ib7yfI0mgPsnN5txEaD4Qxl27ZtXHrp6fX9m0zO6Ree553y8Y3YaDjtkcJlQP46Sc4lp79HTv+Qst5FSA6BQJCMRblRFIXY5edREZkCigRMYelOLPpit2N33StRt7yRafXVhmtrglh0rIhhIhbCRFxH0IprM5ahRmyk2nILvfJVhHqWIg9TK3xJkaFTXE2HeCFHwz9ueu8CB4eVCyaPCuHgsoZe8Xps0UuJHWgdIEUaRwzRKa6qCnQT4b8vGCnaCo3PRPhvrLE/uGA7IWy6xLVM6S/VHbfoQJJG46FiEbdeaKz8togiasO62pVRXGw/3fKmJc07p+9Z8LzfpL5kNDev5mM/js6tJ2QKpb2mcb2z+hYyGLHRYDAYDAaDwWAwGAwGwxy7ZvbwPwe+zg9H7ydQAcWwxKOTj1fPH8ofZTDRz3BqENdqvmF+Prccvq1tsfE7R++o1m1MWIlqHcSFmC7PIJCknSSBDvC9oKWIJhBIBEprpIiPCIHS0dKWLW3Gy5PV6NQKSiuemN5NISjWzUkgcWTj8nWgQnbO7GFb9+YFBcfrVl8VOyxPPaVYXPRCr05orFApKbSY4CiRJJp8rafK0/xw9Me8YesvnYLZGgyGpys7duzgyJEjTExM8LKXvYxUKtWy7cTEnOlmYKD92rftYsRGw2lNWe9jRn2drLqLgHECfQKPERQFolAGF4uAgFFAoJhtMZIiJAcIQj2FTT+u2BhJlWIbLpso6f8LgCYkYJSAyXm1DCNCSgic2EHpIHDQKCy6ECQBjcDCEt30itcwZL8ZgEBPEjCG0mUskcFhDVIk0CeZHa90Hp9Rxvh7bN1bPZ5kOymxHYvopUtrn1n97WVfp6R3UNYHSIgNC7brka8kr+/H0wfrjjtiGKULcdxtRcCtJRJzNeVYvLWRpBFIbAZwxWrS4rJF5xnoybiWpiTQ4y3bKV1AkWs4HonLtbU6WxXz1gSM4bIarf3IgUoeTUgp3IPFED3y53HFk2N7NxgMBoPBYDAYDAaDwXDm8O0jt/N/H/9nQhVWj+WDQl2bQAWMFE8wUZ5ka/emqqNvIY7mj1EMiqTs1gvMFb5x+FtLnndZRRuyi0GJlJ2iFJYJVLSuo7SuE9M0mjB2PioNQsjIEYkk42QQCIpBseEa+3MHG54FwOausziaP9ZUrtNas2d2H8/qPbepIGlJixevuWbJ99suKTuJF3ocL43iSBtfzV/fEyyUtlVtJQSubC767ph+4uQnajAYntZ88pOf5JZborTGDRs28NznPrdl2wceeKD68fnnn3/K52LERsNpidaKcfVPzKhb0Cg8vS8W/zSaUhzVoIEyIR6CYuwwXCjKIUTjIXAJmEDrkC55LSutdzGlooLaCo+y3omiCLSKrdDxtTwEaSRuNWZzvhDXIV9Q/dgWfdj0NbgUhbBxxVo8fXhJz0hpD4+9hGSj8RmqO1/Sj1MKH2dcfIoB+ZtASKhbibHtMaNuYcj63QXbWCLDKus9HAvfXXdPFoNIRoHpJr0EYM37ugUoctgMkRDbWGm9C9EiSkTpEln9PWbULXj6QPV4QT+ApBNbDGHRM2/0+XUaK7SfqR8whtYlAqaYL0pOqy8xq75BSlxAr3wFaXlh2+MaDAaDwWAwGAwGg8FgOPM5nDvCA+MP8qOxH/Otw9/FkQ69iR4c6QDUCY+1eMrniZndnNOzjaSVbNqmlnxQWFRs3DH1BAeyh5Z8D7ViYjEo0mFnyPpZfNXa4Rj1i9b3lFYkpMtFfeezZ3Yfoa6/57LymCxN1R0TQrChYy2DyQE0mqP5kabXCFTIWGmcVekVDedet+lV9CfbL8WzVNJ2Gl8HaK1JyEQTsbE9bGljy8aySwDFsMhDE49wQf+zTmaqBoPhacxznvOcqtj45S9/uaXYWCwW+Y//+A8AHMfhuusWLpe2HIzYaDjt0FpzQn2YnLoLjY4jUyORTNfEo1bbo2K3nFVTgLn57iGFhxXHoApsUlyMJboJmULjU9KPx646YsfiwruQNAUUIHFRcX3GCq5YS1q2t0OgS97AePiJttpCJK6VeKIa9ynJYNG84HWoZzgRfghHrG57/FaU9OOLNwJsMcBq638zof6VrLojEmd1Ia5t6aLQQEj0bKP6jc0FYonAZqX1Z7hibdNrZdX3GFMfR+nGHXBgEzJNqKcRJEmKzUiiXYEL1eSsp3mGrSYgJFsXuTq/BWiK+kGK4UMM8Gv0yJe2eU2DwWAwGAwGg8FgMBgMZyJaa+458UNuOXwbj0w+Rll5PDz5GDp2/B3MH6HP7WE4NbhgfcZAheyZ3c/Gzg2MlcYph2WU1lgicgkOJvuromWyjTjUn4w/tKz7scWcEOYrH8d2qpvu20EKiUIz62c5p2cbazvWoNGMFI4DMFYcr668SSHoS/QynBoiE7s6V6dXEqiAE8XmZYFGS2OsTK+oW7156fqbePXGly/pPpdKJBRHM7elTUK6VRcoRPcdLJJmJhCkreYisRSS/kQfdx2/14iNBoOhJTfeeCN/+7d/y/T0NF/96le59tprueaaele37/u8/e1v5+jRowC87nWvY3Bw8JTPxYiNhtOOKfVFcuouADx9qCo0Ai2EnUrdvxBd446TZGIHYm3NRYXNIC6rEcIly3fo169HE1DW+6tCIxDXg3TrjjVDU0BjI+aJkkupL9glrmaCTzeNbW24ng4os6vuWThiaIEe0Sxz6k4s+rBF7yJtWxPFk7aHJToYst5Cv/xfZPV3OR7+b4R2sOhFEqDx48hUn8aoUokkg8s6HDFEQT9AmgsarjGtvsp4+MmWc7DpqdZl1JQo6cdJiG1YdLR9HwKn4ZgmXPRZiLq/XjXj4T8jSNAtX9z2tQ0Gg8FgMBgMBoPBYDCcOXihx988/H/44ej91WOjxbGq0AiRGDlRnmKiPEV/ovUaja98Ros5Zv0stqhfwp3yZjhaGKHX7WFr92YydvMN6LXkgvbXdGrprKmJKIWkEBQJCbGEjLdZK5SuXxMTca1GKaLN5Y60OZQ7TIeT4efXv5ib1l7PTyYe4vGpnfz9jk+wIjVEwkrQn+htWmNxfcdaklaSkcJxPFW/2d8LfWa8GXrcbvqTfbx648u5ce2pd+zM51D+MAkrGYmpWpGyk+hAV+cnhVg0PMsSklab3PsTvVjCYtqbOcUzNxgMTyc6Ozu5+eabeetb34pSit///d/nVa96Fddddx0dHR3s2rWLT3/60+zatQuI4lP/8A//8EmZixEbDacVWvvM6K9EHxMQMLdrSVfdcPNR1RbRx1bcOkQ2cftJOhAiKrysdJac/j5aB4Q0/uMtSMZiZfNYi7kZRC9sRf0omgBHrKEY7kbxRTrF1dhi4dgGKdL0ylcwqT67YDuAgFFUjSgpSWHRv2g/kPgcxtI9CNH8RWYxBO0VJ6/FEh1keA42/Q3PISRPoCdQzMaio8CiA4t+LNFTFY5n9Xfo069Hirnr59QPGQ//ecFr2wxVxUaIRMKy3kVSnDdPDKy9R5s5Z6yNoDHKIvp6a2hyDohrTTaKlGPhx0mLi9sQhw0Gg8FgMBgMBoPBYDCcSYQq5K8e/FCdg1CjGS9NtOwzUZ4i1CGWqF9fKIUlSmG0+d0LPWy7cQ1Da81keYpjhREemnyEC/sXTteaf412SdspOp0OprxpQhVSCkt1GppAxHUZBRXhLFp2mlt7cqWLBibKk1y58gUIIbhk4EKe1XsO/7nvv9uax3BqkKHkAFPeNGOlcYpBqfrs1mRW85Zzf5PLBy/FahFJeqp5eOIxjuVHkEKQ9QtIIXCkgyUsyspDaVWTwNaIJKpn2ey8EILhdLR21Cpq12AwGCrceOONeJ7He97znmpcaiUytZYrrriCj3zkI6RSi9f4XQ5GbDScVuT03dW6gpHQWOt4a/zHVzc44mpDVD2gWa59/T/SWfWduFeTf9wRSDKxuFTfT1fFTQX4hPgoZhE4KF1mlL/FCYcY59N0yufTI19OUmxree991mvxOU5WfbdlG601fo0AK3BJiK1txVcIEijGUMxi0b1o+2Y4LE8km1G30uz5WmSwxOK77yqicJe4unpsUv2/pmPWIkUKS3fNc8cGBPo4Fv34HGvSa87RKmmMIal1YrYSLO2WYmLIrPom/dYbFpy3wWAwGAwGg8FgMBgMhjOLz+39YkNU6YyXXbSWX6AClFDVSNRyWK4KjRDVb0xXV7vqEUCv28P7fvo3vP/Sd7OtZ0vL6/Qmetq+l1p8FVAIiuT8PEop5pcu0mi0jlbopJANoqYUsnpvAoHSc2t55XDhNLH5iDhmtW+eI/TVG1/G84YvX9JYi7Fv9gB3jHyfyfI0oQ7pcDJc2PcsZv0stx6+jYcnH+N4cTSaF1AOPcp4SCFxpYslJLkgTzCvtqWIf0V1GhuLCglgY+f6arxqp9N+OpfBYHjm8rKXvYzLL7+cz372s9x9990cOnQIz/MYGBjg/PPP56UvfSlXX3314gOdBEZsNJxWzKpvVz/29fws9mZuvPliU0UAlIBC4zdxmNULcz4nULocC0yNMa0CiaQjjvz04nEVc+KjrrYUpBC48c6lMp4+jM8YWpXJqR8yZP0eXfKahmtUGJJ/gE0/0+q/m9YUDJmpxrpKOkiKLU0ddM2wxQC+PorP6LLFxk557bL6lfSjy+pXS1E/ShfRX4hF9Siebq+oucsGSuyoe54B4zhiDVJnmsahRg5O1SJC1atp1yhGCmxsBlrOZ1Z/mz79OoQwf/0aDAaDwWAwGAwGg8HwdMALPW498p2G4/68yM9muJZLKSjhSIdQhxTDxhI7Ch07B+vpTfSSsFy80ONDj/w9n7jioy3TrF644nl8evfn6yJdF8NTPo9P7yRQAbawKIsQNFXxbL5UFgmJGqtmzSNlRUYAKSQDiT7uPvFDblgbrS+l47qMJ0uqRd3D5XDnyN187dA32Tm9u+64QvOxxz5Bzs8zmBqgx51bW0vbaWTsRlVaUQpL2MIiZSUpUSLUqiqy1gqNQN3XSwrBxs4NdWLqpYMXnbJ7MxgMT29WrlzJH//xH/PHf/zHT8n126/mazD8DPCIipRGPsP63U2iJo5hYWp3V813PoKc53ZUuoRiFpvWRVEjh2MSSWcsMM3fUSaRpJAkGl60olqBT6ApMRr+H7JxPcqm1xGCfutXWG9/ij75y9iiPh5VU8Smn6TYTkqc07bQGM3QxaIbRa7tPrVYopcO8dxl9Q2XUOuxFbWi4Iy+te1+UiRJsLXuWWkCQiZwGG7ax6Yfl41NzujY2RjVcmzmKHXFugWdpqGeoXgKxFeDwWAwGAwGg8FgMBgMpwffP/4Dsl624XirCM1aBALXcgGNFzZugo8HaiDjpNnYub76+fHCCX48/tOW1xlKDXLpQPvClUaza2YPxaBESZUIVIjWuu6edM2vCkprQh1t0E/bKRzpIIRgU+cGUnaKyfJUta0lLbZ0b2p7Tq04ewFHZ7sorfjYY5/gQw//fYPQCLBvdj+T5Wk85XM0P8L+7EGS1twm9KSVpNvtJGklkEIQ6DAWm0VVeExaybgmZbR2aAkLS1g40mZVegXn951bJzR2u908f/g5J31vBoPB8LPAWGsMpxVzAmO0E2o+lXjLpYxY3z8SDGuRpFBkcVhByBSKQtxTxW7GgDlxUcRikwVVp5ykVe2+2vsq670kxdmMhn9HSpy3YB1HW/TRZ72WXv0qAsZQFBC4TKn/IVvj/lwqjlhJSe9cVt8e+fPLduO1ihtd2hhzYmFZ722rj9IemiKaEIfVhEzFkaqaUBdwWYfFFCFT8TVcbIZwxEpAgFZN6oYCSATzd80JXLF2QVdjhZDptuZvMBgMYRji+z5KKYQQuK6LZf1sapAYDAaDwWAwGAyG9rj9WPON5XabdRItYdGX6OGgf7jp+fluxS63ky1dG5GifrPzLYe/zWWDF7e8zi+sv4H7x37S1pwmy9PM+lnyfgGNxrUcUBDqEK0Vat6am0ZXN+BrrUk7KVzpYguLTV1n0e12AY1uzxvWXMvumfbWeZpxdvdW0naa/dkDuNKlL9FLyl660/EfH/8U3zrSvLTRaGmcyfJ03bFCUIwCZbWufn0EkmQsKiqt0GhsaVfrN0K03hioAKU1K9PDDCT76Xa7mgSqwnVrrozFSYPBYDj9MX9bGU4rJOlYWJPMz4CHKLayXmyM4lJbU/8PtcNgwwtaSp5HTn0fBCT0Voo8imK6aYxpfXxqZXyrLddlyCyKIhKYVd+kz3rdgu0BhLBwWFH93KZr0T4LYdFJUmxB46HwAY3ARpBccPYZ+Tx6xCuXfV1bDOLpA8vuD5HbsIKi2LJdVCdgFp/RWNSrL1suySBwsBkkKTeR0Jsp8SiKEhY9dS93CXEWkhS+Pl4TnyrjMeZe6CUpHLEGm/qaAa1pP7LEYDA8MymVSszMzJDL5RpijjKZDN3d3aTTpyZyyGAwGAwGg8FgMJwc4+WJpse73E6kEKg2okvLoUeH04EXenjKI4xjN21hxVX+oNvtYig1RLfTWRWzate5Hhh/kFJQImknm17jwv7zeeVZL+VL+7+y6HyO5o+R9/NowBKSlJXCVwGWsNBCIqsOxrmt2RqNLayoTqOwWd+xloFkX10dx455NQh/buXz+ZddnyHnLy2Jy1c+Y6UJQh3ya3f9XvW4LW2eN3w5N629jnN6z25rrHtP/IhvHm6Mwa1wIq7NqFGUQ59AB+j466O0rjo4a6kIwQIRR91WkrIkjnSxpc3Gzg0NgnGF9Z3reMWGX2hr/gaDwXA6YMRGw2lFQmwi0OMASDINkZ8CWVdbMaqNOF+UFDUfWTUfO02jUrvljYTMUlA/Rok8Wge0ThiuCJtzdRqrGettfDv5+gQJsYEZ/S169WsQi+xwU7qMxyGUziOEi6D5y2I7aDQhk2jtoSjhcah6H4IkjhjCZrDumQF0yqsZkr/XMvO/HbrEVRS4f9n9K/OoIEnUSb4VtA4os5uQxuiSuEX1z5QjVrLSeje26EdrxbT+MjPqGwR6tK6HwwpsMYwij9A2RXbEr/gWFhlsMYQ1zy27GPIkRWODwfD0RSnFiRMnyOdbx0/n83ny+TzJZJKVK1cap6PBYDAYDAaDwfAU06o2oy1s+hK9jJcm646HOqAcegQqqEp1+SCPJSxc6ZKwEoQ6QGnFivQKet1uklaSaW+aXTN7KMV1HQWCjJ1hZXqIweQAtrTJBfmWYiPAG7b8EsCCgmMhLDJaGo+FRouMnUYIgS1tfOVH4qcQVaGsskFSx6t0HXYHCTvBcKpxHe78vnPrPnctl9dvfjUff/xfWs5nPkfzxzhWPEHGTrMyXV8iJ1ABd43cw10j97CtZwt/esEf0ZdceHP41w99q+W5rJ8j7xcohqWmX2etNXk/j5SShEyQqIlWhSiedTA5zFhpvCo4SiGaOlMrrO1Yw3sufgcZJ7PgvA0Gg+F0woiNhtOKLnEDeX4EgCOGKOvGXU2CFJqQOYehjD+ujTqtOPYqf8QtEmxpiAFNiM0kxTa6xY1k+R5lvTceIY0micaLY1MVc0Jj5XoVkTMEEgis+AUxmkez+IOQCWADoZ6kqB8jLc5v+hw8fZQZ9Q2y+naUnltw1jqkzH5s0d/gwluIkBxlvQeNh8t6HDGMpTtj998EmhKePoTHERJiPTbDZOTldIsbScsL27rGQmTEc7BEH6GeXLxxE5LiXBJirg6Bwyp8RuraaB1Q4vEFXY+1KHIcCf+ENdbfYIt+esUv0iNeTkHfR1bfQaCngABJB2l5MZ3iaiyR4Ujwdkp6x7LuA0CKDClx3rL7GwyGpy9KKY4ePUq53F5ceKlU4siRI6xZs8YIjgaDwWAwGAwGw1NIxk7XFGGpZyg1WBUbQx1QCErVmoa1KKXwCSiFZVzpkLJTJK0kGzvXs2/2AEcKxyg3qelYDEtMezPstw+xOrWSIGxM6qpFCMEbt76O8/rO4WsHb+UnEw81pKmMFsegxqVYVmUEAguJX11/qx8TqK5ThTrEC30CHdZFyW7p3sTW7s0Nc7pp3fWMlybaclzuzx5krDRByk6ypXvTgmtjO6d380c/+jM++Oy/YKiJ8AlwOHeERyYfaznGSOE4WT/Xsv6mEAJHOgQqoBiWCHRIxk5R+4xyfp5ze87mUP4IM16WTZ0b6Jzn8IRIeP25Fc/nV7e9vsEBajAYDKc7Rmw0nFakxcU4YiW+HsGiD8GhhjhTgUDSgSIfC4FQ72wM4iMShYckTZKtWGL+P9IW/fJNAKS4BF+PUBvJGrkokxC7CRVloBi/XNRGt6rYNZhlfsSqwEWQqEZuakI0IQKrWiewFq0V4+qfmFG3NH0+QlgIbVHWuxEkSYqtyEXcjiGzlPSuav3JgElCPY3AwRK9OHptHBsbxaqiocf6RQasNy447lIQwqZb3MSk/n/L6t8jX1r3eZe8nkL4QN2xMnvbFhpBYjNAoMcYCd/LGuvvECLalZfmMjSaMnvRFBEkkHRUa0Z2y5sohcsXGzvF1UiRWLyhwWB4xnHixIm2hcYKvu9z7Ngx1qxZc1IOdIPBYDAYDAaDwbB8ntV3Lgeyh5qe67AzDKUGOJofoRAUWhZWsaVNoMporSgEkXDV7/Zy1/F7KQYlpGhdwifQATk/YF94kI8+9o+879I/X7TW3yUDF3LJwIWMFI5z58g9TJanCFRAKSyxZ9delFIooWGeMBqqsMbV2Hw+ZeVF7kwVYtdsjLxhzbUt5/PGra+jP9nHZ/Z8gbzfPOnlWOE446UJehPdbOzcUBfP2orx0gTv+clf85HnvL/qOtw7u5/DuSOUwjL3nPgRxaDYtM5jMSwxUjzRUmisoLSiw8lQDj185VMIiqTtubIXvvLpcrt4y/rf4kUrr+CRqR38aPTHZOPo2G63iytWPIerV/2cERkNBsMZixEbDacVQgj65Os5Ef5NlGEuVuPpg43tosRzIIjrKDZGqYrqMV1Tb6+CZMj6fdLyAgBKPIjDECEzLK+eXpnGbycdC3zlWHRM1cwJ5tea1FpzXH2QvLp3wSs5rCCI3YglvYOk2I6keeHrkDwF/VBc51IhSKBqIkYDPY4gEUeorqjuBptW/0VSbKVDPrf9R7AIvfJVlNlFXv1oSf165C82zCMjLscWA9XI3VDn4q9de9j0VV2vZb2Pgr6fJGczo25hVn+rOm4t4+KTdImr6RIvxhI9hHp6SfcRIeiWNy6jn8FgeLpTLpfrolO11oRhiFIKrXV1Q4Rt2w2iYrlcplAokMmYiB2DwWAwGAwGg+Gp4IY11/C1g7e2PD+Q7Odg7nDLFSeBIFAhoQqrwpZEMlGeQlGpDSiQC4p8mlAFfHLnp/nh6I85v/9csn6O9R1rObd3O4PJfi4duKghmnNlegWv3fQKAO4+/gM++NDfcaIwBi02MwohCXWI0hpLWk2dhUorQh0g5VxM6Lm927ly1QtaPiOAl6x7MdeuvpI7Ru7mlsPfZt/sgeq5lJVCCsmz+s4laS1tE/fh3BFuO/o9EjLBLUe+zZ6ZfdVzB3KHGC2O0+l0MJQcoDfRU403PZA9hNKq1bBVtFZYwiJtp9A6iad8OpwOkpaLJSw2dK7j33/uH6uC5vbebbx648uXdA8Gg8FwumPERsNpR6d8IQGjTIT/jsMwGq8uMjNyB/pVF1tUY9BCkIhjTzUWmaoTDcLIpaaDSFQTAwzKN5ORl1bHnNG3YIkeEvosyuynmeAY1Yec+//8NlF0a/MXsWheIZJOiGsiSup3Kk2qT9cJjZFbcppAj8X3qgALSRqHIXxOoAko6V2kxHkNtRY1PkX9ELrq9rOb1nzUlPH0YUJmSIgt8TiaSfW5Uyo2CiFYId/OCT5CTn2/rT698tX0W7/SZCxJj/xFxsNPABAw2tBmgZlgi/o8/wn1ORQzTUXGCkrnmNZfYVZ8m27xUqb0F6Fp5cio1mbkFBUIHKRwAeiTr8cVq5cwV4PB8ExhZibaMKGUIggCgiBoiDKCyMlo2za2bdf94D4zM2PERoPBYDAYDAaD4SlibccantV3bss4zsO5I5FYRhRJqmre9TUapRVaRJsM0VGdRKUVIWF1rUmjCbVGxfGmc4Jj1F9pjSU0s36WH4zezyNTO6rOP1taDCT6OatzPVeteiE3rbueTV1n1c3xByfu438//FGKQRGxgGOwIsSFOhJHWwmOQkhsYaPQDCb6uWntdeyYeoKsn+P+8Z/w6OTjFMIitrAYSPZz9aqf46pVLyRlp7h+zdVcv+ZqvNAjHxRwpcMdI3c31HUsBiVGS2Nk/RyhDpFIXMtlINlPr9tdnWsxKPGO+97DWZ3rG+ZZIevnyPo50sUUW7s2ERCS9XNtlTCqsz8IQcJykUKwsXMDEAmtzZyTBoPB8HTCiI2G05Je+UosuhlXn8RlLWiJxwEUxZp6jRXJTyBJIXCxGcIRqwj1FAET1IpBIdMMyTfTJ1+PqCnArLUmr34MgC0GENrB4zCKwrxZtfp2mXu5axUfUZkB+KBBiGRd3b5Q55hWX61+7jOKr481cWT6hJTiuxdUImMDxnGYE9A0IUX9OIp8PEMHQXrBF6SQWcp6DwmxFYHA0wco6h2kxDkL3NO8MXSWnL6HUE+hCbFEB2lxMa5YF81DOAzLP6FDvJAZ9Q2K+iHmi7YCm4x8Dt3iJlKydW3DHvnzePoQM+E3CFi4FqTGQ+ER1WDMUNKPxX9eBrFEF9PqSzisJ2Qa8NCAJInNQEP8rtJFpvWX6JEvY0Z9pRrzq3VIwDgBYw1/dqTuoFe+kl5pdq0ZDIZGlFJks1nCMKRcLjcVGStorfF9nyAISCQS1VqNhUIB3/dxHKdl31oqrkkAKaWp+WgwGAwGg8FgMJwkv7r19bzj/vdQDutLIxTCItk4FjRhJUhYLuXQo6w8lA5RWuEIG1va2DJFIYg2jVccdfM3t2utCQmxhA1ogtgNKRAoNEpFAmUpKJNxoijPQIUcL44y62cpBkVuO/o9fnnzq6uOxqnyNB965O/ROvJV2tLCElbT2pIwJzgqraKo1CaRrR12B8cKI9Gmfa141wN/xaHcEfJBgQ4nw1BqkL5ELxLBVHma3TN7+bfdn+P61Vfzxq2/hCMdXMvFtaIN3N888p3q2Dk/z5H8MWb9bMN1i2GJGW8WRzoMpwbpdXt4YmYXvgoYTA3QYddv0pwfxVoIijw+s6vazpEOnvJZCNnEBTrrZSmGJVJWkksHLlywv8FgMDwdMGKj4bQh1FmK+mFCsggkFr2sk//MpPo0o/qjCOzYvZiHWA4CGddDjP5Rl6Sw6MIW3STYEAuTmsjVB0X9cJ3QCMTC0NzLkyW6SdFNqLMEjFZrMUZOyTTRHjJFK1fbovfJDN3yRqSY29GU1d+pCotlfZCAE4uOI9BoJJIUoZ7GqXHrefoIitn4mbnx/S++EytkhoATOKwAYFbdSspaXGws6d3MqK+RU/c0EUg/RVKcS7e8kQ5xBUJIOsRz6JDPwdPHKOj7CXX8NRc9ZMRzsUXvotcEGJS/i9IFCuqBpucVXuzsjIRgWeN4jRyzRynpx9CE+Jyo1tasUGYflu7CZS02g9U/O5qAWf1NVlh/zrT6Mll1Ox774z9v9UjSOGKYkt7BgfB/MSzfRlqe39b9GQyGZwZBEBCGIaVSqen5ivhYG5+qtaZUKpFMJqtC4WJio9aaXC7HzMxMw7VSqRTd3d1kMhlT+9FgMBgMBoPBYFgGm7s38s4L38r7H/wwXji3NjJaHAPiIj9VkbESjaqQSIQQWMLCkQ5JS1HwiwvWCVRaI1CRI7LSTtQLlJ4qk9apuvf7QlBk5+wetndv5bN7voCvfH5ly2v51pHvVudsx+JbQroUwiKtkEIihURrjSUsdHUuAoXCkTZDyUEcaTNZnmJv9kD1Z5ucn48EQ+soW7o2kYnrG5aCEl85+A32Zvdz80VvJ2knq8/pYO4wANPlGfZk99W5Q5vhK5/DuaPs1ftJSBcQFIJCg9jY43YzUqhfhyuHHlPeNGkrjSNtZCzktsIWzZfYp70Zuju6uG71VQvO1WAwGJ4OGLHR8JRT0ruYUbeQU99vEKo0Pr4exWUDAonCo8gOausd1gpEAaOgA1yxmahsdv3upJLeSVnvJSE21fRv7uawRCcWnXXHAqYp6kcQyFhwXEp9R4nAIeBEQ92+GfVNIBIJ2xEa5+YuURRxGKJPvg5L9KF0gVH1f9DaI2RqCfOL8PUoFsMopplRtyJJI3BxxAo6xAuxRP0zmVL/xUT47yz0LEr6MUrhY2Tl91gh344U0cuiK1bhipcueY4VhBD0WL/ArP4Wvj5ByERV8FOU0JSInnsSSfRiOYcmYBqo7E4T8T7A2jYBIZMUmcZhmKTejohfIJXO43OMDnkFef0jbD2IIh9f30KSwBaDdX+GQj3DSHgzK3gHGXn5su/bYDA8vWgmNGqtq79rqdRurCwYlMtlUqloAaHiVGxGoVDgxIkThGHzjTLFYpFisYht26xYsYJksjF222AwGAwGg8FgMCzMJQMX8v5L380/7Pgk+7MHgcjh5imPYlCsWzmxhYUf12cPtaIYliiFJdJ2GktaBCqoad2YplVxHQqiyNL5tQVDrQh12CCEFYMSRwsjrO9Yyxf2/Q+bus7iW0e+Wz3vWm5ce1AjVXnRmoUiriOZcaJkqEJQQArJ5q6NWEIy62XrhMZavNDnieldbOvZUicCPjq5gw889Le8+6K3Y0krenZak/VzbQmNFXwdUAxKhDIkbacJm9xLp9NB2p5zlFb7hj6BCLCljWu5lOY5VmupuC/nE6iA5w9fTqfb2fS8wWAwPJ2QizcxGJ4ctNaMhZ/gSPBHZNV3G4RGRYmCfhifoxR5hIBJPI6iyaMooCmgKcW18eYI+P/Z++84SZK7zht/R6Qp213tzZjdmTWzVquVgZWEvF2tJOwButMJxCHgDh4O/3ASRjoQP4wwB4d7MEIYAc/BDyOzMivvvVnvzZienvZdvtJEPH9kZnWZrOrqnpndnd1472t2ujMjIyOre6oy4xOfz3cDX58aeN5tdXPX91JkU2sZpmExwc7N3d4i5yS5+O8CsuN8Wof4+hSKZldtyr3gcwJwKclXIEUOi+JITsZeNIqQLRp8mRb3tYXgLfVvrIZ/ysPBGzgT/i9a+iEANtU/sx6+k1FF17r6MqfDX0XrYPfGIyLJIcmREUfIiaeRFVdgMYXAQlLEYhxJhl6hMWQbun53NBAOWDmo8DlDgzvRHREi6+HfsBr+MZIMrriIrLiKnLiWnLiKjLikT6yOzhKwHL69/RoaDAZDs9lsP3hrrdsRp2kP41prlFKEYdgWI4Mgek/trOHYSaVSYWlpaaDQ2EkQBJw6dYp6vTdK3GAwGAwGg8FgMIzCFROX8wfP+S1+65t/hRcuPpdm2KIeC40SQUa6jDljUa3DnlQRDdSCOvRGp6acJ4lOtaQ9sBTDIHfkenOjLbz9+d1/zVpzvWv/bHYGIQQFe3hJngRf+YCmFUbi5EWFQ1hxOtQj1RNDS0WEWnHf9gNxHzt8de0bfObM54Eofhbg4crxkYVGAC8WCD3l4ysfOWAqfC43k7q9pbz2+S2Rfqwt7L4o1oQxp8gbLn/dyOM1GAyGCxkjNhoeM1bUH7Ct3jNwv69XSGozKuo0uA2fE7G4GKAJ4lp8NULKKHZWGAWciZ2H/bT0A33bivK5I446ROLGNRAF3YLj4JuvqF6iE9eUPERT39vep4jcLIFeYW9OyU4Udf1FAKrqM/G2vYmhmhBFFU2zo9aj1dPGo6I+wsngp1kL/oL18G/2PNKGvpV1tffjBmEzjxRR3IZAIhmLI2QdxADzdkilT6SO0Az+GSgCVmnxSHtLXX81jtndG5oWm+qf9nycwWB4YlKr1RBCtIXEUUkEyURsdN3+1bTNZpOVlZU9jUdrzfLyMp7XG4ttMBgMBoPBYDAYRuWqySt4ycEX4FoOJWecCbfEuDtOzs5hCYmvBi/ETvaJ+D/Z/kogxc73Sa3AYZGraQQ6ZL25AcA92/f1ufpmslNYQmIJi6JTaNdoHIbSCoWmYBeYz80BO3ULd8NXASvNtb7tN5+4BSCuZ2mP1FdCqEOCjgXjnvLIxqJlL7PZGcZ73IdCSHzlo3Uk6hacQp/gKBDk7HQDgysdvu/y1zKVHa1UkMFgMFzoGLHR8Jiwpd5DRX144P7IYbcW/11B0yK6dfIHiIgKTaMtkmkCQtZT2iU1GrvpjTVNPU43aelH4vEkSe1J7UhrwEoviaSAxRiuOEJGHEH0jEGSRaMI6L+p2gsNfWs0UU253e+oaFT82qn2lmF9aAKW1W8T6P2Nuaw/hNKj3yAOQ4oMY2In+z5gDc3gG3ZNOEBoTH5+wyb6QwKW0dpH6Xoszu5vMr6mPkegt/Z1rMFgeOIQBAHNZhMp5Z6ExoTE5ei6Lrbdv8BifX196CriYf1ubu49ittgMBgMBoPBYDDs8I8P/AuOcFLrou92n94pIEphtQU3S9jQFhkZGnM6TCTc9LbiccBqj9BnCYuLi4fbX4/ZRfJ2vl3PsRNBJKzN5WbJWVkOFhbbAtxKc3XoNXay2lzrE03v2LyLRyrHAchaeyv1kBYrO6gPgeDy8UsYc3aiXF3pdPUjkRSdIhnLjUs3Rc7PXlejEILpzCTXTF7Jt138qj2N2WAwGC5kjNhoeNTRWrGl/nVom5ANFB6KKv3iz+CbMY3fFhx9nX5DkxaZmhWXkxVXpI9FV2nqe2hwGyGrRG7LsKMmpEDgIsghyMaOuhwW07hcTFZcQ048FYe5jjE4O18LC4vCUIFsFJSux47O6PWxmWGY27LrWOqkiWy2mOtvTORwDNmixYMoPTizfvBYa1T0x/d83CA6xeLIITqYqI7jsBt6PXRFoKZJwCo+yXn2Lg5E/QSU1Yf2dazBYHjikLgS9yMIJmitKRQKfds9z6PRaKQcMRrVanWk6FWDwWAwGAwGg8HQz8OVR7hj8y5mslOp+4fN2PSKhP1i5c7zw6BnCUtYA+M9AcLYPWlJCy/sX0g9k53m4uKhuCakwJUORafImFOkYBco2HmKdoExZ4y8naceNJjPzXKocKDdR9kbPQ3KC322vO2+7V9bvxWAWlBLFW1HJSNdtv3ywP2WsLhi4hiL+XlsaeHKKDmmc45IIMhbOS4uHubKiWNMZibIWVmyVoaiU+BQ4QDXT13LpeNHec78DczlZvc9XoPBYLjQSM8YNBjOI3X9JYIBQmCC0k00DfqFHEFUV08OzIyPBEcPQbpzzhGLqdvnrZ/hZPizhHrnxiPQW7S4H00LTUjkoEzGlERZFOKagJHA53J015sfh/mu73PimVT0J4YeMwxJDkuME1JF6hy+PkPIBppWHC8rYhHUQfSsMYiuq1foFAiySMZTzxe5MHX89Qouh/c85or6KCV5456PS8MVhynKF1BRH0t1ru6gUXj0i41pN+3pP0OFj8eJOHC1SaDX0PjYTCOEk3rMIDxM3UaDwRC5CJVS7SjV/eA4/e8/5fLgB+lR0FpTLpeZnDSxPwaDwWAwGAwGw175wMmPADCTmeZkbaldIzFBCBHZClNIIlNBI8XgOTCBGLhgelC8Zy9jTpH1Zno62HxuDle6nKqfbketRiJmd7vZ7DRTmUlK7s48ktaaUO9t8WIjaDLZUx2i6tfwQg+tNbPZGVYao7klO18zicC1XIIh0bVJu8OFgxwsHGC9ucG92w+Qs7JY0sIWFuPuOHPZGVyrv4RFL6+66BUjjdNgMBieKBix0fCoU9a7u7k03ghRl4NXZ0XiYPpNVUmkC1yOWOSA9TaWwrcS6g0CvUmD2+MI184bQh1/LwC3qy5gJMJJMhwZODZHLJIV13RtK4pnsYJkvy45ixmUbrAWvoOq/hReu66gFfep4xqXzVhwzLZFR02/M1Hg4LBIVBuzPyJW6Z0VbwFrOPogYoT8/k4CRo/SGIV5+RP4+hR1/eWBbfTQmozD2TlWEbAV/9wVIVuEbONxAltPYrOAJYoj9an0/h1HBoPhiYGUsu1ulFLuy0kopaRer+O6bjuK1bZtWq29O897ORd9GAwGg8FgMBgMT0aOV08CkXNwJjvNmR6RzJEOYZh+vy2EQCJRWiFT578iIbK92L1nqkMKSUb21ycMddiOBQ21ItQhtrC4bPySgdcxmZlgMjNBxa+y2lyjFtTjcUmyVobZ7Ay/+LSf44/u+vOuxZNCiCjudQ8LKtPESVvabafnxcVDNMPmSI7JSKSNKDiFSL4d0RkpEcxmpzlYWKRg59lsbY16CQC85uJX8oyZ6/d0jMFgMFzoGLHR8Kjj6+Vd24RxFGov0YqtUUStkDRRyREHyInrBx6VEUc5bP0eq8Gfs8yvpQpxO+nDkTioqCAptKNRA1aw9RSWSHcFjstX9t3cOPIATjiPz+ndL62DSEAM8XiEJnfSUFG0hKIeR7s6SPLtaNlIMvPQBEgKgOwSdTtXw3k8hKcfiq80jy3msJmJRcqOuA58Qrax2ZvzJV1M3j9COCzKX6KsPkTAOumiYqfYmPwcdxd4o9el93cqis/dEcA1ARsEbOLqwzhiYdd+pcjt2mZUmvo+WvpeQl1DigwOC+TFMxFDIlMMBsNjT68j0bIslFJdD+mD3I5CCKSUaK1ZX1+nWq127W+1Wmit0XpnEkIIgW3bWNZo7w37qSNpMBgMBoPBYDAYoBHuLDA+XDhILahT9XfmuzKWS3OA2AhRjUBHRlO3qmeOQyLQIkncEoQ9UZ9Fu9h+BtBa4yuflvK6xDyN5uvrtzOTneJ7L/lOPr/ypaHXMxZHqPZScAq88MBz+ct7/5ZG0L2o2hE2nh59/ict9nUqM4ktbYpOkapf5VjpMh4sP8xGa3iNeSkkWTuLLex2v84eE6lefdEreNnBF/GWr/z6yI7KGw+/lB+64vv3dB6DwWB4ImDERsOjjqY/B35vbfbvAJyU/2HXVUyagCqfRJJDY8W1FJNYTYnERdPoEssUtT7B0UqJIJVijHHx0r7tWXEZefEsqvoThGzseh2asF1nUVBEUYvdh8m1yVhglEhySApx++TmU8XHFNiRGCMxLak72YmijqcfxuckjjgQ16rsHM/wG8doolx3uR8lo7n/9oIlJsjJawnUOgErBKx31cIUWAgcNGHbrbmb2zFNaNy5nU+LzdB4HAfNroKjw8GRr83Tx6mqz6OIYhEl4+TFM/F4gG11My19f98xlphiXLycknwltkivEWEwGB5bhBA4joPv77yPJgJir+jYi9aaMAzb8ataa4IgQClFEASEYdgWGqWU7c+/IAiQUuI4DrY9/FbwbGqiGAwGg8FgMBgMT2ay1k7ilhSSY+OXcn/5Icp+5MoTSFzp4Kn0OZWcnWU2O0MjaLDhbbUdiRAvIoznZoSQEO8TCIpOgUwc8xmogHpQ7xMrBVENxlCHrDbX+cyZzzPhTlD2ynt+BviRK99AxspwpHgRd23d07VvOjvF6fqZkfvK2d2Lsl3L5dlz3wTA8xeew80nPoREcNn4Uar+HGeaq2y2NlE9z015O8d8bpaM5XL3VjRfIoVkIlMaeSxCCF556GUs5Of5nRvexj8/9G4+vPRxan66QeKy0iV860Wv5EUHnj/yOQwGg+GJhBEbDY86kvyubSIxK11UFHF8aSQAjX6eknwN4/Jlu557OfxNWvqB+FxuqqAkyBNS6Rqfoo5kDIGM3W0eQrgdx7gsWr+EJcZSzzth3YQfnMTDjiNG0yeYI6GxSnRbOha7L8NYOEyuPUtIAISxEJrHYhyF1xELq9A0O4TGyKlnDREBNQGePp4ipPaPVWsPn1UC1mLxWIMWWBSxmWMsRXQ9W4SQjIkXsS3eh8vFOPpwu95m9Dtl0+BrhGx1HCXp/l3qFG0hea26zhPHzIb0Fy5P8DiB1GNYojCghWBcvnzo9WitqenPsK3eR0PfjtINQspoQjQhAWeQ5HHEYqqzNNQbbOp/ZFu/hwX5ZvLyuqHnMxgMjw2ZTIZ6vbvebCIy9tZx7Fyd3NnW9/22uJi4EZM2iShpWVb7eKUUrVYLpRSuO7jeSFotSIPBYDAYDAaDwbA7FxUOcfvGne3vbWlzrHQp661NVhqr1II6OTtH4IddQqIjHTLSxZY2Y06RS8eO8GDlYdaa67E7UZGxMggkzbCJQGAJiRQWBSuPY8UL4VVALaj1zdhorbEtC18FCAQLuTksYVH2K2gBaE0tqLfPp7XCkhZjdpGZ7DR27LYUQvCDV7y+La7dePglfWLjXHaG5fqZkQrauJbDhNstBj5v4dmMudE82k2HX8bNJ3ZKMxWdAkWngF88RN2vE+gwjo91yXeIlpNuiU1vm+nMJPYe0p8SoRFgIjPBG6/8Pl5/+ffyydOf5Rsbt1P1qzjSYTo7xYsPPJ9jpctG7ttgMBieiBix0fCokxHHaOkHd20ncNE0B+4TCBQN0kU52SU2TsrXMm29btdzNvXdNNU9PWJU+ggsxgipsiNURRGlgiyRJFjGZiYajRhj0fpFcuKqgT2OiZewId9FRh/B0fMEbaFux5mn43NI8rFDT8dCnuxxI4rYzViD2AUpKSLJABk0IZHYKDrGb2MxSBjrJhK8VLvuY6fTUWuFx8MDokw1IRVCKlT0x8mpaxiX51Z0LMlXsa3eF41LSATdq+IcvRALxWE8dtkhuEZbdgqsK9JFbQuLyVi8HVR3URNwBov0ugd5+UwcMTfwOrQOOKN+j0oYuV0DVuJxR30nYnfIJoFexWGBjDiWWjRe6Rqnw7dwgF8hJ58y8JwGg2EwnudRrVbbgp5lWeTzeXK5s49DzmQyuK6L50Wu/sSlmNC7sjjN7dgrMKbRKzgCbUflIMFxfDw9Ehwih2SlUmm/JlJKcrkc+fzui4oMBoPBYDAYDIYnOq88/NIucQwid91sdprZ7DRVv0bZL9MMWpxprBLoAEc67fqEApjNzeBKlysnjtEMW6w0VtEocnaeQAWsNNdwhM1sdoYTtZOEsWiptKIW1NuzMlpHM0iJqCkUBCpKfdr0tjhZW2I2O0MlqNIMm6w21vBV0DX2zdY2J+tLTGWmeObM0/iRq97Ac+ZvaO9/3sJz+It7/pZKRz3FjJVhIjMxUs3D2exM14yGEIJXH76x/f3FYxfxlKlruG3jjq7jHGFTcgc/t1wyfpR7t+9nLjd4DqaXG+aeyQ9f+Ya+7Rkrw8sOvYiXHXrRyH0ZDAbDkwUjNhoedUryJsrqA0PbCGwkGUI8+t2Noi02Suy4BmF3O0kWKQqU5GsoyVfiisMjjW1b3cygeo9po7QYQ+OjaAFBPI4oJkPjY4s5SvJGxsXLscTwqAZLFFiQP89S+CtIkcPlIhx9EEWTxHkYsBa7HmmfI7refpFQILEoomii8VA0sBhrjxwswI8DYjPQJd4KwEbiQIp4FZ07iAVOgSbE5wxah3HdySDengh2O3USoxLnRSBgJfx9Ar3OlPW9w1/qPeCKw+TFM6jrr6Tut8UcQh9HU++IUE1+d6LvEzE2/ffAQZLF42F0LEb2xs4m+KwhtEvIVvyz0kRBJ5PMiZ8ZeA1aa5bV26mGn6LF/X0OyiRCNxmzpoXHIyjdIieuS/2JaQJOq1/jYvHnA921BoOhn1qtxtbWFo1G/8KCzc1NXNelVCoxPj6+78jR8fFxKpVK26G43zqJSqmuMfS6IiESHHujU33fx7ZtpOyuiZzL5VJFyEajwdbWFrVaf3xQ8pqMj48zPj7e16fBYDAYDAaDwfBk4cjYxVw1cUWf2y8hceYBXFQ8xHJjhdXmOn4cq1pyS2Tkzv34dVPX8JqLbuSFB57Xvs+vB3Xe9vXf5vaNO8lYLveVH0Rphae8dtEcpRVKKzQagcCWFslC66KTJ9SKpfoyD1QeQiAp2nmum76WbW+b1eY6zbCJ0hpLSAp2gZnsFKEOmclOd12PIx2+++i38Y57/q77dSheRCNoDK1P6UiHuexs17YfvOL1XFbqXsD9U9f+KD/7xV9kozm8XmMnlpD86jN/kdP103z89KeHLtC0pc1Nh1/OD17x+rboazAYDIbRMGKj4VEnI46SFVfS1HcPbGMxFbvxCnFkaGe9PLft3orq5mXRZCAWiCxmcVngsPXH5OW1I49L64Cq+jSDxLVBCBysuA4ghNhMI8UYU+J7mbN+ak+Tz3n5dBZ5E8vh29G0EMJquw21VoTc13OEQlLsq6HYOTpJDsihYkdkFBsqENiEbMdlxNMcpF5c2dBFkGm7GHd6tmJxUuDxSBTdR41IdNVx+3SxzuFgu47Yhvo7bDE1UsTtqMxbP8OJ4Gdp6jtRcexoIlLbTOMwT4sHgaDnSIfBbsaoZmci2EZbJAo/dpvuPABE6wWjup6tvn0hjsiyEv4uFfVhZq0f6RPDt/W7qYafpsk98e8/XccPqpEZsExL58mK9OgOpWuU9YeYFN+Vut9gMOygtWZtbY3t7cFxyRA5HldXV6lWqywsLGBZo8fyJHSKekopwnBwTPiwB+Nk/26fO4nDsRPf98lkMl3bJif745k3NzdZX18f2r/neaytrVGpVFhcXNy1LqTBYDAYDAaDwfBE5bWXfidv/epv7Hofb0ubQ4UDHMgvsOVtU/GrvOTAC1jMLzCVmeSFi8/tEt6Se/6CU+BXnvFm/v8PvZsPnPwwlrR4YPthyl4ZiITGUIcdUavRnFAkHOaRcaxoM2zSCuOkFTSbrU1mszNMZ6ZSx7vtbfOWr/w6b7/hVzhUONje/h1HXsNSfZkPnPhwe5sjba4sXc495ftpBP3zT5awOFa6FKcjnvUHjr2Ob7v4VX1tZ3MzvO0Zv8hbv/obrDRW+/an8Z8v+16+99LvbH/9/hO3cMupj7Pt7TzrzWSnecWhl/CKQy9hMjMxUr8Gg8Fg6MbM/hgeE6bl97EU/nJXRGgnjpjF16cQWEjG2nGgkWCU6Wuf1HF0xWEc5smIy7uExlBXaen7UVQjwUnMkRFHuvoIqaDxEcICnV4vchiJW9AS49jMkpfP2JfLpSBv4LD4PbbUv1FRn4hrLELIRvv1EtjYzBBSiV+b3ZG4SApkxFEAPH06dueJWBhMu14d1zz0kBQQ8VuGohXHh0osSkSiYkAkNCYRrTtn3hFDBRIXn1MErJPVlyNFjjX1VxTFC5BicN2wUVG6QVl/EE0dn1N9r4/HidiBarHjYhXx98SvQ/JzS65CkNTIFD1vm5JM7K6N6kJqVMfva9JfR2txCTZTgKahv8HJ8OdYtN7SjtjVWrOt3hs5FXuExmhE3tDr93gEVx9AivQYw7L6ABPiO/ftwDIYniyMIjR20mg0OH36NAcOHNiXm29ycpLl5WWUUu3j0xyOaW7FYaS1Tzs+CAJc122/N8zMzHTFoYZhyNraGpubmwghkFLu+j7SarVYWlri0KFDxuFoMBgMBoPBYHhS8vSZ6/nhK9/A/3PXX43UXgrJdHaKX33mL/DiAy8Y6RhHOrz20u/iu49+O19Y/TLvuv+feO/xD1D2y1T8GraISikIRFQP0nKxxM7cRqCDLtdhK2zxSOUEs9mZoeet+lV+77Y/5nee9Wtd23/s6h9iwi3xfx7813Zsq2u5XF26gpXmGivN1bawmbFcjpUuJWflsKTFs+a+iW+96JVcPXnlwPMeLh7id5/1a/zrw+/lllMfbwurnQghePr0U/m2i2/iaTNPbW+fy83y/cf+E6+//LVU/AqNoEnezjHmjJl5EoPBYDhLjNhoeEzIyacwx3/nTPi/SBO5BA42kwRsxHGgY0QRlEU0dbqdjlkcMY/NTFvUKsmbAKirW9lQf0NNfymO/XRjJyBkxCWMy5sYEy9EisQZGWEzRcDavq4tcvVlKYhv2tfxEEWBzlk/zrT8L1T0x/D1Kcrqw4S6hiXyWExHhcB1ehTHIBLxKmADjweJXlMLSWYX0TJyLUqKaPy2E1JgkeNaPE7icbIjfrT7rJGc5yLJtcU6TZMmd5HVVwBQ1Z9iXLxkT9fTS6DXWQrfiqcfBiArriJkA1+fia9Px69BGMuJGaKfmEJAPP50JONdDsVOkt8ryRge99PrjBRkcMQcNrN9YmVUT/FXOGS9HVccoq6/gqdOxDUv+9lNbISQFsfJkX5j7utlGvo28uK6XfoxGJ68VKvVXYVGpVS7tqIQAiEEzWaT9fV1Zmdnhx6bxtjYGLVajXq93n7ITXNJhmG4q+DY625Ma98panb2bds2s7OzlEpR9Hej0WB7e5tyudwXJWtZFo7jDBUeE+fn/Pz8kKs3GAwGg8FgMBieuLz6ohvJ2zn+6M6/wAuHP9Nn7Sw/de2PdtVCHBVLWjxn/gaOV09yvHqCBysPs9bcaMepCgbcs6eMqexXRkpNuXf7fu7bfoDLS5d2bX/dZd/DKw69hPefuIUPnfooW61tLGmxmJ9nITfHXG6OA4UF5rOzZOwMs9lpXrDwXKay/ekqaZTcEm849jped9n38Onlz3Hn1j3U/Dqu5TCbneHFB57PYn5h4PFSSEpuiZI7vOSRwWAwGEbHiI2Gx4wx+SIkRVbUHxLqjb79rriYUNfQtJDkyXApUuQ6YiQVAhvRIwAV5HNw9MU84H8nVf15+oWfLI6YQ+uAVvggG+JdLFq/hMtOlKXN3L7FRoHNmHzhQGfZXrBEgQnxaiASCrXqjpsYVCtwEMlr19IPdt1sChwEuditOPhoxU6R7+iYPAiJqw/js4SOHYC9jsDI3ej2CW2agCb3kdPXsK3ex7jcv9gY6gqnwl/A16c6xiixmcEWM4TU8PRDcX1NN47gdbCZJ5LwTqCpdIy/c+yS5Pcoeg2DrmsUOChqWBSQFLpEV4eDuOLI0HBepausqXdwwPplKvpjcV3OdKfpKPVEA1bQ+vLIpZu6f7SoEYPhycogoVFrTRiGBEGQGnVqWRZKKaamprAsi1arRblcxvd9tNZIKclms4yPj6cKiaVSiY2NDXw/PSq5cxwJozode9v1HiOEoFAosLCwgOM4hGHI8vJyW2BMG1MYhoRh2L6uQRMR1WqV6elpE6dqOKc0w5BbTm/wdw8ts9Ro4ilN3rZ46kSRN152kGsnio/1EA0Gg8FgMBjavPjAC/immadzy6mP8f6TH2a5fqZr/6HCQW46/DJefOD5FOI6jvulFbYIVMBGK6prOEhkhGj5taf67/UDHbLtlZnI7C7G3XziQ/xE6b/1bZ/JTvP6y1/Lf7z0P3CydoqqX8ORDtPZqb56j/vFkQ4vOvB8XnTg+eekP4PBYDDsHzPrY3hMKchv4oh4BzX9ObbVzTT0be19khzT8vU09d1dbi6BNbBGYV58M1KN84B+NSFbqW00TTx9HI9TZMSloOFU8GYO2m8jK66gqe/BEkWkLowcUbqDwGK87aw8l/SKqrAfB6bE15GQtSOpOfGeDBqJokl6zUJid6iFIBv/iQQ5n9U47lX0CYo7x7YgJQJX4+GzgtAOWvsIsTcBNWFV/XGX0NiLJBfHwWa7tgecxuVSHA4QsEISoRrVY6zFTk6NphmLiP3Rv5omAocWjyDJtiN1ARwxN1IV0Lr6Mr5cIdQbQ1yNo0YnhgRs4JDurtrdHWkwPHnxPK/PwQeRE7DVaqVGmyaEYUij0eD+++8nl8vhef3/1mq1GhsbGxSLRaampnCc7vc813WxbRvf9wnDMFUUhNFqM/aStE+cmEmNxiQWdWxsrC00njp1qj1+rTVBkB57DtFr02g0yOVyqWPSWlMul5maSq/3YjDshVBp/ujeE/zdQ8ucajQJev6N3Lld5V9PrHJNqcCbrz3Cc+dGWx1vMBgMBoPBcL4Zc8f4zqPfyncceQ2PVI9T9ioIIRh3xrh47KJzdp6cnWOtuY4aYVGinyI0QjQzstZaH0ls/OyZL/IT1/aLjQm2tDkydvGu/RgMBoPhwsaIjYbHHCEsiuK5FOVzUboZx1xG0alCOIR6mw31j1T0R1G6ntqHIxYoyVfRVA+xrv9soNDYTUhL3wfiMmwmOR3+KpPie9rRpBkuocldA+tKpmEzxYz1xnZdxHOJI/rjHyQlBJl2XcfdkGRiQU21hSuNbIthAgcLB00Q12pMahpGe0ECDpIcoFD4NPUdBGzFbtOktSRyBHZOOquoJmaKG9PnDGjJ6fDXAIUQLg4LjMuX44rdb7gDvU5VfW5om86al9FovPh1C2lyezx2P3bLZuLxZ1B4RC5DFUfkptUd07FjUsVXn43/LiIZ1eGq2VY3o/HjvvoZthqxt69hvxOSs1slaTA8kalUKn3blFI0m82RHIRKKWq1Gp7nDXT7aa2pVCrUajXm5+fJ5/MIIdpuRyklmUym7aRMEELged5Q4W9UhBB9TsPk/MvLy11C6Sjn01rTbDYHXnOlUjFio+Gs8ZXiR794N+87tUY4ZAFOU4V8ZbPM6z97B79x/WV875HBEVoGg8FgMBgMjzZCiPMqvh0pXkQjbO7eEAYKkpawRu6jHtQJVIAtzTSzwWAwPJkxnwKGxxVSZPucZ5YoMWv9CNP6+6noj1PXX0HpKmBhi0mK4oXkxdPZ0v9GRX0In5U9nFHT0g8gxTWgIaSKFGMoXUGKHFl9BU3ubQtpuzEtf4BJ+V17OP/ojIkXs8676HQdCiFw9Dwex0fuJ2QzdutBJA1WCRFxzGkmjhe1+2sL0iAS0wIUDTStWOj0UwTZRJwTgNUWyTRel9gYRZK24j4a1PQXI1dgfK+7pf6drLiGSfldFOTgGphr6i/xOR2/NhJJDsl4lzQX6q34nD6qp+5ndH4rdnsG8R9N5E7sjExVkCo2JvsiB2TUKorr3QsN/TUsJrvG1o2IxzS4tuROu8F9ZMXgQusGw5OdXmFNa02r1RpZaEzaJU7IbLb7M01rjVKq7VysVqtks1ls22ZsbAzLstoCY5og6Lpu3xh7I1J3czwmzsbebfl8nmaz2efsHOXak2tWSg2sNWkwnA1aa37mK/fynlNrIzv962HIz33tPiZch1ccODdRXQaDwXAhobVm43iNViV6ns2VHCYPm5hpg+GJzjNnn0bOzp1VH67lovTgVBeDwWAwGHoxYqPhgkGKLCVxIyVu7NundcCW+pchte6GofD1GTLiCBVuYUb8MCv6dwGNFAWy+hoCThOwFjv9ehFYjFGSr2HB/rl9XNlo2GKagryBmvps93bmCdkmJL3GGES+O00Dhd8WTiMx0QaaJE64SEB0B7jxVNxXGMeGitgBCAI1YNpPA2Es5Al0x89GE8QxtZ1H9k9QN/UdnA7vYEq/jinrtTuj0U0q+qNsq/ezpd6NpreeZQZHzGEzi8BGE8ZuxnR3rOi4vp3aiP0iaiSsioFRvlEfTQQlLPY2sRlSpShfiFDWQEetwN2ltiaAQ9prCZCXz9yzCGowPJkJw3BodGqC1rpPlEtqGibiWxiGfcJlImZKKdne3m6LkK7rImX/4gYpJVLKPYt3nedUSiGEwPd9bNtGCEGxWMSyLNbW9levOMH3/VSxcVTB0mAYxCdXtvjn4yt7iBSPaCnFz371Xl668CwsubfoYYPBYHg8orXGqwdIS+Bk06d0mhWfuz98ijvff5Lt093PDlMXFbjmpsMce/Eibs5MCRkMT0SkkDxl6mqOV0+O0Lb//kgicKWDFIMWW3eTtbPG1WgwGAwGIzYanhhU9WcJ9VYsNu6dgHVcDhPoNSxZYMb6YdbCPyMSHF1cLsbRhwhYR1FFE8YOQBebGcasF7Egf/7cXlQKE/LbqanP0ymoCiHI6Mto8UBqfKxGoajGrsVE2rPjKM0k/rPT5eeh0KlRmzp2LEa3nk47UlTHtRvT3XSR4Bi93SR+yrBPaJQUhoaEbqh3IUSGSfkd+HqZpfCt7RqNaTUINS08fQKfZbLiGIrWQKExGWW3i7HzWkTPdt12PnZHmyZf24CbFMUcGYHFuHgpFhMDa3FKXMJYIB7Ui8TBIn3Fckm8avQBGQxPQnoFvlEjSweJaUEQYFkWQRDQaqXHGyul2qJkUq8xiSTtHU9Sa7FeH7BwomeyYNC4tNZ4ntcWBxcXF9tOy7MhqTPZO4404dRg2At/fO/JodGpw1hpefzz8TMmTtVgMFywaK05/uU1br/5JCe+uo5W0fuhk7W49HnzPOXVh5m5ZByAez66xCf/6C4CL32x1MbxGp/607v5wt/cz4t/+hqO3mAWIhoMT0RuPPQSbjn1MbxweFKXkyISZqwMIMhbo7kjnz03OInKYDAYDE8ejNhoeEJQVh+MoulGrF3YT0jAOg5z+JxiQn47DrOsqXfi62glmBAWDnPAzsOYFGOUxKuYkv9p19i6c0FOXMWs9SOshn/StV0Ii4y+nIAVAlbiyNOISGi0ke06hCqOTI0EQkmhT/iLokYbcW3GTqIHVoHV5X6MXIuSwfGeGo2KHYa6L8YUwGJi1+tfD/8Kl6OsqN8j1Btd/Q9C49PUdxPu6gbsrE/Z30u/atgpotKxXxD9Pp2gxjZSu1hM4TCHFMPrN9pMY4kxxuUr2VB/O6CVQJAd6G5MnKmWGO/blxfPIC+ePnQMBsOTnXw+z/Z25BRPRMBRGBRjmoiNg4TG3nZCCFzXpdVq0Ww2yeVyfZ8vtm3jOA6+vzNxkESjJuMYJDImfSV/J8Lg6uoqMzMzqcdZltV1rt1Ii1LN50etX2sw9LPSaPG5ta2z6uMvH1gyYqPBYLggOfmNdT7+B3dSWemvneY3Q+6+ZYm7b1li+miR6aNF7vrgEpYtYJfnU68e8MFf+wYv+ZlrufwFi+dr+AaD4THiVYdfwV/d+/fcunE7gRr8TCOQONLBV9H9fsZyY7ERZrMzI5/LYDAYDAYjNhouaKIozY+zpd5NyFYcJSrimoPu0KjLXrRugoj6BCjIGyjIG6irWynrm2nqe1HUo3hO5hmXL6cono8U7nm6unRK8iYEDivhH9FXv5F5HOYJdZmATUI2sShhM4PNFIICTb7RFQcrYpdjv+DYArIkIppuR4taWIzRLb5JIiFyWK1AFf88AnpFSYGTKo71ozkV/nSf6zISMQdPhA+LT416TVb9Dhs/7Lxlqna76FgrrkXZLVhqPDSyLQJbegyXo0iRJY2ifAEAC/Ln2VbvHhiNK8mg4vqQvXskWWz6VydnxVUsWP/jURHFDYYLmXw+j23bBEEwcvRnb7vef2ee1+++7j3e9/22+y8RHBPnoev2f85ks9kuMVRK2RYbB8W+JuPqdBk6joPruvi+z4kTJ7qcnEnNSMuykFKOFCebXE8vpVJppGMNhjT+/eQqrRF//wZx21aVVhCSsUe/NzQYDIbHmvs/vcxHf+d2wiD9nqRV89l4qEpltUnwwRAdaoQlsF2LsdksU0eKZIrOwP61ho/9rzsYX8gzf4X5rDYYnkgUnAKvOvxyAuVz7/aDNMP+BQsJGcvFVz5ZK0PWiuYrcnaWcXds1/NcOn6UKyYuP2fjNhgMBsOFixEbDRckWvusq7+lrD+E0jVCNuIad511BVtEcaG5kUTHRHDqdZ/l5XXkue5cX8JZMS5fRk48hW31fsr6FpSudO23xTQT8lupqa+hegQrS88QcKZrWyQ4jsXimEfyOipaSBJhzCJ6PfP0uvyi2o1Rbcb+OocJGoGVEnkqsShhMbnrdYeUaen7yHE9Quz8TC1KA2NHozO3dkk0jQRDhroz6YhM3Tm3jkNkJdn4PJ3X3z0xGlKhyV1k9TGk6BZMpSgwJl4IgCNnOWj9JifCHx8ookqyKEQsOGoiobGIpIjNzupDgc2YfBEz8r8+6sK4wXAhIoSgVCqxvr6+L7ExcRh27kuLFe3clxzfK0omAl/a8UII8vk8QRC0RcekXafDsbN90qcQAikljuO0XZeJuKq17hIjwzDsu6bd6G2byWTIZtMXWRgMo3C6OVywHwWFZrnpcXFxtDgwg8FgOF9orXnkS2vc/8ll6psttIJM0ebwM2Y49sKFdh3G03duDhQaQ1+xdPsmtbUmyUe+DnX0dagJvJCtpTrby3UK01kOXDOB5aY/E4eB5qv/9BCv/MXrz9MVGwyGx4rvOPJqPrX8Oa6byrLlbbPSWGXbK3ctsXYth0PZAzTDJmvNKEFKAAfzuzueHenwtOnreNf9/4dQh4w5Ra6ffgpHx46cl+sxGAwGw+MbIzYaLjiUrrMU/k+a+s6OrYnIZdEtFgUoqnE9wOG/7okgmeHoOR7x+cERC8xYP8CUfh0N/TUCtoAQyRh5cR0+y1TUx/uPY65PbATiGpRZNBkgaMeeuhxBUqDJnShkHMfae6yDjqU4jU16JKlAoaBLPLOwKGIz11P7MB1fr6AJCdnGZqq93WZuF7ExHNi7bkfDRrGy0ajTBMcUoSCOUo1ctE68bXh9N41Pk/vI6qu7xL9x8dIux+Ok9R1omiyFb40djv2TDNHPItPl4s1wOUJY2GKWcfFyxuUrsMXuQq7BYNihVCpRrVZHjlDtJK1mYppQl4iIw0ichJlMhkKhgO/7KKWQUuK6LuPj40gpWV1dpVKptPtstVpdMakJlmW160JaloVSikaj0W4jhEgVNxO3ZK8QOYjONkIIZmZGi18yGAYyovC/azfnpBeDwWDYH0ppbv23R7j9fSdSI1Ef+vwqX3jnfRx78SLPeO0lfP6d96cKjUEr5MTX1mmWO56rtN4RHTWgQFigFVRXmxz/6jqHrp/GyaYLjo98aY3qWpPijFkcZDA8kThYOMCbrv8pfvVrb2fCLTHhlvBVgK98FApbWGSsbEdRGMFqc51DhQNMZQbPI1T9GttemensFP/80L/37b9q4gpuOvwynrfwHCxpUiUMBoPhyYIRGw0XFFoHnA5/vUdoBEmekBaCTEpcpkZRQ1Ic6nCUIo8rDpOTTzkPIz9/SOFSEDf0ba+pLw5on8PWC/gsEd1K9roUBeC0t9piuj07l+UYPkt9bruolwyaZly/0WInajTxFFrsTPNZyLZIZuOI/tjPXjQBIZvtrzuxRBGpkyjY9KOT/4s+QTpyBe5ci4xb94qGaXKl6jnWiV+H1pBjonhVnyUyHAEgK65kSn5fX7sp6z/iiqOshL9HU99DwEY8LgHY2ExiizkkWQryBmbFjyOFE72uwtzQGwz7RUrJ4uIiJ0+epNkcHDeUkDgJE9fgbowiNHZSrVbJZDIsLKTXm5ufn2dmZobt7W22trZoNpttN2KnuNiJ1ppms5nqgBzkpEyOG3aNtm131YVcWFgglzNOMsPZMZftX+i0VywEM5nBUYIGg8FwPvFbIR/6jVs5/uXBCyQBWrWA295zgns/epraRgs70/35rULFqVs3uoVGIlGxe0MkPiafyc2yz9JtGxx+2jTS7l84pJXmzg+e4ptfd+neL85gMDyuuX76Ot72jF/k17/xu2y2tnCkjSPTp4OvmLic/zj9HzhVP82J6snUNivNNSp+lYP5RbJW+j3aXVv3cNfWPXz09Cd501N/mpxtngcMBoPhyYARGw0XFBX9MRr6613bQspAiKJO5DWLagd2i2gaRQOLYmq/AgeLKcblK8/TyB99FA1CXSFgLY5H9VE04hhTK478bBFNv2VioWyQoObiiiM4zCJ1kRb39Al+UZSqQuPF/VhtiU/iAhY204SUO0RfSUYcQ7B7vGckcA6enHe5mCZ3xxG6Pp3uSh0Ln9FvRRbi1yMZeX8srIwF07CjTScScIBWu6Llzp5cfLbWUDdtwDquPkxeXs+C9QsDI06L8lkUxD9Q119iW91MQ9/afu0tMcmYeDEl+UocMT/wXAaDYe/Yts3hw4cJgoBabdBChgjLsoYKcGmxqsPa9KK1plKpkMlkmJiYGDiGqanI8T1KbUXP8waOY1jNx0RsHCSW2nb0vpfJZJiZmTFCo+Gc8G2HZ3jb7Q/h982mj86x8TxFxzz6GAyGRx+lNB/+rdtShUatNY0tj8pKE68WoEKNtARBK0BIyaHrp7oEx8qZJs3t/jILvZ/LWoPoqSPRLPtUVpuUFqOyIaGvqK428erReb/wzvsIWiFXv+IgEwe7Sz4YDIYLm6smr+Adz/8jPnPm87zv+Ie4a+uerv0HCou88tBLeenBF1J0onmzW9dv5yNLn2CluYavfAp2nlpQx98ImMuOllzytbVbeetXf4O3PfMXcaRZ9GUwGAxPdMwTt+GCYlvdDESSYsBKHKvZiLft1GyMYkCjSnog42esII7T7Hd82czgigOMi5cS6ipV/WkCvYrGR1IgJ64hJ689/xd4jiirj7IWvoMmdxFJbfUeN6KIYz8T4a0eS3HZvpjUjDjGtPX9rKk/x9ensESBrL6KFg92OQkjgTFHFEXairdJJPm26GZzoC0KCxwy4hgWoz3I6o76h2kinsRFMobPCXprJUaioUIjEYRICqh2bcpEmOzFoVOIlLixKCoQ2CgGu50kOSCLpISiwqAI1IK8gUXrFxBil4hfISmIGyjIyMGqdCRkGveiwXB+sSyLgwcPcvLkSYIgaNc1TLBtG9u2kVK26x720iveDarhOIojcmtri1KpNLTtKNGvWuvUsSbj2G0sWmts20Yp1b42IQS2bTM5Ocn4+Lip0Wg4pyzmstwwPc6n17b23ccPXHLg3A3IYDAY9sDdt5zi4S+u9m2vrjXZOlkj9LqfXVSg8eohKgx4+AurzFw61hb/tk7VCEMFqkNgFCI1bVrr7iWTKtRsn6yRK7lsL9Wpb3pdMdXrj1T5xr8+wjf+9REOXjfJ0777KIevnz7r6zcYDI8PbGnzgsXn8oLF57LWXGeztYXSiqJT4EB+se8Z4Lrpa7luemce7M7Nu/kfX3rrQFfkIO7cvJu/uvdd/PCVbzgXl2EwGAyGxzFGbDRcMDT1PbT0/WhCWvq+2NG4Q+Qoq8WutLhQRexu01hxPcEWgnzXcQKHrLiCaflfWFV/RlV9Mnb/deOqiynJmxgTLx3oQnus0Vqzpv6UbXUzITWiaoVV+sU0HV+jQFJofx/9sbEYQ5InK67kIvsPAPA4znr4DiCKYs1xDaGuErASx3uq2DmYRzIDWLEYGZ1bkIlqB+qjCAEWUwh2r/2V0OmGtBjv2qd0nSb3ovFiIbFBdwyqJPp9UCgaCGwyXITDIZrcFdd7TCJfZezytOPXLoh7yNEZmdrt7OyfmHc4iCsOoWgS6I22M1NgY4lS9BqL7K5CYxpSnH2cnMFgGI1cLkc+n6fRaOA4g1fj2rbdJeAlE4CdTsZOobLz690EvqQGYuKyLBbTXfqj4vv9joje80kpU0XSzmvpHLPjOBw8eJCxsbGRhFODYa/86LFDfGZtO77H2xvTrsNrj6THEBsMBsP55vb39UcRbp2ssb3UW/5jh+Q2IWiFbJ2s4zdChBTU1luoUPc37twkenfv7Kxve5z6xgbS6v+s7tx26tZNlm7b5Nk/eIynftvFgy/OYDBckMxkp5nJ7m0xwb8/cvOeSkF0csupj/GfL/se8nZ+98YGg8FguGAxYqPhgqGuvopGpwqNkNTLy6FpILBiZ2PkaAPiWMze6E+bMfkSSuImlsNfp98Rt4OnH2E1/BPK4hYWrbdgi4k9X8Nuta7OlnX1l233ZyS+tkh37bVH1K5nKTschjbzOGKWSfm97W3j4qVs8HddQqwlilgUcfXRjvNYHbW9wthVGTJhfStT8j/i69OcDn9lz9cm4hqPFqUugU7pJk3uaTs3BRYWxThONYpLFexEqUZOSyv6W0gcvTiw1qMkGwvYNvQJox1CQU8MrCCLIxbafbgi3U2h9PBoRoPB8PhgcXGRU6dO0Wq1BraxLAvLsvB9v0ukGxY5mpCIiYNIokkByuXyULGxtzZjGru5H4UQZLPZtlszLfq18/Msufbl5WWq1Srz8/MjjcNg2AsvWZzmNQdnePepfnfQMFwp+Z/XXUrGGn2Bk8FgMJwrlu/aYv2hSte28pnGUKERiJ9fIh0xEhxrhIFCqxEm+pP4VK1RITvHaFC+RisfN29jubJLl3RyvfWd4bN/cS92xuKaGw/tfl6DwfC4ZdvbZrWxjqc88naOg4UDe4o13Whu8oXVL+/7/M2gyUeXPsmrL7px330YDAaD4fGPERsNFwwhFXy9lCo0JkgyaETsXosiVHXsaCOu5RghcMXFHLB+FdCshn848jha+n6Wwjdz0Ho7lhgeARroTcrqg1T0Rwn0GpoAKfLkxFMoiVeRE089Z+JjQ93Glvr39vchG/QLZGn017P0OYmj5ynJV7S3WWKMknwVW+pf+3qIrqH/7UQIC4sxLDHJrPyvWGIMh4PYYo5Ar+zl8uLw0lksJrq2t3igJyI2aW8hyBFFuxILj0E7JrbFQ0g9hsUEAjfVzSqwYxE27XVMfm4yjqRNtrpkxbGh9RrbbR+nDlmDwdCNlJKDBw9y5syZ1PqNSilarRZKqT7H4igMW4jSWw9yUPxpQqFQYGNjY9fzDcO27bbg2Gw2BzohLctqOyAbjSjSvNFosLGxweTkJBMTE+TzeeN0NJwz/uibrqQRKj68vDGSwzErLd50zcV898WmrrHBYHhsePBz3c88KlBsnei+l1BKE3qqy7GolG6Lhn4jBAE61KlxqcCOOpmgY3ek2BEa27tCjVcPEA1wcpHoqHwFGsrLDaQtyJVcLCd6Bvr0n97Nxc+coThjItINhgsJrTVfX7+V9534EF9a/Sqqo/b1mDvGSw+8gJsOv5yF/O73SZ9Y/jSh2r1cwzCM2GgwGAxPfIzYaLigCNh9NXvkfnNQcSxoUnEvid+clq+nJL6dovwWPI5zIviJPY/D0ydYVX/IgvXzqfuVbrCq/oSq+lRP3GbkZqvpz1Pj8zjiIDPyhyjIZ+x5DL1s6fd2fe/rFSQuIU2GOTYjuutZanwy8nIsUepqNS3fgM8pauqLI49LihyL1i9jiTEgmnyflz/NUvhLqSLhMGasN1JWHyJxUYa6OtCV2IvAxWEhjnwF4rqfrjiMrWfxOZV6VFYcQ+gsHg/H9SaTPSJ+/nfbWyxKZMTRLvFxGDajFVU3GAyPPlprarUa5XIZz/NQSiGlJJPJIISg1WqhtSYMw7bj0bKstpDX6W4cRuJ8TPrvpTe6dbd+M5lMWyTcL4mTMggCwjBsj6tTpExExjSXZBiGlMtlGo0Gtm0zPz9PLpfb93gMhgTXkrzz2Vfz9jsf4R8fOcNK00OliI4ZKbmkmONnr7qYVx+afQxGajAYDBHN7e4FjdW1VttpGAaaoBUS+v2f7VppVKhJ1utIO75HSFyLZ4NIzgGtig8SbNfqdlsKyE9mGJvLkh13ueP9J7nh9Zed5YkNBsOjxWpjjbd9/e08WH4YSJzSCimi95KKV+FfH34v//bI+3j1RTfyxiu+r70vtb/m+lmPae0c9GEwGAyGxzdGbDRcMAR6NdV9lo6IHWwZ6JiGKojncMj+7Xar7fC9DI8ZHUxVfZZArmGLbsEo1GVOhb+Ipx/atY+Wup9H1A9TlM8mIy7FYoycuJ68eMaenCCB3qCmvtD+XlFDUQVAUoy/Hj5B3VnPMnIP9p9fCMmCfBMr/CEV9ZHBfWlNyCZa+BT5FjbVP2BRoiC+hbx4Ojl5DQv8D5bD3xz5Z1qSr2FG/hAWRTbVP0XXzZmRjgWwmSYjLkWyhKdPEYmNqzj6IA6LhJRRdEYcSTLiEmymQECOawl1hYAVQrZjN6TGYgybaWwxh2Rvq33H5Ev31N5gMDw6bG1tsbm52Y4QTd6PlVJtZ6Ft2+Tzeba3t8lms20BsjNadFC9xoTO9/m0/a7r9sWR7ha5ClAqlYaKjcOiXRO3IvTXdtyJyNZdImQavu+361guLS2xsLBAoTA8DcBgGAVbSt507VH++5UX8Z4TK/ztQ8ssN1t4SpO3LK4uFfjByw7w7JkJLGlctQaD4fFFZSVKAgg8hVcfklYgQIhIEIT4PuEcvqUlYiYACmSut9Aj1Dda1DdaFGez3PXBkzzzP16CZZtIaoPh8c5y/Qw//6W3cLp+hpXGKmvNDXwVl50RgjGnyHxulgl3ArTmPY+8n43WJj9/3U8OnIdKjj8bfDU8ocVgMBgMFz5GbDRcMITU6c+HGYWdWhQWY+2tStepqI+fxYgU2+r9TFuvb2/R2ud0+LZdhcZAbxJwph0Ju6n+CZej2GKCTf4NRyxQkjdSEq9Git0FrJr+Ap2iaRgLjRDJrpHgWGOYsJq4DG1mccURWvre1Gg/IWzmrZ9kQn4r2+p9VNQn0LTi6w/xWUbjYYkxbGZp6ftpxT+yMrfgiEVK8iZK4jUctH+TDfUu6uorDPq5uuIwE/K7GJcvAWBKvp6ADcrhhwnY3PW1AbAo4YpLAHA4gCWmCfQKAauEbGOLSbL6cprch8bDEbPYzPY5FC0x1v4dcvVVhGKFkP2tzsuIy8kKszrYYHg8obXmzJkzbG5u4vt+XySqbdvYto2UkiAIWFlZQUqJ67qpLj8hRNd7aBiGQyNMO99zXdftczUCZLO7fyYUi0VqtRrVajV1v23beF5KdLQQuG7k1lZKpbooh7kwe9uFYYht22itWV5e5tChQ2QymV3HbzCMQsG2eO3RRV57dBE4/3WxDQaDYT9kx3fKJgReSNAMCfxdhEbihUECkmckFWikJfYuOPbedsTHd0a2SksQego7a6V2X11tcvwr6yzdvsnh66f3OACDwfBo0gpb/MKXf5UvrnyFzdZW31uA1pqyV6HsVXAth4uLh5l0J/jM8uf569zf84Zjr0vtt2if/aLBgp0/6z4MBoPB8PjGiI2GCwgfixIhW/s8XuCw2P6uqj+HZv8xcwAV/VGmeX3H9x+jqe8a2F5rjc8JfJaJaiV6sbMvpME3sPQ4AgtPz+Dph6mKT7NovQVbTA4dR6i3e87TM+GNxGIMjR/XLuxdlSaiWoNciyWSG0AVux3TJ7Yz4hLmrB9nWv4XPP0gvl5lXb0DjYdkcFyer0+zFv4ldfFVFqw3c8B6C748Q1l9gKa+B0UNQQZHLDAuXkZOPqV7pEIwb/0klh6nrr469HUBGYunFyE6Hp0lGVxxGIeDTIjXkJPXARJJnqa+i7L+AIFeS+3RFrOMixuZlN/Btv4Aa+Gf7TKGdCbkt+/rOIPBcH7QWnP8+HG2trYG7vd9v+3Ys22bMAwJw3CoU7CTJHq00+3Y6250HAfHcQaKeaVSKXV7J0II5ufn21GwvaSJjUmNxuS8g2pDJmMfRdQJgqAdyaq1ZmNjg8XFxV2OMhj2hxEaDQbD45Gjz57lG//2CBAJfFqDv4vQuEN8b9GOPdUIKdoxrPtBiB2hUQgQVrQwSimN8lW7TmMv9Y0Wt73nuBEbDYbHOe89/gE+svQJGsHuc11e6HP/9oNcVDzMfG6W9xz/AN915FsZc8f62l47dTX//NC/n9XYrp266qyONxgMBsPjHyM2Gi4gFA7zQ8TGSLyL6g/qdq1GgYvAxmYKITpWluqzz4sP9EbXZPG2et/Q9onQqGjGbsDOB0XVru8YcIZAn8HTJ1A0OGT9LpYYfSWZEDLVKChwYreeRqNIin4ktRp7RcJRag9aokCGY6ypvyJke6jQ2Eldf43l8P/HovUWHDHPtPX9A9t6+gTb6maq+jOEugxoBC42EwicOAK1QXLRkVg5F7sTB7/NCSSuvIiifE57W57rmNTfTV1/iar+HCGRkGtRoiieTV58U/T6AiVeTUveu2eHbEm+hjH5/D0dYzAYzh9aa5aWlgYKjb0EQdAVsep5Xl/caRqdTketdZegKIRo14QcRDabHdkZKIRgYWGBra0ttre3u8TDxKXZGQnbK3AOcjX2jnsYvQJsrVbrEiANBoPBYHiis3j1JNNHiqw/XEUIQeCFjLA+CYjdjewIjFqDlIBgeB+day90/3YhQEiB6ImaDrzBYiPAg59ZQYUKaZkoVYPh8YgXerzt6789ktCYoIHj1RO4lsOkO8GHlz7Odxx5TV+7p08/lcX8Aqfry/se36sOv2LfxxoMBoPhwsDM9hguGCRFLFHC0Yv4nO7Yo2LxzqfzaUq3//ZiwfEwkmLH/rPPnI/qICrAoqnvoaUfHNgy1Fux0Nhox472EjkJd/5ZhmxRVh9khT9g0X7TwL4tMd71vWC3yegdgXFni93lCrDFNELsPnkOsKHeRUvfN1LbTur6a2zpdzMpviN1v69XWAl/n4a+tW+f0nWCOMJUUiQnromvW3a5GHej83ciQQhJQdxAgRuGHiuEYE7+JAKXsvrQSOebkN/FtBwsrBoMhkefjY0Ntre3d2/YQSLGddZzHMYg52OvcJfElCYkAqWUkunpvbkJhBBMTk4yMTFBvV6nUqm0RdJsNkuj0ei6ht1IxNWzcZCVy2Wmpqb2fbzBYDAYDBca17zqMJ/8o7uQNoTe8PuFXqQlEFYUqap15G50cjZ+I2gLjqJTfOz9iBYgraj2o5u3CVqqv018bBgolNLIAfVuvXrAg59b4bLnLuzpGgwGw6PD39z3jyzV9i4GRoLjSSanJnj/iQ+nio1CCF55+KW8456/29fYLi9dyuWlS/d1rMFgMBguHMySNMMFQ14+EwCHQ+04VE1ISDWOIh20vNNCkMXnBB4PtSNGLdEvMu0VQbYtyDX0bUPb+pxB0RooNAJtZ2Mnigbr6h0EanB9woL4Zrr+OWuJoklIuf1HURsqsFpMdH0/Jl42sG3X+LRHWX94pLZplNX725Pwgd6gqe+joe+ioj7DieCnUoVGiATBpH6iokpT342isSehUWCTE9fte+zROCzmrB/ngPU/KcgbSCukIrApyudy0Pp1Zqw3mKg3g+FxRBAEbG5uDowNHUTi8kvevwaJjYl4mNR07IxRTbYl/SilaDQaNJvN9p9Go0Gj0SCXy7XrKe4VIQSFQoGFhQUOHTrE4cOHOXToEEePHh3olEx7n0pEz7Oh2Ty7+HKDwWAwGC40rnr5QS7+phnClu5zEw5DiEhobH9NJCoKCXZWIgSg46waQWo9RyHAydqML+Sic3e00UqjAkUY/1G+oln2aVb8yIHZ0Y+0JZkxh3s+vLSPV8BgMDwavPf4B/Z9bCv02PK2OV1fxgv7a7sDvPLQyzgydtGe+3akwxuv+L59j81gMBgMFw7G2Wi4YBgXL2advwbRxOUwQmdocheRszCNJEI1g0AgsPHUCVbEHzJv/QQ58ZQBx41OXu4IVUr318Xa2dckZHuEGpHpgmlImRX1hxyQv5S63xYzFOQ3s61uxtOn0DSSM3f0rGKxUSLIIHvcjzbzHd9ZlOSNu4w1oqo/idKVkdqm4ekl1tU7aekHaehvEEW8ejT0nfG4ZrGZRYr+SXabOUKic2sCWvpesuJq5IA6k70U5HOwxcS+x95JXj6dPE/HlyvU9ZcI49fEEiUK4gZsYZw8BsPjkXK53Bb89kNnlHbv172C5KDjwzBaBCOl7GsrhMB1Xer1Og8//DDz8/MUi2e/WAai+NRDhw6xubnZfh0SOkVFKWU7dnU3B2cnadGyezneYDAYDIYnAlIKXvZ/P4WtUzVOfn2d0N/9s1BKgZu3aVZ9dKhRcZ1GaQls10JaNiqn8RsBQUu16zB2ionSEuSnMhy6fopm2ef07dHiVa01Oq4fmYYKNV49RDRC3LyN5UiKMxmkFJTPmEVDBsPjkbu37uVU/fTuDYew0lhlwi1RD+q4Vv/8S9bO8tanv4lf+PKvcqo22sIDW9r87HU/ztWTV57V2AwGg8FwYWDERsMFgxR5xuQLKatotVbIJpIcGjcW0ZKHNhFHkdpdLjebWYSQVNSHKYpnUZA3kBVX0tR373tM4+Kmju8G/3MK2ED11WhMY/BK1y317xwgXWyMjszQ0g+0zyFwU+pCQlQbsoFCtesrRhG1OzUhx+TzscVocX1V/ZmR2qWhaNDU9+GFD5MRR9vbPb0Uu1XB5xQ+Szj6AK442HW8xSQCp+3Y1AT4+iQZcdlI5y91/fzODY6YoyRedc77NRgM5x6tNeVyeV9CoxCiS0zsdAKOKjT2opRqx5RaloVt21iW1SVgLi8vMzc3x/j4+C69jUYSzzo1NUWtVqNWq7Xdl9vb20gp26Jhpxg5Co7TX/f3bJ2RBoPBYDBciDhZm2954xUc/8o6Z+7ZHhinKgRYroWTtdrOxURo7HUvSkuQKTq4BY1XC/BbYfQknJFYrsRyLQ5cO4ntWhSmZFT7UWtUMNq9idbQqgW4BZviXPTcGHp7uxcwGAyPDnds3oU8y/C6ih8toM/ZuYFtprNTvP2bf4U/vPPP+fzKl1B68OKJi8cu4r9e+QNcO3X1WY3LYDAYDBcORmw0XFBMy/9MQ3+dln6YkDIAAquv/mAvklw7ehVgS72XgryBkryJZrg/sdERi+TF09vf22JmYFuN3xbPhjNYbAxYpqUfISMubm8LdYWy/jDr4Tup6y+hqKPx49ckgySPIt1xqWmhEFgUybAj9GXEZczKHx1hrMkY9lbnLEFRo6nvQROgO5yIUTTuet9ofU6hddB1/UJIXH2EFveTiKoBm7j4CPonuTsZly8jJ6/Z19jT0FoRso2igSSLRWnkmpcGg+HRJwgCNjY2qFQq7ThT2KmRuFvccSI2dpK4//YjNHaSyWSw7cG3aKurqziOQy43eCJgrwghKBaLXa5Jx3G6alnuJQK6UyTtJE2ANBgMBoPhyUBuwqW0mCc/keHUN9bxmiG6LSQKLEdguVb7idBrhOiOhaNCxvcnPR+vQgjcgo2K3YpaRYJltujg5u32sdmSS6syuKzGwIhXpQl9hZO12v0ZDIbHF/WgQc4eLeFpEKEOmcnOkLHSyywkjLljvOn6n2atuc77T9zCx09/mo3WJqEOKdgFnjZ9HTcdfpkRGQ0Gg+FJiLlTNFxQWKLEAetXeDD4HnZ3CUZIcmS4AiF2ft0b+ht4eomieB5l8eGBdQGH9Tojf6RrIrUonssafz6gJqMCkodFTXf0q2j/kQyrx6Vp6fvIiItR2mNN/TkV9VEUDer660CIIEsSQxq5/aJ6lekOx0hwdLgaKaIJ65y4jgXrzficZju8Oa6DWEfg4ogFxsXLKIgbekS0vU+ma3ya+r7UGpUB62jSV8wGnEHqLI7YiXy1xSRaH8Hj4XgsGl+v4ooDA86tyYijZDjGtroZSZGcuHbfMaeBXmNbfYCy/hCh3qmraYlxxsRLKcmbusZrMBgeW3zfZ21tre3g66yhCHQ5FYeJjoOEtERs3A+JgNlqtZBSDnQBaq3Z2to6p2JjGpOTk9RqtXY9y0RM3Y0k+jUN13U5c+YMnuehlEJKieu6lEolstmzmyAxGAwGg+HxzMyl4xRns1RXmxx46hQrQxyOWkPYCqN6yfGjl5ACaQlkiigYiZWSwFMoFcWkTl8y1tepsAQ67L9PEaJHbIybWK5EOpLtU3WyV5aYv7K0r2s3GAznF0c6jDlFclaWRri/uGMpBK88/NKR289kp3n95a/l9Ze/FqAv7cVgMBgMTz6M2Gi44HDEIg5HCNggYINhNRttpqP6jsImpEyoy2iieJmV8PdZsN7EgvVmlsJfpqXvHXEEkjnrv1OQz+jaaokCY/L5lNUtfUdodCygpY01edgT7ZYi1eFoo6ihdJ2l8C3t+NeANYjFuag2ZR6FFTspw/h6cyQiZDKGpJ6lokZOvJiSfBVSFzgdvjU1WtbXJ6nzZWwxTUl+GxPi26OHX/ZeO8zXK11OT9HxVqR0efixLGHrKBI3wRGzSO3icQJFHUUZ6BYbNR4hVQQ2LfUQq+KPOvZakdNVvKqrDucwtA5ZU3/GtvoAaT/XUJfZ0v/ClvpXxuQLmZX/V2rdSYPB8OjRarVYWlrqiwNNeyhOREcp5VDBMREWLctCSonjOPuqAdkZlQqR83KQYAdQq9Xwff+8OgVt2+bAgQOcOnWKMAzb1zis7qIQgkwm0yeUJvUeV1dX+45ptVpUKhUymQxTU1MUCoW+NgaDwWAwXOhIKbj6xoN88W8fwM3ZLF4zSXW1SWWl0Sc6Bq0QBNhuFIkathSBp7DcwTGJdsaK6kEKcHJRFGtnf4GnkFbkjOyt2ZgIjVrpHbelFIS+IvQVXjXAyVtc9vyFc/iKGAyGc8Vcbib+e5ZHqif21UfWyvLygy/a9xiM0GgwGAwGIzYaLkg0DTLiElx9mIA1Ajah7ZKzsZnCZgaEJGA1FrcaXX2U1Qdp6XspyGczLb+fqv4EFfWxdv2/NBxxiBn5xj6hMaEkX0NZfYRO8UnpRhwLOnhyNkKgaaEJkRT6BEebEmib0+rXu8TAQK/09STJAJk4ojRyOVpMIskjsJDk4uhZC0fMsmj9EjX9BU6rt6a6DTsJ9Drr4Tvw5IPMyZ+iIG+gEX59l2vbQaMJ6J5stpjo2D+4DohGR/Gr3InQ2VhcdbGZwRIlcpQIdRVEiCMOoqgjyaC0j6cfwmIiEin77oFDauqz1PgsRf185uVPIsTgCXytA06Hb6OuvzLSFVfUx/D1GQ5Yv4IUwyNJDAbD+cH3/T6hsfOBOC0WFWi779IenjuFtyT2NAzD9vZRBcfevrXWBEGA4zhDH9rL5TLT06PV190vruty6NAhVlZWaDQaOI5Dq5Xm4I9ejzSh0fM8fN8nkxn+/tdqtTh9+jQzMzNMTEycq0swGAwGg+Fxw9WvOMRX/uFBwkBjOZLSgTzjizkaWx5ePUCFGikFmyfr2Jmd+w8rL7FcRXbMwW+kPy8l/bUqPqGn8OJ6iwDV1SYCsF0rqu1ox7WnFQgZCYsqUIBAWP3JDkppyqfrvO8tX+M5bzzGtTcdPp8vk8Fg2CPPmbuB/8d+JzOZKZYbZ2iFo5Tx6ealB1/IRGbi3A/OYDAYDE8ajNhouKARwsFhsaseY0IU1XnXwJqFiexVVZ+iyqeZsX6QI/Y7KetbqKiP4OtVIECKAllxzUiut4w4yqz131gNI9ec0h5N7iFyL0oGC46io+5kgKLWJzjazBGwTEN/veMKPFSPiNrdq912DQokWY6lTlxvqn9hU/0fGCL09VJRH0eQY0a+gXX+Gs1oUR0hWz2uRheLya5R96JRsRDrAZoAr8tR6XMaS49hs4AtJsmKKzlkvz0e56c4E74dS4yPNL6q+iSKOovyFwfWXFxVfzyi0LhDU9/JGfU7LFpv3tNxBoPh3LC+vp7qaEyiQaWUffsThkUC2baNbdtYltVu2/n3KPT2n7gqwzAcWrvR9wcvjjmXOI7DwYMH8TyP7e1t1tfX24KjEAIpJZZlpdZp9H2/7cAcdi2drK2tIaVkfHy0922DwWAwGC4UciWX5/7XK/nEH97V3iaEID+ZIT8ZLcpRoWJ7qU7Xc5GA+StKFKaztKo+1bUmQUvF0aiS7JhNYSaLZUvqmy3WHqjgNXbERq8eR6JnLAI/jERGIXByEmkL/HqItAa7JgXg5GxCX/GpP7mbVtXnGd9zyTl/fQwGw/7I2lledOB5vO/4Bzk2fil3bd9LoEaf35nMTPCb3/TW8zdAg8FgMDwpMGKj4YLEEuMEuj+KLUETxvUGhwlxTtcRa+FfgAWT8ruYlN+177GV5I0ArIZ/gs+JtrAmcAcIcqIrRjQiciQKogdOi3FceYSmvqer1W4uxO62LRTbXS7CaLtmQ/1tyhh2p6zez5h8EWPyRZTV+0c6Runun4nNbLe7qKdupSaMBWPVta2XkAohFZRepCC+BQBPn2Al/D12qyuptUdILXaCBvjh+7EoMSd/rM/h6OlTlNWHR7nUPmrqczTlPWTFFfs63mAw7I8gCKjV0heedNYhHBQR2lnHsRfHcTh69ChBELC9vd2uBTmI3rjUznPsNXpo2HnOJWEY4vs+SinGxsaYmppic3OTlZUVgiAgDMMuoTYRYKWUeJ6H67p7jntdXV2lUCi0RVyDwWAwGJ4oXP2KQ/iNkM+9417S1ib11VQUMHVxkcJ0VNs4U3TIFAd/ruYnMxx8qsOhp05RWWlSXWuiwiQaFbJjDkFLIW2BkIJWxR/6tCQAJ28j7R0x8ot/+wClA3kue+6TI1Y1uecaVE/bYHg88OqLXsEHT36EnJ3jqtIV3Fd+gGaYnkjSyUx2ijde8X1MZacehVEaDAaD4YmMERsNFyQF8Wy29bsH7vf0Q0OFRhB9ohvAWviXZMU1ZMVlZzW+krwRh0UeDl5P4miUFAhp0V2jUSJIf2CJRMoMAhuXi8iLZ1BWN/ddx17wWem77pAt0GCLydRjdmNbvY9Z+SM09K34+tQIR+xMSEuKOHQ/oNpiph0Nq1F9QiPxnkH4nG6Lulvq3UNjcUNdJmAFnzU0zbht1PdSeBub4b8wLf8TE9a34YjIPRuJqnurxdbJtrqZrGXERoPh0aRcLg90GiaOvDAM2y69QSJebzSqEIJSqYRlWWQyGQqFAuVymXq93nfsKEJimsNxGOd7wqvZbLK9vU21Wu1ybCb1F6WUbbG2c6yJAGlZFtlsdl+CodaaSqVi4lQNBoPB8ITkqd9+MRMH83zl/32IM/dsd+1L6icCZMYcSgfy5Ep7q/1uOZIrX36QYy9aZOm2DT76u7dz5p4y0hJkxhxUqFl7sEJ1pZEqeCZIKXByFpYjo3qPHXzlHx58QouN9XqdcrlMrVZr3+cIISgWixSLRTzPo9FooJSKXKKOw9jYGLlc7jEeueHJyqHCQX7q2h/lt2/73+TsLE+ZvJpNb4uVxiplv9rV1hKSqcwkc7lZnjZ9HT969Rsfo1EbDAaD4YmEERsNFyQleRPb6j2kiT4aL67hOBiLcaRIewjQbKv3krV+8qzH2NR344qLcThEyAZKe/jYhGyg0X01GfsJAU2Gy3DkAjb9q8wid6ZgVPErpNy3LdBncDg00vFp1NRnmJU/xAHrV1gKf3kEwVHG/y+Q5XKEsNB4+HqFgHU0HiEVIoFRoSHltRr82gky1PRXqKkvUVEfT22jdUiLB6OfC/VUQVIT0OA2Tqu3saH+iUnr25gWb6Ss9+dqTKiqTzMj34glxs6qH4PBMDpp4l8nmUyGRqPRFvuklG03I9CONO0UAqWUZLNZarUatVqNQqHAzMxMW4Q7G9dhcp7dBErX3dvEYy9JxKnWGiklrutiWRZKKc6cOdPnBtVa43le2wkKtAVFpVTb3Zj05/s+YRiSzWb3JYxub29TKpX27Pg0GAwGg+FC4OJvmuXib5pl7cEyd7z/JFsn6/iNADtrUd/ycPM2bn7/UzZTFxWQUnDoqdNc9oJFahs7pSwsB+aPjdMsR9tCX3WJjpYtsDNW5H6MP4edbPfioY3jNZZu3+TAtftbtPp4pdlssrKyguf117wLw5C1tTWWl5f7alU3Gg3K5TKu6zIxMWHi4A2PCc9fjFKe/tftf4KvfKYyk0xlJmmGLbzQQ2mFJS1yVhZb2lw3dS1vvv6nyVjDa6sbDAaDwTAKRmw0XJC44iA58dSu+oUJUa3F4eKbzdzAfVX1KWbkD561GFTTXwAiD6XNLAhwWKSp7yRgHUWLfseeaAtrAgeLaWwxw6L1S9TV1/rOIbCxGCdku29fOiFK+8g4GjRyDnpICvu8ykiUq+tvMCafzyHr7ayHf82W/jcCvUEkmEqkyGAxFb0WYgp0iMNBENDSDxCwQefPTJIhpAZxTKxGAFaH6DjYJeOIeQSwqv4kNbZWa0WLewkpE1JlWJ3KqE6kS5O72AwVTXE/IdURhOLBaDw8/Qg5ce2++zAYDHtjUC3GBCEEuVyOZrPZXp0uhEBr3RYNk22JGNkroNVqNarVarvN2ZAcv5sjcD+TWFprarUa29vbNBrdCQDJav1Go9ElKCb4vt+3PQxDGo1G+/VKSNyPEIm9+Xx+z4JjIoaerahqMBgMBsPjmZlLxnnBj13dte3L//AAX/r7B/fd5+xl48xdXmp/f/UrDnLrvz3SJShW11pIKXDz9s5neLTSM/Vepjib7dt2x/tPPqHExnq9zunTp1PTJcIwpNVqtfcppWg2m333hJ7nsbKyQqvVYmZmxiyaMjzqPH/xW7i8dCnvO/4hPrL0Cap+layVIdshKF47dTU3HX4Zz5m7AUs+PsoWtMIWnznzeU7Xz9AKW+TtPJeXLuXp0081/44MBoPhAsGIjYYLljnrxzgZ/iyh7hbaAtaGHmczMzQyVONR1Z+hJG48q/GlCoBa4HCAkDKCViyvdQqOAnAQ5JHYSJHjoP1rZMWVNMRtqeexxVzfa9B5NQovjmSNJtvrfBWpXWxmscQkTk/NxFHRWrWdgRvqXdT1N/D1MoFeQmgXgU1IFY1PqAUBZcblS5gV/xfL+lcJ9NrAupoCF0GzoyalJqpjaSMQSNInnm2msONY1rr6OjazSNHd1uORuL5jjWFCY0TyswlpcR9aB4RskhFHR36d0nsd7rIyGAznllHe44QQZLPZrvqEvUJjUovQtu3UPlutVtsB2Su+pZ1v2P5B50goFovY9t5u43zfZ2lpCd9Pj5fWWrO6ukoYhti2jeu6XTUme4/rFGMhcnumjVkpRaPRIJ/P7/nzZjeh2GAwGAyGJyJXveIQX/l/H2rXWtwr19zUnVwzcbDAweumOPmNjfY2v7GzgKj9+TzgYzoz5qS6LDePV1NaX5i0Wq2BQmMiLPbG6WutaTab5HK5vnuc7e1thBDMzMyc97EbDL0s5hd445Xfx/dd/lq+sPpl1prr+Cqg6BS4dvIqLioefqyH2OZ0fZn3Hv8gH1n6BDW/1rd/IT/PjYdewo2HXkrB2f9CeYPBYDCcf4zYaLhgccQCB6xfZSl8K6HeeWgaVqPPZhqXI7v2Hej1czDC7oeNyFH3ACGbCGwkBTReyngVmgYWFzEpvoesuBIAi+nUs1hMIsig6S78rWjG2zofliQCgcbHZwlfL2NRwkYNrB3Zi9IeAcsErLXFwJr6Ett8IHYSSmymcVjAEfNdx3r6YZb0m8lwOVX92V3qakbj7RZjA3QcHauooeOoWRBICnE9SgVINJEwKNkZQzT2ZNz9rp1haHxCtghYw+EAkv3HjAj6VwUbDIbzR5pDMIlJ7Yws7RQUk5XrSRvbtslmh//b7RTekgmo3eou9pKMZ5iQKIRgcnJvLgLP8zh16tRQ8a4zCjWpw5jJZBBC9Dkak2jZUQnDsL36fy+Co1nFbDAYDIYnI4WpDFe94iB33HyyvS0MFM2yh98MoyWqWZvsuIO0u5/jSgfyXPb8/lqKT/+eo5y6bROt4iWvowqZIuozDa++t2eqx4pWq0W9Xm/fq1mW1bdwa319ve++LbnfSeL2e0nuHz3PI5Ppfz7c2tqiUCiYOo6GxwzXcnnewnMe62EM5EurX+W3bv19mkF3KlWgAnwVAJoT1ZO8896/54MnP8pbn/4/OFBYfGwGazAYDIZdMWKj4YImI45y2PodNtTfU1GfTBHXIiRZbOaxmRtx4vLsnRQ2EwScAbqjOxMEDgIHjQLCuI5jNNrIFbhF2FF7siiezSr90aACyIrLaeq7YvENFI0+8TE5Z+d3GS6lxf009d1kxRWIIfGkAKGuRg6/LoFU49NZp1ERsErAOhl9SRSb2tXao6w/REhlF4FTIbDQbcExmcQPUOysdoteRxeQNLkfoR0cFuNju4XcgChiN+21Saf7dyWpBRroFVyx35WAAkf0P/wbDIbzRxINqrUmCIKuiE+IJoosy8JxnHa9xk6HIoDjOIO67yOpWbhfV55t20MjVOfn51MntJKaip0Taa7ropTi9OnTu46n17kYhmF78iwRGxM346B4sUHuxuS1r9frOI7TjqPdLSp2t/0Gg8FgMDxR+ZYfuoLKmQb3fvw0mydqVFeahJ7qetq1XMnYbJapI0UyBYfchMur3vo0nEz/5+fB66Z47o9cwaf+5G4ApDXagp7Jw0VypfRkGSf3+J1S0lpTrVbZ3t6m2ewvr7G+vk6hUKBUKmHbdl+N78TNOOi+JzlHcv/VmQjRyfb2thEbDYYUvrL2dX7t679NqHbqvm96W6w016h4la73uoKdZ7W5zv/9pV/m9274dWZzxjFsMBgMj0cev3eGBsOI2GKGOeu/My1/kIr+MK3wYZSuI5AIXGymkYzvyR0hxf6iGbT2Camg8ciJp9PU9wDgc7JLaOwkEtxkSmKNpq6/iqeP44qLkCLHmHwRZfX+/vGSJyOuoKXvjaNL08U00XbjSTJcgi0mCfUUAeu09P1kxBUDqxEqXafFPW1Bs72dBpK0la6RkxMtscVE156QjZT26Yi4XmMkxap4i0P09mUTiY9NOgXiyNFYxKbbWRmwikYNdb92n7v3Id1HkidgFYdD+6rdmBNPxRGDa4YaDIZzz9jYGKdPn6bVSn9v7BQhE3GrU5izLGsk0au3zqOUskvUHOV4KeXAGoVSShYWFsjnu99zwzCkXC5TLpf7BEPXdXEcB8/zhn4OJq9BL0EQ4DhOO1Z2N7dmUvMyqV3U2bbz+GQsUsqB0bSZTGZPIq/BYDAYDE8kVKDwmyFLt23RqqY/v4SeYutUnfJyg8WrS7z2T55NaTHdhQhw7U2HcXM2H//fd+Lkdrm3EYLpI8XUWo0Jk4cfn5GGSinOnDlDrdYfyZiQiJHVarXvfqMzNnWUlAqtdbtGde/9TK1WIwiCPcffGwxPZLa9bX7zG/+rLTRW/RoPVB6iFXqp7WtBnYcqj3CidpKf+NzP8/cv/stHc7gGg8FgGBFzt2N4wmCJAhPi22jqu6mqT59VX3nxtD21b+r72Vbvo6o+GddHBK0DmtyLxQQ+K3seg6SAJMuWeg9z1o8BMCFfTVl9iDTnpUWRrLiSmv4ykSOv+6EocgDaccTpPFJED6E2cwSsE7KNYhuLUl/fWmua3INqi5gSgYyFx2GCm6bFA1j6qQgRvd2EuoyigUBiMYWiMkD827kGjY6/tuOziXhrte86IxSKMj4NhNbt6NzO2pWjIFKiUiUFAupovNT9u1GSN+35GIPBcHasr6/vKvolk0lhGLYnlZLJoiQ+a1i9RoiEsyR+FHZceaNMVCWiW1rMaCaToVQqUSwW2yJeQrlcZnV1dWD/nuexvb3djkQdJJoOG5/v+3tyae4WsdopNiql8DyPIAjIZDJd11cq9X8eGQwGg8HwZMBvhfzLz3yR295zAssWZMcdgpYi9EJ6P7ItR2K7kvKZJrf85m185+98M7Y7WEg89qJFDj99mlvffZz3/tJX8Bvdn9lWxmJsNktxNovlDC+1cc0rDw3d/1igtWZ5ebnPqTiMcrncToRI6jAm92/D7iE779mSe5re9IlEiBwfH9/7xRgMT1BuOfVxGkFUVmfbK3Nf+QHUCMJ+oEI+uvRJ3nHP3/FfrvjP53uYBoPBYNgjRmw0POEoiZuosn+xMSuuICMuGaltoDc4E76dhr69b58QNlJnafEAmiaC7J7q/CXut4r6ODPyB5AijysuYs76UVbC/516TMg2kiyQjetBRjUNBS4uR3CYa4t+CZYYQ+oCihq+XsESO5O7GkXAOh7HCemtYynj/++2mjWMnYCL8Xc7Dk9FhSzXodjAZ6UvHlXjxxGpLehwGSqasdip6BYbRU80q8TnDIomGS7dZZy9RHG2/VuzSIrsJ2rXFYcpiBv2fJzBYNg/m5ubbG9v4zgOYRimThgNmkhK4lATh+IgUSwhiWBNhLthglvn5FTiBMzn8xw9erQr5jWJd+0kqa24tbXF5uZmV9xrL53X3Gw2yWQy2LbdFYfa+SetH8/z2vv2WoMyjbQ+EgdBNpttv+bFYvGsz2UwGAwGw4XIx37vdu64+SRBK7qXkFLg5ix01kKr+DMbgZAgZLw4ylPc9p7jTBwu8PL/+7qh/edKLje8/jIqKw1uf+8JVBB9Nktb4BYGL6zqZOJQgYPXTe3a7tFmY2NjoNCYJDUkJPccEC2uSu7lOhMdhtF775QkQvTeJ+43Wt9geCKiteb9J24BoBE0ub/80FChUcdzPkmylAZ+9/Y/4pkz13Pd9LXnfbwGg8FgGB0jNhqecOTkU3DVYTx9Yl/Hj4/oPPP1CqfCNxHowa5Fl4vwOE50e9RAoWMxcDg2U1jMAqBpUtWfZly8PB7fy9FoVsM/Iar12D2mBIGLIBIDM1yOFOmxfAAZLqHJXYRsofCQuISUaen70QSEVFOOUmg0iiqCHJLB/fusYuuF+EFs50FL46NEBZtZbGZRuhGLpFFdRY+HUT2ux0hADeNajv03pLodteoiyaIJUFTxeHjg+NKQDKqrYZHlstiVmfa6DDhKTLJovQUhhq8ONhgM5w6lFJubUa1VIQSZTIZGo9EnLA6aSEoT8RJRLJfL9e3rdEWm9dX7fe9ElOu6Q2NUW60W29vbVCoVfN9vx8IKIdquy94+e8fTbDbbwmvnvmRiLbnmzvGeS6Gxt8/eba1Wi3w+z8LCQqqgazAYDAbDE52tUzVue+8JvHp/vLkQICzBoHQZvxFy278f55v/82VMHBgcp5rwtO86wkOfXSHwRo99T3jma492fb9xvMrSbZu0aj6WIylMZzn6rNmhLstzjVKK7e3trm1J4kIQBKmin2VZbWExabOXGPw0wXHQvZzBYIA7Nu9ipbEKwOnGMqHu/3cZ6hBPeXih1zXrY0ubjHRZa67z1/f9A78z/WuP0qgNBoPBMApGbDQ8IZmV/42l8JfR9D+gDSMnrmNMPH/Xdko3OR2+dajQCMSxo6LtMNRxPUVJLnbs9WMxhSsu6Xp89PWZrjZZcYy8eBrb+t14eolIdBOx8JdBYCHJYTOHzQxCDH/AkyJHVl9Bk/tQ1FDUaOn74zErSHkdBVmgGbepx0JqunNT00RRw6II9EyEs5PJL0UOOkS+QK8RstzRVrEjVg6a9NbxmAI0AQIbgU3INtFb3u4PjoJcXBeyH0keKcY5ZP0eZ9Sv09IP7tqfIw5ywHorjpjfta3BYDh3VCqV9sp03/e7ahImwlmvgJYmCvaSiGLZ7M7ikcT5OCqJuJcIakKIgRNbYRhy5syZrlX6nbUZtdb4vo/v+9i2jeu67XGnCYqe5/UJeZ3tk0mzToHxXIiNo7gklFJMTEz01aU0GAwGg+HJQBgo7rj5JJWV5r77KJ9pcMfNJ/iWN16xa9vpI2O89Oeewod+41ZUOPrn/DNeewmXv2ARFSoe/OwKd9x8kqXbN/vaZYo2V7zkANfcdHgk8fNsqVarXfdTSilardZQ8TARF7XW7TSHvdJ5X5m4Gzvve0ap/W0wPFlYba4BEKiAjVb3+4ZCUQ8aBCp9Li9QAYEKaIQNPnPm8zxceYQjYxef9zEbDAaDYTSM2Gh4QpKTT2Gen+VM+NsjC45ZcQUL1pvbMaOBXmdbfYC6/mrsYJPYTFGUz0frxlDnZKgreJxAxeJiFAGaCI4+Cg00EGTaTkdBFkfMYzPft041EeQ8fYqV8A9o6jsBcDgQ11zcQFHDoxZHj+bIiMuGuPP6kaJAVl+Ny2HK+oPsiHlptR8jQTNk5yFY00AjB4p0SV3GfpF18MOcy2F8luJWmjTRc8DVEImvNSRFXC4hYA1NkyQeNf33QgwVgiUFLFFkTL4IVy5wSPwOVf05yurm1CjdrLiCcXkTY+J5CJH+uhgMhvPH9vY2QRC0HYAJidCWFo+VOA6TSalB0aLJxFQSt9VsNtvHjzpJlYh/SY2gRqPB8vJy+3jbtsnlcmxsbHQJmcNW3Cc1IzOZTJdDsTMqdpj7snOybFi06l5JEy97sW0bx3H2JNoaDAaDwXAho5TmkS+ucvv7TrB81zZ+I+D4V9dpbnlYrsTOWOz1Y7hV8bntPSd41vdfvmvNRYCjz5rjprc+jVt+41Zatf5npKAVUlltUl9voQLF5MVF7v7QKbaXamw8XGXjeC2l13gs1YBb//04d7zvBC/6qWu5/PkLe7uYPVKpVNpfJ2kUe7kvOxs6I+qT+zuI7nnMIiqDYYdARQvI15rrXfGpSiuqQXWk2o1Kax6oPMzv3PZH/O/n/NZ5G6vBYDAY9oYRGw1PWIryW7Aosab+fKj7TOAyLl/KtPxBpHDx9TJr6q+oqS/QW5fP5ySN8Faa+m4keRwO9cViBnqDFg+wI6IJBBqw4i0KsOLagpEAmRFXYDMxcIySAk19P0vhL6N0pWufEDYOcyjtEbAab/Vo6rvIiGOxmxC0Vm2BTWCnxnlK4SKES05fR8g6ITUU9VgwlXE06+AHVkUTa4DYmLweNlPtaNloLINXeQrhtutJ0hWnOuyJWxK9vsmYaoAmJ64ipEagV/EJ0F0RqDYSd6DImOAQ1dEsxVG7QtiMiecxJp+Hp0/g6UdQNBBkccUhMuLosO4MBsN5RClFo9HoExo7GRTlmRYhmobv+2QyGcIw7Iol3Ys4p5RqC2xaa8rlclsM1Vpz5swZtNbtmFQhRJdDM40wDPE8r11bMq0mZaerMmGYUHouXI2d15XUjUy2J9cGUKvVCMPQuAAMBoPB8ITm/k8t87l33Ed1bWcBZ+grwlaIUhrVDAmaIZYrcfL20CegXmrrTWobLcbnR1t8evj6aV7/zudx78dOc8fNJ1l/uEroK9YfrtLYamE5krHZLMW5HJYjqaw2uf9P78Gr+mSKDlNHirj5wdNLYaD5yG/fhlaaYy9c3MOV7I3kHilJoRjl/qWz3va5IFmEls1msSyLQqGAbZupN4MhoeBE4nstaLS3aTTVoDaS0JggELz/xC18Ze3rPGPm+nM9TIPBYDDsA3PHY3hCk5PXclj+Pk19D9vqfTT0bYS6ihAODrOMyRczJl6KJQoANPX9nA7fSqi3B/YZUiGkHP+pktXH2m7IUJd7hEZiF13i6oPEdScptNsEehVLTAx8gLTFLKfDt/YJjZ0IbHYclKAJaOp7cDlEyBYhZToFUEuXcJhDUupwvvi0uA8psh11FL0htQltut2GIZowVUBMtgnhYOspAtbjreMDrym6fbQBF9jN6RJVqIxE0U40igowh0WBceslVNVnaejbYmFwcM2TTiR5LKYpyG8mI4707XfFYVxxeNd+DAbDo0Oao7GTYZNKSe3C3Saekpo8vu/3xZUmxw8SHju3+77fFaeakIiYAJ7noZTCdd2R6gglEV57qXvY6z5M27/fybjO4zKZDI4z3O1txEaDwWAwPJH5xr8/wuf+8l56P1aV6ha+NBB4CqV8MgVnZJejDjV+Y28lRZyszTWvPMw1rzzMia+t8YG3fYOx+SwTB/K4eYvOk28er+JVo2fcVtVn+a4t5i4fJzs+ePGm1vDx37+D2UvHmDxc3NPYRiV57ZIY/VHa76U+415IBMdSqXRe+jcYLlSunrgCW9pdtRpboYfSo/9btIREIAhUwDvvfZcRGw0Gg+FxghEbDU8KsuIKstbwmhW+Pr2r0AiJUy75ukqL+8joKwCBx8P0x466bbFxh7Dnu01CNrGZ7DufIw5QV7ftIoBWCVhB00JRZ0c8C2lSQdIb26JjAXILQZaMvgRLFEHodqxrghQuUudQNOhF4qJ64kg1LUTP+aIakjsPlDbzBGxgMTHUTRhFsgoiQTMRNtOesNNExh0ioTWiIJ6FJSfQyqep7+qqGTl4HBkyHCMrL2Ne/uyu7Q0Gw2NPvV5/VGoMNhqNdnTpbsf3Cn+7xZr2Ohg7V+uPQiI47gUpZVvgTONc1G4cZXX/+Zr4MxgMBoPhsea+Ty6nCo0AUorU+w8VaLxa5CIcBWEJnNz+pnuaVZ9P/9m9BJ4ilyIehr6iuta9oEuHmpX7yixcNbGrw/G2957g+f/tqn2NbTcsyyIIgq7a1sMYds+zH9ISMjKZzDk9h8FwoTORmeA58zdwz9Z97W2e2lsZhYyM3pukkDxcOc4dm3dxzeT5eV8xGAwGw+gYsdFgiFlTf7Gr0Aigda9QWCFgFUkW1VHDsN0eGdcb7D5O0USSIRHPAr2CLfrFxqJ4IVvqn1PHErCNr092CKACUGgUnQ5HkH0i4s74mjS5m6w+RkZeTqDP9LWxmcPjkb7tkVDYoFNg1fQ/sFnMIMSOQ8USRVx9EVKkj2mnf4lFkZDN9hax57ctGdfJbCLJInCZs34Mm2nW1d/S1HekCqk7Yx8jw2Xk5TNZsN6EFKPXwTQYDI8dlUrlrGJBtdZYltVVv7GXMAzbE0q71SNM+uysF9lLZ5SoUip1Amy3CNXetp11G0fFsqx2zaHdHJp7QWuN67oj9WVcjQaDwWB4IhIGis/+xT2pQiOA5Ugs14KU2olhoAl8hb1bHUYBhekshakdkevkN9a58/0nWXuwgtcIcbIWE4cKXP2Kg1z8zbNIufPZfOu/H2fr5OA6jNXVJskFqFCjlI4eBwWsP1Rh8Zr+Z9pO7v3YaZ79A5fjZM/9dFQul6PZbO4qIp5PRyPsxORblkWlUjHuRoOhh5sOv4y/v/+fAPCVvydXo0DgWJHYmLWi97mbT9xixEaDwWB4HGDERoMB8PUKNfWlkdoKIXvNiwSsIHrEPI1C04xdjZregzRNQloIHCQ5QsptQSzBFYcRyFT3nc8Knn6kq1+BhUbSXd9Qx25HjWSQUKbweIS8fhoBaWLjDB4n6RVMIYoXVdRiQZW4PmU3Sa3DTias7yDLNayrv6DvBe3qfwI4EYuMe5/sTpyT0c8hiyOisUxZr6Ukb2JbfYAN9bc09d0dDkiJzRQ2i4xbL6EkXkVeXrfncxsMhscG3/dptVrYtj1wZfsoDj0hBJlMBqUUvu93TUolk1h7qdPYWROo99xJHcOEYRNgo4p/necbJpqmIaVsC6Oe1/0ZtBO9rUd+HQEcxxnJaZnUp0zOoZSiVqtRrVbbcbJCCBzHYXx8nGKxuKe4WIPBYDAYHise+twK9c3BDh4hBeMLOVoVjzDo/3wNWuGuYmN23OUprzmM5Uju//QyX3rXg33iYQMoLzc4/uU1ijNZnvbdR7j2psOEgeLuD50a2n91tUnghQQthQoUnRpBq+IT+oqpi4vkJzKpj29+I+SBz6xw5UsODD3PfhgfH2d9fX1om+Te4lzWaexFCEEYhrRaLdbX143YaDD0cM3kVXzbxTfxp3e9oytOdRRyViYuhwOz2RkA7t6695yP0WAwGAx7x4iNBgNQVu9nmODViaA/BkXRQFNt1yXUqLjOoYqPkXHvYbsX4pYaj5AASZGQ7bbYaIs5Fq23sqb+rO98ARt9QuPO+CQa0bNPxxGrYoDDUeKKS+J6kynXLCxcfREeD3Vt14Ro/B4nZUhIBUEGiYvDYp8bMCeuZ17+DFK45OU1bKn3UlWfTBVVc+JqfH2SRDQdJfY0ueLo1XDaW1xxmKzYidO1xDhT1vcwKb+bkA1a+iECvYHAwRYTuOJibDE10vkMBsPjh0QIHCQ2Dppk6hXwEmejlBLbtts1FDsdg0nNxt5zDyIRynqxLGtPbr5RBcdEYBRCYFlWe3y7HWvbNq7rts/V6eJMju8UM3vH1otlWSO7GsfHx1FKUalUWFtbo9FodAmltm3jOA5hGNJsNllbW2NiYoLJyclz4sA0GAwGg+F8ccfNJ3dtMzaXZfN4lTAlzUAFkZOw04nYd/x8jmtuOsxX//khvvDX9/ft11rTqgaEXnQvVN9q8fH/fScbj1Q58JRJahuDa157jYDKajMWGXWqQ7N8ukFtvUVuwuXgdVNYKeJodbU/Eehc4LoumUyGZjO9//MtNCb3WwlhGFKpVFheXmZhYeG8nNNguFD5xaf9HJ9c/ixfXvvayMdkrQxu7GbMWVnG3TEAakH9vIzRYDAYDHvDiI0GA1DTXx65rcUkAqerDqNGo2ggEHENw4B2lgxR4eodwVER1RjsJBIntfZAQE48lXnrZ7DFJEp3r0LVKDz9MGlCYyT6BYBFJP7p9p7o/000LqLj/AKHjDiGRQGfM9h6CiH6nSeOmAUd4HESTRi7JYO4j0TgDONrDtHUUagux6cgS0neyLT8foSI3n4y4lLmrZ9gRv4gVf0ZQr2BJkCKAnlxPQB+cJqmvjOufzlYbIwERkUi8oKNogJIAr1JUb4w9TghBDbT2GJ6YN8Gg+HCodPNZ9t2O3p0t8mlzn29k0WwIwiGYYiUEtd1kVJ2iY2jOAjTxrCXej67uQl7RcBE4EyuSUrZXnHf22/iKux0CmYyGYIg6ItwTfpMzpc2pkSszWQyIwmBQgiUUjz88MN4ntfnqgTaY7Esq+083djYoNVqsbCwYARHg8FgMDwu0Vpz+o7Nvu2hF9Io+6gweqKyXMnYQhb/4Roq7P9sVYFCutE9igp13EaDEGSLDtd962FO3brRJzSGvqK62oycia2eewAp2DpRY/bYYAee1wg4fccGyldRdOqQ61Shprbe4qHPr3Dkhllst/ueKvTOba3ETqamptje7i+Pcr6jU6G/PnfC1tYWjuMwPW2eNw2GBFva/MGzf5ObPvg9rDRWhi7/lwiydhZX7tSRXczPt7/OWP31ZQ0Gwznm3gyE+cd6FI9vHji/9xkXAkZsNBggFqRGI6oaOIvPUnxsC00rFh8TkS95c4nEr/ixMRblJBY5FD6JWJe0lWKMw/YfkhEXd5yvW/gLWY/rMPajabXHqLHpFt522ghyiDhSVOpxtK7jx05MjxO4+mjqZK0jFtFa0OLOnrETx1jYCHKARmAjcPA5gcMMM9Z/ZVy8GCnSP5gsUaQkXtF/TTrEFQcBRVPfG9fF7D73Tk3M5PY0eb13riFknY3wXUiRY0K+JnUMBoPh8UEYhl2RmYmwNzY2NlJcZqdI6LpuOwZ1LwyqdRiGYVu8S2osdgqMvd+n0etKzGQyfde123UmdRU7SRNTk23JORM3pmVZXSJhsi/tmguFAplMhqWlpdSxdIqOvdfYKXKOgmVZbG1tEQRBqtDYSRiGNBoNcrkcQghqtRorKyvMz88PPc5gMBgMhscCrx50OQGbZY/KSpP6ZqtrHakmEhQRsZCoIyFRiEgU1IqdGNMOMVJKgSy5bC3VuP19J8kUbETsgGxseaw+UEaniJcAWmmqq002HqlSOlhg9rKxrnsCrTRn7tmmVQn35Ar0GyHHv7zGJc+eg47+nPz5m4oqFovkcjkajUbX9vMZm9p5jkGLnjY3N8nn8+Ryg0qbGAxPPq6dupr/cux1/PFdf4kXenjKQ2kVL90X2NLClS6O7C6rs5ibZya7I97PZWcf/cEbDAaDoQ8jNhoMQOI01HHcqCZEIBG47WjUThwxh6/PoCh3OBxFLHqlPcRoIEBjIbDbdRw1suM8DgIbh+5JUlvMdHXp65WBV6E7aiqK9nVJwEbiotEILFwuRyAJ9AoeJ3rGrAkpY+tZbGaRwu3YExBwBkkxjk/14nMmt4IZclwTuT+7HrIENhMDhcZhCGExLm5kQ7+LnLgGT5+mxf0kgmO30CjodJPunD1LhmMgFGvhnxHqMtPW6/Y8FoPBcH7xPI+trS0qlUrqhND6+jpjY2NMTEwMrf2X1AbcTWAc5hActD0R+BzHab/P2bbdJYwlguMgt19vu7RrSRyBg0TLxLXped7AlfpJ/531JRPnoGVZA8XFXkqlEmNjY7RaLTY2NgZeU9JXEpnqeR5hGI4cn5q4ULXWuwqNCVprms0m2WwWIQSVSoXx8XEzkWcwGAyGxx2Ju09rzfpDVWpr3VGfGgiaIYEXolVHUoEGdPTUQ+xklFb356q0BRMHC8xePs6Dn15l83gVt2Azd6xEq+qzen951KohbC/VERJmLtkRHGvrLby4HiNCkJqfOoBWNWD9kSrTR8ba2w5cMzny8XtFSsnExET7fiK5R9rtnuxciJG73SNtb2+bexSDoYeffsqP8ZGlT3Bf+UEyenjaiwAOFhY5kF/s2v7Sgy88fwM0GAwGw8gYsdFgIBKjPH2KgNWemE6BzSS2mMdirGOriyRPyHpXS92OT00jERwFO7UcIXI+Bmgkgd6grD7ChPWq9lFj4iWUuSXuwUdR6+245xz9SLKx0zAS53xOomkMaJtH0cLnFD7LZPQl2CJ6GPT1Sod70onjZHcchS5HB9Q41Kyrv6EgvmWkCWetQ2r682yr9+PxMIHapsHXEWRwxCxF/Wwa3IGiFl9HIjCmOYEkOa7FEsX2lk31jzhigXH5kl3HYjAYHh1qtRrLy8tDJ3qUUmxvb1OpVFhYWMC2bZrNZpfTMJ/PI4RgfHyc9fV1giBouyOTPjpJm1xKRDjf9/sceVrrtpiZkNSG7I1hTd7v0kTHJMq0N7K0E8dxaLUG101yHKcttqWRnD+5xuScnue1z70blmVRLEbvnwcPHsS27bbzsDOGNS2CNZPJkM1m24LoIKSUlEqlduRZGO7NNaGUIgxDbNtGa83Gxgbz8/NtQdVgMBgMhscDliPJlRyOf3k9cjN2oDW0aj4q6Il0t5NFQpIw0OhQo5VGaY20JXZGMjaXY/KiIplC9LleWYme87xawNJtG7E7crQxCikIWiG19RZuzqZ0IN/uM2glKQ67ddf/2bt1ssb0xUUQgumjYyxcNTHagPZJqVSiXC6Ty+UIwxDf9/vuW5J7jXN5rzDo/iW5N6pWqwRBMNI9mMHwZGHcHec7jryaD574CKvNdVaba3iqe9GoLS1mMtPM5WbIWtmufQWnwAsXn/toDtlgMBgMAzB3OIYnNVprNtTfUldfxudUWgsCNgj0BpICWXE5ApeQMooKkmIs/mkEWfTAONbOeM+QtH96ApuAZc6o32JcvhgpohWPOXkNrjqCpx/uci6mk/agFLkpo1HoeLxBXzzrTg8ZMuIgLf0AEEYuQn0ZlpggYLXdT+Rq9OLriQRbnxOEeg2bOWym23UZAXx9mrr+KgXxjKFXsK0+wIb6B0K9sTMmAY5ewOM4LV1B4OCwSEg5jrNNe10ix2iGS7HFRN/eDfUuxsSLzUS0wfA4oF6v7yo0JiSr1B988EEcx+kTA23bZnx8nEKhwMbGRrvOYKfrLjlPZ23HNBEyiUxNJogsy6JUKvXFcgkhyGazNJvN1GvodRAm4mgSDzuIRCxL6zOZpArDsB2pmiZ2Js7JxDGY0Gq12u7JYUxNTXWJlgsLC2Sz2bbgOGiiLpvNMjU1RT6fJwxDyuUy5XK5y22ayWQYHx9nbGyMSqXS/hnsNfI2uR7f91FKUa/XqdfrSCkpFouUSiWy2ezunRgMBoPBcJ5xC06/0Ei/0NhJlJYAwgIhwc3ZIMDJ2hx4yiROdueZK2iFBM2dZ6Nm2UcryBRHm/qxXInfDFGBorzcYGwhR9AMaWx5O3UaxXBzo0i5tfAbIbWNFoXpLNf8f+z9d7xkWXkfev/W2rnyyaFPT/f05AAzDAyMACOSCDMCy0hXVxiEgmXZ1hWyrl9bKHCJluBaur6SrfdjY/mVBL5XtiQbocAQBDI5DAMMA5OY3OGcPvlU3nGt949da59dVbvS6dz9fD9q0V21Y52erqr928/z3L0y1rGMQwiBRqOBZrOZhInqRqlCoYBGo5F8VuydVT1qlvdBqdne6c+eat+MMTQaDVQqlbO+X0IuZW+86m584fRXcEhbwnJuEfWggVDG3zV0rqNoFMCz/nEB8PqVV8PShldEEkIIOT8obCRXtA3xe6iLz0LHTKed6OAZWwJNtOXDsNlNCORaJ2yTYLA6VYM5CDQGrJ2+CCs767GeJeIPR4E8hbXot7CsvTsJ6yr872Mj+r2R5xPPhOx9bP9Dl0zmHQ7+IMZgQcc0wDT48klIhPDwJAx5OJlNKdBC+n5WBge8s02BNnw8Cx8nYcmj0Nl+H/2a/ATyGBw2bkV/iD3xF5nPGWwRUoYIsAqJAAFOQcCDhmKnMjTdzjVuHWtipTPvsV8oN9GS9yHPXjTweAgh554QYqKg0fO85IKNECKZ16eEYYidnR3s7e3Btm00Gv3/LqfDM0VdiEqHi0B8wSifz6NcLiOfz6PRaPSFjUB8IXBY4JimaRo457Asa+gND4NCzHR7VBXQqWNWFYymaXaFiaoKNL2dMAyHhp1TU1Mol8t9j1cqFZTLZbRaLVSr1a7qUdu2US6XYVn77zOapmFqagpTU1N9MyIVVdU4qCXsIOkLhek5SVEUJW1V6/U6bNtOqmEJIYSQCyEKBKprTQRe1FVtKIWMg8Yh90BKISElYOX0/XBRArXT7a72pOnAUkqJKBCQEpmtV7NwzsD1uHRRhAKtHQ+cs7h9agdjDODInP+o5kr2Hb8EGpsujr5oHje8cqnv+UkJIbCzs4NarZb5uaHZbHZ9Nhp0M9i5muOobixT+04HnRsb8ViUYrE49kxrQi5315aP4Rdu+jn8+4c+FHepMYujVwLw/Nnb8dZr/9dzfHSEEELGRVdcyBVrJ/pvqIvPAgAY06HLmaRybxCBFpryPgi0u74LMuQARODIp4I4VcWY9QVGAKlZkAxOajYkQ1s+gJr8NMrsbgBAib8arnwUVXFvZ7sDZkHA7GoDG7d7jS8kS4jUc4O/aBosHqytowyN3YYQ2wjlBkKchoTfOT9AVQ7GIWPW9iJ4eApSRjDYPADAld8fuN9d8d8HBo2KyVbApY0Aq4hQh0QbAjJpE6tw2DCwHM+7HKIqPoE8p7CRkAspXdE2jGoXml5WXbxRAZIKnlTo1G63kzmA47IsKwngpJSwLAuHDu3ftJDP5wfOUuScd7XrylpG0zTMzs5CSolmc1hb7P1t2rYNz/P65kVmnZeu65khptqOmqMIxGFjevZketnp6emhd90zxpDP55HP50eeQ+96vftLz2ictH1qevl02Ni7Hdd1cfLkSRw6dGjovE9CCCHkXNh+po5PfeBBHL9vCyKUXeGdCONAkHEGzlnmVysp4nCSG92VPc1tD1MreXC983hq3cgXSfVh6EUwc+Nd/jGs/e+pjU0XhTm7r4qR8fj2TtETOGYFjYpmctz9rtuT2ZUHFYYh1tbWhraaB/Y/Jwz6nKk+k6S7NJzNuY1ZN1ep49/a2sLOzg4WFxeRy+XOeJ+EXA5es/JKaEzD7z/8nxCK0d/fXrr4A/jfb/0FaJxCe0IIuVhQ2EiuSEK2sCv+R9djJg5DoAExYJahQLtT2ae+rMRf6BhMMJiIg8UIvVWMA44AKmxMVwXGfzYgZBvr0b9DIFcBMGisjDL7B2DchBc9iQh7mVuNAzcNQAQGCxz7w+clgs7x8K5gLo2jCI79LzsMGgzMw2DzcOXjiNAERwFx9eA4H+gkfDwLLm1orAQhsy+sh3IPO9H/O8b2AJ3NQpMz8PA4Avid88qDwwSDAwPz0FhprG15eGqs5Qgh546qaBvF9/3Mi0Wq5WbvDEEAXeHcqIo51W40XfnGGOtbh3OOYrE48LjTswuFEF3rq4rGubm5pEpznCBUBYXp9mBSyq51NU2DYRhdbbuGbScMw+Q1U+dsmibK5TKKxeLI9qpnU/o1Gre19aAqhfQ20y10OecIwxCrq6s4fPjweT0/QgghV7Znv7GJT/+f38Wp72xDSsB0NHhCQkSy834WLyeFRCT2KxClVP8PkCJuoypCAU3ffw+TkURj20NpIf7epxk8uTdViv33SVXhOM7brGZw5KYttHY8BG4UH09WZWAncFRVl4yzgWGjZnBc/QPzyE2dWatDIcRYQWNyjIxB07Tkc1lvW/l0Z4tJOiuMK2t+d3pfa2trWFxcnPjmLUIuV6869IO4qXI97j3xt/js6ufRCLq71DDGcOfcHbj78Gtwx8xtNBaHEEIuMhQ2kitSTf5dp6XoPsZ0WPIGePh+qnovJtDqqhiMw0LeCRr3Az0GAww+ANaJGYdVIRpJC1Ygbq4KBIhQQ4htQALbwoWGuH3ENj4Ch92Gaf4P45mG2MvYNoOOWQg0+wJFdfwMg1vmGWxx4HMRqgDE0PXVOcjUjEoGHQFWoaEEzrJnZtXEpyExfuURYwxMWuCIv5QZmIPJrhp7fUXI1uiFCCHnjOd5SUXbML3BWvrxIAiSlpmD1g2CAIZhwLKsrtajQBzA6bo+URurSqWCWq028u73rJmIpVIp2dfS0hLW1tbGChwZYygUClhaWkIURXBdF6urqwP3M4ymadA0DaZpYmZmJgkXR70GUkq0Wq2kypIxBtM0k2rPgzrIRYJBFwTT1a3p11WFwEII1Go1mpVECCHkrIsCgSe/tI4T396G3wzBNAa/FeKZr23AcHQEXvzexTiDldfhNUNEfs9nCYmBLVWlAPxWBCkAw9aSYK+92x022kUTbs3vygel7O4AMIxua5i7pohdS0N9ow0zrwM8+3st61RiahoH44AMU98EOaAbGjSLw7B1lJfOvIJvZ2dn7KAxLQxDOI6TfDZIvxaMMfi+D9d1h23iQLLmQqqbvdSNbmtra7jqqquGtrYn5EqynF/Cz934Nrztup/ANza/hR1vF5GMkNfzuG3mVsw7cxf6EAkhhAxAYSO5ItXEJzIf58yELW9CgHWE2Oi0DfV6gsZYb+Vg/JiBuOJRIJ6NyBF/UxRdS8XTFXNQ3yLjKY4tSERdYV4k96Ax1ateoi0fgASgs2kY8ioI7CUhHYMODRVwZsGXxxHgdM8Ri85+s+8mNXAIOqbiJWUbIbaTakgJiQgNZH7rBSARdWZY+uj9EhrHj21ocgomrutfVwrU5CcztzsMw/6F7RBbMOQK2ICB4YMMCj8JIedHbyXiIKp6MW2S2X6q3aqUErZtT1Q9J4TAxsZGEmhqmoZCoYCFhQWcPt377+xwjuNgdna/vbNlWVhZWcH29jYajcbA8JJzjlKphOnp6SQUVHfppwkhkvatalvpMLX3vFXF36i2olEUoVqtolarZQaj6vjK5fJYLUp7L3aqc1Kv8ajWt1mvU7p97qB1giBAEATY2NhAuVymO6EJIYScFW4jwAP/4xk8+reraFf3vzdKIXHyOzsQgYCZ0xG0wqRqkXEGq6DDrQeQfqe6Mf32JuOwLh6CKCE7H5mkBEJfQIQSZkGP5ymG3e+LxQUbbs3vf58bs0Nocd4BGMPU4Tymj+SxfOsUTn57By03DvkY22/1ynUG3dKhGTz5pphuS7q/TRtXPX/4iItR1A1DB6U+d/a2Nj1XcxvT20/vT/1eCAHP8xAEQdJSlRCyz9RMvGTxrgt9GIQQQiZAYSO54kgZwZfHBz7PmAYTyzDkEkLswcVDnRAxDgkZWKcqMjvYYrAgk1asItXatHup/qAxAEN3+JVV7ccAcDgIsAGLHc48BpNdBUgNAU4l+wBkpxKw9+Iqg8lWYGAJodxFiHVE6P4SJ+AC8CEhIcA6FZmdL0nwITG8QlAigItH4bDnQsoAjO1fjA6wilAOn5WZhTMn+cIsEUKgmVSBjsvAodELEULOmXEv7mQFT+MEjenZOyo49DwPtj38RgNVFacqInv3Va1WYZomisXi0JAwLZ/PY2Fhoe/Cn67rWFhYwOzsLGq1GlqtVnIxTNd1FAoFFAqFvsrB9J+jKBrYZjaKoiTEU21ie4O+YTzPG1l9KYTA3t4earVa5uyhMAxRq9VQr9eT9mVqvmW5XEYul0OpVMLu7m5y3pOEjb0XNbPmI6W1222srq52zeIkhBBCDqK+0cbfvOtb2DvV/32oteNBdGYz+q0QfjME1xmMnB5/G2QMXOOQOrpmOCYYwDUWVyWmk0IJCCHhN0NY+f5LOrmKBd3SEPrdc67DIAIL1HtlXAXZ2/aUaQyFufjm1HbVh2ZwrH53FzNHC2jteYDohGecQbe1uMKy5y23/+YmoLycww2vXh74Oo6j0WiccatTy7K6umqEYdhVKTlo9nP6+TMJJ7M+dwkhsLm5ienpaapuJIQQQsgljcJGcsUZNJOxF2NxnMalCfS0Do3gYdCtoRwWBMJOUBhHiawn4IvDy5jszB1kMMDRewF8wNwt5KAzgQgNaChkLmOyQ9DlFAJsIMIOgGLXnEUGDRpmYbB5MGnDx7MIsJ65rXgWZbyOhAuJABx5SASpYHUUiUCuYU18AEv8nUkVokBjxHrZNEyD4XgSyEqMVyGVVuavO9C+CSFnxzitS7Nm8426yJN1R726q1y1UU1f7FHhotpXupKyt3pQ8X0fvu/Dtm3Yto16vZ5ZqZnL5ZJALQxD7OzsdAWKqlKyXC5jamoKU1NTI14RJOfmOA7q9fpY7cSklEkgaZpm8tr0BoO953jq1KmxL+ypOUrLy8twHCe5eJYVyAoh0Gw20Ww2YRhG13mr2ZaD9pveVtbfhXEqFmu1WvK6E0IIIZPaPdnEs/dt4ov/4RG0qgF0k8Mpm13hXX2juy0n11knAAxh5uLPF4ynZiv2vn2pakeB/Zsso3iuIwCIiAEshFPp6VzDgLlrS1h7ZBd+S0JEAKRE6Ha/r7J2BM3g0Cwez4HsrMcYw8b3q2jv+Zi/vgyZAwrzNnIVE+3q/mek0IsQ+RGsgpFUbGYpLjq48dXLsAujux8M02w2z2h9IJ7jbRgGms0mgiAY2M5/UKh4JkHjsJuhhBA4ceIErrnmmgNvnxBCCCHkQqOwkVxxBrURzRLKrczH41ang79QceQg4CEO6QR6KxtZ5xjiKNLrBI39F3x75y6maShAZ/PIsxeiLv+ua/6gkC2E2AUgYLFrkGc/gZb4Jny22jlqHRrKSSvS4UFjfKTx8didcDFChDoAOeRV6MbhgIGjJb6BHfwJZrS3dp452D9DDBwaZhB2jrs30B1FYyUU2EsPtG9CyNlhWVbSPnMSw9poDnoufXEnCAJYlpW0HVUtVoHu1q6MsaS6cdDFIdd1oWkajhw5gna73Ve5ZxgGgiDA2toaWq3+qocoirCzs4Pd3V0UCgXMzs4ODGHDMES1Wk1mJrbbbbTb7ZGVfL3bAOLXPp/PDwxTpZQ4ffr0xBUEUkqsra3h8OHDWFtbG2smp2ptKqWE67pJFaraN+d85Pmp58dZVh3n7u4uSqUStVMlhBAyFiEknv7qBh669wROPbiLzSdraG3v3/DDDY7CrI3ivA3GAK/R3QZeNzlCXyD0BbghoBtxyOeL7Gp+KYBIDn4flkIidCO0qx4iP4JmasnjUSTiqsko/jPn/e91EkAYCISBgOFoWL51CnbRwPqjVXiNALqtwS7HASFjDIdum8YzX99E6KUrJuPzHBQ45mYsrDxvBne+9cxDtEk/L2YRQmB5eRmnT5/GxsZG13O9AeOwKkbO+cibn3q3PU43iXa7Dcdxhi5HCCGEEHKxorCRXHEYM6CzeYRyY+SyEtnVInFYOOwCLOsEjlntRfWkwpBB68x+zA5AdTY99PhCuYECfylm+E+hLj+Hmvg0WvLbCLEJzmzomAcQoSm/hpDtIpQbMDAPDZXk4mood0cEjfGRAho4TEgwCDSBpMXr6MqkOEzdrx6pynsxJX8cnJnQMd3Z/uR3iRpsoRMId8+6HEeF/4Oudq6EkPOPMdbVPnMcvYEiY2ysQEwIkYRyYRiCc94XhPVuh3OOIAgQhiFs2x54kajZbKJer2dWyXmeh9XV1ZEXyKSUqNfrcF0Xy8vLXbMPPc/Dzs5O1x39Ukq0Wq2u12PcoC0MQ+i6PrSqr91ujxUUZomiCMePHx/77n81S7H356qqG9XPZdSFuklCVyB+HVqtFvL5/NjrEEIIuTL5rRCf+q3v4OR3dgDEbU/bO93fFUUgUFtrob7eRvlQ/42kXOfgGoOIJEIvgm5wDHyr7H1cfV3KeJsLvQhrD+9h9poi2tUA9Y02vGqAMBBxpaXofFYQ3fMWgf12qprB4dUDtHb9JCStHMp1va8ato4jd87hxDe34Lf3P9eowNEumUlLVcaA4mIOK7dP44ffewcKM8Nb2J9PqtOF6joRhmHS9aJ3rmPWZ0z1eWTYjW5pnPORQSMQf7arVqsUNhJCCCHkkjX6Ew8hl6ESe+1Yy8lOoCgRdVqG+p0WohZMDL87k8MCgwYOCxyFzpxDGyYOwcAibNwIHZWBQSNHMbPasVdd/k8w6HDlo/Dk49BQgMWuhoGlrnatGiqQCODhaXh4HFLGX6TCkUFjvLYK8xgMMOSg+vrIESEhgwmOPDj2vzQJWUdDfgFAHKg67LYxjqEfhw2LXQuOQjzDcUxF/gpM8R870D4JIWdXqVQa+vywgElVwI2SvmAkhEhaimZtT0nfsa4q7obtq1qt9j0WBMFYQWPvOmtra8k6rVYLJ0+e7Gsdpp5PvzbpYG4UzvnQFqpZ5zMuFeINIoRIZmK22214ntfV+lZVdqqffXp+Uvr80iHruBfyFLVsrVYbsSQhhJArXeBF+Itf+QYe/ewqaqfbqJ1uY+upOga95UohsfNMI3MOo2FrYABEKCGERBSKvrmJWV+vGEPTKaZGAAEAAElEQVTm1RvGGEQo4dUDPHvfFqqrLQTNEKHaNwPMvA7NiFeOuy8w6JYG09Fgl0yYOR2cM1RXW6iuxe/flZU88hkBoZnTcfSueUxfle+a1yhlHHrqlobKSh6H75jFHT9+Nd70Oy/E9FXZYz8mNU77/XG24XkeXNdN5lk7joN8Po9cLpd03VCfP7JuYlIBo/qcMuxmr3EqGtPLNpvNs1LBSQghhBByIVBlI7kilflrsSv+azLvL4uEgITfaRca9T3HUYOOWYTIbrUaN/bstJ2BDo4cbFwPzvYv7vry5MD9G2x+rHMJsYMN8Xuoi88NXY6BwWSH4cknEWEPHp6AIQ93zi+bChL35zOq+ZMSDHoSxmbvz+z80jvnM9f1fE38LUr81QCAMr8b7eiB0SebQUcZ0/wfooHPQcjRczzK/I2Y5T93oH0RQs4+wzAwPT2NnZ2docsMmks4biWbpmmQUiKKor4QC0ASQqb/nN4HYwyu68JxnMx9+r7f1/pqe3v7QBeMfN/H3t4e8vk81tbWMu+YV+1Q1bGlg1F1d376MbUsEM+hVHf1Z124i6LojOYiqQqB9PbVax+GYfKapF/z9AU9XdeT9rPq55U+F3URUJ3rpG1Q04HmQas3CSGEXBlOP7qHj73jG3j2vq392YoA3HoAKSQ0g0O3tP4WoiyuhuxtL6oZHEZOQ9CKEPkCkADjDEyia/tdm2IA0xg4YxChSKoh1eNRKBGFnRtJvQhRqs2pbnKYOR1CSPjNECKKQ06dA7rV/Rkg9AVEJDF/fRnFucGViJrBsXBjBbPXCNTX22jueIg6rVhvef0Krr5rHje/bgWF2bNbzVgoFM54bmOhUBh4QxVjDKZpJp9Z1GOjWvSr0FLNBU+b9EYoNUf8bASrhBBCCCHnG4WN5IqksTJK/DWoinsznxdow5Xf77QL7b1QzMFgIELc9o/BBoeFCDX03oYazynUoWMeBhbAWG+rz+wvlDpmoWNmrHPxxGOdfY+mYwaS+fDlCUSoQiDIPAYVtEr4nfMoQKIN2WlXqkLGeOaj1qlaVF+sWCdg3P9SnVWlGWK/jW2evQgGW0QgT491HmkaK2NW+xnM4m2oyc+iJj4BX57oWoYzB0X2CpT5PTDZVRPvgxBybk1PT0MIgb29vcznNU3LvNgzbsikLvSk216l25SqYGzQNtUd7GrGo2lmt21uNBpJ2BiG4RldEKvVaqjX6wMvcKVDzHTwps5HLde7njq/KIrQarVQLBa7lmm1Wtjd3UW73e66a1/X9bEufKUvtKn9CyGSOZPpY+mddaT+HAQBHMfpCoiVdIWA7/tjt2pNU2Fr+hgJIYSQNCklvvpHj+Nbf/p03Do14z1VSiQzGA1bg2Hvv09yzsA4Q+hHMJ3uyy66qT7XAIg6N3dqDAL9gaMKFJPZxDqHiCQgZTyTUUrISILr8Xtj4EZxgMniMFEdE+cMdtFAFAqEnkDgRmAag9ZZz8jpEJGEbjFYufEuE2kGR2Ulj8rKfjvyH/jZ63HVHbNjrT+pQqGAra2tA1f+aZqGfD4/tH0/YwyWZcHzvOSGp2GzGxljsG2767Nk7/PjSN8IRZ9NCCGEEHKporCRXLFm+T+GL1fRlg90PR4HjY9AIgSD1TO3MZ7FyFI9bCRcSGhw2HMQyp3O8gKABoPN4Qj//2Fd/nZfABZvrf8/QR0zMNnVY5+Hh6fHDiYBxO1VmQFfHkeEvU5b1M5FV8jO+XjJ8XHkEQeHFiQanefVFykO1qlyHIzBZMt9j4rU68oYx6L2GzgVvQNCDm69l7FlLGq/Ac7iVrQV9gZU+BvgyscRyR1IRODIw2bXT9RmlRBy/s3OzsI0Tezs7CRVe4q6Y9z3/SRoGrddqFof6A60VHimZjKmlxvG8zxomjawIlCp1WpndLFIVXLq+ngf1dRFKlUtmK74UxfK0kFjFEXY3t5GPp8H5xz1eh27u7vwfb+rHavalppzaRjG0GPq/bkIIeC6bmbwOUgYhknQyRjL3J+u6weuSkxvb9KqSEIIIVeGL/7HR/HQvSfR2HK7gkbZqUCUAl0zFAM3/gyQDhx1iyNoR5C27Hu/0QyO/JwFvxmhsdFGFEpwjSFKhY1MA7jWXRnHOGDZcVvUKBDwmiGQ2raMJAxHg2HryHqL03QOTY9nRVoFHbPXlMA1Bq4xHL8/7tjT2HIxne+0PpVxG1kRxZ8r1HzHQWqn2wOfO1MHmfWdVi6XhwaH6f3Ytp10ZFAdG3pnS5ummdzAlNVydZKqxvSNUJOsRwghhBByMaGwkVyxGNOxrL0L6+J30RDx/EAJAVc+lrRXZeCddqEhAA6OPBj6LzALNBHIk7DYtV2Pl/kbYGlX45D8beyJj6ImP4VI7rdt0VBChPjPHDkYbAEa5jDupc8I9Xj24oTXSnXMQmPTcOXDndAv6gSNrSRkjVug7p+ragUroMLA+Bu2HDH61WRXQUO573EN+a4/W+wolrV/jbXofYjk3shz4CyPJf4bcNhNfc/Z7LqJXxNCyIVXKpVQLBbRarVQq9WS4Itzjnw+Dyklms0m2u322GFjVijIGEsCrUkDKzW/0XGcvotB6YtQw2YWjiMMw4FBG5Bdvdh78WxUJWK73cba2hps2x7rwp2qUOytDE3rPYb0PMZhy/U+53kebHtw+zUhBGzbRqPRGHncabZtd/3cLCt7bjIhhJAr16OfOYWH7o3HXTQ2XUgAolMRqOYwqnamjHXaoDKWVAvqnTBOMzUE7QhRIKGb/V9ONENDZcVC0Io/k0gBtGv7n0t0M/U+zpAEfen+MVxjMBwtfpwxeI0AXOOZQWMa67R5ZYi3K8L9z1VREJ9nY9NFfcNF5Kcq9hjglE0U5x04lf5OD6F7bucNTk1NodVqDWyvP4ht26hUKgAma8GvaRpM00QURcnnUiBu8Z/+LKTCx3Y7DluHzXHspW6qU/sc9BmLEEIIIeRiR2EjuaIxZmBR+1dw+RtRFfdiV/yPpHWoomEaEZpg0JMKwCwhdmHABUd8cVRjU5jib+r8Po8Z7ScxLd+MhvwKWvJbEKhDyBBV8ZfQ2BQ0FCY+/gh1GFiYeD0gDlI1lMHgwmLXwJdPd+ZPDj5HBhMcDBEaUGFjf5tZhcNiR6Eju42OPSAkPKz9HqriXtTkpxHJ/ovfnBVRYq9Gmf/w2HMtCSGXDsYY8vl8V7iogsf03L50S9BB2xn0vJpXOEl1ZJqUEkEQ9AVV6XDvoNtW2++dp6hm+Kg5huNsXwW1av3e1qWe52F7exuc865gb1TLMBXQjroYlm7t2vv4KOocB93dL4TA0tISnn32WQRBMHJ7qi1abwBbLvffDEMIIeTK9sBHnwXQeb93I3iNIG5dmqLeJ6WMqwkBCc4ZQi9KwkaGuD3poFmMuYoJM69DszREXgSw/c82hqPBsIbfNBR6Udwu1ewJtsbtrCCB+qaLqcN5sNRcyXbVx6kHtrM3I4H2no/2ng/d1jB/fbmrmtMYswXrQXHOsby8jNXV1bEDR9u2sbS0lHymME1zorBS3fyl63pS7Zj1+aRcLkPX9YluOFOfT9TPr1QqUdcFQgghhFyyKGwkBIDNboCt3YC2eAwMvNMmlIPDBGc5hHIXHp7AoBmLMYlQbsBkV4GzIpa1d0Nn3UEbYzqK7GUo4mXJY3pURl38z9RWBICw05VH72rZ2svEIYAdvE2fxsqIZB1S+gixg3HKARkMaKggwh7ioFF2qiH1zvMWDDYPHXND26uW+d2Zj+tsGjPaWzEtfwJN+TV48hkItMDhwGQryLOXgPfNviSEXE6klNjb28Pe3l7f/Jv0zD4VWqUvymS1TE0/ng7Sep+fpO1pGIYwTbNr3/l8fsgak1PHpFq9ptubjkO1/Bp2bqqFrBAiCePSbWsH8X2/q+VX+piBuB1YbzvcSanXeBDGGObm5rCzs5OEsFnLqNavvcdqmmYyY5MQQggBgFPf3cHuiXjmctCO4Nb9uGVqD8YZ0BMiCiEhPQHhxC1RAUA3OKyCHrc7TS1u5nVYhfimneK8jb0TzaQdqmGPDhrV/tT8x+S4GJLtjCNw91vJGzkdzW0PEBJmfvSlotCNcPrhPSzcWIbZCRkXri+Nve+D0jQNhw4dwu7uLmq12sAZjpqmoVwuo1KpdIWDpVIJ9Xr9wPvWNA2HDx9OPpel28y7rounn356rM4Zql1r77ERQgghhFyqKGwkpMOVjyPEKWisv8pBZ1Ng8gZ4eLpnhmO3EFvI4y7YuAVr0QcQyV1IhNBYAQ67HWV+Dxx2c9c60/ytaIlvw8ezCORGJ8Tb/yaqoQSdzUPDVGq2ooCGIjgK8PFMX8vTcWkogSOHEBsYHqR2Y2DgcCDhgiMPDVMw2aFOOJobGVla7BhsduPwfTAdBfZSFPDSsY+LEHLpk1JifX19aHtMznnXfEIVIPbO0+ndbm/wlr44xzkfeLFqkDAMk+o+XdeRy+WS585kpmCa7/tJy9eDzIAcVAXaG0BGUQTXdWFZVnL3/qjjT5+/otbTNO3AYaM6VrX9YRWqlUoF9Xo9+fvQ+3diWBuz6enpAx0fIYSQy9djn1kFAIhIYvOJ2uCvSJ32qb1Vi1JIeM0ATmn/ZpnctI3yiobqqRa8elyNX5zfv9mlMGOheqqFoB3GcxtDAdGU0MzetqndNJ1Dt7pvTOUaT4LOXhJxFWb8/s/AOCBTH310kyP0IpjO+N8rRSiw8f0alm6tYPmWKcweOz9hGeccMzMzmJ6eRqPRQLPZTD7HaZqGQqGAfD6f+RnAcRyYpnngz2n5fH5gG3bVrrXRaCQdKbLa3KvZ4enjK5fL1EKVEEIIIZc0ChsJ6QjkqaHPa6wERz4XEaoIsdGZtai+OMQtSSV8+PI4Aqx2rRvJGhryC2iIL8BkRzGn/bMkdBRoQCKEJ5+CRH8ruAg1RLIGBhMGDkGgBYE6LFyDAA8ixDYADh3T0NlC3yzEUWx2PZryaxOtE5+xBYG40lGgCS5zYGycf1I0zPCfmXh/hJArw+bm5sg5fCpA4pwn4dKoIC5d0SiEyGyzOm51o1ovXWXY2/ZKzZ48E6qdavrPkx7rqO33/t7zPDDGkhlFwy7EBUHQVzHIGEOhUBirtekgva1fB4WFuq7DMAwsLS1hdXUVnPOBbVd7zczMoFCYvH05IYSQy1t9wwUANLddhG7UCf+y3285ZxCdVqppIpDJPEcAsApxFaNTMuG3Qhi2hoWbymjv+th6qo7mjofZa0uor7dRX28jcOPuMVEgwHg8u1G3ta7Q0SoYMAo6gkb3jT1OxUToR10hqRASkS8QelHfsUoh0dy24UyZ8BoBGGfQzPHeS5XIj9DYcHHLv1qZaL2zgTGGYrGIYrE40XpTU1NYX18/0P6mpqYyn2u326hWq8lMySiKkhasmqYln1+zPtcUCgXMzmaPHyGEEEIIuVRM9imSkMuYGFKxqDDGoLMKbHY9cngBcrgDOdwBB8+FgAuBJiSGV3L48hmshu9EU3wdbfkwTobvgEADNm6ChjIGtTKNsIc2HkCEPVi4Hpw5neXjow+xBVc+BFc+NvIY0grs5WCYvI0cgwGHPRcMJgABgXEuqnPMa29Hjt8+8f4IIZe/druNWq02cjld77Rt7oRiWfMIFRVKql+D2qwCGCuoSl8gUusbhgHTNNFoNNBqtRBFEQqFQt98wHExxmCa5sCg8SDS6w+r9gOQzDEyDGPoHfZZr2GhUMChQ4eGHsuoWZvjsG07OTbHcXDo0KHk78Wofc/Pzw+8UEgIIeTKFnrxe68KHfVh7UwZwDXW17VUShkHfuhulwoAx148j3/8P16Jl//izRChhFUwMH1VAbmKifnrSpg9VuyqTJQCCNwIfjOElAA3OfKzNopLThx2ht1dHUpLDpzKflVl4EZwawECtz9oBIAokFh7eBdPfOE0Wns+TEdD5A/uFDFI4EY49pKFida5kIrF4oE6HMzNzXXNuQYA13Vx/PhxnDp1Co1GA1LKpFOEmrnteV7mDVyqS8PCwgLNaiSEEELIJY8qGwnp4MiNXigl/jKgQ0LAk49Coh0/PkY7U4kAq+F7AcaT1qic2bBxA4RsI8QGQux1QkPZCfIYOIqQCCCZD8CEhmkwHO8KFyNU4cpHYLObhs5MjI/VQJ7fBVveC1c+BoHmeOcOHRa7HhoK0NjN8OSTnTmXg+lsFnP8F5Dnd461D0LIladarY61nLpLXM0x7J3H2Lts75/VBbSsqjlVLTls32pdNU9RSonTp093LVMoFOA4Dur1OqIo6mrxyjnPnCGYZtt2clEqK9Q7aPiYrvAcRFVUqupGxlhmG7D0cXDOUalUkhAvl8uh3W73rZMOf7Ne53ErE8vl7pbntm3jyJEjaDabqFaraLfbXc8bhoFyuYxisXjgEJgQQsjlz8zr8BoBglb8/YrrnXajovOeJ1LvwSwOGpnGwERcJSg7j0eegGFpKHTapToVE7e8fgV3/PjVaO14+Pi7v43mTvfNrowzVFbyiAKB3ZPNTugXPyelgBQCkBzNLRfNLRdCSLiNELxTjWgXDRRmbVhFA24tgFcPEPoDPtPI+HhVuCrCeDnD0iAEENQCaAaHbmkD27Iquq1haiWPU9/ZwdEXzk3+ol8g09PT4Jxja2tr5LKMMSwsLPR1RWg2mzh9+nTm5zTVJlV9DoyiCO12G7ZtwzRNlMtllEol+lxCCCGEkMsGhY2EdFjsGsRVhZNdwA2xkVT1MVgY9z8rH8chZLtvhiNnDkwcgYkjAABPPoUQ6S+JAoE8CY3dCMY4dDmLAKe7tiHQhisf7wSOgxX4S2GwGTDosNlN8OUJhNgCBgaHDBrKMNlh8E41JIMJm92EWe2fwZdPoy4+Bwk3Wd5ht6HM70GevRCMUTE1ISRbGIZoNse74QFAEjb2tgIdFVapoDKKooGVkOl5kL3PpVuxZoWG6g72zc3NrjC0NwhVcw0Nw+i7yFQoFNBqtZJ9ZbU7PYhRFY1pQRAkx2UYRvKaqXNSQa1pmklLUs456vU6tra2EARBZqipgsys4+h9HQa1GtN1PbMFqgp5C4UCwjBMfsbqdSaEEEJGWbypgkc+3T1eg3GGyM+oDJRxuMhY52YancXVhxoDGFBezuHm1yzjhlcfwrEXz0PT488oX/pPj/UFjUAc/jW2XHiNACKUEJFMHheRROgFMB0BI6eDIW7jqulxm1fpRwh9Da1dD/kZG07ZRCtjH/FhS8go7vOafpdlGoPfDmEXTTAOhG6EKBAwczo0I/vzlV02MXtNEZrOsf10/ZIKGwGgUqmgUCigWq2iVqv1ze82DAOlUikzFHRdtytoVJ9xwjDsm7et/ld15FheXoZpmiCEEEIIuZxQ2EhIh8mW4bDnoi2/M/Y6EkAgN5I/G5gb60KulGFn1qKI5x0OmLMopNtZrluEGgRccNgwsIwIexBJwNdZF3VEqEJHuW99ANDYNKb5T4LDBhDPALHYEZhYQYgthHK7M0NSgkGHhjJ0NtdZvl+e3YEKvxvz2v8GIduQCMGRp4CREDKWrCq4YTRNg6ZpfReFhs34A+KgyjRNuK6LIAiS0LCXekwFa2rWDoCk6s9xnK5KxyAIkjBOVe2lLzSp8Cy9nSiKYJpmEoaVSiUUi0U0Gg1YlgXX3f+3/UyqGdP/O2wZRR2/EKIruFPLqnmJKysrybHv7e0l1QEqiO292JZ1LunXJC2r+pMxhqWlpZHvtbquj9VWlRBCCEm76bWH8D9/9yEA8XuV3wwhQgnGWRzQZZCy8/lDAlznsEsmDEfDP/nLV2P6qu6bY+obbTx732bfNvxWiI3vVxF1KhHNvI6gFSJwo67Q0W+FEELCyhtgDNBtHbIdwszrYAzYeqoOEUq4NR9WQYff2l9fnZMMZdwClsfvpZrOIGXnhioRt5I1bA26qSH0o3iGJEMSljKNIT9joTjvwMztv9f67eGdbi5Wuq5jZmYG09PTcF03+QykaRosyxr4mWN7e7srTFRzGgfdsAbEn6uazSYef/xxLC8vU1t3QgghhFxW6CoMISllfjfa0fhhY4RqVxWfjvHu5IyrB+MvMYHcgMWuHrDcBgZVWsbrXQXGdFjyerh4DLJn7mQo16Gz/rBRYxUsa++BweLjzfMXoSm+0jkLDQYWYLDxZ2447Lkw2GLyZ84mnwFJCLmy9YaG41Ah1bC2p2lqBiFjDJZlDaxuTFOtRNXcnXa7nYRt6aAxfXEqfTyqui+9bG/A6fs+TNNM2nO1Wq1k37Zto91uj32O4xrVblYIAdd1M38u6hyEENje3sbc3BxarVZfGzJVDZm+wNa7T/U6ZP0cesNCTdOwtLQEy7LGPEtCCCFkMoUZG4s3V7D9TB1+M0QUdm604QwcnVapAz46SBn/P6diYPaaEspL/WM6Hv7kyb71/VaI04/sdYWZDIBmcISeAOfq5qV4HyIQ8BoByss5lBYdiFBi+5l6/LVRAhtP1MA64aBd5IhCgcgXiAIBEcbVmJrGoVvxL65xiKqfHFfoCxi2BsbitqqGpYFpDIs3VcB1Bk3nYLw/gDPsS7sdKGMMjjPe91jf95OW7enPgeN+nvU8D+vr6wiCAPPz8wc+ZkIIIYSQiwmFjYSk5NldsNkNcOVjYy0vZC35vYFFMDZem7b0bMQItcxlpBSdUHL0Njiz4cib4eNZhNiFCijjMNQHg2rRwpDjL8Ac/6cw2P6XmjK7B018Zaxjz1Lmdx94XUIIAdAXxqVbpKpwr7cNqArxOOeZ1XOKmpkjhIDneck2DcNIKgt724NyzmEYRtc+08ekgrBhQWPvufUGayqwVEGm2mb6HDVNS0K7sy3d5rT3dR3ngpmu62g0GgNDSVUNql6TrOpFKSVM00zaz6ZfG7W8pmkolUool8tUrUgIIeScu/Mt1+DBvzyeBI0K43F7VCb7Q0fG4ufNogHD1jF1KJ/ZevSZr3dXNUohsfH9ambVZODFFYVMY2Bg8ZxFSGimBsOJZynmpuIbcLjOsP10XNUYulEcNhY676M6h6ZzhF4ECQkzp0M3elqXq8pNKSECiXbNjz8bMIB3gkm/GaIwl93lBgAqK/3h6uUqPWfc9/2urhbjcl0X1WoVuq5jenr6bB8iIYQQQsh5R1dsCElhjGNJexdORb8KX54YubzszDbUMQMDK2PvR3bNRMy+mCvhQyIcspXu5xgzYOFaGNJHiE1E2Omsr8FgKyiwu1Dir+8KGZUcfy4scR08+fjY56AYbBl5dtfE6xFCSBrnHEEQIAiCzNAwDMMkIOxtr5me7ZeuelMXflQFY7rVVW8AaNuDL56lj0HtQ83tUa1Y1XaHVUqqmZLptqbpeT3VahW2bfcFar2VkZMaNMtShY29z41zsUzNtgSQtKS1bbvv52LbdlcYmyUMQziOk4S/aj6SqirN5/Njz5okhBBCztSNr16GUzERtMO+KkQVwDHePZeYcwarYIBrDM1tF9e9chFZ3HrQ9efGlpu0Tk0TUVyF2L1zgIGB8Xh/7T0fQTuE4ejITVlwyiaa2x5OfXcHUWfmI9c6n48KBsAZNEtD1jsq0xiEl5pLGQJqGoYII4RehNOP7mHFmoZd6p816JRNXH3XlVOhl65qHNQyfhTVgn93dxflcrlvJiQhhBBCyKWGwkZCemishEPav8F69NtoyW8NXZZBh4ElGFiZ6EJo3IRn/0/ZRlWxZM9C5MyEiUMADgEAFrRfQZH/vZHHtKT9Bk5G/xKhHFxN2UtjJSxp7wZj9MWIEHJwURShWq0mwV3vBZt0mOj7PqIoypyhk57TF0VR36zBNPWYEKJrm8OOUQgBy7K6qhpVAJne5iC9z4dhmLR1BYBGo4HZ2VkYhpGEdKrKsLcd6zjbTxsUVhqG0RUEqt+Pek9Lv/6qKjQIgq7wVG3Htu1kjlEWFQpzzjE9PY25ufHmHxNCCCHnwvH7t1CYs9He8+E3g66Zh0CqwlHduKQxmDk9CfakAAw7+1JL6An4rRCMAdzgqG+4A5cbR33DxfSRQnJchVkbdsGAEBKFORvlpRy4Hlctrn53N/ObZ+BGmYFn33LtCGsPVzF3bRGF2e6btG56zXIy0/FKoD4vBUEcHh90rrb6LFir1Wh+IyGEEEIueRQ2EpJBYwUs6++FJ59GVdyLuvhcajYjoLNZlNhrIeBjT/z5xNtnsFO/778zNDb8yxrH6CqcSZbT2QwOaf8Ga9F74MvjI5c32CKWtPfAZMtjbZ8QQrJIKXH69Gk0m82BLajS4aO6sOd5HnRd7wr70kFju90e6y5zFeKp6jqgv7JP13XYtt3XxrU3yBznQlNvlWIURV3hZaPRQLlcRrlchuu6yfmlW56OOpdBesM+1VpW/W/6HIaFfenKzvR2e8PT9H5s204C4PTPLP380tISDGO8duSEEELIufLdvzmB1o6LwI0AxsC0OECEiOcdykjGbVN1Biuv97VLLczbOPXADp73pqMAAL8d4vt/t4aH7j2BZ+7bRNCK3wdFJBG0Q+idmYgMAHj8v1KMvoEIiCsjp65KdQBg3fMlzVz8GcOt+ZnbCrwoPk+kWqkOIYXA9tP1rhaudtHALXcfHrre5aa3xf5Bw0b1WbBaraJSqdDNVoQQQgi5pFHYSMgQFrsa89r/hln+jxFhDxIuOHLQMA3GOEK5jT3xUYyuQuymYxYB1gBI6Gw2c5k4hNQGbltnc2Pty2Tjt3c12BwOa7+LhvwSquLjmbMrLXYMJX43iuzl4GxwFRAhhIxjd3cX9Xo9maU4iqr0A+IWqGomIOccmqZBSgnf98e+8NN1wa7RwLFjx7rmDGqaBtu2Ua1W4brd1Qe9odlBpMNG9WcAKBQK2N7ehu/7yXGmj2vU+aQDRKA7QFXbUtLtZXtbvabpup7MsexdV/0+CIIkMOzdhqZpSWtUtS9VtVooFChoJIQQcsGd+u4OHvjoM/DqIayCDq8eAIgDR2j9742BG4FrLK52BOBUTEwfzifh3qOfXcWXPvQo/FbckjU/bWGvEzZGoUDoif2wT4vfE7nGIIaEjelwU0YSIpTgOtDa9eE3Q0SBQOQLMC2AiAS4ln0TqxASQXv/uybjLJlHObD5DgBIYPvpOuySCdPR8NrfuK2v0vFyp+t68nnzTKmb3no/ExJCCCGEXGrokwwhY+DMBEf/DAqdzSDPX4Sm+MqE27OhyRIiNKBjQNjINOhyBiE2+p+DDY7SyP047Lkw2NJEx8aYgSJ7BYr8FfDkU/DlsxBogcGByVZgs+sn2h4hhAwipcTOzk4SNKrgaZx2pEKIpJIuCIKkBWp6RuMovWGYlBJra2u47rrr+pbNmnl4ti4wZf2ZMYalpSU8/fTTXcc7zuujwkIVOA4LENPbVsv1tkLlnPfNyVRUNWq6LW0QBGCMJeFkVqVj1mOEEELIhbT9TB2f/NffSVqKcs5gFQ34jXBg+CciCa8ZB5OFOQczVxeAznvaV/7wMXzu3z2M5paHKBRAZxNeM4SIJKIgiismlUgCGiAiQISdtuZ8P8gEAK6zpF0rAEgJ7J1sorXnQwQiOabAiyCExMkHdpCfsWCX+m/oyWqdyjUGybPfl6WUcZDZ2c/Osw38g9++E8u3XHntP4vFIlqt1hlto/c1HmdmNiGEEELIxYzCRkLOUIW9EU18Fcm3xzEZWALHHhgGzzs0MJ8ZNppsaejNpkqZ3zPRMfWy2DFY7NgZbYMQQgZpNBp9F2pU9d6gQC39uBACuq5jamoKjUYDQDw7Z9ygMStAbLfbcF0Xtt19h35vAHdQo0K1dHtSy7IwOzuL1dXV5Jw450lVYNZ5qmpP1WJ2nLaovetLKfvOv5eUMpnDOGgmZhAESaVjVuiYNklVo+d5qFaraLfbSRtYwzBQLBZRKBQyf66EEELIKF/+g8fgt8KuykHOGaySgSgQCL0IIuyZ38jiALC0lMP0VfHsRL8d4rt/fQJf/ePHAYlkPiPnDFICoZ/ajkRSRSglksARjEEKCRlJcCAJHHVr/3NCHHQGyXGISCL0I0SBgAgFpJBo7Up4jSBu1dpp0ap2G/rZHXRMR4dmcoSeQORHEJGMj01KhJ4A1xh0S4PfCvHF//AoHv7kKdxy9wpufNVyX0vZy1WxWMTW1tYZbYNuvCKEEELI5YbCRkLOkMNvwQzehu3owxOtl+cvQo7diR3xkYHLcJaDJqcQYTd5TMcCdIxuoWqyo8izF010TIQQcj7t7u4ODMzSgdqgMEtKCcuysLCwAMMwsLOz0xWwDTIoaFTb3drawspKdwtq27ZhWVZXu9esi0TD9p11Ean3sVwu1/Xnqakp7O7uIgiC5NzSFY7qz6qNrPqlQkD1OGNsYNvXdDipKkOjKOoKPntfI9d1kzvwR523CoBN08x8DTjnfeedpd1uY3t7u6+dLRC3tG2329ja2kK5XMb09DSEEKjVamg2m4iiCEEQQAgBwzBg2zZM00SxWDxrQTIhhJBL1+7JJk49GH/nciomGpv77zUMgG5w6AaHEDJuMyo7lfoaA2OAW/Xh1QPsrTWxd6IF0Tv7sB1BMzhEJDrv450AD+gPHAXAOJKqR9EJHA1bg94J84SIQ0QVSnrtsCsIZZwBLG6JGnpxSAgWB4m6yTthZP/rwHgcaDIGmI4GnwGiHYExCd3UYTjxZ4ooEGhue9j4fhXbz9Tx9Fc38PAnT+IN738+7OLl3xadMYZyuZx8xphU+nOo6j4x6HMXIYQQQsilgsJGQs6CKf5jADi2oz/GOBWODrsdS9qvxWEiy2Mz+hCA7LYpFo7BxSMQaMHAMowxZjDqbBbL2rvBGH1hIYRcvIa1n1Kh1KAQSwVc1WoVjDHMz8/Dsiw89dRTA9tQqZBu1J3jg46rXC5jY2O/2lzX9a4LTAcJG9MXlhzH6Qu+NE1DsVhEo9GAYRiIoqjr/NJhYlo6WDRNM2mP2hvGqnASQNe5hGGYedGrN2gc57zV9hhjmcFeqVQaWY3YaDSwvr4+cj9CCOzs7GBnZyepkk0HtUAcWtZqNei6jp2dHeRyOVQqFeTz+aHbJoQQcvGRUsJvhdBN7Yyq6h6690Ty+9yUBd3SEHr9IRLnDOD97+etXR/t2jZCV8ShX8ZHDVXRyDkD1+LgMStwjB+MQ8z0257h7F++8ZvxDEjdYPAaAXrfHlVgGKZapUoh4dZ8OBUz8ysrY4CVN1QXWARuhNCNOn9mMGwtqXZU+2tue0lb12+dbOLkt7fx5v/4YizedPm3Vp2enkatVkOtVptovfRnUfU5rlgsUmcGQgghhFzyKGwk5CyZ4m+Cw25FVfwNGuJLkAj6lrHZDSjze1BgL0uCwDK/Gzn2PFTFJ1CTn4GQ9a51OMtjjv0SfDwFXz4z8jhMdhTL2ruhs+xZkIQQcjGQUg6stFPPpyvnhi3neR5OnTqF+fl56Lreta4yyQWcQWFloVDA3t4efN8HgCTkG1WFOSjk7L2LvVTKnsVbLpfRaDSSOYhZx5sO1NT5q+2rc1ftRgdJh6dhGGZWIvaGnenzU1WWg6iWqullVGXAMK1Wa6ygUbVu9TwvqepMt5/tpSo5pZRot9uoVCqYmZmhNmaEEHKRiwKBJ754Gg/dexIb368mwZdTMXHTDy3j5tevoDjnTLTNE9/a7vpzYc7G3snmWOsGboTAjcA78xbB0R/mSUBGar6xBAPA9ewKQykBJjvViUImcxujQEA3OaJQJJWTUSD6gkbGAMPi4DoH4/GxxY8zgANePYSZ7/48wTmDWdDjMBVAFIpkPQDQTZ4ZanYdt5BYf7SKP3zz5/Hj//4uXPv3Fsd5+S5ZjDEcPnwYjz322NARAL3rpD+TqM9loz4LEUIIIYRcCihsJOQsstn1sLV/gVn+j9CQX0YodwGE4CyPHLtj4PxDgy1hVvtZTMu3oi2/gwhVABIcBeTYbeAsbi/XFt9FVd6LpvgaJNIX6Rly7HaU+T3IsTvBGN0VSQi5uA0K9ABkhoXjbC8dSJ3J3eFZAVuj0UAURbBtO2njqdpejWrdOug5XdeTfeVyORQKhczlHMdJ2qn2blfNTVSEEMlrp4LHRqMB27Yzg8q0dHiq1h9WMZmmqghHBXVhGHYFnqoF7iBSSmxubo58fdV8SPVaSCmT3zPGEEVRZvtcIQQ8z4Nt29jb2wMAzM7SzTqEEHKxevhTJ/H1jzwBt9Z/Y2d7z8e3/vwZfPu/P4NrXrqAH3z7zTCd8S55eI3u97fSooN2pzXqMFGwH8pFgYRmqFbwcfgmRU+7VPVbISFZKnDszHYEADAGrjGYeR1SSPitzo1AXgS9M0uxs1h/0Ii4ApLr8fudYWvQTI7IF50ZjfFKYRBvU9Pj+Yu9VaFqH0AcNGaFmsD+LMm0+nobn/7Ad6CZHFe/aH7o63ep03Udy8vLOH36NFzXHdqRo/fGM1XV6DgOLMs6X4dMCCGEEHLOUNhIyDmgsTLK7O6J1+PMRJ7dOfB5hz8HDp6DkO/Cl8ch0AKHA4Mtw2CX9xc5QsjlRVXdZc25GefOcCUdHk2y3jAq/HJdF9VqFY1Go2vbjLGklei4d7KrSk4V+HHOk/04joPFxcWhQd309DSklEkgJoTouqg17DiEEGi1WtB1HbZtDwxiVeVkEATJMfduZ9hcItu2k2rBQYIggKZpkFImAd/u7i4453AcB6VSqSsUbbfbyfFkSQeuowJqFUCqkDh9XkEQwDRN7O3tIZfLjTVDkhBCyPn1jT95Evf/16dGLicl8MQX17F3qoU3/ObzYRdGzxBUrUAVxhnmryth4/FaV+DIZYhitAVDuhBMx67rwEMxnuPI4oDQrQbdLVIzDxIQoYRmsCRwVCEjAGgGh6ar92uGoB1CRHFwGQUCmhEHgF3HzAAzp/cFh5wzcFuDbmuQUXwjUnk5h6AV9oWsQFx5KQIBzhl0myN0s8+F6wxZH12kkKhvevjMb38Pb/3Dl8IpXd6zkaemphBFEba3t9Fut/s+Pw3qbqHmRy8uXt4VoIQQQgi5clDYSMglSGdT0NnlPweDEHL5UmFbEAR9F2UmCQ175wqe6bwbXddRKBSwu7uL7e3tzGVUKOd53sQVmKo9qW3b0DQNpVJprNadjDHMzs7Ctm3s7u5ib28veZ16A8BBLU3DMES73U72ncUwjCRU7F1/2Lnqup78iqIIQRD0LZ9u8co5T6oRlXa7je3t7eT14ZyjVqsl8yN7jycdNKZb2Gb9feoNF3vPX1VcMsZQrVYpbCSEkIvMw586OVbQmLb1VB2f/NcP4I2/+Xxwbfjng9yUieaO1/UY1zkWbiijutaCWFvFVc1vY8V/CKaMOxxICYhQYIMfxvf4S1C3lxEFYnTQmBKFApoetzyVIrVS6i1PNzk0w0DkC+gWh2F3Zi133kK5xuJlTD708wQDwDQGgMEpmbj1nhU89ZVNNDZd+K0QstOyVYQSsiDjEDQQECL7ZHQr+7MEADS3XJQWHTzy6VO448euHu/FuITNzs5C13VsbGwMrXAE4s+ulmXBtm0sLS0N/ExGCCGEEHKpobCREEIIIRdEqVSC7/tdgdMk4R3nvK81qKrMO2iVo2EYkFJ2BY3p1qRqv77vJyHWoDmNQHZwyhjD3NwcSqXSxOFooVCA7/tJxV8QBH0tuYa1oU23Dc3aN2MMlmXB9/3McC+Lrutd8x1V6KhCS7WeajfLOe/bt2qFGoYhWq1WchyqZa0KeHVdT9YNw7CrbeokVOCZ3n8URdB1Hc1mM5kvSQgh5MIL/Qhf//ATB1p37aE9PPXVDVz70uHVY9e9fAmbT9b7HmcMuC38HBbE1yH0CJEQkDJ+vxOhBNMYFvkpNKPv4SmXYw8TtuKWgIgEuM7BNQaraCD0BDSj+z1Yt3VMHbbxlv/8EvzFr9yP9e9XoTWCrmrIiXYrJQzHQGHGglPurjzcebaB+nr8OSL0sz9PMI6+Csq0sFN1+cgnT+F5P3r0ipiHXKlUUCqVUK1WsbW11VXlqDp6GIaBXC6HUqmEYrF4RbwuhBBCCLlyUNhICCGEkAuiXC6jWq2O1QIzS3rmocIYQy6XQ7PZnHh7hmHANE3U6/UkfMqq0FMtS1XYqI5B/b535mHWMoZhHKgKU0qJWq2WBHaDWnMNCkFVEOl5HhzHydwHYwzz8/OwLAvVahWe5yWPp6nq1EGzINOhoud5XRfc0qIo6npeHaeqDFDnosJIy7KgadrA+ZHjyHptVNgIAI1GA1NT1EGAEEIuBk98cR3uiNmJwzz08ZMjw8YbXr2M+/7LE93hmpS49qk/x8z2dwF0WpumAja/GQKdUE3TJNqhBR1tSO5gSNfxPmq+I9MYOGew8jqWnzsFBgYhJLjGwHWGXMXC9JFiPIeRM0T6wbs5cJ0DUuLlv3QLPvtvv9dzPOpzDPpatQJxhaSZ0zE0Juu8zdbW21h/tIrFmyoHPtZLCeccU1NTmJqaSm6gUjdGqYpGms9ICCGEkMsVhY2EEEIIuSAsy4LjOEk7zEmrGk0zewaQupCzs7Mz9vYMw0jai/q+P7QFVm/LznQVI2NsZIgYhiH29vaQz+fHPj6l2WwmIduwsG1Q2KiOU1VrDjrWSqUCwzBQKpXgui6azSZc103aq6YrDEcJwzA51qzWrL1BY+/z6fZiKoQ0TbPr78tBKlmzqhuVYbMpCSGEnF8Pf+LkGa2/+r1d7J5oYOpwYeAydsHAda9YwiOfOpU8tnLq75KgMUv6nSfULESBBkBCF20I5ozdShXohI2dIDM3bUE34/e+dIPNm16zjPy0hemr8lh7eHf8jfdgGoNV0MF1jutfsYTADfHF//hY0saVd0LM7O4MnbmQI4LOdLVla9cbsuTlS9d1lEqlC30YhBBCCCHnzZkNNiKEEEIIOQMLCwtJ0DeoQi5NhXmWZQ0MuzjnWFlZwczMzMg5OGpbjuNgbm4O7XZ7ZNA47LFxQ69Go9HVPnZc7XYbAEZWgw5ry6WOcdD+S6VSVwtR27YxMzODQ4cOoVgswjTNiaoy06Fouho1Xb04TNbzwwLKcQ1b/0y3TQgh5OwQkcD6Y9Uz3s7pR0Zv4yU/dz1mjxUBADzysLj+laHLq7da3dLgMwesEz8yKcGRcdPKkFJAKQHGGRhnKC32dx5gDLj59SsAgFvuPpyEkQeRn7HANY7CrB1v7/WH8cbffD6uev4MGAPsUuczQM8ISc3ksArG0PapSrINYODMR0IIIYQQcnmhsJEQQgghF4xhGDh06BAMw4BlWTAMIzMoUyGjCgeHzdNTgdahQ4ewsrKShGeapoFzDk3ToOs6LMuCaZpwHAfT09NJ9d5Bwq90peM4pJTwfX+sZdNUwDhO5d2oQDBrG7lcDnNzcwPXKZfLI/ebpuY2KulAOT3PcZBh7WDTjw8LV8edh5ReblRITQgh5PzwW2en0txrjr7Bx7B1/PD778DCDWXMbT0ALRr+Ps11Dt3SYDgaGlEJFt+v4DMQgPMx3n9SizANmDlWhJnrv/nqmr+3iOJcHEJe/8ollJdyo7c9QHF+fzvK8q1TuOc9d+DN/+kleMk/uh7TRwtwyiY0g8OwNdhlE1ZOH3s+ZHFhPzC1CjQDmRBCCCHkSkBhIyGEEEIuKNM0cfjwYUxNTcG27SQQTP/inEPXddi2PTRoBIBiMa5KYIyhUqng6NGjOHLkCGZnZzE1NYV8Pg/DMCClBOccQRBgc3MTm5ubCMMwmck4qUnXOcicynRV4DjLqrmO4yiVSlhaWhq6fLFYHKsCVUkHjernqIwzc3FUO9j0cmcqHTDmcge/iEsIIeTs0cyzc8li3EpAp2Tijb/1fNw29zgMe8A6DHDKJpZursAqxu+JodTh8PZ+digFOBf9geOAtyuuMRTnHeSn++f5zV1TxMvffhOAeIai6eh49b+6FZo1+Y0xxQUHZk7H/HUlLFzffwNReTGHF73tOrz+nbdj8eYKCnM2DFvDJG+zVsFIAlMrr2PxxsluVCKEEEIIIZcmmtlICCGEkAtO0zTMzMxgenoaTz31FFzXTZ5jjA2seOxlGEZfUMQYQz6fRy6Xw8bGBlzXzQzhVDCmquayljkboZbazkG2NWnFndqPOqf0jEm1vVKp1Nc6dRDOOZaWlnDq1KmxwlIVCKqK1PTj41Rnjnqdes/noBhjyWtrWRZs2z7jbRJCCDlzhqXBLhlwa5O3Hk8rzo//77pmcEzpe6g8dxpuzUe7GkCEIn6vMDny0xb0ThDp1gPU19vgTEBHiLxWRyPq3PQkBZimQeMMUqj3YcSBY+d+GcbiCkm7qGdWAC7eXMbRF83jL37lfuydaCAKJTSdYfpIAUdfOIsnvrA+dhCYn7EwfVU8t/I5b7xq6LI3vfYQvv+5NdQ32th5pjHeDhC3gp26an8m9fWvXIJh02UnQgghhJArAX3qI4QQQshFgzGG+fl5rK+vH2j9SqWS+biUEqdPn0a9XkcQBF0tPFUFYG8FnRAiM3DknB+oKjG9vqZpME1z4nWLxSJ2d3cnmpkIdIeOpmlC13WYpokjR45MHNZZloVDhw5hbW1trOpETdNgWVbXfiZ5/dTPoLeSsffP6rH0c+O2V02H2ZO2iiWEEHJu3fjqZTzw0WcPvH5+2sLhO2bGX8H3ITvvU3bJhF0a/H5dXs6htevD4S3UoxIc7oIBaETFZIYjGMA0BobO+1MncNQNDUxDp3KQwehUKjIGLD93CrqhYfV7uzj9cPe8ySiU2HyyDikl7KKBdtUf2t6UGxylBQfl5fhmrFtev4LrX740cHkgrgS9+13Pw1/+6jewe6IJGY3RUYEzzF1b6gpNb7n78Mj1CCGEEELI5YHCRkIIIYRcVIrFIlzXRbVaHb1wSqlUGhgUbWxsYHt7O7OaTkqZtE9VVPCUFTieaSWdagd7kLBRzZg8aNjJOU+CtXK5fOBzsSwLV111Fer1Omq1GjzP61sml8uhWCyiVqud0WvGGINt213zNAfN9UxXUo5qNasCWzXDM33MhBBCLh633H0Y3/mLZ3GADucA4io9rk1wk45pgnGeBI7DaAbHwg0lXPXAKh6qlwAANnehswA1bR5Couu4GWNgHCjM25g6XEBuyoRXj6s2X/HPb0FuysLc9SXc/ydP4YkvnB66b8YYFm4sY/vpOgIvgqZz+K0IIhLgGoNuaSjO2chNW2Cddq7PfeNVePHPXT/Wy+CUTbzp/3oRwBm+89FnIcXgH4BZMDB9Vb4raLzjfzmKqZX8wHUIIYQQQsjlhcJGQgghhFx0ZmdnwRjD3t7eWMuXy2XMzs5mPtdut7G+vj7RTMV0e87eVp3pKrpeo0I1xhh0XT+j6rlKpYJ2uw1N08ZqRZqm63py/KVS6cDHAMRhXblcRrlchuu6SWCrWqYahoEwDFGv1/vWnSR8VLMeHcdBEAQIwzCzfapqt5tuGTsolFXrpqsuHcfB4uLiWWuVSwgh5OwoLTg49uJ5PPnljYnXNRwNN79uZaJ1GGPQrroK4TPPjLkPHc+7rYnHvy7gB3GoqbMIZk4D0wxEgYDsvB0ZtobZa0tdsxntkolb7l7B7W86CgD40n96dGTQmBwrZ5g5VkR7z0+22fvxhHGGI3fO4tZ7DuPw8yao8ARg5nT8xP/3xbjmJQv4u3/7PTS2vPh8pATXGJyyicK8AyvffWnplrtX8MKfvHaifRFCCCGEkEsbhY2EEEIIuegwxjA7O4t8Po9qtYpms5kZ7uXzeZTL5b45jUoYhjhx4sRYQWNvgJhuydkbbnHOM4O+UUGVZVmwLAuFQmHk8QySz+cxNTWFra2ticJGTdOSuYyFQiFz/qOUEq1WC/V6PQn1VNBXLpcHzowcNONQ13Xk83k0m82ux4cFtr3UMTPGYJomDMNAFEUQQkDXdQRBAM55EqQKIeD7ftLitbfdavq10HU9mVs5MzNDQSMhhFykXv5Lt2DvVAvbE8wP5BrDa3/ttq5gb1zW616H8D/+x7GXd3IMz7lmFw+tLiL0BIJIQ6jnwVgc2DkVC8V5O3Muo25yPOeH43aje6ea+N7fnJjoWBljyE3F5/jD778DW0/V4dYCMAbYZRPHXjyP4pwz0TZ73fkPr8HssSLu+3+exM6zg38GhTkbz/vRo7j1HmqfSgghhBBypaGwkRBCCCEXLcdx4DgOwjBEs9mEEPHd9JqmIZ/PJ+0vB9nd3c1s8dlr0pCJMZZZWThsO6p16tLS0hmHWjMzcWXC+vo6giAYuXy6gs80zb4qUCklqtUqqtVq5vba7TZ2d3eRz+cxPT09UQvYcrmcGTaqoHDUcffOp0xXh87MzGB9fT0Jo1XIqMJh1U41HTjatp383SkWiygUChPPwCSEEHJ+mTkdb/ytF+AT738Apx/ZG7m84Wh4za8+d+JKPsV65SvR+uM/hnTdsde5/fCz2Gs7WK1WYB8+jMrSXDKfcRDGgFf877dg6nB8E9JD9548cLtYKYGTD+zgrp+67mAbGOHqu+Zx9V3zWH1oFw/dexLbT9fhN0MYjobycg43vfYQjtw5B87pxh1CCCGEkCsRhY2EEEIIuegdpPWoEAL1en3s+YbptqlKurox63nOebL9rMBKBWNqTuPS0lJSqXemZmZm4DgO1tbW0GhkVxmoGY2apoExBsuysLS01FWhKKXE+vr6wG2kl2s0Gmi1WlheXh5Yzdgrl8uhUqn0tcQ1DGNo2KiC0UHK5TI451hcXMTOzg5Onz6dVDOmt6F+6bqezKvUNA2Li4sDKzUJIYRcfOyigTf+1vPx1JfX8dC9J7H28F7fMvlpCze+5hBuft0hFGbGe5/KwhwH9o/8CNr/7b+NvQ5nwMuuewxfPX0b1ubmOhsavLxucrzq/3Mrjr14AQAQeBEe++zqgY8ZAB7921Xc+ZZroOnn7iaa5VumsHzL1DnbPiGEEEIIuTRR2EgIIYSQy1K9Xp+ozWg6VMxq75meBZim6zo458jlcvB9H0KIpKpOVWCqVq9nu01nLpfDNddcg1qthq2tLbipCgx1XEAc7JVKpSSgS9vY2BgZNKYJIbC6uoqVlZWxKxxnZmaS6klFhYm+7/ctzxiDbdsDKw4rlUqyb9X61TRN6LqeVDamQ2D1S3FdF2trazh06BC1TiWEkEuIpnNc94NLuO4Hl7D9TB2nH63Cb4bQTY7igoPDd8yctaDN+Yf/EGJ1Fd4XvjD2OmYpj3t+/2dxulrCQx8/gWfu24IU3Z8bclMmbnrNIdz8uhUUZvcD0Z1nGvCaYe8mJ9Ku+tg90cTs1cUz2g4hhBBCCCGTorCREEIIIZeldrs9UZCUniOYXi89t7F3WbWcatdZKpVQqVSSZQzDOGuVjMOUSiWUSiUEQYCdnR00Go1krqGaEVksFvvCu2aziXq9PvH+hBDY2NjAysrKWMszxjA3NwfbtrG3t5e0tjUMA1LKpMJRVR2apjnwZ1csFpM2sgCws7MDz/OSgJdzngSOABBFURI6pytNXdfF7u4upqenJz5/QgghF97M0SJmjp67UI0xhvy//JdglQrcv/5rjOpvqi0tofiud0FbWcFhAIdvn0Fjy8Xq93bhNQJoBkd+xsbK7dOZgajXGN0WfRz+GQaWhBBCCCGEHASFjYQQQgi5LKUr28Ztpco5z6yG5JwPDL9UOAbEAWcURTh06NB5b9FZr9e7gjwVLAZBgN3d3a6Zi5ZlAUBXpeGkXNeF53nJtsZRLBZRLBbhui7q9TrCMEwqQn3f7wpwe2mahkqlgqmp/dZtQgjUarXk957nJXM909WNwH5ALIRAEASwLAu1Wg1TU1NU3UgIISQTYwz5f/yPYf/wD8P7xCfgfeYzED036Ri33Qb77rthvOhFYD3v/YVZG9e/fGmsfWnm2anI1AyaQ0wIIYQQQs4/ChsJIYQQcllSAZJhGEkANw5N07oCKyB7HiMQtyrtrcLzff+8t+jc2trqm4mYpdlsot1uY3FxEYZhoNVqndF+q9Uq5ufnJ17Ptu2+mY9SSjSbTVSr1aQdrZo5WS6XUSgU+l7PRqOR/Kxc14UQIvlzr/TPkzEG13XBGEOz2UShUJj4HAghhFw5tKUl5H72Z+G89a2Ijh+HbLUAwwCfnYWm5jOeoXRL1YNiDMjPjn8TECGEEEIIIWcLhY2EEEIIuSzpevwxR9O0gXMY03rbpKp2qr1Bo2rFaRjGwDDRdd3zFmJtb2+PFTQqQgisra2hWDzz1nPNZvOMt6EwxlAoFCZ6zdSsSVXROO6MTlX12Gq1UK/XKWwkhBAyFmaa0K+99pxsu7yUw9LNFaw9vHfgbRy6bRqFmTMPLQkhhBBCCJkU9dcghBBCyGWpVCoB2J+dOIgKnnp/qbDRsiw4jgPHcZDL5eA4ztCZgsqZtCgdl+d52N3dhZQSURQhDEOEYTiybayUMlnvTERRdMbbONP9CyEQhuHYQaOifu5nMzAlhBBCzsQt9xw+o/VvPcP1CSGEEEIIOagrqrJRCIG/+Iu/wMc+9jE89thjaLVamJubwx133IGf+ImfwJ133nmhD5EQQgghZ4llWbAsC57nwTAMSCkRBEHyvGqrOSwsY4wlswRt2x6rLaraZrPZRKPRgOM452x+4/b2NjzPywz9OOfQdR26rmcetwrnzvTYLvS8wyAIxp7JmaXRaCTBMiGEEHIhHXvxPApzNhqb7sTrlpccHHnh2WnpSgghhBBCyKSumLCxXq/jF37hF3Dfffd1Pb66uorV1VV8/OMfx0//9E/jV3/1Vy/QERJCCCHkbKtUKlhfXweApBrR9/2RQaOUEpzzJIBSMwFt2x44v1FKiTAMEQRBst2TJ09C13XkcjmUy2XkcrmzEmpJKbGxsYHNzc2Bywgh4Ps+giCAZVl9oSJjDEEQnFHYeK5C1HFxzrte74MQQqBWq6FcLp/FIyOEEEImp+kcd7/rdnzsHffDb4Vjr2cVdLz+Xc8D53TjDCGEEEIIuTCuiLBRSolf/uVfToLGl770pXjzm9+M2dlZPPLII/iDP/gDnDp1Cn/0R3+E6elp/PzP//wFPmJCCCGEnA3FYhGu6yYtTQ3DgK7rcF23q8pRYYwlMxl723KqwNFxnL7AMAgC+L7ftz0VgrVaLbRaLRiGgaWlJei6jkajgXa7DSFE0uq1WCzCNM2h5ySlxPr6+thzGtNBaToc1DQtCV4PGoBe6FmHuVzujNu4qpayFDYSQgi5GMwcLeINv/l8fOJ930Zrt/+zRa/CrI273307plby5+HoCCGEEEIIyXZFhI1//dd/jS996UsAgDe96U34wAc+kDx3++234/Wvfz3e8pa34IknnsDv//7v441vfCMWFxcv1OESQggh5AwJIdBoNOC6LqIoAuccruuCc57M+FMBW+//6roOwzDQbrf7tquqF1UYqeYkqjaeKqxUekM8z/Pw5JNPDpwhubu7C8dxUKlUkM9nXzTc3d1NWn9OwvO8rspMzvkZVyZe6IAun8+fUQtVIP4ZNZtNCCEGVq0SQggh59P8tSX8+L//ATz8qZN4+JOnMtuqlhYc3Pz6Fdz0mkOwi4NnUxNCCCGEEHI+XBFh4x/90R8BiO++f8c73tH3fKVSwXvf+1685S1vged5+MhHPoJf+ZVfOd+HSQghhJAzFIYhdnd3Ua/XkxAq3d5UCJG0T023SFXVjOqXeiwr0FOtSaWUyfYU9XvGWFcbViAOQF3XTWZHZlVIAkC73Ua73cbU1BRmZma6nhNCjF3R2Eu9DunKyUGh5zgcx+naltp+FEXJ63mu26yq1/hsVDf6vg/bts/SkRFCCCFnximbeP6PH8PzfuxqnPjmFnZPNOG3I5iOhukjBRy+Y4bmDRNCCCGEkIvGZR82njhxAg8//DAA4BWveAUqlUrmci94wQtw9dVX4+mnn8YnP/lJChsJIYSQS4zrulhbW+tqf5oO+IA4nEo/n65qVO1M1Z91Xe9rtarWHRVyqSBSVcr1HocKHIe1TN3d3QVjDNPT08ljjUajq4pyUmEYwjCMZF1N01Aul5M2s+PSNA3z8/PJNmu1Gmq1GsKwe75UPp9PZlWeC5qmQdO0vv2OS70OquKVEEIIudhwznDkzjkcuXPuQh8KIYQQQgghA132vaK++c1vJr+/6667hi77whe+EABw6tQpHD9+/JweFyGEEELOHt/3sbq6OjRoBLorD5V0cOi6bhI66Xr3PVnpbavqyFHU/tVsxLQwDEduY2dnB6673zqtVqtBSpm0b1WtXNWv3krLXmpdJZ/PY3Z2FqVSaeS5KJqmYXl5GYZhYGdnB88++yx2dnYyA79ms4nV1VUcP348c0bmmWKMwTTNAwWv6WCZc04tVAkhhBBCCCGEEEIO6LK/qvLEE08kvz969OjQZQ8fPpz8/vHHHz9Xh0QIIYSQs+z06dNdlWlSSnieNzLMywoNVUCYnmmYVfU2atuqWtL3/a6AL71+1uO90lWHqsWqmkXZG7KpisphVXrp58rlMhhjmJ+fx/z8PCzLGno+pVIJhw8fhmmaOHHiBFZXV9FqtdBqtdBut+F5Xua+fd/HyZMn4fv+yPOdlOpaMUng2DtXU9O0vnCZEEIIIYQQQgghhIznsr+qcvr06eT3y8vLQ5ddWlrKXI8QQgghF692u90XYqkqv3Gk5zeqP0dRBF3XYVlWEuxlrTdIel6j7/tgjGWGYYOOMd1utV6vY3Z2Fu12G61Wq2/fWdWa6hx650amGYYBx3GSP5dKJZRKJbium7RElVJC0zTYto1SqQTOOfb29rC+vt5Vcan2KYRAGIbgnMM0za6ZjVEUYW1tDYcPHz6rVYTT09PY3NxEGIZdcziz9L4WjDEYhoF8Pk9hIyGEEEIIIYQQQsgBXfZXVdLVAPl8fuiy6XlC9Xr9nB0TIYQQQs6erHmDg2b4ZQVvWcFUEATQdX3g7MZh8xp7A76sMHNQACiEQBAEfcf/5JNPJi1SB7VwTT+mti+E6Ar80s8vLCxkVkYahoHp6em+1qJSSqyvr6NWq8HzvMzjT5+H67qwLKsrxAuCALVabeAM7YOwLAulUgm7u7uZwesgqn2qYRgol8tn7XgIIYQQQgghhBBCrjSXfdiYrnSwbXvosunnz0WbL0IIIYScXVJKNJvNvseGtSfNCgp7A0DVipRznvyvelxtI2u7WRWMquIvC+c8CQ8HtVsFgFar1bWd3jagw84nffxAPItyaWmp63OP53moVquo1+td27IsC+VyGYVCAdvb26jX64iiaKx5lWq7ap9KtVo9q2EjEHevaDQaCMNwrIpWFTSapgnbtrtuOCOEEEIIIYQQQgghk7nsw8b03fyj7nJPXzg7m+29CCGEEHJuZAVf48xSHBU2preTfk7TNBiGAc55Mp9wUItUYD+cHFTNGEVREiQO2kZWWNm7/LBzSj9umiaOHDmSzGYMwxCnT5/ua4mqeJ6HjY0NrK+vJ61le6s8R/F9H5qmJccbBAFardZZDfhM08SxY8fw1FNPJX8nsipAVcgIxG1kbdvG0tLSRPMeCSGEEEIIIYQQQki3yz5sTF/Icl0XpmkOXDbdEmzYcoQQQgi5eI0TNg5rgzpsO6rtpgqnhrUTVW1PR1HVjOkgLGs7WWFob+CYddyMMWiaBsuyMDMzkwSNQRDg1KlTA1vOpvm+n8xwHHcWZvo4VVCZ3t7ZribM5XK45pprcPz4cXieN/C1V4GxbdtYXl6GYRgT70tVUKrXlm5SI4QQQgghhBBCyJXssg8b03Ma2+02SqXSwGVbrVbye5rdQwghhFz8skKecef1AfvB3KB1VMimgiVd15OQT1XrZYVa4wSNveuqIK93TmLvLMZxtpv+vZq9mJ5NGEURVldXxwoapZTJcr7vD63CHCQMw66wcdLAclyO4+C6665DvV7H1tZWV8Wm+vk5jpO0hp0kJBRCoF6vo1qt9rXbz+fzKJfLcByHqiTPARlFkJ4HRq8vIYQQQgghhBByUbrsw8ZDhw4lv19bW8PCwsLAZdfW1pLfD1uOEEIIIRcHzjkcx0G73U4eG6dyMR1Y9IZnKuDrrVpkjCEMQ4RhmFTHDWorOu5Mw6zWp+njyQpD0+c2KvhLh5VLS0tJe/larTZ2O9TeOZJCiK429eNuI32s5zIw4pyjXC6jXC7D8zz4vp+EuKZpJpWdk6jVatja2hoYkjabTTSbTZimicXFReqQcRaIvT14n/oUvE9/GtHGRvwgY9CvuQbW3XfD+sEfBKPXmRBCCCGEEEIIGahWq+Gee+7BxsYG3vCGN+B3fud3ztm+Lvuw8brrrkt+f/z4cdx+++0Dlz1x4kTy+2uvvfZcHhYhhBBCzpJSqdQXNo4zW1CFkqoNpqpgVFWMQNxyU9f1vuAxiiJEUQTTNKHreleFYG8glRV8qoq6rFCyN2zMCubGqXBMLzs1NZWEbFJK1Gq1sdYd5xgn2Y5a53yFcZZlHShcTNvd3cX29vZYy/q+j5MnT+LQoUNnvN8rlQwCtD70IXif/Sxkb+WtlAifeALhv/t3aP/hH8J585thv/GNZ2e/UkK2WhCrqwiffBKy2QQzTfD5eRjPfz6Yftl/bSKEEEIIIYQQcpl5//vfjw11A+85dtl/a7799tuTC3L3338/3jjkgsR9990HIL7zf2Vl5XwdIiGEEELOQKFQwNbWVlcFnmEYY1fuGYaRtET1PA+MMZimCc55Ekj2BoqK7/tJqKSez5qZyDlPQki13UFVcuO2TU1XCWYto45dzSdUWq3W2K/NoP0eJGxUy+u6ftbnNZ4rjUZj7KBREUJgdXUVhw8f7modS0aTnof6e96D4HvfG7msaDTQ/IM/QLSxgfzP/dzB9hdF8L/6Vbh/8zfwv/hFiPV1yHod0HXw2Vnw+Xkw2wafmoL1Qz8E6/WvhzY7e6B9EUIIIYQQQggh59NnPvMZ/NVf/dV529/4g2ouUUtLS0k146c+9Sk0Go3M5e6//348/fTTAIDXvva15+vwCCGEEHKGGGOYn5/ve8wwjJHr6roOTdOStqkqtAvDEL7vw/M8uK6bBJlZoZ7neTBNE47jZIZLqnrSsqwk1BxFSplUXI6aK8g571o2/We1r2Kx2HW8k+g9Xs752FWVWUql0iUxd09KOXHQqERRhGq1epaP6PImpUTjt397rKAxzf3Lv0T7ox+deH/el7+M3Z/5GdTe+160/+zPED7xRBw0AkAYQpw+jfC730X4/e9DbGyg/Wd/huo/+2fwv/nNifdFCCGEEEIIIYScTzs7O3j3u999Xvd52YeNAPCTP/mTAIC9vT28+93v7qskqFaryQtvGAbe+ta3nvdjJIQQQsjB5fP5vnnLaqbiILquwzRNSCnRbre7Kg97pWckZgVtYRiCc56ElyrwcxwHuVwOjuPAsiw4jjPWvENd15OKyXGDOVXJ2Lu8bdtdr8OgispBsgLSScNCXdeT0LVcLk+07oXSbrfPqAK0VqudUSh7pQm/8x34X//6gdZt/8mfQDSbYy0rpUTj3/977P3cz8H71KcQfPnLEKdOQe7tQTSb3a1bpYTc20PwyCOQvg/pumi8//0UOBJymRn03n65kFKiseVi+5k69k414TUP/t5GCCGEEEIuDe9973uxtbWF6enp87bPK6K30z333IOPfvSj+NKXvoS/+Zu/wenTp/G2t70NCwsLeOyxx/ChD30Ip06dAgC8/e1vx+HDhy/wERNCCCFkUsViEZqmYWdnB67rgjEGy7LAOUcQBMmFRFX1aBhGUtEYhmHS7nQY1Q61N2wLgiAJ1NLbz6oKtG0bQgh4ntcVZqXDQrWt9P6SwBMS8f/FrUwFBPiQ+8d6w72DtD/tnYGpAtV069ph1PksLS2NFbZeKEEQoFaroV6vo9lsJn8vNE2DYRgj/36kRVGERqPRVVVKBnPvvffA60rPg/eZz8D5+39/6HLh44+j9o53wP/KVwApgWYTSP8dDgLIIIDUNLBcDkz9XXVdRN//PvSbb4YE0PjgB1H+D/+BWqoScgnbO9XEQ584iSe/uI7Wng9ICTOv46oXzOLWuw9j8abKhT7EM+Y1Azz6mVU8/ImT2DvVSh7nGsORF87hlrtXcPj2mQt4hIQQQggh5Fy499578clPfhKcc7zzne/Ev/gX/+K87PeKCBsB4Pd+7/fwT//pP8U3vvEN3H///bj//vv7lvnpn/5p/PzP//wFODpCCCGEnA25XA65XA6e56FarcJ13WRGoBACUsqksk/NTVRViONSgZNpmoiiKAkBbdtGsVjE5ubmyEBPhY6DqinUPnRdh+/74JwjjEIIKSCwX5kYIEAkImjQYDELBjPAsL9vx3H67mIbp71sr96wUQW5vu9nzrLsPRfTNLG0tNQ1O/JiEoYhNjc30UxVx6Vb54ZhmFSvqgB7HJ7nUdg4BrG9jeCAVY2K94lPwH7jGwf+txc8+CDq738//PvvjysWhYAcVLkaRZCNBpDPg3WqgmWrBbG5Cb6wAOm68D7xCeQ63VMIIZeOxraLz/3ewzj5wDZ63369RojHP3caj3/uNGaPFfGDv3gT5q+7NKrxe333r4/jax9+AqHXf1OQiCSe/uoGnv7qBmaOFvDaX78N5aVLY5YyIYQQQggZbmtrC+973/sAxHnXbbfddt72fcWEjYVCAR/5yEfwsY99DH/1V3+FRx99FPV6HVNTU3je856Ht7zlLbjrrrsu9GESQggh5CywLKtvjmOaCvmefvppSCkP1C5TStkVns3MzCCXy6HdbneFVoNkVQwC6JrTqMLGpmwiYAEMGNDkfjAasfgiYoQILdkClxx5nocGDaZpYnZ2ti98KRQK2NramqidqgoMfd9PjksFjoZhIAiCzNBR0zQsLy+jUqlMVBV4PgVBgFOnTo0MTYG4BW273YZt22MF1JO2rL1ShY89BjnotRICYnsbYns7CQeZpoFXKuBzc0AnPI9OnYKs1cAy2vRGJ06g/pu/iWh9HVAzS0fNLpUSstkEikWwzt/daGMDvNOu2fv0p+G8+c1JGEkIufjtnWrir9/5LTS23JHLbj1Vx1/+2jfxut+4DYefd2lV/933/zyBb/7p02Mtu/1MAx/9l/fh73/gBZi+qnCOj4wQQgghhJxr7373u7G7u4urr74av/zLv4zNzc3ztu8r6tsx5xxvetOb8KY3velCHwohhBBCLiDGGKrV6hnNaArDMLNVarlcHitsBPYrBtOhlGpPqsKsTb4JI4wDlYAFiBBBh95V4ahEiLDBNrBkLcE27cz5iJxzFItFVKvVsc8V2K+IVC1j09uzLAumaSbVo2r5lZWVZPbkxSiKIqyuro4VNKZ5ngfbtsdqu0tGy5y3KASiU6cgNjeBnp+PBBA1GohOnQKbmoJ2+DCYZcXhYMbf+daf/Elcmbixsb+NcW4wkBLSdcFynYqfdjsONEsliL09BPffD5NuViTkktCu+fj4e749VtCohF6ET/3Wd/Aj/+cLMHusdA6P7ux55G9PjR00Km4twMff82382O++CE7JPEdHRgghhBBCzrWPfexj+MxnPgPOOT7wgQ+c9+sxV1TYSAghhBCi1Go1AJPPMFSklIiiKAneVDjoOE5XFeAgURQlsyTToacQIpk5uct3sck2UeEV5EQceAgmUOd17Gl7sIQFDg4JCcEEXO5CQGBP7OG1i68dWH1XLpdRq9UmDlsNw0C5XAZjrC9QVbMNOecolUqoVCpdoeTFaG9vb2BV67CZlFJK+L4/si3sQVrWXomY2XNxO4riasdGY/iKUkLu7CCs16HfcAPQux0AYmcHwde+Fi9er3dWk8C4Vae+D+k4yb8TstEAK8WhQzq8JIRc3L7zF8+idro98XqBG+FrH34CP/zeO87BUZ1dIhL4xv/75IHWbWy6ePiTJ/H8Hz92lo+KEEIIIeTK9Nhjjx143eXlZSwvL0+0zvr6On7zN38TQNw+9XnPe96B939QF/cVIEIIIYSQc0SFTJxzMMYOVOWoKhINw4DZCToYY1haWsLJkycHhlW+7yf7Z4yBc57Mj1QiGQEBMMtmsa1vI0KEoiiizdvY1XchIRFq/RV5Ta2JL5S+gLzM4w14Q+b+TdPEwsICTp8+PdH55nI5LC0tgTGGMAxRq9UQhmFy7LZto1AoXBIVfVLKJHBW0pWZnHOEYTgwjI6iqO9nlsYYQ6FALenGwdMtj4VA+P3vJ0GjDAJI3wfSoTDnYIYBWFbc4jQIED7+OGS7P0jwPv1pSFUZOeC/x5E8D+gEyzK1DTnihgJCyMUhCgQe/dvVA69/8tvbqK61Lvq5hs98fRPN7f0W0X47RGPDhVsPICIJxgDd0lCYtZCbssB49/vbw588hef92NXg/GA3YRFCCCGEkH1qbuJB/OIv/iLe/va3T7TOO9/5TtRqNRw9ehT//J//8wPv+0xQ2EgIIYSQK05vNaFhGCMrEYdR1X7p7R06dCizRWc6aFRUUCeEQBiGceWcjI/HlCZmw1k8kH8AJ8wTKEdlHPOOwRTdVVwbxgYedx7HSfMkBBO4170Xb8hlh41APLtxaWkJp0+fHitoLRQKWFhYSM5T13VMT0+PXO9i1Wg0EEVRUqHa284WiMNHxljyq1cYhknI3KtYLI4115EA+o03Qjt0KGmbKuv1OGRst7MrEIWA9DzA8yANAzyXAyuV0P7wh1F85zu7Fg3Td5NyfqDAUUYRkp9+Klxm+fzE2yKEnH9Pfmkd7Wr8nioigdATkEKCcQbd0sC14eGalMBD957Ei//R9efjcA/soU+cBAB4jQC7J5vwav2V+6Ebwa364EYTpQUHpaX9yu3Gpotnv7GJq180eOY1IYQQQgi5+Pz5n/85vvCFLyTtU0d1YTpXKGwkhBBCyBUnXU0IxMHZQcNGxhiKxWLf46Zp4vDhw6jVaqhWqwjDMAm10uvqug5d15MKOcMwIITACf8EIhlBMAGPefC4h6ecpwAAD+YfRDksw5QmIhahxVtoat1tTU+GJ/E9/3u41bx14LHn83kcOXIkOcasSsx8Po9yuYxc7uKu6JhUu91GFEXwPG9g2KoqXqWUyd+ZtEGVq4wxVCqVs33Ily3GGKzXvx6t//yfEW1sQPo+ZKs13spBAFGvw7jhBgTf+AairS1os7PJ013zIE0TaLfjn6uu982CHCj19yPd8lW/8cbx1ieEnDPq3+dhVh/ahd8MUd9oo7ntQYrUf9MaQ37GQnHegZkbfHlk7aHds3bM58rWk3W0dj1sPVnvOscsIhDYO9mE1wwwd00pqXLcfqpOYSMhhBBCyCVkdXUVH/zgBwEAP/VTP4U77rhw7f8pbCSEEELIFcmyLLQ7bRcZY7AsC57njVirm6ZpmJ+fH1jBpmkapqamUKlU0Gq1sL6+ngRUnHNomtZ3kZQxBnCgxrtbfB7xjuDb+W/D4x4iFmHH2Bl5fKeiU7gVg8NGYL9CcWpqCq1WK6n245zDcZyLfu7iQXmeB9d1AfRXuqqfiQqk1fO9bVMHhZQLCwsDKx5JNuvVr0brQx+KqxrHDRoVzhGdPAleLML75CeRe+tbk6eYZSW/1+bmEB0/Hj9umvvtVUdR/41qGninmle/6SboV1892XESQs6YaDbhfeYz8D79aYjTpyF9H8y2oV9/Pay774Z5111gqfdkvx3iu391PAkLpYirG6UEwAAeMTQ2XDQ2XDhTFmaPFTMrHb1G9nzfi0l9vT1W0JjW3vWx9XQds8eKYIzBbx2w3TQhhBBCCOnyrne9CzfccMOB1h13XqOUEr/xG7+BRqOBo0eP4pd/+ZcPtL+z5fK8ekQIIYQQMkK5XE7CRgBJqDYocOxtsckYw9TUVGZVYy8VZgohxgqhBPpbR3LJca17LR7KPTRyfcWV7tjLMsaQv0LaQgZBgFqt1jWjEUBf4Jhun5oVOGYFxYuLi1fM63g28Xwexg/8ALzPf37CFTlYLge0WhCbmwi+8Q0gFTam50Hy2VlEJ0/GrVkNIw4Rx2ghzDo/bz4zA3RCDPueeyY7TkLIGZFhiNYf/zG8T34ybqOcfs51ETz4IIIHHwSfnkbubW+D9apXwW+F+Ktfvx97p1qIwrh9ahRkvL9qDJrJIXc8rAcCCzeU+wJHzbj4ZxFvP9uYKGhUWtse2jMWchULuk3tvwkhhBBCzoYbbrgBL3jBC87pPv70T/8UX/nKVwAAb3vb2/D000/3LbOxsZH8vlar4ZFHHgEAzM7OYm5u7qweD4WNhBBCCLki5fN5aJrW1QpT13UwxhAEQfJ4byAFxKGSYRio1WpwXReVSgWlUil5PggCVKtVuK6bhFNqm+PM8dOQvcxR9+hEYaPDnLGXvZLs7u4iCILk55pVodjbPjXdUlU9np5fWS6XUSqVaE7jGZD1OphtQzabY4WA0DSwfB6Mc0gpEa6vQ691VwRbP/RD8D796fgPug4+OwuxsRH/7BxnvCpK0wQYA19YiDdz000wX/KSSU+PEHJA0vdRf//7ETzwwMhlxc4OGr/7uxCbm/jsI9dj4/EaWrsevMbgSmYRSYh2hNCNICKJradqmL+u3LVMYe7CzL0Z16kHd1LvaUAUCIhIxn9gLA5UDY5BHWcbGy5yFQuV5curZTohhBBCyOXsgdTn4/e9730jl//85z+Pz3du8P3FX/xFvP3tbz+rx0NhIyGEEEKuSIwxzMzMdN3lBcStTzVNQxiGSZtNtbz6X845DMNAFEVotVpotVqo1WqYmppCtVpFKyPA8H0fQRAk6w5rT8oZh83svspER0wWHl6tU5vHXkEQYH19HcDgNqhpUsokJFYVjbquJy1yS6USbNseOTOMjBY++iiYrgPFIuD7kL4fVyH24jwOADmHdF3IIIgvqFer8D79adT/zb+BfffdMG69FcaNN0I/dgzhU/G8U+2qqyDb7TjYNE1ACEh3SAWwpoHpOrRjx8AcB/rRoyj+H/9HfJyEkPOi8W//7VhBY9ruh/4YbvPvYWP3+sxqxixSAn4zQANAeTmAlTeS565/5XitrC6U7338BHIVE41ND5EfZd6vwRigmRp0i4Pz7vesdtWHpjMcewnNaySEEEIIIQdD35IJIYQQcsUqlUoIggC7u7tdj0sp4ft+V/WawhiDpmlwXbcrrHJdF5ubm7AsC7qud832SxNCwPM8RFEE0zQHhlTz2jyOh8e7943xA61rjWtxnXHd2MtfKU6ePJlZrTpKFEXgnIN3qugsy8L8/DyFjGeR2InnkDLOAdsGLAsIwzgQFCIOHqMo/tVo7Fc/MhYHkJwDQQD/i1+E/8UvQj96FPlf/EXYP/qjaPz2b8fLcg79hhsQPvEE5N4emG3HoWW7nVlNyRwH2rFj4LOzMF/8YuR/6ZfAc1T5Q8j5Ejz4IPwvf3ni9errbSyufgLfNg+BcxOazhCF49xgEgeOtbU25q6Nw0anYuLYiy/uEO6xz66iutYeGDQC8bmFXoTIj2DmdWh66nOKBKauKkA3qTqfEEIIIeRS8cEPfhAf/OAHhy5z8uRJvOpVrwIAvOENb8Dv/M7vnLPjufgHDxBCCCGEnEMzMzOYm5vrCo08zxsYRgkhEIZh36w/FWD5vo92u40gCIbuNwzDocvM8lnwno9qARu+zbS77bvHXvZKoX42Bwkbgf25nUIIlEolChrPMlYodP+ZMTDDiOckBkH8S4WN6Z+flPFjYQikKg7DZ55B7dd/HSyfh/0jP7K/POfQr78e2nXXgZXLYJYFVirFsx/V+oyBz8/DevnLkf8n/wSV//yfUfzVX6WgkZDzzP34xydeR4QCzW0PrN3Cih/PpNGt8UM0KYHaWhsijP/Nv/Wew93B3EXm1Hd3sPbQHmQkxzrPOFAN4zarHUxjWLpl6lweJiGEEEIIucxRZSMhhBBCrnjlchnFYhG1Wg17e3tdcxwBJC1PwzDMDJhEqtWjEAKapsH3fUgpYZomgLg9a2+4GATBwCpInelY1BaxGq0mj62Za2Odz2H9MH7Q/sGxlr2cqRBYzc3c29tLZi4CSOYwTkIIAdM0MT09fS4O+YqmTU8jKhQgG43kMRkE8VxFKfdDxSE/M9lsQuzugk/FF82l76PxwQ+i9MEPglkW2n/6p8myfGoKfGoqbsVar8dhJeeQhoH8T/0Ucj/903GrVULIBSG2txF8/esTrxe4EQI3fh8/4j+AZ63boBkchq0lj4/chhfCdyPc8Ip53PHjF29L8tCP8OkPPggp4n8XDVuDiOTI1rFSAl4zgF0ywRkwd20JuWn6944QQgghhBwchY2EEEIIIQA456hUKknFYTqQEkJ0zW9MywqrpJRgjCUzGtWMP855VzAJxIGjZVmZ2z6kHYIHD9vRNgDgcfvxkecxq83iPeX3wGRX7kXDMAxRq9VQrVaT4FhKCdd1+17/gwSOuVyOqhrPAT43B76wgKgTNsoo2g8agbiN6rCfFWMAY4iefBLsppvA8vl4O66L1oc/jNL73gfzB34A7sc/Dv/zn49nQgJgth3/siyYL3sZ7HvugX7NNef0XAkhowWPPAIZjRcOpolIIvLjf+vL0SZ06SJkNgw7rvobJ3CUAigvOnjtr93WN9/wYvLEF9fh1gJwnSeVmFZeh98OEXojAkcByEhi/pYK7JKJ3FT2ZxFCCCGEEELGQWEjIYQQQkhKs9nsm9U4rN3psLBRrasqI3Vdh98JOJQwDAfObmSM4Zh2DAYMPMIfwY6xM/TYrzOuw6+Xfh2z2uzQ5S5XUkpsbW2hWq1mPi+EQBRFXUGy+t9JAkdNo5lW54L1qlchfOopiGoVcmsL8Lz9cFFVNg7CGKBpYKYJGYYIH34YLJ+HjCIwxhA+/jiM5z8f9g//MAq/9EsQP/uzCL71LchqFWAMrFSC8fzng3cCSkLIhZeucp4E4yyp9AMAQ3oImR3/3tagGRyBF0H4Aln/qmgGh25yXPv3FqAZF2/7VAB46N4TAIDctIXGRjt53HR06KaMZzQGou+fT84ZdIvDLhmwSyZ0k+Poi+bO56ETQgghhJDLDIWNhBBCCCEpvS1UVUA1yKiQSq2vaRp0XUcURX3bi6IoCSR7McZwxDiCGxdvRAklfKr9KVTFfpimMx13WXfhbvtuPMd8zqjTu2xJKbG2toZWqzXw+fT/qt+nA8feZdPSzw+qRCVnxnzVq9D6yEegHT2KMIog9/b2nxwWNnaCRug6RKsFBAEkABZFYJzHYUKrhfq73gX3ox+F/YY3wP7RH4X1sped+5MihBwYM4yxl40CgaAdQUQCIpLxvwGd50TPZQ+uMVg5HdIBoiCCVAWALA4aOY+rpM38xX25ZG+1hY3v1wAAxQW7K2wE4vM0czqkBEQUB44McRjLtfjV8ZshQi/Cja9ehl0Y//UmhBBCCCGXhpWVFTz22GPnZV8X96dnQgghhJDzrDdoCsNw7GUHCcMQmqaBMQbLsvraeQ7bDmMMi4uLyOfz+En8JN6cezPWo3W0ZAsmMzHDZ1DghbGO43K2sbExMGgEBlcxpgPH3mUHbcekOX7nBM/nYb3mNXD/+q/B83nIXA7S8wbPaWQM4Dz+JWU8czHN9wHbTv4o63WInR20PvxhhI89hsI73gE2IOQnhFx4fG50pZ1bC1DfaKO963f92y6izg0mXIfP7Mx1GQN0M7tSPTdtIj+dvd7ForXjJb83HR12yYBb6+/EwBig6YMrNKNA4JZ7Dp+TYySEEEIIIVeOi7snCCGEEELIedbbInPSeX5Z0sEiYwy2bQ+sZEyzbRuHDh1CPtXaUWc6DumHcJ1xHY7oRyhoBOB5Hur1+tBlelvjpk3yMzZNE5zTR+hzJfczPwPj1lshWy0w0wQvFsGKxbhyUQWLnAO6Hv9SQWMG2RM+pme/+V/7Gpr/9/99Vv77JoScG/pzngNtfj7zOREJrD9axfqje2jteH3/LWs6A4TEM+J6tBv9bURHKc47mLu2dNBDPy+isHsm48yxIjRz8ven57zhMOYv8nMlhBBCCCEXP7qVlxBCCCEkJZ/Pd838G1V1OMnj6ecty4JpmgjDEJZlJRV3mqbBcRyUy2Vq15kipUS73UYQBBBCgHMOy7Jg2/bAGY29DMNAGIYTz2hUNE2DaZowJmjtRybDDAPFd78b/le+gnAnnlHKNA0wTcieeaf7Kw34761/SFnXH70vfAHmy14G80Uvgmg04P/d3yF89lnIdhvMcaAfOQLzla8EL1CgT8iFwBiD9drXovVf/kvX4yISWH+kCr81uPOA4eiIAh9P8udCRBJePYBVNAb+c5FmlQwces4UFm+qnOEZnFt2sfu9SDc1LNxQxvr3a4i8we3f0yoredz2I0fOxeERQgghhJArDIWNhBBCCCEp5XK5K7waJzjsDa5GrZNezrIsHD16lKrlBoiiCLVaDbVaDUHQ3x7ONE20Wq2kTe0wuq4nP69JA0f1szIMA47jTHweZHzMtmG++tWQUQSxvg5Zq/UHioyBmSagaZCD2uf2tsfNaH/b/q//Ff7Xvgb/C1/oCzM9AK0Pfxjmy14G501vgnaY2gwScr5Zr30t2v/9v0O29+cRbj1ZHxo0AoBmclSdw6hFi4CQEELCawR9AV0vw9Ewd23pkmgrOn2kgNyUidbu/r9dhqNj6eYKauttNDZdiED0r8gAu2SitOhg9uoipo/QDRWEEEIIIeTMUdhICCGEEJJimiYcx0G7c2FzVAjYG1plLT9sG8Vi8ZIPGqWUaDQaqNVq8DwPQohktmGpVDrwObqui7W1NUTR4AqNdrsNz/OSSsdh+1HH5Hle8udRgaNqv6rrOjRNQ7lcHjtMJgcTPv44wocfRnTiRDyvUdMAxwGiKJ6xqGlgnepSMWxOJ+dxK1U/nuXGdR3h44+DFQrgc3OQjQbaf/Zn0G+5BczOns0mfR/eZz4D/0tfQuFXfgXmnXeek3MmhGTj5TIKv/qraLzvfZBRBL8Zor03oMo5JTDLeGL5J2CdBLxmCBFJiEgiCgQ0I/t9wioamLuuhNmri7j+5Ytn+1TOOk3nuOk1h/DNP326+3GDY2olj8pyDq1dD249gIjim2x0kyM/a8Ow45bxN732ELh2aX8GIYQQQgghFwcKGwkhhBBCeszPz+PkyZOIogi6rsMf1L4R3VWMg0KoYW03y+XywQ/0IlCtVrGzs4MwDBFFURLeMcYghIDnedje3ka5XMb09PTYQZ3neTh16tTY1YdCCLiuC8dxhu7DsqzkWNVxAtmBcPo5y7Kg6zqKxeJYx0MmF3zve2j94R8ifPzxuIrJdZPnGAAJQHoemGUB6r+pjGpXAIAQSdAIAOAcMgiA3V3I3V1Ezz4bb9+2Ifb2oC0ODxak66LxW7+F4nvfC+O5zz3zkyWEDCSExLP3beLRz6yiueUi9AWmiv8A1zz63xDUB99goLSdOTx6/U/CtqZQihqonW5BhBKhLxD6PWEjA5yKheK8DbtkoDjv4J73PA+GfWlcKrn5dSv41p8/Ayn63ysZZ8jP2MjPZN9MwTWGm1+3cq4PkRBCCCGEXCEujU/QhBBCCCHnkWEYWF5exurqahI4huHglm2c86Q1Z2/QxTkfWG03OzsLM6O146Via2sL29vbCMNw4Ouj6zp0Xcfu7i5838fi4uLIwFEIgdXV1YnnKkopk8BxmFwuh1ar1VcxmXVcuq7DNE1wzrG0tARN0yY6JjKe9kc/iubv/z5kEIBpGlguB1YsQtbryTLMsiCbTUjXhYwisFyufy4jEFdDSgmI/faBqoWqDAJASshmM17G9+Pfj0GGIRof/CAqf/RHceBJCDmrpJT4zseexXf/6gQaW27Xc7tYxonST8P8/t/hqHwIOTMA17r/zW7bs9iYfyE25u6A0OL/RqePFKAZHNXVFjQjfq+evaYIxhm4xqDbGnQz/nd98aYKXvOrz0V++tL577swa+POtxzDff/lyYnXvfMt11xS50oIIYQQQi5uFDYSQgghhGSwLAsrKyvY2dmBlHJgmKZaczLG4HfaNaYNChNnZmZQqVTO9mGfN9vb21hfX0/mKA4KEFUQaRgGpJTY3NzE/Pz80G3X6/WhrVPTeoNcIQSiKBoaCjLGkMvl4LouwjBMguL08yok5ZxD0zQsLy/DooDprJJhCP/LX0bzD/8Q/qc+1R0cdgJHKQRY52fMDAOwbUjXBYIge1ZjFMUho576mqPr8X+X9Xr8nBDxcp3lo2eeAS+XwWdnRx6zqNfhfeELsH/oh87k1AkhPaJQ4LP/1/fw5JfWBy7TRAlP2D+IR+VLsBQ9gcVpD44lEGo2moUV1ErHMtcrL+dQmLfR3HRR33TBDQ6nFL836ybHNS9dwC13H8bCDZdmp4Hn//gxuLUAD/7l8bHXue0fHMEd/8vV5/CoCCGEEELIlYbCRkIIIYSQAQzDwMLCAmZnZ7G5uYmtra0kTFQhlKZpXe02gyBIgknLsvpCL8dxUKlUkM/nz+/JnCWtVgt7e3tYX1/vC1YZYwOrOFUoWavVUC6XhwZ3tVpt7ONhjEHTtK5wMgzDkRWIjDE4joMoivoqUtXvTdNEuVy+LOZqXmzC48fReO97Ea6vI3zwwf4KxSiKqxp9Pw4cczkASGYrqsCxi6pm1DRA/Tw1DQjD+JeSqngEAAQBggcfhHb0KLSjR5NwcxDv4x+nsJGQs+zzv//I0KARQNIqVDAdp/QbsVplWLihDKswuFW5oukcpaUciosOXv72m7F0yxR0k8MumzCsS79i/SU/dwMqK3l860+f7qsKTSvM2Xj+/3o1bn4ttU8lhBBCCCFnF4WNhBBCCCEjaJqGxcVFlMtlrK+vD6xyVLP9bNtGLpdL5hYyxmAYBkql0iXbNjUIAqytrcH3fbium9niVEqZhHdZ4VwQBOCco1qtDqxu9DwPnudNdGy6rveFjaradBRVtWiaJjzPS35euq7DtrPnXJEzEz79NGq/9mtxS9RqFRjy82a2Del5EM0meCegZ7YdVy76ftIWFcB+0Mg5mGFAMrY/s1GRsj/Y5BwIQ4j1dUAIaNdcM/TvTvjkk4hOnYJ26NCBzp8Q0u34N7fw2GdXRy7X2zZVRhLbzzSwfOvU2PtijKG8lMPUyqV5w88wt7xuBTe95hCevW8TD3/yJHaebSJohzAcHdNH8rj59Ss4cuccOB9vdjIhhBBCCCGToLCREEIIIWRMjuPgyJEjaDabqFaraLfbXc9bloVyuYxCoXBZVcL5vo9Tp04hiiJEUZTZLhbYrwiUUkIIMTBwrNfrmJmZyaw+DHqr1cagaRo45xCpirXe1qiDmKYJx3GSgJGcW6LRQP29703mJIqNjZHrMMuKw0XTjAPFMATT9bg9ahiCCQHk85C1WlyVaBhxoNhojD6g9N9Rz4Pc2YFwnJFBotjdpbCRkLPkex8/MdZyuqWBaQwy2n//CVoh3HoAuzi6uhGIA8vKZRg0KpwzXH3XPK6+a3i7ckIIIYQQQs42uqJCCCGEEDIBxhgKhQL+/+zdd5hkV3kn/u85N1Tu6ljdMz1Ro5nRzEijgBICIRCSkEYCA8aAl7W9BmP/sL3rxaQleEWyMayNw9reXbNgjL0GG7AxMCMhgSQUUEJCEpIm5+lUHSvXDeec3x+ncuiuDhM0ej/PM2j61r237q3Uw/3W+77RaLQSvimlYBjGeRlWCSEwOjoK13UrLWJbBY0AKssZY1BKtQz8pJTwfR+O4yBcao3Zah+LwRhDIBBoW3HZTrmqsZNQkqwM5957IaenKz+rVKqj7VipHaq5c6ee1ViqLjYMA8bwMPyjRyFrgmqVzzdXMLZSEzYqzwMDICcmwFetmr+damMrVkLIkmSSBZz46VRH6zLOEO0LIpOs/6JPNlnoOGzccM0AIr2nd/6u8CXyMw7cvA87bCLcG4Bhnj9fQCKEEEIIIaSV8++KGCGEEELIGVKe2Xg+S6fTyOfzlYpD2UHIUg78hBB11Y3l2Yi+79e1Pa211IpQzjmCwWDHgaNlWVi9evV5GRCfq5RScO66q/qzEJ0FgmVSQs3MgA8N1S0OvOENEF/9at39qE4qZGtnO+oNoQAw34eamQHr72+7KYvFOj9uQkhbx386taiPgdhgc9iYn3PbrN3s4tvXdn5ni5Qay+OFPaew/0ejKGaqn0GBqImtr1+NHbvWont185dsCCGEEEIIOR/Q1RVCCCGEENKSUgrJZHJJrU3LyjMQy/sDqqFjK4FAoFIZuVicc4RCocp+3MZ5fTh/W92+FPjPPQcxNlZdwJgO+FwXKFUIM8Z0taFtt6wsFMlkU9jIe3oQfsc7kPn85/X8R9dtH2Iypv9wXt9CtYFMJsHbhI18YADG+vULnzA5o1x1Eil5Fxx1ABJ5MARgsSF0sVsQYpdRBfM5ysks7veLFTIR7gsgP12d9aqEgpIKbIFZhKsv6cHwzt4lHed8hCfxwP98EQcfGKt89CipIIUCNxicrI/n/v0Efv7dE9h0/RBe93vbYdrn9xeVCCGEEELIyw+FjYQQQgghpKXZ2VkUi8UV368QAul0GpFIpCkAME0TkUgE2U7m7bXAGENvby96e3tRLBYr8yXLrVYDgdPbPo+0J05W57KpQgFydBQyk6lrSVqJCItFKMsCCwZ1C9Wa5VCqriKR9/aCBQKwtm+HGB+HOHas/UGYpp7p2CpAZwzlvSrHab69JHjrrfO3WCVnVEHtxYz4BxTUz5tuc9QhZPEwLLYaPfwX0cVvOQtHSOa1hAy4f2MME66sBpVs4f30ro/iDR+7dPF3tgDfFdh9588w+vwspJDITjrIJAvwi9XqfcPmiA4EERsI4tCD48hOFnHHZ66AFaDAkRBCCCGEnD8obCSEEEIIOYcppVAoFJBOp+F5HpRSlZah8XgcltXZnKqlmJuba1q21KrDRvl8HlNTUxgYGGi6LR6PLytsjMfjAIBgMIhgMLis4yQrR5WCazk3B3H4MCAEmGnqysZWPE+3Qw2HwWy7ulxK3QIVAAuFYF99NWQuBxYIwFi7FvB9iJERoDRftKlaUinIVrMia99LbdoFM9NE4BYKrM4VGfkgkuLPoODPu56nRpEU/xOOOoJ+/ltU5XgOCcXthVdqwDjD4NY4po5mkJ92wA0+73O69vI+3PyRSxCIrPzvy/v//AWMPj+L1EgOqbEClGz+/ShcidRIHqnRPKL9QSipcN8Xn8cbPrry4SchhBBCCCFnC4WNhBBCCCHnqFQqhbm5uZZtTIvFIubm5hAOh9Hb27vioZrv+6elqhFAZU5iKpVCKBRCNBqtuz0UCiESiSCXyy16393d3ef9HM2XKhYKQaXTEAcPVtucBgK67ek8VD6vqw4tq9oCtSTwuteBhUIwQiFYV18N97HHANPU4aRtty52YkwfS6F+7hurrXpt8xoKv/vd4N3dHZwtOd3y8mlMiC8CaD3/tZWU3A2OEPqMXzt9B0YWZeO1CTz8v/dB+Iv7EgvjDAObuuCt9hEdCEIKBa9QfS2YNsem64dw8e1rkNgcX+nDBgBMHc3g0EMTmDqSQW6qg9+XCshOFuGVqh6Th9JIXNh1Wo6NEEIIIYSQM43CRkIIIYSQc4xSCpOTk0in0wuum8/nUSgUMDQ0hEgksmLH0K6ykHMO2abqq1O1rUzn5uaawkYAGBwcxNjYGAoNgdB8urq60NfXt6xjI6cPHx6Gf/hw3TxFZhhQpgn481emqXwerKsLCIUqLVQZ5wjcfntlneDtt8N97DGwDt4HLBDQ8yLLgbpp1rVrZS1ek6H/8B8QfOMbF9w3Of2U8jEh/wKLCRrLZuW3EOXXI8AuWPkDI4sWitu44FWDOPjj8SVtb4VM/MIfX4WuwSDSE0V4eR9WyEAsEYIdPr2XO17YcxJzp3KdBY01nIyHqcMZvLDnJBL/ZcdpOjpCCCGEEELOLBo2QgghhBByjpmenu4oaCxTSmF8fHxRwdxChBDgbebStVveCcuy6rYvFotwWszH45xj9erV6OrqWrDlYXlOYyKRWPJxkdNPzc7qmYkNWDhcV63YemMF5bowap7j8G/9Fsx16yo/W5ddhuCuXWA9PYC9cGtGFgzqYNI09THU4DXtfY21axH90IcQ/uVfXnCfBPAPHEDhn/8ZuS9/GfmvfhWFf/93yOnpFb2PnHoMQs0sefuU3LOCR0OW6+I71i5529UX96BvfRRW0ETf+iiGtnWjb0PstAeNbt7HvntGkR7LL2n7/KyD5793Ek6uxfxYQgghhBBCXoKospEQQggh5BxSbo+6WEopTExMYP369Ssyj0wpBcMwWs5oZIwtaXYj57yuqrEsm822XM4YQyKRQG9vL9LpNNLpNPyaCjjbthGPxxGLxZYVgJIzo7h7N3giAXn8eN1yxjkQiUDlcm1nJQKAcl3w/n6AMUTe+14Ed+1qWif8W78F5XnIj45CjowseEwsFIJ16aWAlBDJJFShABYMwrz4Ypjr1yN4222wdu5c/Mm+zCjfh/vAAyju2QP/4MGm2wt/93ewrrkGwTe+EdbFFy/7/lJy97K2z8gfo4//Ogy2ctXgZOmGLurGK955AZ76xpFFbRfqtnHj+89OZeDcSA6zp3JYzgjj2ZEc5k7lMbj19LR5JYQQQggh5EyisJEQQggh5BySSqWWvK3v+8jn8yvSTpVzDsYYTNNsmhnJGKu0U10ocCwHk5xzBIPBlqGgv0ALTdM00dvbi97e3sp9lo+PvDSIqSn4+/eD9/dDTkwADfNAmWEA0SjgulCu2zJ0ZJYFc/t2hN/zHljbtrW8H8Y5ov/lv8BYswbpj38caqZN9Rtj4L294MPDYKV5p2ZXF8AYYv/tv8G+7rrlnfDLiMzlkP3c5+A9+2zbdZQQcH/yE7g/+QlC73wnwu9619LvTxVQUM8veXsAUCiioJ5DlL1yWfshK+fqd22C9CV+9q1jHa0f7Q9i1ycvRywROr0H1oaT85FJLm+ucWHGQXqCwkZCCCGEEHJ+oLCREEIIIeQcIYRoOyuxU6lUakXCxnA4jJmZGZimCd/3W1Y3cs4hxPwz0wzDqFQ0tqs+XEyFJFUwvjSpUrUuMwyYW7bA37sXqAmxFUoVjsEgEAjoGY6+X5nvyOJxGNu3I/SOd7QNGmuF3vpW2Fddhbn/7/+DOHasGmCaJngspiskLatpu/B73kNB4yIo10XmU5/Sz2eHCt/4BgAsOXAUWN5nZJlcof2QlXPtr23G4NY4nv3OcYy9MNdynUDExNbXr8Zlv7gBkd7mivgzxcv7EO7iZ4bWUgrLDiwJIYQQQgg5V1DYSAghhBByjsjn84tuTdpuH8ut+gsGgwgEAnAcB4FAAMVi8wXRhdqp2rYN27ZhGMa897XQ7eQ8UPMaYcEgjK1bIV54ATKd1iGgUlCMAZyDBQKAZYFZlq5AHBwEX7tWv6YX8f4w1q5F95e/jOwXvtCytWctHosh/Ju/icBrX7vUM3xZyn/1q4sKGssK3/gGrO3bYV1++aK3ZVipimaqjD4Xbbw2gY3XJjB9PIv9PxpFdrII4UkEIiZW7ejBhTcMwQqc/d8ZdkR/Pi2rjyoDrODZPxdCCCGEEEJWAoWNhBBCCCHnCDnPvLrF7mclArx4PI5kMgnDMBAMBuE4TssKx0bl9quhUGft7cLh8LKPlZzbWCxW+bsYHYUcH4eSUrcwdV0oz9MX7YWAyucBwwBLJGBu2aLDxxb76YQxNIT4F78Ib+9eOLt3w33kEaiatr3m5s0I3n477OuvB7Pt5Z/oS5wQAqlUCrlcrvJ5ZBgGIpEIurq66j5XVKEA595767ZXQkDNzEA5jg6RDQMsEgGLx5s+K4rf/e6SwkaOGAAOYHmflwaodeW5rG99FNe9e8vZPoy2ov0BhHts5GecJe8j1G0jOhBcwaMihBBCCCHk7KGwkRBCCCGEtBSLxZDJZFAoFGAYBkKhEHzfh+d5ldCxtrKxXOnIGIPdYXBjmiaFjS8DxtAQ+Jo1cB98EGpqCkApqLYs/adU3QildLUQ50ChADEyAmPjRt22t7sb5ubNS7p/a9s2WNu2Qf3e70FlMlC+DxaJgK9Ay+Hzge/7mJqaQi6Xa/pCged5KBaLmJmZQSwWQ19fHwzDgHPffVClimdVKEBOTEBOTwOtWisHAuADA+CJBJip/y+o+9RTEBMTMAYHF3WsnAUQ4VchJx9f2skC4CyGELt0ydsTEu4NIL46vKywMZYIoWvw7MycJIQQQgghZKVR2EgIIYQQco5YiWrE8izFlcAYw9DQEMbGxlAsFsEYg2VZME0TUspKKOG6bl1AEQgEOj6XeIuKJ3J+4rZdCRobsXbzPKemIE0Txrp1CNxySyWoWipmWWC9vcvax/nGdV2Mjo7Cr6n4bEUphXQ6jUKhgOHhYTj33w8AkDMzEEeO6MC4HceBPHUKcmpKV6sGg4BScB94AKF3vGPRxxxnu5DD0sPGLnYTOKNKVrJ0dsjExbevxfSxLNyst/AGjdtHTKx7RR/6NiyuWpuQsymfzyObzUIIAaVUpfI9EonQv+UIIYQQQmEjIYQQQsi5IhwOg3O+rHaqK33BxzAMrF69GtPT00in05V5kLVhIue8EkZ2MqOxLBAIIB6nVoYvB2J8HP6hQ4BhtK58m4ecmIAxNITAbbedpqN7+fJ9v6OgsZbneRgdHUV0agpydhbi8OHO59YVi/D37YO5fTuYbUPOzCzpuEPsclhsGJ4aWcLWHHG+a0n3S86sYtZDYc6F9CUCUQvR/nOr5eiOXWuw9wcjGN87B9/p/HPNCBgY2NyFHbvWnsajI2RlKKWQSqWQSqXgec3BeiaTgWma6OrqQnd394p94Y0QQgghLz0UNhJCCCGEnCM454jFYkilUkvex+kI7zjnGBgYQG9vLzKZDDKZDHzfr3yrPRQKIRgMYmpqquOg1LZtrFq1ii5KvUw4d90FcA5jwwZdBddpOAUASsHYsgVGf//pO8DTSObzcO+7D94zz0DlcoBhgPf2wn7ta2FdfvlZrQaZmZlZVNBY5rounLk5sMUEjdWNIQ4dgrl9u57VuQSMMQwZH8Yp/yNQKC5q2wHjfbDY0JLul5x+SimcfHoaz+8+iRM/nap7eXWviWDHbWuw9fWrEIhYZ+8gS4Yu6sbwpb1QUJg8mIabW/i9ZIVNJLZ0Ib4qjM030OuQnNuklJiYmEAul5t3Pd/3MTMzg1wuh1WrVsFcZhcCQgghhLw00b8ACCGEEELOIfF4fMlho23bCIVO3/wnwzDQ3d2N7u7ulreXA8d8Pt92H4yxurlv5PynXBfOvfcCAHhfHyAExPHjUL4P1AZdnAOm2RS+8eFhqJkZKM8Ds+YPGJQQkLOzQLEIBIOQ6TTE/v065LMsGAMDsK66asH9rAQ5M4PC178O54EHKrMNazn33w9j9WoE77gDgTvuOOOhoxACmUxm6dsnkzCXWIWtslnIbBY8Gq1fXiwCjIEFAgvuI8AuwGrzUxgTn4VUnZwHQ7/xHsT5rUs6ZnL6zZ7M4u4/fBZzI61/h8ydyuGRL+3H4187hOvesxk7bjv7lYFv+OhO/OsHn4BhcRTmXGSSRRTTLtCQwQe7LMQSIYR6bNhhE7vuvBymTb8DyblLKdVR0FjLcRyMjY1heHiYvkxGCCGEvAxR2EgIIYQQcg6xbRt9fX2Ynp5e1HaccwwODp6mo+qMbdtYvXo1PM9DKpVCPp+HEKLSdjUajaKrq4tCxpcZceIEZCnUUlLqVqqWBZXJ1IeNgA4cbRuwbfBoFHz1avDeXh0anjgBc9Om1vcxOQnnrrvg3HMP5Nwc5Ows5MQEVKEA3t8PnkjoOYEAeFcXAjffjMCuXTASidNzzidPIn3nnZCTk/OvNzqK3N/+LbwXXkD0Ax84IyFoWSaTqZu1uihCQOXzkEqBLzEklckkzJ074T33HIq7d8N78slKpSMLBmG/6lUI3n47zM2b2+4jxLZjrfGnmJXfRkb+uE2VI0OYXYFu/laE+c4lHStZeYWUi0MPjSM75UD6Em7Ww4t3j4BbCwcUviPw4N/sQzHt4RXvuOAMHG174Z4A3vz5q7DnUz/D9LEswj0BeEUBr+BDCgVuMFhBA1ZIX3qJ9Aaw687L0L+RZjWSc1s6nV5U0FjmOA6mp6cxMDBwGo6KEEIIIecyChsJIYQQQs4xPT09UEphpsN5ZpxzrF69GoEOqoHOBMuy0P8SbXlJVp4qVboq34c4eFCHjAB4NAolBJTj6NBRSkApKN8HCwTA164Fr2kLrLLZ5n37PvL/5//AueceHWQKAf/gQah0urKOHB+HnJgA6+mBsXEjZDqNwre/jeL3vofohz4E+9prV/R8xdQU0p/4xKLmEbqPPIKcYSDywQ+esQrH+SqQF8L27QPCYd1CdYnHq1Ip5L70JcjR0ebbikU4P/oRnB/9COaWLYj+/u/DGB5uuR+LrULC+F308V9HRt0HRx2ERA4MQVgYQhe/mdqmnkMmDqTw8++ewJFHJiB8HXYLT2LshVkIV8IKmYglgogMBMH5/K+tJ/7xMGKDIWx57aozcehtRfuDeOufXo1DD03ghd0nkTyYhhWs/1JN93AY229bg4tuWn1OtIAlZCHLaemfyWTQ19dH1Y2EEELIywyFjYQQQggh56De3l4EAgHMzc2hUCi0XKfckrSnpwfWGayIImRRTBNKCIh9+6DyeSilwBiDEgJwXcDzqnP/lAJ8HyqTgf/zn8Pcvh28t1ffZtt1u1W+j8xnPwvvqaf0Ainh79unW6Y2UgpqZgbCdWFs3QpmGFCui8wf/RFiH/0o7Fe+csVON/+//teigsYy58EHYV1zDQKvec2KHct8hBBL3pbNzgLBIBCNAksILZXv69mNx48vWM3pHziA9Ic+hNhnPtO2shUADBZBN3vjoo+FrJxTz07jxbtOYepIBm5BwAoY6F4TxrY3rMGGawbw/PdP4if/d3/TmM/MRAHC1S15vYKPmeNZZKcdJDZ3wbA4imkX2cki3IKAEgrMYLDDJqIDQTz5j4ex+Yahszr7FABM28BFr1+Ni16/GslDaUwfzcDN+zADBrqHw1h9cc9ZP0ZCOlUoFOC67pK3l1Iik8mcljnihBBCCDl3UdhICCGEEHKOikQiiEQicF0X6XQanudBKQXOOYLBIGKxGLUkJec+zuE98wzU7KyuXkSpnWq5Kq5V5YOUUPk8vKefhnnZZTD6+/W8xxq5v/7ratAIQBw71jporKGyWYjDh2Fu2VJaoJD9kz9B/K//GsbQ8qvfRDIJ98knl7y9s3v3GQsbFxt8sH37wJ95BiybBTtyBGx0FIjHAccBFhFcKiGgikU9r7HDmY8yk0HmU59C1xe/CIOqps85hx4ex5P/eLhp1mIBQHqigBNPTcPN+3AyHmKD9XOFlVLITDa3v3WzHk49OwPT5vCLza8vL+8jN1XEzPEsHv3KAVz3nq0rek7LkbiwC4kLu872YRCyZNkWnQQaKaXg+z5kzec45xxmafZyNpulsJEQQgh5maGwkRBCCCHkHGfbNrUlJS85MpdD7q//Gu7990OlUtVgSYj6kElKHTi2CR3Fiy/CevOb6+YripEROD/6UXU9z4PscM6pmpuDzGTAY3pmmnJdOHv2IPzudy/6HBs5d92FprKtRfBefBH+sWMwN2xY9rEspKMvKggB/uCDMB54AHxsrLp8bg5sakqHxZalZy2WLjAvyPfBIxH9d7Pz/zsqZ2dR/OY3EXnf+zrehpx+T3/rKB7/+0PzruNkPYzvnQMU4BUEejdEK7flZ11Irzl09ooCXlHAtDnscPvXiZf3ce//+DmCcRtXvG3jks+DEFI1X+W7lBKe58FvnLlc4rouTNOkL8MRQgghL0MUNhJCCCGEEEKWTXkeVDoN5bpQvo/sH/8xxIkTEGNjYIGAbp0pZXM1m1LVyrgWgaNyXfjHjkGMjFTm9hUbQj2RTC4q5JPJZCVsBADnhz9E6D/+R7CGVq2L5T766LK2L+/jTISN0WgUudpKUN8Hf/pp8J/+FCyd1j/v3QsUi0BPT/1zE4lAMQbOmH7uONftb+cLHBkD6+oC5ub03yMRsEVejHbuvx/hX/91sGBw8SdMVtyLPzi1YNAIAJlkEVDlvxfATYbuNTpwdnPNgYXv6KARAHxXwgoCbJ7Rb27Ox+N/fwiBqIUdt65Z/IkQQuqoNr9Pfd+H4zgLbu/7PrLZLHK5HCLlL5cQQggh5LxHYSMhhBBCCCFkyfzDh1HcvRvugw9COQ6kEBAvvgglBIzBQcjJSTDLgioFUm3NFzj6Porf/S4i73ufrkT84Q/rbpdTU4s6ZjU7C+V5lXmBMpOB+/jjCFx//aL207TfdHpZ26/UPjoRjUYxNTUFUSjAuOsuGA89pENGAJAS7NgxsFIrPTU2BvT0QA0NAYYBWBZYPA6WyQCADg0NAwiHwcJhfQ6+X6l8NPr7wQcGIMbHdZUrAD44uOhjVoUCnPvuQ3DXrpV5EMiSOTkPP/m/BxZcT/gS+Zn6cCI1lkekPwgraECJ+lBDSQWvUF9V5bsCVrB9MC1L+3jkb/fjgusSCHUt70sDhLzctapK7DRoLGOMYXx8HKtXr0YoFFp4A0IIIYS85FHYSAghhBBCCFk0OTuL7Be+AO/55wEAKp+HnJiAGB2tzE70k0kdIgaDgGXp+X7z7lTWh42lCjh4XqWqTSSTzbMZXVcfAwB4XrXKkbHW7T2lhCoUKmEjoKsdX04YY+gyTWT/7M/ADx+uv21yshI0AgCTEpieBrJZqI0bAduGMTgIlMLGinwerK8P5gUXtLxPVd6naYL19i7puP29ewEKG8+6/T8aq1Qfzic/7UDJhioppSsce9dFwRoyDd+VaKypEq6cN2zkXL+/hSex954RaqdKyDJFIhGka774opSCW/o9245s6FpgGAaklBgfH8eGDRsWPSeYEEIIIS898zQjIYQQQgghhJBmYmoK6Q9+EN7zz+s2p/v2wX/+ecjJSahisX5lKYFcTgdTSrX+U1b7M+dg0aiumiuFg86DD0Ll8037V0JAFYu6jWsuB5XP6z+5HFQ6DZnPQzXOoGr4WS1wIbUTrKvrnNhHJ5Tnwfzrv4Z57FjDDQqYmWm5DXMcsKNHYQCwenvB+/qa1pHJZNsWfOXnwFi3DqzVjM5OjrsxaCZnxQt7Tna0nt9iHiMA5KaKUFLVhYhKKfhu8/rtXk9lZs0+9t49suD6hJD5hcNhmDUzdX3fb/m+UkpBSgkhBJRSdX+EECgUCigUCpibmzuDR08IIYSQs4XCRkIIIYQQQkjHVLGIzJ136gpDx4G/d2+l9afy/foQT8rWsxQbKxwaAkcWDILFYpWZfuX/ypERsECgblM5OwuVyeiQs3EeZHnfrqvXKRSqVVMNbeL4CsyVsl/5ynNiH50ofu978F94AcFAALz2sUinwTyv7XbcdWGUqkCNjRvBurvrV3CcSqvURoxz8LVrwfv7l37gy5yrSZYvkyxgbiS/8IpA21mq0ldwsh7CvUEwQ38eKKGaqyDb76IiNlCd4ZmeKGDmBAXShCwHYwzxeLzys9+iBXo5aGwVQparGJVS8DwPIyMjKBQKp++ACSGEEHJOoLCREEIIIYQQ0jHnvvsgTpyAEgL+gQP1rVFrL0hK2VQ9WKGUDhxr/wC6happ6rCxJpBk0ajerFgETyTAShUXcmoK4siRpuCwHeU4uuIRaAotzW3bOtrHfAK33dYcpC6CuW0bzA0bln0cC1FKwbnrLv0DYwgGg7BsG4xzsHYVKIzpsJBzqOlp/dxyDnPzZvBVq+qeA9miMtIYHIR9000wVq1a1rHzgYFlbU+Wz8m2D6MbcaP9JQcpFLjBEO3TYWG7UHG+txS3OMK99e9lJ9P58RFCWovH4wgGg5BSNrVILQeN7fCGynUhBE6dOoViY+cDQgghhJxXKGwkhBBCCCGEdKwcUsnJSaCxUqGcFihVDRrbJQWtkoUWASWLxcDCYf33cBg8EoF93XVQuRzE0aO6ErIhOJyX6+oWrTXbmJs3w9y8ufN9tGEkErCvvHLJ2wfvuGPZx9AJ76mnIMbH65ZZloVQKAQDuiql9g/nHAbn4OXnUkr9/AMAYzDWroV12WUwNmwAi0TAADDLAo/HYV91FWJ33on4l76EyHvfu+xjD9x887L3QZaH8c4D9WCXteA6saGQrm5ss1tutr9s0TUUajoebtBsOEKWi3OOVatWwbKa38OdBI3lQLL8x3EcjI6OUptjQggh5DxmLrwKIYQQQghZKiEE0uk0HMeBlBKMMViWha6uLtjUDpC8xHgvvgj/2DF9EbHUSrNOOYxqV9HYiYaLmDyRqPzd2LQJABC4/Xbk/+7vKoElsywozlu3UW1FCCgpK3MDg7t2Lf14G4R/+7fhf+ADLav75mNffz3s669fseOYj/f4421v44B+LBcgRkZ0lagQ+nEMBMD7+2EmErB27kTXH/5h0zbWK14BY2ioKejslHXxxTDXrVvStmTlhHsCYGzh9qYAEIhasCMm3FxzG0bD1q8zK2hgYFMXxvfNtdyHGWj9eoz0BxFfFW5aHuqm362ErATDMDAwMIBCoVCZ29guaCx/OaXdOr7vI5PJ4NixY0gkEoisQOtyQgghhJxbqLKREEIIIeQ0cF0XExMTOHbsGKanp5HNZpHP55HL5TA3N4cTJ05gZGQEuRzNliIvHf7BgwAAlckArdqhlROIxhRiMa1FlYIqhZUsGgXr6QEA8J4e2NdeCwAwhof1fMjauwiFOtt/qd2nmp0FAJhbtsC+4YbOj2+h3ff3I/aZzyxqLqF93XWIvv/9da1jTydZmrHZktn++6hKSqhCATKVgpybg5yagpqdhZyehhwdhf/zn8Pfv78yw7MRYwzBX/iFJR938M1vXvK2pHNOzkMmWUBuxoHwmkODUNzG8KW9He8vlmh+b5pBA4FItWIq1G1jaFs3uNlcpWi0qGyMDYbQf0GsafnAhV0tA0hCyNKYpgnbthEKhRBo6CJQDhgNwwBjrO0Mx1rZbBajo6OYmpqiKkdCCCHkPEOVjYQQQgghK6xQKGBsbGzeNlPl9QqFAnp7e9Hb2/mFW0LOFpXP6/9ms61XsG2gXYDeaSkU57rVaV8fjC1bKtWHgVtuqcxqdO6/H8YFF8Dfu7cSejLLAkIhqMbWrg37ZpGIvig6OQnr8ssR++//XW+7gsx16xD/kz9B4etfh/PjH0O1mVNlDA0hcMcdCL7pTWcsaKwl5+Z0cFyav8gCAbBoFKrF3EYlBFQuN3/1qFJQqRTcn/4UhW9+E6Ff+qWmVYJ33AH/4EE49923qGMNvf3tsK+5ZlHbkM75rsDBH4/jxbtOIXmwGhYbFsemVw9ix641GLqou7L84tvX4tQznVXvRvoCyE4V62YpRqNKV7hKAXADLBRCKB7H0LZuTB5KQzgSUipYoeosUFaa7xhNBGGHW1/K2LFrzSLPnBAyn0AgAMMwIIQAL83tbbTQDMfyOkopCCEghEA+n0cymUQwGIRt2zBNE4ZhIBgMIhqNnpXfiYQQQghZHgobCSGEEEJWULFYXPRMmpmZGXDO0d3dffoOjJAVUJlz2KZNKmMMarkXCBkDbBvmtm1gpSpE3ttbN89Qjo2BWRbMbdsgDhzQIVj5+DjXgWPjhU/D0EEj5wBjYKEQrFe8AoV/+ie9bTwO+7rrYG7YsLzjL+F9fYj87u8i9Ou/Dve+++D97Gf6OE1TV2m+7nWwrrjijF9Qlbkc/CNH4D37LOA4TbcrQM/itO3K46+k1AFz7edau+M2TbDeXuS/9jXAthFqUckY+b3fA7NtFO++u6NjDr/rXQi9850drftyJ6XCsccncfTRJAopF4Cem3jBKxPYcM0AuNEcFBy4fwyPfGk/ijVhYJnwJA7cP4YD948hsaULt3xkJ2KJENZfPYCuwRDSE/OE+yWMMwxs7kJy3xyc2TyY5yCoZiBY/e9JFggg3NePQMiGDBqIrw7DDptQSlc4BqLWvPMYgzELF75maMHjIYR0jjGGrq4uzM7Otv237Xz/5m11Wzl4LBaLcF23MhvYtm0YhoGpqSnEYjF0d3fDnKfanhBCCCHnFvqtTQghhBCyQpRSmJiYWFJbqKmpKYTDYZrjSM5pxqpV+i/zzfQzDMBvns8GoD6gatVq1bLAYjHweLwaNEajiN15J3hNGK9KIRmzLBjbt+tWnpOTUOk0mGXpGY6eV13PtvW+LQssFILyfciZGRT/7d/qDqHw9a/D3LYNwTvugH399SsSBPJIBME3vhHBN75x2ftaLpFMInPnnfD3728ZNAIAQ+lCcCYDhMNgtq1D0trnSykw04Sq+RnQF6V5f3+lGjX/5S/DuvzypjmLjHNEfud3YF9/PYp79sB77LFK69zKOrYN+zWvQXDXLpibN6/E6Z/XhCfxzL8dw4t3jSA71VxJe/CBcUT6Ath+6xpc9tb1MG39/nr2O8fxky8f6Og+kgfS+NcPPoFf+NyV6B6O4A0f24nvfOSn8IoLz2jlnoNe/yRS0kAk5IKxFgGE4wCjI1gb5nCGtiDQ3/lMN8NkuOW/7YQVMBZe+SxQSlGlFnnJisfjbcPGcnC40L99a29v/Hu5BWuxWEQwGAQAzM3NIZPJYPXq1U3tWwkhhBBybqKwkRBCCCFkheTzeXhec2VIp1KpFAYGBlbwiAhZWdZVV4F3d0NOTbVfyTD0nzbVj+WZiZBSzwdUSgeNhqFnNDJWmRtorFmD2Mc/DmNNfWtEFqmGEIwxsN5e8N5eqGJRVzUKATAG5fu62lIIMMOAnJmpzhMsXdBs5O/di+zevQg8+aSuwCsdiyoWdaBZLIKFQuADA9VKz5cAOTuLzEc/CpFMgnV16fNv094VwSDg+7ptrhD6j5T6T+kiscrngWxWP3elalFlWWBKQbmuDniVgrN7N8z3va/l3Vg7d8LauRNyZgbuU0/pgJMx8Hgc1tVXg0ejp+vhOK8Usx7u/swzGHtxbt71ctMOnvx/h3HiqSns+u+X49Sz03j0K50FjWX5WRe77/wZfvHPrkH/BV244zNX4K5PP9OyKrJMFYvw9+6FJV28aechJKJpHJ5K4OjUAPKeDSkZbFMgEUtha2ICA7EMTuaP4nH/jfBgIzdVhFcQkEKBGwxWyES0PwBemuVoBgzc/OFLMLzz3GlHnp4o4IW7TuHgA2PIz7qAUrDDJtZc3ocdu9Zg+JJz51gJWYhpmujr60MymWy6rZM5jfMpt18tt2ctFosIhULgnEMIgZGREaxZs4a+jEcIIYS8BFDYSAghhBCyQtLp9MIrzSOTyaCvr6/lPBxCzgXMNBG45RaI6WkdCLaoYGSGAVV+DTcGjuVgCgALh3VA5bp6eTisgyylYG7fjtgnPgHr8stbVgOZW7a0Pr5gUIeKk5MQyaSe/QjdGlTlcoDn6QpH2wbv65v3XJ0HHoASAsFf+iW4e/bon2vCORYOI/C61yF4++0w1q6dd1/nguyf/7l+TFCqQEwkIE+caLku4xyIRKByueaqxrLyc6uUDiE5B6JRqIkJ+MkkWF8fjPXr4dx/P8K//utgbcJdoNQm9+abF3U+yvd1qBkKvSwrxoQvceKpKcyezOGJrx1CeryAQNSEHVl4/ujEvhR2f/JpFObcjsaoNtJB2km84u0XYOiibrztz6/Bc989gf0/HIWTa1HVfPgANvWM4qKhMXSHdNvVHatGsWPVaNv7iCKFntGncFDuRHbKgZL1Bzp3KodoIohL37wOr3z3VvStX14wnZ9zsPcHIzj+5BSKaReMM4R7Atj06kFsuXEV7FBnl04KKRcP/OWLOP7kZNNj6+R8HH54AocfnkDP2ghe8zvbsHpHz7KOm5AzpaenB0KIulEByw0ay8r7MUpfRnJdt1LhKKXE+Pg41q1dC8w9CUzsAbIHAJEDuA0EhoDELUD/jYDZeTU0IYQQQlYehY2EEEIIIStASolcaW7ccvZRKBQQidDFEnLuCu7aheLu3eB9fZATE80r2LaumCtVu1Wq4coVjID+byCgK9fCYchsFii/fwwDKpdD8dvfBopFWNdcU2mpWrmLV70K/Etfgkyl6pbLVAri0KHmkNNxdNAIAJ6nW6zm85WQTOXzuoWjlIBhgEcigGEg/5WvoPCtb8EYHGw6TZXPo7h7N4q7d8N+9asRff/7dTXfOUicOgXv6afrlvHBQahUCqrhMSxjhgFlWfqxKz935YvKjeEe5/pPPg8VjYJxDjU1BVEsAlu3wv3pTxF49auXfR5yZgbFu++Gc++91epaxmBu2YLgbbfp1rfnyHMgczm4990Hcfy4rrYNBmGsW4fAjTeCx2JL3m92uogX7x7B3h+cQn7WxdxIHqmR6u8eO2Iilggh0hcA480hrJvz4TsC++4dhecI9G+IwbAX/wWXF+8eweVv2wjOGWKJEF71G1tx9a9ciEMPjmP6aAZuXsAKGojkRzDkP4SAuXCr1bITs734yeEL4UuO7k0u4sO9yJaqG5VUYJzBDhmIDAQx/mIKJ5+aQjZZgBQKgaiFxJauSpvYhWSninj07w7i6E8mIPyGQHMkj9HnZ/HYVw9iy42rcO1/2jxv6JiZLOC7H3sK6fGF51jOnszh+3/wNG7+8CXYeG2io2Ml5Gzr7+9HsVjE7OwshBArEjTWEkLAMAwIISClrHz5js8+DD/5A5j+TMMGRSB/DDj2t8DJrwGJNwBr/xPA6VInIYQQcjbQb2BCCCGEkBVQbgO1XKJd60lCzhG8rw+xj30M6Y98BHJyUgd0NRjnOqTyvEp7VCgFcF5pO8q6u2EMDkKMjQGTk2BAteKxpwfMtuE99xy8554D7+9H9IMfhLVjR/U+TBOBm29G4VvfqiyTqRTEgQNNVXgK1RmPFYYBlc/D+9nPgEAAyOfrbhY1FZesUGgZNtZyH34Y6elpdH3mM+dka9Xinj1NyxhjMC68EOLQoZaBo3Ld6lxHxqphY/nvQDVQLv8sJVQ2C8Riun1tNgtx6BDk7Oyyjl+5LnJ/8zdwS9Wm9Tcq+Pv3I7t/P/iXv4zQr/wKgrfdtqz7Ww4xMoLCv/4r3B//uPl1B6Dwta/BfvWrEXzLW2Bu2LCofZ98Zhr3fO45uHldPaiUQnayPthycz6mj2aQSRaQ2BKHYXFIoZCbLiKbLFa2dXI+pJAoplyEewKIJYIIdnUe1GYnizj+5CQ2XlMNyqyAgW03D9etl/nUP8NdRNA4OteNhw9thlT6NSWTSZh9fYivCtetJ4VEdtLB+ItzOPTQBAa3dlWOPxA1cdFNw9ixa03TdrVmTmTx/f/+NHLTreeXlnlFgRf2nML43hTu+PTlCHc3v8ednIfdd/6so6CxTHgSP/wfP8cbP/sKDG3r7ng7Qs6mwcFBFAoF5PN5/Tm/woFjOWT0fR+2bSOSvgdds9+Cb5ow5/v9KorA2L8D+ePA1j/QVY+EEEIIOaOoRxchhBBCCCFkUaydO9H1hS/A3LGjucoNup1p3XLbBo/HwYJB8EQCfM0aiBMnKm1OKwwDxurVdYvk1BQyf/AHcH/607rlwTe9qdIKVbmurmhsddHT85oCUdg2VCYDOT0N1WL+pMxmdQvRbBZyehpybq79g1Hi792L7Be/uOB6Z4P7wAMtlzPDgLFlC/jatTp0raEa5zkyBliW/mOa+k85bKwlZTWkBKBSKYiDB5d87KpQQPpjH4Pzox81B40NZCaD3N/8DfL/8A9Lvr/l8H72M6Te/34499zTMmgE9GvVue8+pD/wAbiPPdbxvkeem8Fdn/pZJSwE9PxE4bb+ooub8zG+dw7FtIvRn89g5li2blspJJQEfFciP+NgYl8KyYMpSNF5cDB5cP7W4XJuDu5TT3W8P09wPHykGjQC+jltfC0WUi5OPTOD2RNZ+EUBKIXJwxnIUqtVJ+vj2e8cx9d/6xE88f8Ot7yv7HSxo6Cx1vTRDPZ86hl4xeZWsS/sOYXZk4vvbuC7Ej9Z5NxMQs4my7IQCoUA4LS0/a9t0RrKPoquWf2lIiF8KHTw+ZR6Bjj0p63/PUAIIYSQ04rCRkIIIYSQFbBSF1xoXiN5qbAuuQS9//ZviPz+74M1tP5lhgEWiYDZNlg0qtuSQlct8uFhyCNHmi8Ecg7jwgv1LMcGyvOQ/fzn4R89Wl29pwexT34SvKtLt3NtE0SpxkAzENBtXksBpHLdumNRQlRbrgKA58Hfu7c5sGzB/clP4B85suB6Z5LyPMhMpu3tjDEYq1bB3LkTxpYtYP39+jHiHMyydGVqYwXjQvfpunWXhL3nn1/asSuFzOc+B3///kVtV/iXf0Fx9+4l3edSeS++iMxnPqNbpnZAuS6yf/zHurp2AYWUix/80bNNbT7zs/MHZW7Ww8mnp1sHkqVd1d5WmHWRPJBqmo/Ydv/5FvMZa8ipqUVd8D86NQDXb9H+tOY9nJ919DE2hKLS06FpLaWAp75xBA/9n31Nu3zsqwc7DhqFK3VL1Z/P4Ol/OYI/f+1d+Idffwj3/+ULmDqShlIKL959qqN9tTKxL4WpI8ub+UzImSSlhGmap60bh1IKkC665v6lZuEiPk5mfgKknz0tx0YIIYSQ9uhqFiGEEELICuCcV77pvVSMMYRbBC2EnKt4LIbYhz+M/iefROQDH4B1+eUwNm6EceGFsK+8EtYrXqFDx54eGFu3wrjwQsixsebgLhCAcdFF4PF42/tSxSIK//zPdcvMDRsQ+6M/aq7Cq9uwZs5gOKyDxPIypQAhIOfmIFMpyPIMQyHqA8hCAeL48Y4eE6dFy9KzqsOLwYwx8O5umBdcABYKgUejOkQOBBZfISKlno3p+1D5PJy77sLUG96Ambe+FXO/8RvI/7//Bzk9veBuvCef7CiMayX/D/8w/+tiBSnPQ/Zzn4OqDak72U4IZD7/+QUDyr33jMDJNQd70msfgCup4OR8+K6sVPy1XK/huXUyHmaOZxc4cs0M6GBQeBKixbEs9vE4kBxqfYylzwu34GPqcAbtipsyydaP4/PfP4nn95ys/FxIuTjycIt5sw2kLzF1OI1Tz84gNZKDVxCQvsLcSB7ZySL23TuKb/7e4/jqf/wxpo62D/Q78fzupYeVhJxJQggUi0V4nrdiLVRZwxdZlFIIF58CF43Vwou4v/HvL//ACCGEELIoNLOREEIIIWSFxONxFDqsamklFotRZSN5STL6+hD78IehPvABeM8+q4Mk1wWLRmFu2wZx5AiKu3fDfeKJ6nxAxsC6usATCbDu7qaLja14jz0GOTMD3ttbWSZOnYK5eTNkJgOZTELNzNSFY8yyANvW7T9dVwcXSulArBx6lmcRAnpZ+fbyzEnoKi1jzRq9n3k4P/4xQu9+N/g58sUBFgyCGcaCLUhrqWw1bGKWBdUw03JB5dmNjIHZduVnZdsQExMofOMbKH7zm7Be+UpEfvd3K5WvjVrNmuz4HHI5OA88gOCtty55H51yH364o1a7rahcDs799yO4a1fr25dYNed7svI28B0JO1RfMcgMBuW3vnCfnXIQHw7DtFtUGZY4WQ+HH5nA898/Ca+oX1tWyMAF1w1ix641GNwSb1ml3M5cPoS5QhitwgRm6ssWmfHCvFWXbtaHVxSwgs3H/bNvHcOO29aAMYa994w0VYk28l2B5P4UvELz+0a4Avk5B+Ee3Xp47IVZZJJFDFzYhVB8aXPixl5Y3lxTQs4U13WRy+WglDotMxvLuvIPNy3r5N8JFXNPAs4UEOhfwaMihBBCyHwobCSEEEIIWSGRSASmacL3528t1058nqouQl4KmGHAvuKKpuVGIgH72muR+9KXUChfmDRNMKN9mNGKEgLOPfcg9M53VpbJCV2hxGMx8FgMauNGwPd1YGiaEIcOQaVSUICeo1eqZqyr1qutdGxcLoSeT6gURDIJY3h4/mMsFiHHxsA3bVrUuZ1O5mWXwVvE7Ly6akjTXFQLVUipt+dcB7XlWZANYacSAu7DD0McP46uP/xD8J6e+kMYH4f39NOdH3MLzp49ZyRsXG7L1uKePW3DxpHnZpBJtq7Q5GbrL6copSCcaqWhcAXQEDaaNofri9YX75VCdrKI7uHmENjN+5g+mtGhXsgEN6rbewWB/T8axf4fjSKxuQuv+88XgXd3tw1iXWHg6NQADk0mkEzHkMx2gQGwDIFwwEHYcsFNDhYOQfqyo7an0pcAmj9XspNFHHtiEhuvSeDwIxPIJAuQvoJSCtzkCMXtSkgphcLkgXTLoLGsMOci3BOAUgq+IyE9ieTBFIa29SAQWfxlFie7tH83EHImKaUwOjpaFzAuN3Bs9RnElIugf6Lu9w43DDAsImxUEsg8DwReu+RjI4QQQsjiUNhICCGEELJCGGNIJBIYGxtb9IWXnp4eBMoX5Qk5T4kTJ8CW+Tr3Dx2qX9Awk5FxrisZyz/H47qa0vd14NUYNC5EKcD3oaSEnJxcMGwEsPhKwNMsuGvX4sJGw9CPV1mpOnFB5aAR0FWNgUA1UG5TtS1OnkTm059G1x//cd1rw9+7d/HtWxv4R49CFYtgweCy9jMfkUwueqZk0z6OH4d/7BjMDRuabmsXNAJAqNtumlMIANJXda1Tyxl67TV9wzbACgKG1fp5aRU2FjNeZV5iNBGsCxobJQ+m8Z2PPo0br7gZsYe+WXebUsCzI2uxf2IVPKHvXyi9L6mAom+i6JuYY2HEEzZ6uYHc5PxVjbX7bufJfzyMQw9O4OffPQG3oS3tLAOCXTZiiSD8oph3HqVSCk5WPxaFlAs3L+A7AgzAyLPTWH1xD4Jdi6twbPc8EHIuyWazyLf4/bbUwLFdpaKhik23WeYSLl/6jW1YTyNRBLIHAD8DMBOwe4Ho5jN3/4QQQsg5gMJGQgghhJAVFA6HMTg4iImJiY4vvHR3d6Ovr+80HxkhZ99Cs+k62keu4eLhArNSeX8/5KlT1bapi70gWq7qKxZ1YCZl2+CssslpDLeWwrrqKhiJBEQy2dH6LBCAKoeNnOvZjVLqEHW+x682kLQssJrnZr6Q2T90CM499yD4xjdWljU9z0ukcrnT8nx4e/fCf/55+AcPQoyMgNm2bu+7yGrdMtWm+k+47UPeSG8AsydzTbMbW4ZyDWkjA2CFTBh2mxDYlZBCVQLFQsrFxL4UpC/BDIbYwMKPqZPzcd/jg3i9G0LU1u99qYBHDm/G8Zn633m+4HCFASnrj8dJGsg+NYVgbP72xWXcbB1ezI3kMPLcDIZ39rZ+CSugmHJRmHMgXAkjYLSsoRK+hJcXcPOiUsHIeGUXcLI+xl6cQyBqof+CGOxwZ5dcoh08noScbbOzs5BStgwXV7KlqmXX/15njMFYStjIO/vcWBIvBeSPArkjwOyTuoqyUXA1MHg7MPB6wGzdLpwQQgg5n1DYSAghhBCywqLRKEzTxMzMTMtvgJfZto2enh7EYrEzeHSEnHkimYRz111wfvhDyHLgZRjg3d3gg4N1odRCGoMj66KL5l/fNMH6+iBPnmwflM3XIrR0m/K8+hah89wfHxqa95jONMYYwu97H7Kf+YyeWbkAPjAAUQr7eE8PYJpQk5NANApVLAKe17xRbfmcYYDXfq6FQmDR6Lz3Wdyzpy5sREC3qFSpFOTkpJ4BWXrsWSAA1t8P3t+/cCtee2kz9FpRrgvnvvvg7NkD/+hRANCzQkdGAOgKRd7Xp1/Ti5zZ2W6mpj1PS07GGaIDQaRH63/PtHyV8+bXeM+6CHxHoDDrtthAtyQtzHnIJIvIjBfgl0LNQMTE+L4Uon1BxAaDsELtj9FxOfb23YarMv8KAPjp8Y11QaOQDDP5KFzfhFIN7yvOocCQm3aQn3FgWBxWyGhbDWUGjJbzGmdP5ZAezVeqBw2Lo8UruHTOCm5BwPBVUztU4Um4eV93aK6pRDQtXtdy1XckOPcxsW8OiS1xBKILBx5bbly14DqEnE2O41T+TdsuWFxM4Njufcw5hxnshuI2mNSfTXbAXkwD1Sr7NHyRL7MXmNgNTD0MFI4CxfHqbWYU4EFAOoDI6Vau4/8OGFFg428D698LWPRvfkLIS5T9K0Bg/v/f9bJn7wNw59k+irOKwkZCCCGEkNMgGAxi9erV8DwPqVQKjuNUvg1uWRa6uroQWkTAQshLkZyZQe5v/gbuE09U5x+WQxUhIJNJyGQSLBaDsXFjRxVorLcX/rFjgOuChcMwNm2CecEF8I8cqayjXFfPaSyFYiwQaJ7HWKscWDFWrV6s/RnQ23regpVr9nXX1Qdt5wj7yisR+b3fQ+4v/7JtsFXG+/shTp4Ei0ZhbNoEFIvwJyfBDAMsEtGBpePo/ZQf0/Jsy1L71FpGIrHg8YlTp+A99xyM9etR3L0bmT/5E4gXXtDPBWOVSklmGFC+D5XLQZ46BZ5IgK9Z03ruVzi8YMjZKf/YMaQ//nGIo0ergWc4DFZbbVNqtSunpmBs2AA+MNDx/tu9Zoa2dYOx9i/d+Kowiim3ri1o4yPBjeZJZ1bYRPdwBIwD00ezyE3Vt2uVUmFs7xykK6GkgvAkGAPssAnD4lBCIZMsIJMsIJoIom99tG1of8JZh6te9XqkfvgTHEgOVpYLyTCV7YJfqmY0uIAvatru1rzXpK+gpD4WO2JWQg0pFFTp7RruCzS1i83POZUwlpWqNMO9ARRT7QNWQAeLXlFU5zj61aARqA8bGWcwLA5RCmOlJ4GQAekrJA+ksWpHN8xA+8+NQMTElteeW19QIKRRsVj9jGgXFJZvW2qFI2NMjxRgHIXw1QhnH4YdsGEaS7h0afcC8cuWdBwtSRc49CfAzKP65/xRwJmsv704qgNGxnXAyHipFXsGOPQFYPx7wJp3AuveA3C6HEsIIeT8Q7/dCCGEEEJOI8uy0N/ff7YPg5AzToyOIv2JT0BOVi/Gsf5+oEUrT5XJwH/xRZhbtrQNh2Q6DZlM6uqye+6pLOfxOPjQEJTjQDmOXmd2tjmdaVWNB1TbpFZ2yKsBl2HU3aaUWrCFauD22+e9/WwK3HgjeE8P8l/5ig5s22DRKAK7dkGMjekZmOFwdfYlSnMxQ6G6AEum09XHrTZsNE3wTj4DfR+ZT30K/pEjECdO6EpG368+j74PVSxCmSZYNKpDPiEgx8ag8nkYmzfr42o43/kuinfCe+45FP7t31D4+79vau3KIhGwREKfr1MzO1EpHUoCHQWOvLcXxgUXtLytazCEta/ox4mfTrXe1mBIbIkjuT+FQtqFkuUATumXLmMwA/WPixU2Mbg1XmmR2n9BDNGBIDLJAgozDoSv4BV88FI1pJQKZtCAGTBa5onZZBHSkxi4MN6cdEK3ZD255c2YPjwA9uJJKCGgFDCdi1aCRgAwmITPDIAbze8zpt9/wgfcvA/OOXxXVF4ejAG5KQfFlIdofwDRRAhW0EBmvNq6udzSNNIbwNzJLKTfHIhIqSptaN2Cr4NEk8ErVu+LG6ypXasZMCphoyrVlioFCFdgbiSH/gu6mh+YkotuGYYVpEsz5NwmG6riOedNyzrFOa8LJBljlS/jWZauBPb6bkHA/ynMJbamxsAtAFvito2kB+y7E0iXWqUWxxuCxqKe2VimJCAygBGr6bOsgOx+YOSbQP4EcNGdAF+5yntCCCHkXED/oiWEEEIIIYSsKJlKIXPnnXVBIwDwaBQyEmk9j8/34R84AHP79roKR+W6EIcOQWWzYLEYWENbTJlKQc7Ownv6acB1gWCwdcDEuU4kaqsWy8trMaYr9GrXKe9igWpk6+KLYW3fPu86Z5t1+eWI/8//CW/vXji7d8PfuxcylwOzLPBEAoGbbkLgda8DbBvZP/xDXZUKwNi0Cf6LL+rZla2U52GGw9XQj3OYW7YsWA2qXBfec88BhQLAmJ4NWdoetVWYpepSNTcHxGKVCkqVSkEcOQJj06a65z64axeU78N76qlKUM0iEZjbt8Nct27+YyoWkf385+H+9KfwDx9u+ZpVuRzU0aO6wpOxprBTHDumg9EFXjeBW26pr5BscPGuNW3DRikU8jMOpFKQnqoEcEoqSAUwpqCkoQNDmyPSH0T3cKQSNJYFYxaCMQvesI+RZ2fRvSaMcG8Qhs2RHsujmGrXeFTLz7qYPZVFz9oolFJN78FTz85gIr0a1uV9kFPTyJ2YhidqztkwwG0bljLhFfVzrpQCagqSlVRgSkG4ALdk3X3o9qq6AjE9XkB6ooBYIohiunrcsYT+XOEGQ6Q/WBdESqEfO9/RsyrLnJynK0tFKTNoEd4CgGEyWCEDXl5ACoVCyq0cdzHjwcl46BoKI9IfrHvsh3f24JpfvXDex5aQcwFjDLzmM678c6vAcb7qRsMwYJd+jyulIKWEUgqWZSEUCiEUCiEejyMYvBBwL60GfIvBA8DgrYvfrp2jf1M9DiUBZ6x6m3Tqg8YypQCRBcwYKt/CUAJwJoD0c8ChPwW2fHTljpEQQgg5B1DYSAghhBBCCFlRxW9/G2J8vOVtfNUqiEOHWm/o+xCnTsG8UF98V64Lf+9eXTXGGPiq1nPNxPHjYJxDeh6YlFDhcPvAsbYUyjDqWqgyy9Iz/hiDymSqwWT59kik7Tkbw8OIfuxjbW9fLKUU/GeegXPffZBTU1CeBxaNwrr4YgRuuQW8q1opJaamdCUg53oOZlf7Kqoya9s2WNu2zbtO9KMfRe4v/xLO/feDmSbMbdsgDhyoBG+qHP45jq5CNAwgn4csFsGCQZg7dizcxlQIeC+8oKtRLau+ArVcZdp40Vop/fxwrp8zAGpmBmpgACweBwCY27bBeeABOPfcAzk313z+O3YgsGsX7Ouuawr6lOMg/YlPwN+/X7fknZmZ/xwY049/JFI/Q1IpyIkJGBs2tN/UMBC8df6L4uuu7Me6V/ThxFPTdcvdgo/kgTSEo8M5K2TADBnwiwJuXkGVKvecnA9eZIj0BXQLVKXQsgQRQGHOBTcZ+jd1gRs6WEidaj97GNAzIoUrMXkwjfSYDvAYA8yQidhAEJH+IFKjebh5H+AGeCKB/GwAXDa3MrWgqwu9gqhUGNbekSplz1IocEOHGlbQgGkbTevOHMsBDPr2kIFgV/WLCt3DEThZD27Wh+eIupmLdbuR+vyUVIAETJvBaLwv6Jeo9JWeMyoVVF1FNFBMe/CdLGZP5dA9HEbXUBjrr+rHzR++BIapH+fxvXN4fs9JTB5Mw837MG0DXatCuOjmYVxwXaKyHiFng2ma4JzXBYzlwFEp1RQutgocy9WLds2XhoLBIOLxeOv55Rd+CHj+A4Db+ssWLTGmt1upeY3OFDB1n/67ErqiUTgAmP4YlW2+gAPoDxDp1VcwutNAaC0w8xMg9RwQ37kyx0kIIYScAyhsJIQQQgghhKwY5bpwfvjDtrfz3l6o4WHIkZHW28/O6gDLMOAfOFBpT2msWQPe3d20vkwmdQUlY+CxGGQ2C2SzOrgqz1w0TTDOoWrnMXKuq85KYVWTaFSHaqXKOhYI1LcHrWFedBFif/AHKzarsbhnD4rf/S5Ei8fIe+opFP7pn2Bdcw2M4WG4jz4KceJEdQXGYF12GYK33w7rqqv0efs+3Ecegfvgg5Czs4CUYLEYrCuvROCmm8DbhKjMNBH9/d9H4Lbb4OzeDfeRR4Bt26BmZiCOHdPPVWlGo1JKP7bleYaWBXHoEGRfH4wNG5qq/sr8sTGo6enqTM3GYNEwdJDZqBw49vZWYjOZTILH44BlwT94UAfVbXgvvADvhRdgXXwxoh//OHhNKJr7q7+Cv39/ZZ9tByaWHyfOgUAAKpcDawh65fQ0jLVr21Z3hn71V8H75r8ozhjDzR/Zie//96cxsU+3svUKPib2piozBgF9mG7Og/AVwADD4nWZYn7WgfAkUiN5dK0KoXu4+XnPJIvoGgpXgkZ9fu2PzXME/JoWo54jYAUMXYSa9zFzXAdsTtZFKK7fP15RoJhuPTPRdwSkK/V91r4cGp8CCUilEIgYlbmKtZQCfE9AlKo9Y8EQZk5kER0Iwg6Zlfazp56ergsa9ZcUVP2OKo8D0+dVELBDRt0qTtaDFAqM6wpHKD33sby59BUQ0OeXGsmjZ20EG1+ZQHayiEyyiMf+/hCmj2aaziM9UcCpZ2YQ6rax8xfW4fJf3LDs1sCELEUkEoFhGLAsC05N2+hyC9RysNiqPWoZ5xzd3d0wTROGYSAWi+kZje3YvcD2Pwb23wkUWv+boQ63gQs/APRes/gTbGfie4CT1H/8rK5WlD502Miq7cOVgv7sKH8ml77UIbIAYgAzUSmTdqeBQAKY2E1hIyGEkPMKhY2EEEIIIYSQFeM+9BBkpvmieS1jeBjgHPLUqZZVayKZ1OFePg8wBmPdOvDBwZb7EuPj+uKm60K5biUcVIahq+TKcxYNPQeOxWK6Ek9KKMdpGzYyzoFYDPA8XfnGeX0oxBjsq65CYNcuWFdcsSIBgBICub/4Czj33z/vemJsDO5f/EWlTWldxaVS8H72M3g/+xn4wACsiy+G9/TTkKV5i7W8Z55B4R/+AfZrXoPwr/wKeE9Py/srV0HK974X/osvovCd78AtPS9gTM9OTKUgp6frqwSVgpqagigWYWzdWl/1V7pdnjpVDYBbzf8qt7UVovm1IoQOo8vtVOfmdMhcLC7YurXyGDz/PDIf/zi6Pv95sGAQIpmE8+MfV26X09PzbF1zmIGAPl/XrW/1KwTk7GzLuZWht78dobe+taP92yETb/rDV+Ch/70PB340iuTBdFPQ6GQ8yNKsRjtsgpscbtaHLFUIKgm4OR/BmIXUSB7ClejbWA3IC2kXwZiF7uFw3X0bNofvSSihSk+Bvg/hq8qcwvLrXzgSVqD+sVdCITWSRzbpYODCGLyC3xweAvBdAbcU/DHGwEwG6UuoecbCuXkfwpewwyYMk0NKBb8oIDwJ4ZeqDCWDXxTIjBeQGS8gELPQNRTSAQl0RahwpH6cWE1uUMIYA+M6bAR0YMgNBtPmUNCtVsutVxkDrKBZ2YfwBJTU2+rnhkEKhSOPJDF7IgdmMORnXEQTQUR67KbWzZXnZs7F439/CNNHs3j9By6uzNMk5ExhjKGrqwszMzMt26eWPwPm+13Y29uL4eHhxd1xcBDY8UVg8h5g4i6gONq8jhEGBm4EBu8AQovc/3ymHgL2fxrwa6q7Kx9IshQ6qobPs9rzL5Vji6yeH8mDOhAtV0POPg64MzpUJYQQQs4DFDYSQgghhBBCVoz37LMdrWesWgXe06MrE6em6qrXVCoFZRjgAwPgg4Ng4XDLfah0Giqfr6tALGOc65aaNW1TZTarZ8PFYpC5HOD7UEI0h2DlfTAGvn59paoy8v73A8UiWCgEvnYtjBYB0nLk/vqvFw4ax8chy5WMQsDfv79pziUAXc344INw7r4b5tatbduZKseBc++98J99FrFPf1oHwW3weBxyZgb+iy82VePx/n54ja1ny/eRzUIcOgRjy5a6C9Fybq46C7GxhWqtcsvbcvVkTRKkCgVdSRmJgPf2Qk5N6erGRfCPHEHur/4K0Q9+EM5dd9UnTW7rCryWhxkM6mA7ENDzJ9vswxgeRugd79CzMRfBtA287r/sQGJzF773iaeRnSzCd4QOvLIewAE7aMKweeVydyBqlgI5fU5SKAhfwjA5spNFmAED8dVhGCbD+iv7MbG/GkoLTyIzUUB2yoGb80stQqFD1XIRD6ADutJIVKFUuZ6nTjQRwvSRDKaPMwSizQG/FApevnY+JyBFqTKwttiQVW8HSoGeK+FKH2bQ0I9HTadkVpqPqNuu6r87GQ+TGa/SBdEKGDADBmQpoJQGg3D165hbrBIQ1vKLAqbNITypqxZLDNuo5IWMAdzgcAoeuMlh2vWXX6aOZcAN/VwV0y4yMQuJzV3g87RLPfTgOOywgRt+59yeDUvOT11dXZibm0MgEECxWGw7l7GVQCCA1atXL+2OzTCw6s3A0C8A6WeB7H7Az+nZjMEhoPdVgBFccDeLMr5bz2r0G9pIK1nq5yzR8lsTdctqPrCUBEReb1cOLJUAMi8Cfa9e2WMnhBBCzhIKGwkhhBBCCCErRmWzHa/LgkFdtbhmDVQ6rcOn2VmITEZXEwGQU1NgXV3giYRuo1oTVomxMX1/rQIu1wWCwbpwi9m2DrciEfBoVAeN5crG2n3YNoxEAnxgQIdgAIJvexvsK65Y1GOxGO6TT8K5995515Gzs5AnT9Yv9H2Io0dh1sxfVFJCHDqk24wC8A8c0PMT52lXJ5JJpP/gDxD/kz8B721dZaGKReS/9rXWOzBN8P5+3Xa01bapFFQqBVbTCldOTVVDYttuHzYCpeSIVedu1lzkNjZvBu/pgTh5Ulc1LjJsBHRFrvi1X6t7DlSrtq4LkRLmxo1QUkJOTkLl82DRKIx162CsXYvgrbfCvPTSZVXCHnpwAvHVYcRXhwGlkJ0sYupYtuUURsZ1uCd9Cd+VEJ6E78jK/L/cjIPX/dcd2PmmdXjxB6eQPJAGoCsGkwdSEK7U+1WqLlSru56u9ExDhVJXQaFgGNWjscMmov1BZJJFZJM64Gzku6Jul5WgsfG+KicGcIPp6/5KQUoFJ+uBcQZuMhiWDv28on59+Y6AHTZr9q9QzHiwgroNK4NuO2tYgPA5wHx97py1fFyl1KGt71QDUm6WWqjW3IeT9SrF1bWUUnCzvp6zWZoB6WQ8jO9LYWhbvK6NbaMX7x7BRTcNY3Dr4l/nhCyHZVkYGhrC2NgYgsEgHMdpqnBst92mTZvA27TT7hhjQPwy/ed0mnkcOP5/ANXQwluJ0rJOfy+0+OqFKAIiV/NzDoQQQsj5gsJGQgghhBBCyMppNwNxPrkcxLFjlQowbhjV8E8pqFQKIpWCCARgXnABWGk2okwmW7feBKoVcI1ho+/rwLGrC8wwwLq6YF54oQ4npdTzHcvzHkvsV70KwTe9afHntQjF3bsXXEe0ajsLQGUyOtQqVYDKZBIqna6u4PuQ4+Mw1q+fd/9ychL5r3wF0Q9+sOXtzgMPQOXzLW8DAGPdOl1p2iZwlhMTdXM3Ven5ZqEQYBgdX76tBI+Abo1rWToMm5qatzJzPkpKFL/znbp2s6xcUdlQNbvgvopF8P5+8NL8xvB73oPQm9+8pONqlBrLY/T52eoCxpCdcloGYrW4yWGbXFcnKobBi+IwgwYMiyPcbSMUt2GH9OUBrygwsW+uEi66eX/hzLV0Tb08NzIQsSqVhLGhkP5vIojprKerMGs3VapSSQiUgss2AWPNaesgkOsblNTv9UrbVEfocxWqZWWicEXlXA2Lg3EG3xXVdqol0lOlx6xcvVk9CK8gqm1kOdMzMmseh3LQCOiKx7r7L81z9B1ZCRsBPedy+kgGA5vnDxKf332SwkZyVkQiEaxatQrj4+MIBoMQQsD3fYgWn5OGYSASiWDt2rUwzZfQ5ceTXy39+6H83lSAdPTMRiwcrtZr8WHmpXVwyQyA2c23E0IIIS9Ry/xaESGEEEIIIYRUNbbXXIicm4O/f399q8mGtqZKKSjHgZyagvv443AffRTuk0/q4Gu+FKTFbSwc1oFo+f5KZUcsGAQLh/W8vZpAIXDDDYh+8IMrMpOxHTE+Du/pp+ddR6bT9a05G28vVRQqpVpWF8rp6fbBbA33kUdazncEAGfPnvk35ly3bC2FbI1UOg1VLFZ+ZoyBhUK64rI2QFwMxgApIefmdGWkvfQLt84Pf9i8+1isxZoLaHicrZ07l3pITdIT9a8BN+83hXetKFQLQsvtRU3bAGMML9x1CgDQu0G32p08mKoEjV5RwC9V+FXmBLZ7y5X2XZ4NqZRCbDCEaL9ubxjpDcCwOYppD3ZNK1Xh1YeLSi4cO9e14/V1K9NySFlu7chKrykpFXxHwMlVQ9Pap8grCBTTLryCqAsazYCBQEy3pS1Xdkqh70eWqhprj7W8H7cg4BWr92WYrBK8lvmlcFUK1fRw5ufcSkVmO4cfnkAxs/DzTsjpEIlEsG7dOvT29iIQCCAYDCIUCiEQCFR+7unpwfr167Fhw4aXVtCYeg4o6M/EStjop0tB4yIr3cvqh8Dq/bqlecCBlW3HTgghhJxNFDYSQgghhBBCVsxi5tCpfB7i0KGmcIaV2mAq6Jl8Kp2GKhR0hZmUUNmsbhEqhJ716PutQ8cW4RUDwMNhHSzG40Cbi6DWJZcg+pGPIPKBD4Cd5gul7uOPL9iuU05Ozn/7rK52U+m0biXayPd14LgA5ftwfvCD5uXFIvyjRxfcHoYBc+tWGBdc0DwnUimobBbMthF4/esRfMtbKq1dGWPVlraLwZh+Dh0HsKy2QWcnVCbTNIOMJxKL31FNWG5u3QrzgguWfEyNaisAAR3qzUcpHRgW0x4KKbfy37EX5jB7KgffFZg9qf87fEkvzACHVxClbVVdm1BmMDDeWSAspYIdMdGzLlLdnjMktuj3djBWfU/VPublKsImDXdbPg7py8r4s8ocR1m7XvVjQHiyWm1YuhOlVF0wWNmOAVbQgGFyBKMW7KhVatuqA0fGGZjBwE1e95joakWhw9ZSENnYNlbN15K29HMm2f6LBeVzGXluZt51CDmdLMtCX18fNmzYgKGhISQSCSQSCaxatQobNmzAunXrEIvFTusXdU6LiZov1TiTus2p9NB+RmMnarbjpXbmThIIDAKxi5e4T0IIIeTc8xL6ehEhhBBCCCHkXGdu3gxz82b4Bw8uuK4YGWmutrNt8IEB+JOTut2p3yJMkVIv51wHjkrpnw2jOhyN8/kr5YSAsWYNQr/yK+CxmA40lQLv6oJ97bUw1q5dxFkvT13L03br5BaY61R6nNTc3Pz7GBhY8L7cxx9H6O1vr1smFzGLE4zpNqL9/VC5nH5shQAYQ+C22xB9//vBYzE4jz+O/Fe/WpnVqBhran27EB4Og4VCUHNz4AMDYMuZCWYYMPr79SzJ8qnE40Aw2DrAbaPczhYAgrt2Lf14WqidOwigqT1omQLgFwQ8p3WFnBIK6dE80mN5RPqCKKRcxAZCsILVYEy4simE45xBSDXvNXfD4jpgU2gKGuywicGtcRgWR2HOg1fwm+Y/1iq3S61UHJbalFZmOrY6DlWdk6Zb4QKsXNEoFNy8X3qN6Tar5WrI8rEyBtgRs64a0Qoa+lhrjpMbrL4isybAVFJXaFohrturNh5eC1LoeZpSKBSzHoopF4ZtIDoQRLjHbnosO6loJeR0Y4wh2vjFkpey7H79X28WSP98kTMaW2Gl7UttWXmp+l7kgf4bllbRTwghhJyjKGwkhBBCCCGELIkYG4Nz113wnn1Wz+kzTfC+PvDVq4H9+6vBXwvKdVsGYzyR0BVxrts6aCwrzVesPyBRacfJbHvBOXZQCqG3vx08EllozUXz9u2D99hjuv0pAB6Pw9y5E3J8HHJmBpASLBqFdcUVne2wgxaogK5MXPY+MpmmZeUKxMVikQhYzeNrv/KVEEeOILd7N7zHH9ezGmvvr/wcLhQWAzrUHB7W6wWD4Eu94C2EDkWlhPXKV0IcP145ZsYYjHXrIA4eXLD6FNBtV1lIzyi0Lr4Y9mtes7RjaqN3QxSmzSttOFmLt5iCrngszxNsOkZWs50CclNF3PuFn+MNH9uJ/KwLM2DAd0TlPlrvpPV+uaGDRsNklbaiwa761raBqIWbPngxJg6mcO8f/7yuerJ8Tb86k7E0j7HhoW8bNKL5aWKMIRA14TsCwlcQnoRh8/rZkKV8khsMdthsantarvYsH4+SCszgkJ4EVJs5k0wHtsIVdTMba/fMSsGpVxT11Y4A3LwAK4hS6MgRGwyhayhUCR25SY2qCFlxIq8DxvTzpaBxpSjAiKDSYI4ZQPc1K7h/Qggh5OyjsJEQQgghhBCyKP7x48j/3d/pOYMNV9jF6CgAQE5MQPk+jDVrWoZGMpls2pZ1d4OvWgVVagm6oNK8xboQTQgdQnYwu8/cuXNFg0YlJZwf/hDO7t3wjxypLs9mIScmIGdnwUIh8MFBsL4+HRp85Stgtg05PQ3W29u+5dxCFXudtHrttOqvVfvZSAQsHNZzMttQQkBOTUFNTUG5rn5+DAMsFtMhciQC98EH4T3zTPWw16+HVxs6G0a1PS7nTfM7644pEIAxOAgwhuh//a/If+1rnZ1f+Xjzef28zMwAQoDFYvAeegj+3r1AIACeSOgKze5uYONGiKNHFwwc+eCgPq/NmxH9+MdXvAVvMGrhwtcMYd8P9fusthKxzCuItkEjAJg2r3udGRbHxL4UfviFn5danXZhbO8cVKuqybYvT91WFEBdZaGbFwi26GzbuyGGzTesws43rcc//cZDOPTgBJTS7UHhy8rxlduWNt3/IgqNDJPBsHSFoRQKvisq+wZKIanJYIctGGbzCSoFCFfoVr8GoLhu1coYgxL1bWDLD2t5iZQKxYwPO1LzXLFqrRM3GNys3/p0aqp8hSsxdzIHN+ejf5NuTRnuXvp8UkJIG9wGMvsAUUDpmwRYemUjq/6X2dUZkIwB0c2A3b3swyWEEELOJRQ2EkIIIYQQQjrmPf88Mp/97IJtPdnQENSJE/D37YO5ZUtTaNQYWrHeXj3njzGIZFKHhY4zfzVeOWzUfRCrywxj4XaaloXg7bfPv84iKMdB9gtfgPvEE3XL5dgYxKlT1RlxuRzEkSNgc3P6fDmHzGT0sulpGJs2gbUI2FgwCDVPK0/e26vXs6y2l0VZMNjRuZRnZtYt4xyB170Oxd27m25TUkKeOqXnSoqGtp2+D+U4EFNTAOdQmQx4zVxFPjgIPjgIOTFRuiNWDRylrH+Oy0kOY4Bl6ccqEkHk/e9H4Prr4T7+OPz9ugWechyo8uuHcz2jsxz8KQVx/DjExIRu4Vp6jbHeXighwIeGIEdHIY8fhxwZgbF5M3h/P2CaECdOtG2pyiIR8IEBBG68EZHf/M2OH+/F2nH72krYGOyyYQYN+EX9uEtZP2exldoqOwCIDujjPP7kFKRQCEQtDF7UjWOPJlsGjpVqQ0B3BuQAeLUVae0Wrdq8JjZ3oW+9rkLtSoTwW/9+M/7pNx/BoQfHkUkWKtuUKwg51+lcy+rBRgqVwiFAX+o3awJZbjDYIRPCV7BCCsLTO7SCZl3QqFRpHmQpAJWl6sraY/AKvp7XKKszJCuVjzXnrZTSlZFKwQqZYIyBW7xUaTnPubQI/fMzDmYMhjWX92H40t4FHgxCyKLxIOBOr0D7VECHjEbpvzUVjZELAasXMJc+Y5gQQgg5F1HYSAghhBBCCOmIf+wYMp/5zLzVbWWMMRjr10POzUHmcnUBE4BKKFWpeitV9alCASqd1pVNkYhuz9oqYaid71cOp8o/d1AFaG3fDvualWlhpoRA5nOfg/fUU3XL5eioDhpbbTMzA6EUjAsvBAsEwOJxqLk5iIMHYWzZ0hSW8oEBiHnmMfJEAgDAenqA8fEWK3AdmHXAftWrWi4P3n57U9iohIA4cKBl69W69VwXkBJi/35g06ZKOAoA1qWXwnv66bpZiZXntxw41jIM8EQC4Xe9C6G3v70yXzNw661wH30UMplsnoPJOVhvL3giAXHiBFQyCeV51X1zDjkzAzU7CxaPg4XDkLkcmO/rY96yBby7Wz9P6bS+j0ym0vaV9/Qg+uEPI/iWt4C3CGtXUuLCLlx4/SAOPaQD2lgiiNkTOvwX87U+BWAGeH2LUKa3B3RbztRYDoGoBTtkIBAx4RV1lWQlNGSoDkAs/awUoPxylaDSIZ3JdKhmNAdm229bU/czYwyveu9WpMcL6B4OY+S5WUhf6jaiZv2xQnQQOMrq6lbYhNGi3agUUlc6+vpzqDxXUYe1uvVp+X5EqVVqZaeqet6cMSiU2qqWA0dWXkkrz4T0HAnGhW4zazP4Ts0BVYJMVdmHk/Ng2gYMm9cVlGYnixja1t3yvAghy6QUIEtVjcvCAVZzyZWbQHAVEEgAPAD0XgsYp+cLKYQQQsjZQmEjIYQQQgghpCP5//W/Ogoaa/HubgBA+Fd/Vc/nm5uDUgpFy4JKpcDC4br1ZU1IxAwDiEZ1FWVthWM5UCynAeXAsVwF53ltj4dFIjA2bULgDW9YsVCo+G//1hQ0qnQaYmRk3u3U7CzkxASMoSHwwUGIuTkdZJ06BWPduvrj7u4GAgFd7dmAdXVV5gTyWAyyRbtT3tvbUatVFgggcNNNLW8z1q6F9YpXVM5VKQVx6NCCQSOgw0YeieiqwsOHwUwTrCaAtq64AuLIEfjHjwOFQv1zWzvDMRCAsXo1zE2b4D33HIJvfjMAQIyPo/jNb0KOj+uAupGUUFNT8I4e1fNAG6pHWTCoAx2lKrNEmWlCCQEmJcShQ2AXX6xngcbjda8da+dORD/ykeZA/TS68f0Xo5j2cOrZGUT7g0iNFSA8OW9Vo2Hxprarkb5gpdLRsDi8go9CykVu2oGT9ytzBBlnOhBTClD1YVoj35EQnkIgbMAM1gdi4R4bm28Yatpm/VX92HjtAI4+NqnD01P5ppatjDHAqAab85GehBkywVu0RQVQqshksCMmpK/AGOAVBbxi/eOnRMO8yBZzGZnBoYQOZJnS1ZONsybLf3cLAobNYYVM+EUJ6StIIZvnTHJA+gqu74MVADNgwAwaOutlqFSyEkJWkCgCMw+1+ILT/J95TZgJgFfDRGYAXZcCZs1c4cGV66xACCGEnCvoq3CEEEIIIeScJ6WE53nwPA9yvraa5LTxjx2D9+KLS97effRRhN76VoTf/W5E3vMeBF//+qagEUBTG05WmvnHwuH6sKwcQjEG2DZYVxd4by9YNKpbsNa2IDQM8P5+mDt2wNyxAywYRGDXriWfSy0lZcvWomJiYsH5foCeXamUAo/HK9WJcmoKqvFxYAx8qDmkgWXB2LixblF5P9UFvPW2Ldg33DDvHMvoBz4AY3gYgA5LVSqlAzrHgczlILNZ/SeX05WD0NWPLBisGWin4B871rRvPjysqwdjMf0cGkblDx8YgHX11QjccEOlLa+cmUH6Yx+D+/jjSH/wgxCjo/q2Vq8rQLehLbdW9av9K1kwCNZqxqcQ4F1dYKtX68rHcqtXAMy2EbjpJsS/+EV0/eEfntGgEdDB4K5PXo7ttw7DDBgY3KLvv10RsBXgsMNG3azGQNRC34bqxW8dJAJjL8wiN1WsVPvV3i6FqrZQbaF21qJbEHVvQyto4LY/uAym3aJNMGO46UOXYM1lvYgmghBu6zCNMdZRRZ+CboNaTHtNAaLeDwAGGCbH0LY4pFAtg0bZ7lxriztLgSMrtXqVfqkat2YdJXXVpPAkvLwPKMCweeugkZXasSpV+qODUDenZzv2XdCF0Z/PIDfT/MUDQsgyzD4JeHP1FYlLwgBu6ZasPAhYPfVBY+QCoOviZd4HIYQQcu6hykZCCCGEEHJOUkohn88jlUoh31ClFQ6HEY/HEQ6H6y6ek9PH2bNnWdv7Bw/CP3gQ5ubNAIDALbegePfdzSu2eD5ZOVC0bR3C1c7YK7dcLa9rmmCmCevKK+taZNayr78e1rZtyzqfMu+JJ+rbf0JX8al5Wp7WKRb1OcTjMNavB5SCnJyEmp4GawgNjcFBoFCATCb1AtuGuWULWCBQtx4bGABLpaBmZwHG9GzINgFc3f7XrkX43e+edx0ei6Hrc59D5tOfRuE734HM5dpWkirPgzIMXXnZmKiUz7urHJIp+AcOAJ6n28o2nBPv6QHv6dE/eJ4OZB0HSghMv+lNMDZsAO/v18/9tm3wjx7V51+elen7utqzdranlGDRaOugsXwO6TR4LAbz0kvBgkHEPvc58GgUvKfntM1k7JRhctzwO9tx5S9vwt4fnMJPvnIQhx8ehyp9H4NzBsPmMG1eafFZFuq2MXBhV93ymeNZODm/8n4ybT0LstwGtNX8xUa1+zMsjqnDGQxtN9E1GMJtf3AZEpvbVxObtoFdd16Ouz77DEafm4WTaz3QUNUlfXU3NC+DDuqUVLDD1csf3OS6hexQCIzrdq+BqAnfkZCeLL08FjhfVZ1TWQ4cpZD68W/xa4lxpisWFZCbKkJJgBms2hq2OgZTt24tb8d0e2ilBMI9NiJ9AQhf4dQz09h64+r5j5EQ0rnJH+j/cht6oOpSvtxWqoJkNf/uCAxW/27Fgc3/bRkHSQghhJy7KGwkhBBCCFkhUilMOR5yvoDNGXptCyGzuYKDLKxQKGBiYgK+3/picz6fRz6fh2maGBwcRKjUQpKcPu5Pf7rsfXhPPVUJG83Nm2Fu3gz/4MG6dZhlzbsPVqp0Y9EozG3bII4fr6s4AwBYVrXqsYF12WWIvv/9yzuRGs6Pf9y0TE1NdVTVWCanp3VbTsZgbNyoqzNdt+W6fP16sFAIqlAAX726ZVDGGIOxaRPk8eNg3d3VkG4exvr1iH3yk/NWNQKAchy4TzwB/+RJyNFRXSFYnpVZ2+6Uc31stg2VyUAx1rRvkUzCLIeNqZRun9rufl0XKpuFGB+vDxFdFyqfhzhwAPLUKfD+fvChIZgXXqirLZNJHQbXtuItHyfnHbWWlckk+KpVgONAjo3Bes1rFtzmTIr0BnDlL2/ChmsH8I33/QSZySJyU05zq00GhHsCiCWCCHbVv24yyQKyySIYA2KrQpW/G7YB3xG6nWgbjENfW2esErIxrlt/Ms7QvSaMt/3ZNQh3B9ruo8wwOba8dhWOPzGFyUNpzI3kqvfNSmGdp8ANVhcGMgaAoxK0AqjMVgQA35VghoAV0L+TI30BxFeFYUdNjL0wV7lvw+R6vmLBh0K5ShPVas7ah4GhFBjqH6UshYalykTGUAloy61VgVLFZE3AyBmrzGpsRc+GBBgYCnMuslNFRPuDKGbat4smhCySnwfmntZ/5wHdUhVANXBcTCtVBrDS510gAdh9pb8PAFvv1LMbCSGEkPMQhY2EEEIIIcs053r4weg07h6bxpRTvfhnMoZXDXTj9uF+bIvPfwGfVOVyOYyPj8978bXM932Mjo5iaGgIkQVCErI8nczlW4hs2EfobW9D5nOfq1vGenp060wx/0wyvkpfrDPWrwdsG3JsrNIak/f3N63PLAuBW29F+N3vBusgYOqUmp1tXtYmKGyroTKQDwyADwwg+oEPwLnvPsjpacB1waJRWJdcAvvGG6FmZ1HcswfOfffpmZY1jKEhBG67DfZNN8F78EEUv//9uvmRUkr4vg8lJdDVBfba1yL8treB9fbOe5jFu+9G/qtfhZqbg1ueUWmapZaRNRWDpTCYlSpKVekcZS5XFziqVKp6TJOT8963yuXg793bFOKq0gxL5bpgtg05Pg45MwNz82Y9n3PtWvBEAv6zz1bautZxXWChCkXPg5qdBevr0wHrOSo6EIIdMtG9OoLu1RG4OQ+eI6GkDucCEbMyn7GWUgrp8WrQ2zUUhmkbmDuVgxUyIH0JMV+upQA7bJbagur5h93DEYS6bYS7bbhZv6kl63wY0+Hc4NY4uofDyEwUkJtxAaVbs0pfAVwBstpyFEyHnYqr+nCwhu8ImAEDVtDAa35nOwa3duEHn3tOtzVtIDxVbQnLZOU8m05d6iRQqYb2skqBGRxGaXZjbcWnkqoaiipAlnfcYkYl4/XVor4rMbFvDuFXJsAbqlUJIcvgTqHS/lR6gGHr/1Zm1JZLljsIHJmpv3ERSADh9TpsTLwBGNylKxsJIYSQ8xSFjYQQQgg5b/18NosHk7OYdT0IBcQsA1f0xvDqgW6YfPmjq5VS+Kdj4/jWiST8FsGYrxR+nJzFj5OzuKgrgo/u2IDewPxVWy93juN0HDSWKaUwPj6ONWvWIBBYuHKGLJGxAlW6je1Mr7sOoXe+E4VvfKOyjBkGeF9ftVVoq90MD9dV6xmrVoEnElAzM5DJJIzBQahym85oFNZll8F+1avA4nE4e/ZA+T5YJALrkktgrF5eG8LG2Yp6Yeev37bruy6sHTtg7djReptIBJHf/E2Ef/VX4e/bB5nN6vmWPT0wt26tBCXGHXcgeMcd8J59Fpl770V+fBzCdaEiEciLL4Z8xSsA00Qul8P0sWOIxWLo6emB2RDI5r/+dRT+6Z8AAOLEifrKy8YqUimhslmgFDgyxqqBY7EIphRUqSLSe+YZwDR121nLatkWWbkulOc1VUYqIaqhtO/r2ZCGAbgu/P37YW7bBhYKNbW5bdw3gsFWXS/ryKkp8L4+qHmqL8+2YNTCxlcmcOghXelrRyzYHXwHo5j2KlWQgZgFK2ggvjoMM6ADRy/vg3PWsq1oOewzAwa4wWCHORKb47Aj1dePVxQ4cN8YLnnjuo7OI9RdrboMRC0EohZ61klkp4qYPJSGGdCvKeEKNI7wZYyBGYDigBk0oMozEZmuIOxaFcKWG4bw2v+8DcKTuPuzzzTdvypVKOoAETpbaPWWLi2TUjWH4OVtGGBHTBgWh3AlfEdA+K0Ga7bYvVJgqvkGNy8wsS+FYDf9e4KQFSOLOiC0+4HimK5uVB6A0r9bVKmEW6+M6gdDQy9nZgHdVwO9VwOxi4HohUDPNdUSaEIIIeQ8RmEjIYQQQs4rSincPTqN741M4WS+2HT7/ROz+L+HRnHzql68bd0gIstoc/o/95/EveMzHa27L53DB58+gC9cvhn9wfbzwV7uZmZmFhU0limlMDs7i6GhodNwVATQM/NEQwXdUvbRKPyud4EFg8j//d9XLtjzwUFd6db4WmBMV6q1eJ6ZYQD9/QhccgnYwADcn/wEyrKgMhkUd+9G4etf1/vu6wNPJPQMQ8ZgXXopgrffDuuaa5Y0/5NHo80LFxvMtqi0ZK32WyLzebj33Qf/0CGoQgEsEICxejXsm26C0aKqUymF1PAw5t785nkPQ0qJVCqFXC6H1atXwy61aC3ee28laITnQc508Lknpa64jEb1+bmuDgazWaja83VdqEJBz1MszeZEKFS5fKukhMrnwVq1Sm5MmqSsPva+D//wYVgXX1ytNGWs+TUlZbUN7DzKFZSdzL48k1LjecydzMEtCNghA+uvHqiEjZ3KTlZ/V8YGq49zpC+ASF8AJ5+e0jMMfanbf5ZCR2aw6nuGAX0bY4j0BZrmQwLA1NHOK6NX7ehBqNtGYa4aaBuWbnFaboMKAKbN4eT8lrMkTduAHTIA1L8XGYBdd14BxhhM28DwpX2YOZ6D71S/NKBUufpQdfS9ASVUNW+oWV8KCW5yGBYH5wwsaEAKCd9FxwVSUupmrtyof0wLcy44zSsmZOUYpc92uw/IH6/2SJalNyzjqL7Ry1+cUg29mxmw4beAnX95xg6bEEIIOZdQ2EgIIYSQ84YnJb649wQenpxruk0phVnXR9rzIZTCz2Yz+NqRMfz5K7bgkp7You/rX45PdBw0lk06Hj758yP44hVbYBvLr6w83/i+j9wywqxcLgff95sqssjKsF/zmmrgtBSMwb7++pY3hX7xF2Ffe61uC/qjHwEAjA0bII4e1SuYpp7Fl0iAlVpeKs+DnJzUIZXvA4xBFYsQx47pqrpCQVfEFQqVMInZNkRpO2PdOvDBQXjPPAPvmWdgX3kloh/5CGCa8F98EbLU4pN3dcHcvr3tLEnriiua5lmyWAwYH+/4oeHx5rZq1iWXNC0TY2Mo/uu/wnngAahi85cpCl//Oqyrr0bwrW+FtW1bZfn09DTm5uY6Ph7f9zEyMoI1a9bAZAyFf/iHym2VELiT6nAhdAtSxvTfa1utlucmln8u/Vc5jl43EtFBlutWQ8hGCyVB+TxkOl0NJQ2j0mq3aT8LBTel+zIuuGD+9c4AKRWOPzGJ53efxMizM3UPA2NAbqoIZjCEuu2OAvRy0GYGDYR7mh9nbnIEaioVldJBWqX7p8EQ7Q8iOtC+HW2rVqXtGCbHtluG8fS/HK3fR6F+H4zr1rBO3tetVWuYdvPr0w6bGNrWjUhvtQKecYah7d2YOpxGMa17xZZnKtbfGeYPB1u1WC39j5PxwBiDlBLCa7OT8uJWFY5SQZXbxZbPJWpi3w9HsenV9AUbQlaEdHVFY3EUUAKQ+gsmYFz/rCR0yFhbyc+qFYuMAXYC2Pbps3DwhBBCyLmBrsQQQggh5LyglMKf72sOGn2pMFF0MFn04Kr6Kpgpx8UdDzyLt69P4JfWDeLq/vnnqEilkPMF5lwP3zjeeZBQ63iuiB8nZ3Hzqr4lbX8+S9XMb1sKpRTS6TR6F5g7R5Ym+IY3oPjP/9y6bWgH7KuugjEw0PZ2Y3gYkfe+F+Ff/VV4zz0HlU7DfeIJOHffrav8yvP/CgWI0VGomZlKAKQAHTp6HjAxoSvpOK9v9VkOshwHCAYhjh8HoKsoAcB55BG4v/iLYPF403xKHo8jcPPNCNx2G4xEov68brwR+a99rS784z09EIGAvq+FmGbLWYmB22+v+9l74QVkP/tZyGy27a6UlHAfewzek08i/L73IfiGNyCfzy8qaCwTQiCZTKL/yBHImrmUlb9bVutKwcrB6NaSlceyPNeRsUoFIisHiI2BmO/r5zMS0VWJbdqrNm3XYh2ZTIKZpm52Fwjo9q0L7acVw4CRSMC68sqF1z2NcjMO7vr0zzB5uHWloFJAMG5j/MU5pEbzSGyJLzgvUUkdKCa2xFs+zo2ViozpQLB+H/MHv2ZwcZcett86jGe+fawu9GvZxrUUOEpfwXclpCcBzqrnzIBgl41YIqjbszbswg4bMCyOwYu64eZ9pMfzmDpS/9iWHxJV+Z/OcEPPkpS+gpSyueNiK23WaZz7GIrbOPn0NFLjecSHzq1qW0JecqYfAg7/mW6bqkSphaqv/wB6BiNktYpRGdUPBmYA3AasHmD9uwGr+2ycASGEEHJOoLCREEIIIeeF+ydm8WByrm6ZIyQOZPIozBOOFKXAntFp7Evn8YtrE/i1C1Y1XWw9kSti98gU7p+YQUFITBRcHM8XEDYMJII2+gPWotqZ7R6ZorCxBaeTYOYM7IO0xnt7YV13HdyHHlrS9sE3vrGj9VggAPuqqwAAgde/HqG3vx3F734X7sMPQ0xNQRw8WJ3TB+h5e55XbZUJ6NDR93Xg2KKlqSoWASkhTpwAi0Sg8nkdPioFnkjA2LChbn2ZSqHwrW+h+K//ivBv/EbdufBIBIEbbkDxBz+o28YYGIA4dWrB8+V9fboFbA1z2zaYNcfgHzqEzCc/2bKasRUlBHJ/9Vdgto3URRd1tE0rhUIB2fvvr993KaxjjAGWVf+4A6WyN1mtJmysGizfznm1WpHz5uDS8ypVqeVq1ia11ZWMtX6uZ2fBSo8lsywozuvbr5bvewE8GkXg1luX1Gp3peRnHXznw08iPTH/3EjD4hjaFsfEgTTG985haFv3vIFjsMtCbDAEK9i6/a8VNBasTGxs89kovrpFG9x5xAZCuP59F+HHf7V3wftgjMGwdMCoAPSsjVRmSFpBA2ZN61UrVH8JZODCLoy9MAdAVz6G4jaCXTacrAfp6faota1jO21/yrg+LiVUaaZj7fE2dF5s1CJwLLd2ZZzBDhmwwyaUAib2pihsJGQ5ph8BDv0PQPoAGOBndOAIlN6oEpUKRmZCv0E5YIT0z4wDdi8Q3gisestZOw1CCCHkXED9uwghhBByXvj+yFTdz56U2J/OzRs0ls04Hnyp8O2TSfzj0WrFYtrzcedzh/E7T+7DntEpFIS+Oph09MX1vBA4livgmdkMkkW35b5bOZwt4EB6ebPvzkeycf7aPIRScISEJ2XdjMfF7IMsXuR974MxPLzo7UJvfSusyy5b0n2amzYh+v73I/rxjwO+D55IgA8Ogg8Pw9y6FeaFF9ZV3gGohkm1oVcD5bpQxSL8gwchjh2rBF1yaqp19Rt05WDub/8WhW9+s/783vWupopHPjgItJozWCsYBF+9um4Rs21E3vOe6n0qheznP99x0Fgr8xd/gVwHgee8+5gvXAsE6n+WUgfBtUFjrUqJWClw9LzSYlatcqzleWDRKFiblq3MMCrzLlm76keldFvbUhvcxuCS2XZdrqOgH3Mlpf5TWm5s24bArbe2PI4zQSmFuz77zIJBY5lhG1i1oxvdw2HkZ1p/CaP/ghhu+N1teM1vX9Q2aAQwb3vUMjvaus0woEPCi25a/OfG9jeswaveu7XysmkMChsxg2Hwojjiq8KI9AYQitt1QSMA9G2sn4W647Y1dVlzZqIIBujZkKw6u7H+jhY+9nIhb6tqzI7GErdqy6oUrJABK2Qi3KPfe07O62BnhJCWnCRw+IuAMwmkntFtVFnNZ0Y5YKy0Ui3/20DpvwdXAV2XAJELge4rga6Lz8ZZEEIIIecMqmwkhBBCyEvewXQeBzP5umXHc0UUW4UMpQKDcuEAY4CEwqTjYlUogH85MYFLe6JYEw7io88cwmih/iKtVKopwPSVwrFcAZ6UGA4vfFEWAI5kC9jSFVnEWZ7/FqoYEkphvODgZN5B3q8+BwZnGArYWBsJInwWq45eDngshthnP4vMJz9ZaUO6kNBb3oLQf/pPy7pfpRTy//t/g/f2QoVCULkcIASU60IcPtwcKNZezRdCv9FbvDZUoQBVKIB1dVUDLSn1TMdVq9oeT/5rX4Oxbh3sa64BoNumxj79aaQ/8QnIqdIXHwwD5tat8PftA1oFhYEAzC1b6mZBMttG9MMfhrl1a2WZ99OfQrSY/6iUWvA94xeLMB58EOIXfmHe9dpRSqG4ejVqp9oy09TtaFEK+0Kh6lzMTlrslp8Lw9DblYPGxrazpgnWwfzVSmvUxuCz7i4Z+MAA5Oiovi8pq+FtKeRUSgGOoys1a19PjIH19iL6oQ+BxxY/33elnHp2BskD6UVtwxhDpE//TtKBogm34MMKGuheE8HgFt06PDWWxwt7TrUNwUJxG2bQgF9s/fwygyHa1/7x3/jKRN2cxMXY+aZ16F0fwTPfPo6TT09h9kQO0q9/vzPOEO4NIL4qtGAgueO2NXU/dw9HMLyzF6eenYHvCDhZHd5xi4NxNM2CXAwlal9HS96N3pwzGCaDFdBzNcuVqqbdPiQmhCxg4i6gcALIn6guM8IAFCDLQT7TfxhH5f89mBGAWUAgoSscIxcCWz56xg+fEEIIOddQ2EgIIYSQl7yfTM3V/ewKiVm3vjJJSsCREm5NpQoAGIwhwDmmHQ+rQvpi6L+fnMSU6zUFjYAOG1tRCjiVd2BzjoFgiwqdBnmfKvAaWZaFQqF11c6JXBFHsgW4LQJkXygczxdxquAgNVfEb/YPoNtuX2VDlsfo70fXF74A53vfQ/Huu6vhWgNr504E3/SmSiC3HO4TT8B74QXIiYmmeYpyelq/AWtbcTa+T0szApv4vl7ueXVhlZyamjdsBIDCt75Vd27G8DDif/qnyH3pS/AefRRKCDDbhrV9O/yjR6Hm5iotRVlvL4x16+qCRnPDBoR/+7dhbdtWdz/F3bsBlKrt5uYgk0mobFafE2NAIAA+MADe398czikF45FHIO64o/X5L4AxBtVQncl7eyFy1cpsFgjooG6eWZINOwWLx/Xz5Tg69DNNHVyGw/o5CQTADAPK8/RzM8+xM8sCotGmVrR1TBN8cFC/VhxHVzeWXyuc69C5XQtmw4CxYQOyn/kM3OuuQ/S//tf2bV1Po+e/f3JZ2488O4Nb/tulLW+LrwpjzWV9OPmz6bbbx1eFMX209ZzISG8A3GxdfWqYDJe+Zf3iD7jGmkv7sObSPqTG8vj+HzyNw49MQAoFbjDYERPRgWDTDMlWYokg1l3Z37T80resx4mnpuDm/ErXXwZdHSpcuag5jQAqwaKSOohlXP993rmWjeW1Jdxg+ksFrPqFnNhg9T3ZSdUpIaQF6QGn/gkotPhsNSIA8oByG97/pTeqyANGTFdGrnknsOn3AYPei4QQQgiFjYQQQgh5yZtrCBaTjgtVujqgFJD3Bbw2IaFQCnkh4BQlBgouBkM29oxNo8s0YBvNFy9rZzP6UsGREl7NBcRnZ7PYGA1hKGQjYra/+B1sse+Xu66uLqTTzZU7B9I5HM8VF7zeK5TCP06k8P0HnsW/vmYn+jsIfcnS8HAYoXe8A8Ff+iV4jz8O75lndLWhaYL39SHwutfBWLOm7fZibAzOXXfBfeQRyLk5AADr6oJ99dUI3nEHjLVrK+vKdBrpD3wA4tChpv0o162Gi/NUMFbWaZwdWN6uMcRunEPYgr9vH/wjR2BecEH1centRewjH4GcmUHxBz+A9+ijkJkM7KEhHajZtj7ffB5KCPBoFNbllyNw++2wtm9vug+Zy8F7+mnI6WmIkyebj0spoFCAPHEC8tQpHTquXVvXdpTNzYEdPAi11NmNwWDdCDk+MAAxMlL3mDHDgDKMedvWgjEdMFpWNWQNh8EA8FWrYKxdC6UUxNGjUKUAm5km1ALVkqy7G8a6dRD799dXRpZvLweRhgFzyxb4+/cDrgs+OAhj40b4zz8P2eJzB5YFFgzCvOgi8J4eQCm4jzyCdDKJ2Gc/Cx4+c3Py8rMOjj/ZOtR3Cz5y0w6kJ3V2ajA9dzBe31b26GOTKKRchOKtPxev/pVNGHthFr7b+vmLDgThFnxkxuu/EMJNjq5VrR8LxoAb/vP2SgXlYvmuwMEfj2PfvaNIj+d18Mf1HMZgl4Vg1+I+46/8D5sqj4kUEscen8Tzu09i5LlZFFIeJg6k4GQ8GBaHaXMIR6CjIY0NqzBD/6xEzezGxn2Un5pWu675mGKc1S3vXhNBoNSyNtofxPClvQufOCGk2dzTQPZA+77GRhhAEJAuIJ3m9QwbiGwAtnzsdB8pIYQQ8pJBYSMhhBBCXvIaLxNMO7r1kVJAxhOQHZQlKAUczxfgKYk514cfsNAXsOBKBaUAkwMBxqEYwBSQ9gVEiwsUErrV57TrImoYuCAWQrBFxc1weGkt5c5nwWAQgUAATk1gcDxXwLFcZ7PqTnoSE77CaCqHG3/4FH7jwmFYnKPLMnBtfxybYmcuHHi5YJzDfuUrYb/ylR2tL6amkPurv4L39NNNF+7U1BSKe/aguGcPrEsuQeR3fgesuxupj3ykZdAIoHX71FYXDluFjfPN9+xw9qfzgx/AfN/7mpbz3l6Ef/mXgV/+5bbbdtIGVaXTEMeO6Za15fNirHWLUSl15WehAGPzZh2wlfbP0ulFF2eVmX199R0gTRO8t7euolU5TqU1KjhvDoBNs3osLdqdyqkpGGvWgDEGY+NGyEAAcnwcEAIsGtXVjo041/M7167VbVK3b4d/4IAOvWtXGxys/J2FQjC3bwdTCrBtHeB6HnhXF5SU1cpTxoBQCMaGDeBdXXX78w8eRPZzn0Ps059e8PlbKenxQlNVXH7GQXqiACfTPLMvM1GAGTQQHQgilgiBGwxSKKQnCm3DxsTmOG760CW49/PPQbRpHdq7LgrGGGaOZ/XxMKCrJwAv78MK8Lr3l2lz3Pj7F2PTqwZb7ms+vi/x5D8ext4fnIKTbX7uQ902JvanYNocPWujCHUvHDq+4p0X4KLX6/moIz+fwY/+9Hnkpqu/ayJ9AQxcEMPIz2cgPAnfEbqFaqmDIoD5M0deyhwVwBmHgoJiqtJ1kbHmzZnBoMr30U5N0h/tDyK+uvp7bPutw+D8zLwGCTnvzD2lKxQrZM37rfzG5wAP6j+QNf+OKP2ekwIQBd1KlRBCCCEUNhJCCCHkpS/WUEHoS32BL+t3FjQC1WukJ3JFuFIi7wucyBehlIIr9R8FwABQlKUKklJbs8ZLfY6UsAwDWSHwYiqHi7oiCNcc46pQADu7o0s/4fNYT08Pxkvz6aRSOJxp3Va1lR9nPGQ9AV8p7M/4+MrhUayN6LZW3zg+gS2xMG4f7sfrBnvOWEhAqsTJk3qm4cxM+5WUgnIcuI89Bu+FF2CsWQNx5Ej7yoP5li/mOW5ct+ELAsp19XF7nt63aYJ3d0OMjnZ+H013Of/xuU89hcwf/ZGuxGtBGQaYbTeFdyqdhjhyBMaFF8IwDHhAx+FpK7ENG8AsS7c0LTHWrYPMZoGi/oysCwNrq0tr29sCulrQbhEMeR5UOg0Wj+vAcXgYfGgIcmoKPBzWcy/Lz2m5bezAQH3galkwd+zQ559MQs3O6lC2pwcAwKNR2K9/PYK7dsFYvRreM89g9td+TQehvq+rQTkHi8fBEwk9x7PNc+Q98wz8Z5+FddllS35cF8NzqtWdSinMHM8hm5z/s9EvCsydzCE/4yCxJQ7D4m1nLpZtvDaB2z99Be7/8xeQSdZ/ycMrCmTGC8hOF8FNBqUYDIPp0HM8DwbACpvoHo5g82uH8Nr/sh29azv/PZedLuLFu0ew754RHHt8EvlZHWAHYyaiiRDCPXbl+QhELQxt68bkwTSSB1PoXR9FLNH6Yr9pc1z7nzbjkjeuAwAcfTyJe/+4daAaTQQR7Q8iN+vAL5TeMzVhHzNYtVNzQ/jLmG6XilII25wsVjv3lhkmA7M4hCehSl9sasWwOMwAR2JrtULUChnY9obh1hsQQhY2+zj0bMZy5WLDjFVmA9wGWPn3DG9odywAd0q3Y6WwkRBCCAFAYSMhhBBCzgPX9MfxnVOTlZ8VAFeqlpWH7VicQUhdCSmUDis5Z3BENa5UCnCVgoS+3iAUwJTSBT217VVr7tdXCvvTeeyIRyptWW9b3UdhVxvRaBQ9PT2YnZ3FiVyx7rGczw/SLp7K11f4nMwXK2EjABzI5HFg3wk8OpXCh7atb2qTm3J9vJjKIucLGIyhL2Dh4u5oXetcsjRydhbpO+9sHzR6HkQyCTk5WWkVqqSEymbBVq3SYVBjJR9QDbYW8V6vqLvqXx8usqgOSWQ6rasFy/MWa8/p1CmoTAbOjTfCvuGGutaly6GUQuHv/g75b38b/tNPt19RCD1r0PPAI5G6wFTNzkKlUuDd3eCGAUQind13Pg/l+4BS+vEOh9E9OAhx000o3nVXdUXThLl1qw5CGyoJ6zQEjXye41C+X3cdlxkGzNWr0f3lLyP/z/+M4ve/r8PABd6PrKsLZlcXIATC7343zB07wMJhGKtW1QWd3gsvwFi3Dsa6dZWqxnnnPjYo7tlzxsJGO1R97XcSNNZycz6S+1MY3BaHFao/v2LG+//Z++94Sa76zB9/n1Oh881h7uSgSRpFFEAgIaLCIBy1YHuN8drLLvbP9tpes4ABk20Daxt77V0bMMYGvgbbGIzQSOQgERRRHs1ocr45dK5wzu+P6u7bfTvcvjOAwpz363Wle6tOnTpV3V3Vc556ng+5qRKhp3CTFj1jSdZcPMB//ti1HLl3ksf3nODED6fJz5SZOphFa0j2u2RG4khHMn+qQG6iFAltAoJyyPzpAscfnObf/sc9bLl2lMt+bgODGzNtx+eXAr7913s5eHdUh3HqUDYSGgG0prTgU1rwsVxJ/7oUqcHomu4mbVZf0k9xziM7UcJyJMn+ReG9Z1WCXTevZccr1xDPRLGjkwcX+NoHH23r3BRCEEs5ZCdKSDsSAatIWy5+xLRu0CUgEh+FELhJG600filySVVFRCGoXatE5UGleMYFrSnnApTSkXBf36+AeI+DtATxHgcnHr1+0hLc+NZLSfaZhASD4azJPg7BQps0BKJ6jcqLcpHtFNDiHh8WKusMBoPBYDCAERsNBoPBYDB0yWNzOfacnOLx+Ty5IMCVkuG4y8tH+3nF2GDH+oRLCZTC/hFNzANc1JdmfTLOsULkxLCFoLxCJ48lBLkgQBOJiWWlo6i9OkKtayaH+v9HzXSD4FjvhvC14nSxzIZ0gn7X5hWrTI2lTgwODiKl5O6Jg8u21RpuXyjz9RZRgvkgZN7z6XWdhuU/mJrnT584wtsv2oQUgr3zefacmuK7k3MN9TcBhmMON60e4saxQXpd89X5bPAfe4z8Rz6C/+ijkSCXTCLTi46n8MQJ1OnTzRN+5TIohR4fhyCInHypVKOoV43tbPV5X87dWBUpK3UEG1YND0fjWsa5qObnyf35n+PedRfp//W/EPF4x/bdUPyHf6D4+c+jZ2ai46o479oSBKhcDplpFHTUxASyrw+3p4fitm1tN9dhiJqaQk1MoIuNIpZbLKJTKeK33kp4/Dj+Y4/V1olYDOfCCwmOHSPM5VpP2KZS4HktHZjNA2ne3nn+85GDg6Te+EYolSh/85sdNtfo+XnUzAwiDHGuvprw+HGs1auxt21rEBJ1GFL+ylcWj+Us7kf+PfegpqeRg4Mr3nal9K1NYruShfFiTWjUGkIvJPBUg6AlrcgFZzmLx+QVAhZOF+lbE02Kn3xkhsduP86ReybxClHNx9BT2DHJmssGuOI1m9jxyjVsev4Ih74/zh3veZg1lw4gLYGQguKcx5kn5tChRloCaS1+zpSvGH9ynrEL+9j/zdMc+u44L/+Di9l8zUjTcZWyPre9/QGmDmWjvxc88lOtY7NDTzF1MEvoqVqNSCEEyf4Yyf4YTsLip95/BRA5H3tWJZqE6fv/+VDbmpRVAj+sXRrqn2PQSiMqxxmJq81ORB1qVKBQ1VKP9eJkXZKzsAS2a9UEyFjappwPou3qvkpZjqyd87510WvnJCxueMslrLv8x/++Mxiesxz9eyid6u5BJR1CkAM7TZPgGBuuFGk1GAwGg8EARmw0GAwGg8GwDA/NZPnYwZMcXVI3z1MhuVyRj+WKfPLwGV6+aoBf37K6yS0GURzmD6bmuePUNE/M5/BUJMyNxF1esWrgRyLkvGrNEP/vqRMAJGxJWO7e6WQLQTFoDFwNtcaudwrRnIpWpyeidDTZbVUcHkuZKvtc0JPkjy7eTMYxX8GWI9HTw59NFHleQnJpwiK+5KSWtOahYsj3cgHHvdZCjAYmy81iI8B90wv867FxThc9vn6mfaznZNnnk4dP86/HxvlfF27gqsHetm0Ni6h8Hu8b36C0Zw/hsWP4P/xhVL+vuj6VQo6MoBYW0NPTTdtHpoLI4UgQRKJiGKJzOUina+KQcBx0ZV3DpGErt+PSD2b171isMQo5FkNns+hKnG8nRDISPbz77iP7vveRede7Wjswu8S7/36Kn/88EImFAMJ1I7dhJyouR5FYjHLT8/PocpnE7t2Eg4PkcrmmzdTcHMHBgw2vTRXh+6S/9S0Kc3MUP/tZEr/yK4hkEu/eexcb2XbkDJyaQnteLWpVCIEcGcFas4ZwcjISTpejxXmL33JL1J+UpH//97E2baL0hS80OGR1pValmpiAchmRSiHXrkXNzlL+xjcof+MbyMFB4jfdRPznfg7huqjp6c5xvl2glSI4dAj3JyA2xlIOW65bxbf/5gkgijQNymGz2KUhDBRhoBASnLiN7UafFQ3kJkt848OPM3lggeKCR/ZMkeK813Bzmz2e57EvHWdkay/X/PpWHv3icaS9qJyVFjwmnmrjBqqgAsXEgQVWX9RP4Cm++If3c9nPb6R/XQo3aTOyrZe+NUnufP9DNaERaIpubcXs8TyWK2sOxyp+MWT+VIGt14+13C47WeTofVMt1y32EeDlAuy4hV8MEVKgw+g4tdJQJ6oKISInYh0aCH2NkAIpJVo012XVWqPDqE2tLxkJjlGtSIWq7NN2JUIKhrZk6FudYueNa9h181rSQ+f+UIPBcN5y5ktw+gsgnSgiVS9zf4UoYjXIgZ2hIUu159If1ygNBoPBYHhWYma6DAaDwWAwtOWbZ2b4y33Hl40jLSvFnlNTHM4Vefclm0nUuRy/njruNwABAABJREFUMz7LPxw6xVTZx1OKOS+ouMc0pwoljuSK/PORM7x0tJ83bl3bUqzshhvGBvj+1DwPzWaJy+6fMpYIpBAEuq4+E83CompzDvSS37XWCA3FUBGzJNX5REsIXrV6iAsyya7Hdj4zU/aZCBV35hRfzwdsciQpKdBAQWkOeYqAKPa2FRWzKYUgxFcKZ4lzSWvNBx8/ylgi1lVpv1KoeN9jh3nrrk28YMgIjp0Ijhwh++53o6aiiX01NdUkZul8nmDvXrTvN7sVgZoNqEr1RapGq/b0LK5yXXQYdnb/1cd51vcpZU0wrC4T/f1thUbteZEIWh3b7CwIgTUygv/wwxT/+Z9Jvu517cexDKXbbov2UzlOAFwXisVlHRja8yJnZV29RJ3NkrjlFlKjo2ityddFnqqZmUhobNGv8H36vvMdnLm5qKsgoPDxj5P45V8m86pXUb79drz77qvFrYpMBrJZRCaDVamnWHWLWqkUQaEApQ5Ckm0j615TgMRrX4tz8cWNy372Z4n/1E/h/eAHlL/yFcITJ/Dvvx81P4/MZJBbttQicOtR09MUPv1pvPvvJ/NHf4QuFDqey27RnSJkf8Sse94ApQUfLx80xHu2Q6vI0aiVhRO3iKdtPvlrd+EmbeZP5pk72eEcaJjYP89tb38QaQtWbe/Fci2U0kwezHblBvLyAVMHFyjnAoJyyFf2P8yaSwdrLshYxmbmSJ7kQFSLMfQVxWp86jLMHs+THIg1CXmP3X6iJjYW5z2O3DtZi2Q9eu8UfinAdtt/P8hVXJVOzAIdibqirpaiVosioZAiesqo7pxRdURWFjkxi9BrfJBJCIF0BDpUhL6oOVCFEFiOhZASFSiEFKza2cfzXruJDVcOseHqYSz7R5cIYTCclygfTv5z9LtMgIxB2IXYCNFFVXnRNhCJlYPX/njGaTAYDAbDsxQjNhoMBoPBYGjJgzMLTUKjqsSIWm3Umb0Lef7k8SO88+LNWFLw+eMTfPzgKbJ+wHjJY9aLYkrrOVYo0+NYTJU9jhVKvOeSLSRXEMlaxZaSP9y1kfc9dphThTKWEMuKpBJBypbkg8WJWy1AtCrfsmROsR3R+YkE2LJS9Ng2m9NxBuMu836XExoGHCFqMbW+hv0tou+CJXU5q2Kv0ouv0XTZ56HZHL2OzUjcodexEUJwsljmVLFMwpb0t3A+tkJp+NATR/jLK7ezNmmcJa0Ijx8n+9a3oupcdCqbbW6oNbpcjv6/xK1YXb+0fS0uValIWKvW36uKcUIsxqlWazlWaRWTaVmRKFYVKYVArl+Pnp1tHm6pVBtvDceBYhF18iTq1ClEfz+lz3+exGtf21AbsFvC06cjByg0CKcCIJlcXtjSOjovdXGl7jXXYK1bB8CqVauYm5tjfn4ef36e4NChluc5duoU6Ycewl5YaNpF8VOfIv2Wt5B5xzsIJyYiwTifx9+7l/IXvxiJjkvuD8K2sXfsINi/H9qIfHJwsOE1SrzmNSR/+ZdbthWWRexFL8J9/vMjUXv9erq9YwT79pF917tI/c7vdLlFZ34UsbndMnu8AJquhMZ6/FJILOMwczyPtATJvlhnobGCBvLTZWxHcmbfPGM7+yjMeagu9q9CTTnn4xUCYqloykEFmvxUicxo5L49es8U5ZxPbNxheFsPpQW/69KroacozHqkBhqjec/snePkIzPs/cpJDn13vKE24/i+eUoLHok+l8xIgkRv82c0KC8emxO3EFJQzvvoSj/1QiIiqp1YjbCtj0yFKOHAiVtIW+AVFh+2ECKKeZVW5IxMDcXJT5XwiiGhF7WzXMno1l6SAzFmjuQYu7AvclYaDIZzY/pu8Cv3ttgweDORaKiao/hbUhMbBSS3wPANP7ahGgwGg8HwbMSIjQaDwWAwGJrQWvO3T50k1Jp8EEZCYdknrEg4EkGfazMSd+lZEgn6w9ks352cI9Savz9wkhOFMqdL7d0KGs28HzDvB8x4ATEpee+lW5Dd2M2WkLAt3n3JZopByMlCmTnfbykMCsCRkrglCZWm6juoRqItNSw0jrc19XUcU7aFFBXXpIR8qBgRglzQ2oVnaCbj2sSkRUm1OWcV92iodC3itn6+t4pEoNHM+T5zvk/CsticjjNRiiI6p8t+12IjgKc0t52Y4je2rT2bw3pOo4OA7Lve1SA0Ai0dh9r3F4UupdD5fCRUVWnx+RfJZM3tp8vlKHazXF6MW61uV++KlLJlPCeOg71lC3JwkGDv3locqEgkCI4dqxuoRuXzzccgRKPQpDV6Zgbv3nspfvazZ+Vu9L7zncZx1+/OcSLBcRlHXr3YKEdHid14IxAJvuVvfAPn6FEGikWyx45RHBxEDw2hHQfhebgTEyQOHMBaZh+lz36W2ItehDUygjUS1eGL3XADwUMPoSpOyKUI18XeuTOKO52cjOpx1lYK5OgoCIHzvOcRf/Wrca+4ouMYAEpf/CL+Qw8t2w4iB2LVkeo/8ACl225DxGLR++gckKtWndP2K+H045EILm2BCroXnqQt8AsB0pFIKZjLdefGVEFUCzLwQvx8wPTRHEFp+XtYVWjUFWG0vnRqdiISG71CQDkXTe6Xcz7je+dIrTAaNDtRbBIbZ0/k+fc/uDd67qBST9JyIlezDqOnUIqzHsVZj/RwnIGN6QZ35FJBz3YllhujnPUJPBU9wyBBICp1FyV+KcRfcl6EgFjKRsioNiM6inlFgJuyF2tcClE7D05c4sQllmMxvLWHeCa6L008tcDEXz7BvZ88yE1vv5SRrcZZbzCcNRN7Fn+3e8GqXHd0vss41TBql9oCYz8NTmb5bQwGg8FgOI8wYqPBYDAYDIYmfjib5XCuwKFciVwLoUChmfF8ZrxIvNmUipOuEx2/dHKSU0WP44UyZzoIjUuZKnv8+/EJXrV6kBeO9J/V2G0pee3GVezLFjhRKHMoV6y53yKRUeBKWZv89OrkQ6vOTbe0FhN0djRW+5ci6seuq8c0WfZIWJJYK3eVoSVxy+L5Qz18e6LRZaZ1FGnqKUWgNIrmKFuIXgsBxK1G0aoYhjwym4PK6+SfhVvkm+Mz/JctY8StlTtwn8t43/0uYaXOYAMthMMmkScM0UGwWO+wKhhW41fDEC1E5Cb0fQgCdL3zTohaXcfFnehFh2MiEQnRWiNcF2vjRuSqVTgXXUTita+ldMcdEIaEJ040uP1aCo1Ewqdo9fr7PrkPfIDYDTdgjY62OVOtaaghaFlNxyNcF6REl0rtI2O1hngca2ys5trMffjDeHfdVRNlte9jPfQQ6YpbVA4MIMfGGuo9diI4fBh/716cnTsXx2bbpH7v98i95z1RpG0LhGVhrV6NHBtDz82hFxbQYYh79dXEbriB2EtegjXWut5e82Fqynv2dG4Uhqipqeg9WSw2rMp96EM4V12FLhQaY3RXgL19O/bGjWe17dlw6rHZiojl4BW6i1K1XYmTtClnfWytCVTktuuGWnSohsBX5CZL0XVVNn+e67epCo2LyxbrFvrFIBIas40uIr8Ykj3T+BotRznb+BmYOpRl7nie7HixVmcRQDqS9FC86Z6emyyhNQxtXhQLpNXiIQcglnGQxQCtqDk1a2MvhU0Ox1jKRtZFntpxm2RFGPUKAVTc914+OhfxjIO0JanBGD2rki1fo/xMmS/+4QO8+n1XMLrdCI4Gw1mRP7j4uxAQH4PCEbBSoEqRc7HTt30hwR2BxDoY+9kf92gNBoPBYHjWYcRGg8FgMBgMTXzq0GmemC8s1jHsQDEMeXKhwNZMkl43+mpx9+Q8XqhWJDRWmfF8/mLf8bMWGwGuGMgwFHORQjAUczheKLHgh00RrrAYi+ZaEpvIhSiAhCXx6yYnRVWF7IAgErBaNTtd9BiIma9eK+E3tq7lrom5mvNUacgH4bLxuBC9VFLQUugqhgqFJm1bLUXl5SiGirsm5njl2OCKt30uU7r99pbLhes2fyZaCVLlMlpKdLEYCYr1DkUhFgU2paLtLavRAShE5GKsblepA6gBpCR2/fUkXvta3OuuQ8TjUa3IyvvDveYacv/7f0fRohV0udws6lVqPIpWbskKKpul8NGPknn729u2aUm9sCgEcnAQtUS8FbaNSKcjQc/z0EGwKKpKiUwmcS65pHae8h//ONQ7PyFyFta5StXUFGp2FmvLFmRfX1dDLd9xR4PYCOA+73mk/uAPyP/5n0fO1TaISl1M+vtJ3Horyde/vqt91uPffz/hqVORk7L6XrFtRE9P5FjMZgmeeqq9KFsuEx48iDp9GjkygrVhQ8trRSfiu3eveNxnS2G2zNyJyJFYdc0ppQnKitALm8qbWq6FHYucjCrUqFATlBSI7sXG+g9tWFZISxAGuuP2wZKxtCIMFCpsbuQVAoQlkB3EzMbxRceFgDNPzDF/qhBFl4aN41O+YuF0Aa8YiXxO0q454PNTJdyUTU8l2tVNO+Snm7+3CMBN2CT6XWzHIjddQocapaIxCCmwXIm0JLG0jZuy0SoSL2Npm/RwvFYrMiiH5KZKZMcjcVVagp6xBL2rk8hlalb7pZA97/khr/3ra0j2xzq2NRgMS1BBc1xqbATCIpTHwarUcNR+JDrqyuNsQoCwQbjR/+0kbPk9SG16Wg7DYDAYDIZnMmbGy2AwGAwGQwNTJY9/PzHZldBYRaE5kC2wszdF0rYYL3kUziEy9J6peU7ki6xNdee0WYotJTeMDfCZo+MkbYvtPSlKoWKi5LHgB4RaIxDYAhJSkgsCpBDUz38mbItYJUZW07qOYz0SsCqTpK2mSn2t6He6j+s0wEtG+9mcTnAgV0C3EBqr0antEETbpG2r4UUJq64SpRECcn5AqCNxMiYl7jITvgCniucWv/hcIzx5MoojbYEcGmoUzdqoEdrzIuGotmGlRmN9DiM01mWMxSKRrtJGSAmui7BtrE2bEKlUtM6yEIkE8Z/6qZaOROeSS+j7+MeZ/+3fpvzVr6Kz2Ub3pW1H8aSO0/Lz3XggGv+++wgnJ7GGh5drXUOk0w1/y5GRJrGx1tayFt2a9VSiXfXCAjqfR7ZwCupSqbnDMCR86inE9u2Inp5lxxoeP95yeezaa5FDQxQ//Wn8hx9u+1rbGzcSv/VWYtdfv+y+lhIcOULuAx+IIlSXitZCQDwexe1aVsfXSheLiN5e1MQEOgiwL7ig6zFYIyO411674rGfLWf2zhFLN94/pBS4CQudsKL4z4qrTkjRcNxVYS8MVPdCI0s/chqtNaHXuY+w3Py9IfQUXqCgsmr6UBY7ZjV9rKUjCT2FdLsXfTWayX0LZCci4c5y2l+7bdeilPXRKsBNLwqO2TNFMiNxhBCkh2LMncg3OCPr6VuTwk3a9K1L4RUCSgseM0dzCFkRSQWM7erveI7smEXPqgQLZ4o4iShiVdqSoKRwEqKjcxSgtODzxJ0nuPIXt3Q+OQaDoRFpg7CiKNR6khsiEbF0qpKTHKvUZWyBsGD1a2DwJ3f9NxgMBoPh2YTJ8jIYDAaDwdDAZ4+OU2wTg9eJEM2JQhmtNfNeQOEs+qhSVop/Pz551tsD3LR6kGSdqBC3JOtTcXb1ptiYSpCwJPlQMe+HZIPo/8UwRGmNIwRSgC0FPY5N3JIdTS/1QiPQst5kxrZ5cDZ7Tsd0viGE4O+ev4M+x6EUqiZHY6cpWYvodQi0pqyWToBHE+eFIGS67PPEQp592Tx7F/I8PJdj30KeOc/v6Hosh92L8ecD4fh423UinW6Mqmz1YapGplZdidWfagxqdZuq+Fi3jUilkJkMsrIfUXW4DQ1Ff6dSiHgcNTWF9/3vtx+nbeNccgn2zp1YF1wQjbunB9HbG/VdJzTqMEQXCqhsFrWwEP2/UIiiYC0LrRTlO+9c0Tl0Lr+8cTzJZFfCXz2ytxeUIjh8GDHYxnnb7tqsNcGBA4tibgd0sX3kpbNjBz3vfS+9//f/4lx5ZVSLU0pEJoNz9dX0fOAD9P6f/3NWQmPpttuY/53fwX/00ZbHocMQdeZMJBYXCp3N6EGAtWULJBLomRnCkye7GoPMZMi8611RrO1PiHI+IN7jYLcQsQSR8CgrrsCln67665h0u//nv7Qb73sC0fGaGAYKVRdLrUONChR+KUQFFReg0viFgPx0mdKCh1cMFs3L0VbLxpVXsVzJzOEcxblyrYalHWsv8klLVNyZCr+w6HgNyiGleb/SJooybUUs4+Am7Vpf8YxDvMfFsmXNjRnvcZcVdLXWzBzNUVrwo5+sz/ThLKcfn+XEQ9PMHM011YBcyhNfPoky9yCDYeXEV7denlgDPRdDfFUkSi7FikNiPfRcCkMv/vGO0WAwGAyGZzFGbDQYDAaDwVCjFIZ8e2J2eedOG+b9gEIQ4nUxWb0c90wvLN+oA4Mxl7fu2ohdN1taCEIenYuEpVnfR6Nx6yZno0S2SKDyK8cgRCRUxi0LSyzWAqz/ser2YVeEyqWMJVyO5yNHUTEImS575PzgrGI8zycu6svw0efvbKlPtXuXLRV/y1UrI9FEb6gh0FEs69KpeY1m3g/Yny3w+Hy+raiYtE29xgaWRHUuRa5atWTBkn+GVIXGIGj8USr6EFbFx/prSzVadcm+RTIZiYUt3jTL1fmzKxGkOpdDSBn91PWjfR+VzUZiludF467GunoeOpeLxMfpafz77uu4r6U4l16KtWZNwzJry5bIvdklcnQUNTODtX59ezGsU63RIEBNTy+7n071HVU2S/Hznyf3nvfg338/OpsFpaJo0/vvp/Tv/4734IMrvvYVP/958h/5SGO87lI8b3Gd56Hz+fbildYI28besQORTqPGx5cVWq2REXo+8AGsdetWNPZzxbKj92FmZOVu/+rbt+a+W8F29U5BYS1Tr7F66jSoivC4tL2QICyB5UTLg7KinPNrIqW0ZdffP2Jph8JMmcBTlW1Fy5qL9VSFwMBrFEZzU4tu396xJLKFQ7JnVfO5t+zF/Qkp6FvTuf5nUA45/dgc04dzDTU3q9cYFWiy40VOPTLD9JFs2/d5frrM0funOu7LYDC0YOTG9utqguJlkNkBqa2Q3gqZCyFTESLjI9B31U9suAaDwWAwPNswMaoGg8FgMBhqfPPMLMVQ4Uh5VoKhRjPl+Sg0zjk+05T329TaWgGXDWR49yWb+ePHjzBeLLMvW2iu9yfAlZKyUjhC4FqSfBiSDxRJi7pITV0RFTV1c5QsLQsYWyqiAKsTMdK2zaFckTfes5eTdRGcPY7NK1cNsHvNECPxn5xT5tnEgh9wZX+Gw/kSk2WfUhh2jFBVgK80kigaVQnwtY5qcgaqJnIIETly2wVjFsKQvfN5dvamiC2JVt2a6TypfL7R4FxsgRwaQmezUb1AiOrqVd1xSjWLiLUNZSSOKRUJiy3cjrpchlisVgvQ2rSpZVQqgP/oo6jpaWQb15/7ghcgBwYI62o3VtHlckdHX/34w4MH8TvUdWxH7OabKXzsY4tdOQ72jh0E+/fDMvsWPT3Inh7o7+84TpFIdHSPqfFx5DLxr9aGDS2X+48/Tu7970dlW7u4tVJ499yDd889uFdeSfrNb0ZUol+reI89RnnPnug1UCqqUak1wQMPIHp7o0ZtIqkbom8hiuUtlWrxsg1UXh/hOFg7d6Ln5hB9fZE4uuQ+YW/eTOyWW4i9+MVRnO5PmNRQtM/0SJzCTJlyrn1NzKVEgnmIHZPLRnQuxY5ZBJ6qCJUSp6PWWamtG6rFj+mS/dmuVRPWbFfil6P6jV4+IJZ2kFLQvyHN7LHcshnZunIj1ioSMauuw05YjsSJW/ilkKCscBOL4mP9MY9s7WF83zx+MUQrTaLfxS+GlCyPeM/ifdpJ2Nhxi9BTDG3JNEXd1uOXQs7snUP5qjZ2WHSlLiU3USL0FSMX9La08c8ez7Pp+csessFgqGf4FXD8k6A6ROELCXabVIHhG1o7Hw0Gg8FgMABGbDQYDAaDwVDHEwt5AAZjDqfPsiZdIVBYQuCscFJzKU4XdfO64ZL+DO+7ZAu/+oPHsYVoGcU5EneY98JImNI6MlChWfBDYkrjSFGb91wqONYfpqT5uNcmoknuh2az2EIwEGucjFzwAz53fIJ/Pz7BdSN9/Pb2dcQ7OY/OQ74/NU/SsdnVl+Z4vsShXBFfaVTFStNqTloDIZF70ULjhQpfQKA1UkRxgAIItcZTKooiFKLBpQrgacX+bIFdvalaPO5QzOGqwZXFWz7XkWNji85E224ZlWpt3AhCoCYmEK4b1Q6sugJbdioXXXhSLjoZq39X0RrZ24u1cWNXQpCamWkrNgrLInbjjfgPPNCwXHted0KjbdeEzuDIEYpf+AKJn/mZ5berEL/5Zry77iLYt29xTLEY9oUXoqamohqOrcYRi5H8tV8jdsMNZN/1ro77kENDqJMn0RVXqK53AwqBKBZR8/O1SNba6yRl5Ja0beI339zUr//YY2T/6I/QfndCmHf//Sz80R/R8773oYOA4qc/TeEjH4nqQdaPp6cnEhEroqE1Oors6yOcanR2ad9v6QTTngfxeJNeI/r6Fn+vCtWjo6Q/9CGCQ4fQhQIiHsdavXpF9Rx/HKy+qJ/MSJzsRInhbT1M7JvHy3f3QI4Tt3DiEjdpU1roXqSEKC7UdhdFyvRwgtK81yCW1RACHerFl26pq5FIYKxiVYRMraO6kkE5xIlbpIfiWJZg+nC2rYE10evWjkVaAifhdO3adOJRDd+gFKITUV3P+uPxSyH5qTIqVATlEMuVBMWQuRPR9yM7YZEZSZAZjiOkYGRbD6GviWfaC40q1Ezsn4+ERq0bjsuKtf+uU5z1mD2eo399ummdXzz7qHqD4bzFTsHozXD6C2exbTra1mAwGAwGQ1uM2GgwGAwGgwFfKb43Oc93J+Y4kiuiAS+MRLZOtQpbEWjNlnSiwb23Uhwh2ZRq4UQ5C7TWfOzgSaZK/uJks47cjP0xh9GYQ9y2OFMs8+RCIXJ0isgtoYlq84VaRCIklYhOIZCwpJ4VpOxoElMiGIw5jMRdJkoek+Uo5jHRIXpTA9+ZmON00eN9l24xMZ11LFRcrmeKZU6XyiRsiaM0nqfQROe+nQlGE8XjerVYXFFzo4Y6en191RiD61oCR4iaA6cYhsx4PkOxyNFy09hgy7qc5xva8/DuuovSnj0E+/cTHD6MnpoCKZH9/cjRUUS6bpJciEgQ7OuLRLNCIRLO6mfeq45Fy2pd27HaJp1G1ImaIh7v2nGml6knG//pn6bwsY/VXJho3bWjsT5eVDgOxU98gth117UVN5u6cF0y73gH2T/6I4I6d6WwLKzRUazRUdTCAjqXi0RaKRG9vfT+7/+Ne8UV+I89tvxOtI5qTi60iKrWGl0uEzz2GMJxFiNs647R3ro1EirrULOz5N7//q6FxirB3r3M/+7vRu+fJ55oFp61Rs3ORmOVEhGGhEePRvUoXbcxQrfd66pU5HCsd0NaFnJoqKlpeOoU1urVWKvb1PV6mhBCcOHNa7nnHw9g2ZLRHX3MnciTmyqhwzZXPxGJcv3rUhQXfGIpm5ljObzcylID3JRNvMelNO/RuzpBotdh5kiuqZ2UizUdpSWaXY0Jq2GZlAI3aVOuiKaBF5Loc5GWIDUUx0naLJwpUpgpN4iBTtImPZLAKwRkRpKU8z7F2c4xzktxYhaWI0n1xyhlvZqzMD9TZvpQFiEFvauSpC9JEPqK7ESRwkwkygfFkNljOQTwU39yJRuuHuJTv3pXgztyKfnpEkGlDqMQovaxEiJye3YiO1GiZ3USy24UJbtxchoMhhas/1UoHoe5B5ZtWkO6sO1t4A782IZlMBgMBsNzAfMN1WAwGAyG85jpsseXTk7x1dMzzPsBB7IFZrxostjXiqKvcaUkZsmWdQhbYQl4/aYx/uSJIx1T0DoxHHe4uC/Tcp3WmvtnFvja6Rkmyh5eqEnZFjt6k9y8eoixxKLg8LXT03z6yBm+fGqasH40InKsjZfKzHs+KdtixgtwpagITxpLCkIVCYxaR663QGkCIqdc0raIS1mL81wVd3GkIB+ECASeUjw5nycXhLhW5JjrJib1qWyBP3n8CO+5ZHPLmnPnK4Ug5HghErBDrckHjcLCcoJjoKM2Ukd1xKQWKHRTzcZAa4JAI0UkHledjhOlSGwcibvcvKZZpDjfKH/nOxT+9m8b4jKt0VGCqSlQCjU9jZqeRqRSUf3EOiFQ9vUh+/pAqcjFVnXWWdaig7EdVSHS8xpqEi4nINYjU6ll1/d84APM3HorlEqNzr8O4xLJZEN8qxgYQIchpTvvJPmf/3P34+vtpecDH6DwsY9R/uY3o/3Xr+/pgZ7IWetcfDHJ//bfsDdujFYuE3+ti8XINVlfC3MpYYjO5dBCRAJfKtVwXFprFt7yFuydO8m87W3I3l7KX/4yKtcsQC2HOn0a7wc/iPbTrlH1+JWKRNZ0GqanV1TLUnteJJ5WkIODraN2uxGVnyZ23rCGB//lMH4xRFqCgQ1p+tYmyU+VyU+XCSuuOSolaoUErxgy8dQCPWNJ3KRNZjjBdK51xG070sMJBjakQFcchVkfFWjmTuYbLroqiOJWBc2uRidu4cSaz7flSGIpG68QoBW4ycU2btJmaHMGtT5FfrqMXw7JDMe57NaNODGLhz53BIQgO15csdgIkdiZGY0ztCXDphcMk+hzuf+fDzF8QQ+xnkWnpBO3iGcc9GaNCuvEVCF44J8Psf6KQa7/rQv5+p+3F/qzE6WGv4WMXKBOwl72gS6tNLnJEr1jjVHVg5ua3Y4Gg6ELhAXb3g6HPgxT316+vZ2B7e+AzM4f+9AMBoPBYHi2Y8RGg8FgMBjOU/Yt5Hnvo4eZr6uN6NZFEyYti6wKKSuFpxQp28LuQnF88Ug//33bWj528BQT5bOYAESwKh7j5tXNTqAvnZjkCycmGS8197t3Ic8Xjk9y+UCG120a47uTc/zbsQkmSl6j0LiErB9ypuQRtyRxS9LrWHhKU1YKIaNY1drYRDS3Wo3bjFmSkbhLxraZKHucLnqoyr60jhx5GiirqJaj0N2Jhw/NZnlwJssVJqoTgF7HZrzkoSvnthio2utQH4tbPbvtYlWrLsiqs9GmvXNXacj5IWknEhxzQYAFvOvizfQ45/dX6NJtt5H/6EebhCqRSiEGB9HT07VlOp8neOIJ7B07Glx/+D46m0X29qJmZhbFxk7Uux2DAB2GbWsztu1iZAS5du2y7dyrriL+0z9N+atfRR09uuy4lgqNSFlzzpW/8hUSr31t5MTsEhGPk/qt3yLxq79K+Wtfw/vWt1CzsxCGiHQa5/LLie/ejbVuXeN26fYChPb9qPaj50VjTSbR+XxjozCsRaYCkcCXz0dOUimRa9ci+/uByJW48KY3kfnjP6Z0551dH1sVNTNDcPw4OpeLhONWdRUrY1g8CB0JjpkMlErRdt24KevF6ESi7XtgudqjTyeJHpcb33ope979wzrRS5IZTZAZTVBa8Jg9UcBbUs/RciXJXge/FLIwXkRYAhXq9uJuHfFel4H1KZy4zU/98RUMbkxz4K5x9n75JCcemWHqwAJBOcRN2lGspxQN8a7SEtgxqyE+dSmWI4mlHcJAtXT5SVtywYtXsWv3Wi548SosW3Lq8Vke+vfoc5kaijF7It/e4dkBaQmkLXneazbxxT98gNRg+0QFIQSW3XjWFsaLfP3PHuPV772Cct7nux/Z16Tfl3M+fqHRTWrHJFrT8bzUk5toFBt7ViVYd3l3bmmDwdACacMFfwCjt8D4Hpi5G9SSe0liHYzuhqGXgf3MvTcYDAaDwfBM4vyeKTEYDAaD4TzlULbA2x8+SClsdMEMxRzOlCL3mBCQcSxyQVhzknUSHAWwIZXgN7etJW5ZvOGCNfzZ3mOUVPeOIwFsySR40XAvQ3UuQKU1f7XvOF8/M9Nxew08OJPly6emcaSgz3WaHHAN7TUUKpPQpTCq2xezJK4lcC0LpaLajXFLsjoRw1eaY4USSkdxmxf1psmHIfuzeYIlM4yeUg2ilyUET+UKrFWxBvdlO24/NWXExgrPG8jw9wdPARAqXTvXUY3NRnFxuenmEFBq0bkoERTD1nK0BvJBSMa2SNs2/2XLatb9iOJ9n614P/hBS6GxirVpE2EQoOfnFxdWRC5n166oniNEEaWVPkRPTyQEe17runuWhYjFanUedaniEiqXoSIO1bscOxG76aauHcOJW28l2LsXNTUF5XLj2ISIxEPXbSkiisHB2nI1M0N48CD29u1d7bcemU6T+Jmf6bruo7VxY1STcUk9QwB16lR0zqpjdBxIpdCFQnRcVaGxGmVb21Chy2XsCy9EDg839BmePs38b/82Opvt7EhtQXjyZFSDU6nIedhObKxH65rDUbgu2nEi12y53Fqsrr5elRhskUhgb9vWVvi1Nm9e0TH8pFl3+SA3vf0yvvqBR/BLi/e23FSJ6cO5ps+OHbcY3daL5VpYLgxtyTCxb57QU2At9XU3khyMMbQpg5OwufGtlzC6rRetNWMX9jGwPoW0BIm+GAtnCjx+xwnu+9RBIHLi6UBjxWRT9Gc7pCUY3NTDzhvXcOFNa8lNlAg8RSxls+rCPoa3NN4Le1YlasZcaUlSg3FyEyt0pQqwXIsNVw1x5N6pjjGonTjx0Awzx3JcfMt6elcneeAzhzmzd6623ltSWzGWdhge62H6cA4VdLfPoByila45RnfdvNYkHxgMPwoyO6If/79Cbj+EeZAOuEOQ3vZ0j85gMBgMhmcdRmw0GAwGg+E8I1Sa9z9+pEloBEjaFhnbJhsEoKO4ypiU+FoRqEhw7HEaY78kggHXYTThsjmd4MqBaFLwlzeN8YOpeX4wNU++i4hDiWBLJsHaZJxf2dxYL+vvnjq5rNBYxQsVR/IlBLCjRzY43yASpwKl0Wh8pQk1tYjYYqhwpKjV45MyGpctJKsqAuFAzOHJ+TwhmlPFcmvnpIZyncsiXhEwNZrjhRISwWiiszhy//QCEyWvq+jV5zpJy6pNipeXxETKSu1FWF5orFKNxg2UJm1LXGnX3KxL3y+2EKxKxNiQSnSsuXm+UPjEJzpGigopsbZuRR071iAoUi4Tjo9jrVkDgCoUIvffqlVg26hjx8C2EZV6grXtLAtR57jGdaEiNmqlau8LObB8HSVh28RuuKHrY429+MWUrryS4JFHIJFodGZ2IpFochyeTcTo2SCkJH7TTRQ+9amG5ToMUXWO01p7x4GeHnS5DNUx1p9vKSMxLxZDVByNSwkOHEDEYjXHYzfohQUoFqP9QiQgat1aQKmqSkpF/68KjkpF74WenqiNlNFPVTSttq0eU7mMGB7u+P6N3Xxz18fwdLHhyiH+0/95AY/ffoInv3aSuRMFpg9nGy6AdtwiMxInNRSjnA2YPZEn8EK0imoE2nELaUFpIWhwBAopSA3GSI/EiaUcxnb18cJf30bPqiQPff4IT9xxgvnTjaLe6ov72bV7LfMnC0w8tYAKFeNPzjc4HJcjsypBz6oklmNxwbWrasuzk0WO3jfFsfunQEC8x2XTC4ZJD8ZZd8VQtBzoXZ2gOFeORNQuSfbHSPS6XPmLm7n9XT/sertWPL7nBNe9cQfrnzfE+ucNMXU4yxN3nmDmaA4emyM/VcJN2KRH4rVai0EpZPZ4fpmeF1FhFO+eGYmz44Y15zReg8GwBKcH+q98ukdhMBgMBsOzHiM2GgwGg8FwnvGDqXkmWsSQVhmKOUxWaiGqFvKNBgZdh5iUuFIyGHNqbsefXjtcmyweiDn8yWUX8IcPHeBgrsh4yau5COuxhajVwutzbf5w10bW17nHHp/LsedUs0unHRPlKG5TA4dyRVIVgShQmnKo8OsmmoNKTUalK3PVCDyliVuNE971Zs6UbbGzN8VT2QIHc0WSLZwbGmrnLmFFNS/rOVYo0evaxK32rg8NHMgWjNgIFELFYMxhouwRqMb3pBSRiLuSBD1B5DQNtKYQKpL2optV1zklZaVxzo/et4kOr9f5gP/ww5EbbRmElJHDbs0a1ORkJDqWy6jJSazVq7HWrkWkUqj5+chhFoaokycjkajqGOzQN/F45G6sOiMzma7iLxOvfz2yt7f7AwaSr3sdpS9+Ed3CKdhyfMkkVgvnXINg+mMmduONFD/zGXSwKPbo6enIRdiC6jVb2zZoHUWxVt2NUtbWq8lJrLGx5g6UQo2Pr0hsDCcmos9ZFxGoWqnmsVfFRykhCBCui6oKlfX3maoIGY8jYjH05CTB1BRicBBr48aG10UODOBec03Xx/B00rsqyQt/fRtX/tImPvIz36BvTQoVaqQlcNM28YxD9kyR04/PE5bbPOwjBLt2r2FwU4bxffN4uQA7ZpHsd1lz6QC7dq9jcEOax/Yc5z/ecn/N+Vec98hPRc5DNIzvm2fvV04S+orUYIxYymHVzj6mDmcpzJQ7PgUiLEHfmhQ9qyIhP9Eb3e+O/3Cax24/ztH7ptBLrvnf/bsn2XTNCKt29NbERtu1GNnay/i++a7dgv1rU9z0tkvJT5cpzrX/TlRa8PBLkVArbYGbsnETjZ/v/d88zbX/fXvtszK0KcOLfyOq77b3Kyf51v95oqnfnrEkfll17ciUliDe4/Cqd11OPO0sv4HBYDAYDAaDwfATxoiNBoPBYDCcZ3QS7qbKHkfypQaxbCn5IEQKWJOIN7jzXjLaz6vXNkbsbUgn+Isrt/OXTx7jkbkcOT8kFwSEWiOIah72uzZSCLakE/zO9nVszjSKBref7F5oVFozVVqcvC6pkISWFIKo7mQ99aJS1emm0BQDRUyKBofNUrEwaVtsSMXJ+XmS0qKoGr2NjhTELRnVaWxh1NFoJkpeg6jaik4RsOcTCs26VJxsEDJHs2AiER3rcja3X0x89JQipiRKR/GslbBFpIhqmEogH4bkg5ANqS6dbc9RSnv2rKi9cJxIXFy9OnIrKkX6TW8idv31ZP/0T/G++92ooWVF0Z/j4931G49HsZgVUUmOji67TeI//aeuo0jrkUND2Js3o3p6UOPjzTUOq7gucmQEOTraso6k6Otb8b7PFtnXR/LXfo38Rz5SW6YWFtq214D2IrFFJJOR27FVu4UFaCU2WhY6m0WXSt1FocJidOsyqFIpikht1bYa+ao1WmvI5xeXSdkQ6ypiddHVWqOnpgg9LxKGK4Jj4pd+acU1QJ9ujj84g+VKelcv3jdVqBjfO09hrhqJLhCtIlO15sg9U/jFkN1/dDmrdvY19f/AZw9xbyUeNTteZGG8SFBqvi+V5j3CQDF9OMfqi/pIDcYZ3tJDsDYkO1EiN1VC+Yv3YCdpkxmJkx6K16JBAbZcO8K3/voJ9n65/UMNYaA5cNc43DWOXwqxY5Eg7lYiV6cPZSnnOovYvauT/MLfvpDhLT08tud403oVKLKTpSjOtYVYG8s4ZEYSJAdchBB4hQCvEBBLNX92+ten2o5jcGMayxHMnyp2/DzYMYuBDWlufsdlDbUbDQaDwWAwGAyGZxJGbDQYDAaD4TxiuuzxyFzrOL/JksfhfPSEfcKSCGgZtRpqTajgZLFEqDXrU3FuGhvkN7atbdnvSNzl/ZddwPF8idtPTnH35BwLfiQYpWyLqwd72L1miO09zRNyc57P96fmm5a3oxAqPF03Zh0JqEuFxk4EWlMMFcm6yMzhWLO7cLLk41iCjek4MUviVVySdsUh+dBctuN+psoea5OxWmRrK2I/QTfUM5m0bWEJwfaeJJMlr6k+pkLXJtKXTteKumWCpb9EAvW8H9BYijTaohQqbBGJ4qNxd1lx+LlOsH//WW8rLAssi/DIEbj++qaYUWvdOnQu117MW9pfIoGIx7FWreoYoWqNjJD4xV8k9opXnNW4rZER7O3bCYiER5XLoWdmavUbhW0jensRfX1ta6hZ69Zhb9p0Vvs/W+KvfjU6n6fw6U9HCzpFWSsFSiEq7r+2tHNGplKRgFcsImIx1NxcJEwGQST6OQ5yaKhRiFw6HstqOH9a60iQLHYQYSpRqbpUihyS1T6ljLap9mfbLUVEvbBAeOQI9ubNJG69lfiNN7Y/9mcoj9cJZSpQ5KbKTB5YwCs0vlZCCmxXYsesijgbLc9PlSjMeex59w/5mQ9excD6dG2bp75zhns/dRCtNVOHshSmy3RC2hKv4HHioRnWXj5AaiCOHbPoX5eif10KrXTNfSla1H7uHUvw1LfP8NS3znR9/FJCcdYjORC9b524xaoL+/DyAdmJIoXZMiqoOKAtQbLPZXRnH//5o9eSqmwT+o3fD8o5n4n9Cx0dkuWsTznrExt3GN7ag+XI2n6WsmpHH4Mb00wfaf3dq29NivRwnNxkmdxksTEKVkRuz2t+bRuv/F8XmzqNBoPBYDAYDIZnNEZsNBgMBoPhPGKm3HqyOOeHHMk3RnnFLYktBJ5S+BUhrYpGI5B4SnHL6kH++7ZG4aAV61Jx3rhtLW/ctjaaSIaOQhvAAzPZJmGpE+GSuLWyUhRDhUTga1WbYBW1/7SmFCpcKbGlICYlfU7jVyalNXN18X+OlDh1uqDWGmsZt11QEbn63fZxaMvVdTxfeN5AD4LIadjvOmT9gLJa9N7Wv0VE9aci+mqoOGkbRUdN9H6JfIy67Xsx0JogCNErcE4+V9HF7uL+OvZRERNjN9xA8bOfbaipZ2/fTrB/P7rL+obJ17+e9DvegX/vvZTvvJPwxIlI8EoksDduJHbzzThXXXXOE/Tx3bvJ7dsXDTOdhnR6mS2WbP801QFM/MIvIFevpviZz3QUikUsBqlUW0fjYsPW51Gm04TJJGpigvDoUfCaIynVqVOInh7kqlXIvr5aPKuuOBPrRU6tdSQy1kXl1uo2tiIIIrHRshbjX6vuRq07ui31zAzx3/s9kq9/fedjfwYSeCEnH5kFID9TZvpwlqAUNgmNELkdvYKinPcjp2Od2Hfq0VkG1qf4xl8+zq1/9nwgeg3u+3TkaJw+nFtWaIToumq7Fn4p5Mzj86y/ysGJL4q8QgqsFiJjlfRIgie/dorCdLlWX1LagljGIdHT+l5ouRZuGixbENaJfW7KZnBThsFNmVoMq5CCwU0Zdr/zsprQCBCriyQt53zGn5xviG5VSqPDikArQEqBrEStR+3nWLWzDzfVfmpl1+61fOf/Ptl2ve1a9K1J0rs6QVAKUaGOzpcjcRMW171xhxEaDQaDwWAwGAzPeIzYaDAYDAbDeUTYZrL2dLHcUkqxpcCWVhQxqTTV5+3HEi5rEnFcS/LD2Rxa6xVNhAnRItKtBTm/tTjavt+6PzQUAkWoNZbQ1OuQuvKfJsdb3d/lUGFLi7FErKlBUCe+2i0mT4UQtRqDnVhaf7CeDal4S7fn+chI3OXKwR7um15gMObga0VcR25STymCirOxKjDWvw8EEOqoRqNi8X0Q1r2G1Rjddm9hSwiO5kp86vBpfnlTixjJ8wThul07D9tSEZWs4WHcq67Cu/fexXW2jb1jRyRaTUxEYlOrcWQyOFdcQc973hN1+aIXEXvRi85tXB1wr7sO+fGPo+a7d1lXEYkE7stf/mMYVXfEXvxiYi9+MQt/+IcUv/CFSDAOw8hJmEhgDQ9DPE7w0EPLd+a2Fny056GLRfT8fEfBUi8sEC4soMfGEPE4ulwGx4mEwvrtyuVo3VJH+lLBsSoqQrQ8DKGuVqaIxbA2bkSXSui5ucZt43GskRHk0FDzfp4llHPR/TE3VWL6cBY0LSM/tdLRT+XwNTqKkq7cu7y8z9TBLLPH8ux4+RgX7V7PiYdmmD9VoDBbJj/V+nPYCjtuEQYKvxQwdWiBsQu7q+M5uCnFvq+dYuZorqlGY7XfzEiCzEi8yRVpuxYDGzNc+rMbeHzPcU4/PtewXkjB6ov72bV7LZuvGUEuiUVffVE/QkAYKCb2L9T2HwaKoKyanI8Q1U+0YxLLsfCLIaWsj9WifnOVbS8d46F/P8rCmc4PbAghcJbUg9y1e12tlqXBYDAYDAaDwfBMxoiNBoPBYDCcR6Ts5ig5L1QNLr1WSCFwrcUJvqGYi1uZsDtZLPPwbI7LBjI/2sGeBU5lElJryPphzRUphMBCE7bR9iqGhQZ8rRlwHUbi7Sf54lJSCjVT5RKhjmoBxixZ2245sbETN68eOuttn4vsXj3EfdMLjMRdxkseWmhcSyCFXNb9KiuahIVAEjlTu/UpSgQp2yJlW3z26DiX9We4qG9lzrbnCnLVKtTs7Dn1Ya1aVfs9fuut+A88UKu9GO1ERu63VavQCwuoubnIuSZEFMc5OIhIJEj/7u+e0zhWgnAc0m95C9l3vAPdJkq05XZSkn7Tm5DJp7/GWuKXfgn/0Udbr9Q6EumWOTY5ONi8aRAQPPlkFMOaSrV0NS5FnT6N6OkBKq7KughVrVQkNLb7TLcSHKtiodaLTw1IiUgmF+N6g6AWfYtlNTgpy9/4Bslf/VVEGzH1mYqQUMr6TB/OgQYV6gZ3H1CLLl2KCnXlurh45wt9xX+89QFSg3H2f+M0ANmJ7oVGiO6jsZSDl/cpzJTxigFuovOUQ6LP5cQPZyPBtA1BKWT2WI7cVInRbT1YbuN3mckDC/SuTvIzf3oVs8dzzB7P4xVD3ERU67BvTfsHd3pGE6y7YojHvnQcFUQJCF4haCkyVlGhxiuECBkSSzmoUDN7PEf/utb3Bidu86p3Xc4X3nw/xfnuvxdsuGqIa359W9ftDQaDwWAwGAyGpxNTCMhgMBgMhvOINckYw7FG58lk2V9RQKQtZJNo+eXT0z+C0TXT1yFitBUJyyJpWeSDEL8yAV2dSpVCYLVxrkGjy1EIQVxK4u1qJorI+VgIQvZn85wqlhkvlTldKnMkX+Sh2SzjJY+U1fmrlt3GStfn2LxstDtHyPnCFQMZXjzSR9yS9Dh10XxdeGTjda9D0rI6RujWYwlB2rHodWwSlff87SenVjbw5xCxV77ynLYXrot7/fW1v52dO0n99m+3tZSKnh6s9euxNm/G2rQJa+1aRCJB8nWvw73mmnMay0pxLrqI9Nvf3jGSsx7huqTe9Cbcq676MY+sO5yLLsLasKH1SiEih18nYjFEX1/T4vDoUSiVkD092BdeCF2eH72wAEJgrVnTWHPT8xZFw3YIEUWmShn9LF2dTiN7ehr7sO2ozmcy2VSXUufzqPHxrsb9TCKWdsiOL9a0DJfUGNS6tdBYWx8uPoxTJT9d5msfeoyTD08TlEJKCyt/YEYIcNMOid4YQbm9YDewIc2Gq4YoznrkunRP+oWAM0/Ot6ynuO/rpwDoX5dm8wtH2fHy1Wx+4WhHobHKrpvXkJ0oooFy3u8oNNajFXj5ADsmeWzPiY5t+9ak+NkPXUXf2u4SC3a8cjU3/uGlyA7RswaDwWAwGAwGwzMJ42w0GAwGg+E8QgrBTauH+OTh07VlpXBlEXJDMaepvt3p4vL1nM6GKwd7iFtyRWN0hCCoc67VD1WKKGJT6fpIuUZiUpK0LSwJXos4t3Ko2LdQIFAa1249CajQTJY9qJhsWk33Wgh6nOavYnFL8s5LNtfELUOEEILf27GecqjwQsUTC3k8peg0DyuIxEVLCnwVEJMSjcYSAln3PtA0vk+cSq1OWwgQMFrnbv3B1DwzZZ+B2MqE8OcCseuvp/jxj6O6rKm4FPfFL45qHtb3+fKXQyxG/i/+Ar2MK05YFsk3vIH4q151Vvs/V9wrrqD3z/+c4uc+h3fXXS3HK2wb90UvIv5zP4e9efPTMMr2xF/9avJ//dct18mRkUhwayPyyZGRpqhs7XnomZnF9a6LvXMn4aFD6OUiZy0Le9s2RCaDLhYJ9u5F+/6y7wFg0bmYSIBlNUf7VgXIFdT71YVC122fKRRmyo2vyZLbpO4gNEJdudS6gsMqUATlkMkDeSynzc2rCwRR3cShzWle9nsXcfj7E5SzPghBotdh8wtHiWUc/vV3fgACQq/7e3xQCpk6nGVka2/D8tzkylyY9TgJGydhUZgpo4KVHbQdl0weyLL3yyd50X/d1hTTWk/vWJLX/s01HL1vksdvP8GJh6Yb3qZu0mbbS8fYtXstA+vPTwe9wWAwGAwGg+HZixEbDQaDwWA4z7hhbIB/PnKmFj2pVjCbKBAtY0VXKlh2S8q2uH6kv3vnpI7GUj8lLpfY2ASRY41KGp/Si7UoBZCyZW2ueum58ZVi70IeX2k2pRPLx6QKUDpyMIZL+hqMOVhLlLI+x+adl2zmgszTH7v4TMSWkrddtInPHh3nX46Oc//MAmWlcITAXyIs2EIQt2StpuZwzMVTIVl/0fFafR9YQpCwJAJRi1ytkrIs+tzFr8yB1nxvco5b1g7/2I/3mYZwXWK33ELxM59Z+ba2Tfynf7rluti11+Jceinlr36V8h13EJ4507BeDgwQu/FG4jfd1OiCexqw1q0j/bu/i/r1X6f8jW8QHjmCLhYR8TjWunXEXvEKZG/v8h09DcRvvJHg8ccpf/ObTetEPI4cGEBNN19rRX8/si7+toqanIwuoolE7XURjoO9fTu6UEBNTET91cfkJhLISq1E6Tg4L3wh3t13Y23bRrBv32IkaqcawJV6kyIebx1rG4aR4Git4IGNLh2ZzySe+PJJMiNx8tPNIpuue6CmE0I2Gr2rvxdny8QyZ/9Ahdaa0AuZ3L/A9OEsg5syjGztYXhLT63Nt//miVp9xFZ1GjtRnPMIyiF2bPE17taN2IrcZIm+dSnmjq+sJq2TsLBdC+UrZo/nKOeCZesrSinY9PwRNj1/hNxUiexEkcBTuEmbgfUpnLiZojEYDAaDwWAwPDsx32QNBoPBYDjP6HMdXrdpjH84FEWOWd1mSgKr4m5DJCUa8mHIvB/wvck50rbFtp4k8ZVM8i7Dq9YM8ZXT011Jogt+QFkrUrbFnBdUIlHbtxcVoUnoqJ6jJaJajbHKObGWbHwwW8QLVSWO1mXWC/B15wlOISLRcn0yzqwXkK1MjteLtmsSMW5ePcgrxgZb1tU0LCKE4Bc2ruLW9SPceWqav33qJA/PZZkq+QgRiYyulFTfpinLYlUixoBrcyBXZMEvNvWZqBMl64lLydaeZJOTd87vvm7fc43EL/0S4ZEjeD/4QfcbCUHqd38Xe+PGtk1kJkPi536O+M/+LOGBA6iFBVAK2dODdcEFiB/hNeVHgcxkSLQRT5/JpP7H/wDLovy1r0ULqsKcEFibNqHLZXSdc1UMDGBt3tzkagRQMzPgutjbtjVdaEUyibVxI3L9+qgWZLVWor34z0+tFPbWrVhjYxT/9V+xt24lyOUid2O7C7fjRFG2TkUIaxV1Xa3Vm+7OGSZcFzn87Ht4YPZ4nljGwU3aeIUAUX9rXk53q9xQQ09RmPOie6EjcVPR62PHLUrZzrWcW3arNIGnCCs/5VzA9z/+VG39yLYedu1ex/orBnnqW4sPFcg2KQHtUKFm/Mn56IGeioMzP11i1Y5edt64ltRAbJkeGgk8RWGqjJuy8Ushoac6irVSCuy4he0unvTsRAm/tLzYWE96KE566NkndBsMBoPBYDAYDK0wYqPBYDAYDOchP7d+hGwQ8G/HJkjZFlNdJNcNx1zWJqMJvFBrJkseEyWfkgqZLLn8yeNHgMiN+LLRfl61Zog1yXOfRNuUTvArm8f4x0Onl20740WTo7YUJG0LT3XndIicbYJAg680VbNEplIbUGnNyUKZibKHJQQni2VOFsuECjylGsStVmiifnf2pigGIVcN9vDC4aj+4Fgixq7eVMvJfEN7bCm5Ze0wt6wdphyE/MJ3H2PfQp6wEl3rSslgzCFdJ95ekE5wqlCmXPe+sIRoWTszY9tckEngtBAzwhXEMz7XEEKQfvObyf/1X1P++teXb++6pH7/94m96EVd929v3Xquw/yxEU5NETz6aCTIOQ5ycBDn8strIpoOQ8Ljx6P1to0cGMAaGXmaR11HGOLs2oX3/e/j3XcfemoK7UfXTeG6iEQCrVTNqSkGBtpem4TjYG3fjnDbiytCSuiwXs/Nkfy1XyP28pdT+Nd/JX/gAPg+eF4UbVp1RVaiUwF0qQSlElrKqP6ibUeCZm2n0Xhll+fdve46ZPLZ5yb3i9ExD13Qw5kn5pBKI4oVD327a1T94rqXVetIcEOEnH5sluRAjHJuZQ9VhEEkLuqwEmMuQROQmyqRGoghpGBi/wIT+x/HciTlvI/tRtfnWMahtLC8uKmUxi+GhL6inPNJ9Cy+t8JAc9//d4gHPnuYTdeMcN0bd3Qt/LlJm9xkCSEEbsJGxzSBHwmmWlWztkHaEtuVSFs0fS6ieNccPaPPvveSwWAwGAwGg8Hwo8CIjQaDwWAwnKe8fvNqVidi/NOh0xwvlNrGqTpCMpZwWZWIhMZ5L+BAttAQC1rv0ssHIbednOK2k1Pcun6EX9k0ds5C2q3rR/GV5v87cqZju2o0rCMkl/Ql2JctUA4VvtItj05Qqc9nyZqL0RaCAdch1JorBnqYKHkczBYphmGjqxOwJNhakAsCLCFI2VZbQ85k2WdNMsZrNozy37auXekpMHQgZlt89Pk7edMPn2Kq3H7CWoioTmZZhZSVJlSapG3VJt0l0Ws/EndIt6inWSVjn99foYVtk/7d3yX2yldS3rMH73vfa4qzlP39xG68kdiNN2INDT1NI/3RoLUmePhhSrffjn/vveglDzHIvj6cF74QpMT/3vcix18dzoUXEnvVq3Bf+MIGZ99PmtKePRQ/9SnCuTnCo0chn4+cglDJlFboYhE5PIxcvToS+opFqBfihMC5/HLiu3eT+9CH0OVzq9erK2KitWYN6d/6rej8jY+jg4Dw+HHUmTPtXY6V8daEtWo7KSEeR3QZZxvfvfucjuHpwklE7yUnbjG6o5eJ/fM1Ia4lbYTGeuyYxCsE+KUQJ2HhhWrZuo0aom0KwWIdSClQgUZaMH0oy+yxPOmhGL2rk0hbMnM0R26yxKoL+7AcSXo4zvypYnuRlMjBWM77NdemVjUNEGEJ0kOxWruDd48zeWCBV7/vCnpGE50PAOhflyQMFs+bkAInZuHEundU23GL0nwXT24ZDAaDwWAwGAzPUc7vmRKDwWAwGM5zXjk2yMtXDfAHD+7nSyenKAaKUGtkpYbdcNyl37VrMZKzns+BbJF66S5t25Fg04J/OzZB1g/5re3rznmsv7hxFRdkEnzh+CSPzC3G/KFhzveZ8wLmvACJoNexiVkW/Y7DggjQWuMpjdK6NjkphcCVze4ER0o2phP87Lph4tLin4+eYSzh8uBM60l1xxKkhUUpVGT9kLRj0SKRE0cKblo9aITGHxNDcZcPXr6Vdz1yiGOF5hpmVXpdm6myYsC12ZpOgBDRex4qDtXlhfErB3uWbXM+4OzahbNrF2puDv+xx9C5HMK2Ef39OJde+rQKaz8qtOeR+4u/wLv77rZt/CeeoPzVr0ZRpJs3N9WV9J94Av+JJ5CDg2Te/nbsCy74cQ+7icI//iPFf/u3SMTbtw+dj2rTCctqqm2oFxZQSmFv3w5SEr/1VpydO6O6jqtW1ZyaoqcHPTl5TuOSmUztd2HbxF7xCkq33YY6eTJyMNp2Y83HVggRORstC2wbYduRK7OLh1ycSy+NYmCfhQxuTHP4+xNA5Mwbu7CP6cNZZo61qDvYRmisP0eWI5GV659WGhXoKKI1397hqIFyzicohQ2ar6j0Y8eiB3RUoFg4U6Qw5zG6vRe0JiiHTB1cYHRHH7Zrkex3KbS5zyrVKDQuJTUYQy55GGjhTJHb3/kgP/uhq4kvU38y2R8j0etSnDt7sTAzmsAvLfNeNRgMBoPBYDAYnsM8+2cADAaDwWAwnBNSCP5g50ZOFT3yQfuJsmIQcnCJ0CgQrI67BEqj0VhCNNW3+/LpaTak4rx67bnXxLpqsJerBns5ni/x5VPTfGtilr3zeUqhIuVYDKKZLPtMeR5TnocrJYHS2FIQs7pzV9pCMBRz6HNs/uHQaUKtKQUhvtJRjFqLbiwpSEkLpaPI1R7bRhG1jUmL4bhDj2OzOW3i1X6cDMdd/vLKbXx3cp7bT06xd6F50v2awV6eWMgz4Npn5bi9qDfN+pSpsVWP7Osjdu21T/cwfuToMCT7/vfjP/hg2zbhsWOR+w5Aa8KDBwGaBEcANT3NwlvfSuY978HZufPHMuZWlG67LRIalSJ86qma0NgJncsRHDiAvX075S98Affyy3EuuaShjXvFFZTuvPOcxuZccUXt9/D4cdTsLP6DD6JmZ6OFUoJSHR1vQCQ0hiHE41gbNiD7+5fdt7VuHem3vGVF49XFImpuDsIQkUp1tZ8fFztvWMMDnz0cxXwClmsxsr0PO24zeWAhigAF0K2d/bAoCgoJTqJRdE70u8QzDpNPLbTcVgNePmiob1h1GSIaxcsqQSlkfN98rU5hacGnnA+IpWx61yQpLXiooHm0filsEhpFtBssR9I71vreOneywA//7TDX/JfOgrKbtMmMJM5abBQycla6STO9YjAYDAaDwWA4fzHfhg0Gg8FgMLA6GeMPd23kPY8ebqhnV894yWuIWlUa0rbkYL7YUMMubduMxB0GXKcmPH7u+AS7Vw915Rrrhh7H5odzC5wplumP2ThCYEvJgi+ZrIvR9FQ04mKgSFiybXRcPWuTMd62ayO/9+B+nlooMuf7hFqzUBFiLSFwpay4Ihu3lSISbwdjLqOJ5lpRQZc1JJdy79Q835mYZd4PSFgW23oSvGr1MIk2jtLzGVtKrh/t5/rRfo7mihwrlCiG0eu/PhlnQzrB7z2wjwPZ4ln1v3vN4I94xIZnKsV/+qfOQuPExKLQWEVrwkOHEIkEItEc36hLJXLvfS89f/VXP5F4WV0qUfjUp6Lfp6fR2Wz3287Po+bmkH195P/u7+j7m79pWB971avOSWy0t27F3roV/7HHKH760/iPPRbtt75WYxhGQuNiPmfrzoSAWAxr3bquajU6F15I+u1vR6bTXY3Vf/hhSnv24N9zTy36FcDeuJHYzTcTe9nLEPGf7EMI6aE4G68e4vAPGt2l/etTBOWQ6SM5BLQU76CSOisioTGWdpqEwcxwHDdp0/eSVTz17TNNcaqhpwh9VRM7hYiERiFES/GySlAKKS0sinq5iSKxTRnchM3wBT1MPLWADhd3pjUor/neKW2JtCXD23qxO8SdPvnVU1z1n7fU6kO2wnYtVl/Uz8KZQle1I5fSuyaJtCTDWzLLNzYYDAaDwWAwGJ6jGLHRYDAYDAYDAJf0Z/iTyy7gT584wkSp8en+UGumKyKe0ppSqHGEoNAi3i4XBORyAcdFmfWpOIMxh+myzz3T87xwuO+cxlgKQ75yepp3PnyIU6XFuDWBoMexGIm7xKWkVCfqOVIgAF9pnGXcjX2Owx9fuoU3/fAA904vujnqtwq1phiGlEJIWBZuiz4nyl5LsbFTHcCllIOQjx06xacOneZwvtjkTHmHe4hXrR7it7avY2N6+ZpU5yMb0gk2tDg3/79t63jrQwcohSsTf58/2MO15/geNjw70MUipTvuaL9ea9SpU61XKoUaH0du2BAJdhMTkZuwEvUpYrFIcPzTP20pSP4oKX/zm5F4B6jx8RVvryYmkH19hMeO4T/2GM5FF9XW2Rs3Yu/cSbB3b/cdao2am0PPziL6+5l5/esJHnwQ0duL7OtD53LgOItORaioWCJSnapiY/2DG7aNSCSwL74Y2dND6jd/E++uu/AfeaTRESkE7tVXE9u9G+fyy7tyNocnTzL/9rcTPPRQVJ9SKbAsZDqNGBwkOHKE4P/9Pwqf+ATJN7yB+Ctf2f25+BHwvNds4tgD0w11GoUQjGzrpZT1KcyU25pCpSWxYxZ2rNmBGO9xcBI2WsOOV6xhbFc/D33uCLmpMqpS2zAoh+hK59ISDS7JWKpZvKynnPVxU1FEa366TP+GNFIK4j0uq3b2MX0oi1cIFvfToo9kn8vozj6ceOeHbkpZn4N3j7P9Zas7ttu1ey3TR7OMPznfMTp2KZnRBL1jSUa39zK02URsGwyGFRKWQbrt6xMbDAaDwfAswoiNBoPBYDAYamztSfLR5+/kvukFbj85xUOzWTQwXfYJ0cSlpBgqkpWYtE74WnEoV8RXilWJGHeemj4nsfE747P836dO8MR8jvElYqhGM+8HzPtBLcrUrpvotKUADaNxF09p5rygFgdrIRiMOYzEXV480s9f7z/BY/U1IYkmb20hCOpmbTVQCEMUkviSWlHFMGTBD+ipExcFcHFfdy6aI7kir7n7UY7m27vvZjyfTx45zedPTPCnl23lP20Y7arvKoFSHM6VyAchlhAMxGzWJM+PeNALMknetmsT73/8cNeC4+X9Gd504cazil41PPsof/Ob6GL7z5+enQWvfeRiePp0FLe5tE0QoIOA4r/9G+GJE8RvuYXEr/xKVDvxx0B5zx4AVC5XEx1Xgp6fR5dKiHic0u23N4iNAKnf+A0W3vzmjucqGoBCnTlDODEBnofo7SU4eBB18GAkCE5MELouaB19xtLpRYG2ihCgVOQgjMUqiwS4LvbWrYhUCpRCz83R8773EZ4+TVip/SgSCawNG1bkJi1+6Utk3/IW1NRUU4xrODUFx48jh4aQY2MA5P/qr9Dz8yRuvbXrfZwrI1t7ednv7eJr//uxmsOwytDmDCdzPlpptKIWqSqEwI5buEm79dy2gJ66WNIze+f4+T9/PpuuGeHhzx/l9BNzlLMe04dzWKHAL0XXUCHAciVOzKoJj/WoUNeckJooHUH5CsuRKF8hK+5EN2kzdlE/5ZxPdqLEzLHF+7GoxLPGehzWXDbQ9fX4+A+nlxUbt71sjHv+6QCrdvYxdTgb1Y/skN4rLEHf6mTtXF30qnOvTW0wGM4DlA/Td8PEHsg9BTqMLm7uMIzcACM3gtP3dI/SYDAYDIazwoiNBoPBYDAYGpBC8PyhXp4/1EvOD1jwQz55+BRfOzPDU9niih681WiOF8q4UnKyWF5+gzbsOTnF3z51glBrpkqdI84kVJyNskFwREDOD9lVEfxU1ZFROaCRuMu+hTzZIGxZu9K1BEGLOLpSqLAQDa7JUGuO5UukbAtNFL16zVAvwzFn2WM9kS/xU99+qElQbUcuCPmDB59Cac1rN65atv1kyeOOU1N85fQM836je2NrJsmr1gxx3XAfrtUmrvA5wmUDGT5w2QV84tDpmqjeih7H5ubVg/zihlU/shhgwzOf8le/2nG9mpxsu06Xy5H4FoYIt9nhDEAQEJ46RfHf/53g6FEyb3sbwln++rASdKlEcORI9PtC67p7y3ei0fk8Ih4neOSRptX2pk1k3vEOsu97X3sxMwgI9u+PXIuAyGSQmzcTPvpo1L9SkShbKKBLJRAicnymUoggQHse+IvXfV0qIRwn6md4GDk01CDWhidOAGCNjWFVhMCVkv/EJ8i+4x0dBWXCEDU+jpqZwd62DZFKUfjHf0SOjBB78YvPar9nwwXXrcKJW3z9zx6jXOfIS/bHiGcccmWFsCr1FIXASdrYTvvre/+6NInexfdtORed+52vXMPOV67h1OOzfOVPHqGcCyjnfFToY9kCy7Vafj8IfEVQDpviXMuhj5ACvxgyf7rAwPp0g0gZSzvE0g5eIcDLR2MQFZfr8JaeFT340Y1TMZZyuOznN3Lfpw8yvKWHYG1IdqJEbqqEqnOOOkmbzEic9FC8Nt6hzRm2XLuyB34MBsN5yPgdcOJT4C+5J2sN5Qk4/ik48RkYfjls/G+R49FgMBgMhmcRRmw0GAwGg8HQlrRjk3ZsEpbFnBdQbBGbuhyR4FhifTJ2VmO4f3qBv33qBBqYqjgsOyKieNNiqCIXRN2EZD6MhMSUbdVERoD1yTgX9aXYc2oarXXLfThCIIFWPrhiqHCkha8V5VATaI2vdEPM7N75PP/tnr3ctHqQV60ZIt7CyRQoxa987/GuhcYqJRXy9ocPckl/hp29qbbt/vnIGf75yJm2Z/CpbIEPP3mMTxw8xdsu2sSODn09F9icSfKeS7dwulhmz8kpHpnLkQ9CHCkYirm8bLSfa0f6cNrViTM8Z2mqxbiEdk4+7XmL65ap0Vpt5z/wALk//3Myb37zygfaAZWrc2ifxbW7RsVdqPP5lqudiy+m5wMfoPAP/4D/wx82ugCVWhQabTtyAq5di56ZiWJJSyV0nZCIUpEAmctBuQyOEzkZE4nF+o1CIIeGsLdubT3cQ4fIffjDqOnpSPBNp3Euu4zYS1/aVWxt8fOfJ/9Xf9VZaKzH9wn27cPeuRORSFD81Kdwr7vuJ+qC3nDVMK/7xHXs/9YZHt9zgunDUW3ORF8MLx8QeArLldhtBEEAhGBgQ4rMSOM5spYIk6t39bPh6iGK8x6F2TKTT7UXsr1CtO9WaK2JJR3KeZ/smSJePmBkW2/T/iJ9UdT+GNzUKIZ2g1wmQr3Klb+wmexEkSe/ego7ZtG/LkX/uhQq1GilG+Jiq/SOJdj9zsubxm0wGAwNHP8knPyX5dvpACa+DMXjsOPdYLVJHdEhzPwAZu4Gfx7QYGeg//kweJ0RKg0Gg8HwtGDERoPBYDAYDMuSsCQTyzgKO1FWisIK6+NV+eTh0zVxrFo3cjmEgKQVxZvqyv6rTJd9UnYk9GVsi1eMDfDz60b47fv3VbYVSARqiSQnhCBhS/JB83GEWpELaIhZrWfAdeh1bM6UPD5x6DR3T87xzos30+c2Opm+PT7LvmzrCf3lWAgCPnbgJH92xbaW6z/y1AluOznVVV9zfsDbHj7Auy7ewsX93UW/PpsZS8T49QvWPN3DMDyD0MsJTS3EO611owjZrlheiz68u+/Gv+kmnEsvXckwOyJidQ94nIvwVRXb7fb/dLQ3biTzjndQuu02Cv/0T4RHjoDnofJ5KJex1q9HDA/XHIjB0aPobLb9PrWGsFIT0PcRqVSD81Nns2ilEHUPAqjxccLxcYJDhwiPHWvozvv+9yl84hPEXvpSEr/wC8j+/pa7DY4cofDxj6MmJpY5KUs3DAgPHcLetYvw9Gn8Bx/EveKKlfVxjjhxm103rWXXTWuZO1WgNO/x8H8cZd/XTiGkaOnSg0hMTA/HSY/Esd3mh2DSw+3jtd2UvVhPcwmdhMYqQkBqME6i1yE/VWZ83zyrdvY1iINVEc9J2PSvT61YaARIDnT/sNNLf2cXqcE4D/3bYcJgsSYlLQTL1Rf3c8ObLzmrMRkMhucQQR4mvwbT3wF/DrQCOw19V8LozTB7T3dCYz3ZJ+DAB2HbOxrv4cqHU5+DiTvAm2nebub7cPTvYfgVsOa1YD+3Hxw0GAwGwzMLIzYaDAaDwWBYFk3knjsXWkWTLse+hTyHcouT975aZvK+HgG+1lzalyYXhMyUA3ytGI47XDvcx5WDPbW40Lsn5pj1FmPWYpZs6eJ0pCRhRU7GehRQDlVDzKZVKWrZ69hsTicaalweyBZ5x8MH+cDlW0nai5O7Hz1wsq1g2Q1fOT3NTNlnoBLXeqJQ4q6JucrPLJYU9Dk2GWf5r4Ce0rz/8cP81ZXbGYmbiVTD+YVIJjsLjlI2C46+3yi6LCfwLXE3l/bs+dGKjalUdByFArSLc+2mn4poKQcHW67XQUDxs5+lfOedUZ1KwFq7Fq0U6uGHQSnCEyeQxSLW2rWEc3OR67DlzuqEK6Wi81yJciWVQlQFzyBAZ7OI3l7QmuDgQfRMNOkqhodbj7NYpLRnD95999Hz7ndjrWuusVe+/XbUzExDbGu36Hwelcsh02nKe/b8xMXGevpWJ2F1kniPw6HvRsJp/7oUfWuSlHN+TUSzbEEs7bSssVhlW4tah/FMdI+xXYtEn0txtjEmPfDV8kIjAr8UojUUtUZI8AsBM0dzjF3YB0T1G7e/fDWnn5ir7fNs2PaSlcXpXv2ft3DxLevY+5WTPHHnCbITpdo625Vc8OJV7Nq9lpGtvWc9JoPB8GPEm42EP28yEufsNGQugt7Lzu3hm6WERTj6cZj6Jqgl5SK8KSgcgVP/Gv0/NtbepdiO2ftg/kHoq9xPgjzsfx8sPNZ5uyALpz8fbbvj3eC2vn8bDAaDwfCjxoiNBoPBYDAYliVlW9hCnJMQ5pxFvbs7TrWZkO6SUGtmvICRuFtzEV433Mf/2rWxod3BXGOtseGYw7FCa3E0ZkmkgGKoUbr606AlIirtRuMua5OxlnF6R/IlPnbgJL+zYz0AJwsl7p/p4PTpgqmyz52nptiUTvClSiyo1pqHZ3N4Opr4PV0sk7QsRuIuwzGnY9RfPgj54olJ/qtx/RnOM+zt2/HuuaftepFINMZ/EtVqbKBFVPLSPurx77kHNT3dVtRbKUJKYi97GaUvfQk5MIA6dmzZaNcmYjFET0/068te1rRaFQrk3vte/MeaJz51vWinFGpyknBuDkqlprY1pFwco9a12FS0jkTTTGbxmlWJdw0PH64JjUiJHBrqeEhqcpKFd7yD3j/7s4ZzrQoFyt/6Vtu42G5QExPIdJrg4MGG5cHBg/gPPhjFw1oWsr8f97rrkH19Z72vbuhbk2LNJf2cfGQWACEF8Z7uhedEn8vmF44AEAZRNLm0JJteMMJD/34UgMxIvFlsLHd4uEiDUlEsqdYa25WEdcJkYaaMXwrZev0qXvSG7bgpm0/92l0UZlcWL15lcFOGVTv7Vrxdotflef9pE5ffupH8TBm/EGDHLOK9Lk6s82fbYDA8TWT3wpkvRvGiemmt1n+B+OrIaThy08qFv6X4c7D3j6BwuHO78mT0489CalskfK6E8T2R2KiC7oTGegpHozHu+qBxOBoMBoPhJ4IRGw0Gg8FgMCxLWSmGYi5nSuXlG7cgLi3cs6h7dzDbKALaZ/EwcmGJo7KVq6+wJBp1KOZyolBuilKt4kiJIyOnZc4PakKjQCAFrIq77OhNNdSLbMW3J2b5L1tWk3FsThc9vJUKAUsIteIzR8bRdbud9YKa0FilEIYcyReZKfts7Ul2HOfXz8zwK5vGcC1Tj8pw/hDbvbuj2ChHRggXFmvV6Urs52ID2RD72YRtIwcGGhbpMMR/7DFi119/1uNeSnz3bkpf+hLCthEDA+ip7qKUq1gjkdAkbJvYjTc2jjcIyP3xH7cUGgHU7Gzzwmy2WZStJyrQ1+hurIq2SkUCY/W8CoGamUHVHZMYGOh83qtjm54m///+H5m3v722zL//fnSpdE71LfXMDHrTJnShgA5DvG9/m9LttxPs39/UtvDxj+Nccw3xV78aZ+fOs97nclz6MxtqYuNKWXf5IN/88OMcuXcSvxidl3jGYfOLRkgNxshPl4n3OJG7cS4SA1WoUUGbB5M0qFCBBmFL7JjV5KrUSrNwpshT3z7D+L55bnnv89i1ex33ffpg6z6X4eJXNztYV4IQgvRgHIwxyGB4ZnP6C3Ds450jzEunoojRqW/C9neCO9C+bRWtYPZemLs/cgwKCVYa5u5tHWO6lHIlllsFkN8P6Qs7C53+AngTkWtSh7DwKIQFcPpXJjRWKR6L6kVuemPj8iAP+YMQ5kC4EB+FxLldLw0Gg8FgMGKjwWAwGAyGZYlJyaq4y1TZJ9ArF8TWJGNnJVYtrfPY6zrkiiubCF7qxrxyMIPWmgdnsnztzAzjJY+HZrMczZdI25HjL25JhuMu48uIq1JEE5G2AImgx7WwEFyQ6SzgVfGU5qunZ/i59SOUQ8VKUmKraK1rkmgpVOxdyLOjd/Hp5clyezfIQhCwf6HA9p4kss14c0HIXZNzvHxVFxMyBsNzBOfyy7HGxghPn265XvT1RdGk1ajVJdeZhnqJLZBDQ4u1EOvQudxZjbcd1rp1uFdeiXf//VijowTT08vXkqxi2zWXYCsXXvmOO/Affrj99kGjq0RrHUXTKtUoIjYN2mrattZHubwoJto26uTJxZVSYq1a1fGQ6vHuvZdwYqImqOr5+cX9ny3VY7Ntsu96F/5DD7VtqoMA76678O66i+TrXkfiNa85+/12YMNVwzzvNZt48F+Wcd/U4eUDvILPvm+cQitNbrJMca4ciYgCjj0wBVKgPMXQlgxDW3qY2DcfRbT6bb4jVIRGrSOHpR2TOPHW5zo/U6JnVYKFM0Vuf+cP+ZkPXMmZJ+Y4/sOVpR1sf9kYO19pnPkGw3Oe0/8RiYjdkj8Ee98Guz7U3mkYluDMf8D4nVEkaj2lU1A8AXYSYqPgto7vRocQ5IDoIQt0CIVjkGlRX92bgdLJSGRcyuS3onVCQHys/f7aMfUNWP+rkciZPwhnbo9iZpdGv6a3wuirYPA6kKaEgsFgMBhWjnlE3WAwGAwGw7KMxl1cS7KtJ1mrRdgtaxNxBmMOq86i7l9syWT8SNxBrHD/9aLfaNxloujx3+99knc9eoi7J+d4KlugEITkgoAzpTKPzuXYt5BnwLXpXaa2YX0NSSkiZ+MFmSSxFQir356IHCdJW3YlUFYJlCYfhMz7IQt+yJwXkA8U837AQl39yWLQWRzOBgEnC51F1WP5DrGHBsNzECEEyf/yX9rWdhJSIsfa1IGTEtGpRqJtI0dHW69zHLRSBE89hffgg/gPPURwuHuRqBWp3/99rLVrEakUcv367jYSAvuCC8BxsDdtIvUbv9HUpLRnT+c+loqaYRgJcctd54RYFPyW9hEEaKUiode20dlsbRvrggsQyWTnvpeMr3znnYt/VpzlywnFy3YbhoQHD3YUGpdS+OQnKX7mM+e03048/3UXcNUvbe6qbWnBIzdVIt7jMnMkx4kfzjB7LEdpwccrBHj5gOKcR2G6RHaiyNH7pvBLASM7ekkNxSOXbwui2NTosxVLWrjJ9vdX5S/2MX+qwA//7Qg3/uElbLiqc0RuPTtesZqX/M6FXbc3GAzPUnJPwbEVCI1Viifg8N+0XufPwRNvheOfahYatY5iUQGCAuQPRwJefRqK1pF4mHsSgoVIcAxzkTOydCyq4ajqHgYsnYL8gdZCI0R9qHIkgOYPQ/H4yo41LMLE1+DgX8CjvwuTX20WGiE6lwc/DA/9t0iQNRgMBoNhhRix0WAwGAwGw7JcO9JHwpKkbYudvakmEbAVEsHGVILVyWji9hVn4Yxbk2yc9HWkpN9dWTBDVfjTlfqKf3vgJKeLjf/AHog5NRFVo5n3A/YtFBiOuQy47SP5VN2kasKSbM8k6V3h+OYqwuCmdIJed3lHTag0WT8kF4QNYme1bmSgNE9m8zw6lyMfhIRtomDrmSx7DceylFJ4bvGuBsOzEfeaa0i94Q1txTFrdBRZccXV4j+lRKTT7QU1KbEvuKBJ0NKeR3jyJIWPfpTp669n9pd+iYU3v5mFd7yD+d/5Hebe+EZKX/wi6izqCcpMhp4/+RPsrVujMW/Y0FHw05aFtX49GpCDgyR/8zeb60s+8gjhiROdd7w0zrT+GlNfm7HloGUkOLYap9bI4WGYm4v+tm2s7dvPqv6h973vLe4yk4n+Pzh49u5GIVCnTrV0rS5H4dOfxn/kkYZlulRCTU+jstm2Il63XPmLW/jZD13F1utXYbXIJBcCBjemkbYgPRJn/Ml5cpMldBvLvRACJ2EReiHH7psiLIcMbc7QO5bEiVuIJadAa420JYk+ByfR+T659FD3fe0UCMFNb7+Ml/zOhQxf0NN229UX9fPKN1/MS//HLqSJ/zYYnvuMf6l7x/5SZr4H5SViYliEJ98ZiX+tCOabhTpvelGcC4uQfTTaPmiRVqBVFG268HAkeJbGo/93Ilxy7y+djgTKrtFw8EMw+Y3FRcqP+ilUxNLCYSiPR+5LbxqeeEskPhoMBoPBsAJMjKrBYDAYDIZliVsWLx0dYM+pKZK2xSV9GeZ8n4mSx4IfNMhZcWkxEncYjrs1p15MSl4xtvKCRzeMDfCDqfmGZeuTcXJ+2FSHsBVKQd4P+WExi6cUlhC4UjIYcxiJOziVCWFLCAZjDhN1kaMKzaFcke09SUbiLhMlj1kvQC8R7ySChC25YrCnoS6l0hpPRQJndb+t5s3DygRJn+tw49gg/3S4dWwjLLoZl06pVKNUbSFwKvWvimHI3vl8JCIuYyQKtGa67DPcxn2aMBO2hvOU+KtfjejtpfCRj6Dm55vWWxs3RnGep09DOo2oCI4tcRzsrVsjMbKCXlggHB9HjY+jSyXCEyeiPgCOH0dkMjUXZP6jH6XwyU+S/p//E/cFL1jRcci+Pno+8AG8u+6itGcP3gMPoCYm0DMzNdFP2zbCcRC+j5qcRI6MoCYnWXjTm7C3biW+ezfui1+McN2O9Szr9xm2qtsIkbK1XI1aKSOnotZRncdqeyEisVFrLNeN6jSehbgH0fmv4lx+OcK2ayKrmphYcX8ik0HPz3fvIF1C6YtfxN66lfI3v0lpzx7Co0dr62Q6jfvylxPfvRtr9eqz6n/Vjj5W7eij+IbtHPr+BMU5DxVq4hmH9VcO8f2/38/EUwuceWKOoLx8ZLkQAjdlE5QVZ/bOs/ayAey4hVP50VqDhiBQ6EBWRMjlHfxyiRhazgcc+M4Zdr5yTe1n4ql5jtwzSSnrA5Dsc9ly7Sj969pEIhoMhucefham7z777bWCiTth3S8vLjv2ic6uPr/5uwAQCXQyFtVo1NWEkWodYhX9VL/BB/mohmNYipZbidZ9dqJ0EtzBaJ/LUTwe1YLsuSjad/kM+DOtRdriiajf2CrY9x645G/Aaf+Ah8FgMBgM9Rix0WAwGAwGQ1e8eu0QXzszjac0QkC/69DvOvhKEyiFJhLtYlI2iVs3jA2QslfuFLlyoIfRuMt4aVEErMa57lso4LcRHAOlKYYKKWDWB6cyea/QlFTIyWLIqWKZAddmYzqBJQSrElFNSlUn5Sk0h3NFLu5L05NJ4ilFzg8JtEYKmCkHzHk+a5PxmtCYD0ImSh7TS/pyhGQ47jAScxvqV6brzsvrN6/mc8cmyIfNk7yhbi00RuOMiFuN516hKQQhccta1mjTSWxcn4p33thgeIahg4DgscdQc3OgFCKTwbn4YkR85e/l2ItfjPvCF+LdfTelPXsI9u6trRPxOKlf/3Wcq6+m/O1vk//LvwTfb9hepFLI0VHkwECDEBkeO4Y6cwZdKqFLJUQ8vig0QiSyLSwQLiygBwexNm1Cl0pk//iPSf/P/0ns+uubxqpyOcpf/SreXXfVjl1mMjhXXkns5puJvexlxF72MoKDBwn270dNTka/P/kkwb59CMtC9PQ0CKIAwVNPkfvLv0R+4hOk3/rWBpGuHWJwEI4fX6y/uPRpC9uuHWdL6uJoRSwWxZxqTead7yTxmtdQ+sIXKN1227Lj6EjdvmV/P84LXoB3993IVatQU1PLC6JLcV2snp6zcjZCJDZ6997b8pyoXI7Sf/wHpS9+Effqq0n93u8hU6kWvSxPotdl101rG5ZFkaiTzB7PdyU0VhFCVIRFxYbnDzG0JcPjt59YdDL2uhRmy5Sz/vKdVUj2Nd+Ljj8w1VB/cWRrLyNbe7vu02AwPAeZ+W5jHOnZMPXNRbExKET1DevRQRSb6k1G+wpyoH0QdlTXUFRc/FpFbkarLs5bexWhsUUdYh1AWLfeapGKoDVIG5CR4xC52EbrSNhMrOt8fDqI2sl4JIgWDnV2guowau/NRMc7+RVYfWvnfRgMBoPBUMGIjQaDwWAwGLpibTLO7+3YwAefONIgeDlS4Mj2QuIlfWl+bcvZuTCEENy6foS/2d8YL5S0LS7sTXG8UGpyG/oqEtg0kLZsRuMuvtLMeI0TnRrNtOdTmFfs6EkStywuyCR4Klts6K+kFAt+SI9j4YUKQeQgtKVgJO5gCcHqRIxAaQ7mCsz7LSYUAF8rThXLnC6WGY3HWJ+Mg4ArBhefFr6oL83Nq4f43PGJJgdlOVQdA1FtISKxcQmOJSiqkFSH1ygaX+veM7bFtcN9Hbc1GJ4phFNTlO+4g/JXvhKJbXWIRILYS19K/JZbsNYtMzm3BGHbxF7yEmIveQna89D5PNg2oupmBNyrr0ZNTuLff38ksAkBrttS4AyPHo3cjL6PLpVqbduhpqfRSmFv3Qpak//wh7HGxrC3bYvW5/MU/v7v8b79bbTnNW0bHDlC8XOfw73ySpJveAP2li3YW7YAUb1A/8EHuzonan6e7DveETk6lztnUiKHhlBnzkQLqrGo1WuNEIhkMjqXLa4/S+teCsvCueoq0r/1WwDInnN3WoglfcRf9Sq8u+9GxONYW7YQHjjQfTxfLIYcHET2np0AVhWfCUNkJ+ei1nj33IN6y1vI/PEf1+Jfz5Un7jxB6CsKM51r+LYdVgiHvzfJ/++OGwj97zN3YjH2L7+SPgWkR5o/M6UViJUGg+E8wZ/pvq1WUc1EVY5+lw44/ZGoVmXijsgFGJaii5o/F8WiiiXfobWOBEflg5CRM1GFkTgnY1F7VYr6aT2YigCpox9VAgRYqYrzPwBdjvq3EpVxe9G+hFsROWVUTzKxho4VsspTi4Jm/hB0/NdE/RADyO+HE5+BsZ9fvt6ywWAwGAyYmo0Gg8FgMBhWwLUjfbxl10bcLmLQAJ4/2MMfXbwZ+yxdHgA3rR7iljVDTctjluSCTJLL+tOsicdISInQUAxCLCHYnE5w1VAPqxMxZr3WAiBEcaP7FwoorelzHbZlkrX6jRDNJxzMFXhkLscTC3meyhU4kCvw5EKeYqgYiTvkgpC9C/m2QmM9GjhTKnMgV0AAu1c3xsv+2RVbeclIP6JuDErrhvqMS7GFoNe1W84D2EIQquXny9utf8XYQIMT02B4plL++teZf8MbKP7LvzQJjQC6WKS0Zw9zv/mbFD71qbPej3BdZH8/MpNpdCICmTe/GWvDBkRvb+QQbCE0qulp1Ph4NKZSNBEpksllo0D17GwU10rk3Cx+7nNRf7OzLLz5zZS/+tUmobGxA413330svOlNBE9FdZjKX/86xX/5l+4OvNqN7+PddVdt7J2Qa9ZAMnJ5CCEaBUQpEbaNyGQiobX+XFrW4rkTAtHXh71jB6nf+I1aE2eFUbKtcK+5puFv56KLiN98czS8/n6srVu7q98oBMnXvx5706azGkd45kxNlNV+d6JacOQI2fe+F93CCX82nHpsjmyHGo3dMHs8z7EHptl1c6NrciVz1IleF7tF/WJpm/uQwWBYgu7i+hcWoXAEZn8Ac/fD/EOw8AjMPRC5GOfug9O3w+H/B3vfBvnDUT3E/KHI4RdkIVioCIKapvgWrSK3oypW/vYiYbAmNIrKj27zQ/R/VYz6CbIQ5ipCpoiERVnnnlSlaDxhMXJGBoXOx+9NRP0HeboWGuuPbfae6BwZDAaDwdAF5hu7wWAwGAyGFfHC4T7+9uqdvGb9KL1Oc0iCAK4a7OGdF2/mbRdtIvYjEKr++9a1vGb9aNPyrB9wLF/iVMmjqBTZiqOxGvEKMO8311lcSj6Mok8Bel2bS/szrE/GEQjmvcXalL5SaAUjMZeLetOMJWLkA8W90/PMev6K/g0/40Uxq2uSjWJE3LL4xxfu4hc2jBKviA+ean8EfY7NplSCdvqvEIKYJSgvEwdot9g+Y1u8es3wcodiMDztlPbsIffhD6OD5QV/gOJnP0v+ox9FBwHeffdRuuMOSrfdRvmb3yScnDzrccjBQXr+5E+w1qxp26YmNIZhFPGaTCIcp6v+w/Hx2pMB/j33EJ48Sfbd726o7bccan6e7LvfTXDqFMXPfKbr7eoR6fSiY7FTO8uK3JcVwbHevVk9ZiElMpmMxNlEApFKYa1ejRgYQK5ejX3JJdjbtiFHRoi99KW17e2NG7F37my7b60UulBALSygcrlmEU8IYhVhsZ7kG99Y24/s68O59NLI9RlrURdLSuSqVfT+9V/T8973wlkIfzoMUSdPLi5YQXRrsHcv3ve/v+J9tsLL+xTnzi2OUAWKI/dMsOvmtYxd2FdbbjndfQ+QjmRgfeuai8n+LuqSGQyG8wtrmRqtxZORqJh/qhJ/uuQarVUkKN7/87DvfRVBDggLjdGnWkXiYZCN4lOXouucilotCo/RSpr/gdDqS7sGXVyMhRVETkdEFNUqllxHVRlUIRIlO6HK0dhajbsblAeT3zy7bQ0Gg8Fw3mFiVA0Gg8FgMKyY4bjL6zaP8YsbR3lwJstU2cdXirRjc3FfmpE2tf/OhddtHuMlo/3ccWqK209O8ehcrqG2oUUUbZqSFr5WHMwVcIQk43RXK3Ki5LMqHgMBthSVGokBVDyGouo1FLDgh8StAFdKnMp8QSlUaA2JLt0XthDMlQP++cgZJJFTc00yxhUDPThS8KYLN/Dz64b5l2MTfPboOKVwcQLaEoKhmMOmdIIex2am7DPTwQ3TjeDb4zZ+LYxJydsv3tS2jqPB8EzBf/xxCn/3dyvaRnse+f/7fyl+9rOIRKJhnZAyqnG4ezfO857X5F5cDmvVKnr+7M8of/nLlO+4g7BOlNP5PDqXi9xyUkYxrN0456p4HmpuDtnfjw5Dch/8IMGhQysaH0SCY+79728Y20oQ6TT6xAl0GC47fuG62Dt3ok6ejOog+j66XG6OSXUc5OrVkVDbwuWZ+o3faHqt4rfcQq6uhiZEblE1MRHtq158FgLR24scGUH09hK76iqs0eaHWISUpH//97G3bqX0hS8QTkwgx8aQY2PobDYSLZVCOA7OC15A6td/HXv79mjbVArm5zuej6WoqalGkdJe2T/Ry3v2ELv22hVt0wrLtVDBCmtULkFIQTnrYzmSm95+GXve/UPG982TGowtG88qHcnotl7seOv309brV53T2AwGw3OQ3kvbryseixyNbaNMIRIBFWgRRZIKEdU2bCfgVYVEYS0RLitiog4rP3WOxZpoKejuqcRKbUYr0ygQyljkZqxH+VEtSbe//XjrosvPmtl7zn7bsyH7JEx/B/zZ6BjsHui7EvqvahZdDQaDwfCMwoiNBoPBYDAYzhpbSq4eOrv6VGfDulScV44N8vUzM6xLxgkq/4B2pGCq5BN6jf+I97XiTDGaDHCtzv/ILqmQBT8g7Vg8lS1wLF+KpgREND3gSFH7d3pJhRwrhJwqlrEQJG1JPlCUlUKGkZOwLRoCrbEtwVO5Av/7iaOsTkaODV+pmqgYtyROZcK9x7HpcWz63ejHlVbDnEG0TOJ1cMSkbYs+1+Fkodzk9BREbs0qA67D2y/axNaeZMdzZjA8Eyh97nPoFbjBwulpwn37arUSZW9vFNuZTEbiU08P3r334t17L+4LX0j6f/7PJmFsOWQqReLnfo74z/4s/oMPEuzfj87n8b73PXShgBgYiMaQy630cFFTU8j+ftCa8ve+h7Xq7EQY7xvfQK5fHx2b1ovCXEUIXQ45OIiansYaGVm2rbAsrPXrkWvWoGdm0LkculiZNLVt5MAAcmCg9X6FIPWGNzS4Gqu4112H+/3v4919N1pr1NGjqMnJ1rnQWqPn5gjn5hD9/cQ/+MGOY46/+tXEbrkF/777KH/1q6iJCbTnIVIp7O3bie/e3eRgdS65hPDUqWXPRz1qiZN2aR3J5fAffZTw+PEV1yFdSnq4OfJ3pViurE1oxzMOP/XHV3DPPx3gya+eZOZojtBr8TkVkOyL0b8u1VZo7F2dZO1lA+c8PoPB8BwjtRnS2yG3r3G5NwWFY8sIjVQEQ7loNFR+VC9xaY3Ghm06fd/QlfhTq67/eqqRqpW2LamuX3I/lLGob70kwcGfjcYsW0zvChlFsIZnH48NQOkE+PPg/Bj/zac1TH4Nxr8EuQPgTy/WzhQWnPg0pLdBz6WQuRDQYCUhtaVSt9JgMBgMzwSM2GgwGAwGg+FZw0zZ512PHKIQqiYn3qFcsfVGQlMIFFJY2MvUmpzzfcbLHqeLXsMUQP3UQD2e0mR9n6RtkbYlpVDjKUVMWi03UDoSFG0pakLpRNljNO5wquhxpuTVhEBbCLZlUqQdCymiONhsEDBRstjWkyBe5yYSQjAcczlZbD+pIhGsTsQYijlMlnwmSx5eZcKkz3VwLcmFvSletXqIFw73nlOdTYPhJ0U4MYF3//1dtdVaEx44EEWO1omTulRC2Da6XCaYnYV4HGvtWuTAAN73vke2UCDzznciVug4g+iz6V5xBe4VVwBEjruKuHTWtfYqdRnV3Bx6fh7d2xs546RExGLd1RgEVKGAPnYMIQRqZqZBoBOZDHJ0NBI127gh5PBwtK8VHIewLOznPY+eD34Q73vfo/Sv/9rRXWlv2kTil38Z9+qrW/cnBOnf/32yQUDxU59Cz8wsPwjXxVqzhtwHPkDPBz7QUawVQuBefXXb/S8ltns3pTvv7KotRFGvFOrqbcVikfi9QoL9+89ZbNz+8jG+/w/7oXj2NSDTQ3ESvYvCvO1avOi/bufqX97Cnvc+xIOfPUzoR589aQkSfS6Z4ThWixqN9Vx8y7oVO4wNBsN5wuirmsXG0qlKjcVOVL4HNDjlBJGzULBs1SlhNwt/QEMdxlbxqTW3oq6IkfXt6q5zugyi0c2PnYxqNFb3K6xoG28K4i3uZckN0XHM/qDzsSyHlYxEzR+X2KgCOPgXMPkVKI9DearZOaq8qNbm6c+D0w/pHZGQKgT0XAKju6H/Bcb5aDAYDE8zRmw0GAwGg8HwrOE/Tkwy47WONgpbOVkAR0Txp8VQkZEWWoOvIlFQ1SULOUIwWfIJtMZf4pJypGgpHoaVWoqFICTjWCRsidaawZhDWUX9aCLhMGVbzHg+svKPYKXBU4oFL+Su8hyeXhQZXSlBwpMLeXb0pIjVCX8lFfLEfLQ8aS9O0K6Ku8x6PoU2E//VKFVXRnGtqxMuxVARtyRv3rWRHT2pH0v8rcHw40IrRf5v/obgySfR5XIkIEqJSKWQIyPITGaxrdYE+/ejjh1rcr1pz2sUEkslwoMHwfOQq1bhP/QQhU98gtR//a/nPui6WE9hWSsp87qI1uhikfDw4SjWsz62U8rIJTgygki3r2WlSyXU9DRMTjacp9r6bJYwmyV0XayNG5F9fS37iV17Ldrzuq4baG3YQOZd70Km08RvuIHYK1+J/+CDlO+8k/DkyUj4TSaxN28mdvPNOB1qMlYRjoM1NoZMpwnzeSi3ieuUEjk4iFyzBuG6qJkZsu98J71/+ZeI+Lm7+iASR+2dOwmWRLu2ZUmN0W5coq3Q+fxZbVfPxquHGdqU4eTDXQi2LYhlHNykzZZrm6NpnbjNq9/zPGxHcuyB6RX1u+maES665dyEVIPB8Bxm8DqY+gbMPxT9HSxEtRU7OhCprG8jTGnVhWglQLqVGoticVl9pOrS9g2Oycrf7QRL5YGIgfai31sdj6x8b/cmGsVGOwPDr4A1r4Ujf3tuYqOViPpb7nyeLVpHQuOZ2yB/oEVdzbDyetYtD4uRmNz7PMCC+Yejn8wO2PYOcFaWEGAwGAyGHx1GbDQYDAaDwfCMJh+EfP3MDF85Pc2XTkzhKYUlBRnbYiTuknGirzOiTS0UIQSOFHhKk/dDAr00RDTaLFCKQqiwRPSsc7226LZx+dU/u1wONQlbIIQg1HBhb6qh7RPzeUKt0RqKYYhXUTqV1oSammsj0JogDCmGkLAs9mcLXJBJMFH2an0FWrM/W+Ci3nTNrWlJwbZMkn3ZAsUWguNQzGk6L6Nxl3despntPamm9gbDM5nSbf9/9s473rKqPt/P2uX029v0PsMMDEWaiCCidDSaqImJRo2oMbHEGrtiRWOJEUssUX/2XsMgItgAMSi9Te/l9nrabuv3x9qn1ztFBljP53PgnrPXXnvtfe7cc9Z+1/t+f072Rz/C/eMfK8U2lJDmj4/jJxKYixdj9PQQ7NmDPHiwfrxmPYFeSvw9e1TEZ38/+RtuIP4P/4CROLJoYZEs/VsT8fi8Y1QlSgiU991HkE7X1ksMAoKxMYKxMURnJ9aaNTU1AGUmg/fww+05Eh0Hf+tWWLkSo7+/sp90GufOO7FOOAGRTCrxMwgw+vtrXKBGVxfRSy4h9pznYJRfgyrn5+EQzM6S/9//xViwAGPBAuX4HB9Hhg5QTBOjs7PkxCzDP3CA/G9/S+zSSw/7+NUkX/YyZt72ttLxm1H22SISCYzDFBuJRg9vvzKEEJz7snX84HV/RB5G5F7ngji9y5Is2li/dphhGlzytlO58SP3svuOsbb6XP3kQZ72ho3a1ajRaBpjWLD2bbD5aph9CPKjEDSvEas+TY1aQVGI8Mt9wW3Y7G+PC2a3cir6+VCME2X7lrkVhUF9YbNJ/4EHcrpJm9AlacSU6Jhar2o39jxRCbAFIbLvKbDt43XEzzaJhp9LVu3CpKPC2M0qOjW9pfI7mpRKUPQzFF2oxW2Bep8n/xiedxizPfswPPhWOOmjYOm5jUaj0TwSaLFRo9FoNBrNcYnjB3xlxwFuPDhBPggYyztkAzVR9gPJuBMw7rgkTJNlyRgRQ+A2mEdHDIOs5+ELidngpqUM/+tJNb81harRaArRMH61/FUnCIhJJTbmqm7iz7k+c56HlDDn+RUuzEBCvRKPEsj4PgGSrB+QNE3SZf06QcBIzinWewSImAYbOpPsyeSYyLsE4VklTLMoyhbG/YTeDl6xZjGLE0fH0TNfDmXzXH9gnN+NTDLleEhUXckz+zq5clG/rhepqYuUkvSnP03+l79Uz5vVasxk8LdtQw4O4h86hHTru6LrCpAh/u7dGL29yFwO56abiD3zmUcyfKxTTiH/m98AYAwO1tTra4YE5NycEvJMU90UbVJLUs7M4D70kHIHFsQ/18XbskU56kyzIk62cUcSf+dORCSC6OwkmJggOHgQmU5jpNPI2dni+ciZGYKDBzEXLcI86SSsBQuwzzyTyJOffFgxtO2Q/+UvK95bo7sbGjgx6+6/adNRFRutdetIvfWtzH34w60FR9NU741lYa1b13YEbk03Q7VuwsPh9Oet5E/f3sHO20bmtV/nwgSJnignXdncgWhHTS5752k8fON+Hti0j7Eds3XbDa7t5KQrl3LC0xZqoVGj0bTGSsCGD8Cer7QW1oSpBEq/HUGyeQ12CJSoZ0RAxlVNRT+jnvsFd2Srv2EGKrq1vO/Q7VgUMEVlXwIVbSpMJchFB2Dd25XYWE3Xqaq25dzWFuOogxmDSL+KY40OzH//djj4k9DRWC00ZpSDsWEGhAR3CtJbwRuE+Ar1vSi7F7Z9DNa/59iMV6PRaDRN0WKjRqPRaDSaluyay3LToQlGcg6ulCQtk5O6kjx1qKeidmA9pJRMOR4Z3ydqGHRHrJb1ALOez3vu3cFDM6VouHQDJTHj+2yeydBtWxViXGkAkPMDhFDCXqM5fyCVO1CGzkdfShV/2uT8jLKboBJwpSQiRDGetcBwzgGpXJqVQmNr90jOD9g9l2VNR4Jtc5mKbaN5h4XxaEVJNcsQrErFWZqIMpp3mci7rO1I0GVbdNgmZ/V1cvmifhbGj9wJczhM5F0+vWUvfxqfqbl9MO163HRogpsOTbC2I8Gr1y1hVYcWHTUlsl/5SlFohDaiSKXE37VLiZKN/r3V+3sUBErACgLcBx/E6O4m+61vHbHYGL3gAjL/8z/IdBqRTCJSqbbdjTKTAc8ruiONVKq1WJjN4m3dihXGkfqHDhVrPopIRMXPtnVwibd3L0ZHB0GhxqJpYvT1VTQTnZ2YnSq+TB48SOwVr8Bau7a9Yxwm+XnUSKyHt2MH3ubNWCeccJRGBJGzzqLzQx8i/bnP4W3f3rCdYVlEzj1X/Q4cphhrDg1hnXrq4Q61AiEEL/7aU/jslTcysnm69Q5A16IE3UuSrHjiACddvqRle8MQnHjpEk68dAmHHp5i5+2j5GbU72S8K8KqcwcZXHuM6oJpNJrHLkYEVvwzTNwBB38QxpsWPiMLLsBIWGux0UIQUfaYB1YK4ish/bB6bkaV8NgOQoA0w7HW+54SuiQLwqMwwUyBKEssye6FiT/CgstqdzcisOyfVFRpvv0FThg2pNap4w1d0f5+82FuC0z+n3JxFpAS/HT4HrWaJ0klSOZH1X7JVerlqT9BZndYs1Kj0Wg0f0m02KjRaDQajaYht49N8+O9Izw4XVsP6jfDk3xl+0EuGOrmsoV9pGyLpGWSDOsITjseNxwc5xcHxhjNlybcCdPkwqEerlzcz9JkravODyTXPLCrQmgEaJbqJpFMui5BIDGqXIhOIPGkxBAC0WTSKuqUZTSEqKtDFDAN5XwsCIgFkbFchAykZNJxccNxlBPI8LgtnBvTrodtCBbGohzMlcSBfBAw7bp0R+yafWzDYFE8yj+vWcwr1ra+AfyX4GA2zzvv2c5IrnW84NbZDG+5exvv2riSU3qOUXST5lGFt2sX2R//uOI1kUzCWONIRimlcpf5fuEfW02b8ihS6XlKgCtzysnhYchkyA8PM/XKVxK9/HJil12GOIzoShGJEL3oInI//SkAxqJFKqa0xcIDGQRKJLRtdQ6GgbliBf6OHS2PKWdnCaanlVBY7qS07doY1mb9jI/jz8wU9zH6+pruH0xPM/vud9P50Y9iLmn/b5D78MP4e/dCWL/RXLkSa9Wq+mPK55WAeoR4e/YcVbERwDrhBLo++Um8zZvJXXcd7p13qtqKponR20vkgguIXXYZ0nWZesUrWv4ONCJ62WVH1f0X64jwyp9exHf+9Tb23jlOdtqpud8rDEGiJ0LHUJxoymbF2QNc/O8nz3scC9Z3s2B991Ebu0aj0RDpUnUGjWaf0U2iSYtOwhgI2Vw0FBZEBiG+WIlyybWQHwZnjAbVHer0YYPMtdOQhmKoEYED34ahi6vqQoYMXQEHf6qOlW8QKV+OlVDnYkTVtey/sI3xHQbjv4d8lZM+yCtnZ7s1IoO8cnk6Y2B1QjSMfB/eBCv/5eiOV6PRaDQt0WKjRqPRaDSauvy/HQf4wZ7GUWpZz2fXXJbfj0xyzf27WNeZIGGZLE1EiZoG22ezFXPsQErG8i4Zz+f+qTk+s2Uvp/Z08InT1zJY5rK7dXSKuyZro9UaJJlWEABCyoobnvky549pCOKmQd6vFf6McOJuIEAocTKQkkA2P3bUMMiEjspClwmrpFC6gSRAVoyj8ritkcBY3mVlKo4hBPuzpZsSGS+gu0GS4vOWDfKPKxe2cYRjz6zr8Z57d7QlNBbI+QEfvH8XH3nCGlak4sdwdJpHA/lNm2peM/r7lTDVyOHneeofZuEfZz0xJIwilbkcMld2w6+wn+8TTE0BkPvpT8ldfz2Zk0+m67OfxVq2bN7nEX/uc3H/8Af8kRGM7m7ksmUEu3c33ymfByEQ8TgIgbl6tapFOTLSljMyGB5WAqpXcg8Iy8JYsoRg166W+0vfR+ZyiCCAREIJZgsWtD7u3Bxz//mfdH384837z+XI33wz+euvx6szHmvdOmJXXEHk/PMRZdGxMput7cz3CSYmlMgcBAjTRKRSiNBxWff4mUzDbUeKdcIJpFoImZFzz8W59dZ59210dBC95JLDHVpDEj1R/vEr53PXD3dz3093M75rjiBc8WPYBonuCKZt0L04wcYrl3LSlUtrFvpoNBrNI0JyjRLJ/DqfDwWEFToK64huhTqORuiENALlsgs8Kuow2h3Q9YRKcc/ugPUfgHtepuJUZQt3oxGpqvUY9l1qEL4kqJgx+OnQ3RgeOzIAzgRM/AH6zqs9TmwhLPl72PctiA6BMxLWtiz7Ti4E2D2qRqNV9nm54l+U4HgsSG8PazKGSAkyT2XNyxZIn2Lkbf5QSWwc+zUsf5lyaGo0Go3mL4YWGzUajUaj0dTwzZ0HGwqNXiDZPpdh2i3dtPalZPNMhg2dSX4zPMW442AiWJyI0hOxOZjNM5538asmjjceGudJv5zin9cs4fkrFrAsGeO6A/VdSlGztSxnCYFtGLjhalgvkBWxpQZqu22oMTu+EgI7LYu05yOEIGIIZlyvONK8HxC3Gh87YghyviBAFnWMwVjpZnggJUFAjbgJ7bkaC0yEYuPiRJTuiMVIzlHXtKrfiCE4f7DnuKt7+OO9IxzMthnZWEbG9/nqjgNcfcrqYzAqzaMFmc2S//WvazeEUZ4Nax+Wi4xBUBuZalkqijWfrxQag0A9CvsbhnoEAeRyuHfcwfiFF5J41atIvfrVFQJYK4zubjquvpqZd76TYGICc2gIYZr4u3bVFU0lIF1XCWaRiBIau1TMpDE0hN+G2CinpwmqXIhGfz/m4CA4DsGBA833D+NWpe8jDANzzRpErL16r96WLSrKtUGcqr93L7NXX40/0nhxi7dlC3NbtmD+6Ed0XH015oCqHVU+BpnNEgwPE4yPKydrNbEY5uAgxsBATW1EEX9kFzOk/u3fmDl4EK8Np2oBEYmQeuc7MZqIqEeCHbM4+wWrOfP5K9nxhxH23DFGbtZFGIJ4d4Q15w+x5NS+1h1pNBrNX5Khy2H3lyG7p0kjEToK6y2AM8Iv6KFIJQzlcqyeCsSXlMQ6YSlRq+sM6H8ynPMLuOM5MPtQqf5i8dACRKHOo69qExbrNpbNCYQBNEgPkGF8qJVS/RXqKQ5vqi82ghIb/bRyOMYWQ2xRWLcyPG4hYrZ8nMteBgPHyNUI4FUl50j38Fz+MlDCq58Bb05dFz8L7nRJfNRoNJrHCcPDw3z961/nt7/9Lfv27QNgaGiI8847j+c973mccJTTXKrRYqNGo9FoNJoKNs+k+c7u4brb3CDgoekMuaD2Rq4rA+6Zmi0Kbj6SHXNZApkh0kQonPN8vrBtP38cm+aFqxbWjWwF6I/a7Mvkkc1WugrlKpTSYMbzagS+iFmaxJtCkLAESxIxFsajPDydZiZ0/UQMg5wfECDJ+gES5W6MGEaty1FAyjKZ9TxMAXHTpNMufcUyDVFXaDQbrahuQC4IkGHsatIyWZmKszQR4+y+Ts7sUzebO22L03s76LCPr694XhDwy4MTh73/nROzHMrmWfAI1ZnUPPK4Dz5YKQaWYSxcSDA5WeHaK1L4g1T491b4R1TYHIsp1165Qy4I6otVVTX15Nwc2S99ieDgQTrf9755CVbm0qV0fvzjpD/9adw778To70d0dxOMjRGMjEDZuYp4HBGNYi5ahOjrQ4Sip8znEbEYorsbGTovGyJlZZ+pFEYYbWouWQKmSbBvX92/SVLKYp1HhMA84QSMjvlFG+c2bSL1b/9W87q/fz8zb3kLwWytm70e/p49zPz7v9P1sY+pGNdYDGNgAO/hh/F37mxewzKXw9+zB390FOuEEyoEYnPp0nmdz9FGxON0fPCDzH3wg7j339+yvdHRQeqd78Q+8cRjPjbDNFhz3gLWnNfayarRaDSPOIkVSnDb/53mUZxGtNLZV3w9otx/3hw17jopQ0HMg8xeYG9pW2wIlr9Cxa4ml8NZ34M/XArOZPjZWvj+YZa+hxRcfQU3pSz/7tFikaX0VPvoAjVmUOJmM5a/DOLL4cD3IXcQzAaLhhIrYckLoPeJzfs7UqqPX3SCHl6sOADOuBIbQUWsajQazeOIX/3qV7zlLW9hrmox6s6dO9m5cyff/va3eeUrX8lrXvOaYzaG4+tOlEaj0Wg0mkec/91f31kYSMmWmfpCI6h59LTr0WGZmIbADyDt+cUgnGbOxFnPYyTv8OEHdhE1DLoitV9RbMOgJ2Ix4TSPJJpzfU7v7WDccdk6kyWHutFgAHY4uRcIeiMWQ7EoKVutGh6MRZie9cgHAU4Q4EsZhvJInPAGds4PsIUgahpYZaqjYUBf1MYEFsejSAlTjsus5+MFElcGBGG8q0A5MJOWwZxHjTOxEVZYG9IqE0osQ3Dxwj7OG+xuq49HiltHpyucsPNFApsOjPHS1YuP3qA0jyqaRYWKWAxr7Vq8LVtqRcJysbHKRSwSCYRlEZRHaDYSGoWo614MJibw7ruP2WuuoePqq5UQ2CZmfz+dV1+Nf/AguU2bcG65BSMeRy5ejLAsrA0biDz1qYhUirkPfABQcZ/+yEiFe09KqWJWpYRotOUYREcH5tq1Fe3MhQuLDtFgdLQkLoISKaVUbkDbRk5OEgiBkUq1fa7OrbdCldgoPY/Z9763baGxQDA2xuwHP0jXJz6hxr5ihYrYbXfxRjaL99BDSqizbczly7E3bJjXGI4FRipFxwc/iPvHP5LbtAn3nntqzslcsIDo5ZcTvfjieQu+Go1G87hh+UtVjGbuYOM2wlQiXXWUqBGHSJ9y+eULNYHDz8sgqwRMqyo5xExAdBHs/gIc+A6sej30nAlnfBPufbUaR7WwKX0gUKKnEYUgF4qPMoxHbSP5RAglHhb79NRxjCZpC4MXw8BFMH0njNwA2X2luoeJlcoZ2vEX+kzsPh32fLX0vCgOzyeW26hsXx5dayUPf2wajUbzKOOuu+7ida97Ha7rYpomf/u3f8tTnvIUUqkUDz74IF/84hcZGxvj05/+NMlkkpe+9KXHZBxabNRoNBqNRlNk2vG4dXSq7rYJxyVd7yZ8SKEmYT4ISAizKDSCEukihmgaGTqcc0haJnvTOU7r7VDOvyoWxKNMOm7T9a6Fo/ZHI6TjPgeyynXZY9v0RC1sYdAbtbGrLIoJU9VedAO1vxmKe9W4UuJ6PjHTIFYmoC6IR3n9+mVsnk7z432jjOZLNxWkBF+CgcQ2BDHTKEa2Zv3WN8jVtautHdllW5zTf2wi9I4m9021jnlsxf1T9R2vmscJbQho1vr1eNu2KeGtgGWV4k8LfRiGcgvatvrHWRDWpKzvjCtEqNp16v5ISTA6invXXbi3307k3HPnfWrmwoUkr7qK5FVX1d3u798Pvo+3fXtdB6MQAmIxpOMgMxmkYWAkaiOUjcFBZBBgDAzUFSRFJIK5eDHGokXITIZgZETVPiy4QYMAPI/g0CE4dIggmcQYHET097eMg5bZrIqCLbuGzh/+gH+wyY3gJnhbt+Ledx/G0BDuHXfMv4N8Hm/HDqwTTiB2xRWHNYZjgTAMIk96EpEnPQl//368LVtUPclIBHNoCOvkk9uO3tZoNJrHPIEL47fA+G+Uo016qpZh16mw7h3w0DtUlGYjzAQgVT+golOFgMyu0vMgr6JOpa8ciGZCxaAW9o8OQqS/5E50Z2DL+2H1G6H/KXDq52DLNUpw9Av9mCpCVJQJZcJEuR6hLbHNsEFES8eFyvjXZggB3WeoxyPJ0LPAenvoIj1MDKvyGhQEy+ggWF1HNj6NRqN5FPG+970P11WfZ5/61Ke46KKLitvOPvtsnvnMZ/KsZz2L0dFRrr32Wp7znOfQ1XX0/05qsVGj0Wg0Gk2R/xufLopt1Qxn69U1CZHghKKZG0hcoWohlm3GDWRFjGk1M65PzDDwkYzlXIbitatyU5bJ8mScXelsnR4UomyCbhsGpgGDkSirUvGGN2m9QLJlNoslBAUpUwgwERXnUU7OV5PZmGmQME3+bd0SbhmZYn82z8pUnKFYhHwQEEiYcz02z6RBCPU8FCujhiDntw4LihgGljAwqsZ/ycJerHk4qR4p0l5jkbpd5o5CH5pHL0ZPT8s2IpnEPvVUgqkpgpER5PQ0QkqIRFQEq22ruodl/45kuYOvELNac/BQnGxwXH9sDGPRInLXXXdYYmNLEgm8rVuRMzNNm4lIBBGJqFhYlHNSAsKyED09dH/2s8y+/e3IJotGAAgCgn371PUDMIzi36hyd6dMp/F37kRMT2OuWtXa1Vl1bfObNjVv34Lcpk2YC1S0pzE4SDBcP/674XCmpxHJJNELj2E9qiPAXLwYc7F2c2s0Gk0NgQf7vwsj19cXE+c2KwGq7ykw8Qdw6qe2AGAmQeSVqFjtCBRG6H7Ml+oyCktFptq9YDVwmMsAtv8n2N1gxGDpC2D0Jph9WD0XQtWU9MrGbiVD0cxS7rwgXxWrCqq2ol1WX7Fqe6S/JsXhuMaKweClcOCH4Qui7P8G0CQGF1Qbo6rEQqHu5OBlj65rodFoNEfA/fffz4MPPgjApZdeWiE0Fujr6+Oqq67iwx/+MJlMht/85jc861nPOupj0WKjRqPRaDSaIlNO/ajLtOc3dTVKKIpyEsjVaZsPJBGz8bElkoLJbyTv1BUbQcWdGgJ2zuXq1m+0DYEnYSLnkPMDLCGIGgZznt+wluGudJZc4BM1DQIgHwqJQkDSNDGFwPED3Kqb5W4gOaEjxmfPXs/ntu5nJFcSLhKWSQJ1wr0Rm/3ZfIVglvMDBAZR0ygKl/WwDYFlCAailSuVOyyTKxb1N9zveMKqKXQ5f2x9w+BxjXXiiRgDAyriswVGdzdGdzf4PtLzwHXxHnoI0dWFuXw53sMPl9yP5f+m67kahVBxq/VcjagIU5HLIfN53Lvvxt+3T9VAPIqk//M/VW3JFmJjAWGqvzsyCLDWrAHAPvVU7BNPxH7iE3Fuu63hvjII8KuFzYKIGP4blGX1G4VtIycm8KXEXLOm4YIOEY1WCJX+wYNt1SZshnPbbYiYqvdkLFuGzOWQ000cLNVYFvZpp82r1mY5QTqNc9ttyPFxpOchUins007DWrHisPrTaDQaTRv4OdjyQZi+u3k7GUB+GDo3qsfILyG9tfS5L4RyvsWWKOei1QHeDORHVaSpDJTb0A9CIbDse4AzoWolNhxjHpy98Od/UNGkBYSl+o4Oqv39HJhRiAyo+oXTdyshVUSUoCj9yrqTwqx08VUvgxqovbn8FyV7QAnA6e3KxWnEILZICYodJ9TfZ82bYfSX4M6q6yPDuagwmtfcRJSicMuxO1U/g5cclVPSaDSaRwOO43DRRRexZcsWLr744obtVq1aVfz54GEmzLRCi40ajUaj0WiKNHLYlTvTAlkQBSUCEUZ7ljmFAC+sT1iOL6W6Md9ENEpYBoYjyPo+Gc8nYdVXJ/ujETosi5G8w2jOxQsno36gYlTvnZylO2IxGIvgyoADuTwHcnkSpslgLEJ/1C66BB0/YLKsDmTcNDAoORejhoEQYBtm0fgkUfcoUpbJJQv7uG7/eIXQWI0QsDwZZ9tsphg3C5D1A1KWgW2Iuo5SSwiSpolAiawFIobgHRtX0h9rUpPlOKI/2kakU6s+Ykfeh+bRizAMYpddRubrX29/J9NUwls0WqxRKKJRrA0b8HfsqBTU6rkahVBOyGjlqnkpJbguMp9XgqYQePfcA0Iw8653kXr967FPOeUIzraE+8ADuHfdpZx7IyPz2ldOTCAzGUQiQezKKwGI/fVf495+O7KesAoEBw/WipqGoepDCoFMV8YZy7CWpQwCVQtyQf2br5Fzzqk8zqFDddvNh2B8XImYiQRCCMy1a/F37UKONXGwFIjFsNatw7377nkf19u5k9zPf47z29/iDw8TjI6q34UgQJgm5urVJF76UuJ/93dF4Vej0Wg0RwEZwLb/qBUapVQuwfxoGHkaAAaYcYgOqDZnfgsCH6bvAj8LdpcS/HZeqyJYQYl+kYFSv4EDM/eArHLuBy7MbYHOk2vHmNsP2f1AoCYMBQekMJQrERu8WfDnlOhod5f2jQyU1ZmUocPRpTRDM0rOxoLLsYAwlZvvKOAHkmnXI+cHxE2DrohVk65SwexDsO9b4bWq+i41+wCM3gjJ1bDoedD35MrtyZWw6g2w/ePqfQtyhRMKH/Vmp6HQaMapEFwNW13vRX+j3l+NRqN5nHD66adz+umnt2y3f//+4s+Dg4PHZCxabNRoNBqNRlOko4G45wUBjh+Q9QM8KSsWBRuouNKCkCgKStxhYBsGfVGb0bxTIb45fsC44+L4AQFKhOuwTZbGYyyOR5lxPUZyDiM5l8WJGH1RGzt04wxJNWEGyPg+u9JZxvIuazvi2IbBSN6pmcZGTSUAxk0TgSAX+KXzFYKeUMjstC3+PDGLRBaP14jBmM2hrInhKyGzcEwnkCRMgxwB+fCcBSo6NW4aIKAnYhMN60N22RbvOnklJ3QmD+saPxI8baiXH+yZn1BSrw/N45vopZeS/e53K6NP28Q+8UTiL3oR6U98AgBr/XpkJqPiSfP5yptjQiihsqMDYVVOl6TjILPZ2vYAUuLeey8z73gH1sqVpN72NsyFC+c91nIKUaMikUD09CAnJ+e1fzAyQvSpT8V+4hMBsNevJ/HKV5L+7Gdr2sqw/mTFa7mciqBtJJpJqa5fPk+waxfG0FDdBSXRUOws7nYY72HNoR2nwnEqDANr1SrkggWq3uT4uBJJyxCdnarOZE8PIhRPZTbbtrsxt2kTmc9/Hv/AAfxDh0r1PgtjArz77mPm9a8nfe21dP3XfxE5++wjPleNRqPRAGO/hcmqOr3OGOQOKJdgNUEe3CnI7IH73wRnfReSK0rbR28qCY31cMbqx6uDEizd6UpRK70TcnuVSFlw5c3craJaI31KXDTD7+9mCmbuhcQqiITfcaODZbUdnTo6WwC+pwQ5YUOsbIFP31NK/Rwmw9k8mw6M86tDE8y4pbSbnojFJQv7uGxhX+1Cx7HfqchYWT8dp0h6O2z9MGSeD4ufp94XP6fqXq54hfp53/+DdBhpC2VOx/ILYYS1M+O1EaqRfhi8CJa88HAvgUaj0TxmmZiY4Mtf/jIAiUSCC49RKQktNmo0Go1GoylyZl8nYqsk7ZVERS8I2DKTIePXVi+UUlXT8MNtQkqihqiuINI2MdMgbkYYy7tIJLOux6Gcw5Tj1hz7YE65DgdjEaSEKddjKB5hQbxy4tkdsRmKRRgucx7OeR6bZzJs6EoynnepR9Ky2NCZxBQqLtWTEkOALQzMsljQA9k8UUMwVHXcamzDYE1Hgi2zGaKGgRNI8kGAG0hiJiQsiw7ANAROIIsRsQnTZGUqzqpUnCsW9fPUoZ6i8PhoYWkyxsauFPdPzx3W/l22xZMH9ArlxztGVxepN76R2Q9/uPHNvzqIRILU296GtWIF5tKl5H7yE5zf/Q4Aa/Vq3FxOOf1mZlR9xmgUIpGaGoRF4a26/3JBMtzH27mTmTe9iY4PfQhr+fLDOFsIpqcrIk/NVavwH364xl3YDDk7S+otb6k4l9jllyNsm/SnP11Rv1FOTlaIZ4XzFdEo2DZyrvm/32BmBn/bNqy1aytet1auxN6woeI1kUi0fQ4NkbKuCCoSCcwVKzCWLkVmMkpwDN/XapcqgHTdtsTG3HXXkf7c5/B37iRowz3p79jB5AteQNdnPkPsEh3nptFoNEfM8HWVz7P7lNDYiiAPY7+Brf8Ba/+9cX/V+Jnm2/MjodgoYfZB5WqsmSyFUaj5UfWwu5TLz4ioGNfMDhWhaiZK323qCo3lfUrVphDn2nEirPzX5mNtguMHfHrLXn4zPFn3sJOOx3d3D/P9PcNcurCPf16zRM2FJu8oORLbwc8owXHnZyuFUbtHxZ6ufiMc/DGM/x6CcGGXMMLalZJSdGqsNj7VSsLKV8Hyl+lajRqNRhOSz+fZt28fN910E1/72tcYHR1FCMG73vUuenp6jskxtdio0Wg0Go0GgDnX49bRKQ5mHfZk1A11L4zRCWRpzltv+ibLHr5U9Rer3S2GoHmEqmmSDJ2Vy5IxJvIe406t67CcfBCway5Hxvfpi9isTMbqtlueUDeSywXHjO+zO52rG1+aME3WdSaKtQYjpqA6sNQNAsbyLrvTWZKmScYPSFgm/VEbs8F5dkUs1nUk2DabIWJCxDRBwvJkjMFYpHh9fClJez5LEzFesWYxy5IxVqQOr67Y8cKzlw4ctth4xaJ+rBbOUc3jg8i555J64xtJf/KTqh5jC4yuLjre9a5iHT1r2TJSr30twVVX4fzmN/gHD8I3v0kwMaEWSYSxxdVI160rNAJQJmCV1yUMZmaYvfpquj75SYyu+Yvl/t69BHNzyNnZYoypGBqC8fG2ahOKRAJz3bq61yl60UVYGzeSv/568jfeSDA7SzAxUdwuXVdFo6ZSJTE1GlUuxiYE+/cT9PdjhJNXEYuRfN3ratqZK1eq+NUjcDganZ11xcMCInSntkIkW7vEva1bSX/+8/i7d7clNBaQMzPMvOlNGF/+MpEzz2x7P41Go9FUkd4Bc5tLz3OH2hMay9n7DYgvgyXPh+xemNvavL1ssXzSm1Ixp5ldypFYb9JSvTjKnVaxox3rITYEs5PqXOJLS+dnJkN3Y5NZkGErB6UzDmvfrgTLwyDvB7zn3u08MN16IVMg4foDqnTEO09cirXjv9oTGqWvnI3ulHrujCpxsOBMdCdh/3eVsDhwCSx8Lmy7Rl0r6ZdqMwoDvEyli9KMQvIEOPW/IbV6/hdAo9FojiGbN29u3agBixYtYtGiRYe9/3333cdzn/vcitcWLFjA1VdffcxcjaDFRo1Go9FoNMDvRyb5r4f3kg8C4qFTREqKQmM54brSGgpVNTwpEYBZ1SjSMma0dJM+ahgElgSnUa2OEoUaiLJZy7BmYqdlcSjnMBvefJ/Iu/hSFhfARg2DgWiEoXikoWCY9nwOZfNMOB5SSrJ+QCDBQ0Ie9qVz9EVtFsSjxOo4ELsiFif3pBjNqXqTDgG+rBRiT+5OceWifs4d6HrMiGxP7O/ib5YO8qO984tTPaO3g79bPnSMRqV5NBK94IKSQ/GWW5BurTvZSKWIPP3pxJ79bMz+/trtyWSxjqG5fDnpT30KqF9LUIKKTq1HoS4kgGUhursrNgdjY+Suu47EP/xD2+cngwD39ttJf+lLePfeW9vAthE9PRAEqr5iVaRrMSq0u1tFhWbqOzPMBQtI/NM/EX/BC3Dvvpu5j34Uf88eME38/fsR1eJqPK6O1UQglEFAcPAgRk8PIpmk4x3vwFq1qqadkUoROf988jfd1NY1qUfsr/5K7T8Pl2s19kkntVVXMfeznyFnZ+ddNxMgGB1l7hOfoOfrX9c1HDUajeZwmfy/0s+Bq+JK54s7Bfu/BQNPUy7DlrT4Di4lZHcrh2ND0a3OfMLPKqEztV5FoeZHAL8sPtRWdR4DpzKStdilAZFBiC1UrsDRX8DSf2zjfGr5xEO72xIay/nzxCyfvfv3vNZrvfAJ6cHsw5UuUSnVOceXVrUNYOQX0PNEeNINsPuLMH1n1ee8VH3KQImVvefBylfqGo0ajea45H3ve99h7/vqV7+a17zmNYe9/4EDtQtyRkdH+e53v8vAwAAbN2487L6bocVGjUaj0Wge59x4cJxrN+8tCnVdEYuBaISdc9kKobFc9qsnOIrqNrLS3RgxGrsaI2GtRoA512fO81iSiNFhWwxn80w6HrUhrmq/vJDETYErA3alc6ztaBzP1xO16YnaZD1f1YAMJFnPJ2WbdNoW3bbV1H05nnfYMZcrRpwWz71sFx/JSN5hwnFZ05Gg0679uhUxDBYnYiyKx5h2XZ7Y18W5A90kLIP1nclHvYuxES9ZtRBLCL63Z7it9uf2d/HGDcsrYms1GgBr1SpSb3gDwcteRv7mmwkOHFCRn8kk1urVRJ7ylAqXYTOiF16I8+tfI3M5guHhOi4EF4I6NxGFqIgDNfr7a6JXAfI33ED87/6uLaEpyGSYu+Ya3LvvJpiZqd/IdVXkqWFghA5BfF8Jn3WiQkWsudtBRCJEzj4bc/lyFZ06Nwf14mJRbklpWcrlWe+aADKdxlq/ntRrX4u5dGndNgCxK688fLFRCOJ///cEk5O4f/7z4fUBRK+4omWbYHoa59ZbD0toVB0EeFu34tx+O9EnP/nw+tBoNJrHO95s6Wdn9PAWmkhP7Tf8C0id0Lq9GYP61RZCAsiPqUjTRoiq7wUyFBW9aeXoE7aqwehOqShVUZg3GGFcaAykWxIcjagae7m4NvJLWPz3YMzvFu+WmTS3jbUhGNbhVweHeU5PksVWE6FSSkhvqx9H64xCfDF1Bd3JP0J0ANZfrVyfw5tg6v/C3wGh4mf7zlfRq0dYp1Kj0Wgeq6xYsYLPf/7z9Pb2MjIywnXXXcemTZv49a9/ze233861117L+eeff9SPq8VGjUaj0Wgex9w/NcdntuyrkfGWJWJsnW1ep6RccFRORoEQKnoVVNROwd0YMQRGAxHPEoJ1HYmik3Ak57A4oW6WpyyTVEcCNwiYyLvkw1qGphB0WBZZz8cJSjfFpxyPvB+0rGkYt0yWhJGtk45LT8Ru2h5gMu+yYy5bea2EOne7zrl5UrJ1JsP6rmQxHrYaIVRNyQuGenjmkoGWY3i0I4TgH1ct5Am9Hfzv/jFuH5vGr3Oz6OTuFFcs6ufJA11NxV+NxujsJP7sZx9RH8KySL3jHcy++934e/Ygp6YqtteN+gyFxqKAaBgYg4N1+w8mJnD/+Eci557bdBwyn2f23e/GC+N2RCym/kg0uqEaBAQ7d2KuWVOMLa0Zpm1jDLXnDBaplOp2tLnbQ0QiEImA56lrEwRqjIaB0dWFtXEj1po1TYVGAGvtWiLnnYdzyy1tja+c2DOegdHbi7lwIbk9e5C+r4TeaFSJvlbraa7R3d3yPQFwfvc7ZDZbETM7X+TYGPlNm7TYqNFoNIdLQbSTsokrMVCuRxkAYb0/YSpBD0qrA0d/CZ0ntz5mpF+JXY2yUwIHRKR5lGghKlQGYTRqWQRo4IBpU1yyGeTCesTxMtERNX4zApEBFb0qqj7j3CmYuA36n9L6nMrYdGB8Xu1L43aR3hzXZ1bwss4HGrfzZsBtsHAq8MCbA6uz/vbhTbDwOcr5ufyl6qHRaDSatjnhhBM44YTSwpqLLrqI8847j7e//e1ks1ne9KY3cdNNN5EK54BHCy02ajQajUbzOOb7e4brij2HcirGxxTU1Gusbm0ARig0AliGwAuUq1FKiW0YxBuIfxHDYF1HgkQoxrlBQNIya8RC2zAYitfW5pqpqkUmkYzmHZYk2q9bUs95WI0bSLZXC40hScts6LzzkWybzXBKd6qpaHZ6b+u6Yo8lNnan2NidYiLvcuvoVBjXK+mwLc7s7WRpg9qbGs2xwkgm6fzQhzCXLCF97bXIdNlKfb+qZpNlIeLxktAohKpB2MRB6G3b1lLYSv/3fxeFRlCinujqqhE/K5ASf/t2xMkn161fGDnvPIw2ahICRM44A/fPf25ZlxHChSaWVSPqmcuWISKRunG09Ui9/vXMzszg1ouLbYB9+ukQiTD1kpcQTE8rN+bUVPHvc7BvH6K3F3NoqHE9RiFIvPzlbYmSwdiYcpk2cHK2g3Qc3HvvRTpO245bjUaj0ZRhd6v/B7lS3GgB6anXpFtfFxSGqvlnhG5AdxoQyrnoN6jHDEr0szvD9nUIPLBiNBQjhake0gd/rnbxUMGxKF01RjNZem51q3qEwlSCnN1TGaVSzdSf5iU2zroevx+ZbLt95bjV/OtX2aW8qOMhIqLB52O+RSJA0KT2tgxg5PrDjofVaDSa44F3v/vdFYLffDiSeo2NeM5znsNvf/tbbrjhBqamprjhhht4znOec1SPocVGjUaj0WgepxzM5rlrYrbutrG8ygwyhMAIjTUBEinVTeYAinUZ6zkWLUNgCsGSeIxp16vJXE2YJoOxCH1Ru6I24vrOJNvnGtRGq0M9w8943p2X2BgzDVYk402dnKM5p26MK8CSRJRDucbxSfkgYMr1GronT+lOsXge430s0Ru1HxeOTs2jAxGNknr964k997lM/+u/4t1/f6kepGEoYSoarYxDNQzMlSsx+vqa9t2obmKBYHIS5ze/qXndGBzEbyY2gnI4jozUdRK2ExNaIPK0p5H52tcOX1SzLESvijNrR7AEJah2vPe9ZP77v8n/6lfIamG3vK1tYz/pSXgPPIB7552lw65Zg7d5M3I2/DwLAuTYGN74uHpvqmt2CkHy5S8n+pT2bspK1wWvyQ3RdijUFp6bK14jjUaj0cyD3vNg7/+rdAaCEhn9FnMHGShR0YgoN6ERAXzoe6qqEdiM6MLQnVc9D5BlsaUNREAzBgTgp+tPWiRqHFKWnJvCABGFIAvJFSpatR38ufbahexO53CCBiJpm6QDmwNekhV2nflk4IA3dUT9H248rEaj0RwvnHDCCZx55pmP9DAquOSSS7jhhhsAeOihh456/y2qHWs0Go1Go3mscsOB8UbrcItRqAWEUDGplqEeEUOEEan1J9e2EPRHbW666HS+8MQNPKmvi5XJOKtTCU7qUq62wVikKDQOxiK8et1SzuprEKXTAKuO0DnfiXPKMvmXtUuI1qm1BoCE0Xx9MbHLtliaiBFvUYttpIkYeeXi/obbNBrNXx5r6VJ6f/ADuj7zGWLPfjZGXx9GZ2dlbKppYgwOYp10UkuhEVrXTcz/8pfIOoKW6Opq7M4rIxgdrREJ7VNOwV6/vuW+BYxkkugFF0AbtSXr7l9Ws7K8lmUrhGWRfPWr6f7yl4n//d9jVIlxxuAg0YsvJvb855O/8Ua8nTuR5edqGFjr16u42PK/41Li79xZEX9qLlhAx1vfSuyZz2x/fG06Q5tScFA2+pzRaDQaTXNiQ9B1BhVzj3aExgKF3eYeVoKlsGCojQU5dickltXZIEPRktq6jKCERmGDn28es1qYjYmqz17pQ3Z/6/EVCceQH4OJP8Lor1W0anpn3dbZJot7WiJKCygzskEpCj/Tuq5mKxHRnQL38CPMNRqN5vHC7OwsDzzwADfccAOyxd/e7u7u4s+u27Qw8WGhl4doNBqNRvM4ZU+mcWxQO2XyDCGwhCBmGhT0PQGYhnJDxkyDhGXyrKWDPGvpIPdMznL9gXF2zWXJ+gEx02BJIspli/o4s7cTIQRf33FwXufQaVtQ5x6DlLLtWn+n9nSwtjPB2zeu4JoHdpHzK28IzHk++TpOn07LYk1HAiEEK5IxNs9kGrofZ1wfNwiwq240P7Gvkyf1d7U1To1G85dDRKPErriC2BVXMJlOq5hP31cOx2gU0dNT6XBsgbFwYdPt+RtvrD8OITDXrsV76CFo5hb0PIKpqaJQZy5dSuqtb217fAXiL3wh2R/8AG+6QWRcwx3jGGVRP9a6dfM+ttHbS+If/oH43/89MpMhGB3Fue02nJtvViLj/feXHKKWhTEwgDE4qOJjhcBcvhxz8WKCsTH8sTF1vYIAf88eouefT+y5z8U+44x514G1N24Eu3Vd32aIjg6EZRXrYmo0Go3mMFjwDJi4Vf0s/faFRggFMkM5HNM7VSxpYhkMXAyj9T+Di0SH1L7ZXSUBLbqgFBMqooBLsaK9GQMjjFeVjRcchjuHcat1Ira9qTInZhOkVPUPN38Apu6oFTdTa2Hwcui/oNhXw0WW7WBYYHWAN0tMNHD+yxZipmGrPlrhN0+G0Gg0Gg28733v42c/+xkAP/nJT9iwYUPDtnv27Cn+vGDBgqM+Fi02ajQajUbzOCXrNV5l26jGYjUSFZlaj5Rl0V12g/bUng5O7Wk+qUzZpZv3UkqcQOJLiSkEtiFqIltTtknCNMmUrc41BfO6mXzFIuUsPL23kw+ftoYvbz/AvVOlKCK3Smi0hGAgGmFxIlocT4dtsToVZ/tctq7gKJF4gcQuu6yn9XTw5hNXzPvGt0aj+cuSeOELmfvkJw97fxGLNY3slFLijzSuayQiEawNG/C3bq2sJVlNKEbaJ55I6p3vxOiYfy1Yo7ubzmuvZfLKK9uOQiUWw1q3rlj/UFgW0UsvnfexCwghcO6+m/QnPoF01E1aOTtbGUXreQQHDxIcOoSxeDFmQei0LIwFCzCqJs7WyScTOcwII/vUU7FPPBF/587DjlM1BgeJnHtuWzUiNRqNRtOA7jNg0fMgvb11PcByhFEZRyqdMOIUWPUq8GZh8vbmfUQHwO5SxzUi6rk7EToXHSWuFb7T+zkIwjqMTR0mUrkszbgSFTGV47K4WarjxZc06cKH9DbwZ1Vtx3rMbVWP/d+G9e+F+FIWxKMIGlabbE10CMufZsBsJPjWmUtKv3REq5+G8bMV3dTWo9ZoNBpNJWeddVZRbPzBD37Au971rrrtgiDgBz/4QfH5eeedd9THonNcNBqNRqN5nBK3Gn8NGIhF6kaUVk9Im00Rn7G4H7OBENmIJ/R0kPMD9qZz3DU5yz1Ts9w/Pcc9U7PcPTnL7nSWXFXsz2CscrVvl93+zdxVqTgbukoReas7EnzwtDV89qz1PGNxP8sSMTpsi6hh0GFZrEzGObWng6XJWI3w2RO1Wd+ZJGVVup18KXH8gJGcw0jOwQkCnrN0gPecvJJom6KuRqN55Iicf/5hCXcFohdeiIjHGzfwvJZRYyISwTzxRMy1axFdXbX2cyEwV62i8/3vp+PDHz6i8UZOPpn4P/4joq+vuc3dMBD9/VgnnqjchYX9zz0Xo+vwHdv5X/+auY98pCg0Ao3FWCkJ9u3D37u3eZ+//GXTepCtiD3jGRgDh1lfNpHA6OiYV/1MjUaj0TRg+ctU/KlsM/pNmGClqJi1RAZg+Bel7eveBoue01rYivTACe+A82+FdW+H+HIgUOKj1Ym6xRoeR/pKdJSealNBoF6XvhIZZaAiYb058GbUz4VZl9ekFqP0VSxs4DQWGsvJj8IDb4bMbgZjkZaLQJti93BOYoYOo877ID0l4BbOx50EZxy8afW6PwfOCMzcC7lDtXU4C5gxiLSOqtdoNJrHO1dccQU9PT0AfPe73+UPf/hDTRspJR/60Id44IEHAHjyk5/MySeffNTHopdWajQajUbzOGV5MsYd4zN1t/VGbBKmybTrESCRslJoFKgY1UZiYtQweNW6Jqtwy5h1PW4+NMHOuSw3HZrk7olZXBkQMYyK+9yelAznHIZzDr0Rm1WpOEZYG3I87zIbOk6qxcdGWELw8jWL625bmozxz2vV+O+emOVd925vq8+UbXJiV4q067EznWMi7+IEEhmOvcu28APJD/aOEjFMrlzcT1dEfx3TaI5nRCRC7K/+isw3v3nY+zZtY9uISKRCXKvbTghETw9GTw8yn0fmcira1TQR8TjxF7wA+7TT5j3GeiRe9jK8LVsIZmcJRkeRk5PFmpLCttU4BgZqnHoikSD+/Ocf9nG9LVtIf+pTleJrECAnmtdsCg4eRMTjGP31a+AGExN4996L/YQnHNa4opdcgv2zn5H/3/9tXYOqHCEwly3DWrkS+6STDuvYGo1GoylDCBi8DPZ/F7x0Y6FKGCWXoJemojai9GH0V7DsRaW2y16iXJOjN8HILyC3X4mAwoTEChi6HPqeCmZU1UXc8xUwk0polDLUGGNKYKxAliJFhah09wkLjKQ6hhEJxclAxcMGedV/o/MDyOxU55ZY0f7189Kw+b1wyme5cnE/d0/Otr9vOUJw5YmXwIHblYsTwjqTe8EZU+ch/VAUDs+38PEpoupnPwfZPZDbB4mVtcJi31NbR8hqNBqNhlQqxXvf+15e97rX4bouL33pS3ne857HBRdcQH9/Pzt37uQ73/kOd911F6DiUz/0oQ8dk7Hou1sajUaj0TxOuXRhHz/cM1I3PseXEkOo/9fbLsM2ed/HFIKIWSk6XrSgl6F489XBe9M5frh3hN+PTJLzA7bMZJjxPDwCsn5Azlc1DmOGQXVZkQnHxZkJWN+ZxBCCtR0JHp5R8YIdbTgbLSF484nL2djdun7Whq4kScsk7bXnivGCgD2ZPGnPwzDAkCCFAAHTnse057E3m+OuiVk+uXkP//GENTx1qLetvjUazSND7O/+Dm/3bpxbbml7H2EYpN78ZswlrRdeWOvXq7qQ7fYdjVa4CQHs9evb3r/leFauJPXWtzL3oQ8hbBvK6jE2G1PH29+OuXTpYR83+8MfFkXNArIN5yeAf/BgQ7ERlOB4uIhYjK7/+A8mDx3CveOONndSblNr8WJSb3nLYR9bo9FoNFV4M9BxUujqc5WgJQOKNROFqV4PwkU8UqptItw+txnSO2Drx2D1a0uClpWEhX+lHlKqfqvFLj+vxLrcIeW8s3vACT9fjBiqLmSW2jwYv+oloRyXBUHUiEHglcRFGSgHoJWgLn4WnEmI9EJ0cH7XLz8KY7/h7MFLWZaIsSdTLZC25oTOBBuXrIPk22HbR8CdVde1UGMxyFcKqwWEGZ7bLJip0vPMDtW+/FyGdCKARqPRtMull17KRz/6Ud71rneRyWT47ne/y3e/+92adhs3buS//uu/jkm9RtAxqhqNRqPRPG5ZEI9yem9tfE7eD3hgOo0E5S5ssL8AEIKM75P3S/FAa1IJPnPWCU2Pfcf4NK//8xZuOjSBE0h2zmWZCW8w20JgCYEEnCBg1vNwg9obzXOez7ZZVSfEMgSn9KR4/vIh7BbRrStTcd5/6mrOHehu2q5A1DR4+oL2xEA/kDw8k2HGdUl7ARkvwJOSaLVaCngyYHc6ywtuvZ9v7DjISM7hFwfG+P7uYX60Z4RfH5qoEDjHcg5f33GQt921jX/702bedOcW/uOBXfx5fAY5H6eNRqOZN0IIUm96E7HLLmuvfSxG6p3vJHLOOW21j15++ZEMD2vNGqwTmv/dnS+RM86g4wMfwBwaatnWXLiQzmuuwT711MM+XjAxgfvHP9ZuaPfvWzZLMFPfrQ8cUYwqgNHbS893vqPeq1a1dmMxzHXrsNasoeP978dcXN9Fr9FoNJrDQPpKqEutU9GnRkzVZCw4DYMwvlQWXIU+KsrUKEWXBmnY9jG45akwflvtMYSo76obuwkyu0rPE6uUSFmgEKlqxpVjsjRoSmKoBVYPCJsKrCQYVTUb3ZlQSK0iP6piXZOrml+rRgxvwhCC95yyip55pqwMxSK846SV6knPmbDhg0pkDML6jYELfhp13WXZowwplZhaODcpIbtbCcmg6nMmVx7euWk0Gs3jlGc84xn84he/4J//+Z/ZsGEDyWQS27YZHBzkoosu4j//8z/5/ve/z5I2FsMeLtrZqNFoNBrN45i/W76Aeybn8MKbub6UbJ7J4AQBQkCHbTLnQj4Iatbnlmt6WT/AEIIn9HTww/NPJh5G63lBwB/Gprnx4ASHwnqFWc/noZk0/dEIScsk6/mMO6V6H0IIkpZBOhTqJJD2fFKWiVUlJE65LnOeT2/E4t9PXMET+7uYdjxuPDTOzYcmGc+7pD2faddDSklf1Gba8fj81n2cO9DNpQv76I1WTfTrcMWiPn6+b7Suy7Ocneksac9jzgvww2sqgEgjAVRCNgj41zse5sSuBAOxSqdSzDTY2JVkxvXZNpehWnPdTIbfj06xMB7l2UsGuGJxY1ePRqM5MoRpknzVq4hecgm5667D+f3va6JPzcFBopdfTvTii+dVtzDypCdh9PYetvvuWNUDtDdsoOuLX8T905/IX389zp/+VBT/hGFgn3UW0SuuwH7CExCtBLgW5H/1q7qCYHVUazOC0VGMzvp1q4xUayd7K4xUip6vfpX8LbeQ/q//wv3jH9XvgJQqzrazE2NwEHPRIqIXXUT8r/8ao0/Xm9JoNJqjihmKe1ancjjmD6magEFeiVxShp9Vhc8UEQp/5Z9TApCQ3g53vgg2vB+W/H3rYw9fX/lcGJBaD+lt4E6HrwkVFYqpahQigdCxKCw1blHP+yFCt18+dAaGAqk7WRkxmlih6iFaR/C5ltkJc1sZTK3lo09Yy3vu3cH+bL7lbqtScd5z8ip6yudPMw+A3QVdpyl3Y/ZAeP0L51gmlhbFXwMwlUBZeD+lhNxBGDwZ1ry5bJ9w/7rXTKPRaDTlDA0N8YY3vIE3vOENj8jxtdio0Wg0Gs3jmA1dSV5zwlI++fAeJDCac8gFpZu9Qqg6hHYgyPlKQAskmELVbAQ1VU9YJus7klx/4WlETBMpJT/cO8LP9o0y6SihL+0HOH7A1tkMQVjDMGmamHVuUBcEx5wvcUKhM+35dNkWNVZLKfngqWtY36Umql0Ri+cuG+JpQ718cdt+/jA2TXe4YjdARbBOOC670of47u5hntTfxctWL2JXOseNhyYYyTnk/YCkZbK+K8Hli/pZnIjxd8uH+M7u4YbXMucHTDoeWb8kNAIkTLN2zAASMr6PEyqIm2eypGyLuGkWmwxnHW4dnSKQkqWJGAvCaNpZ12PW8wmkRCAYyzvsy+TYMpvhtScsLb43Go3m6GOtXUvqda8juOoqvIceQs7NgWVh9PZinXTSYYluwjRJvOQlzH3iE/Mfz5o1RC+4YN77tYsQgshZZxE56yyk6yJnZ0EIRCqlIlabEKTTODfdRP6mm1TtR8dBJJPYGzcSvfxy7I0bi239ffvqd2KaiFRKXedWZLP1zyESwTr55Nb7t0n0vPOInnce/tgYzk034R08iHBdRCKBuW4d0QsuQER0nSmNRqM5JnQ/AfaEP5tRSCyH+BKYvieMTg2APErQaiBQlbsKvVnY/p/KJbnobxofd+bBSldjsS8TUicoV15+RImDUiqXomEr4RCjVEeylWhmRNVDGBDpV3Gt/ReC3Qm956mo0Ttf3LyPdsgdgNRahuJRrj3rBG49NMp9u3/NzNwBLOmRFzH2mKsYNpawsSvFlYv7Oae/E6s8sUVKGAkF2Ow+FadauCbFNgYll2OBQO0bAGYoqqrGsPI1amzDm2DyD6rOpAiF2N5zYehK7XrUaDSa4xQtNmo0Go1G8zjnaQt6SVgmn3hoNyM5t2a7EMphFzMMPClJmAaWIZASbEPQH43QF7URQnDP1Byn93Ty8Yd28/vRKZwgYDTnMppXrkbHl2RC54opBDnPJ+dLEpZZU/dRCEHcEsSkwAkkTiAJgIgwsISgwzYZjEXojlgsTlQ6Avdncrzrnu2M5mvPpxw/FEW/sHUfC+JRYqaBbQjscBL90Eyan+wd5fTeDv5x5ULSns/P94/V7Ws05+DLoCgeAsRNE9usIzxIJZ66ZaJkzg8YyTosT8WBQkxsBj+cmO9OZ5lyPFwpydZx/1gix75Mnqzn87aNegKu0RxrjI4OImeffdT6i154IcH4OJn/9//a3sdcvJiOd7/7LyZsCdtG9LaOlZauS+YrXyH/y18i85VOCZnPk//d78j/7neYy5eTfNnLsE87DZlrXDPKGBzEb0NslEGdqDkgct55GB21seFHitnfT/zv/u6o96vRaDSaJiRWQMeJMPtg6bXAUa45M65cf62qRlVHpOZHYO9XoetUSK6uv8/c5uZ9Wp3qETgqVlT6yu2Y3kpx5aEs+w4vA5AOIMOEVaFE0IJQF18K0TDKfMnfQ2yh+jl3sPk42iUIP3fzI9jD1/PU0Rt5qpgmk/CLi0xNIbA61tO55K+gbwVUl4aYvlONJ7tPRbsGTs1hEAKkqc6TQm1NwvMOY22jC5SIKiJw32tr3x8plSg8coN6dGyAVf8GcR1TrtFoNMcTWmzUaDQajUbDOf1dvG79Ml735y2M5JwaMStumgxGbfqikZoo03KuPzDO7WPT/H50iuFsnj2ZPLJsFWu+7EawLyVeAJ6UzHkeHcKqW29RCEHUFERN5RLc2F0ZGeQGkr2ZHCd2qdenHY/33LujpdDo+QEPzKQZy7v4UnIg69Bhmyo+1rIYikVUDRMh+PPELPdPpXnLSctZnozzgz3DHMpVTqZH825RaDSFKAqX9cgFQYXQCBAgOZhzWJKMYQrBzrlsUWgMpCTtBUy7ubpxsqCu43Auz6c272VRPMqLVy9qev4ajeb4I/7c52L09pL58pcJpqcbNxSCyNlnk/y3fzsmItqRIHM5Zq++GveBB1q29XfvZvbqq0m+9rWIeLxhO6O3F3/PHghr+zZClDnDy4kdo5hZjUaj0TxCDF1ZKTbmR8I6jb6qGQiN6+sKUzkMy/Gzqj7ioetg9Wvr7+dn2hubESmJZWYMcnvBLyy8kaEw6kBQ7zMtp8YXHVSP4vmNlsRGM9HeOFphJmHy/2Drf4TuS0XCNFUyS/HY25Xzc3gTnPBu5bAsMHUnSBfyoQAqG9RHFgIluBphxGrZPMiMq/qb3hykH1bjSrWoQz37EDzwZlj/Pkitmc9ZazQajeYYosVGjUaj0Wg0AOxJ5xiKRRiKRcj6Pl4onFlCELfq38Ct5o9j0xhCcCCTZ1+20qUSSCriRcvxJaRdX8WdNkkgzPg+Od8nVnVDOe2VJrbf2nWI4VydVbVljOUdHpxKkysTPwMkOT8gbhnMeh6zcx4RYbCmI0HKNskHAdc8sIsPnLqaLzxxA3dOzHLDwXH2Z/LMeR4RQ2Ag6LAszGaLqSU4fv3r4AWSScclYhhFwTeQkjkvIAivXd4PsIzG70eA5CMP7mJtZ4JzB7qbXgeNRnP8EX3a04g85Sk4t9xCbtMmvIceKm4zOjqIPP3pxK64AnPhwkdwlPWRQcDshz/cltBY3Mf3Sf/Xf2E/5SmNGxkG5ooV+Nu3F2tG1iVRewM2dtllWCe0uGmp0Wg0mkcGZwLmHlZCk7AhOqDqMLaKJO87D8ZuVmKZM6ZqEBbcjYUagTIUtxCl/oRQ4lY93CkY/y0svwqsZO32arddO8gAjDgEvhIYZQD+nBpTo0mPMMHLwNxWSK5RcarljkGrUwmR+ZH5j6d4DKFcl7u/UKqJ2Iq5zfDQ2+HEj5SujzerhNBmn831jl1+7n5OPdJblABrtDkebxY2vxc2frxSmNVoNBrNI4YWGzUajUajeRyS9Xz2ZHJkPJ+oaTAYjVQIdnHThPb0xQp2p3N02VaN0AgUxbJGuFKJfTGreeyRG0hiVWMr1DnM+T6/Hp5ouv+hbJ4dc9kKobGAEwTEpVGc/zoy4OGZNOs6E3TaFm4g+eTDe/n82es5o6+TM/rUyt4px+Ufb3uAO8ZnKpycjcYfNGgjkTi+ZMop3VDI+kHFtXPDuplNDKZMOB7XPLCL/znnRAZjum6YRvNoQ1gW0ac+lehTn4r0fWQmoyJMY7FHemiAEhWDAweQ6TSYJkZvL0ZvL85tt+H++c+H1Z93772ISATp1F8sYvT2gufh797d8KamOVh5szH6lKeQ+Jd/mfd4NBqNRnOMmb4Xhq+DyT/WuuFiC2DwMhi4BOwG7n1hQN8FsP+7SrCUBZdg+eeDRNUKFKpuoGEqV2C1q7HYPIz0nLwdBp5euz02z0U+QV4Jgt60+tySYYRo8XxFWL8xnPsYtooRNcJ6ku6UimBNrasUP4WAwUth79fnN55yEmtgz1faFxoLZHbDjmth3VvVvpk9kN6u3I0S9X8kLWNsywkcyO4pOT1b1bQsx51SvwOrXtP+PhqNRqM5ZmixUaPRaDSaxxG75rJsOjDGr4cnyfmlyaVARX9OOi7dtoVotZq4Dm4QMOV4FaJlSwQV9wSybYiN1beYBbAgFNRuPjRJ1m88aZ50XPZm8hVxrtV9O4GsqB8ZINk6k+Gk7iQx0+RgNs9dk7Oc3luKEEqYJlLKlkIjKEGzEQJBgGTKUZNtX0rcoLZPJwiINbFPSiSjOYfrD4zx4lU6TlWjeTQjTBNxnESlBtPT5G+4gdwvfkEwOlraIAT2KaeUhMDD+AwJJiaw1q7F27q1YRtjcBAiEfx9+yBTGWcnkklEUt2MNbq6iD372cSe85zD+jzTaDQazTEicGDbx2HitsZtcodgz1fhwPdh7dtUHcVqRm+CHf+l6isaEZibDTfU+y4exnaKqHJPtsKdrP96zxPB6lCOupZ9TEN6WygsCpToKVGrOcsmQBIQEswOMOrconWnlXMzUVWPffBS2PftMpF1nlipiujUeTFxK+z6onoPp/6kImgLyADlLA3C7wJtrl51xkoi8HxjYsd+C8teWt+NqtFoNJq/KFps1Gg0Go3mcYAXBFy7eS83D9efPEtCx186S9w0WdeRINo0C7SWGddHIsk0EPtEnagggXLoFfQ0T0qCQGI0se1ZVTeOz+jtpD8UG++cmGk6xv2ZPL4M6gp4BdwgIFIV0+ojOZR1WJFSsUub9o9ViI0R02B1R4I7J2aLdRYb0SBBFVDnJqDofGwUt9pk+EU8Kbnx4AQvWLEAy5jfe6nRaDTVZH/0I7Jf/zrS85D5PDiOEhZNExGP49x+O95990E0irV2LaJOpGlLhEDE48hstmETo7sbo7sbOTtLMDJCkE6D72OffDLWxo1EL72UyLnnIiw91dVoNJrjisCDze+D6Xvaa++l4eGrVZ3A7ieUXp+5X7nrpAQExJcpUc6dqlPbt+AeFBDklKDXyNlYiEltVHfQsGHgYjj4oxbjnlGRoAUXvhEBr+DaL0S6Vs11ggwYKeo6AqWsHbPdBQufBQd+2Hws9UitU27Ew0EGym258zMQX6rE1woMlNgoS7UZRUFgLXZS9bx89amAyMD8xhTkYfRX6npoNBqN5hFFz8A0Go1Go3mM4wUB779/J3dONF+F2xu12Z3OkfV9HpxOs6Er2dQ9V3McKTEReEi8QJbiQiUIIbCFqmlYHSFa/Vo+kMQNAVJFjjpBoKasEixDMJp3GBKRYt3GKxb3Ffeda+KqnHM9MmW1KBtRdz20lBzK5vGkOreHptN4UnJqTwcXLeilw7a4fFEf1x8YY9o9vBXGlhAYBnSU1cds5oJsh2nX408Ts5zT33VE/Wg0msc3ma9+lewPfkAwOUkwMoKcqVrYYVkQiSCDAJHP4z30ENb69UWnYbv4u3bR8a53Mfv+9zeMUy0gOjowOzowhSD5ylcSu+KK+Z6WRqPRaOaDlDD1ZxV96s0ocU9Y0HUadJ+h3HfN3OR7vty+0Fg8pgdbr4FTPqPqOYKKzawWBCP9ymFn5JrXDwzyYNa5FSoE2L3q5xoBrYwFV8LIJlVjsICfBWcU/DzghbUUjVC8FKHYFkaLNro+MlDiar1jWx3KRdhfVdt46YtVvcTx3zUebzXxpTB4Bez4ZPv7lAaphEZ3GvyM6stMqt+BgsNSiLBWpiztI30l+BZiZIsUhOCy1+xOMA8jMn72AS02ajQazXGAFhs1Go1Go3mM88VtB1oKjQCGEPRFbUbyDq4M2DKTYWN3EqPNCLqIgHwQMOv5+NWTfClxCMuVICv6FEKlBxX2CKQk7wfk/drahoYQDOccRnIOHZbFuQPdnFnmMDSbjHUk76qhtHU2hWFLnECSDySBlLhZh2gYsfrHsRn+PDHLN3Ye5PzBHp6zdIBlyRj3Tc017VNURccWiBoGCdOkM2Ir0VY2quxYsxa6LnboDh3LNb9hr9FoNM3I3XADmW98A2/LFsg3iFzzPOTcHDKXg2gUEY/jbdmCvXEj2G1E1oVIx8E66SQ6PvAB5j78YYKJ5jV4RTxO8tWvJvqUpzRtp9FoNJojIHDh0M9h5HrI7oP8GDgjlYKbGYeO9bDsKlVvsbrWojsLIzcc3vH9LAxvgmUvhux+mKkjWEYHILc/FL7c5udiBLV1Aa1Q5BICup5Qf1+A6CCseQts+QA445A/qM6t2H8+FB1RTkpho1Ze2s1FUFCinPQqXYzRQXVtx39TKzYKAWvepITWQz9rHanafTqseTOM3ty8XSPyI0poBHUdZaDcnmYMvPL5j4GKjS0QhEJj9QwmdD/K8HpZNsQOs/yDlz68/TQajUZzVNFio0aj0Wg0j2Em8i43HBxvu/2CeISxvEuAJBf4jOddBsKI0mYEUmIbBjOeT9BsIi3AD1R7qywq1TQEfqAqHjpS4jaID42GcaASyAUBE3mXP03McFafcu71RBrf1M60WUuyIIRKqSJhyyNX/UJcE6WFyU4guenQBHdOzPC8pYNsnsk0dSRaQtSIsZYQ2IZgMLzWPVGbkVzjOirVUbI154CgO7zB77a6saHRaDQNkL5P5gtfwHvooTrRdA32yedBSkQigT88jLlkSfsHFAJME3vDBrr/539w/vAH8ps24d5/f0Uza8UKopdfTvRpT0PE2nNA+AcP4vz+9wSTkxAEiFQK+4wzsE88sf3xaTQazeMNbw42vx9mH1TiWmZnKBxV4Wdh6i5IvxP2fRNW/RsMXFjaPvorVa/xcBm9EZb8A4z8or5oJyyI9KljBE3ERgjdjfHK16JD6v9dp0NsYfP9e86E7rNh56drHZbldRClBOmEdSOSgNv6GgROyXkZ6YH4cvWz02DxjRCw/J+Uq2/kl+r6OGVzPzMGfU+FoSsgGdZ9lIf5PuRHKp9LT4mPBedmsT4l4f9bJbQUXJ9B6Toah+FqBCV6ajQajeYR51EjNu7du5dvfetb3H777ezdu5dcLkdXVxfr16/n8ssv51nPehZ2i1WzjuPw7W9/m02bNrFt2zZc12VwcJBzzjmHF77whaxfv/4vdDYajUaj0fxluOHgeK3LsAkx02R1Ks62uQxSwp50riicmUKQskxSduXXByklWT8gbpqYdWJSyxGAKVTdQi8oCY6CkuAYSFnXoRg3jaLAlwjrSiLgww/s4gOnrmFDV5ILhrr57Uj9upSF69DKqFlwBGb92tqOMjw3A4FZtTp30vH4/eg0z148wPf3jhTbVhM1DPJlYqQpBEnLJGmZ9EfVd5nBWITRBmKjgSiOsRG9Ebt4bVOW2bStRqPRNCL/61/j/N//tSc0ltWGlY4DhkEwOoq5eHHrP7yFLrq6EGE/wrKInn8+0fPPV/Gt09NKJOzowBxov56Tc8cd5P73f3HvuqvmBnX2e99TwuUVVxC9+GJd51Gj0WjKCRzY/F6YfVjFhGZ2tXbnuVMw+xBs+zgEWSVyAYz+8sjG4k6r+NbMrsZt4suUw83PNq67CNSIYLEFYHern4fKIrmz+5V4l92rXJxmHBIrVO3JyT9A5ynquuRH1LWSXqUQa9ggIkqAlBklOBqGcvI1uo7SAZFS4mdsSenzs+n5AJFeWPJ8WPy3Spj0M0potLtLtSgLmKnmfdXDm1HXFdQ5BnmYuS90Y5a7FpuNs7xWY0FoRF0jI6p+Tm+B1IZa52kr5lvnUaPRaDTHhEfFbOrb3/42H/rQh3Cq6naMjY1xyy23cMstt/D1r3+dz3zmMyxpsHJ2eHiYl73sZWzZsqXi9b1797J3715+/OMf8+Y3v5mXvOQlx+o0NBqNRqP5iyKlnJersYBpCKSEsbyLBMbyDoYQWEIQMQ26bIuhWIS+qI0hBEPxKIeyeQwh6IqYjOWbr2I1whxRXyoBsCgsSiW8RUx1fK9sEh4zDaKmQadlMRiL0BOxEOF+TiD51OY9fO7sDZzZ28lQLMJwnejQgmPRFoIGSaZKyBMCNwhw6tR2FOEEuSdi180ynXY9lidjXLV6EV/dcaDiHIrHMJQz0ZMS2xAkTJOEZbK2M1EcY8oy6bBt0l5Q00fEFC1zVIfikXC8cFLXYdxQ0Gg0GiDz3/8NLWonFrHtsFaT+psl83lELEYwOYnR21t3Fykl+L56GAaRCy6o287o6cHo6ZnX2KWUZP7nf8j99KdN23m7duF99rM4t9xC6u1vx5hnnUmNRqN5zLLv20po9GbbExoLuFOQH4Zdn1exmF2nQe7QkY8nd7AkeNVDmNBxgopRdcaaCHplr8eGlEgJ0HcedJ8F0/fCge/Vry85/jtVH9DqUucWWwTRhUqMc8ZK7j5hlQQz6SqBMsiq+osyqkTFwKkUEYWphMGOE8FMVB7XbPOzSRgQ7W/eJrlaHVuYNK0jWY4bLuaUAfhzar/C2IWBEnALYmKz3xMZHtdU/zcTkFoH6e1qs5cGd0JFw86HgafPr71Go9FojgnHvdj4s5/9jKuvvhqARCLBC1/4Qs4991ySySS7du3i29/+NnfeeScPP/wwV111FT/84Q9JpSpvquXzeV7+8pcXhcZnPOMZPOtZzyKVSnH33Xfz+c9/nqmpKa655hr6+/t5xjOe8Zc+TY1Go9FojjppT8WgtosbSB6cnmMk5yApiI5q0ihRcZyu55P1AmZcD1Mkuea0NXx/z3BRJFuejDPWxjELgmNQvsAVMIRyMBph1KgtDHqjNj0Ri5hpEDfru/T2ZfLcOznLKT0dXLm4ny9vP1DTJm4aZH0fEUaW1hMTo6GQl/fqT5LDco0MNYmWvXdqjs+dvZ4Lh3r48AO72D6XJeOXbiRYQrA0EWPScRFC0G1brEzFsIzKFbwrUzGmHY/ZMkeRgShGyTZicTxKMnQzntrTwaJEtGl7jUajqYeUEvfuu9tuL4QA21auRoAgIJibgz17kFNTYFmIjg6M7m6k7xOMjhKMjkIuV+iAfCIBQhC74grMhS1i7FqQ+dKXyP3sZ223d++9l9n3vpfOD3wAEWkdH67RaDSPaQKnVGMxd6B9obGAM6Lcefu/B50nt64n2NaY8sqt1wxhQ+epkNmhBNLAoySEFZDKdRhfrhyBAD1nweo3qLqUuz5fPyoWlItRSiWoetOQWKXiW+2uUOSss9BTWIBTWZNRRJWbr/y6FkW/Ot/1u5vUkWwHLw1jN8Pw9cqpmd6uRGTDVq7A6EDJXViPwCsJjTIAqxBDK8N6jYKS0NhMcLQAA6ykikxNrVc/5/aXaoDmR+YnNiZXqXqhGo1Go3nEOa7FxnQ6zTXXXANAZ2cn3/rWt1i7dm1x+ymnnMIznvEM3vOe9/C9732PXbt28YUvfIE3vOENFf18+ctfZvPmzQC8+tWv5jWveU1x2+mnn84ll1zC85//fEZHR7nmmmt42tOeRiJRtYpIo9FoNJpHGVm/VZ2MEnk/4N6pWSad0o0Agbp5HDcMTEMQSDVtNFAORAl8ZsteZly/GNnZF7XpjdhMOO0JjoaAiGEgUGJn1BQkLYu4aTAYi9Bpt/9VZdOBcU7p6eBZSwa4f2qO/xufqdg+EI0UxxU1DJygMubHFkrI86Ws60gENdaEaZKym0eTbto/xivWLuGpQz3cPjbDz/aNct/UnCpHhqArYrE8GePOiZkakbFA3DQ5qTvJHeMz+FJiIEjZZtPFxwtjURYnSjdhrlw8z1XBGo1GE+LeeScynZ7fTtEo5PMQBOrh+8jpaYJQgJQHDyoXYxAgCk7IENHVhXRdcj/5Cbmf/pTIueeSet3r2q7JWI5z++3zEhoLeA89RObrXyd51VXz3lej0WgeU4zfosQoP6dce/OlsN/MfUqsNCIlJ5+fQ8VtGkpwMtr8vm8mIb60vuOwHEHomksBc0oElOHnjRBKVAtcyO5W4uXyq2Dx82Hst7Dzc437lX6lmCilEjWFAXZPKRa0UKsxcIDw87AQL+qH7sbiWOt9sQ+UOFhwAZopGLy0+Tk3Y//34MD3S2IeQHRQvb+Bq96f3EElvCZWlM6jZlg5JTQKQ4m6oOJai25OM/y50RzUKLk9g7wSha3QsRldUIrI9ebU+VttujkXPLu9dhqNRqM55swzBPsvy69//WsmJlQR5H/913+tEBoLGIbBO97xDnrDaJ6f/OQnFdtd1+Ub3/gGAIsXL+aVr3xlTR9LlizhzW9+M6CiWX/84x8fzdPQaDQajeYRIW629zHvS8nDM2mmnPorjoUAy1DxplFTYJsCIWDHXJa7JmfZMVcZZ7S6I06igQOxbv/AqlSc7ojFGb2dnNydYk1HYl5CI8Ad49OAEjHfcuIKntTfVbG90zaJGWpcpiGIlV0f21B1ExHU1GksEDGUI3J1Kl53ezl/GFNjsQyD8wa7+Y/T17LpwtP4/vmn8KMLTuEbT97IB09bw0dPX8fieONVxJ22xYldSbpsi66ISaNSjSnLYm0qwdJk6ab8Gb0dnN3X2XKsGo1GUw9/zx6Yx99yAFxX3WQt1KWVsngjVUoJ2Sxybg6ZyRDMziIL7WIxrFWrSv1IiXPrrcy89a0E8xU84bCExgL5G29E5nKtG2o0Gs1jmYlb1P+d0fm7Ggu46n4ew7+AyKASk6bvVjGksw/D7IMwfRfMbQZnsvVxUmtai24ygNnNkB9VwpbVCVaPius0IspVaHeqR3xp6JQ0lMC189Mtzme6tnailJDZqY5rJJUgV6hvKP2yz8HQ7VfYLp3afgI3rIX4QHiNHgzF2r1w6Lr6rslW7Pg07P16pdAISli0y0RPpOp/bnP9+pBCqPGBEohBtSu8hgz3a/EeCkPVvjQS6lwLRAcr41/9Nj/7h66AgQvba6vRaDSaY85xLTbecccdxZ+f/vTG+duxWIwzzzwTULUZJycni9v+9Kc/MTY2BsAzn/lMbNuu28eVV15ZdDP+4he/OOKxazQajUbzSJO0TPqj9T/3yhnJOUy7XsOpodHASieRjOVdpl2XXFlMaE/EZk1HnJRlYjdSx1D1D2OGqgEZMQwlUlrzvLFdhhNInNDNGTEN3nbSCl63fhlrO9TnuxCCRfFSNF7EMOiwzFI8a6F0ZIMLkbRMTuhMEm9jjDNurXArhBI07TIn48buFJ87ez3vP2U15/R3VVyvpGVyxaJ+fnj+KXzxiRs4o7eT5Yk4fRGbbtuiJ2IzFIuwsSvFiV1Jesre6w2dSd560oqG751Go9G0QmYy83IVynxeiXSGUSlSFv7m5fOliFWAIEDOzSmhcf16sGoXmHjbtzP3oQ+Fkd7t4e/di3vffW23r0am0+R/85vD3l+j0WgeE7hT6v/NaiS2InCVCLf/OzBzTxhBGgpSgaOcde4kZHbD1B0w/ltI76h/zPhSFceaWKFqGjYivU31W05B4LI6VD9dp6v4zkg/YMC+b8K2j4ZOxCbIBsktgafOLbsrjF+tXxm+NNkIwMso4RFCt+dsmcBWtr9hq5jW/d+Bu66Cvd9sPsZy9n2nFIVbg4DkWrCqUt3cKZj6s4pZzexQkat+OhySVOKsEc6nCterEA/bSmgEEBHlLBWGumblJFYq0bHQZysWPgtW1BpKNBqNRvPIcVzHqJ5//vmkUilGRkZYsGBB07blE1CnbBL75z//ufjzOeec03B/y7I4/fTTueWWW7jrrrvI5/NEo7rGkUaj0WgevQghuHRhH9/cdahxI6nERsdvVKNQFCNS6+EEAQYmIzmXZcnSzeUF8SimEOxO5/CkxAkCVZ8RiUBgCkHEUPURo6bB360Y5DfDU4d3omWUD1UIwdMX9PL0Bb1snclw29gU4zmXn+0f5f4pNZk3DUEQSDKemtDWq4coUPGwQ7EIc65P1vfptCwibTpHWyGE4LTeDk7rVauL836AKaiIV12eitMXjfDl7fsZbVIT0zYETx/q5eVrFh+18Wk0mscnIpHAGBzEn51t2VZ6HjJbdnPYMJQLQkpENIr0fWQ+X7mTYSCiUUQk0rRGonvvvbh33knkjDPaGvfREAqd3/yG2GWXHXE/Go1G86ilIPa0I/o07EMqpxwSOjZAdo9yEAa5+qv7AkcJXPmRMNJzVSlidejyUrvFfwub31vbhztVEknrIQREG9QD3vctJUCKw/z+nN6uhMFCXGy9Y0uTYpwqKLdhIXK14IA0yj4PDQtS60q1FKWvREd3Cla9qvl43FkVndoMYUHqROXMzB1UTsPC+x246nxAbZOeirE1yhaySqd9kVEdUL33hShbb1YJy2a8tD2xQr338eWqJmZ17UxhQs85sOBKJT5rNBqN5rjiuBYbL7roIi666KKW7VzX5c477wQgGo3S19dX3LZ9+/bizytXrmzaz9KlS4v97dy5k/XrdYFhjUaj0Ty6uXRhH9/dPdywBuGU6zHn+QQNJonRJkIjqDW6+SBgLO/QE7GYcFy8QCIBSwhWpeI4fsBI3iVXVSMxaZoMxiI8aaCL03o6j1hsTFpmw/qHazsT7M/m+OneEbJ+QIdtMuG4uL4aZ8pWtRodX+IFElMIDNTUOWGZeFKyP1u6US6AbttmMBahy7aKC5ULzDcCtpxoA5HwvMFuzh3o4o7xGTYdGOPh6QwZ38c2BIPRCBcv7OXihX1HdGyNRqMpYC5bhujpAdtW8ajNcOrfWBVdXVinnYa3bZtqU7iZallgWSpUbnYWmcs1dVHmr7uubbExGD+MmLnqPsJSHhqNRvO4xQqj+BvV72sHT5UVwO4CQoHJH22+jwyUk84REDykBMDkShgouzfYfQYsexns/mLlvvnhJh0LJWDZdUoM+GklgjrjEB1o0kWD79iBp/owOlWbZoIjphLsgjB23E+X+jViapxCqOufWF6KLC1n5BcQWwSL/rrxWEdvbO3UBFT8qRuKugEEUr0H0lEuRDOl3IbZfWE8bEZF0hLMU2gsw8+Gv1dm2F9ViQqrE1b/m3q/pv4P3LBmqN0FPU9UYqRGo9FojkseE3ejvv/97zMeTirPPfdcrLIInkOHlJvDsiwGBweb9rNwYWmF0/DwsBYbNRqNRvOopydqc9miPv53/1jd7bOeR4MShZhCNI1BBbCEwZzvM+lIHpxOU53aOZJ3iBkmA1GLrkgMXypdzjaMoqj2pP5uzu7rJGIInEaDaYPzB7obbvvS1n385+a9TOTdorAaMQwcPyDtBxhCCasxUxCYgjnXxzIMYqZRc06gptWTrsuk69IbsVmVildElp7bZCxHgiEET+zv4olhPUopJUJHpWo0mmOAffrpWAsWEAwPE+zd27CdDAJkPTFSCMylS5Ww6DiNxUQpCUZHVdsGOH/6E/7ICGaL+RwA/hG4cApD8urXMNZoNJrHDd1nqfqKZry5W7AR0gcCELbqIz+iBD0zVls/sN6+0lOiVHYvPOF/agWphX+lXtv5mbB2YFgLsR7CCB1z/fW3B+HffGdERZbKUAg0rEqB0exU51SsTSjUdhkuSJThRMdMqO1Bnc9GIxI6BENRNQj7slIq5tXuUYJnPZGxnAPfV+4+o04ygJQwcn3z/dWJw9yWMHbWUMcsP27nqaX+c/tDsTitBEIjOs9anuX1KoJQiDZh7mF1DKtLiZpWlzr/rieo923oinkcQ6PRaDSPNI96sXHHjh18/OMfLz6/6qqrKrZPT6uVVLFYDKOB26FAoWYjwMxMgy8pGo1Go9E8ynjZ6sUcyjr8aaL2s80PJPVWpBpA0jKaC1kS8r6PH8imiUO5wGdv1mfa9VnbmcAs69MKo147bIvzB3u46dDhu0muXFz/BsJ/PrSbjzy4G1l1noaAmGUQk+BJtTVqmPREbLZ7GZwgIGoa1abFGiYcF39Wsq4jiVDJsFyxqK/FXkcHLTRqNJpjhRCC6OWX44+MIHM55GgDN4rn1d5wFALR0YG5cKES7qojVKsIxsaaio1Iib9tW1tio+joaNmmFUYqdcR9aDQazaOagafD3q8pASh/aJ7CEkqQMyLKpRfpV6ISlNx7jaJUCUJ34wzIKLgGPPDvsPJfoPdJ4EwqIW30ZnDHwXdU3UdnXDn5RFnMpxFV44/0V8Z/1h1vWHfRS1e+biWVAOm7kN8P7nSdaFlRG79qJkHkwmjS8hqMkdI+SPXc7lLi7nzwZmHsdzBYJw3Om4ZckzIaBbIHautbVvQzV3IRFkRXM6HExiBLY1ejqNpWmK8E4SPcXLhmUpYicM0Y9L/q8ONsNRqNRvOIcszExl/96le86lUtMsQbcNNNN7FkyZKW7UZGRnjlK1/J3NwcAM9+9rM566zKD+hC/cZYk1ieAuVtnHpRQBqNRqPRPAoxDcE7Nq7gs1v2cWOVmGcIgaiS00whSFpGhVOvBglpz8cL20NNkmgNM57HlpkMJ3Qmin2fO9BFV0R9HXnG4n5uPjRxOGE8nNSVZEUqXvP6dfvHuHbz3hqhsQIBJuBJGHccDuXyGAg8KZl1PTosixbrlZh2PfZlcixNxji1p4PFidbfOzQajeZ4J3rppeR+/nNMKQksi+BQnRvOQXU9JYFIJpV4aBgthUZACZYtkJlMW2O2TzuN3M9/3lbbhn084QlHtL9Go9E86rGS0P9UGLlBuc3m424s/5ywOkJxquzvvBENo0bdsjqBQVl9PoFy2iXUa5O3K0ExP6z2K3coGrYSFGWg2iAgtkA5BM0kdSNKyvFzqk6kNxf2F1QKXflJyOxV4xNmqKGVfw6GP0tfnadVtuDFiKlzDZxSpKmIgGGq88NT4qiUSuxsFNPaiOFNEFsY1lR0wIhDai31Z2VBGN0anoewlJOzGeWiqt0dXiOhrqs3S62oWI/CWGTV8/C16phe6cH476D3XOg7r0XfGo1GozneeNQ6Gw8dOsQ//dM/sXv3bgDWr1/P1VdfXdPONNUHVzsr/2XZF6JWLkiNRqPRaB5NWIbBa9cv46+XDnL9gTFuHp4k7flEDIFlCPDBNgQRw8ASrT8380GAG35uWgJMwyDj+6rMB2oaaRmCiCEqRMtZz+NANs+SRIz+qM1VqxcXt63pSPDClQv5+s6D8zq3btviDRuW17y+dSbDxx/aTbpFpF7eD8gHAYEEX0p1DkIiBLiBZMbzsIQgZhrqWjVgJOdwYleSV69rvWBKo9FoHg0YHR10XH01M297G2LpUoz+foLRUYKxsVqB0DQR0SjYNmZ/P+bixcXXWyJlqZ5jI6LRtsZsn3UW5uAg/kiLm6iNCB2dGo1G87hn6T/CzD1KZPKm23c3mlFlYBMCYotVFGoNQgmHRiSME3VC4ansc0D6SvjzZmH6XhVvCipeNV7lhhdGKNZJ5cQ0oiqatBl+RjkuA0/tL4PK4/uOclgWBUUvFBwNig69coKcWr1YqHdZPM+oinxNnVASI3MHVB3EinNt8xat9MEZhdkHIL21dntyNTgTEOlR55gfUc5PWT5mqYReI0p9cZJK0TUyoMZc7EOqcwqc8LUyMVEI9f9i3GwdcbZwHn42jJ0N3aHJUCzd9nElGHee1N410Wg0Gs1xwTETG1euXMkrX/nKw9q3s7NOweYytm/fzstf/nL2799fPNaXvvQl4vFaR0MhGjWXa5EJD+TLVt1GInVyzzUajUajeZSzNBnjFWuX8KJVC9mfyXMwm+fqe3ewM51lzmuzzpWEvK8mikF4czgiwA1UHGkBz5fkQhEzZhiYoVA3mnM5pTvF+05ZTW+0MtLob5cPEUjJN3e1Ef0D9Edt3nvKagZjtZ/bP98/ykiucVKBlJKcH5AP60TKUGhUP4MpIBAQSPCQzHk+SdPENutPyE0huGRBL0Px9m6IazQazaMBa+VKuj76UWbf9z78Q4cwly3DWLIEmU6D7xNMTBAMDyNME4TAGBrCXLasrANLCY7NFn6E+zbDXLCgrfEKIYhedhmZr32trfbV2Kef3vaxNBqN5jGN3QXr3wcPvUsJaZmdrQVHMwHxJUoES6wInY3pxu39bFjbsN5nQHgsLw2GEwpjKCefsJSrr0B57UIpIbtbuR7tnvrHDVxVr7DguDQiSnwrbvfAn4Vq5570UZko1YJjOP4gr87XTJZtMiC5ptL1KKvFyjZLI/jpcNyh8Fpvoc7cVshsh9l8GB9bp28/EzpLc7W1GgsYZXMawwa7F5yxwglQEhYbLCoSZkkgrotU4/Cz6np1n166RtKDPV+BjR9rsr9Go9FojjeOmdi4evVqXv/61x/1fv/whz/w2te+tlhTcd26dXz5y19mYGCgbvtkUn3A53I5pJRNnRqZsmierq6uozhqjUaj0WiOL2KmyeqOBKs7Ely6qI+f7htl62x7EXWelPgyIKAkykUNg6hBUbwrjy11A4kX+CQsk6hh0BWxeO6yIZYm60eNPn/FAtZ2JPjJvlHumZytG87TYZlctLCXv14ySE+0tgbLrOtxy+gUM65H1gtUTcZwLm4AEcPAk7IoNELtmluJEhANBEIo12Pa90kJs8LhaCLojdosjke5d7rJzZQjYMb1uGtilhnXwxCCLtvizL4OYu04hjQajeYIMZcupeu//xvn9tvJb9qEe++9xdqIIhpFTk1hDAxgDA4qd2MZwjAw+voImjgNRYu5l7VyJdbatW2PN/bMZ+Lccgvejh1t7wMgkkmSV101r300Go3maLMvk2PT/jHunJhlzvMxhaAnYnHBUA8XLeilw/4LhpTFFsLGjyvh5+BPYW5znZqFKGEp0qcch8KAxGpV70/6jQVK6VcKfLWdqnhQ6VFz+zK3X7ntjPB1qysUDMOFhlJCdk9jsTF/qNQWSsJa4Z5hkKWue1FtVGKnFEDBvVdGwa0nDDWu+OJK8REqXYPCUOchA3AnQrdgGDFqd5T29dMw+3Dp+guj/kId6as+/AwEZujwrGony2on+jn13EyUtpvxWmdofKlymQbZsusQBVnvPZQthMbiyavzKIjZnaeUNs1thvQOSK5qox+NRqPRHA88qmJUv//97/Pe974X11UfWE94whP4/Oc/31QYXLx4MXfccQeu6zI2NtZQlAQ4eLAU2zY0NHT0Bq7RaDQazXHMXy0Z4HfDk3TZFtNu67pZTiAJUI4/U0DcNIvz15hlEJOqjRsExSm6QM2Z13bE6YrY3D05yzOXNP5MPqOvkzP6OtmfyXHzoUlG8w5OIEmYBid1pzh/oJuI2Tjy/Du7D3Hf5ByjOZegIq5H3RJwPA9fgiEoxrxW3wYJwu2mAUnLxAskThDgBpKeiIUlDDptk75opCg+Hsq2UZtsHjw8nea6A2PcOjqFG1SOMGGaXDjUw5WL+xsKtxqNRnO0EKZJ9MlPJvrkJ+MXolQdB5FMkv7Sl/AeeKDhvsbgYFOx0RwcbHrs6BVXzG+ssRgd73kPM+98J/7eevF9dfZJJul417tUrUmNRqN5BNg1l+WL2/Zz79RczbYJx2X7XJZv7DzIU4d6eNnqxcStv9CiM7sLVr8Oll0Fw9fBwR/C7EMqXrXgHoz0Kyfj0BXQ82S49xVKxBJNShQFjRNIACW2FV2RdcQyZ7TkbhQijPrcX2rj58GdVuOv3LnMoVc8mBJIc/vDaO9mYwuXJQqjNK6KKFHUdek4qdIdCKGTMxcKiq46R6tLRc0645W1LQGyqPqZkf5wbGVCbz03IhLSW1DLKynVkqwWO6sJnFD0DPuMVn0u+zkVxxo46n0PPMBTImlholden1G2mlOGImO5KzJ3SB0/ta702vB1sOo1LfrSaDQazfHCo0Zs/NznPscnP/nJ4vOLL76Yj33sY8RizW+urS1bAbtnz56mYuPecCJq2zYrVqw4ovFqNBqNRvNo4YTOJK9ct5TPbNnL5pkMc9V1uKrww/qGAkhYJpHqWFEBEVMQqeO625PJc3LEZsppLWoCLE7E+MdVC1s3LONP4zN8Yet+ZjxPLfats5i6cDvAlyo+1WxSi1GEE2fLEFiGOqehWLQmAhaUyHo0kFLyxW37+fn+6hshJTK+z3UHxth0YIyrVi/iWUub36zXaDSao4U5MIBZNq+K/9VfMdtEbBSJBKK7Gzk1VbsxHkc0KaNh9PYSfepT5z1Go7eXzo9+lMwXvoDz+98j3cYOC/uUU0j88z9jlce/ajQazV+Q+6fmeP99O8m0qDXuBJJfHpxg22yW952ymq7IX/C2nt0BS56vHgDZ/eCp1DGsDhWfWqD/Qhi+nmJtxhphsYWgV6i/WBTX6oiWzghEF5TcfdEB5VgsF+Tyw7ViozNRiiEtnlvoQDQsmN1cJ+a0evgBGKYS8YJACYjl+JkyoVGqY+ZHlDMQlDjnZ9VYvQyYscY1G710acxWkqKoVy0IFs7NSysRU1hK9AtcMIIq4bfO3KdQw9GIKHGzcJ6ZnarfwqTKTAFZ5WgsuFaLYqugNNNqRFjTsV78anYPJFaG8a8oUVuj0Wg0jxqaLDE6fvj0pz9dITS++MUv5lOf+lRLoRHgtNNOK/78pz/9qWE713W58847ATj11FOx7dobiBqNRqPRPFa5cnE/r163lBO7EgxEI0WBrR5+6GjstC1iTdyF9cj6PjOuV+k2PIo8PJ3mmgd2FkW/euUVy2szgpoO+03qz9TTIYcb1IJMHqUV5tdu3ttUaCxHAl/afoAf7Bk+KsfWaDSa+WI/6UlEzjmnaRtz1SqIxytfNAysJos8RSJBx9VXI9qY99XDSCZJvf71dH/lKyRe/GKsFSswurowOjowFy4k9sxn0v25z9H5wQ9qoVGj0Txi7JrLtiU0lrNjLsv77tuB47cQxY4l8cXQsUE9yoVGUA7HApE6i/6bxauCEr0K7jhh1Bfi/HxVFGoEkqsro0UL4l7FflVlD8wYJMKozuiQElVb1VAUQoluIhLWi6xq7+cAqUTImfsgvb1yLEKENQ19dZ7eHPhzNBTpAqfUDlmKra0mX5YiUIhyhdq4WqPO9ZShIzG5rjS2uc3KcVk+LmEo0dMeUE7EmijXVkIjjR2vMoDsrtLzZvU+NRqNRnPccdyLjT//+c+59tpri8/f9KY38fa3vx3DaG/oZ5xxBgsWLADgxz/+MX6DL2/XXXddsWbjpZdeeoSj1mg0Go3m0ccVi/v59Jnr+dd1S3hifydL4jGihlEUHg0EKxJx+mM2vREbQ4DrSxxf4gay6f2CckZyDinr2KzC/vSWvTiBxAwnvZE63xfqDTOQDTag6lFWM+t5dQXKEzoTNa/Nl037x7jx0MS89/vajoPcNTFzxMfXaDSa+SKEIPXmN2OXLfSsaWNZWOvXI1JhDSjDwFy9ulj7sRqjt5fOa67BWrnyiMdndHURf+5z6br2Wnq+8Q16vvUtur/wBZKveAXmkiWtO9BoNJpjyBe37Z+X0Fhgy2yG6w+0tzjtL05iBfSEi1CiA/MTpAquvEIbEalfmxBq4zrtbkiuVX0AdesuljsfzQSk1pecdABY6vjCQt02LcSlGiXhszhG1Gtm9RxAKnfj7EOh8Fi9OVBRpKLsuEGZmFg93sKYC7Go0aGycwzxs1WCphEKomati9SI1l5TIZR4aIWRq5md9cVaUAJrajUQlOpLCouS6NpIrC24H5vc080dpPTeayOIRqPRPJo4rmNUDxw4wNVXX118/trXvpaXv/zl8+pDCMELXvACPv7xj7Nz504++clP8sY3vrGizb59+/jYxz4GQE9PD3/zN39zxGPXaDQajebRyPJUnH9dt5R/WrWI28emGXdcPD8gblmc2dfB3ZOzvPHPW9mdztV1J0YMQcQwijUM6zHpeJzVW//m8uGS831+OzzJ/VNzGEKQCB2XliEwEG05KQu3IspbBlLFlUbrnJMXSMwq6+QVi/pr+s14PjcfmuB3I1NMuR6BlHTaFmf2dnLZor6KOFYpJT/e27iuWTMk8KO9ozyht3EcoUaj0RwrRCRCx3veQ/Zb3yJ//fUEc7U1x4RtY27YUHQX+gcO1DhbzGXLiF15JdELL0RUOyE1Go3mMca+TK5ujcZ2uf7AOH+1ZADRSIx7JFnzRnjo7TC3Fey+OnUS6yAMFU0qRKhLhTGszdpXY3dD50bl8nPGGxwjoQS7SF9tH8XnolbQKzWqfGrYQFIJgQVBbW5bbVxrkQCslGrrpSnOQOrWWKwWokUp5rScei7AguAoXXXOfqbs3GwlQgojFB/tkrDpZ8Po1AbY/eraGmEfxY/y8s/0BvUsWvleAkcJtGa8flSsRqPRaI5bjmux8XOf+xxz4SR13bp1XHjhhTz0UOu87mXLlpFMlj6Y/+mf/omf//znbNmyhS984Qts27aNv/3bv6W7u5t77rmH//7v/2ZychKAd77znaQKq201Go1Go3mcErdMLlzQW/Ha3ROzXHP/LiadxnWvnEDiBD4RQ5AwzbqLWg3gnIHuozLOPekc1+0f49fDE9w/Ncd4ODaBwAkkBhAzjZarxRtJkQJwA4kb+JhCEDMM7FBgrD61xfEop/aUvkNkPZ+v7jjIzcMT5PyAQMrQDSmwhMPW2Qzf2zPMOf1dvHT1IgZjEe6cmOVQg4jWdrhncpYDmTyLEtHWjTWa4xDpush8XtX4a5BkEqTTeA88gJybA9PE6OnB2rixYXtNfYLpafK//jXB8LC65skk1vr1RM45BxHW3PX37sV94AFkOo2IRDAGB7HPOAPRwJ0uLIvEi15E/PnPJ//b3+LcdBPB6CjSdRHJJPbGjUSvvLIYnerv34+3fbvqPxbDWLgQe/36v9Ql0Gg0mkecTW3G5jdifzbPPZNznHaUF/IdFcwYbPggbP2P0MmXC517UFdwEmENxILYJ0wwEvUFRdWgsfPNiEJ8KXSfCcteooTHwFECVu4ADG9qPG4r2Xhb8dB1xmTYIFJhtKtfG10KYEZV/GjuAMWljlYoUhbqRAYuGH5J6CyfqBhR5T50xmqja2WD+Y4Qyh2aXKOO6WVCR2ig6m7KoNLlKH1VD7MRZgKsOKQPqOtvpkr1J4uDLTgYawYTnnez72xltToHLmrSTqPRaDTHG8et2JjNZvnJT35SfL5lyxb++q//uq19v/a1r/HEJz6x+Ny2bb70pS9x1VVXsXXrVm6++WZuvvnmin1M0+Tf//3fecYznnFUxq/RaDQazWOJP4/P8IH7d5IPArWY1hTk/MZuQSeQSOmrGoZV88y+qE3qCGsbOn7Apzbv5bcjk8XXppxSjJJExbrO+T6WEEQNQT6oP95GZ2EKUTHv9qUk7fvEMIiblU5HAbxk9aLiqvJpx+Pd925n+2yWKddlJOcw43rFYwkEvRGLwViEW0eneHB6jvedsppfHqyz+noeSODGQ+O8eNWiI+pHo/lLIH0f59ZbyX7/+zi3305w6BBIiejsxFywAPu004hdcQWRpzwFEYngbd9O7rrrcH73O2S+8gae0d9P7NJLiV56KUZPzyN0Ro8OvK1byf3kJzi33Yb0vJrtRk8P5ooVyLk5vK1ba7d3dxO95BKil1+O2V/HWYFyOcYuvpjYxRc3HYu5eDHm4sWHdyIajUbzGOCuyQYxlfPgzsmZ41NsBCXurX8PzD4MB38Ce78KzmQYuxnWBTTsMCrVKolewoLUWkjvaCyi2d31aw+WM3gx9J1X+Zo7C6O/qqz3WE5sqaqx2Oi4oES/eghTnXN8qTovPwcEJSHV6gJ3kop4V2GB2aHch0FeHbcgjBb6NKLK4VkQIOuJjQ1F2bKxGbHKyFe7G+a2VF2LoLGr0YxDap2KWC0fv9UR1pScLYmmamOdTmQYvdrIjSvUNquj9r3TaDQazXHNcSs2btu2Dcc5/JX91QwNDfGjH/2I73znO2zatIkdO3aQyWTo7+/nrLPO4iUveQknnXTSUTueRqPRaDSPFYazeT784C48KbFD51DUMHADWbduYQFXSnJ+QMwqTXyjhsGKVJy4aTDjehzI5Mn6PoaAAxmHrB/gSUnSMjmlO8XSZKy23yDg6vt2cF9Z5JSU4FfJhlFD4AQCT0oCSoKjoBTqUz36wpTXqhIay8n5AR2WhSFKDsd/XruEc/q7AMj7Ae+9bwd3T8yyI53FCWprxUgk447LuOOSME1Wp+K8594dRM0jd2YNZ4/e9yeN5lgQzMyQ/cEPyHzxi3gPPojMVdUyGhsj2LsXb8sWnNtuw1y5EqO/H3/XrsZ9jo2R+eY3yX7/+6Te+EYi5557bE/iUUruhhvIfO5zyEZub9/H+cMfkDfcANEo1rp1NVGmwdQU2e99j9zPfkbq3/+dyFlntXVsb88evPvvV47USASjv5/I2WcjIk3i8TQajeYxTtqbf63GaubcI+/jmNOxHjreCiv/VbkKx26Cydshe6hSJDOiqsZjZECJdZE+5UqsR6uITWHB4KW1r9sd0Hc+jN5Ufz/DVMfPH2rQr9G8lqBhgdXZ2CFZz/FYcB8akVDkFJBaEx7HhNkHqJi5BA417kGjdt5UxIyVakxWvJ6Ajg2Q3atEUGGp2pGyav4iDPVexJcp56Vbp068sJQYWVjIVDhedV1NdQLqvOpRuL5DVzaP0NVoNBrNccdxKzaefPLJbN68+aj2GYlEeNGLXsSLXvSio9qvRqPRaDSPZf53/xg5X004u2xL1UAUkpRlMuf5TQXHfBAQlQZCKKHxhM4k6zrifPTB3fxhbJo5z2M45zCWcwmg6PbrsNVXlJO7U1yxqJ9zB7qK4t61m/dy39QcgZRMOh4zrocXSDKej0BgG0K5DgUVY/QQdFgmbiDJhKJmNQLotC0sAzJe/TagBEcvkPRFbV65dgnnDXYXt/103yh/HJtm+1wW2UatyIzv89BMmgAVvdofO7JJdT1xU6M5XvD37mX6LW/BueUWgrExqCd6SYl0HOToKDKdViKjlBgLF2IuXdq0f+k4zH74w6Te+EaiF1xwbE7iUUr+pptIf/rTjRsEAd7DDyPTYc2nfB5v82asDRsQ0VoHh8zlmPvAB0i9850NBUfp+zi33UZ+0ybc+++v2W50dBC96CKiV1yBuWDBYZ2XRqPRPJoxj0KtxWa10o877E5Y8nz1SO+Au1+u3HAYSliyOisdb9EhcEZr6vtixlXbZvRfoJx79Vj4bBj/XeOaiolVKkq0WnSDxq7GAh0bW4hkLeYHBfeimSwdy+4Ed7qqmyp3oNWhroufre0zMtDYSWhEVcRq4EBiJXjTytkJoeDbq2pEFsRDd7TxORhRIE3JuSlKDtbKwdffH9R71vdkWPL3jdtoNBqN5rhEFzbRaDQajUbTEMcPuOlQKUbHMgR9UbWSV4RiXtQw6gbkgJpGeoGkPxJhXUeCXXNZ/jwxy+9HpziUy3PfVJrhnIOvgk8Zd1wemkmzeSaNF0jum5rjIw/u4n337SDr+RzI5PnVwQn2ZXLcMznH9rkMo3mHSdfFC5S4Oef5zLo+ThAUxxgxBL6UBEhilkG3bWJC0eUoUF+KkpZJxBQYQpCyTTrCfcvPz0DQHbE4d6CLrz7pxAqhUUrJd3YfaltoLOBJyZaZDMM5B/cIxcLEEUbUajTHCn9khOk3vUkJjePj9YXGKuTcHMH4ODKTITh4kGB4uPWBpCT9yU/i7dhxFEb92MA/eLC50Aj4O3eWhMYCjoO/fXvDfWQQMPcf/4E/OlqzLUinmXnnO5l973vJ3347MpOpiW0NZmfJ/vjHTL/qVeRvvbX9E9JoNJrHCD2RJg65v2AfjwjJVXDyp1QcaHQA7K5aQcyMKwGsHMNS4lgzoTa5Bla8svH2xApY9brGfdgpSG2o3W5EmouNHRtg5asbb4eSmNiKcoEuOlTVh6gfm1rP7SkMJTa2wozChvfBundC16nq0XEiRBdUuiLrOhWLB6sjtBp1zlnWCsiFsQ49A9a+rXUsrEaj0WiOO/Rfbo1Go9FoNA25bWyK2ap4p8Ey550QELcMOm2LuGliCYEpBAYCSwjipkGHbbEsGWPbXBZPSjpsi9Gcw44mgty06/FwKDgC/Hlilqvv28E3dh7kwZk0B7J53KqVxnbZqm5fSjJeQMbzAUnCMum0LaKGEkdNoQRFARhCxabahiBWFWNqGiLcVz16bItz+jvZ0JVkTzpXsxr9/8ZnuHdybl5CYwFXBjhBwGiuwQrrNlnf2SCy6TAIgoCpqSn27dvH7t272b17N3v37mViYgKvTq03jaYZcx/5CO5996kYzXZ+f6SEIIAgQObzSNfF37dPvdZqV88j99OfHoVRPzbIbdpUtz5jEcchmKhfn0nOzdWKkOXbcznymzZVvObt3cvE3/wN2a9/He+++/AffBDv/vvx7roLb9s2gpnK+DXpOMx95CPkb7ml/ZPSaDSaxwAXlC1aOxwEcMHgo7hWcedJcMLVjSNHQbnqkqvUxMOIQGp9qZ5h3T43wob3q+jQZvQ/RYlajVyI8cWQOqkktBnRynqH5QgTus+EM78NsRZOfavN+prlAp3VpdyNrfqI9NeeT3Rh69qWAH0XKLEytrC5kNsKM0FpOWcBI7yORun1GiHXVuLmyZ9WP2s0Go3mUYcWGzUajUaj0TTkYJ36f0nLZHG8cvIuBETN0A1om3RGTFK2SdQ0cIOAbbMZ3CBgdSpOxvPZlc7V9FtNxvfZPpcpPr9rYoaPPrSbbAM3VNGybUUAAQAASURBVL16h04gyYYRsIZQTsv1nUk2dCXpiVh02BZmWJ/RNoyG82ohBBHDYH1XklQY8Xoo57A/W1lz5ZcHx5k9AhHODSSTzuGLjXHT4MKhI7/hFAQBo6Oj7Ny5k7GxMXK5HK7r4rou+XyeiYkJdu/ezaFDh7ToqGkLb/NmvAceQE5MINutyy5lxUPm8+D7Kn61DZxbbiGYnT2CUT82kI6Dc1ODulQh/midiLry7SMN6mWF5G+8Eel5SClJf+lLTFx+Od7dd4Nb9fdMSuTEBP7DD6vfh/LfBSlJf+IT+Pv3tzoljUajecxw0cI+osbh35o7taeDRYkWsZ7HO12nKIfjwr9pLKKl1sMJ74Y1b1SCWj061sPqN8D694OVau/YvU+CJ/wPLH1h/X6TK2DNm2DZiyGxrL7zsutUOOljcNb3lFCXWNlcrDMTleOTgarjGOTC/ztKaCwXDYVQbs2CKFvtdCy2MyG1riRURgeUaNqK1Amw6jXq50ivEk4b0UoIFGbohqy+BmGkqrBK9R3NWCkSN7kGzvwOmFpo1Gg0mkcrx23NRo1Go9FoNEeXsZzDDQfH2TmXJesHxEyDJYkYly7sa3iTIu/XdxAtTkSRSA5UiW31+P/s/XeYJcd5no3fVd198uS0GZuRARIUgxjFAAaQFKlsUVmmZMmfFT5b1I8URVGZVqAkW/LPsmVFW1awZJqSAVIUg0iRYCZIAASwAbvYODs5ntjdVd8f1SfOOWdmFgtwd/He1zXXzHRXV1f32T3Tp556nrdmDCqGowN5sr7H6W1EjK6EEZfKVSJruVCqslwLyXnNqNNWtIJAKcKOCfOasQTGEGiNxSZipavpmPYUnvKoxqbnRI+Hi47dkU1vcD6uhe3C55cWn6SwoZ5c7Z6XT42SfZIxqlEUMT09TbXa/7W11rK+vk65XGbXrl2ku9R0E4Q6lXvvxczNYeN4owDVi1YHozEQRVhjMLOz6MkuMWEd2FqN6kc/SvZNb7rMUV8fhF/8Yl/R1ZbLxE88gS2V3H2uv4dqDZ6HCgJXP3P/flSP9yezskLts5+l9qlPUfvoR7ckCNtikeiRR9pqQtowpHLvveR/+Ie3f6GCIAjXIHnf4xU7RvjAxYXLOv4b9/QQ3q410pNwww/A3u+CxU9D+bwT37ycq5848txmrOb+H3FtwgUnzHkF52bMH7y8cwfDsPs7YNe3werDEC65qFCv4JyXdWEwXIO5f4TSE04UDEZg+DlOmGurMzkOw8+Fpc/1PmdqEmpLYGs96kZaKJ1x96XuplS+E12rM+7cvfByMHgHmPLmdS3BXcORt7eLm1Ovh6XPd28fjIA623eREoXDEO2AtUd61L3MtMfR5vbDs/4QCkc2H68gCIJw1SJioyAIgiBc55xcK/HXZ2b47MIKpuMz4ecWVnnfuVnuHBng2/ZNcsdI+2ribBe3YJ09uQw5z+NSpcZ6D3ebRuF7iluGCmQ8TWQsC9UtCA3W1V+sGctjq0UynsdKGBFb53isGBd7mtYavyU+Net7xGGM6RAzq7ElSC4ltuC3zAf4WrE/n2csHTBfrVGJLcZaPKXI+x7jmaCnAOh1bJ6vbtGx1YfBwNWJrHW+WJswFPh8y77NBZh+GGO2JDS2Escx09PT7NmzB9+XR0thIzaKqH3yk8SLi1uLT+3aSeJwrNWaotgWnCDm0qXLO991hFla6rrdlsvEZ85gVlawa2vtQiO4mppR5NyHlQrx8eN4+/ahst2j68p//dfEp04RX7iArVScsBxFbgLW81CeB0GAan3dajWi48fxb721sb360Y+S+77vawiQgiAI1zs/eGgXJ9ZKnFwrb+u4N++Z4LljQ0/RqL5G6BSMv6x/Gy8DEy+/8udW2rksexEMwK5v3lpfU/f0FhutgWjZCZbd6h8qnHuwOgvVOeeorDsZtQ+3/wfIH4KZD8DchyFqWVCU2eXOPfEqJ04u/BPM3AfFjjrWSsPIC1zboTs3jmHoLneOYpe6zTrlBMda9/h1/IITOf1BJ+QWH3fX0XqtOuWuJbMLdn27c452qzcpCIIgXFPIjJAgCIIgXMfcP7fMex890yZcGWsJjSVOBLVAK768tMaDy2v8q8N7uGd3c4X04YEedUkSRtMBo+mAYhQzXw2pxgaL67fgewwFPudK1YYjcLEWbhACO7EW1qOYOJn0NhYUTQehsc7FGBpLaGIynm70rxUUAo/1DsExss3r9VR7fcfd2UzD2bk7t0ltF6AYxdRig8Fyer3MVCbNUMo9Umku35VYZzDwedst+/m1rz5B1G/FcAt53+Pdtx9sq6d5OSwvL29LaKwTRRHz8/Ps2LFJjRrhGYldX8eGoXM0bvHfdPeObPP4ON6S2Ggrm0c2X/d0iZ42a2vEJ06416VU6toGaIuxNYuL2GIR78gR9ED7whQThkT/+I9QqxGfO+dExo7X2moNSmHTaVQ6jaovTiiXsQsLqIkJ165YpHb//aRf/hRMJAuCIFyFZDyPX7zjEL/00GkeXe1dI7eVb9o7wQ8c3PUUj0y4bIbucm7L1Yfbt1vrxLdwyUWiRutgO/4GqzTNqleJw9EayO6CG34Ixl7sdt3wg7Dv+yEqOuHSy4Hf8dlt8jXuq/i4c0TGSbvCYUiN9R6/UnDju+Crb3NCYSfpye5io063uxO9HAze7sZfuQBx0Z138nXO/Tn5qt41MwVBEIRrDhEbBUEQBOE65YHFVX79kTMN0a4SG2YrNearIVFLnI2vNOPpgMlMiv984jwZT/OKHaMA3DU6wI5MikuV/o69vO+R7xLfuVaLyHqa+WoNY2GxGjbEwm50Co3g6izWjEVBVymvkkS9tgqOA4FH1RgqsWn0tR7GpLSmFhuGA599uQxZz2sIhf2IrXNkzlZqlJJJ+YLv83vHz/P7Jy7woolhXr97nL35NI+tbW2SqBdHBnK8YHyIX7rzEL/z2FlmNrn3BwpZ3nbzDezNby6U9sNay+rq6mUfXywWieMYz3tyMa7C9Yetuxmt7V/DaDtssR+V679g4pmAyufbfrflMvGJE04QLJed4LgZ1jpHqdbEx4+jbr65cW+tMcQPP+xE5SjqHZNbj8WtVJxbMpOBbBYFLho3ERsBzFyXiU1BEITrmIHA55fvPMSHphe47+IC50obF8so4NmjA7xx9wRfN7aFeEzha4dScPSd8Mg7XOxqnepFJzS6Rs4FGJddnKrFORq9LgkC1VlXU3HHGzvOo53jkh61LuvkD7mv7ZAag1t+HY79Qvs1gHMtZnZCZbplWw7yN4LqUnNRacjudfUgb32vi5oVBEEQrjtEbBQEQRCE65BabPjNR88SWxcJ+sR6hYVa2LVWYmQNlypVZio1RlM+//HYWZ4zOshQykcpxWt3jfEnp6a7nKU3kbHMVWvMlGvUjGmohOXIUDWmawQqQLlFHGzFJBqFUt2NUZXY4CvV6M+5Hl1DjWq4HNOepmYtg6mAO0YKfHlpfdNrWY9iTqyWCDvqjUxm3AfpyFo+PrvEx2eXqMWGnOc1BMnt4ivF10+4OKzbhgv8wfNv5guLq9x3YYEvLq42Xj1fKV44McTrd49zy1Dhss7VSbFYJLrciEucWLmyssLo6OgVGY9w/aALyb9R39+SG7FBt//wSrl+thjZ6x8+vPXzXacEd96J8jwXawrEZ8+6eNQwdKIf9H5zraOUcz9Wq6AU8enT+LfeirWW+ORJzMJCs81mJKKjrVbdn4ZsFlssYtbXG/9WGuMSBEF4BpHyNG/YM8Eb9kzw4NIaDyytsR7GeEoxkvJ56dQIO7MSMX3N4Bfgln8Pj7/X1T+0xomGbaikJmPW1TGERHi0TqDzB5yLMBiG8rmtndda55g0VSdc+vnNj+lFehxu/x1XI3PmPlh50LkTTQg658ZlY8jscD/3S3lJT8JNPy9CoyBcq3xyFB6RJKO+LHa+xz/zELFREARBEK5D/nlumdUwwljLsdUSa1sQkSyWhVrIw8tF7r0wx1sO7ATgtbvG+YfpRabLW4vXXK1FHF8rEVnLeCpgvmYaQUB1M1I9ArUuOtaSWNdq4lLUCpRSLR9XLaDQKOIeMaxVY/CVRyk2TuBsOaeHIqUVNw7kGEkHrIYRDyyFnFovcyCf3SB61lkPYx5bLW6Ifg2UZjS1cdVuMY4JjakPd9uMp1O8blfzA7hSiueODfHcsSFiY1mPYrRyTlJ9pRxiCaVS6Yr0IWKj0InKZPBvuon43DniSsUJjsZs4UC18fcgQI/1if1qQQ8Oknrxiy9jxNcXemyM4HnPo/bpT2OrVWziYLb1yOS647TXa5LUXASajsREHLSrq64mZOuxmwmX4Nor5cbg+6gggHIZErGx040pCILwTOOOkYENtdSFaxA/Dzf+nHMGnvxtWHmgfb8OIDXhhLi2ONEuHyaWP+8iTdMTdKVyyQmCG+o47nSxpROvSlyQ20R5MPRsqC04wbO0DMSgPcjucz8rD+JKd1emPwATr4Rd35IIkoIgCML1ioiNgiAIgnAdct+FeQBOrZe3JDS2shZF/NZjZ/nO/TtQSiX1AA/wji+fZKnWv6+LpSrH10rUjCHjaeZrNVbDGI0i7Sm8FvHAWud0LGPwtcLYpu8ytq6BBjzlREZLMufdQ2QLjaVoY8Iek9xpz2O4JTLVU4rJdIrjayVuGsxtEO9CYzi+tlFo1CgOD2S7in3DgY9WCq3YtDZlJwXf4427x9nRY8W6p9WGyFdrLV9ZWuf++WVWktdmMPB5wfgQd40OoLYhSMaX6ca80n0I1yeZe+4h/MpXMJcuQSq1tVqKdVHSWvez76M8Dz01taVzpu++24lYApl77qH26U9jZmddJGocu7hTY7Ym/NYxxh0XBMQzM7C62qzrWH+/2YrYWO9La+dwDIKG8xIguOWWbV6hIAiCIGyCjWHpC652YLjstmX3wsjzIXiKY2lz+53QN/xs5wq0iUCnU3RfodhlmzUw/1HY/R3t2+MKnPpdWPznHhEw03D2j+D8/4Cd3wx73tI9jr46C8tfgngd0BCMwMjzoHgCjr8H4mRhYjDS/RrjEgSjMHAz2Mg5Owdug/GXSl1GQRCEZwgiNgqCIAjCdcZ8pcbxtRLFKGaxtoVaXF04V6rw4UuL3L3TOYh25zL85l1H+fdffYITaxsdcNXY8NhqkdmkvmDW06STGoqBVoTGUo5t42OzsdYJigmxtV0/Gydhe6S1JkoaFHyvrRZjo62FqjVdRUCN4qaB3AbxbSjlY7BMl2vszrWLfHOVsHHOOp5SHC7kGAi6P0IppdiRSXG2WGEkFbC0lVpouPqPNw5k+fYbtiaixMZy38V57r0wz4UujtMPTi+wM5vmdbvGeOPucfwtRFduR5h8KvsQrk9SL34x3uQk8dAQ1hgXx7kVQUprF82pNSqdRg0PozKb1yfVIyNkvvEbr8DIr33CRx8lOnUKNTBAfPEihKGrrRjHW3sNrHVtPc9NTkYRBAF2dhbleSjfx7b+39+qc7UuUkYR1hhU8j7lHzqEf+ONl3m1giAIgtBBbRFmPwgzH4TiKajNQrgK9YWBXhaG74Ib/pVz4OmnaKq0Ogto0E8iCrczhjUqwWM/C+snmtts7FyItQWwYbIgyHOC6tk/gdocHPyJ5uKg5S/CzL2w8sWNzwVxqemm9Dapg+3lIFwELw2H3n751ygIgiBcs4jYKAiCIAjXGSuhc7jVhb/L5YMXFxpiI8BkJsVvPecoj64UuffCPJ+aWyaylkoc8+hKiUock/E0Ka3aRKe01oTGOVYsrp6joX29rrW0+QBtx76qMSgLgacJtCJQHsWo3cVorO26SNc5GAOG090dTiOpgOHA8rLJYT49v0LNOOFzttq8fx6KsXTAjmyKTBIn2IupbJpibMh6mpG0z2ylxnrU3fGX0ZqJTIqpTIq3Ht69pbisShzznq8+wZcW1/q2my5X+aPHL/L5hVXeedsB8n7/cXubXNdWuBJ9CNcnKgjI/ciPEM/NYYtFyOex65vXTK07GvE81NAQ/sGDmx9SKDDw7nejn8GRvrZWo/rRj1K97z6i06eTjRaqVWyp5ATD7VB3mHoe1iYLRyoVGB9HeR4sLzcFxn4Fdrv163lQq0HWRa+l77lne2MTBEEQhF6sPgLHf9m5+4qPuzqGncRlWPiUE92Gvw6O/gwMP2fzvq2Bpc+5r2jN1VgMhmHsJTB428b2pt8iROsEUFMFDCgfvAJ4HQusTMvnO2vhxHuaQqM1UD7vxETb5bNHXILKDJTPuGMP/ht4/Hdg4RPdhxRXYO1R11d11rlAM1uo1zb3URevuutbNm8rCIIgXFeI2CgIgiAI1xkW53xbrF6eq7HOA0trFKN4g0h181Cem4fy/Fi8l4ulCj/zlce5ZQi+ulLCsxvdLL5W+EoRJe7FbtPPlu7bG/uSnSlcdKhSinzgERsnRFaN2VDZJFCKlKcJlGJnj2jSWmyYr4XUYsOx1RIvnRyhFMUsVENOrZfwlE/W9xhPBXg96jp240A+w2oYM5TyGU+nKCZ9RsZisfhaMRwEDAYenlb88OE9vH73+Kb9xsbyqw8/wQNL/YXGVh5aXueXHjrFL915iKCPw7FQKLCysrLlfrsxMCC1hYTepF/8YuyP/zjrv/3bRI89BuCExz6ilAoC1M6dpO64w7Wv9V9E4e3dy8A73oG3d++VG/g1hllaYu0Xf5Ho5Mn2HUpBKgVbEXm7kbgQieNGX/7NNxM9+iiqMxrX8zYKml3t60ntRq3RAwP4R46Q/oZvuLzxCYIgCEIra4/BY+9yzrziCSfG9SOuOOHw0Z+FI+9w8Z9d21Xh0vudW7I6t3H/zH1ObNvxelcrsb4aspsz0IROHKzOtguJ4I7zB109x2AYUOC11DReeQBWvux+thGsH4dos7/xFqoLcOxXnJsxWnXOTr9LjGz1UotoaaF81n3P7NzkHMD0+2DHm546l6ggCIJwVSLv+oIgCIJwnTEY+JSNId5mzcBOrIXzpQo3Dua77k97mi8urbEexRQjQ9TnA3ze16xHhlrSRkGbOLiVkXoKQmspRoacr9FK4WnIaY2KoGINKa1Ie7pRNxFgOAgY63A1roURM5UaS7WIeqXIj80scSlxg66GEWlPM5VJ9RXoeqGUYjjl8+vPPsx9Fxf45NzyBtE273u8csco9+waY3duYyxkNTZE1pLzdMMp+v7zc9sSGut8daXIX5+Z4bsO9J4cyGazpNNpqtUuK763gNaaQqFwWccKzxwyr389enyc4u//PuFnP4tZWMBWKk5EbI3eVAqVzxN8/ddT+MmfdAJUHFP9xCeo3nsv0eOPN5tqTfD855O55x78O+98Rsf5mrU1Vn/mZ4jPn9+wL754sSnubtV52A1r0QcOYBcWUL7vXrdUyjkd6yjlBMfNolrrEa3VKqRSDLz73aiU1HUSBEEQniRx2TkawxUontxcaKxjarB+DB7/bRcdOnBz+/5wBY79ohP2+lE+C6f/Myw/AEfe5moWDtySCHYJ0apzJXZzIYL7GxmuuK9gEPJH2sczc1/Szrhr7Cc0Wgu25q7PxoCBhY+DP+QiVr2sEzVT4+73ehTrhus67wTPzWpchiuw+CkYf1n/doIgCMJ1hYiNgiAIgnCdMZlJsTOT4pEnYVJLKU3e9yhHvT+YW2v54EX3IbQS9/8Ar5Si4GkWW9qp5KvfkfU2nlJu7lopYgulyJD3myKcxc1tpz2N3+JAHAp8Dg1k28SHmXKNs6VKQ2SsE5rm7+thzMVylYVqyNGBHNlNIki7YYEDhRz/9uYCbz28my8vOmFWKzeuu0YHG3Ut65wrVrjv4jwfm1mimESvKuCO4QKv2zXG/73QZfV0C5GxzFdrLNYiokS4qbso//7CHN9xw1Tf+o1DQ0PMzs723F+OYmYrNYpxTGxBq2YU7L6JcfRlCLPCM4/U859P6vnPJ3zwQcrvex+1j3+ceHoaWy6jggC9Zw/p176W7Dd9E8HRo80DfZ/M3XeTuftuzOqqi2H1ffTg4JbqOD4TKP6H/9BVaDSLi5jz5+maNb0Zncek05izZ/Fvusnt9n0Xd5tOY1sXK2z1XEqhBgagUqH2uc+Rufvu7Y9REARBEFqZ/5gTvCrTvcW8XkRFCJfg/J/Dzb/c3B5X4LF3uzjWrbL0GTj5m84pOXWPc0NCIoIe3/rCn3AVSqdhKIl3rc7D8ufdz7X5pAZlD2wMcbFdcK3/HJfBL7jvpTNQuQSFoxCv97hv1jkeNxMbwV2riI2CIAjPKERsFARBEITrkFfsGOUjM4uXffxEJtUQ73rxpcU1ZhInoNmCNzECPK3Q1tVsNLa3u1EBGtCJyFgntpYB36diYmoG0g0N0OIp1RAaA6WZyATsyqbRLR3MVmqcKZU3HauXHFM1hsdWS9wylO97L3r1UT9mMPB56dRIz7bLtZDfevRsV9eiBb6yvM7HZ5d4oljhhnyGkVS7UzMylnOlCgvVcONrYWA9irlQrvLjXzjGL995mNEe9SsHBgYolUqsd8QsroUR50tV1rrUeSsSc7wS8Ydrs3xzpHnljmdunTxhewR33EGQxKNuFz04CINbmOh6BhFPT1P73Oe67jMXLzZ/2a6rse6ETI5VcQxRhH/oEPH0NGpw0Dkms1nncgyTCO84bp6v3k8nWsPQEP7hw6A1pd/7PfTYGKm77tr6+ARBEAShk5n7XERpeJmfh6qzsPoglC9Adrfbdu6/b09orLP4aSe8Tb3OORNXvpK4LbeZMKAzcOa/wuF/2+7WrPZeKOiExvWOcxkan7xs5PpRyeccU4X1R8Ef7t1nuOLa6e5lKhpULvbfLwiCIFx3yPJzQRAEQbgOeePucXx1eX/mFYrJTIBWMJXpHWd3cq3U+NnbgoOl/hlXKYWnFHWd0FPgK0gpyHqalFYEWuFp1dUYo5SLIC34HrsyaQZ8n5TWDPqeczIWctw5UmBPLtMmNJbjmDPFysYOW8ZRJ+c3711oDY+vby5QdnKwkN1Su9lKjX/3pRObxqMuViNqxnByrcxcpVnTpRYbHl0tMlet9RV9LZaPzizxU186zoVS9/uglGJqaqotDnW+WuOx1VJXoRFg3ij+qao4W6rxO4+d5b+dvND3OgRBeGqofuADXScuzfo6tpS8X7cKh5eD54G1qMHBRr1FPTnpREhA5fMuUtWYjWNRqv1LaxgeRufz6BG3GMMaQ/lP/uTyxycIgiA88whX4OLfwuP/EU78Ojz6Lld7sTq79fjUDX0ugolg9gPu97gC8x+5/DHO3Ou+7/sBqC1u322p05CegoVPuHjTuOi2R+sQl7ofY63b1/n3uO2eWCcctmIiqE73EUOtc1O2bYqgOgOls86BWT7rfjbhVq9QEARBuA4QZ6MgCIIgXIdMZdO8cfc4/+f87LYrN96QzxBozXNHB3s64ABKLZGonfUIt0RSuLEuCPpKkfU069HWqk2G1lAIfPbkM4ylAuaqtTZxsZPZSrghOrWV4Ra34EDgk/U8yokzZz2KKEbxtq7zdbvGNm1TimJ+/sFTzLaIh70Ik4kBi+WJYoVAKwq+z7G1UmOcmxEZy1w15OcePMVvPvsII11e37rgmMvlOD4zy+n1cte7tm4UJ2LFiQhimvf9/efnyHq6b31IQRCuPNWPfrTrdtMZjZwIhtt3VOhGLUbvwAHimRn8o0eJjh9HDQ1hl5fdO0Eu52pw1s/RTXTUGrJZtO+jJybaBNDo9GnCRx8luLmjTpYgCIIgtFJ8HC7+b1i834lddcIlFwkaJ4sFdbrp3Nsq1jqhbP2k+33+n1y86uVSOgOrD0PhJkiNQeXc1v8O65SLNtWBEylnPgi5G9y+cLnPNURbEzVtCHQskqwfp3p8FjTJZ5e47GJVuwmotUV44Adg4m7Y8UZISfqJIAjC9Y44GwVBEAThOuVHjuzhQCGLYmsuFgXszWWYTNyM9+we79s+0xIrOpiIc3377xiGsbSNLK01nlbkfK/viFsfXpZrIUcGcvzu1x1lV7Z3lE9sLQvV3oJe3c3ZymSHq3MrgmCdgu/xssnesal1/mF6gXM9XIadtM5HWCznSlWmy9UtC43148Bdy38/Pd2znVKKXKHA78xW+IeK5rFI8USkOBMpTkSKf6pq/r6qeCxSbUJjnb88M8PZPi5SQRCuLDYMMSs9CvVWWv4v1t+IN3m/3kAiMqI1eudOVNq936bvvhuVz+Pt3Qt+so61HqPqee7L97t+qXQaslm8HTs2nK56333bG58gCILwzGL+E/DwTzmnn+1I36g792zsXHvx2sY2W8I0HYSLn3xSwwVg4Z9h+YtONMzfCN4W6k37Ay561WsRA+f+EdLJ385+YmKnY3EDisbqzw27vP7HWwu1JVj7KlTnuo/DyyWu07+Bh34C1k9sMh5BEAThWkfERkEQBEG4TrlzpMDXjw9z02COocDvK+AVfI8jAzl2JoLd4YEszx4Z6Nt/p7g3me4duQoQqN6yp8ZFpwKJY8/D7+JSDJRqKJQaxaFClvc86zADqYDX9nESrtQioj6rh0dSPoFufyyaSAdkdHNCfqEaYre4Avk7bpgi0Ir1MGK2UmM1jDAdx1pr+cDFhQ3HluOYxWrIXKXGYjWkHLkP7/V6lHVKUcR0ebNJhHZao3U/PrtEMeo9QfHJuWVWwogFq3gg1Hw61Nwfar4QaqZNfXKiN/ddmO+7XxCEK4cNe8eU2ZYFCSrV8j5djzPdjKSdSqfRAwPoFnFQT0ww8M53okdH8Y8cceJia+RyZ3Rq4mpU+Twql8M/erSr8BkdO7b5uARBEIRnJoufhsd/s7eAqJLFL601g+Pi9qNLld8UBPs5CLdKuOJcgADBIAzc7hyLwXD732PlQXoSBm9zQmNnbcTaAuQPQO5A73NZswWBtc8zgAoA0zuG1lShdLL3fnDXUCdchsfe5aJVBUEQhOsWiVEVBEEQhOsUpRQ/e9sB3vbACQYCn0ocM1sJKUUxsbV4SpHxNJOZFLmWeNCJdMC7bjuI2mQS+uvHhxjwPdYSwWoiEzBbrfV22ilIaU3VND+U1vWzjKfbPu96WlHQHrGBqjFExmKx5HyPnOcxng4YT6d40eQw6cRh+cbdE3x6foVjqxvrloSm9wfhQGn25jauLNZKceNgjkdXi9SMwWAxtr22YzdetWOUyFp+6LOPMtPihhzwPe7eOcY9u8aYyqZ5YGmtIRYaa1muRcxUal1rIxZ8n7RWWGsbr0toLKE1jWjX2Fpia11JtmT8nqLtdRxONR/9asby4ekF3rR3km7cd2GjELodPjazxPcf2klmuw4qQRC2jcpmUVpju7zXKc9rehaCwE1oti5+aJvg7HiDqwuE6bQ7Ry6HHmguRNEDA/hHjzL47/89xd/7PfA8wgcfhGqPhRC+j8rn0WNj+Dfc4MbThUaNSUEQBEFoJVyFk+/tH0Hq55PY1JaFhHXB0R/c2nl02sWXZnYlx19m7cc2bDN+FNzf12DYfUEihqqtRb6aGkzdA4uf6tWgz8GJm7Fxnh7n06mkn4791rioWtVnStlLN6+rTlSEE78Kd/znJ1c/WhAEQbhqEbFREARBEK5jRtMBv/HsI/zCQ6c4tV5mX76/8LM/n+Hn7zjYt1ZjnZSnedXOUd53bg5w4tbRgRyPrRbbBMVW0l5TbKwLYhlPk+qh4HkaconjMK01dw4PtImSQ0HzUSblaX7u9oO8+8HHOblWbuun13REoDQ3DuYagmW38d4ymOfkeimpJWnptQo4UIpd2TQfn10iNBvPuBbF/O9zs7zv3CwvmRxmInGC1ozh+GqJUp841PUoYt1CKTJkfY1WishaotgSakM1tl2dm1q5+xZoRaA04x2v6xcW17qKjethxKOrT6IuDVCKYx5aXue5Y0NPqh9BEDZHKYV/662EDz20cV8uh11fb7QjlcJWqxtFR3Auwy4TgCpZNKCnphrb9PAw3sGDAPgHDzL0W79FdPIkq+98J7WPfxxbKmGTxRMqnYZ8Hm9yEm9iAlL9nfCb7hcEQRCemcx9aPN4UOVDMOpqLra2tcbVJ+xVh7CVuitv4jXu+1ZFyn74BSeE9kJtcYGeUqAzMP4NkD8E5Qts+LTTL4xF6WR/PS6mx/3odFQ2MP2FRoDM7u7byxdg5Usw/Jz+xwuCIAjXJCI2CoIgCMJ1zmg64LfuOspnF1a498I8Dy6vb2hzx3CBe3aN84LxITy99ZWmb9g9wQcvLlCOnYCY9jS3DOU5uVbu6tDTCrKephwb0p4m0Jr0ZlZBXGTq4YHcBp3vxZPDbb8PBj7vedZh/uzUNB++tNgYV2ckq0IxkvLZm8v0FBrrpDzNLUMFilHMy6dG+fT8SpuwtyOT4jU7xzi2VuIz8z1qprVggU/MLlOJDWA5tlrqKc52DBpPK9bDmELgERsnMBaj3scaC+XYUIlhXz5Ad9yHtbB7vNJan3jV7bAeXpl+BEHYnPQ993QVG/XEBGZ2trkhk4E4BmPc9zp9YlVVOo0aG0ONN2v5pl/9apTf/nHSP3yY7Hd+55N2JnotoqYgCIIgAG6BzMwHt9Y2PQm1eSeKtcaJmip4m4iNSkN6AnL7YfCWpL8drlaijQENXsoJmlsVCAGGn+f6fbLkjya1lDNwy6/BZ9/o6ia2XUOf45WfmBvjZPVnL1FRubjW8jkwyT3cSkmJ7B5IjffeP3OviI2CIAjXKSI2CoIgCMIzAE8rXjgxzAsnhrlYqnK2VKEcxWR9j725NLu7xIhuhclMinfcup9ffOh0Q4ALtObmoTzFKGa2UmOhGmJalteOpALuyKXJeJqHl4vEfZfegofiyECuERdaZ38+wy1DhQ3tM57HDx/Zw/ce3MlHLy3xz7PLTKcqTFdqaGA4FTCZCTbUaNyMl0wO89O37qcSxyzVImqxizAdSwf86anpLQmNrcxVapwpVkhp1X9CoIW01oTGsh7GRNZucueaKBSrYcxaGJH3PUqxITYuunW+UmM8k+pof2WQhCRBePpIveAF6NFRzOJi23aVz6MKhXZ3Yz7vatC2io09Io9VKoUaH8c7cKARy6y0Jv3a13Ztn375yyn/6Z82XI2XQ/ruuy/7WEEQBOE6Zf0YVGfcz9Y45yKxE/xU0B4/6hcgNeZEsrhloaWJwOudVAK46FTlw45vhJn73Nf641C52B6nqs66c6SnwMv2H3t6Ekae5x6OCze6a7lcpl7f/Dl/AG7+FXjw37RHtOJ1TzBQvnNXWuPuiwroeS/qDlGvAMUTEFf6i6tKQ3Zfe63Gbix/AaJ19xoJgiAI1xUiNgqCIAjCM4xduTS7cr1WsG6fZ48O8gt3HORXv/oExRZHXN73OFDIsj+fIbL1eoeK1+wa498c3cMDS2v87Jcf58vLa137VShGUz67smmy/sYPtq/f3WfFLE50vGf3OPck7d7z8Gnu36Yg2Mo9u8Yb/e7MNsezWA15//m5Xof1JLKWUhyjlbfBedkLpdx9Xa5FdElq7YpGOSektXxlaZ2MpxpVXGYqNX7wM484Z+tu52zVSjEY+PVqLk+K4R712ARBuPIo3yf31rey/hu/sWFyUe/eTXz8eGO7UgoKBaxSUKn0djX6Pt6RI3i72+PQsm95i4tD7YIeGiL1whdS/cQnLus69MAAqZe85LKOFQRBEK5jwmWIy1Cdda5F27JgRvuQmoDUpKsXCJA/6NpUY3dcHWt6i2aZKSc2Tr4WZv4vFE81+w9G3Xkb/cTJWOYgd6C/m2/yNc2/s1Ovu3yxMRiEsRe3b9v5JihfhHN/7MYTl5O/6ymw1eb4dboZIas8V1Oxn1MxNebaeVkYvAOw4A/A/Mfaj/MyTnBNjW/N6WkthCsiNgqCIFyHiNgoCIIgCMKT5o6RAf7wBbfw4ekFPnBxgQvlZn0UpRR5T/PiiWHu2T3OjYOuVslzx4Z438vu5Ec/9xhfXlqjEhuMtXhakfc8JjIpgh6Rrl8/PsRrdo513WetbbhvWrln9/hli42TmRTPG+teq+VD0wtd6yVuRmgsCqgag6+3EcGEpWmG7C1SKpzLNOtpKrGhatz9rRlXw1EBgzkPC3xleZ2vLK+zM5vm3bcfYHcuw12jA3xxsbsQvBWGAp/bhvvUpREE4YqTfslLsMvLFP/gD9omAvXQEHbfPsyZM41tSinneMznIYrcV/2YpLajf9NN6JGRtnNk3vxmst/xHX3HkXnzm6l96lPYPrVoe17DG9+IkoUKgiAIQitxBc79CaxujAsHnGOxMg2VS5Aeh+wNzmlXOOLEsPI5iPrUI9e+ExkzO2Hnt8DSp5NaiC1k90C0trFmpLVQOg0oJ9B1UrgRdr65+fvYS12U6PqJLVx4B3u+p3uNxQM/4uJhp9/vXIOm4u5Z+QzO5diR6OLloHDUtSs94dp20upQHLwDDv2/sPaIE31NCFjnftSXUWfZXn76gSAIgnD1ImKjIAiCIAhXhLzv8aa9k3zjngmOr5WYr4SE1kWN3jSYZyBof+w4W6zwD9ML7MymeGRFUY1dzcWJJOLUWliqhcxWapQjQ2wtWikODWR5/tggsbUNR+AjK+vce2GeLy2uUYxiPKUYTvm8bHKEe3aPM5lJcefIAK+YGuGjM0vbui5PKb7jhkk+t7BKOY7Jeh57kuhZYy0fnF7Y9r2KjGEljAi0pmYMxrp6lrG11GJLaE1jzl8rRaAVKa3QSlFNLI1aKcZSAcXYEJpmUK0TGRUprVHAehRTNa4/C1RiQ2jAU5pzpQrrUcxkJsVg4DNdrvK2L53gPc86zOt3jz8psfHVO0fxtxlVezURG8tyGFKJDWmtGU751/T1CM8cMm98I2pkhPIf/zFxS61Gb2oK5XnEZ864+FSl0OPjeDfcgFlcJL540bkcAXwf//Bh1GBzkYU3OUnm27+dzGtes+kY/CNHyP3rf03xd393W2NPveAFZP/Fv9jWMYIgCMJ1TlyGR98Ja49uobF19QtNDfJHmtGemV1OjCyfd+KYjZO6h3knqmX3wuSrYfJ1cPaPNwqN4I4rHIX14z0Ex1OuP6+lPEXhCNz4rnZBTgdw47vhkXc4EXSr7P4XMNU9whyl4Ia3wsDtcOnvYPXBZFyRczs2zp12dSPTU86FqFNOSAxXXLto2V1LetK1G3upi23N7XPHlwtOYPSe5HSyJwsSBUEQrkdEbBQEQRAE4YqilOLGwTw3djcC8oWFVf727CwPrzTrp0xkUkS2ymylxoVylZRWhMa21XrMeh6T6RQDvsfvHDvHn5ya5vnjgxxbLfFEsX01bmQt89WQvz03y/8+N8vzxgb5sRv38WM37qUSmw0OR2stcSL46UTAtNayFsbsL2T5vWPnN0SK3jZU4EUTQ8xVao1jtkrVWCyWdCI2hsYQGtvVIRlbSxxbKrETEaNEbExpzWg6IKrWSPeotbYWxlRi07bNJl8pT2GBxVrIYi1kIp1ifz7DWhTz8w+d4ne/7kZ2ZFJcqtS6dd0XTyleu6t/zO3VylylxgcvLvCh6QWWw+aq6wHf45U7Rrln9zg7s1cuhlgQngrSL34xqRe9iPBzn6Ny331Ejz6KLZfxduzAv/lm9MQEZn4ek4iRenwcNTaGHhpCpVKgNVSrqGwWb+9e0q99LcFzn9vVNd6LzKtfjfJ9ir/7u1uq35h+xSvI/9iPbescgiAIwjOAE7/mXIB+wTkQzRZcceGKc+zlD7rfle8ExbEXw22/AysPJNGqxkWDDtzm4lcrl2D5c7379bIwcLMTLcPF9hqO1kJt1ombfh4mXgV7vrtdfKwTDMGtvw6P/447X7+UFL8Ae7/Xxa9uxujz3Vf5PMz/E1TnYe4foXIO/CEXndqNYMh9AQx/HRx5ezOOtpXCTe56ujkht0p2r3OfCoIgCNcdIjYKgiAIgvC08TdnZ/izU9MbhDtPKfbkMuzKpjm2WmKmUoWkZqOnFGmtSHkeoTUs1SKGUz6ni2U+fGmR8XTA/ny2Z6KoBT67sMrZB07wy3ce4u237ud/nZ3lfedmeWK9zGy1RjEy2GRUgdKMBD5aw3AqYLEWdu334ZV1Pr+wwsn1MjcO5Eh5W3e9mWRCwdOQ1ppSZLqWS+ukFhti647Jes5tF1rLUpcxViJDtUNorOMpRabDpTdXrRFby6FClvlqyIcvLfLTt+znHV8+SdV076cX/8/RPUxmLiNS6WtIZAz/5cQFPnRpoWs9zLUo5v+cn+P95+d4yeQwP3Hjvm295oLwdKOUIvX855N6/vMBsMagOv7fxxcvYtfWQCnU0BDe1NQVHUP6Fa/Av+UWqh/4ANV//EfMWrtbWnkewQteQOaeewjuuOOKnlsQBEG4RjGRcwlG61A8DQufdCIf2tUFrFzaWj+1BReL6mWb26bucYLlyHO7HzPzgf7CHzg3YP4gmH2uXmO4nMSCKvd9/7+Cibu7i3Wt+AW48WddvcXZ+2Duw+1Rr/nDbrzjL9t+VGl2D+z9bvfzoZ+Ac3/mHI+mzyJCnYKpN8C+7+9exxnAz8HYN8DsB7c3nlam7rn8YwVBEISrGhEbBUEQBEF4Wvi/5+f401PTfdvMVGqsRREZT1MzltlKSM7X+FpBi8tMWagYQ6AVs9UanlbszXVZNdzCdLnKzz94it+46wiDgXMCKqXQKNJaN+pFZj3NYhgSW0toLHnfw+vxgVsrRTmOeWS1yC2D+S2LT/X+XA1FQ9pzDsetoGi6Ez2lOFTIcmqdNlE0MpZSHG8QdevHp7XuKs4u1kLyFY+d2TQfuLjAm/ZM8O7bD/IrXz1NMdq89poCfvjwbu7uUU/zaiUyhl9++PSWYmMt8InZZWYrIb9050EyPVylgnC10Sk0Ani7dj3l5/V27CD3Az9A9ru+i/CBBzCLixBFqEKB4M470aOjT/kYBEEQhGuA6qwT++b+0TkTAYonobbonILpSSc2Vmc2FwQBF6k6C7kb3K9eFsZf3v+Q+Y9ufbzad2JmZmf79mBoc6GxlewuF4G67186t6WtuZjRbrUZLwelYN/3wa5vcYLm7AfbY2Izu2Dytc6JGQxs3t+O11++2OhlYPwVl3esIAiCcNUjYqMgCIIgCE85M+Uqf/B4l9onLYTGcL5UJTKWYtQUykqRceJgizi2FsVE1kWL5n2PS+UqE+kUmU3EvnOlCm9/4EQjdnUsHTCWbv8g/+hKsRFnulALKa8YbhrMO8Gzg0A7sbJmDMfXStw6lN9SBGDa0/hKsZJcZ1pr0lpTiWNqScSqQrUtKg6UIvA0pdhgsFSNIZsIXcOBz0otYjWMiKx10asdczAKJ466qNjeY5up1NiRSXGxXOXB5XXuHBngvXcd4W/PzvLx2SVqXWx/CnjO6CDfvHeS20cKm17/1cb///j5LdenNMn9fXh5nd945Azvuv3gUzw6Qbg+UKlUw2UpCIIgCG2c/wu48BftIqIJIUxqrUdFiE47gS8Yda7FrVBbcLGdSsP+H213OXZiIudSfLJU5y/vOKWcc5Dckx9DN/wC7Hyz+zKREza9rLun2yG3H3a+Cabfv/0x3PBDyTUKgiAI1yMiNgqCIAiC8JTzgYvdoylbmauERMa0CY0ABucwDDynkMWGhhhogWIUk/c9Zis19uX7uxtnKzW+srTGHcOFrqLgWhix1lFbrBTHnFgrcdNgfkOikFaKsXTAXLVGKY5ZCSOGU5uvQvaUYjgImK+GaBShMdSSGpVKAdbVU8SCn7gt62Jn1TixC2CmUmWmEhIl9WLygUcYW1ajqHEPFeC11KKsn78XNeOiakfTAWeLFe4cGWB3LsOP37SPHzy0i49cWuTkeplSFJPxNDsyaV69c5Spa7SO4YVShQ9fWuzbJk5qgM5WapTjpsPzweU1FPAvD++WOo6CIAiCIDzzWHsUlj4H0SqgXE3AsRc7QWqrnPpPcO6/gym7GojKcyKY8jc6GE3kBMhgqOl+tAYwuE8Gyh1fX6VoI+cUPPDjMLGJq9FuoRbkVrDdSzBcVWgf9BZcjL3Y9y9dzO3cR7ZxzPfB5Ksv/5yCIAjCVY+IjYIgCIIgPKWExvCPm4g51lpmK1WKPaI/q8YQJC6+zrhRC5SimLlKlT25dJuo1oqxlvOlCpG1LIcRI11EwdlK9zoma1HEUi1kNL3xmMlMirmqO26mUtuS2AjOXWisxQCmQ4lVqmnkjK1lPYrJepq0p0lpRTm2RMZyer1Cymu/3hiLp1SjLqTrA8CilcJXCq+ftRGYr7prLXfUfCwEPm/aO7ml67tWuO/iQtd/c3Wmy1UulqrEWLBQMy76NrauyuevP3KGPz51kZdOjvDeu44wmr62alUKgiAIgiBsCxu7OM6Z+6B4auP+C38FAze7+n/jL+3dT2Uajv8qXPxb1+cGjKsxqFNAS3qJNRCtQTAIlRkw1fbDlAKVcscFA3DDD8OON25+XV4mETgjJ3JGK+48NnLn1ylIjW1eP9G/9lI+to1ScOgnIXsDTL+v6UDtRmaXqx859pKnbXiCIAjC1wYRGwVBEARBeEp5eHmd1bD/SuGKMazHcU/3Y2Qt1rrPtVGXGi0GKMeGUmQoBN1r6C3Wwsaxa2G8QWyMjGGx1nucs5VaV7Ex73sM+j6rUcRqGFOLzaa1G524WsNa2GKZR8qxSSJXFZVYEVmLtoYU7dfb6x7WY1VT/uYxr9VE0M1udXDXKLXY8JE+QviZYpmZRICuxoZK8hq09WEMq2HEfRfn+eTcMj9z636+/+CuLcXpCoIgCIIgXFPEFTjx72H5i/3brT3qvpa/AAd/fGNU5/KX4MR7YPEzPYRGwMTufCapYaiSZ14bOydlXGzGgJpa4nBM3I31JzavAMN3JcclHyb6UbjR1Yyszm0UMQEq5yEYgfQU+D2cgYO39z/H9cSub3JC7uL9MPsPULkIpgJeDnIHXX3HwTs3v++CIAjCdYGIjYIgCIIgPKWshj0mEFqIjaXWWWSwg8bUQY9mNWOTONHuYuNMuela7CZYVpNaib1YjSLKUUzW39j/4YEcj6wUqZiYiukuNlprWapFzFZqLNZCVmoRWqlGJKqGTQWqSmzIej4ZrSgb2/Ve1K+hZZqlbQzaun7iloM9pUhp1XCF1l2Rm8XSXutMl6sUo+7/Pi+Vqw2hsRyZhgDbicUJvFrBahjxKw8/wWylxk/fsl8ER0EQBEEQrh9MBMd/BVa+vPVj5j8GGDj075qC0+pDcOyXoDbnRMJeNMRFA/G6Ew7B/Wxt4nrMggrA65EsUrkID/wgpCadCKZTTiicuBsm7253IVbnoHgcyud7j8laqC1CbcnVgszsaN8/eJvb/kxC+87B2s/FKgiCIDwjuL6XqwuCIAiC8DWnn4BXJ7S2qwDYq0dj278sTkAMewiW1kKxpdZetwcgs4Xzr/cQpnytuHkoT97zuvazWA35ytI6J9dLrEaRq0GpNUo16ydGFiJjsX3GkdKa0FjSviatdTNrtQWVbOxMSq2Lj6uRc+iFxja+KrFhNYwpRjGxcTGsu7Jp7hi+vmOgSnF3ATG2lgslt5q9EvcWGuu0/htfiyL+7vw8/+n4OdbDqO/rKQiCIAiCcM1w8X9tT2isM/9x5xYEiMtw/D0umrQ62/84pV2sKbiH+bjYFBrr22wPsdLWnPvR1GDtMSiedNtNDcrn4OwfwZe+D574r0kdyBV49GdaYls3w0L5LFRn2jdPvX4LxwqCIAjC9Yk4GwVBEARBeEoZDDZ/3NiKHmOtpRxbQms3RoVai8IJPeM22OAoiztOkNIb5UZvCy60zn5aCbTilqEC33HDFI+uFnloeR1wdRzPFitYLDnPYyjwmS5XMbYZG+sphVbuGo0FjW1cgwLSWpPyNArnniv4Pllfk0nEx9ZKl75S1ACd1G2sO0JN/bu1PetahsYSmZihwOd1u8aue2deukftyvlqSIxzjlZ6CJLtuH5ia6nFhgeX13n0wSJ/cPICvtLsL2R4w64JvufADvzrPJpWEARBEITrEBPBzAcu//hLfw+Tr3ZOx2jNbYvLmx+n0xBHzTEoRdtqOxtvXHxnKi5+FcBLhMPqDGR2Nt2S4ITFS38PpTPOnVe55PrO3eDEya18QCmddY5LPw9Dz4LRr9/8GEEQBEG4ThGxURAEQRCEp5TbhgoUfK+nKxCcM9BXqqe70QmJcfJzt4BQx3SlRmgthwdybeJhq6akUIx1qb2Y9jReS6xpN3qJdHVSnuLNeyf4nmAn54oV/v7CHH96apoDhQwZz6Pge5wtVkBBoDW1OG5cilJJjKoCjSLva7RKfIrJaSPjxMOqMeS05kAhS87zmK+GzFZrlOOYlFaUk1ud0orQ2IbQSM8718TiamhOZbayqvvqZaUWcalSpRobsp7H7lyaXEcE7mQm1fXf3Wy9TuMmjsY6Cst6aIgablvwtOL0eoWsrzlfrvDJuWXe88hpfuDgLn78pn3ku8TxCoIgCIIgXJUs3g/h0uUfX3oCVr8KM/e1bOzznGWj5Cupw2ita29Vu2C4YQFirSk0Kr/FGRlDbd5FqHay9DlXizF/2P0ejED2Biif2YLgaKE2C8OvhSPvaB+bIAiCIDzDELFREARBEISnlCeKZTylOL5awuIiOgcDn7F00BAEPaVIa00UbxQkjbUoVKPMi1auRl7nR/+6MLcSRhxfLXHjYK4hDmql8JUmsoahwCfdxV3mKcV4OmjU6etGqocTrs6LJ4YZSJyce3Jpvry0zmSHaLceRZQjQ80YYtMM4FTWCY4ahVEuVrbTeVef7zDWuSTr55rKppjKpoiMJbaWi+Uqs5Uq1sJSGG24WdY2y+Z0u8abB/P87rFzPGd0sGv9yauZB5fWuO/iAp+ZX2kTjtNa87KpYV6/a5yDAzkACoHPCyeG+MTscqOdsZZyIgJvVkcUwFMujjU2ztlYP8JaS80YMlY37vVKGPEfjp3jYzNL/NHX38Ke3PVdE1MQBEEQhOuEhY8/+T4u/q1zETbofMZM6jCaaiIyNjdDTPOBVtNYRtf5PFt3Syrt4lDjEo3K78VT4A+Al2s/pjbr6jCmViAYctvSk6ADF7laFy+7obSrGXn0neDnercTBEEQhGcAIjYKgiAIgvCU8E8zi7z//Bwn18pUYsNKGDVq2y3WQs4VK4ylA3Zl02Q9Tc7XlGODaVHGrHXCot8xF6EVtOpACif6eMmEw1oUca5U4YZ8ttFmIh0wXamyI9vbsTeZTvUUG1NaMxRsdES2cs+u8cbPX1pcY7pcbfxurOWJ9QqXyrWGk671Omz9erFoC1Us6T6Lozudh8ZaVsKIuUqNYhQ3nHbWstEL2kNo9JTi9uECWd9jLYr557llXrljtO81Xy2s1CJ++eHTPLZa7Lq/agwfml7kQ9OLvHB8iH938w2kPM09u8bbxMb6a2OS12IzTPJvtFVorGNx2/0WZddieXB5nbd+5hH++OtvZWc2vd1LFQRBEARBeHp5Mq7GOtXp9t+9nBMXwYmL8Xq7yFhHKbAaJzjiHI/KBzpdjrX2eo5xqb0fE8Lqw+AXIL0DUskzbm0hGd9sU2wE53AMRlw9x+pMMr7YnVenITUO6Qk3luUvwuSrNo49XIHZf4DFT0G4nFx3AUaeD1Ovc+dbP5ZEy3oQDEPhaO9VgYIgCIJwFSNioyAIgiAIVxRrLf/lxAXuvTjf2JbxNHtyac6VmiuDYyyz1RrLtYijgzkmMikqsWU9ihqijUmckJ101iP0lCKldVuNwblKyO5sBj9xB9YjM/vVkMz6HuOpFPO1jYLjRDrV93P/c8cGuWko3/j9vpbrN9ZyfLXEahS16Xyt19FKDMTGYKrWRcxqd311p2bO95hoERsvlatMl2uELRM0WU+xHDblstbzdl5GoBRpT3PTYJ7RlojZey/Mf83FxthYPrewwtlihXJsyHqa/YUszx0bbNyP5VrITz9wsk3c7cf98yssfeVxfvnOQ9w6XODmwTyPJiKlTu7OFqr0AO61jbs4bet0226xPLpS4pcfOsXvPfem6742piAIgiAI1zi2dzmELWOi9t9TE4kA10do7IpqCo4q1bItOb6+0m4DSRxrtA7RSYgmIbOvOa5o2YmfumNhYjDULkJ2I1zs+H0VzvxXWPiUG2tjCBGY87DyAJz4NeeMzOx04mWdzE6YfC1M3A3BQP/zCoIgCMJVhIiNgiAIgiBcUf7w8YttQmOdndk0xloudAhCNWs4tlri8ECWaV2j4HsUI+dw1C3xqZ342tVXrLdJe+0NDZb5ao0d2TQK+OEjuzmxWuL++ZW+499fyFBbNaxGzYkBD8Vkprer8cbBHD99yw1t2x5daTrsnlivNPrztCJssWX6WhEndRU7hanQOhUrii2V2BAoRdbT7EqERmstZ4oVZqtd3JhK4SV925a+A60Y8H0szXjarOfqP3YKsSfWSsxWahuiYJ8Olmsh911Y4B+mF1ishRv2j6cDXrNzjNfuHOMXHz69ZaGxzqOrRf7DsbO87Zb9vPO2A7ztgRNMl6v4ytUPjTeRG+v/2gyKjXJxvbZoTxMpFRPz0HKRB5bWuGt0cFtjFwRBEARBeFrxCk++D51ysaS1+aQeo3WOvoYY12/xlWruV557sNUBZPeBDZ3zsjZH44nXJu1VS+Qqqt0xWJ1Nmre4IaM1SI1t/9paxdjqLDz6s1BpcXJGa257uOiiXht1JRWUz8Pgrc5FCe64s38MF/4CDv0UjD5/++MRBEEQhK8B11YRHkEQBEEQrmoeXFrj/efneu7fnctwuJAj77Xng4bWcKFUZWc2ha8Vu3NppjKprq5GcFMGaa0ZSQUUfI+hwG+43FpZrIW8fGqE37jrCN+6b4qfuGkf+/P96+RppTg6mGM8nXJiHIrDAzkC3f2x6cUTw/zKnYfJdFxTKXarqytx3OaUTHfpp58rrp4G5StFylNopcj57lwXytXuQiON6jTOGakUWiVxqhYiawi0YiwdcNNgjjuHB3o6PldqUdftTyXHV4v8m88f4y/OXOoqNALMV0P+/IlLvOX+h/ny0tplneefZ5e5UKowlPL5tWcd5lAhCwrG0yn6lefUSpHxNBbnbOxE0ZzL0n0mzmYrNe67sFGYFwRBEARBuKoYfvblH2sjWD8BK1+E6pyLM60/P6kgER7D5Cvq+Iqbbd0B7ktr52osnXbiXG2B9idqC5hmH9hEeOygNtsuFNrLfO71knSTaB0ee3dTaDQhrD0Ca4+6Mcal9hqQ9bjX5S9C8fH2a40rcOJXYP4TlzcmQRAEQXiaEWejIAiCIAhXjHu3IJyMpgNG0wHrYcxctUY1qdOoFHzPgZ3UYsOn5lc4V6pQjQ2hscQ0aw9qpUjVlTNgKpdmfz7TqAsZWefYC7RiTy7Nv7256TjM+R6/+qzD/NJDpxuxmd3QSnGwkOVQIcNdo4McXy2xFjUnIvK+xyt3jHLPrjF257qLl4FWxLHdUANSKbcvNG4yobXWX12Wav1dKygEXkN4HfB9zpereEoxXe4uNLb34s7p4YTKgcBDAc8aGegpoLaylbqFV5JTayV+9iuPU463FqX1+Jp7bW4ezDdE2K1igfsuLvBDh3czkg54711H+ezCCn91Zoa/OnOJQCnnLk3wk397gVZUjfs32e3u1IVKTyn63eL1KObzi6ushlHfeF9BEARBEISvKROvhvP/04ln28HUYP0xJ+gFhyBVhuql5v56DcRGDkd9uVyjAWCa25R2D7Y6BaYEaFeDse+4jBPxdLZPG5r9Xw51Mfbi3zqnIiTX/ijE1ebvcY8kDmuc69MayB9urlqzFk79DmR3Q/7Q5Y1NEARBEJ4mZFZDEARBEIQrwkK1xmcXVrfcvhB4FIL2D/05z+Pf3nwD7z83y3u++gQoCDxF0MUd5ivNzmyKnVlX4yTre2Q7xKZuQtBA4POrzzrE/XMr3HtxnkdWNoqOo6mAV+8c5XW7xhlNB0TGsFiLKEcxWd9jJOVvKtRNZVKcXi+zUN04+ZHRHpGJMBZMl0HWr9ZT7VMuGsXeXJrYwmOrxb41JFXHPVO4e14/Zq4SsiuX3nhgB0+nCBYZwy89fHrLQmM1EZhDY/ny0hq7smlXv9PTjKV8fK0pRzGLtYjQuD59rRgOfArJdX3k0iLfd2AnKU/jacULJ4Z54cQwGU/zgYvznCtWQDnhsNVpa7s4GqHuanTturlY267XWoyFhWooYqMgCIIgCFcvwSCMPB8u/m8IF5I6h9bVTQyGXf1F3VFywBpYP+4cepmdTshLT0B1hkb9RFOh+bRbf7bqdDK2blOgM2DKySbjYkkbbfsskjM1Vxux8wHaJoKm0i01ILeChdqiG88T/9Vd7+wHwcuCPwTFE+3ioqn07gqSONhFKJ+FXEt5BhPC9Pvg8E9tY2yCIAiC8PQjsxqCIAiCIFwRPjO/StxDgNkqn5xb5keO7uFNeydZi2L+0/FzzFVCynFMbJ34lvE8JjMBo6mga3RqK51xrXV8rXnp1AgvnRrhifUyj60WKUUuWnQqk+I5o4N4LTmavtbbrlv4qh2j/N6xc0Rd7omnnctydZOI0voI3LU7t2Uh8DGJG9IaejrnPOXy8g1OpMz7Xls06Fy1xs5suq9guTeXaYi5Twf3z60w30Wc7YaxlvOlCiu1uOG+PF+ukEpuyONrFq0U1rr73crFcpW85zGZSWHTAQu1cMN1vvv2gxSjmL8/P0/FxGygy31TuNdJ4e55ql8WK+41AqiZrYmrgiAIgiAITztxBc79GSzcD5WLTrRrJVqHygVXczC7z7kOwdVQjEtOfEvvdNu8LGR2QPkimCLuSRX6FxUAJzJmXfu4Q7RruBr7iY3JU7Gtgep4tlUeKOvGHWyhjrY17j7UkkjY/GFY+bKrRVl3NQKYqhM3IYmI3eR5z1p3b2uzTpzVLZ89Fj4FN/wQBEObj08QBEEQvkaI2CgIgiAIwhVhuUdtve2wGkZYa1FKcftwgfF0ivH09kS+Vm4Zym/aZn8hy/5C91ilJ9bLfGh6gelyjZoxZD3N0cE8r945ynAq6HpMnVftGOU/Hz/fc3891tS2xKi2olXTIeehODqQYyjlHt1KkUErGE75pLRmoRoSd/SilCLneURY0lpvEBWrxlCJ4w1u0Fbu2TXW9xqvNPde3Fr9wpoxHF8tsVKL2mJea7ElUJZybKi1WEZznkfKa78BxTjmdLHMYi1kubpRbMz5Hu951mGMtbz//ByRdbGp9W5Vx4vWEBqThN+873UVJFupC6OFbca/CoIgCIIgPC2Eq64GYfGk+71wFNaPbYwttYnLL1p3bbycczB6Gfe7bpl+zOyBaBXKy/WDe5y85UFKeTj3YeBEStXSnzW0x7B29qGb8ah1d2NbEwX+MKSG2PThzYbOrRklySjBsBNZwd2rOtF6sxall98o0Pbr36adkJnZ3bI9grmPwK5v3lo/giAIgvA1QMRGQRAEQRCuSu4YLrA7m+ZCuUdtky1wz+7xyzruc/Mr/M3Z2a51HT89v8J/fOws4+mAm4cKTGQCxtMBr9wx2la/sRD4vGxqhC8vr3U9R2iNm9tI3Hdxh+iocfvSWnPjUFNoBIiSldHFyHB0NM++fIaFakglNsTW4msnNOZ9zYPLRbrLmc4x2YuMp3nFjtHeDa4wl8rVrpG2nUTG8thKybkNO+aDImspRTFhx3WV4hjYKDgCrIQRv3/yAu+96wh+h010IPD53a+7icOFHL/+6BmWw+bEmrW2cf+0cnU+FS6+tuDrDW7KboynnUN3R+bpc48KgiAIgiBsibgKx36xKTSCExEHboHSWYiWkwjSFkzNiXG5/U5ky+7bGK+qlBMPdRrifosVLU2hUDtR0ZaS31UiMta/+tHy/GdjFwGrO6ZDgwG48RfgzB84Ya/rcIwTWqNScsygq6PYqK8YNc9R/9lEQHHjfep5yUm76hykd7VHvpZ7L2IUBEEQhKsBERsFQRAEQbgiXImacwXfa7j5lFK8btcY/+3xi5fV155cmjtHBrZ93F8+cYk/f+LShu2hMVwq15ivhoTWcGIdPrewypGBHIMpn/91dpY7hgu8ee8Ezx1zEUf/5uge/vTURZbDjZMWbdVoEtGxTtbTpBI3osIJh63oZNKkHtGqlWKiR8zrvnyGM8Vy2zZr3flrxhAaTdAR96mAHzu6l9zT6Ljbanzq6fVyI9bU67BrGmup2aYjtJVSHOMrf0PsrIfi5FqJvzwzw3cf2Nm2b7Zc48e/cIwT6yUm0wEprVgPXWyrAmrGUo0Nxrp7lvU0KU+zSXoq4F7D8XSK1+wca4vsFQRBEARBuCqY+XsnrnWi01A44mJCq3MQLjmBDZzjMDUK6Unn+utGXHYuQBviltd1cyQCqHYHIyRiXFIrspGa2hqf2vkzQJwcp91Dt41omw5VCnZ+E+y4B3L74OwfwfqJjcOpnHdCo/Jc7cnMnqZjsvV8tiN+v17fcjPXZNsxNTdO1SLUblbzURAEQRC+xojYKAiCIAjCFeF5Y4P8wckLPYOQtsILxtvrkLxm1xgfvrTIE8XtfbjWCn7w0K5tn/9952a7Co3lOOb4aolqR229GMvxtRI3DeUp+B4PLq/z4PI6375viu85uJPBVMAPHd7NH5y82OaK64UCsr5uxGsCDAYe6cQmFxpDaCyhtRgL/hbmLKYyKWLj6huG1lKLDWEiUh5fK6EVjfqFY+kATyl+5MgeXjo1snnnV5DIbP4vpxqbtvuoFQRKNa7HWLetXcltTu1UjSHboTaOZ1ztzw9eXOBf3DCFrzXzlRp/dWaG33rsDOtR+4RRoBVaaXK+R6DgYrlGJY5Ryv172IKhEYCJTIq0p3nt0xxVKwiCIAiCsCnWwswH+rfRacjucV+dx64+DFgnSFrjRDkv50TIuJyIadZttzHdhbheD7oWbNUJcV7GtTMdSSg2pv2BsP4M77VvV9rVXNz9L9zvg7fBbb/lxMaZe2Htqy4yVWkoVp1jMzWWxLp23o+6MNjlmdaa7sd00ipe2hhoERu93ObHC4IgCMLXEBEbBUEQBEG4Ikxl0zxndJAvLK5u3rgHb+iIPc14Hu++/SA/85XHmd5inKoC/p+jexvuwq1yoVThj7u4KGux4dhqiZrpHtFksDy+VuLO4YHGnMhfn53BU4q3HNjBm/dO8k8zSyyHAbOVGqthjMWiWiZQFK5+X8pTG9x6E+kUi9XQHRs1HZLFKEajuFSuMp5O4fdxxw2nfC6UFJU4Jk6EOQ9FbC2xgWUTsRbFrIQR/+6mGy47fvbJkN+Ci3K2UtswfZPyNGEUN2pfumjTllaJ61Ar5+TM2Pb6lVOJI3QljPjk3DIjqYBfefg0n19YTeJXO1DuNV+PIjLa48hglhOrZaqJEFzGkPX7S46Dvs/eXJpv3TfJaLp/7U9BEARBEISnneUvQHV2+8fV5qFyEWpLzjGomyUGiCuurqONwdRTNzROCNzOcsWkrbVJ/UW9sSai8l2/trVvAzZ5CFTaOS9TozBwM+QPtI/Ty8GON8Hu73Ti4vzHIFzpP6zUWHLP+oikm7kbdUtSSac4mT/Y/1hBEARB+BojYqMgCIIgCFeM1+8ev2yx8cbBHAcHNq7YHc+k+M27jvA7j53lCwurfacixtMBP3JkD88f357QCHDfxYWufZ8ulnsKjXWqxjnuhlNN4egvzlzi2aMD3DyU5+vGBvni4hojqYBKHLMaxlRjw5liBaUsvlJdoz8DpTlbLFPrUuclrTWxtZwtVThfqrIvn2GyS5TqehhxbK2EVZaCr6nEhkpsMcpSiU1DiPO0h7HwZ6en2ZlN8fo9E5vesyvJDfkMA77HWtRF4EvoFrUaJPettU5j552si5DGWqrGkEmcoqOpgExLRO1fPTHDpUqNS5Vqd6Gxg4qJma1Yjg7kOLbmBOlqF0GzleEg4NBAltftGt8Q2yoIgiAIgnBVsPyF7R9TPu+ERnBingnpGvlgqk4cVB6Nmoyd0aNbQfnNmFXtJ3GlrdTrPbbGtKrEDWmd8FlbdILkAz8Ig3c4oXHlS+3iZTAC0brbpruXLQDAH3AiZbeaj0pv7m5UuhmbqluuDdyYx76h97GCIAiCcBUgYqMgCIIgCFeMrxsb5FU7RvnwpcVtHZf3PX78xn099w8GPj93+0Eulqrcd3Gej80ssZrUQfSU4o7hAq/fPc5zxwbRvVSePlRjw0e6jLmcCINbYbbSLjYC/N8Lc9w8lOdtt+zn7Q+c4IlihYznNQSuijEs1brHqyoUkTGNBdidpLRCKY3FYrA8USwTGcuuXLrtuk6slZ2D0VqKkcFYi6cUg4G3QZVbDkOWw5Cf/OJxCoHHy6ZGt3TtV4KUp3nVzlHed26u635rIbQbRd9y7LbV01P7vfoWKEUGXymGUz4HC9nGvshYPjKzyI2DeWYrtd6ddFAzhqUw4gVjQxxbKzJXCdsETTc2d78nMyl2Z9N82w1TfOu+qS2fQxAEQRAE4Wkl2ubiwcp0U2iERDTr8TxV1/1snDy46eRBruOZW/VLilDgN5/j0Dmwa0ltxi5tLUDSvzU0nhiDYfCHYPF+mPmgc2OmdyT1GJM24ZKLU41LkJ6CbO/PLKSnXDvldVyPoudKtMY1tAiZqYn29mPfAL7EqAqCIAhXNyI2CoIgCIJwRfk3R/dSM4ZPzC5vqf2A7/Gu2w+yL5/ZtO2uXJq3Ht7NWw/vJjKGyFoynkcpivnIpUXe/eAp1sIIrRRDgc+LJ4d5ycQwKa9/rOVXltYodnHUzVZC7BZjnVbCEGNtm9h5/9wKK7WIoZTPe551mF95+AkeXllv7J/KpLqKjWmtqfURGgGGUwE7symOrZYwyRjPl5u1LWNrWaiGrEcRnlIUo7ghxuX9jUJjK6tRxI9/4Tj3v+a5W4o3vVLcs2uc/3Nurusd7/Y61GLbqKNZD+HaHEtoLAcL2bbXar5ao2YspSjeUKdxM9ajiMga7hwZIDSG+WrIZDpgvhahgZFUwE1DOV63a5yXTY40anAKgiAIgiBcnWzjWcXUoHK+fZvSiYOwC62OPRsnoqJ24lqj1qLqM4Yu+5QGrwDx+kbB0Vqc0Jj0G5edsBcMQ+4GWH/UORrrbSvTzpWZO9Au+FkLlUvuevOHu9yHqrs2L+sERxvR9sCt/ETo7IL2WyJnFaQmm/v8POz65h73QhAEQRCuHkRsFARBEAThiuJpxU/dfAM3Deb5u/NzXOrhEvOV4gXjQ3z3gR3szm0uNG44XmtKYcQfPX6Oj80sUYk3fnj/wuIqf3jyAnfvHOMt+3f0FHlWwi5xR8Bil9jOXlicOy7lNScVImu5f26Z1+0epxD4/MqdB/n7C/N8cHqBh5eLaAU7MikuVWpt7reVWshstbd0ltaa/YUMKa05OpjjxGqJcmyoGcMjtSKDgYfFshrGGGuJrZsr8ZSi4PtsReu6VKny9gdO8LvPvWnL9+DJsiOb5pv2TvK/z22sEaSVQqMawiqWttfc18qFZFlXU9F0zDMp3D3Ieh5ZXzNXCdmb9xp9zVZCfK22FJ/ajdlKSCHwCbRmZzbNX774dvK+Ry02m4rdgiAIgiAIVxWpka23rc51dxR6BYjWwIZuv1I4h18iLjaWiZnkd5UIkYkoqFpzK7QTCONyIkgm7VtRHngDrh6kDZP0VENTaEywIeBDXISlz7XHl9apzbvzZffUO3diItY5OE0V8kdBBxAuu1qN0Yq7zsZX3BxX/dq0566hFR2Al2/+nhoDL0kq0Wk4+k7ISPS+IAiCcPUjYqMgCIIgCFccpRRv3DPBG3aP86XFNT5yaZH5akhoDXnP47bhAq/ZOcZIOti8sx7MlKv83IOnuFiu9m23FsX873OzPLy8zs/fcZCBYGuPP9Zaoq5RTO1tasY55QyWr64USWnFUMpnMp0i5WlWwohiFPPh6QU+cHGBC8l4c76mHBnGUil2ZzOshRHD6QBjLafXyz3PmUkExpR2Albe98h4mvUoJrbO/xdZi0l+bqlQg8VFkXpW988bTfjopSWeWC+zvyVu9Knm+w/uZCWMusbaDgYey4kwXL/n4CJlPQXl2DpRFfd768tXX5ieTu7bXLXG7lwarRTrUUzFxOxOZzaIlFtlsRay32YabslSFJP3PREaBUEQBEG49hh9CUy/f/N21kKtIwLfGifG1WsXmmTxnm05pk38M+2RqcrrUccxOUanm/UeO1E6Ee6ME/Xicsu5khqOXta5CG3YFP6U7xyErQ/I1UuubW3B1XZsFQkrFxIhNaYpktbHoFxfsecEzbrDUeWcsIh1fSnPXYtulkDAL0Buv/s5NQ5H3wGFoxuvUxAEQRCuQkRsFARBEAThKUMpxXPGBnnO2OAV7Xc1jLYkNLZyfK3ELzx0il+98/AGAWhwiwJkHWstldhQM+3hnqExhBaK5Zjpco3hlM9XltZ437m5DY65lNakUrrR32oUUzaG1TBydQW1QrfMd/hKM5EO2JFNESSCmbGWE6slinFM1tdkrCI0tiGoGQtVY9r6qcQGLGT8zUWwYhxz38V5/vXRvdu6P08GpRQ/edM+9uTS/O3Z2bZI08lMqiE21oxBAWlPk/F08prEba9HZ2kcXymSW0NkLYu1kPF0iqoxKBQTmaBRC3S7mCSeNZ04WzMiMgrCppQrhloE+YzC97dfb1cQBEF4ihi4EfKHoPh4/3ZxycWK1rGxE9iwoLJN7c6UWlbAKVytgNYUD9v83nAktu6LIY6T2NUU5A+6c4cr3cdl48TBCE7oaxUnk++m5XOEjSBad2JffdDROqw9koibQTLuZsKGO3fS1su0xKDSsi1w/dgITMV9Vxr8gWSMkbsucGOMPSg9AYUjMPpiV09SEARBEK4RRGwUBEEQBOGa47+fmt6W0Fjn2GqJ/3N+jm+/Yapt+50jBbKeptwSy6mUc8dFHU43Yy3FyBB3uB4DpdoWQ1sss5Uqf3DyAneODJDrUvuwZgxzlZC5ao1aUnvQWldvMYwNaa0ZCnwmMynG0kFbjUGAC6Uqq1FTHFNKkfIUgdYUfI+aNVS6pLFWjMEzikD3n9yPreVjM0u89dDup92h9637pvjG3RN8YnaJD04vcKZYYTgIyHkeWimMAaUsKrkn7to11S5xunXqImyd+uttsYykfFJak/Uuv0Zl3Wk5kvIpPI21LgXhWuLiXMR9nyrx0S+UWV4zrBQNUQy7xz1e9pwM3/+GAYYK8v9HEATha87UG+DUf+jfxrYs0rImcfKZZgwouDhS5Sdux1oi2NVjVDtcjv1qRSqd1D2sucjSws1O8KvNQLjW0tAkNROhKTQmz7wKJwBaA6ZjgZmNnePQy7nrMKE7p047oVGlwFbbr7vuaIwr7rq8bmkgyt0Pm5zDyyWbvURotK6fzE7I7AMv5dpd+j8w834Yeg4c+FFIT3bpWxAEQRCuHkRsFARBEAThmqIYxfzT7NJlH//Bi/N8677JNuEu43m8YmqUey/Ot7UdCnwWas26jbaH0AhsEONiaylFhoHA59hqiVuG8m01I1dqESfXSxv6KscGL6ktCM5dOF8NGQp8inFELTbEgAdMl2td41Bja9HKCaO9qMaGQPef0NcoKrFhsRayI5vu2/apIOVpXrVzjFftHAPc/f/C4iq//PBpPj+/Sme4VkYrIqO6vj6B2iiuxklm6lDgU/DdY3He98h53mXVbvSTf1Ov3jnWEEEFQXAUy4bf/p8rfO6RKsWyYXYpZmHZNP4fPnEx5FMPVnjvn6/wppfm+anvHuKGnZtHba8VDatFt3BgMK8ZyIurWBAE4Yow8UpYeQAWPtGnUcvzTlxKIlE90InoZk3iUrRJhGgaiBKxr+ZEO2uT2NQez61KO6FPp1w/NoSo5FyXhaOQGnUiYbTqRLraYrPfzvqIKpWMuUeSha2B0S3Rr6ZZb1Knk34j2pyY9XtgqqD9Zv1HG0O8ntSSLDS3ZXa7beWzzg1Zj5CtLbq+cgeb8RzWwvIX4OGfgpt/sRmxKgiCIAhXISI2CoIgCIJwTfHRS4suCvQymauGfH5hleePt8cSvX73+AaxcTKTahMbq8Z2FbI0yjkbcQJf1VgqkRtjOYqpKsOp9RI3D7mJhpVaxPG1Eu0hrA4FVGNLWrtfjHX1BS+Va+R81RCxarGlFMf4dTejau7zlCLt6Z5zNlCv6wj9zI31KNAnc7+vJEopnjs2xI/fuI/vX/xqQ6Ro3Z/3NeuRaRNafaXI+94GYdZTisHA59/dvI9feuh0o0bnZCbFE8XedTO7kfU8Aq3RCl6biKOCIDhW1g3v+E+LnJuJuDgXcX62d1xxtWb56w+v86kHK7z9e4f59rsLjX1zSzFzSzGlquH4mZAHjtV47EzYdvzth1Pc88IcX397Gs8T0V8QBOGyUQoO/b/u516Co24R1mzkHHpezgmJdTGxs8+6cOilwZSdSIkHfnJcI141cSXWI0zBuf+iVYiKEC67r2DYOQq9LGChOtPuQmwbbyoZb4+HZEsiUKqOjapZDzJa7n4sQFwFP0iEzmJyXfmW6/cgXnPtoL1WJbj6kDoN2T3t28MleOzdcOt7IT3e+/yCIAiC8DVExEZBEARBEK4pPjvfozbLNvjM/MoGsXFvPsN379/B/3jiUmPbQOA3XG7W2pao06ZMqHAOvNAYatbV7bPWElkncoXWgrWcK1WJLezIpDhTqnQVGgEXEYohNBYUlKJmHcJSnIhm0BhLZC1RZPGVIudrtFJkPM14OuD0ev+J9poxfWsLTmTcBFK3CNivJa/cMcqrd4zxkZlFyh0ORK0UBd9F4kbGJtGoeoPQqFDcMVLg1559hJ3ZNF8/PsQ/zy0DMJ4OmC5XqZqti6yTaXevXjk1yngm9aSuTxCuJ8LI8kt/uMS5mYjzMxEX57dWF/XCbMRv/cUK5arhyL4U936yxIMna6ysG05dCAkji+8pxoc1kyMembR7L3voZI2HTtYYHdS87XuGue2Q/H8UBEG4bLQPh38Khu+CS3+/sYajl3XiYrjifla+c/PZHs9Q1joR0FSTeFIviSI17lnNy3Q/Tqcgs8tFicZlWHvUCZnVWSc21gmXm67ETrx0M/a0ZwKFTepCtk6XtrRVKvldQ7dneRu547FuzJ11HMGJoTpNz8jY6gxkdnSMAed8PP/ncOgneoxdEARBEL62iNgoCIIgCMI1xWq0/XjLTtbC7pPd37F/BxVj+Juzs41t+wtZHlspUrUu7s+QzJOQzIkoRSmOMfWEJdw+rTbOYyzWQhZrIQraIlVbSWlFJXZxqqZjEiM0FmOtEyQ75jeiJOK14GsmMgGB1oylAtb73K/OPtrHoRkOAoYCn9HU1ffI+AOHdjFbrbEaRsxWaqyFcSM+NuV57M8HDKd8FqsRi7XQicA4AXg45XMgn+G/Pf+WRvztPbvHG2KjVoobB3M8slIi6jVZ1oKHYiyd4vbhAj96dM+m7QXhmcQ/P1Dh2JmQxZV4y0JjnTMXQ971X5bYM+kzmNcsrcacuRQ1DClRbLm0EDOzGDM54rFvh99weC+uGt71+4v8zA8M89xbekxeC4IgCJujlItUnXglXPoAzP+jE/XwIbcXJu6Gk7/hRMB4vbdrsBNTBawTK1UKglEn1plKIvh5bl9q0gmK9QdrL+viU4vHXe3GunDZ6LNOy2I5nQaVToTAPtQjXxvXrtsf6K2hWQeSJC42atmuwB8EYlfHset11xK3Zg+x0cbO4Zie2rhv4RNww78Ev7BxnyAIgiB8jbn6Zo4EQRAEQRD68FSH4n3fwV0cLGT5m7OznFovU/A9jgzk+OLCKlHL3IOGRt3HONlurQt+8gCvSz5pLYkjddMRLv60E6XA14pyZPC79FE1lmyPaMDYWmrGMhw4l92efIaLlRphT4de78mgnZk0SsFrdo7h66uvBtoLxgcZTbnrHAx6P9Luynnsym2sN/mGPRNtdTZvGy7w4olhPpkIjhnP45ahPMdXS1RM/4mp3bk03zA1wv970z6Cq/BeCcLXkvvuLwEwvbC9hSJhbFkrWpSCmcWYVKCohe49y/cU6UBR/69vrWsTxnB4j0/9L0UUw6/92Qq/8WMeB3ZvXv9REARB6EJchvmPwcx9UDrTvq86DflDroZiXKTfs2VXbOzexLVyImJnfGgv/AIUboHKRSfq1cXG1kVi2gebBowTMaPOWNVEINwQmdqC6nTHt34YSDu3JF3qmvcTGq3deB5oRtFiofSEEySD4XZR0tRg7sOw880d/Yaw8ElY+oyLmQUneo6+EEZf5O6FIAiCIDzFyF8bQRAEQRCuKYavgMtuONV/0vklkyO8ZHKEY6tFPjS9wD9dWnIlZuoNGj/YhtBY36xVUu7FWrwOa6OxzWNLcUyg/a4pTqqPpBrGhqyn3XFd5ikUipoxpD1N3vc4UMhwfLXUta9e55lIp5jKplz9wV1XZ/1BX2u+dd8k//XkhW0fm/c9vnHPxIbt//bmfRSjmAeW1gBXs/L24QJLtZDZSo3VqN2VpVG8fMcIv3D7IY4M5i7vQgThOubUhZBjZ0LWy4ZieWuxxFEM5aqh3DInrJRzMdbno621RLFzMucymnrS8+JKzPmUYs9k8+9EtWb5qw8Xefv3DV/BKxMEQXiGUDoDj/081OZ7tyk+DrYGNkwcf9tYGqi8FmfiNoVKLwP5g7Dnu9zxa1+F1QchWnO/6wCKZ5zbshc2oulU7DJu3Sk2qj77EkyXWpGN89UjXlv6MTV3TKvrMq7A+gnwUpAad/GxdUF15ctNsTGuwIW/grkPQbi68XyL90PwBzD5Gtj17Yk4KgiCIAhPDSI2CoIgCIJwTfGiiWG+tLj2pPp48cTwltrtzKZZrIZ8dmEVpRS+VljAWDfpnaw9RuPiVOvCYWyti1XFNtyP0IxerVPtVjPRQmRsV1cjkMS4uhqNcUdMVVprUp5itlJjb97FBt6Qy7BUjZiv1jZM4fhdlM6dmTR7c+7Y1+4cZ+Iqrj/4xj0TnC9Vue9inwmwDlJa8TO37meyy3UFWvNztx/gT05N84GL89SMc1WNpgNG0wGV2FCLDTHOPfqDh3byzfu6RFwJgsDquuHDnyuzWjRcmo+w1jYiTntRrloqNUuHrk9S+hal3KINEzvREQ/Wy4Z8RjdcjpcWYnaOeXgtDvDPPFRhcSVmdOjqqj8rCIJwVVM+B4+8HaI+Yh00nXrKSwQz3bFCr9vKOuUiUqPEDWlrG2sUbpVgGKZeC3wTFE/DQz8O1XkonU5qQuoeNSQ1yZN1M7a1MWacmNgZdao8N3blu+Prz+Ktf9+U6qObJn/MlOfOHRX7R7uaECrTrjZl/jAEQ83XI1x2QnBnHc1OwhW48NdOpLzx3RAM9m8vCIIgCJeJiI2CIAiCIFxTvGxyhD96/CLFy6zduDub5s6RzeucTJervPvBU/zTzCKhNdSnGup1GlFOFAQ3n2CwaAtKNf2CJnHh1OmcZ6/FloymbQ4mtBaDJVCKtKcpRvGG+QqLq/lYbYlHzXi6IVzOV8OG2KiU4vbhAl9dWWcljKjFrn+NIkgG52oOBkxmUuQSi9Dzxgb54cO7N71PX2t+9OgeBgOPvzozs+l6+KHA5523HeDmoXzPNr7WvPXwbv7FDVN85NIi/zC9yHS5SmQtg4HH4dEBXr9rnBdODF2V8bKC8LXmwRNV7v1Uic8+XOXcTMT52Yhi2TkRUwGkgu4x06WqpZoIjVvxthgLxOB5UKoYClmN54ExlvnlmKmx5kfd2MA/fKbMd75GalwJgiBsCRM6IWszoRFc3URrEtEseXO2rREcKhHtkvqHKpXUUEx+t9a5+4KRyxtrqiWFI3/AOQGXP09D2PPyvWtJNgTSRHCsj1X5ST3GDqxNaj9G7rob/dSvKwXeEJiVjcc27kWSsBKt9xBBu2BjV6Myf9SNLa5sTWhsZf04HPsFuPlXxeEoCIIgPCWI2CgIgiAIwjVF2tO8asco7z8/d1nHv27X2KbumqVqyM9+5XHOFiushs5iE2hNpUXc66y2Uo9IrYcw1adY6m4cgEApwpaJDpPEsLaWYEz0S1KexteKwcCnZiw1YxpORoXrM6U1Ommr2wRLg7FNV6WvFbcOFTi1Xma5FhJZy1g6YCwd4CtNSivWwpilWsRKGPGCsSF+9MieroLA1ch3HdjJ3TvH+MDFef5xepGVsN0WdWQgxz27xnjp5EhbncZ+FAKfN+2d5E17JwGIjBFxURD6sLQa8yt/vMyxM2FjW/2/TP39shpaqiGkfMimabwX10KohRZjtheiZywo485TrlkKWdff7JJhqiMB+vHzYZceBEEQhK4sfNK56TbDhi7us15rsI7y3O/1515rQVnwCu0ORpUCW3UCn74MASw1CkPP7hhTrV1YVJ47b1zsIu6pdsERlUSwZjeuEjQhELl41qijJqO17jpsDeKsO0+nK7J+Pp2GuNRfaKwLsZ3nKJ6EiVfAhb/cntBYZ/04XPwb2Ptd2z9WEARBEDZBxEZBEARBEK45vvvADh5eXufx9fK2jnvO6ABv2L2xVl8nv3/iPLOVWlv0qKdd7GjUmDTZeFxrdKrCRa0aLF5SHTHtacIOR2Z9f5PEdZhMMCgFaU+R9jxi4/o4UMjgK4VG8dhqkY3ex42uSl8rjg7mKEUxoXFi43zV1SJcCUN8pZnIBIymUpwpVfihzz7KC8aHeMPucW4dvvrdQJOZFN93cBdv2b+D0+tl1qMYXznH5u4kFvbJIEKjIPRmcSXmbf9xkdml9ve3dCp5H+toX4tc1HQ+4wTHamjb5qS3g7EuCC+KLcYotIZKLYnFazlzsXIZnQuCIDxTmb1v8zY2grXHIC7TvtSORGzzNv4BiIuJ4JhElup0UrMxhYs03Wbc9cSrQbdMbZbPudhQf8DVbqyjPPAGnDjaWR+RJBZVafAGITXk+mjsTtyINgKVuB11yrkxO1HJdlNOBMuO58fUmHM0mk0WwPQSXm3s+pz9UP/j+zH7D7D7O9rvmyAIgiBcAWTWRBAEQRCEa46M5/ELdxziyEBuy8c8Z3SAt9+6f1O33nylxmcW3ARDJW5fcZzaguBUdybW01Hrk+cp7ZyK3eoktqJQZD29cXIGJ3juy6UZSQUMBD75wOPIYK5DrHR9eD1Oc2ggx1+9+HZeNDFMxtPsyaW5Y3iAO0cK7MllSCfOv8haPjm3zNu/fJI/PXURezkqwNeAQGuODua5a3SQO0YGrojQKAhCb+LY8gv/bWmD0AgwXNAEvsLrMnccxVCuOuExii2xab5/tqI6vndiW0TKamgb2zrevsmkrg2ntiAIwtec8jknIm5G8ZQTGuvPtqqe7wFOOOyCtS7StC5KKu1qCOoU256i1GmYfE37tpkPuDEUjriakI3zGjcm5Tux0x9w8apezn33B2HgFhh9Huz9Phi6A/KHoHAUsvudONjqyPRyG8U6pZvxpMpPnJQtf9j8AgzcStcVi+0dNaNWu13zwv0QrW7SRx/CJVj69OUfLwiCIAg9ELFREARBEIRrkqGUz68+6xBv2b+D0VSPD+TAzmyatx7axbtuO0im24x3Bx+cXmhMeHdOfKc81RQce8xbu+hUi1Kq7UFL4cRL3SE26npHFsLYohUU45ilasRyLWI9jBvj8JVmNN1+rYOBz81DeQp+89ryvrchKlYreOH4EL/+rMP82elpPj67RMbT5HyPtKc3jKuVvzk7yx8+frHnfkEQnrl8+qEqpy5EXfcppZgY8UgFnVFwYIyr07i6bonijeJgs5OO712oJ1zX+1DKLc5oZdfENt0ygiAIz1QqM5u3icsQLie/tFQ2bxMce4hq9RqNkMSg3gXB2MbY0H4oBYffBunx9u31aFHlO6FQp5zDMVpt+b4CpuLGqVPgZ6FwCDK7ksvx4bbfhuwe8IegOt39WryCO16RCI355r3QKXeMbbnOwk3JNW4iNupUjwhWID0FpSfaXZuXw6KIjYIgCMKVRzzzgiAIgiBcs2Q8j+/cv4Nv3zfFZ+ZX+NzCCmtRjAKGAp8XTw7z7JGBTWs0tvKpueXGz93cgblkBrtmDL2mC+IkTrUe4qehUe/RWhcfqBR4yjkQK7Fpc1FGxh2LhRBLJTb4WnGkkEUrRWwtC9WQ+WqNSmySuo+KnOehgUOFDCmt0Eoxkgp42eQwr9k5xngmxfvPzfLJlmvcKu8/P8dNg3lePDm87WMFQbh+ue/+Ut/9kyMeMwsxga8II1eXcbu1GbXq376+r24gyaTq3vImr3nB1p3wgiAIz2hsl3jQTmot9RzrEaTW0KyB2KceIbhI0uwRyOxwvx99JyzdD6Uzm59bp+DwT8Ho8zfui5MSC9U5KJ91saN+Pok2rTXHZUP3R8MbhMLN4GVb+ijB4G1wy6/BY++CuLLxPHW8HHjj7nu0DKa++EY5tyQGBm4DP9e8bhX0jmHVAegeqRzBiLtfa0suCvbJ8GSckYIgCILQAxEbBUEQBEG45vG04kWTw7zoCghhK7WmQyfveyzUOmqqKMj5miBWlIipddgfbcsPWkHW88h4rtZjNbZEgMY2BMlSvLEPr7U2ZNJnbC0n18usRTGRtW37wcWeRrFzP+7Mpvneg7t45Y7R9rFZy99dmL+c2wI4wVHERkEQ6lyYjXjoZP9J6VSgOLw34NEnalRrdoODcdOEZgue71yLW01znhxtd4TcdijFvh3y0VcQhK2xVlvjQxc+xl88/jd8eeEhilERhWIsPcrzJp/D9x/6Lu4cuBVjDFprfN8nm81ua3HbVY232eIMC9WW50lVFxhbXO5Ku6hSGyVfyRI8lbgJVeBEQAAvA7u+CXZ9M8x9GGbuc1Gu1ib1FSN3bGoIJu+BqddDdhesn3SiZ1x1feUPu74qF6F8vmW82gl4DRGvpaavNbD+mHMd1gXHerv8ARetWrkI1Rnn5KzXelS+cyumJluEStMUNJV21xlXm/ehfr7We9wqOOpUUuexy7+j1CjkD7a8BJuIuYIgCILwNUA+cQmCIAiCILTQOpc9nk5xvlTFdPHUBJ5iSPsshxHG2iQ+tTl94Se1IdOeRikIlCLQNNqa2GIVrIYxnWUkFarhmlQ40VInAuT5UpUB3yPtd49X2ptLs1iL+J3HzjJTrvGWAzsa+z6/sMpsZQur1Xvw2GqRU2slDm6jVqYgCNcvT0x3j0/tZKigmRzxWF7rsDRuZV4+aaOVc433aYLWbvHJ+FAzMlUp+NZX5Lc0TkEQntksVBb5kxN/zp8c/wvOrJ+lGrc/My1UFzmx+jh/8/jfcWP+ED+w77u4e/wbAPB9n6GhIQYHB/G2ENt/VZM7AKjEJWgAzznu6tGeNkpEt0QMNLXE0WdovskrV7NQpxNhrctzq6kCA7DjzU3BbscbYPzlcOYPYeb/Qm3OCZk6BXEW1o9DuAKl08652IrynChYeqK9vuIGOv74mND1O3Cri1DN7G7uW3vUxaPmDrI5eqMr0cvCrm+B3A1ORF15sGVfzt0fpdyHCNXl340/AOlJSI21nMbf5Pq2gD/45I4XBEEQhC6I2CgIgiAIgtDCQOCzHrlVy75WjKYD5qs9BDrl3I/FKMZaS0xTHATwldogJNZrI45mAs4UK4lrkY5IVounWkqVJdGpdQPkehQTeHpD33uyGSYyqcbvf3HmEkMpn9fvdvVsPnJpcXs3owsfmVkSsVEQBADK1c2dFeslw8xizOnp0MWh6pb6jC3iYWOuFaD+c/IeZ6yrwWhp1mdspd4uHSimxjy8lgzs73t9gefcnL6cyxME4RnE2fVz/Mznf4mPTX+C+crG5yWbWKstlrIt8/D6o/zqiffyROksb937PURRxMLCAktLS+zYsYNc7hp9Viqegpl7oXwGKpea23UAqQlIT7jfbezqBtZFx8ZX4wD3Rh5XnKjo5TcKZNbA2Ethz1ua22Y/BE/8V3eMTkN2b3NfVIQLf+nETZ2CwpGkTmKCiZJ6hutuvF6Ora1qwZ2vNgvZ3TD56ub2uH9U+Nb6rsDYS9xXZRoe/iknlCrPjVF5SQ3MJRfvCu5eBSPdXabBKPiFJzem0Rc+ueMFQRAEoQsiNgqCIAiCILTwvLFB3n9+rvH7rmyK5Vq4Iba0TqAVec9jLYpa1nK7iY2Mt3EVd72e5LlSlbClT0+1y42tUVzGWFrn1w1QiWJygVsB7SvN3lyaQuBxoVQlsgZrnVj6u8fO8g1TI+R9j7lqRyTsZTD/JJyRgiBcX2TT3R3WdS7MRlyYiwjjRCRU4Hnuq1G7se4IT9L1Ggs2FMRxMn1twConOCpoi2JVOEejVjA56rFn0r0vag0//OZBXv/ia3TCXxCEp435ygLv/MIv8U/Tn+wrNLYS2ZiFcIn/eeF/kffyvGX3twBgjGF6epqdO3deW4JjVISTvwHLX3S/B6PtYqMJXZxoZdq598JuC9han2UTJ7tKVorExQ7BUTmh8fBPNVeMXPxbOPsnPca3DuvHmjGmpuZch4WjTZdetJJEmHpuvLa4PVGuOgtTb2jWkQQnWnarrbgdVHMhIJmdsO974dTvtbfxsu11I/sxeTcUT0J4mXUXU6Mw8oLLO1YQBEEQ+iBioyAIgiAIQgv37Brn787PNaZKMp7H4YEcJ1ZLxF3iVMFFqvqxxlrTmDTPe14jSrXOgO+T9TTT5Rq1DnuOsRav06qYoJRC16Nak20VY5n0PCazKTQwWw15oljBdozxYrnKd37yIX7sxr3UOoulXQad9SUFQXjm0q8O4vnZiItzEdZCqWyI6uaXBJUIjzoREcPYfW99i/G8FsExcTpq7b4bm8wpJ2+bO8Z9Du/xGSp43P28LK97YY7J0Ws8ylAQhKeFP3jsT/nq4mMsVBY27OsmNNaJbMxKtMZfXvxbXjT6fG7I7mkcc+nSJfbt24fvXwPTbuEaPPoOKJ1pbvMLkBqB2lJ7W1uDSn1RXkvtwwbJahE0TnCMk3qOOMExGHXuyPQUHP53zTfxxfvh3J92H5+JoHiiKTQ2xmKc6DZwixNAq7Nuu5dx4qmNnDNx0xqU9f5CKNzoRNaZD8DCJ2Dp827cygN/2EWabknAtO7exUVY+qy7b+mdMP4NMPYNcPaP3Rgvh6k3wtrDTpy9HCZe7aJYBUEQBOEKI39dBEEQBEEQWtiVS/OskQEeWFprbBsMfG4aynNyrUS1S4ZfbCwGJxYWfI/xdOC2WzeRnvE0k+kUMfDoSpEwcR62klTE6UpsLUq5WNY6GtiRSbEaxsz2inlNeHB5nf947BxLtZDBwE9clJdH3pfJe0EQHHunfG45EPDI6XbX9PJazIW5iHLFUossUcSGpRrWgonB9yCbUpiqi4r2tSJKFEel3H5rm6XC6ts9BdqDneMer3pelu9+7QCDBc2tB1ME/uW/xwmC8MxiobLIp2c/x/nSxS41ujdfYBXakMVwiY/Mf5wf3Ptdje3GGFZXVxkdHb3CI77CWAvHf6VdaKyTPwT2mBMjIYlOXaf/fdEttQeTKFV0EheqXD3EzA4Yfk67g/Dc/2DDw3Gd2pxzKnbDRE4czO13wh6ACpzgGFecyEeGrjUjO8nshum/hYt/2RxLagxK6064rM27Lz/vzud1qQdsQ6jMNMesNJRONetLnv1j5+icfO3liYXDX+fu3eBtsPIVJ7Zuh8KNsPvbtn9eQRAEQdgCW/hrKwiCIAiC8MziR4/uYShoX5OV9z3uHB7g6ECO4SBoW8dtsPhKMZVJ8YLxIY4O5jk6mOfmoTw3Dua5IZ8l63vMlKtYbCM2sJNucyz1tta2r663wPlybVOhEaAcx4TGUIxijq+WKEUx61FMOYqJ+6zY78Ztw10mVgRBeMby+hdtdIxcnItZLzmhcSuUK5ZUoBLHoiWbbnmHVW6uNh0oBnOKQlZRyCiGC5rn3ZrmXf9ylP/yjgleeleWZx1Ni9AoCMK2+OD5D7NYXaIYbnSZbeUdLLaGiqnx4fl/ohSX2/atrKz0dUZeFSx/Eda+2mOnduJUesIJhXHZuQnbaBYRaBca65vr0aqxi1CtzbvtU/c026w+BOVz3YdgrRPu+lFbcEJoq/NRZ5qxpJvFoCrlakNW52D9RPsDeWps4zVFRRfhGq60b49LsPpVFzdbF0dTY+21Kk0V5v4RLv0dDN/Vf1ydDNwCR/5/yYqbDNz0865u5VYp3Ag3vtvVuxQEQRCEpwBxNgqCIAiCIHSwM5vmF+44yLsfPMVKGDV3KBhOBQynAmJrG+6btShisRpyeCCH7uEaDI1hsRZ13bcV6hGq9d4NsBKGpLvUhezGehhTjQ3nSxXmqrVGPUmNYiwdMJlJbepazPse3zA1ctnXIAjC9ccL78iwd6rIuRn3/laqGC4txMRbiFzWSZ1GC1RrTmSsRaCVExaroaUWNsOhtVZoYKig+e7XDvCW1xY4vDd4yq5NEITrn8/OfoGFyiJRZ0QnbE1tBCIbMV9b5LH149w1dGdjexzHlEol8vmreKHWzL2bNNCQOwDBCKx+pT3ruv5Uqjya8ak9MDVQaSfIZffB8HNbxvCB3sdFa86h2A8bO8FR6XYxVKddzUUbu9hQ0/EcrlMuFjUYcTGtptpuowcnFKbGoTrTcc56hOvNzrUZV2D9sfZzKOXiYrthQlh5ACbuhuXPbRQuO8cw8UrY/8PtQmEwBDf/Klz8XzD7D737CEZg8jXO0ShCoyAIgvAUImKjIAiCIAhCFw4N5HjvXUf4s9PT3D+3QtSxMt1TCs9TjKYCXjU0yidml+mXTrpQDRv1FHu228SQY5JYVvezRW12AICFUmw4tlZCKxdRWI0tGe3OZ7DMVWvMVWsMBT6HB3I9Y1ZfMTVCxpMYVUEQmvi+4hd+eIS3/ccFFlYMpy9GjYUYDerGltZNSf3FOhaohpaBnCaXURSymsVVQxRbwhj2Tnq88I4ML/+6LPe8MEchJyE9giA8eVbDNarx5ikR/bDWEtmIYoezESAMe8R/Xg1UZ2Hli1trGy47sVBVkw31JXAGrOrzcJsslzMxsO4iSAduoa2AQOl07/OaTYTGOnHJuRnNescO7QTEwTtcGxO5sSo/qb2ooHyhKWjqzMa+s3vBlCFc7bi02DkyCzc64bFNzFROpO1XL9JaWPg43PH7sP4ozNwH68dcZGtdqJy42wmFwVD3PrwM7P0e2P2dsPgpWPy0E2gBgkEYfSGMfL3UaBQEQRCeFuSvjSAIgiAIQg+msmnedst+VmoRH5pe4MtLa6xHMb5SjKR8Xj41ygvGh6gYw+cXV6nEG+s51mmt9ZjSGkVM59rwrYb/1XVPX29yhIX1KCaylmziZMx4mnUTUzOWlNd+/EoY8ehKkZsG8xv6HksHfOu+HquzBUF4RjMx4vGbPzHGL/63JT71lY0Tw1q5Gratv2u9cW46NhDFFotm/66A/bsALPmM5k/ePUEmLQKjIAhXFoXqu1hsO+guT3JXdYxq8XTvOomdRKuw4ckVXD3GtKuN2NaXTVyGLc/G1rjjzv9P5+abfA1MvbG/c3FDbGuvdjGkJpKakl0bgD/YfXs9plV5kOpSY1NpyB+B4uNOdG0lWnXRsHGppX0iNKbGNx+3qcHCR2H3d8D4S5vbVNBHwO2C9mH8Ze5LEARBEL5GiNgoCIIgCIKwCUMpn2+7YYpvu6G72JbXHi+dHOZD04s9+zAdE+0prdsEyF66oVKgOmo8GixppXoeU6cUGyJrCXRzIs1TirzvObERhbEQG9tYn75mYk6ulbhpqBn5NRz4/PztBxlNS1yhIAjdGR/2eOubBvi7fy5SCxVhi7qoNRjTdDP2mz+thWA7Yvpe8dysCI2CIDwlDKUGST3JaEmlFL7yGfQHNuzT+ip+72oVyDbDxr3fvHUKVMZFg5oq2LClfmJSeBeVRJpat798Ds7+Ccx/zDn5etFZL7Ffu9SY67dbf736qS01azqmxje2i0tJ/UUL6Z2QHne1HcNEfLUWSk8k9yGJXE1NOsfhVpn5IOz6tmaEq0SdCoIgCNcoIjYKgiAIgiBcAV6/a7yv2NgZTZr1NDVjmrXI+sy+b3QFKVKephabxnpxBfhK4SUKpLFQS8TMdMdEl6cgo93xpS5uzFIck/E0+/IZ7hge4Cdv2stUNt1zfIIgCAAPnqwxkNWUKxZj3PuQtW5+Oows1S2kCUaxxWtxXWdSije+pE8MnSAIwpPgRVMv4PNzX+Jc8SKhubzI00D5TKUnualwdMO+TGYbotPTzXYEMRSNSNINbsMkn6MuksXxxtqHmKYQWXqChjOyeJJGXchu4+kXQ9rWLuPOmZ6EysWOfVl3jm5ESTRq/VhIakDOu5jZzmhcHTgHZXqnux4TQuUC5A9DMNzlurdAbR5WH4ahO7Z/rCAIgiBcRYjYKAiCIAiCcAU4OJDj1TtHewqOBb99pXTgKTJGU44N3iYORa2Uq9GoQGHxccd1C77ylCKtVaNmmqdUWySqsZZSZDAWBlMeWilqxhAa2+jPWFfH8ZahPP/66B4RGgVB2BLrJUshp5lditFatU3t+p57Hwuj/pF91sJAzr1neRp++nuH2DkuH1sFQXhqePWel/PnJ/+avJ9ludYuNiq1ecqopzxSOuCNk68h6KiLl06nSaev4meozG4ntlVn3Xcb03AgBuOQnmgKiDrtHIA63S7AqZZCANaA6XRL2qRfC3hJfy0PvlHRCZBLMzD0LCfYteLnXW3FnvGoOIGvHlma2e3qPNZansfrImI36o7N/CEnSoYrzfqLNnJjazwhKzABmIugpt250mMuhrVb/Op2CHsvWBQEQRCEa4WrOM9BEARBEATh2uJfH9nLC8eHuu4bTvkEHaudC77HSMrv62qs4yk3ca+VRmu6Co0AsbWUYkM5NigL+RaR01pLMXLRqnX90deKnO8xlPIZCjz3lfIBxUoY8dMPnOBcsU8tHUEQhATfg9EhTeB3f0/LZxSpoP/7nVIwOeqRTSve/UMjPPeWq9gVJAjCNc9QaoiX7nwhu/O78Da40jZ/PkupgLHUKK+s19tr7Xuo+zPhVcHqQ3Di16B83glzJkoiTg3EVefWW/kSrH7V7fdybr9OtcepttYWNNWOB1STRJq2iHXdIkJV4NqtP9YuEtZJ9RELwQl9KhF6lYLcISeUQhKv2qd2ok65eozBCIRLsH7MRafGaxAXncBqwuSr5rZFa67OZOmcu09XouhnPXY2WofyBRcH21kfUhAEQRCucmSJqCAIgiAIwhXC04q337qf/3H6En9/YY5y3FqTUTGRCbhYrgLgK83uXJqpbIrT62XOFittNRxbUShyvmI0FVCKDdXYUCPu2haSuSJcDSHVqMboajjGyRJ9v0vBR9UyWWKxlCNDoGN+/qFT/Ofn3kTKk3VqgiD0ZmzIuaXHhz2m57vX4MqlFelAUa05l2PnwomBnOZff8sQr3peloG8vOcIgvDU89Ybv5dHlo4xX1lgtjxP6ztTP3djoHwKfp6f3P+jjHU429LpNAMDG2s4XhUsfBJOvtcJfOnJdtegtWBrTlizsatNWL0EXt4JbcoHlQLrnmfRdedmclyzo5a6jQA6iTrtUTvRyyQC3iknAPqF5r7UqBtDtxqTSkN6R8c25WJZUxOQ3QfxerMuY530JEy9ztVsvPR3zq25ftLdi341JMFdV1wGFULFgvck3avWQvEJmP85WP1y+z+4whGYvAfGXyq1HAVBEISrHhEbBUEQBEEQriBKKb7n4E6+dd8kH5tZ4h+mFzhfqlAzlhvyGSJjKQQeo6mg4Wg8UMhyIJ9lulLlfKlCOTLEWDwUWV8zmU4xlAoYTvlUYsNMpcrJtXJDOGxFo/A9F41qsJRjQ873iI0lNM32Kb35JH69/9lKjU/MLvGqnWNX6C4JgnA98rK7MvzpvWvsGPNYXI2p1rrP0HsachmFRRHFSV1HwPv/2Lvv+MjO8u7/3/uc6epaSdvXu17vrm2MjQu2MZhi02w6oYUWAqaF8iQBEshjIJCAeRKeX3oCJLQHCAQSnARsHAMGG9u4d+xt3l61q67p55z798c9kmakGWkkb7U/79drvNLMKfc5oxfs7FfXdfnSZ97Tpde8oOXYLhzAU1p7ol2ff+anZGV1496bdDg/oKi6Gs/YGS0l4iamrninPnrqB3VZz6W1r8XjWrp0ac0vcZ0wRh+ZCholKd4t+ftc0BeVXRvU6X+/tOFU0BhVQkbju6BvIjy0Qe1+dtovxXmJyuzEBkyicp8jqbDfhWxTL7pAMbe1Ehoad14vXml/2mCu47LXSqd+SCqPSWOPutDRxF142fY0F0rm90kHf+TmPAZjcweN1aKypJwLYhcqGJdy2yWFU9WZ1ca3SON/I+36mnTqB6XuSxZ+LgAAjjLCRgAAgKMgHfN15fIeXbnctW6y1soYo93Zgv7kga0aLk/7xwwjLU0ntXTafEQj6ZSWlHZUWpmmfE+ntKTlyWhntqBIVmElRPSMUcwYGSOVKkWSpcgqZW1N1WTcM6pT2DhDdXvX6/cNEDYCmFV3h6+Lzkrq9oeK2rAqro07yyqVGw88M5LilX+n9jyjM9fE9dYrTtBKIABPan3pXv3Ns76gf3383/WVx76hraPbVAiLsrIylcDRWtfOPu0ldX7HM/SBU67S09vPrDlOJpPR4sWL5fsNKviOtx1fqQ3UjCe1rJdGH3SBYiM2dIGhF3MtVyVXOWiDShVkdXcOq9rWqUkp1sT/tntJKbVMKu6fChVLh6TioUrIaaZmKRojed1TFZjVFZMmJi17nbTyLe77eJvUfVH9c6aXSa2nS4dvaaKiceK6oqnri0IpGJFyu6T0Cnc/mxWMujAx0VU/aKzZdkza8gVpzQelvhc3fw4AAI4hwkYAAIBjYOK321e2pPTF89bpLx/bqU2jddpBVelKxPTOtcv0D5v3zHhtSTqhA4WiRstTrVFlrSq/b67QWnlywWMxjCarGo2M0k38A5iRCzYnbBnLaft4XmtaZ/mtdABPea+4tEW/frioVNLTmWsS2r6vrNFs1LANoSRl0p7WLI3pNc9voXUqgOOmNd6q95z+Dv3u+rfol/tv1fcf/6HuOnSfxsrj8ozR4vRiXdx9vt7Q91ot8jon9/M8T21tbero6FAicQK3uhx7rFJFN12l5b7xpoWG00QlFxp6gQv3wpxrV2p81/40Krhj2arj+RnJm8fs3eQSN28x1ikN3+nalU4yUrJHinW4YLA8JOV2SsWDLjDNrJL6XurCuPg85mUmeqcqNuuxEwFjnXtjPHfdue1SeUBKrZiaFzmbsODattpQSi5ubp3WStv/wbWA7XhGc/sAAHAMETYCAAAcY4vTSX3xvPXaPJrV9fsG9Kv+IZWqWpye1dGqly3v0cU97frFwSEVwtp/3MgHoTaN5RTamZ2uqgXWypNUCq2sMTIyao35TVU1tsdjSk6b0bg/XyRsBDCrs9Ym9NaXtupbPxlXIm604ZSECsVI/UOhBkYiBaH7Hy3fM+po87S4y1drxtO6lXFd9SqqGgEcf3Evrhctf4FetPwFDbeJokhRFMkYI8/zTsyWqdMdvL7+88X97s9Yu2TLUlicWeVn5IK19AoptdTNOiwddPu2PV3KrJaylb+vhlm3g5eYu2Jv+kmM784//hup/WlSkK2sxbi2qdUtS23k1msjKblIOuMLUqqJoG+6wgG31rAw8zVrJYWa0UdXkuS5h40kP+7aqua2u+AyvWL2cxb3T83MnE8bVhtJu79N2AgAOCERNgIAABwn69tbtL69RR9av1LZMHTzHGO+ElUh36FCuWafXBBq42jWBYlGaov7yodhTVgpudmNkawiK5UlpTyjTJNBoyT1pWb+Zv700BMA6nnDi1pljPStn4zLWimV9LRqiadVSyTJtSKs/of5M9fEdfU7u5RKUtUI4OTgeZ68JuZfH3dRWRq8Tcpuk/b+wFUK+ikpscgFe1EglQantjdxKRavVDhWzV00vlywVqlaTHS7R5iXel/gqhd3fc2Fi8WD0yoSmxRvl2Sl7Ba3Rnmzt181nmQq4weCrLTl89LT/2r+5x3fOFV9OSNwrGqZWqNqXqWsq/psWS4VDrr5j168ccViMC7l97h7FWanzp/sazx/sma9m6Ts425eJQAAJxDCRgAAgOPM94zavfp/LStXtbMKrdXmsZyCqnJGY6RMzFfaSsXItUu1VrLGypf75xFv4tFk0JjxfXXF4zOeb4mdoPOHAJxwXv/CVp21NqH/viWnOx4pKJj8N2vX3lmSTl0e0xWXZHT5M9OKx06CqiAAOFmUBqQDP5IO/VQqj7rn8rtc205Jyu92gaMatE41E397nG5axaOfdpV8p33EtTUdulMqDy4sbEz0uXVHpflV+03IbpVGH5banz6//WzlF/u8lAsAo6J7LqrXOtVMhYx2ouLRSpF1QaMN3T65HVKip3aWZDDmgsjSgNvH+C4klSSNSsV+Kdbq5lbGO2df88HrpVM/NL/rBADgKCNsBAAAOIFVB3yHC2WVovrVhca4GYupqn/TsFYaD0KFUaSg8u8gcwWOSc/T+vaMa5dVfXxJp9JCFcA8nLEmoTPWJDQ0GupXDxQ0OBopCKxaM56esT6h01efwLPNAOBkNb5F2vQZqTzivreRC/DsRHjmuVCs2C9FeRewmZm/ZFZfnQAyv9v9ufhKFzZ6KUmj9Xe3UaUtaqVCUsad30+6gG38UbedP485j9UOXj//sDHWPvW1iUl+zK0vGJtW1FhZ70SgqOmvqRIu+u4aC/uljnPcTMfSkAsaE4vcOUoD9dcSjLvKzlSlXW0j2cfnd40AABwDhI0AAAAnsLM6Wie/7i+W5rWvMVJrzFdoPQXWqhRFSvmNW35lfF/r2zNK1GkL9sxF7eqt01oVAObS1e7rlc9dQJUKAGB+cjuljZ90FXNh1gWKpQEX8gVjU5WNZiI4CyozB1OSl6lUNM7Cq/N3wYkqxo5zpUXPlcKcO2+1qCzZkvtzOuO5lqzBmFu3MVJyAbMXJWnwDneM2Dz+P6ftadLg7VP3RnK/sWcrwWy1yaB0mumzKU3MVXomuqUlr5Ye/6LUusG9Vh6efT3WugDX+K61aj1hbvZjAABwHBA2AgAAnMBO72jRqa1pPTQ0rnwYzr3DNMZIT2tvVWAj/WY4K6PafyIxkjriMfWlEuqMx2dUNE64clnPQpYPAACAo600JOV2SZv+1AV9xf1VLTonmKmwbHpeFhbc/EYvXmknWqd1vqnMapxuYs6gMdLa33fVkrkdrkrPWinK1Q8ZK0uSn3LBXOmwpEiK91TmGVbmJ3qxmWFeIzZwbVznEzZ2P0va/8PagNTW+Tv3ZNvUOhfhVWZH2kiyRSkKXXi69z+k3f8qta53FZTGqH572jryO121Z72A11tg5ScAAEcRYSMAAMAJ7splPbrr8MiC9k15vtrjvoyJaXVrpN5kXGVrFUZWvjFK+p6Ss1Q7StLZna06r7ttQecHAADAUTLykGsdOnSHq2Ac31QJ+cJKcJhwLVLD/NRswhrV4VlUqUAMXIA4vbVqvLt+u9XMKVNfe3Fp/dWSNdKOL0nBcCXgrMP4bubjRJBoQ1eNaeLS8H3Tzt3uZjomutTwN+MmL2N+nUDU83yp5dRKBehEyDg9VLSa2Tq1wviuOjPMzgxVy8OujerYoy4ATS5xLWObYa1UPCSll898bbYWqwAAHCeEjQAAACe4Fyzu0pfSSW3L5ue1n5HRqpakjHH/KLMyk9T53e26b2is6WOc2prWn5y1ZvIYAAAAOM6CrLTlGmnkwannigfd8xOBWVSuhF8TwVmldWrd6rxK21DjuZArzEp+61QQaIyUXFx/LX1X1H5vPOn0T0r5PdK+71cyuqqgzsRcJWB19aQNKq1BrRSMSH5bbUvX8qh75BNSy1opNssvwfnzbNvtp6TFL3PB4PjmSvvUaeo9J1WCxri7X7bOfZ0IPqOiFPqu4jM+EZjWex+mKR2S0ss0I2Dte+nc+wIAcIw1WbsPAACA4yXhe/rdtcuU8eu0tGrAyGh1S0qdianfQDfG6I/OPEWvWdmrWBPh4bN6OvSFZ5ymlljz5wUAAMBRFGSlRz9eGzRGgWtDOqOK0FbmMlZagBpftcGVqd12IgCzqp0LmF4pxVo1Q3ql1HF2/TWWD0ttG6R4m2shOvHwM9OCxnIl3MxI8ivzEov1rz0querNRnMPEz2N5xzOZvlvS21nSK2nV9qWTv/n0unBoKkEjX7l/jYIDieCX1sV+paHml9XVHKVqtVSy6SOZzR/DAAAjhEqGwEAAE4CZ3e26oz2Fu3I5jVYCmRn+W3opOfplGlBoyQtSSXUEo/pnWuX67Ur+/Q/+wZ0w/4BHS5OtXxqifm6bHGXrlzeoxUZ5sEAAACcULb+pauQq2bL9duHTlbkWRd8mcr8w4nZjbOxkTtuy2mNqxpX/Hb95w/9zK0n0SvFOl2FXrF/5hqNJ0XWhZATMyWjknt46erFuLVMXM/Yb6TWM2bOkOx7SW1FZLPibdLpn5U2fsq1Oy0OuHNERdWEsDKV43tV52lwH01MUiX8tROVo5WQ1ZY1GVjOZXqAvOx1ldmPAACcWAgbAQAATgKrW9M6q7NVvme0MozUXyxpoFhWKbKysvJl1BaPqS+VUEfcr9v29IplPZNfdybieuPqJXrDKYs1Ug6UDUKlfE8d8ZhiHs0vAAAATjjjW6The2c+HxWamMk4EZqZShAW1YaRNa8b1x401tE4aFz5NmnRpfVf679x6msv7qrxUksrbV6Dqefze2pblHoJFzTaSrgozwV+tjyzenD0QTcDMdnnQkcTd2HjQiX7pDP/Utr1dWngZnfO/E7XinayyrPq3ky0T23ES0hhdVBYtX4Tk7xUVZjZpCWvlPpeNI+LAgDg2CFsBAAAOEm8bHmPHhvNKuF7WpFJTVYeWmvnnKmY8IxetLR7xvPGGHUm4jOqIAEAAHCCOXh9/efDUp3Mqroib+KpcGoO42R1XmU7G7lqQuO7IFBGivJy8xarfhHNi0unXCUtvrLxOosH6jxpaluxRkUpGJ22SWwqcAyLkoLGWZwtu/mOwZhrIbvuE1Kiq/GamhFvk9Z+WDrlndKBH0ubPuPWGJXc+r14pc2qmT1oNL7kxaSw5slp6y9J6VVSYVfjNqySCzUlafmbpJVvWdh1AQBwDBA2AgAAnCSe3duhH+5Oa9t4vub5uYJGSXr1ij61xfmrHwAAwEkpzEsDt9R/zZuYH1idbtULsKqqFycZ9/B8qeNcF4IV+92sQGulKJQ8T0otkfpeKvW+SIq3N16ntfVbuk5X7K8fsnlpV00YFWdvM2pVabeacgHg+Ea3n9fkL9CFBVfBmN3m7q2XlNLLpZ7L3PWteJOb4/jY/5aG75m51qheJalcgOu3yIW5lZa1RjPbu9rIBZLt50jFQ67V7PT75mek5W+UlrxcSq9o7roAADhO+BcnAACAk0TM8/Tpp5+qjz+wVfvzxab3e15fl966ZslRXBkAAACOqnozDyd5lbad+QavN8HEJT8lee1SoseFfVFZOuOzlTaoy5ubFWiM5KfnXstka9LpIvfw4pWAb5aqP2tdCJdaJo096lqgrn7P7Oct9kv7r5UO3VR/Dbv/n9T9HGnpa6S2p0nLf9vNbywNVVqoxjSjSnHCZNCoSovUieuJaWbIKxdEegkXcqaWuSrNiXmaJuYqSFe9ffbrAQDgBEHYCAAAcBLpTsb1l+eu0xd+s0OPjIzPuq1vjF69ole/c+rSpqofAQAAcIIKC41f81OSSUgqaCqcm+ff/fxMVYtVuUo/PyN1nOcq8Oajdb008mDtczZyQZqNXMViFNTfNyrIVVpmXHhnS5U5jlWzHY3vQrr4IhfSTTj0U2nFW6VYpv6xxx6TNv2ZC/VqzlmUiocrAWHkttvxJSmzxp0nuUQqDlTCXisprprw0Hjufsm49VdXPdpIUujasZq4287UuZ/G1FaMmpi05BX1rwMAgBMQYSMAAMBJpiMR0zXnnqbNo1ldv29Av+ofUima+q3vRcm4XrJ0kV6ydJG6k8xiBAAAOOn56Vley0jxVhfMTVYUVtqjzlYZOMFLukBteqvP7mfNP2iU3DzHibAxzLtqwtJApWqvIsq79ZlE1Xmtm8U4MfNQkkzKtUqtbmM68Ut009cWFqTDN7m2o9NlH5c2fqo2tC2PuvmS5RFN3idbdhWP1kr5XVJqhdSyVkr2ue1t2bWrjSptUL1KSBvl61eeeglJUaWDbbnS6jXp3k8zy9/TT/3gE59BCQDAMUTYCAAAcJJa396i9e0tet+65RoqBSqGkTIxX4sScfkelYwAAABPGsnFs7cnTS6WgmxlZmIlUDOm/lzEal7CzUlM9s58bfGVC1tr10VSrEMauUcqDatu4GlVWWexsoaUC0utJD8xc/t6XTq81Mzn6oWNNpI2f742aCwckPK7a9dmy+4eTn5vpcIeFyS2nFYJIifWEbng0BYrIWKdoNH4UqzVhawTAaZUmUdppHhH/es85T1S7+UzXwMA4ARG2AgAAHCSS/m+lqb9470MAAAAHC1+Uup5gXTw+vqvJ7qlwj73tfFckGet3MzACZ5mtP70km7f6ZWT6VVS+9MXttawIEU5N+ewES9RaVtqp/6UXEBn6oSN9dQLSIuHZj43dJerrpzc5qCrWqxmo/ozHK11AWR2q9Sy3gWUwZhkkpIqIaO1bt3VTEyKtchVb8Ylv7XynpQrVY6RVB5y8zElFzK2ny0te73UcU5z1w8AwAmEsBEAAAAAAAA40S1+WeOwUZ4Lw8Yfdd+auKuoi3JTMwSN70IwL+FeN8YFYpk1tYfyM9K6jy1sjdZKmz/nzpleIeX31N/O+O5hQ/d9VHJr81vqVzFOF2ur31rWlmc+d/C6qa/D/MygUXKBZ6Mq0KgoRUkpv1Nqe5qbv1g4KOXzVbMnbWUepVe5NuOuyUvIBY6+uzZF7vl4j7tHXRdKqeVS30uk9PK5rxsAgBMUYSMAAAAAAABwosuskhZdKg38qv7rfkp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+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 837,
+       "width": 909
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tsne = TSNE(random_state=17)\n",
+    "\n",
+    "X_tsne = tsne.fit_transform(X)\n",
+    "\n",
+    "plt.figure(figsize=(12,10))\n",
+    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y, \n",
+    "            edgecolor='none', alpha=0.7, s=40,\n",
+    "            cmap=plt.cm.get_cmap('nipy_spectral', 10))\n",
+    "plt.colorbar()\n",
+    "plt.title('MNIST. t-SNE projection')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In practice, we would choose the number of principal components such that we can explain 90% of the initial data dispersion (via the `explained_variance_ratio`). Here, that means retaining 21 principal components; therefore, we reduce the dimensionality from 64 features to 21."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zxBFHbNPzP/CBD+TVV1/drGsnTJiQrl27btP7qK7rrruu7PwTn/hElZMAAAAAAEDTU5VSanuwbNmynH/++Zk4ceIG8wULFmTBggW56667MmrUqFxyySVb9fxXXnllswsptj+LFi3K3XffXTLv0qVLhgwZUoNEAAAAAADQtCil8tZeQBdddFFDIXX00Udn+PDh2W233TJ9+vRcc801mT9/fsaMGZMuXbpk9OjRW/yO6dOnNxxffvnlOeSQQzZ5fefOnbf4HdTOjTfemLVr15bMzz777LRp06YGiQAAAAAAoGlRSiW54447MmHChCTJsGHD8u1vf7vh3MCBA3PyySfnnHPOyezZs3PVVVfltNNOy5577rlF75g2bVrD8Yknnphdd921ccJTc+vXr8/1119fMi8UChkxYkQNEgEAAAAAQNPTqKXUV7/61SRv/WX8FVdcUTLfFv/4zMY0ZsyYJEmHDh1y8cUXl5zv1KlTLrvsspxzzjlZvXp1xo4dm6985Stb9I63v5TafffdFVI7mAceeCDz588vmQ8ePDj77rtvDRIBAAAAAEDT06il1G233ZZCoZAkGxRI75xvi0qUUvPmzWv4imnw4MHp1KlT2esOP/zw9OrVK3PmzMm99967xaXU2+846KCDtikvTc/YsWPLzkeOHFnlJAAAAAAA0HS1aOwHFovFjc635adSnnjiiYbjo446apPXDho0KEkyf/78zJ07d7PfsWzZsrz44otJlFI7mrlz5+bBBx8sme+99945/vjja5AIAAAAAACapkb9UmpjX4xsbN4UzJ49u+G4Z8+em7y2R48eDcezZs3KPvvss1nvmD59ekOxtt9+++W3v/1t7rnnnsycOTMrVqxI165dM2jQoIwYMSL9+/ff8l+Cmvn1r39dtjQ955xz0rJlyxokAgAAAACApqlRS6m3vyTa3HlTsHDhwobjvfbaa5PXduvWrex97+btpfuS5D/+4z+yfPnyDc4vWLAg48ePz+9///t86lOfype//OW0aNHoH7HRyFavXp0bbrihZF5XV5fhw4fXIBEAAAAAADRdjVpKbY+WLFnScNy+fftNXtuuXbuG42XLlm32O6ZPn95wvHz58gwePDinnXZaunfvnsWLF+d//ud/cvPNN2fNmjX5xS9+kWKxuMV7VlF9d911V15//fWS+SmnnJKuXbvWIBEAAAAAADRdzb6UWrNmTcNx27ZtN3ntO8+/87538/aXUoVCId/5zncydOjQDc5/8IMfzOmnn55Ro0blzTffzC9/+ct86EMfyqGHHrrZ76D6NrYs5ciRI6ucBAAAAAAAmr6alFLLly/P8uXLs3bt2pJzxWIx9fX1WbduXVauXJnFixfnmWeeyV133ZU77rij0bO8c9+fQqGwyWvfuXfQliyvd91112Xu3LlZu3ZtjjjiiLLXHHLIIfnKV76SSy+9NEnyq1/9Kj/96U83+x1U17Rp0zJp0qSSeZ8+fXLkkUfWIBEAAAAAADRtVSul6uvrc9111+XGG2/M3Llzq/Xad/XOJflWrVqV1q1bb/Ta1atXNxxv6rp/1KVLl3Tp0uVdrzvjjDNyxRVXZPXq1Xn00UdTLBbftSijNjb1lZR/ZwAAAAAAUKpqpdTXvva1jB8/PsmGXxxtrkr9Rf8795FauXJldtlll41eu2LFiobjjh07NnqWNm3aZL/99sv06dOzfPnyLF26tCLvYdssX74848aNK5m3a9cuZ555Zg0SAQAAAABA01eVUuqJJ57IbbfdlkKh0PD1z6677pr6+vq8/vrradmyZfbYY4+sWrUqS5cuzbp16xquraury2c/+9kce+yxFcnWvXv3huOXXnope+yxx0avfemllxqON3XdttjafauonltvvTVvvvlmyXzYsGHZeeeda5AIAAAAAACavs3fGGkb/P73v284Pumkk/LII49kwoQJuf7665O8tbTfb3/72zz66KOZMmVKrrvuurznPe9Jkqxbty7FYjGHHHJIRbIdcMABDcfvtqzgvHnzGo579+69Wc9/9dVX8+c//znjxo3L1KlT3/X6119/Pclbe1116tRps95B9RSLxU0u3QcAAAAAAJRXlVLqySefTJLsuuuu+d73vtewv9L++++fXXfdNUkyYcKEJG+VMUceeWRuvPHGHHHEESkWi7n66qs3KIQa08CBAxuWBpw8efImr504cWKSpFu3btl777036/nTpk3L6NGj89WvfrWhhNuYV155paEY69evX+rq6jbrHVTPpEmTMmPGjJL5e9/73hx88ME1SAQAAAAAANuHqpRSixYtSqFQyHHHHZc2bdpscO6ggw5Kkvz1r3/dYN6yZct873vfS8uWLbN+/frcfPPNFcnWrVu3DBw4MEly3333Zfny5WWvmzx5cubMmZPkra+9Ntehhx7a8Dvff//9Wbp06UavHTNmTMN+W6eeeupmv4Pq2dhXUp/4xCeqnAQAAAAAALYvVSml3t5/p9zXRb17906xWMz06dNLznXr1i3vf//7UywWS0qrxnTuuecmSRYvXpxLL7009fX1G5xfsmRJLr300iRJXV1dRowYsdnP3nnnnXPaaaclSZYvX56vf/3rWb9+fcl19913X6677rokb/3eH/nIR7bqd6FyXn311dx5550l886dO2fIkCE1SAQAAAAAANuPVtV4Sfv27bN06dK0aFHage27775J0vAV0j868MAD8/DDD+f555+vWL4hQ4Zk3LhxmTBhQu68884sXLgwI0eOzB577JGZM2fm6quvzvz585MkF154YXr06LHB/Y8//njDfkKDBg0qWabvS1/6Uh599NHMnz8/99xzT+bPn5+RI0dm3333zWuvvZZ77rknt99+e4rFYtq2bZvvf//76dChQ8V+X7bOjTfemLVr15bMzz777LRt27YGiQAAAAAAYPtRlVKqc+fOWbp0aV5++eWSc/vss0+SZNWqVZk/f366d+++wfn27dsnySaXvWsMP/7xj3Peeedl0qRJmTx5ctn9pUaNGpXRo0dv8bO7dOmSa6+9NhdccEFmzpyZp59+Ov/n//yfkuu6du2aK6+8MocffvhW/Q5Uzvr16/PrX/+6ZF4oFLboyzkAAAAAAGiuqlJKHXLIIXn++efz8MMPp1gsplAoNJx7u5RKkilTppSUUi+++GKSbHBPJXTo0CFjx47N+PHjc/vtt2fGjBlZtmxZOnfunEMPPTTnnHNOjjrqqK1+/j777JPf/e53uf3223PPPfdk+vTpWbp0aTp06JCePXvmhBNOyPDhw30h1UQ9+OCDmTdvXsn8uOOOS8+ePasfCAAAAAAAtjNVKaWOPvro3H777Zk3b14uv/zy/Nu//Vvq6uqSvLXPVMeOHbN06dLccsstOeWUUxrue/311/OHP/whhUIhe+yxR8VztmjRIsOGDcuwYcO26L4jjzwyM2fOfNfrWrdunbPOOitnnXXW1kakRv5xSca3vb1sIwAAAAAAsGmlmzxVwCmnnJJu3boleWtfnhNOOCG/+93vGs6fdNJJKRaLeeyxx3L++efnwQcfzLhx4/Lxj388y5cvTxJL2lEzb775Zv785z+XzLt3754TTjihBokAAAAAAGD7U5UvpVq1apUrr7wyn/70p7Nq1aosWrQoc+bMaTg/evTo/P73v8+aNWvy4IMP5sEHH9zg/pYtW/oihZp55JFHsnbt2pL58OHD07JlyxokAgAAAACA7U9VvpRK3vrS6be//W0OP/zwFIvF9OjRo+Hc3nvvne9///tp06ZNisXiBj+FQiH/9m//lr59+1YrKmzgH0vSt33oQx+qchIAAAAAANh+VeVLqbcddNBB+fWvf5358+enVasNX/2hD30o48ePz7XXXpspU6ZkzZo16du3b0aMGJFDDz20mjGhQbFYLFtK7b777jn44INrkAgAAAAAALZPVS2l3ta9e/ey8549e+Y//uM/qhsGNuG5557LvHnzSubHHXdcCoVCDRIBAAAAAMD2qWrL98H26KGHHio7P+6446qaAwAAAAAAtndKKdiEckv3tWjRIscee2wN0gAAAAAAwParUZfvu+qqqxrzcSUuuOCCij4f3mnlypV57LHHSuaHHnpoOnfuXINEAAAAAACw/Wr0UqqS++wopaimxx57LKtWrSqZDx48uAZpAAAAAABg+9aopVSSFIvFxn5kklS07IJyyi3dlyilAAAAAABgazRqKfXtb3+7MR8HNVWulOrSpUsOOeSQGqQBAAAAAIDtW6OWUmeccUZjPg5q5oUXXsjf//73kvlxxx2XFi1a1CARAAAAAABs3/ztOpSxsaX7jjvuuOoGAQAAAACAHUTNS6n6+vosWrQoixcvrnUUaFCulCoUCkopAAAAAADYSo26fN/m+vOf/5w//vGPmTBhQl555ZUUi8W3wrRqlR49euSEE07IKaeckn79+tUiHs3c6tWr88gjj5TMDznkkOy66641SAQAAAAAANu/qpZSM2fOzOWXX54nn3yyYfZ2IZUka9euzZw5c/KLX/wiv/jFLzJ06NBcfPHF6dSpUzVj0sxNnDgxK1euLJn7SgoAAAAAALZe1UqpGTNmZOTIkVm2bNkGRVTbtm2z8847p76+PsuWLcuaNWsazo0fPz5//etfc9NNN6Vjx47Vikozt7H9pAYPHlzlJAAAAAAAsOOoSim1fPnyfOYzn8nSpUuTJHvuuWf+5V/+Jccff3x69OjRcF2xWMycOXPypz/9KWPHjs2rr76aF154IV/4whdy3XXXVSMq5KGHHiqZdezYMYceemj1wwAAAAAAwA6iRTVe8tvf/jaLFi1KoVDIEUcckTvvvDOf+MQnNiikkqRQKGS//fbL6NGjc+edd+aQQw5JsVjMxIkT84c//KEaUWnm5s+fn5kzZ5bMjz322LRqVZMt2AAAAAAAYIdQlVLqj3/8Y5Jkl112yU9+8pN06NDhXe/p1KlTrrrqqrRv3z5JMm7cuIpmhKT8V1KJpfsAAAAAAGBbVaWU+vvf/55CoZCTTjopnTt33uz7dt9993z4wx9OsVjM008/XcGE8JaN7Sf1wQ9+sMpJAAAAAABgx1KVUmr9+vVJku7du2/xvT179kzy1r5UUElr167Nww8/XDI/6KCDsueee9YgEQAAAAAA7DiqUkq9/Rf6c+fO3eJ7X3vttSRJ165dGzUT/KMnnniibPlp6T4AAAAAANh2VSmlBg8enGKxmPvuuy+LFi3a7PvWrFmTP/7xjykUCjnmmGMqmBA2vnSfUgoAAAAAALZdVUqpT37yk9l1112zfPnyfP7zn8+yZcs2677LL788L730Utq2bZtPfepTFU5Jc1eulOrQoUMOP/zwGqQBAAAAAIAdS1VKqa5du+a//uu/sttuu2Xq1Kk55ZRTcuONN2bJkiUl19bX1+cvf/lLzj333Pzud79LXV1drrzyyvTo0aMaUWmmXn755TzzzDMl82OOOSZ1dXU1SAQAAAAAADuWVtV4yciRI5Mku+yyS1599dUsWrQol112WS677LJ07949u+66a1q1apVly5blhRdeyJo1axruXbduXS688MJNPr9QKGTatGkV/R3YsT300ENl58cdd1xVcwAAAAAAwI6qKqXUxIkTUygUkqThn8ViMUny4osvZv78+Q3Xvj0vFAol10Kl2E8KAAAAAAAqqyql1F577VWN18BWWbduXR5++OGS+YEHHpju3bvXIBEAAAAAAOx4qlJKPfDAA9V4DWyVp556KosXLy6ZW7oPAAAAAAAaT4taB4Bas3QfAAAAAABUXlVKqTVr1mzzM+67775GSAKlHnrooZLZTjvtlEGDBlU/DAAAAAAA7KCqUkqdeeaZefbZZ7fq3iVLluRf//Vfc9FFFzVuKEjy2muvZcqUKSXz97///Wnbtm0NEgEAAAAAwI6pKqXUrFmzctZZZ+Xaa6/dovvuv//+DBkyJPfcc09lgtHs/fnPf06xWCyZH3/88TVIAwAAAAAAO66q7Sm1du3afPe7382//Mu/5OWXX97ktcuWLcvFF1+cCy64IK+99lqSpFAoVCMmzczG9pM67rjjqhsEAAAAAAB2cFUppYYOHdrwNcpjjz2W0047Lffee2/Za//85z/n1FNPze23355isZhisZj99tsvY8eOrUZUmpH6+vqy+0n16tUrPXv2rHoeAAAAAADYkVWllPrOd76Tn/3sZ+nSpUuKxWLDPlGXXHJJ3nzzzSTJ8uXL87WvfS3nnXdeXnnllRSLxdTV1eXzn/98xo8fn8MPP7waUWlGnn766bz++usl88GDB9cgDQAAAAAA7NiqtnzfCSeckDvvvDMf/vCHkyTFYjG///3vM3To0Pz2t7/NaaedlnHjxjV8HfXe974348ePz4UXXpjWrVtXKybNyMaW7lNKAQAAAABA46taKZUknTt3zk9+8pN873vfS8eOHVMsFjNv3rx84xvfyIIFC1IsFrPLLrvk8ssvz29+85vsv//+1YxHM1Nu6b42bdrkfe97X/XDAAAAAADADq6qpdTbTjvttPzqV79KmzZtUigU/l+YFi3yve99Lx/96EdrEYtm5I033siTTz5ZMj/qqKOy00471SARAAAAAADs2KpeStXX1+faa6/NyJEjs2bNmiRvLeX39j8vuOCCXHnllVm9enW1o9GMPPzww6mvry+ZW7oPAAAAAAAqo6ql1N/+9recddZZ+e53v5s333wzxWIxu+++e77zne/kve99b4rFYtatW5df/epXOfXUU/Poo49WMx7NiP2kAAAAAACguqpSSq1YsSJXXHFFPvaxj2X69OkNX0addtppufPOOzN06ND8+te/ziWXXJK2bds27DX1qU99KpdcckkWL15cjZg0E8Visex+Uj169LCPGQAAAAAAVEhVSqlTTjkl119/fdavX59isZhdd901V111Vb73ve9l5513TpIUCoWMGjUq48aNy4ABAxru/f3vf5+TTz45d9xxRzWi0gw888wzeeWVV0rmxx133AZ7nAEAAAAAAI2nKqXUwoULG47/6Z/+KXfeeWdOPPHEstfut99+ueGGG/KlL30pdXV1SZI33ngjX/nKV6oRlWag3FdSSXL88cdXNwgAAAAAADQjVdtTqmPHjvnhD3+YH/3oR+ncufMmr23RokVGjx6dW2+9Nf369atSQpqLcqVUXV1d3v/+91c/DAAAAAAANBNVKaWOP/743HXXXTnllFO26L4DDjggt9xySy688MK0bNmyQuloTpYtW5ZJkyaVzI844oh06NChBokAAAAAAKB5aFWNl/z85z/f6ntbtmyZz3/+8znhhBMaMRHN1YQJE7Ju3bqSuaX7AAAAAACgsqq2fN+26tu3b60jsAN48MEHy86PO+646gYBAAAAAIBmpipfSv2j5cuX5/77788TTzyRBQsWZOnSpTnnnHNy+umnJ0n+v//v/8vBBx+cY489thbx2EEVi8WypdSee+6p9AQAAAAAgAqrailVLBbz85//PGPGjMmbb77ZMCsUCnn99dcbrvvNb36T1157LQMGDMiVV16ZHj16VDMmO6hnn302CxYsKJkPHjw4hUKhBokAAAAAAKD5qNryfWvWrMmnP/3pXHXVVXnzzTdTLBZTLBZLrlu1alVeffXVJMlTTz2Vj3zkI3nuueeqFZMdmKX7AAAAAACgdqpWSv3Hf/xHHnnkkRSLxbRu3Tof+chH8u1vf7vsteeee27atGmTQqGQxYsX56KLLsq6deuqFZUd1EMPPVQya9myZY455pjqhwEAAAAAgGamKqXU008/nXHjxqVQKOSAAw7IXXfdlW984xs544wzSq5t27Ztvva1r+WOO+7I/vvvnySZPXt27rzzzmpEZQf15ptv5vHHHy+Zv/e9703Hjh1rkAgAAAAAAJqXqpRSt956a5KkVatWueqqq7L33nu/6z09evTIVVddlVat3tr26t57761oRnZsjz76aNasWVMyHzx4cA3SAAAAAABA81OVUurxxx9PoVDIsccem3333Xez7+vZs2eOP/74FIvFTJ8+vYIJ2dFtbD8ppRQAAAAAAFRHVUqpV155JUnSr1+/Lb63d+/eSZI33nijUTPRfBSLxbKlVNeuXXPwwQfXIBEAAAAAADQ/VSmlisVikqRly5ZbfW9dXV2jZqL5+Pvf/565c+eWzD/4wQ+mRYuq/F8AAAAAAACavar8jfxuu+2WJJk9e/YW3/vUU09t8AzYUg899FDZuaX7AAAAAACgeqpSSh1++OEpFot54IEH8vrrr2/2fX/961/z2GOPpVAo5NBDD61gQnZk5Uqpt/c4AwAAAAAAqqMqpdQ///M/J0lWrVqVr3zlK1mzZs273jNz5sx84QtfaFi+7+STT65oRnZMK1euzKOPPloyHzhwYLp06VKDRAAAAAAA0DxVpZR6//vfnw984AMpFot55JFHctZZZ+X222/Pc889t8F1a9euzZNPPpnLL788H/3oR7No0aKGr6Q++MEPViMqO5jHH388q1atKpkff/zxNUgDAAAAAADNV6tqveiHP/xhPvaxj+X555/PrFmzcvHFF29w/ic/+UmuvPLKhi+j3v5n165d85//+Z/ViskO5oEHHig7P+6446obBAAAAAAAmrmqfCmVJB07dszNN9+cE088McViseGnUCgkeWtpv/r6+oZ5khx66KG5+eabs8cee1QrJjuYcvtJde7cOQMGDKh+GAAAAAAAaMaq9qVUkuyyyy656qqrMnXq1Nxyyy2ZOHFiXnjhhYYSKnnry6gjjjgiQ4cOzbHHHlvNeOxg5s6dW7JEZJJ88IMfTMuWLWuQCAAAAAAAmq+qllJv69+/f/r3758kWb9+fZYsWZL169enY8eOad26dS0isQN68MEHy84t3QcAAAAAANVXk1LqnVq2bJkuXbrUOgY7oHJL9yVKKQAAAAAAqIWq7SkF1bR69epMmDChZN6/f/907dq1BokAAAAAAKB5U0qxQ5o0aVJWrFhRMh88eHAN0gAAAAAAAEopdkgb209KKQUAAAAAALWhlGKHVG4/qV122SWHHXZY9cMAAAAAAABKKXY88+fPz4wZM0rmxxxzTFq1alWDRAAAAAAAgFKKHc6f//znsnNL9wEAAAAAQO0opdjhbGw/qeOOO666QQAAAAAAgAZKKXYoa9euzcMPP1wy79evX7p161aDRAAAAAAAQKKUYgfz5JNPZtmyZSVzS/cBAAAAAEBtKaXYoTzwwANl55buAwAAAACA2lJKsUN56KGHSmbt27fPEUccUf0wAAAAAABAg1aN+bB+/fo15uM2UCgUMm3atIo9n+3fK6+8kr/97W8l86OPPjqtW7euQSIAAAAAAOBtjVpKFYvFxnwcbJFyX0kllu4DAAAAAICmoFFLqb322qsxH1d19fX1ue222zJ+/PjMnDkzK1asSNeuXXPYYYfl7LPPrtgScJdeemluvPHGJMkzzzyTVq0a9V9Ls7GxUmrw4MHVDQIAAAAAAJRo1PbjgQceaMzHVdWyZcty/vnnZ+LEiRvMFyxYkAULFuSuu+7KqFGjcskllzTqex999NHcdNNNjfrM5mj9+vX585//XDLv3bt3evToUYNEAAAAAADAO/kkJ28tO3jRRRc1FFJHH310hg8fnt122y3Tp0/PNddck/nz52fMmDHp0qVLRo8e3SjvXb58eb72ta9Z9rARPPXUU1m8eHHJ3NJ9AAAAAADQNLSodYCm4I477siECROSJMOGDcsvf/nLnHjiiRk4cGCGDx+ecePGpXfv3kmSq666KgsXLmyU915xxRVZsGBBozyruXvwwQfLzo8//vgqJwEAAAAAAMrZLkqpdevW5Z577qnY88eMGZMk6dChQy6++OKS8506dcpll12WJFm9enXGjh27ze/885//nFtvvTWFQiGdOnXa5uc1d+X2k2rbtm2OPPLI6ocBAAAAAABKVHX5vhUrVuSBBx7I3/72tyxbtizr1q0rWbquWCymvr4+69aty6pVq7J48eI8++yzWb16dU4++eRGzzRv3rxMmzYtSTJ48OCNFkSHH354evXqlTlz5uTee+/NV77yla1+59KlS/Pv//7vSZLhw4dn9uzZJXtZsflee+21PPXUUyXz97///Wnbtm31AwEAAAAAACWqVkpNmTIlF154YRYtWrTF9xaLxRQKhQqkSp544omG46OOOmqT1w4aNChz5szJ/PnzM3fu3Oyzzz5b9c5vfOMbeeWVV9K9e/f8n//zf3Leeedt1XN4y//8z/+U3Zdr8ODBNUgDAAAAAACUU5Xl+5YvX57Pfe5zeeWVV1IsFjfrJ0nDcV1dXY455piKZJs9e3bDcc+ePTd5bY8ePRqOZ82atVXv+9Of/pTbb789hUIh3/rWt9K+ffuteg7/z+OPP152rpQCAAAAAICmoypfSv3ud7/L66+/nkKhkLZt2+aMM85Inz598uKLL+aaa65Jq1atcumll2bt2rV5/fXX8/jjj2fSpElJkrq6utx8883p169fRbItXLiw4Xivvfba5LXdunUre9/mev3113PppZcmSc4+++y8733v2+JnUOrpp58ume21117p1atXDdIAAAAAAADlVKWUevjhh5MkhUIh//3f/50jjjgiSbJy5cqMGTMm69evz957791Q0lxwwQW5//778+UvfzmrV6/OV7/61YwbNy4tWjT+h11LlixpOH63r5batWvXcLxs2bItftdll12WV199Nd27d8///b//d4vvp9SaNWsyffr0kvnAgQOrHwYAAAAAANioqizfN2vWrBQKhRx++OENhVSS7LTTTjnggAOSpOHLqLedcMIJ+drXvpZisZiZM2fmnnvuqUi2NWvWNBy3bdt2k9e+8/w779scd999d+69917L9jWymTNnlv13ccghh9QgDQAAAAAAsDFVKaWWLl2apPzXK3369EmxWMzf/va3knNnnXVWw5J5f/rTnyqSrWXLlg3HhUJhk9e+vddVki36auvVV1/NZZddliT52Mc+Ztm+RjRlypSyc6UUAAAAAAA0LVUppd4uc3beeeeSc/vtt1+SZPbs2SXnCoVCjjnmmBSLxcyYMaMi2d65JN+qVas2ee3q1asbjlu3br3Z77j00kuzePFiy/ZVQLn9pJKkf//+VU4CAAAAAABsSlVKqY4dOyZJVqxYUXKuR48eSZKFCxdm5cqVJef33HPPJMmiRYsqku2dy+iVe/87vTP/27/Tuxk/fnzDV17f/OY306FDh61IycaUK6V69OiRLl261CANAAAAAACwMa2q8ZJ99tknixYtysyZM8ueS9Kwd9Q/LvG3fPnyJO/+FdPW6t69e8PxSy+9lD322GOj17700ksNx5u67m2vvvpqvvWtbyVJjj766HTu3DnTp08vue7NN99sOJ4xY0ZatmyZurq69O7de7N+h+Zq9erVZb+gs3QfAAAAAAA0PVUppY444ohMnjw5Dz/8cGbNmpUDDjig4VzPnj3TokWLFIvFTJgwoaSUevLJJ5Ns+EVTY3pnlrlz55bd9+pt8+bNazjenMLoueeea9hPa8KECZkwYcK73nPmmWcmeasse+CBB971+uZsxowZWbt2bcl8wIABNUgDAAAAAABsSlWW7zvttNPSokWLrFu3LiNGjMhvfvObhrKmffv2ec973pNisZjrrrsuU6dObbjv+uuvz1NPPZVCobBBedSYBg4cmEKhkCSZPHnyJq+dOHFikqRbt27Ze++9K5KHzTdlypSyc/tJAQAAAABA01OVL6V69eqVs846KzfffHOWLl2ab37zm5kxY0a+8Y1vJEmGDx+ep59+OsuXL8/HPvax9O3bN4sXL95gubx/+qd/qki2bt26ZeDAgfnrX/+a++67L1/5ylfK7vs0efLkzJkzJ0ly0kknbdazjzzyyLJLFv6jc889t6HweuaZZ9KqVVX+tWz3yu0nlVi+DwAAAAAAmqKqfCmVJF//+tdzyimnpFgsJkl69OjRcG7o0KF53/vel2KxmGKxmOnTp29QSPXp0ycf/ehHK5bt3HPPTZIsXrw4l156aerr6zc4v2TJklx66aVJkrq6uowYMaJiWdh85Uqpnj17plOnTtUPAwAAAAAAbFLVSqlWrVrlhz/8Ya699tqcfvrp6dOnT8O5QqGQn/3sZxk2bFjD/lJv/xx99NH51a9+ldatW1cs25AhQ3L00UcnSe68886ce+65ue+++/LUU0/lpptuyhlnnJHZs2cnSS688MINCrUkefzxx9OnT5/06dOnoeCislauXFn2KzRL9wEAAAAAQNNU9XXijjrqqBx11FEl83bt2uWKK67IRRddlKlTp2bNmjXp27dvevXqVZVcP/7xj3Peeedl0qRJmTx5ctn9pUaNGpXRo0dXJQ+bNn369Kxbt65kPmDAgBqkAQAAAAAA3k2T27xo9913zwknnFAyX7x4cUWXZevQoUPGjh2b8ePH5/bbb8+MGTOybNmydO7cOYceemjOOeecsmUatWE/KQAAAAAA2L5UpZQaOXJkkmT48OE5+eSTt+jeX//61/nJT36SPfbYI3fccUcl4jVo0aJFhg0blmHDhm3RfUceeWTZpeQ21/XXX7/V9zZXGyulLN8HAAAAAABNU1VKqYkTJ6ZQKGTw4MFbfO+KFSuydOnSFIvFCiRje1WulOrVq1d22WWXGqQBAAAAAADeTYtaB9iUYrGYJ598MknK7h9E87Ry5co8++yzJXP7SQEAAAAAQNPVaF9KrV+/Pl/+8pfz+uuvb/SaG264IQ8++OBmPa++vj4vvfRSFixYkEKhkL333ruxorKde+aZZ7J+/fqSuaX7AAAAAACg6Wq0Uqply5Y57rjjcskll6RQKJScLxaLmTdvXubNm7dFzy0WiykUCjnzzDMbKyrbualTp5ad+1IKAAAAAACarkZdvm/o0KEZNGhQisXiBj9v+8f55vx07Ngxo0ePzqhRoxozKtuxKVOmlMwKhULe85731CANAAAAAACwORrtS6m3/dd//VfeeOONhj8Xi8WceOKJKRQK+exnP5uPfOQj7/qMQqGQli1bpn379unQoUNjR2Q79/TTT5fM9ttvv+y88841SAMAAAAAAGyORi+l2rVrl3bt2pXM3/7qqXv37o39SpqRFStWZNasWSVzS/cBAAAAAEDT1uilVDljx45Nkuyzzz7VeB07sGeeeSb19fUl80MOOaQGaQAAAAAAgM1VlVJq0KBB1XgNzUC5/aQSpRQAAAAAADR1VSml/tGyZcty7733ZuLEiZk3b16WLFmSFi1aZOedd06PHj1y2GGH5aSTTkqXLl1qEY8mrNx+UoVCIe95z3tqkAYAAAAAANhcVS+lrr766lxzzTV58803y56fMmVK7rzzzlxxxRU5//zzc95556VQKFQ5JU1VuVLqgAMOSPv27WuQBgAAAAAA2FxVK6XWr1+fCy+8MA8++GCSpFgsbvL6tWvX5ic/+UmeeOKJ/Pd//3datGhRjZg0YcuXL8/s2bNL5v37969BGgAAAAAAYEtUrZT67ne/mwceeCCFQiHFYjHvfe97c/LJJ6dv377p3Llz1q9fnzfeeCPTp0/Pfffdl7/+9a8pFot55JFH8qMf/Shf+tKXqhWVJuqZZ54pW2YOGDCgBmkAAAAAAIAtUZVS6rnnnstvfvObFAqFtG3bNt/97nfz4Q9/uOy1Rx55ZEaNGpU//vGP+cpXvpKVK1fmF7/4RU4//fTsv//+1YhLEzVlypSy80MOOaTKSQAAAAAAgC1VlTXxbr755qxfvz5J8p3vfGejhdQ7fehDH8r3vve9JG8t9XfLLbdUNCNNX7n9pFq0aJGDDz64BmkAAAAAAIAtUZVS6rHHHkuhUEj//v1z0kknbfZ9H/rQhzJgwIAUi8X85S9/qWBCtgflSqkDDzww7dq1q0EaAAAAAABgS1SllHrppZeSJEcdddQW3/v2PXPnzm3UTGxfli1blueee65k3r9//xqkAQAAAAAAtlRVSqmVK1cmyVZ90bLTTjslScPyfzRPf/vb38rOBwwYUOUkAAAAAADA1qhKKdW5c+ckyQsvvLDF9759z9vPoHmaMmVK2fkhhxxS5SQAAAAAAMDWqEopddBBB6VYLOaBBx7I0qVLN/u+JUuW5P7770+hUEjfvn0rmJCmrtx+Ui1btsxBBx1UgzQAAAAAAMCWqkopdeKJJyZJli5dmn/7t39LfX39u95TX1+fr371qw0l1vHHH1/RjDRt5UqpAw88sGF5RwAAAAAAoGmrSil12mmnZa+99kqS3H///fn4xz+eyZMnb/T6iRMn5uMf/3gefPDBFAqF7LnnnjnjjDOqEZUmaMmSJZkzZ07J3H5SAAAAAACw/WhVjZe0bt063/3ud/PJT34y69aty5QpU3LuueemU6dO6du3bzp27JhCoZA33ngjM2fOzOLFi5MkxWIxdXV1ufLKK9O6detqRKUJmjp1atl5//79q5wEAAAAAADYWo1aSn31q19NkgwZMiRHH330BueOOOKI/Pd//3e++MUvNizJ98Ybb+Sxxx7b4Lpisdhw3LFjx3z/+9/P4Ycf3pgx2c5srJTypRQAAAAAAGw/GnX5vttuuy3jx4/PrFmzyp5/3/velz/+8Y/5zGc+07CcX7FY3OAnSXbfffd89rOfzd13351jjjmmMSOyHZoyZUrJrFWrVunXr18N0gAAAAAAAFujKsv3vVPHjh3z5S9/OV/+8pczb968vPjii3njjTcazu2zzz7p0aNHtWPRhD399NMlsz59+qRt27Y1SAMAAAAAAGyNqpdS79SjRw8FFJu0ePHivPDCCyVzS/cBAAAAAMD2pVGX74PGVu4rqSQ55JBDqpwEAAAAAADYFkopmjSlFAAAAAAA7BiUUjRp5Uqpurq69O3btwZpAAAAAACAraWUokkrV0r169cvbdq0qUEaAAAAAABga7WqxENvuOGGPPjgg436zEKhkOuuu65Rn0nT9vrrr2fevHkl8/79+9cgDQA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t3brVIiYvL8/iHP7+/qZ7773XtGLFimLXmjlzphHz5JNPFuufOHGixXk6dOhgOnLkiEVMbm6u6b333jNiAgMDTYmJicXONW3aNCOmV69epri4uGIx586dMz3zzDNG3KRJk4rFmP+s/P39Te3btzdFRESYrl69aoqLizMtWLCgxPtqTU5OjqlLly7G+fr37286e/ZssbioqChTmzZtjLh//vOfJZ7ven7WtowYMcI4T7du3UwnTpwoFrNo0SIjplmzZqaEhASj78SJE6b77rvP6J88ebIpIyPD4viCggLTwoULTU2bNjXifvrpp2LXufb99NJLLxU7144dOyxiiu7lxYsXLeISEhJMDz30kBGzYcOGYte79jwvvviiKTU11SImLi7O1KNHDyNm3Lhxxc5jz3swatSoYmPKzc01TZgwwYgJDg4udp68vDxT3759jZh//OMfpvT09GJx+/btMwUFBRlxJeXvtfnZv39/07lz54rFffDBBxZ/ZmRnZ9s8186dO4v1h4aGGv2DBw82ZWVlFYvZv3+/qXnz5kbcb7/9ViwGAAAAzquCo4tiAAAAwO1uwoQJxrJgqampNmdQOML48eMt9o2SpIoVK+qFF16weO7ZZ59V7969ix0/evRoY4ZPTEyMcnJyrF7Lzc1Nn332WbH9i1xdXfXGG2+oc+fOkqTMzMxiMySSkpK0ePFiSZKPj4++/PJL3XnnncWu4evrq08//VR33HGHJGnVqlXGzBNrZs+erS5dusjT01N33nmnhg8fbjP+WsuXL1d8fLwkyc/PTwsWLFCdOnWKxT344IOaP3++XF1djeOuXcLuZjl+/Li2bNkiSfL09NSCBQtK3Fto8ODBxuy9vLw8/fjjj0bf3LlzlZubK0kKDg7WO++8o8qVK1sc7+LioiFDhmj8+PHGcx999JHy8vKsjq1u3br64IMPip2rXbt2CggIMB5XrlxZs2fPlre3t0Wcn5+fBg0aZDwump1jjb+/v+bMmVNsKcc777xTn3/+ufH+DQsLK7acnr3uwV133aUPP/yw2JhcXV01ZcoUY0ynT5/W2bNnLWLCw8ONJfuCgoL0n//8p8TZgIGBgfr444+NZfvmzJmj/Px8q2OqVq2aPvnkkxJni40ZM8bIt4yMDB04cMDqeazZv3+/0X722Wfl4eFRLCYgIEDDhg2TVPjn0I1cBwAAAI5DUQoAAABwsCpVqui9994zHhct4+cMPD091a9fvxL7ri0c9e/fv8Q4d3d33XXXXZKk/Pz8YntOmXviiSeKndfcyy+/bLR//vlni76VK1caxYGBAwfa3GumatWqGjJkiCSpoKCg2LnMNWvWzFi28EaFhYUZ7ZdfflleXl5WYwMDA/XYY48ZY1u5cuWfurY1ERERRrtv376qX7++1dhBgwbJ399fwcHBqlevniQpKyvL2MvJxcXFYhnBkgwbNswoviYmJmrnzp1WYwcMGFBiQUKyfN916dJFtWrVKjHO39/faF+6dMnm2MaPH291eT8/Pz899dRTxmPz/avseQ/69u1r9R5UqVJF999/v/G4aIm+IuaFw5CQEFWoYP1X/xYtWhh7biUkJBjLdZakR48eVvOqQoUKat26tdUxlYX5fnPX7nFm7tlnn1V4eLj2799vFKgAAABQPlCUAgAAAJxAu3btLIo606ZN0/nz5x04okJNmjSx+mG9j4+P0fb09NTdd99t9TzmszSys7OtxnXr1s3meO677z6jCJGcnKy4uDijb/fu3Ubb/AN7a1q2bGm0bX0Q37x581LPZUtWVpb27dsnqbBw8eijj5Z6TI8ePYz2rl27/tT1rdm+fbvR7tixo83YgIAA/fzzz5o9e7ZRpIyOjjZmvTVr1qzEWWnmKlSooODgYOOx+c/rWrb2xiqa4VZ0XWvM33O2ZudVq1ZNHTp0sNovyZihJ0nbtm0z2va8B+YzwkpiPjvM/PXl5eVZFHTuu+8+m+eRLHOhaD+2GxmTecHKVp5bY17UWrBggcaOHavNmzcrIyPDIq5GjRpq2LCh3NzcrvsaAAAAcCxXRw8AAAAAQKEJEyZo69at+uOPP4xl/D799FOHjql27dpW+8xnNVy7xNi1bM3UMGdrllSRRo0aGbMwTp8+rQYNGkiSTpw4YcSMHDmyTNcrkpiYaLXP1gyiskhKSjJmcNWvX19Vq1Yt9ZimTZsa7YSEhD91fWvOnTtntBs3bnzdx5uPqyw/t2vjipYzLMnNeN9dz3uutFjz+/PHH38YbXveA2szwIqYLxFYUFBgMT7zIs71zvIzf31/Zkwmk+m6risVFv8efPBBoxC7du1arV27Vm5ubmrZsqU6dOigRx55xGIWHAAAAMoXZkoBAAAATsLLy0v/+te/jMebN2/WTz/95MARqdjeONaYFwr+jOrVq5caY16IuHLlitG2tSxgaczPY+t6NyIlJcVol+X1SYUzQUo6/ma6ePGi0b6R13j58mWjXdbXZT6TxtbPq1KlSmU6381435nPvLLG/P6Y3zdnuAeSZQHoz75f7DGmsqpQoYLmzZunXr16GftcSVJu/iy1TQAADDVJREFUbq6ioqI0c+ZMPf744+rWrZs+++wzZWZmXvc1AAAA4FjMlAIAAACcSPv27fX0009r2bJlkgqX8XvooYdKnaFwvcr6gbH5B8POyHxpwby8PKM9ZswYq/vxlMRW7J8tfJjf67Lez/z8fKNd1hk/18v8fv1ZN/K6bB1zK993rq6l/1psPhPJ2pJxN/se3Cjz89euXfu691wq2v/NUapWrap///vf+vvf/67Vq1dr06ZNOnTokMXP4PTp03r//fe1dOlSLVq0yNjnDAAAAM6PohQAAADgZCZOnKitW7fq7NmzSklJ0dSpUzVv3rzrOofJZLL5gfeN7PdyK6SlpZU6G8N8Joj5DJbq1avrwoULkgr3ZLK1x9WtZD6DxnxmjS3mcX92ppY11apVM+7XlStXyjwrroizvq7rlZ6eXmqM+ewh8wKxM94D83OaTCYNHz78pl/jVmjQoIFGjRqlUaNGKTU1Vbt379b27du1adMmY+nJhIQEvf766/rmm28cPFoAAACUFcv3AQAAAE7m2mX8Nm7cqNDQ0FKPMy9C5eTk2Iwt2pPJ2Zw5c6bUmJiYGKNtvrfMnXfeabQPHDhQ6nlycnLKVJD4s+rVq2fMrklMTFRaWlqpxxw9etRo/9k9rawxv1+nTp0qNf6NN97QjBkztHjxYhUUFBh7eUnSsWPHynTNI0eOlHh9Rzp58mSpMeavz3zczngP6tWrZ8z+Sk5Ottg7zJr09PRS/8xwpOrVq6tr166aOnWqtmzZojfeeMPo27lzp5KSkhw4OgAAAFwPilIAAACAEwoKCtJTTz1lPH7vvfcs9rIpiflMl0uXLlmNS05O1tmzZ//8IO3g119/tdm/e/duY6bJ3XffLW9vb6OvVatWRjssLKzUa3333Xd64IEH1KZNG02ZMuUGR1w6Dw8P3X///ZIKZ66sX7++1GPWrVtntFu2bGmXcbVo0cJob9261WbsuXPntHz5cn311Vf6+uuvVaFCBQUGBhrFtsOHDysuLs7mOQoKChQREWE8ttfrul5xcXGKjY21GbNx40aj3aVLF6PtjPegcuXKuu+++4zHP//8c6nHTJgwQQEBAXr44Yf1448/3vQxFbE1ezM/P18TJ05Unz591L59e6tFMhcXFz377LNq2LCh8RxFKQAAgPKDohQAAADgpF5//XXVqVNHUuGSdaXN6Khdu7bR3r59u9W4zz777OYM0A4WL15sdRZXQUGB3n//feNxv379LPp79eplfOj9yy+/KDIy0up1UlNTtWDBAkmF97ZJkyZ/dug29e7d22jPnTvX5gytAwcOWBSlevbsaZcxPfHEE0Z7+fLlxlJ+Jfnuu++MdteuXSVJnp6e6tatm6TCYtvMmTNtXm/hwoVGMdTb21vt2rW74bHfbLaWxzxz5oxWrVolqXA/qUcffdToc9Z70KtXL6O9YMECm0WbvXv3atOmTTKZTDp//rwCAgLsMibJcn808z2ipMK9244eParff/9dFy5csCgEXqugoMBiGU9fX9+bPlYAAADYB0UpAAAAwEl5eXnp3XffLXO8+Qfcs2bNUnx8vEV/QUGBPv/8c3377bc3bYw32+XLlxUSElLsQ/SMjAy99tprio6OllS4RFn//v0tYho1aqS//e1vxuNXX321xA+2k5OT9fe//90oftWrV89iVpo99OrVy1jqLTExUS+88EKJy6rt2bNHISEhysvLkyT16dNHzZo1s8uYmjZtqk6dOkkqLMyNHDmyxDGtWbNGX3zxhaTCWTiDBw82+kaOHCl3d3dJUnh4uKZMmaLMzEyL400mk5YsWaL//Oc/xnMTJ040jnMGoaGhmjVrlvLz8y2eP3XqlF588UVlZWVJkkaMGGGxp5TknPegT58+xvstJSVFw4YNK7GoffDgQY0ZM0Ymk0mS1K1bN4slMW82Ly8vo52QkFCsv2/fvkb7rbfe0sGDB4vFmEwmzZgxwyhKBQYGUpQCAAAoR1wdPQAAAAAA1j388MPq06ePVqxYUWps3759NX/+fKWlpen8+fPq2bOnevTooQYNGujSpUvavHmzEhIS5O7urqCgIG3atOkWvILr07BhQx06dEjdu3dXt27ddNddd+nixYsKDw/X+fPnJUmVKlXSzJkzLT7gLvL222/r6NGjOnHihK5evaqXXnpJgYGBatOmjTw8PBQbG6uIiAijyODh4aGZM2fKw8PDrq/Lw8NDH3/8sQYNGqSrV68qOjpa3bt3V6dOndSoUSPl5eUpOjpaO3fuNAoETZo00eTJk+06rmnTpumpp55SYmKicd+7dOmie+65Rzk5OYqKitLevXuN+EmTJsnPz8947O/vrylTpmjy5MkymUxaunSpNm3apM6dO6tevXpKTU3V1q1bdfz4ceOYvn37WszkcTRXV1fl5eVp3rx5Wrt2rTp37qyqVasqJiZGERERxjJyzZs3V0hISLHjnfEeVKpUSbNmzdLgwYN19epVnTp1Sk888YQefvhh3XfffcrJydHhw4e1bds24/3m5+enqVOn2m1MkuX+aDNnzlRiYqLc3NwUHBysxo0ba8CAAfrxxx917NgxpaSkqF+/fnrooYfk7+8vb29vXbx4Udu2bdOJEyckSe7u7po0aZJdxwwAAICbi6IUAAAA4OTeeOMNbd++vdR9U7y9vTVv3jy9/PLLSk1NVVZWllauXGkRU716dc2YMUOxsbFOWZSaM2eOXn75ZZ0+fbrY2KXCJQpnzZpldS8eLy8vLVmyRBMmTNDmzZslSfv379f+/fuLxdapU0f//e9/9cADD9zcF2FF06ZN9f3332vs2LE6efKkMjMztWbNmhJje/furSlTpsjT09OuY/L29taSJUs0ZswYRUdHKzMzs8T9uDw8PPT6669rwIABxfr69esnLy8vvfXWW0pJSVFycrKWLl1aLM7NzU3jx4/XsGHD7PFSbpivr6+GDx+uadOmKTY21pgVZq579+7697//bXVmkzPeg2bNmlm83/Ly8rRp06YS875Fixb66KOPLPZos4fu3btr9uzZSk1NVUpKij755BNJUpUqVdS4cWO5u7trwYIFGjVqlA4dOiSTyaTt27eXuBxprVq19N577znN3mQAAAAoG4pSAAAAgJOrWrWq3n33XY0YMaLU2AcffFDh4eFatGiRNm3apLi4OJlMJvn5+alTp04aPHiwfH19S/zg3RnUq1dPK1as0Ndff61169YpPj5erq6uatiwoYKDgzVo0CBVqVLF5jmqVaumTz/9VHv27FFoaKh2796t8+fPKzs7W9WqVVOTJk3UpUsX9e7du9Rz3Wz+/v76+eeftXr1am3YsEEHDx7UpUuXjJ/Rgw8+qL59+yowMPCWjcnX11dLlizRhg0btHr1ah04cEAXLlyQq6ur6tevr6CgIA0aNMhilsu1evTooaCgIC1btkyRkZE6efKkUlNT5e7uroYNG6pDhw7q37+/6tWrd8te1/UYNGiQWrZsqS+++EJRUVG6fPmyatSooZYtW+rpp59WUFBQqedwxntQ9H5bu3at1q9fr0OHDunixYsqKCiQj4+P/vrXv6pnz57q2rWrxX5P9lKrVi0tWbJEH330kfbs2aO0tDR5e3srIyPDiKldu7aWLVumdevWKTw8XL///rsuXryovLw8eXt765577lGnTp3Up0+fEmdLAgAAwLm5mIrm6gMAAAAAcJto0qSJpMJl65xx1iAAAADwv8j+X4UCAAAAAAAAAADAbY+iFAAAAAAAAAAAAOyOohQAAAAAAAAAAADsjqIUAAAAAAAAAAAA7I6iFAAAAAAAAAAAAOyOohQAAAAAAAAAAADszsVkMpkcPQgAAAAAAAAAAAD8b2OmFAAAAAAAAAAAAOyOohQAAAAAAAAAAADsjqIUAAAAAAAAAAAA7I6iFAAAAAAAAAAAAOyOohQAAAAAAAAAAADsjqIUAAAAAAAAAAAA7I6iFAAAAAAAAAAAAOyOohQAAAAAAAAAAADsjqIUAAAAAAAAAAAA7I6iFAAAAAAAAAAAAOyOohQAAAAAAAAAAADsjqIUAAAAAAAAAAAA7I6iFAAAAAAAAAAAAOyOohQAAAAAAAAAAADsjqIUAAAAAAAAAAAA7I6iFAAAAAAAAAAAAOyOohQAAAAAAAAAAADsjqIUAAAAAAAAAAAA7O7/AIiS7XZDX4w5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 607,
+       "width": 850
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca = PCA().fit(X)\n",
+    "\n",
+    "plt.figure(figsize=(10, 7))\n",
+    "plt.plot(np.cumsum(pca.explained_variance_ratio_), color=\"k\", lw=2)\n",
+    "plt.xlabel(\"Number of components\")\n",
+    "plt.ylabel(\"Total explained variance\")\n",
+    "plt.xlim(0, 63)\n",
+    "plt.yticks(np.arange(0, 1.1, 0.1))\n",
+    "plt.axvline(21, c=\"b\")\n",
+    "plt.axhline(0.9, c=\"r\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Clustering\n",
+    "\n",
+    "The main idea behind clustering is pretty straightforward. Basically, we say to ourselves, \"I have these points here, and I can see that they organize into groups. It would be nice to describe these things more concretely, and, when a new point comes in, assign it to the correct group.\" This general idea encourages exploration and opens up a variety of algorithms for clustering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/unsupervised-learning-pca-and-clustering/The%20examples%20of%20the%20outcomes%20from%20different%20algorithms%20from%20scikit-learn.png\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "image. <a href=\"https://habrastorage.org/getpro/habr/post_images/8b9/ae5/586/8b9ae55861f22a2809e8b3a00ef815ad.png\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/unsupervised-learning-pca-and-clustering/The%20examples%20of%20the%20outcomes%20from%20different%20algorithms%20from%20scikit-learn.png\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "image. <a href=\"https://habrastorage.org/getpro/habr/post_images/8b9/ae5/586/8b9ae55861f22a2809e8b3a00ef815ad.png\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The algorithms listed below do not cover all the clustering methods out there, but they are the most commonly used ones."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### K-means\n",
+    "\n",
+    "K-means algorithm is the most popular and yet simplest of all the clustering algorithms. Here is how it works:\n",
+    "1. Select the number of clusters $k$ that you think is the optimal number.\n",
+    "2. Initialize $k$ points as \"centroids\" randomly within the space of our data.\n",
+    "3. Attribute each observation to its closest centroid.\n",
+    "4. Update the centroids to the center of all the attributed set of observations. \n",
+    "5. Repeat steps 3 and 4 a fixed number of times or until all of the centroids are stable (i.e. no longer change in step 4).\n",
+    "\n",
+    "This algorithm is easy to describe and visualize. Let's take a look."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x15efe146708>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 432,
+       "width": 438
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's begin by allocation 3 cluster's points\n",
+    "X = np.zeros((150, 2))\n",
+    "\n",
+    "np.random.seed(seed=42)\n",
+    "X[:50, 0] = np.random.normal(loc=0.0, scale=0.3, size=50)\n",
+    "X[:50, 1] = np.random.normal(loc=0.0, scale=0.3, size=50)\n",
+    "\n",
+    "X[50:100, 0] = np.random.normal(loc=2.0, scale=0.5, size=50)\n",
+    "X[50:100, 1] = np.random.normal(loc=-1.0, scale=0.2, size=50)\n",
+    "\n",
+    "X[100:150, 0] = np.random.normal(loc=-1.0, scale=0.2, size=50)\n",
+    "X[100:150, 1] = np.random.normal(loc=2.0, scale=0.5, size=50)\n",
+    "\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.plot(X[:, 0], X[:, 1], \"bo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scipy has function that takes 2 tuples and return\n",
+    "# calculated distance between them\n",
+    "from scipy.spatial.distance import cdist\n",
+    "\n",
+    "# Randomly allocate the 3 centroids\n",
+    "np.random.seed(seed=42)\n",
+    "centroids = np.random.normal(loc=0.0, scale=1.0, size=6)\n",
+    "centroids = centroids.reshape((3, 2))\n",
+    "\n",
+    "cent_history = []\n",
+    "cent_history.append(centroids)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    # Calculating the distance from a point to a centroid\n",
+    "    distances = cdist(X, centroids)\n",
+    "    # Checking what's the closest centroid for the point\n",
+    "    labels = distances.argmin(axis=1)\n",
+    "\n",
+    "    # Labeling the point according the point's distance\n",
+    "    centroids = centroids.copy()\n",
+    "    centroids[0, :] = np.mean(X[labels == 0, :], axis=0)\n",
+    "    centroids[1, :] = np.mean(X[labels == 1, :], axis=0)\n",
+    "    centroids[2, :] = np.mean(X[labels == 2, :], axis=0)\n",
+    "\n",
+    "    cent_history.append(centroids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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k6dOna9asWbJYLPrFL36hdevWeVxWZbfbNXnyZD333HPu57xdbtx0OZTFYnE3hr/ssss0cOBA97GDBw/KarVKavyfgYyMljs0S+pyAbMrI5NGNvIoENnIo98hj5JHI0FIf+Kqqirt2bNH69atk9PpbPPctLQ09+OmlXBEpoSYKGUmJejctG7KTErgyjcQAY5bauVwtP2z3lsOh1MnKv3faTeYDh486O5zNGXKlGYB08VsNus///M/3cHln//8Z6fMrby83P34zL5KLmPHjtU999yje++916sr2E0VFxfrgw8+kCTNnDmzRcB0GTBggB588EFJjUuP1q9f3+qYs2fP9mkOnriWNSUmJurcc89tdsy1Yc1ZZ53VasBE+COTwhPyKBB5yKONyKPk0UgR0nd6Pvnkk3rnnXckSW+99ZbHCrzLt99+637cu3fvgM8NAGAsf5vFt6a2vsHQ8TrLxx9/7H7c1rLYPn36aM2aNcrMzOy0nkDnnHOOYmJiZLPZ3Bu4XHPNNUpMbN6z7mc/+5lf43/yySfugtKkSZPaPPfSSy91P/7yyy91zTXXeDxv9OjRfs2lKVfIHDJkSIsr5Hv27JEkDR8+vMPvg9BFJgWAroE82og8Sh6NFCFd9Pze977nDphvvvmmHnvsMY/nORwOvfnmm+6vm/Y2AACEh/hYY++QSYgL6V9xrfrmm2/cj4cNG9bmuUOGDAn0dJrp3r27brnlFr322muqqqrSI488osWLF2vs2LGaOHGiJkyYoOHDh/u9dKZpo/h7773X69cdOnTI4/OJiYmt7qjpz7w8fT9cIbO97xXCG5kUALoG8mgj8mgj8mj4C+l/gddee63+3//7fzp58qRWr16tK664QhMmTGh2jtPp1H//93+7v8mTJk3SiBEjgjFdAEAH9ExLkNlsMmRJUZTZpB6p8QbMqvO5evVIjaEu1CxcuFDx8fHKzc2VzWaT1WpVXl6e8vLyJEnp6emaOnWqbrvttmZ9iLxx8uRJv+ZUWVnp8fmO7Jr99ddfu5cm79q1S5KUkpLSokejK4AmJSU1O5aZmdlsmTPCG5kUALoG8mgj8qjvyKOhKaSLnklJSXriiSf04IMPymazac6cObr55pt16aWXqmfPnvrmm2+0atUq5efnS2pcQvTf//3fQZ41AMAfifExmjA8U1/sPNLhscaPyAzbpvGh0APQ4XC0eiw6OloLFizQnDlz9OGHH2rDhg3avHmzTp8+Lalxt8vVq1frzTff1OOPP65bbrnF6/e1279bUpabm+t1SIuLi/P4vK87Jzd19913q6SkpNlzL7zwgl544QWP5y9ZsqTF19OmTfP7/RFayKQA0DWQRxuRRxuRR8NfSBc9Jemqq67S0qVL9dhjj6mmpkarV6/W6tWrW5w3fPhwPffcc/ROAoAwdu2k/oaEzOsmntv+SSGqabCyWCxKSAjM7r1tbcZSVVXV7uvT09M1c+ZMzZw5Uw0NDdq9e7fy8vK0bt067d27V3a7Xf/1X/+lSZMm6eyzz/ZqTk17QbW2myYQLGRSAOgayKPkURfyaPgL6d3bXa6//nqtXbtW99xzj4YOHapu3bopJiZGZ511lq644gr99re/1RtvvKF+/foFe6oAgA4YMbCnzumd3KExsnona/jAHgbNqPM1XYLTtKeQJ3PnztU111yj++67r90dpSUpJua7uw3q6+tbPa+0tNTj806nU0ePHtXGjRubXQWPjo7W6NGjNXfuXK1Zs8Z9RbmhoUGfffZZu/Nyyc7Odj92LU9qzfHjx/X8889rzZo12r9/v9fv4a2PP/5YBQUF+vnPfy5JOv/881VQUNDsv9tuu02SdMUVV7Q41tWvqkcqMikARD7yKHnUhTwa/sKi6ClJvXr10s9+9jO99dZb2rZtm3bv3q3PPvtML7zwgq699lq/m9SipVqbXUeqavWN5bSOVNWq1mbsDnYA0BqTyaSfzhrrdxP5+NgoPThrbIeWkQRb010iXRuneFJVVaUvvvhCBw8elMVi8eozN22ifvjwYY/nlJWVtRrannnmGU2ePFl33HGHtm3b1ur7NN3J0mq1NjvW1jwvvvhi9+NVq1a1GYRzc3O1bNkyLVy4UB999FGr53XUjh07JEmjRo1qcczVW8nTMUQuMmnnII8CCBbyKHnUhTwa/khlkNR4tWT/iSq9tO2gHly/Q4s+26f/3ligRZ/t04Prd+jlbQe1/0SVV1duAKAjBvVL0yO3X+Bz0IyPjdIjt1+gQf3SAjOxTjJy5EiNHj1akrR27Vq99957Lc5xOp1atGiRO8DNmDHDq7GbLs/Jzc1tcby+vl6PPfZYqz2ULr/8cvfjZ599tkWAdGk65+HDhzc7FhsbK0nunktNDRs2TBdeeKEkqaSkRI888ogaGhpanJeXl+eef0JCgtef3x+ukDly5Mhmz9tsNvedD2ceA+Af8iiAUEEeJY9K5NFIEPI9PRF4xZU1emVHkY5U13k87nBKW0st2lpqUZ+keM0Z1V9ZqYmdPEsAXcmYnLO05L6L9NuV2/Rtafv9fLJ6J+vBWWPDPmC6PPXUU7r55ptVW1urBQsW6LPPPtPVV1+ttLQ0FRcXa+XKle4NUyZMmKDvf//7Xo177bXXatmyZbLZbFqzZo1qa2s1bdo0JScnq6CgQCtWrNDXX3+trKwsFRcXt3j9uHHjdNFFF+nzzz/X9u3bdcMNN+i2227TgAEDFBsbq5KSEr3xxhvavHmzJGn8+PEaN25cszEyMjJ08OBBFRQU6I033tCQIUOUkpKirKwsSdLTTz+t6dOny2Kx6N1339XXX3+tH/7whxo8eLAqKyv1+eefa9WqVe4G+w8//LAyMjL8/rNuy5EjR1ReXi5J7uDvsn//ftXX18tsNrNDN2AA8iiAUEMeJY+SR8MfRc8ubm/5Kb247aDq7a3vjNbUkeo6Lc0r1LyxA3ReRkqb59ba7DpZZ1W93aG4KLO6x8cqIca/JQIAup5B/dL0/IIp2n3ghN774ht9ufuoHI7v7u6JMps0fkSmrpt4roYP7BHWS4jONHjwYOXm5mr+/PkqLy/XmjVrtGbNmhbnXXTRRXruuee8/uxnn322HnvsMT3xxBOy2+1au3at1q5d2+yc66+/Xpdffrl++tOfehzj2Wef1dy5c7V9+3YVFRXpySef9Hje9773PS1btqzF81deeaU2bdqkhoYGPfroo+73/M1vfuOe44oVKzR//nwVFRVp//797vOaiomJ0c9+9jPdeuutXn12f7iuqmdkZCgzM7PZsZ07d0pq7HnVrVu3gM0B6ArIowBCFXmUPEoeDW8UPbuw4soanwKmS73doRe3HdRD47NbXGF3Op0qqKjWhuJybS+zqMnvA5lN0pheaZqclaGc9KSI+oUAIDBMJpNGDOqpEYN6qqbOphOVdaqtb1BCXLR6pMYrMT6m/UHC1OjRo7Vu3TqtXr1a69ev14EDB1RdXa2UlBSNGDFC06ZN01VXXeXzz9KZM2dq9OjRys3N1aZNm3Ts2DGlpKRo2LBhmjFjhqZOndoieDbVvXt3/eUvf9G7776r999/X/v27VNFRYVMJpN69uypkSNH6tprr9WVV17p8fWzZ8+W1WrVG2+8oZKSEsXGxqq2trbZOYMHD9a7776rt99+Wx9++KH27Nkji8Wi6Oho9e3bVxMmTNCsWbM0cOBAnz67r1xB0lOPpLaOAfAeeRRAqCOPkkfJo+HL5KQpjt+2bNmi2bNnu79+/fXXW9w2HaqcTqcWf7av1SVE3uibFK9FFw91/4Brb1lSUyxLAkLHV199pYaGBkVHR2vw4MHBng4AP/n7bzmc8wwahev3kDwKwIU8CkSGUMujbGTURRVUVHcoYEpSSXWdCiuqJTUuS1qaV+j1mK5lSXvLT3VoDgAAAAhP5FEAABBIFD27qE+Kyw0ZZ0NxeYeXJRVX1hgyFwAAAIQP8igAAAgkip5dUK3NrvwyiyFjbSu16E/bi3wOmC71doeW7ygSXRYAAAC6DvIoAAAINIqeXdDJOmuzhu4d4ZR09LRxy5IAAAAQ+cijAAAg0Ch6dkH+XgUPpA0GLW8CAABA6COPAgCAQIsO9gTQ+eKiQq/WnV9mUa3NroSYqGBPpYVam10n66yqtzsUF2VW9/jYkJwnAABAuCCP+oY8CgCA7yh6dkHd42NlNsmwJUVGcDglS71VCTEJwZ6KJMnpdKqgolobisu1vczS7M/KbJLG9ErT5KwM5aQnyWQyBW+iAAAAYYg82j7yKAAAHUPRswtKiInSmF5p2lpqCfZUmqlrCI1lTsWVNXplR5GOVHvuDeVwSltLLdpaalGfpHjNGdVfWamJnTxLAACA8EUebRt5FACAjgu9dSXoFJOzMoI9hRbio4P/13Fv+SktzStsNWCe6Uh1nZbmFWpv+akAzwwAACCykEc9I48CAGCM4P9WR1DkpCepT1J8h8bI7BYns0EraaJMUlpcrDGD+am4skYvbjvoc2P9ertDL247qOLKmgDNDAAAIPKQR1sijwIAYByKnl2UyWTSnFH9/W4iHxdl1p2jz9WYXmmGzGd0r7SgNmN3Op16ZUeR3zuJ1tsdWr6jSE5nCDWmAgAACGHk0ebIowAAGIuiZxeWlZqoeWMH+Bw046LMmjd2gLJSEw1bljQlyMubCiqqvV5C1JqS6joVVlQbNCMAAIDIRx79DnkUAABjUfTs4s7LSNFD47O9XlrUNyleD43P1nkZKZKMWZbUNyle2elJHRqjoz4pLjdknA0GjQMAANBVkEcbkUcBADAWu7dDWamJWnzxUBVWVGtDcbnyyyxyNFkVE2VqXO4zJStD2elJMpm+a5zkWpa0NK/Qr6U4cVFm3TGqf7MxO1utza78MoshY+WXWVRrswd1aRQAAEC4IY+SRwEAMBpFT0hqDIs5PZKV0yNZtTa7LPVW1TU4FB9tVlpcbJuhybUsydem602XJQXTyTprs1DdEQ6nZKm3KiEmwZgBAQAAugjyqDFjkUcBAGhE0RMtJMRE+RySXMuSXtlR5FUvor5J8bpjVP+gB0xJfjeLb01dg7HjAQAAdDXk0Y4hjwIAQNETBvJ3WVKtza6TdVbV2x2KizKre3zbV/KN5u+Ooa2Jj6ZVLgAAQDCQRxuRRwEAoOgJg3m7LMnpdGr/iSptKC7X9jPCqNkkjemVpslZGco5o2dTIHSPj5XZJEOWFEWZpLS42I4PBAAAAL+QR8mjAABIFD0RQK0tSyqurGlz2ZHDKW0ttWhrqUV9kuI1J8DLjhJiojSmV5q2llo6PNboXmk0jQcAAAgR5FEAALou1j2gU+0tP6WleYVe9VmSpCPVdVqaV6i95acCOq/JWRmGjDPFoHEAAAAQGORRAAC6Boqe6DTFlTU+76gpNTZ2f3HbQRVX1gRoZlJOepL6JMV3aIy+SfHKTk8yaEYAAAAwGnkUAICug6InOoXT6dQrO4r83pmy3u7Q8h1FcjoNaHTkgclk0pxR/f1uIh8XZdYdo/oHvN8TALRn4cKFysnJUU5OjoqLi4M9nVZ99dVXwZ6Cz/bt26ecnBwNHTpU1dXVzY5t3LhROTk5GjlypGw2m8fXFxQUaPHixbruuus0duxYDR8+XJdcconmzZundevWBex3HIBG5FEA6Bzk0cAhj/qGnp7oFAUV1V4vIWpNSXWdCiuqldMj2aBZNZeVmqh5Ywf4fPU/LsqseWMHBLTPE4Dg76wLYxw6dEhPP/20Tp8+rddeey3Y0/HJzp07JUkDBw5UUlLzO6l27NghSRo2bJhiYmJavPZ3v/ud/vCHP8jhaP77paysTGVlZfroo480fvx4/e53v1P37t0D9AmAro08CqCjyKORgTzadfIoRU90ik+Kyw0Z5+OiYwELmZJ0XkaKHhqf3WZj+6b6JsXrjgA3tge6MqfTqYKK6pDYWRfGmDNnjr799ltdcMEFwZ6Kz1xBcuTIka0eGzVqVItjL730kl566SVJUnp6un784x/r/PPPV2xsrAoLC/Xqq6+qsLBQeXl5uu+++/Tqq68qOpqIBhiNPArAH+TRyEMe7Tp5NPw/AUJerc2u/DKLIWNtK6tU4YkqZQcwaGalJmrxxUNV+O9fbPln/GKLMjXuijklK0PZ/GIDAibUdtaFMex2e7Cn4DfXlfXRo0e3euzMkHnkyBG9+OKLkqS+fftq5cqV6tWrl/v4qFGj9IMf/EDz58/Xp59+qq1bt+pvf/ubZs6cGaBPAXRN5FEA/iCPRibyaNfJoxQ9EXAn66zNQlpHPfevr3Xf+QN1XkaKcYOewWQyKadHsnJ6JKvWZpel3qq6Bofio81Ki2MJAxBoe8tP+bS0z7Wz7ryxAwL6swFd1+nTp3XgwAFJLa+sHzp0SCdOnJDUMmT+4x//kNVqldTY36ppwHSJjY3V008/rcmTJ6uhoUFvvfVWRIRMIJSQRwH4ijyKUEMe9R0bGSHg/G0W3xqrwxnw3TObSoiJUmZSgs5N66bMpAQCJhBgobyzLrquXbt2yeFwKDExUYMHD252zHVVPSMjQ3369Gl27F//+pckKTo6Wpdeemmr42dkZCgnJ0eStH//fiOnDkDkUQC+IY8iFJFHfcedngg4f3egbItr98xFFw9lOQ8QQYzaWTdSfjbU1NTovffe03vvvaeioiIdP35c3bp1U05Ojq699lrddNNNHpuUt+ZHP/qRNm/eLEnas2dPq316Fi5cqDVr1kiSPvjgA2VlZTU7brVa9eabb2r9+vXat2+fTp06pW7duikzM1MXXHCBZs6cqUGDBjV7zWWXXaaSkhL315s3b3aHqvnz5+v+++9vdr7D4dA777yjtWvXavfu3bJYLOrWrZvOPfdcTZ48WbfeeqtSUjzfReF6r+nTp+vhhx/WkiVL9PHHH8tqtapXr16aPn26fvKTn7T5Z3XmfF1qamp03nnneXxNeXm5+zMtWbJE06ZN07XXXqvBgwertrZWcXFxbb6na7fM1nbbBOA/8igAb5FHmyOPkkfDGUVPBFz3+FiZTTJ0SZEU+N0zAXS+cNhZt7Ps2bNHDzzwgA4fPtzseYvFok2bNmnTpk1auXKl/vjHP3pcohIoZWVluuuuu1RYWNjs+crKSlVWVmr//v1asWKF7rvvPs2fP9+v9ygpKdH8+fO1d+/eZs9bLBbl5+crPz9fy5cv17PPPquLL7641XHq6ur04x//WPv27XM/V1RUpNTUVL/m5Y9p06Z5dV5FRYX7z/TMq/MAOo48CsBb5NHvkEfJo+GOoicCLiEmSmN6pWlrqcXwsd8/UKpzUhJZ4gNECKN21t1QXB7WIbO4uFizZ89WbW2tJGnq1Km64YYb1Lt3b5WUlGjFihXaunWr9u/fr3nz5mn16tWdtrviL3/5S3cYuummmzR16lT17NlTp06d0rZt2/TnP/9ZVVVVWrZsmUaNGuUOgX/84x9ls9n0k5/8ROXl5Ro2bJiefvppSVLPnj3d41dUVOjWW29VaWmpTCaTrr/+el111VXq1auXTp06pY0bN2rlypWyWCy69957tXz5cn3ve9/zONf3339fdrtdV199tW655RY1NDToo48+0nXXXdfu53TN1+XOO+/UiRMntGDBAl100UXu551Op2bOnCmr1apFixZpzJgxkqTMzEyf/lz/9Kc/qaGhQZJ0ySWX+PRaAO0jjwLwFnm0EXmUPBoJKHqiU0zOyghIyNxzvEr/8eEOje2dpslZGcph90ogbBm5s25+mUW1NnvY/g/oo48+6g6Yixcv1qxZs9zHRo0apauvvlr333+/1q9fr927d+v999/XDTfcEPB5HTlyRF988YUk6eabb9ZTTz3V7PikSZM0adIk93xXrVrlDpmu5UWxsbGSpG7dumno0KEt3mPx4sUqLS1VTEyMnn/+eU2ePLnZ8YsuukjTp0/XrFmzZLFY9Itf/ELr1q3zuKzKbrdr8uTJeu6559zPtXUlvqmmy6EsFou7Mfxll12mgQMHuo8dPHjQ3Rh+6tSpysjI8Gr8prZs2aLc3FxJUkxMjG677TafxwDQPvIogPaQR79DHiWPRgI2MkKnyElPUp+k+ICM7ZS0tdSi32z6Sos/20fTaCBMGbmzrsMpWeqtxgzWyQ4ePOjuczRlypRmAdPFbDbrP//zP2U2N/4a/+c//9kpcysv/+7OhzP7KrmMHTtW99xzj+69916vrmA3VVxcrA8++ECSNHPmzBYB02XAgAF68MEHJTUuPVq/fn2rY86ePdunOXjiWtaUmJioc889t9mx3bt3S5LOOussvwLmgQMHdP/997uvqt99990655xzOjhjAJ6QRwG0hzzaiDxKHo2UPMqdnugUJpNJc0b119K8QsN3z2zqSHWdluYVat7YATovw3NDYQChyeifDXUNgftZE0gff/yx+/FNN93U6nl9+vTRmjVrlJmZ2Wk9gc455xzFxMTIZrPpD3/4g9LT03XNNdcoMTGx2Xk/+9nP/Br/k08+cTdPnzRpUpvnNt158ssvv9Q111zj8bzRo0f7NZemXCFzyJAh7mDvsmfPHknS8OHDfR63sLBQc+bMUUVFhaTGuwb87TsFoH3kUQDtIY82Io+SRyMFRU90mqzURM0bO0DPbz0gm9Fd5Juotzv04raDemh8trJSE9t/AYCQYPTOuvHR4bmY4ZtvvnE/HjZsWJvnDhkyJNDTaaZ79+665ZZb9Nprr6mqqkqPPPKIFi9erLFjx2rixImaMGGChg8f3iKIeatpo/h7773X69cdOnTI4/OJiYmt7qjpz7w8fT9cIbO979WZtm7dqnvvvVeVlZWSGu9I+P3vf+/3nx0A75BHAbSFPNqIPNqIPBr+KHqiU52XkaIHvzdISzd9FdD3qbc7tHxHkRZdPJSeSkCYMHJn3SiTlBYX2/GBgsDVq0dqDHWhZuHChYqPj1dubq5sNpusVqvy8vKUl5cnSUpPT9fUqVN12223NetD5I2TJ0/6NSdXUDtTUlKSX+NJ0tdff+1uHL9r1y5JUkpKSrOdN6XvAmhSUlKzY5mZmUpLS/M49j/+8Q898sgj7t5LF1xwgV5++WV169bN7/kC8B55FEBryKONyKO+I4+GJoqe6HTZPZI1tneatgWgkXxTJdV1KqyoDusd84CuxMiddUf3SgvbpvFNd2gMFoej9aVY0dHRWrBggebMmaMPP/xQGzZs0ObNm3X69GlJjbtdrl69Wm+++aYef/xx3XLLLV6/r91udz/Ozc1tNaSdKS4uzuPzHSky3H333SopKWn23AsvvKAXXnjB4/lLlixp8fW0adNanPf8889r2bJl7q+nTp2q3/zmN61+BgCBQR4F4Al5tBF5tBF5NPxR9ERQTMnKCHjIlKQNxeWETCCMGLWz7pQs3xt4h4qmwcpisSghISEg7+PqVeRJVVVVu69PT0/XzJkzNXPmTDU0NGj37t3Ky8vTunXrtHfvXtntdv3Xf/2XJk2apLPPPturOTXtBdXabprhyuFwaPHixVq9erX7udmzZ+vRRx+NqCVEQDghjwLwhDxKHnUhj4a/yPxUCIham11Hqmr1jeW0jlTVqtZmb/9FrQjk7plN5ZdZOjRPAJ3LiJ8NfZPilZ3u/zKSYGu6BKdpTyFP5s6dq2uuuUb33Xdfm6HRJSYmxv24vr6+1fNKS0s9Pu90OnX06FFt3Lix2VXw6OhojR49WnPnztWaNWvcV5QbGhr02WeftTsvl+zsbPdj1/Kk1hw/flzPP/+81qxZo/3793v9Ht76+OOPVVBQoJ///OeSpPPPP18FBQXN/rvtttskSVdccUWLY2deVX/00UfdAdNkMukXv/iFHn/88YgNmEAgGZVJyaMAPCGPkkddyKPhL3I/GQzhdDq1/0SVXtp2UA+u36FFn+3Tf28s0KLP9unB9Tv08raD2n+iyqsfbk19e6pW1gDumunicEqWemvA3weAMVw76/rbRD4uyqw7RvUP695pTXeJfOedd1o9r6qqSl988YUOHjwoi8Xi1Wdu2kT98OHDHs8pKytrNbQ988wzmjx5su644w5t27at1fdpupOlq0+QS1vzvPjii92PV61a1WYQzs3N1bJly7Rw4UJ99NFHrZ7XUTt27JAkjRo1qsUxV28lT8eaevnll/W3v/1NUmMgX7p0qebMmWPwTIHIFohMSh4F4Al5lDzqQh4NfxQ90ariyhot/myffrPpK20rtbRo5uxwSltLLfrNpq+0+LN9Kq6s8WrcveWntDSvUMdrOyf81TUEPswCMI5rZ11fg2ZclFnzxg4I+11yR44cqdGjR0uS1q5dq/fee6/FOU6nU4sWLXIHuBkzZng1dtPlObm5uS2O19fX67HHHmu1h9Lll1/ufvzss8+2CJAuTec8fPjwZsdiYxsb+rt6LjU1bNgwXXjhhZKkkpISPfLII2poaGhxXl5ennv+CQkJXn9+f7hC5siRI5s9b7PZ3Hc+nHmsqV27djXrmfT000/rhhtuCMBMgcgViExKHgXQFvIoeVQij0YCenrCo73lp/TitoOq9/Lq95HqOi3NK9S8sQN0XkZKq+cVV9b4NK4R4qOp7QPh5ryMFD00Pluv7CjSkeq6ds/vmxSvO0b1D/uA6fLUU0/p5ptvVm1trRYsWKDPPvtMV199tdLS0lRcXKyVK1cqPz9fkjRhwgR9//vf92rca6+9VsuWLZPNZtOaNWtUW1uradOmKTk5WQUFBVqxYoW+/vprZWVlqbi4uMXrx40bp4suukiff/65tm/frhtuuEG33XabBgwYoNjYWJWUlOiNN97Q5s2bJUnjx4/XuHHjmo2RkZGhgwcPqqCgQG+88YaGDBmilJQUZWVlSWoMYdOnT5fFYtG7776rr7/+Wj/84Q81ePBgVVZW6vPPP9eqVavcDfYffvhhZWQEpmfWkSNHVF5eLknu4O+yf/9+1dfXy2w2a8SIEa2O8dvf/tYdlCdNmqScnJwWO256MnDgQHcgB7qyQGRS8igAb5BHyaPk0fDPoxQ90YK/QbDe7tCL2w7qofHZHn/QO51OvbKjqFMDZpRJSosL73+kQFeVlZqoxRcPVWFFtTYUlyu/rPndPVGmxl0xp2RlKDs9KayXEJ1p8ODBys3N1fz581VeXq41a9ZozZo1Lc676KKL9Nxzz3n92c8++2w99thjeuKJJ2S327V27VqtXbu22TnXX3+9Lr/8cv30pz/1OMazzz6ruXPnavv27SoqKtKTTz7p8bzvfe97za4ou1x55ZXatGmTGhoa9Oijj7rf8ze/+Y17jitWrND8+fNVVFSk/fv3u89rKiYmRj/72c906623evXZ/eG6qp6RkaHMzMxmx3bu3CmpsedVt27dPL7+0KFD+uKLL9xff/HFF82+bstHH32kfv36+TNtIGIEIpOSRwH4gjxKHiWPhncepeiJZjoaBOvtDi3fUaRFFw9t8UOvoKLaqytkRhrdK00JMVHNnqu12XWyzqp6u0NxUWZ1j49tcQ6A0GAymZTTI1k5PZJVa7PLUm9VXYND8dFmpcVF9r/d0aNHa926dVq9erXWr1+vAwcOqLq6WikpKRoxYoSmTZumq666yudwPXPmTI0ePVq5ubnatGmTjh07ppSUFA0bNkwzZszQ1KlTWwTPprp3766//OUvevfdd/X+++9r3759qqiokMlkUs+ePTVy5Ehde+21uvLKKz2+fvbs2bJarXrjjTdUUlKi2NhY1dbWNjtn8ODBevfdd/X222/rww8/1J49e2SxWBQdHa2+fftqwoQJmjVrlgYOHOjTZ/eVK0h66pHU1jGXPXv2BGZiQBcQqExKHgXgK/IoeZQ8Gr5MTl93oIHbli1bNHv2bPfXr7/+eovbpsPN/hNV+s2mrzo8zoILByunR3Kz517edlBbSy0dHtufeTidThX8++rc9jOuzplN0pheaZqclaGcCLs6B3jjq6++UkNDg6KjozV48OBgTweAn/z9txyJeaaricTvYaAyKXkUCE3kUSAyhFoe5U5PNPNJcbkh42woLm8WMGttduWXWQwZ21t9k+KVnZ6k4sqaNvuwuJrfby21qE9SvOaEaB8W7ggAAABdRSAyKXm048ijAIBwQtETbkYGwfwyi2ptdncIOllnbbHTZiDFRZl1x6j+2ne8KiAbMnUW7ggAAABdTaAyKXnUP+RRAEC4ougJNyODoMMpWeqtSohJkKRObRYfF2XWvLEDJCkgGzJ1lki5IwAAAMAXgcqk5FHfkUcBAOHMHOwJIHQYHQTrGr4bLy6qc/6qJUSb9dD4bA3tmWxI8/tgtbzdW35KS/MKvW6077ojYG/5qQDPDAAAILAClUnJo74hjwIAwh1FT7gZHQTjo78br3t8rMydsNql3u7QWYlxhuzMWVJdp8KKaoNm5r3iypoO3RFQXFkToJkBAAAEXqAyKXnUe+RRAEAkoOgJNyODYJRJSouLdX+dEBOlMb3SjBm8Da4lTEY2v+9MTqczrO8IAAAA6KhAZVLyqHfIowCASEHRE25GBsHRvdJa7OQ4OSvDkLHbU1lnM7z5fWcJ5zsCAAAAjBDITEoebR95FAAQKSh6ohmjguCUM8aptdmVHBOlngmxrbzCOPV2h+HN7ztLuN4RAAAAYKRAZdJzkhOUkUgebQt5FAAQKdi9Hc3kpCepT1J8h67u9k2KV3Z6kpxOpwoqqrWhuFzbyyyGBb+2RJmkuKio9k/0QdMNmQKp1mY3/I6AM++2BQAACAfhnEnJo43IowCAYKPoiWZMJpPmjOqvpXmFfvXxiYsy645R/fXtqVq9sqOow0tjfDW6V5pS4oz9a910Q6ZAOllnNfyOgISYBGMGBAAA6EThnEnJo43IowCAYGN5O1rISk3UvLEDfN45My7KrHljB+i0tUFL8wo7veApNS5hCuSGTIHkb7P41nTWHQEAAACBEK6ZlDz6HfIoACCYKHrCo/MyUvTQ+Gz1SYr36vy+SfF6aHy2usVG68VtBw0PTN7OITs9KeAbMgWKr4G+PZ11RwAAAECghFsmJY82Rx4FAAQTy9vRqqzURC2+eKgK/90DKf+MHkhRpsYQNiUrQ9npSZKkxZ/tC0rB07WEyWRqvKQ+OStDW0stHR73zOb3geS6I8CIJUWdeUcAAABAIIVLJiWPNkceBQAEG0VPtMlkMimnR7JyeiSr1maXpd6qugaH4qPNSouLbXbVef+JqqAsaXctYcpKTXQ/Z2Tz+87iuiPAiHDcmXcEAAAABFqoZ1LyaEvkUQBAsLHeAF5LiIlSZlKCzk3rpsykhBYh5pPi8k6fk2sJ03kZKc2edzW/93eJzplX6jvLZIOu5HfmHQEAAACdKdQyKXnUM/IoACDYuNMThqi12ZVfZumU9zpzCVNrQdDV/N7Xfk6ertR3lnC8IwAAACBU1Nrs2mbAXYrtIY+2jTwKAAgFFD1hiJN1VkN6/7RmWM9k/SC7j8clTG1xNb9/ZUeRV8Gtb1K87hjVPygBU/rujoCleYV+9aEK1h0BAAAAoaCitl6BiqTkUe+QRwEAoYKiJwwR6Ebx+05U6Z5u8X71BfK1+X2wA1o43hEAAAAQCgpPng7Y2OTR9pFHAQChhKInDOFvryJvOZySpd6qhJgESY1Ll07WWVVvdyguyqzu8W1fbfel+X0oCLc7AgAAAELB9gC2WyKPto08CgAINRQ9YYj46MCHtFqbXftPVGlDcbm2n3F13GySxvRK0+SsDOW0c3U8ISbKHVZDWbjdEQAAABBMrqzYGe9BHm1EHgUAhDKKnjBEXYM94O/xvzuKVF5j9XjM4ZS2llq0tdSiPknxmhMhV5nD7Y4AAMG3cOFCrVmzRpL0wQcfKCsrK8gz8uyrr77S4MGDgz0Nn+zbt0833nijzGaz/vWvfykp6btNOjZu3Kg77rhDcXFx2rp1q2JiYlq8fvfu3Xr99de1efNmHTt2TAkJCerXr5+mTp2qGTNmqEePHp35cYCIE+ge8xJ5lDwKwBvk0cAhj/qGoicMEeienpJaDZhnOlJdp6V5hZo3doDOy0gJ8Kw6T7jcEQAESn2dTacsdbJaGxQbG62UtHjFxbf8RY7QdujQIT399NM6ffq0XnvttWBPxyc7d+6UJA0cOLBZwJSkHTt2SJKGDRvmMWA+99xzevnll+VwfPf70mq1qrKyUnv27FFubq6efvppXXHFFQH8BEBkI48GHnkUXR15NDKQR7tOHqXoCUMEuqenr+rtDr247aAeGp8dEVfYga7K6XSq6MAJbfmiSPt3l8nZ5BYek9mkIcN7a9ykLPUf2IMldWFizpw5+vbbb3XBBRcEeyo+cwXJkSNHtnps1KhRLY796U9/0osvvihJSklJ0Z133qlRo0bJarVq06ZNevXVV2WxWPTggw/qtdde05gxYwL4KYDIRR4FEAjk0chDHu06eZSiJwzRPT5WZpMCvqTIF/V2h5bvKNKii4fyywcIQ0cPV+qtldtVXuq5P5vT4dS+nUe1b+dRZfRO1o2zRiuzX2onzxK+stsD3w4lUFxX1kePHt3qsTND5okTJ/Tcc89JklJTU/X3v/9d/fr1cx+/9NJLdemll+r222+XzWbTr3/9a61atSpAnwCIbORRAEYjj0Ym8mjXyaOhdTkUYSshJkpjeqUFexotlFTXqbCiOtjTAOCjAwXlyn1hY6sB80zlpVXKfWGjDhSUB3hm6KpOnz6tAwcOSGp5Zf3QoUM6ceKEpJYh8+2331Z9fb0k6YEHHmgWMF0uvPBCXXrppZKk/Px8lZaWGj5/oCsgjwIwEnkUoYY86juKnl1Irc2uI1W1+sZyWkeqalVrM/bqxuSsDEPHM8qGYn7pAOHk6OFK/TV3i2xW335G2ax2/TV3i44ergzQzNCV7dq1Sw6HQ4mJiS0a3ruuqmdkZKhPnz7NjqWmpmrixInKyMhosz/SwIED3Y+PHDli4MyB0EIeBRAOyKMIReRR37G8PcI5nU4VVFRrQ3G5tpdZmi33MZukMb3SNDkrQznpSR1ecpOTnqQ+SfE6Ul3XwVkbK7/MolqbnZ0lgTDgdDr11srtPgdMF5vVrrdXbtc9Cy6JiGWENTU1eu+99/Tee++pqKhIx48fV7du3ZSTk6Nrr71WN910k8cm5a350Y9+pM2bN0uS9uzZo+hozzGgvR03rVar3nzzTa1fv1779u3TqVOn1K1bN2VmZuqCCy7QzJkzNWjQoGavueyyy1RSUuL+evPmzcrJyZEkzZ8/X/fff3+z8x0Oh9555x2tXbtWu3fvlsViUbdu3XTuuedq8uTJuvXWW5WS4nlzENd7TZ8+XQ8//LCWLFmijz/+WFarVb169dL06dP1k5/8pM0/qzPn61JTU6PzzjvP42vKy8vdn2nJkiWaNm2abrrpJt10001tvpekZu911llntXs+EE7Io+RRIJyQR5sjj5JHwxlFzwhWXFmjV3YUtRr6HE5pa6lFW0st6pMUrzmj+jdrsl5rs+tknVX1dofioszqHh/bZlAzmUyaM6q/luYVdsrumd5yOCVLvZWdJoEwUHTghNdLiFpzrLRKxQdOqP+gngbNKjj27NmjBx54QIcPH272vMVi0aZNm7Rp0yatXLlSf/zjH9WrV69Om1dZWZnuuusuFRYWNnu+srJSlZWV2r9/v1asWKH77rtP8+fP9+s9SkpKNH/+fO3du7fZ8xaLRfn5+crPz9fy5cv17LPP6uKLL251nLq6Ov34xz/Wvn373M8VFRUpNTW0em1t375d69evl9S4HMnTkiMgXHU0j0q+ZVLyKICOIo9+hzxKHg13FD0j1N7yU3px20Gvw96R6jotzSvUvWPOVVSU2e8r8VmpiZo3doBP790Z6hpCZy4AWrfli2JjxtlYHNYhs7i4WLNnz1Ztba0kaerUqbrhhhvUu3dvlZSUaMWKFdq6dav279+vefPmafXq1a1eJTfaL3/5S3fAvOmmmzR16lT17NlTp06d0rZt2/TnP/9ZVVVVWrZsmUaNGuUOgX/84x9ls9n0k5/8ROXl5Ro2bJiefvppSVLPnt99ryoqKnTrrbeqtLRUJpNJ119/va666ir16tVLp06d0saNG7Vy5UpZLBbde++9Wr58ub73ve95nOv7778vu92uq6++WrfccosaGhr00Ucf6brrrmv3c7rm63LnnXfqxIkTWrBggS666CL3806nUzNnzpTVatWiRYvcO11mZma2OrbT6VR1dbW++eYbvfXWW3rzzTdls9mUnJysJ598st25AeHC3zw6b+wADe2Z7PfdoeRRAB1BHm1EHiWPRgKKnhGouLLGr5BXb3fouS0H1NqGl95ciZek8zJS9ND47Dav6ne2+Gja1wKhrr7Opv27jWmYvW9XqerrbIqL936pTSh59NFH3QFz8eLFmjVrlvvYqFGjdPXVV+v+++/X+vXrtXv3br3//vu64YYbAj6vI0eO6IsvvpAk3XzzzXrqqaeaHZ80aZImTZrknu+qVavcIdO1vCg2NlaS1K1bNw0dOrTFeyxevFilpaWKiYnR888/r8mTJzc7ftFFF2n69OmaNWuWLBaLfvGLX2jdunUel1XZ7XZNnjzZvVulpDavxDfVdDmUxWJxN4a/7LLLmvU7OnjwoKxWq6TG/xnIyGi/n+Arr7yiZ555ptlzY8aM0VNPPdViGRYQrjqSR5/fekBp8TEqr7F6PMebTEoeBeAP8uh3yKPk0UjAb94I43Q69cqOIr+vardW8DyT60r83vJTHo9npSZq8cVDteDCwTqvZ7JfczFKlElKi4sN6hwAtO+UpU5Oh7c/hdrmdDh1qjI0/ifXVwcPHnT3OZoyZUqzgOliNpv1n//5nzKbG3+N//Of/+yUuZWXf7cRx5l9lVzGjh2re+65R/fee69XV7CbKi4u1gcffCBJmjlzZouA6TJgwAA9+OCDkhqXHrmW4ngye/Zsn+bgiWtZU2Jios4999xmx3bv3i2pse+RNwFT8twYvrCwUH/+85/dYRYIZx3NozaHs9WC55nayqTkUQC+Io82Io+SRyMFd3pGmIKK6k67ml1vd+jFbQf10Phsj3d8mkwm5fRI1jkpifqPD3d4XVA12uheaTSNB8KA1dpg7Hj1xo7XWT7++GP347Yajvfp00dr1qxRZmZmp/UEOueccxQTEyObzaY//OEPSk9P1zXXXKPExOa/A372s5/5Nf4nn3wip7Pxt8WkSZPaPPfSSy91P/7yyy91zTXXeDxv9OjRfs2lKVfIHDJkiDvYu+zZs0eSNHz4cK/Hu+yyyzR16lQlJCTo4MGD+stf/qKdO3fqr3/9q/Ly8rR8+fKI6aOErqkz86jUdiYljwLwBXm0EXmUPBopeZSiZ4T5pLi8/ZMMVG93aPmOIi26eGirO9MlxERpbO80bS21dOrcXKZkeXelA0BwxcYa+yspNi48f8V988037sfDhg1r89whQ4YEejrNdO/eXbfccotee+01VVVV6ZFHHtHixYs1duxYTZw4URMmTNDw4cNbBDFvNW0Uf++993r9ukOHDnl8PjExsdUdNf2Zl6fvhytktve9aqppgB41apS+//3v67HHHtPf/vY3ffvtt3rssce0fPnyDs4aCJ7OzqNS+5mUPArAG+TRRuTRRuTR8M+j4fkvEB7V2uzKL7N0+vuWVNepsKJaOT1aXzY0OSsjKCGzb1K8stOTOv19AfguJS1eJrPJkCVFZrNJKanxBsyq8zVdTtK9e/cgzsSzhQsXKj4+Xrm5ubLZbLJarcrLy1NeXp4kKT09XVOnTtVtt93mcz+gkydP+jWnyspKj88nJfn/8//rr792N47ftWuXJCklJaXZzpvSdwE0KSmp2bHMzEylpaV59V5RUVFavHixPv/8c5WVlWnjxo06dOiQzj77bL/nDwRLsPKo1H4mJY8CaA95tBF51Hfk0dBE0TOCnKyzyqD2Iz7bUFzeImDW2uw6WWdVvd2h5Jgo9e4Wp9LT9Z02p7gos+4Y1b/VO1ABhJa4+BgNGd5b+3Ye7fBYQ0b0Dtum8U13aAwWh6P1PnzR0dFasGCB5syZow8//FAbNmzQ5s2bdfr0aUmNu12uXr1ab775ph5//HHdcsstXr+v3W53P87NzfU6pMXFxXl8viM//++++26VlJQ0e+6FF17QCy+84PH8JUuWtPh62rRpXr9fbGysJk+erNWrV0uS9u/fH/YhE11TMPOo1DKTkkcB+II82og82og8Gv55lKJnBPG3WbwRtpVaVGuzKz7arIKKam0oLtf2Mkuz0GtS485ZnTHLuCiz5o0d4LHXKIDQNW5SliEhc9xEz03Nw0HTYGWxWJSQkBCQ93H1KvKkqqqq3denp6dr5syZmjlzphoaGrR7927l5eVp3bp12rt3r+x2u/7rv/5LkyZN8josNe0F1dpumuGmvLxchw4dUn19vSZMmNDmuU2/96HwPxuAP4KZRyUpv8yiGmuDvq2qJY8C8At5lDzqQh4N/zzK7u0RJC4qeN9Op6S9x09p8Wf79JtNX2lbqaXFVX6n/AuYcVFm3XpeP/VJ8m5pQN+keD00PlvnZXS8bwaAztV/YA9l9O7YDrtn9U5W1sAeBs2o8zVdgtO0p5Anc+fO1TXXXKP77ruvzdDoEhPz3d0G9fWt3+lUWlrq8Xmn06mjR49q48aNza6CR0dHa/To0Zo7d67WrFnjvqLc0NCgzz77rN15uWRnZ7sfu5Ynteb48eN6/vnntWbNGu3fv9/r9/DWxx9/rIKCAv385z+XJJ1//vkqKCho9t9tt90mSbriiitaHHP9GUyfPl2zZs3Sgw8+2OzPzJNvv/3W/bhXr16GfyagMwQzj0qSwyk9tXF/szyaUfbdXTKe8mjT460hjwJdB3mUPOpCHg3/PErRM4J0j4+VOYgrZ/53+zeG79TpCoxT+p+lxRcP1YILB+v83mktPmeUSTq/d5oWXDhYiy4eyhV1IEyZTCbdOGu0YmL92+E2JjZKP5g1OqyXETZtKv7OO++0el5VVZW++OILHTx4UBaLxavP3LSJ+uHDhz2eU1ZW1mpoe+aZZzR58mTdcccd2rZtW6vv03QnS6vV2uxYW/O8+OKL3Y9XrVrVZhDOzc3VsmXLtHDhQn300UetntdRO3bskNTY4P1Mrt5Kno65jBs3TlLjXRLr169v9bxjx47p008/ldT4fRo5cqTfcwaCKdh5VJLKa777uTMif6OueytXI/I3ejy3veMSeRToasij5FEX8mj451GKnhEkISZKY3qlBe39G/zo32Q2NS4zaqq1wGgymZTTI1lzxw7Q764YpScvGapHJuboyUuG6rdXjNLcsQOU0yM5rH+5AJAy+6Vqxu3jfA6aMbFRmnH7OGX2S23/5BA2cuRIjR49WpK0du1avffeey3OcTqdWrRokTvAzZgxw6uxmy7Pyc3NbXG8vr5ejz32WKs9lC6//HL342effbZFgHRpOufhw4c3OxYbGytJ7p5LTQ0bNkwXXnihJKmkpESPPPKIGhoaWpyXl5fnnn9CQoLXn98frpB5Zuiz2WzuOx/aCoSzZ892P/7Vr37l8a6Fqqoq/cd//IdqamokSXfeeWezuyCAcBLsPNrUiPyNOn/zBknS+Zs3tChstnWcPAp0beRR8qhEHo2EPEpPzwgTrF0p/eVwSplJ8Zo7pr/q7U7FR5uVFherhJi2f7kkxEQpISYwfUUABN/AnAzdft9EvbVyu8pL2+/nc1bvZP1g1uiwD5guTz31lG6++WbV1tZqwYIF+uyzz3T11VcrLS1NxcXFWrlypfLz8yVJEyZM0Pe//32vxr322mu1bNky2Ww2rVmzRrW1tZo2bZqSk5NVUFCgFStW6Ouvv1ZWVpaKi4tbvH7cuHG66KKL9Pnnn2v79u264YYbdNttt2nAgAGKjY1VSUmJ3njjDW3evFmSNH78ePeVZZeMjAwdPHhQBQUFeuONNzRkyBClpKQoK6ux79XTTz+t6dOny2Kx6N1339XXX3+tH/7whxo8eLAqKyv1+eefa9WqVe4eQw8//LAyMjL8/rNuy5EjR1ReXi5J7uDvsn//ftXX18tsNmvEiBGtjjF27Fj96Ec/0muvvaYjR47o+uuv1x133KExY8YoLi5OO3fu1KuvvqojR45Iaryz4s477wzI5wE6Syjk0aYFTRfX17vGTGz1+OSsnsq48QfkUQDkUfIoeTQCUPSMMDnpSeqTFG/4MvNAOlpdpyqrvcXu7wC6tsx+qZq74BIVHzihf31RrP27S+Vs0izYbDZpyIjeGjcxS1kDe0TUXTWDBw9Wbm6u5s+fr/Lycq1Zs0Zr1qxpcd5FF12k5557zuvPfvbZZ+uxxx7TE088IbvdrrVr12rt2rXNzrn++ut1+eWX66c//anHMZ599lnNnTtX27dvV1FRkZ588kmP533ve9/TsmXLWjx/5ZVXatOmTWpoaNCjjz7qfs/f/OY37jmuWLFC8+fPV1FRkfbv3+8+r6mYmBj97Gc/06233urVZ/eH66p6RkaGMjMzmx3buXOnpMaeV926dWtznEceeUQxMTFavny5qqqq9Pvf/97jeTfeeKMWL14cEVfV0bUFO496Kmi6nL95g3odLVa/Qwc9Hq964w2lx8cqYbr3u90CiFzkUfIoeTS8UfSMMCaTSXNG9dfSvMKg757piw3F5RQ9AbRgMpnUf1BP9R/UU/V1Np2qrJO1vkGxcdFKSY1XXHxk/DL2ZPTo0Vq3bp1Wr16t9evX68CBA6qurlZKSopGjBihadOm6aqrrvI5XM+cOVOjR49Wbm6uNm3apGPHjiklJUXDhg3TjBkzNHXq1BbBs6nu3bvrL3/5i9599129//772rdvnyoqKmQymdSzZ0+NHDlS1157ra688kqPr589e7asVqveeOMNlZSUKDY2VrW1tc3OGTx4sN599129/fbb+vDDD7Vnzx5ZLBZFR0erb9++mjBhgmbNmqWBAwf69Nl95QqSnnoktXXsTGazWb/4xS903XXXaeXKldq8ebPKyspkMpl01llnady4ce7vCxAJgplH2yp4urRW8HQpfu31xvMofAIQeZQ8Sh4NZyanN9trwaMtW7Y0643w+uuvt7htOlj2lp/Si9sOhk3h02ySfnfFqHaXEQEw3ldffaWGhgZFR0dr8ODBwZ4OAD/5+285lPMMvBOq38POzqMZZSW67q1cw8Yb+cwSJedkt38igA4jjwKRIdTyKBsZRajzMlL00Phs9UmKD/ZUvOJwSpZ6zw2IAQAAEH46O4+W9+qrrRdMMWSsrB/NpuAJAECYY3l7BMtKTdTii4eqsKJaG4rLlV9mUZP2IzKbpFizWXUhcjdoXUNozAMAAADG6Ow8umvMRElqd4l7W7J+NJul7QAARICwKnoeP35cK1eu1Oeff65vvvlGNTU1SkpK0uDBg3X55ZdrxowZSkxMDPY0g6LWZtfJOqvq7Q7FRZnVPb5xx0mTyaScHsnK6ZGsWptdlnqr6hocio8269jpej2/te2eRp0pPpobjwEAQGgjj7YuVPJoRwqfh6ZcrUkUPAEAiAhhU/Rcv369Fi5cqKqqqmbPnzx5Ups3b9bmzZv16quv6oUXXtDQoUODNMvO5XQ6VfDvq+bbPVw1H9MrTZOzMpSTniSTyaSEmCglxCS4z3mr8GgQZt26uCj6eQIAgNBFHm0pVPPorjET29yl3ZPDZw/Qx9nn6wc2O33mAQCIAGFR9Ny8ebMefPBB2Ww2xcTEaMaMGZo8ebLS0tJ09OhRrVmzRhs2bFBJSYnmzJmjv//978rMzAz2tAOquLJGr+wo0pHqOo/HHU5pa6lFW0st6pMUrzmj+isr9bu7DmptduWXWjpptt6pt9uDPQUAAACPyKMthXIeHZG/0aeCp9S4q/vw/I2yXDK0WWEWAACEp5BfT+x0OvXEE0+4A+af/vQnPf7447rkkks0cuRIXXXVVXr55Zf1wAMPSJIqKir07LPPBnnWgbW3/JSW5hW2GjDPdKS6TkvzCrW3/JT7uZN1VjnbeE0w0NMTAACEIvJoS6GcR0fkb/S7p+f5mzeo/K23DZ4RAAAIhpAvem7fvl1ff/21JOmWW27RhRde6PG8efPmKTu7cYfFDz74QDU1NZ02x85UXFmjF7cdVL2Pzd7r7Q69uO2giisb/1xO1TcEYnod4mtPz1qbXUeqavWN5bSOVNWq1sadogAAwHjk0eZCOY92pODpUvXGGzr85t+9Opc8CgBA6Ar55e3/+te/3I8vv/zyVs8zmUyaNGmSCgsLZbVadfDgQQ0fPrwzpthpnE6nXtlR5HPAdKm3O7R8R5EWXTxUdQ2hFciiTFJaXGy75/naNwoAAKCjyKPfCeU8mlFW0uGCp0vxa68rdcRwJedktzhGHgUAIDyEfNFz5MiRmjt3rsrKynTuuee2ea7T+V3iqK+vD/TUOl1BRbXXS4haU1Jdp8KK6pDbKX10r7R2G8Z3tG8UAACAP8ij3wnlPFreq6+2XjDFkMJn1o9meyx4kkcBAAgfIV/0HD9+vMaPH+/VuZs2bXI/7tu3b6CmFDSfFJcbMs6G4nJ9f3BoNdafkpXR5vG95ad8Wkbl6hs1b+wAnZeRYsQUAQBAF0Ue/U6o59FdYyZKUpuFz8NnD2hzk6OsH81Wv+nTWjxPHgUAILyE1u1+HfDpp59q3759kqTs7Gz17t07yDMyVq3NrvwyiyFj5ZdZFB8dpVBZbNM3KV7Z6UmtHjeqbxQAAEAgkUe9Z3QeNUnq3S1OUmPhc+sFUzyet/WCKVp/7axWj5/TSsGTPAoAQPiJiKJnRUWFFi1a5P76zjvvDOJsjFdrs6vgRFWzfkEd4XBK9Xa7xvRKNWS8IelJiovy769SXJRZd4zq32q/I6P6RjVdagYAAGA08qhvjM6jY3ql6q7R57ozqafC59YLprjvBPV0POXmGTrbQ8GTPAoAQHgK+eXt7Tl9+rTuvfdeHT16VJJ0wQUX6Pvf/36QZ9VxbTVIN0Jdg0NT+p+lbWWVHR7r+sGZsjucPl/9josya97YAW32OTKyb1ROj+QOjQNEqqioKDU0NMhut8vpdLLpAhCGnE6n7PbGTWGiotrukQ3jkUf9Y2Qevaz/WcpKTdS8sQPcmbTpUvemBU+XpscTpk3XiB/O9Dg2eRQIPPIoEP5CMY+GddGzqqpKd999t7Zv3y5J6t27t/7f//t/MpvD+wbW9hqkGyE+2qz+qYnqkxTfofdxLU03mUx6aHy21/PumxSvO7xo7G5k3yhCJuBZbGys6uvr5XQ6VVNTo27dugV7SgB8VFNT476LLDY2Nsiz6VrIo/4zOo9K0nkZKc0y6a4xE1XaJ0vlvTz3V624+DJlXH2Jss8f2er45FEg8MijQPgLxTwatkXPY8eO6e6773b3TerZs6deeeUVZWS0vSFOqPO1Qbo/okxSWlysTCaT5ozqr6V5hX6935lL07NSE7X44qEq/PcdAfln3BEQZWrcpX1KVoa7UNoWo/tG1drs7e4QD3RFKSkpqqqqktS4PDMxMZGr60AYcTqdqqiocH+dksKGKZ2FPOq/QOVRyUMmNUnyM5OSR4HOQR4Fwluo5tGwLHru379f99xzj0pLSyU1XlF/5ZVXNHDgwCDPrGP8bZDuq9G90txhKys1UfeOOVcvbDsomw9rllpbmm4ymZTTI1k5PZJVa7PLUm9VXYND8dFmpcXF+hTyTtZZDe0bZam3KiEmwZgBgQiSlNT4P3xOp1PV1dU6fPiw0tPTCZtAiHPdDVNRUaHq6mpJjb+Hk5Ja3xwQxiGPdkwg86hkXCYljwKdgzwKhKdQz6NhV/T89NNP9eCDD6qmpnEHxAEDBuh///d/1bev5yUr4aKjDdJ9MSXru7sPiitr9Nf9JT4FTG+XpifERHUo1Bn9Z1HXEPg/WyAcmc1m9e3bVyUlJe6gWV1dLZPJFDK9WAC05Op75mIymdS3b9+wX1YdDsijHddZeVTqWCYljwKdgzwKhKdQz6NhVfRcs2aNHn30UTU0NEiSxo4dq5deeklpaWnBnZgBjGiQ7o2mPY/8WboUYzbp5qF9lZWaqFqbXSfrrKq3OxQXZVb3eN/u5GyPvzvCtyY+OjT+0QGhKDk5uVnQlBr/59f18xZAaHMFzORk+gUGGnm048ijADwhjwLhLRTzaNgUPf/+97/rkUcecf/wu+aaa/TMM8+ETHPUjjKqQXpbmvY88nfpks3h1AtbD2pg924qrKhuttzHbJLG9ErT5KwM5XjRs7M93eNjZTbJkCVFrr5RAFqXnJys7OxsVVdX69SpU7Jare7d9wCEnqioKMXGxiolJUVJSUkhc0U9kpFHO448Ghl/V4BAIY8C4SXU82hYFD3/9a9/6dFHH3UHzB/+8Id69NFHI6a3h5EN0lvTtOdRR5cu2RxO7T9R3eJ5h1PaWmrR1lKL+iTFa46XS45akxATpTG90rS11OL3GC5N+0YBaJ3ZbFZKSkrINJ4GgFBBHu048ih5FPAGeRSAUUKrBOtBdXW1HnroIffVnZtuukmPPfZYxARMydgG6Z70TYrXQ+OzdV5G4y+Nzli6dKS6TkvzCrW3/FSHxpmcZczup1MMGgcAAHQ95NGOI4+SRwEA6Gwhf6fnihUrdPToUUlSRkaGZsyYoX379rX7uszMzLDprRSIZvFRpsaryVOyMpR9xtKezli6JDV+rhe3HdRD47P9vsKek56kPknxHQrFTftGAQAA+Io86h/y6HfIowAAdL6QL3quWrXK/bi8vFwzZ8706nVLlizRtGnTAjUtQxndIP3+cQM0uHuyx+UznbF0qal6u0PLdxRp0cVD/bobwmQyac6o/lqaV+hXGG/aNwoAAMAf5FHfkUe/Qx4FACA4Qnp5e0VFhfuqeiRzNUg3QpRJrQZMKfBLlzwpqa5TYUXLnkveykpN1LyxA3wO4037RgEAAPiDPOo78uh3yKMAAARPSN/pmZ6eroKCgmBPI+A6s0F6IJYueWNDcblyeiT7/frzMlL00PhsvbKjyKulRX2T4nVHBxvXAwAAkEd9Rx5tRB4FACC4Qrro2ZVMzsowJGS21yDd6KVL3sovs6jWZvdqx8pam10n66yqtzsUF2VW9/hYJcREKSs1UYsvHqrCimptKC5Xfpml2V0CbfWNAgAAQNvIo98hjwIAEP4oeoaIzmqQ7lq61NlLihxOyVJvVUJMgsfjTqdTBf8Oj9vPCI9mkzSmV5omZ2UoJz1JOT2SldMjWbU2uyz1VtU1OBQfbVZaXKxXIRYAAAAtkUfJowAARBKKniGisxqkG7l0yVd1DZ4/V3FlTZvLhBxOaWupRVtLLeqTFK85/14mlBAT1WpoBQAAgG/Io+RRAAAiSUhvZNTVdFaD9MntLDkKlPjolp9rb/kpLc0r9PqOgiPVdVqaV6i95aeMnh4AAECXRx5tH3kUAIDwQNEzxLgapPdJivfq/L5J8XpofLbOy0jx+j1cS5c6U5RJSouLbfZccWWNXtx20Oc7CertDr247aCKK2uMnCIAAABEHvUGeRQAgNBH0TMEuRqkL7hwsM7vnSbzGSuEokzS+b3TtODCwVp08VCfd4R0LV3qzCbyZ+7i6XQ69cqOIr9376y3O7R8R5Gczk5uBgUAANAFkEfbRx4FACC00dMzRJlMpoA2SHctXfLnyrY/ztzFs6CiukNN8iWppLpOhRXVyumR3KFxAAAA0BJ5tH3kUQAAQhdFzzAQiAbpTqdTZrNJ56Ylav+JakPHPlPP+BgN7t6t2XOfFJcbMvaG4nJCJgAAQICRR1tHHgUAIDRR9OyC2tud0mjH62x64vP97l0ua2125ZdZDBk7v8yiWpu9w3caAAAAoPOQRwEAQKDR07OL8XV3SqM03eXyZJ1VDoNaHzmckqXeasxgAAAACDjyKAAA6AwUPbsQf3enNEqgdrmsawjO5wEAAIBvyKMAAKCzUPTsIjq6O6VR6u0Ovft1qaFjxkfz1xgAACDUkUcBAEBn4rdzF2HE7pRGOVZTL5NBY0WZpLS4WINGAwAAQKCQRwEAQGei6NlFGLU7pVFS44zZQ2t0rzSaxgMAAIQB8igAAOhMFD27gFqbXdtKLcGeRjOW+gZDxpmSlWHIOAAAAAgc8igAAOhsFD27gC1HK2TQ5pQhpU+3OGWnJwV7GgAAAGhHpObRnvExGty9W7CnAQAAPKDoGeGKK2u0cu/hYE8jIGxOp749VRvsaQAAAKANkZxHj9fZ9MTn+w3fDR4AAHQcRc8I5toh0+aIxOvqUnmNVUvzCrW3/FSwpwIAAAAPIj2PStKR6joyKQAAIYiiZwQLpR0yA6Xe7tCL2w5ydR0AACAEdYU8KpFJAQAIRRQ9I1io7ZAZKPV2h5bvKJLTGbl3EAAAAISjrpJHJTIpAAChhqJnhKq12ZVfZgn2NDpNSXWdCiuqgz0NAAAA/FtXy6MSmRQAgFBC0TNCnayzKoJbJ3m0oQvdSQAAABDqumIelcikAACECoqeEare7gj2FDpdfplFtTZ7sKcBAAAAdc08KpFJAQAIFRQ9I1RcVNf71jqckqXeGuxpAAAAQF0zj0pkUgAAQkXXTCJdQPf4WJlNwZ5F56tr6Jp3FAAAAISarppHJTIpAAChgKJnhEqIidKYXmnBnoYkKTs9SYO7d+uU94qP5q80AABAKAilPCpJSTFRnfZeZFIAAIKP38YRbHJWRrCnIEn6+mS1HM7Ad7GPMklpcbEBfx8AAAB4J1TyqCRVd1KfTTIpAAChgaJnBMtJT1KfpPhgT0MOp3TAUhPw9xndK00JnXgFHwAAAG0LlTzamcikAACEBoqeEcxkMmnOqP5dpon8lBC6kwAAAAAdz6MxYdgUlEwKAEBo6BrVsC4sKzVR88YO8DlohlvA7JsUr+z0pGBPAwAAAGfwN4/GRZk167yzAzSrwCCTAgAQOih6dgHnZaToofHZXi8t6psUrwe/N8iw3TYDXT+NizLrjlH9ZTKFV6EWAACgq/Anjz40PlvjMruHzQ7wZFIAAEJLdLAngM6RlZqoxRcPVWFFtTYUlyu/zCJHk72FokyN/YemZGUoOz1JJpNJY3qlaWuppcPvPfKsVO08Vtns/YwSF2XWvLEDlJWaaPzgAAAAMIw/eVSSYZk0kMikAACEHoqeXYjJZFJOj2Tl9EhWrc0uS71VdQ0OxUeblRYX26Lh+uSsDEMC5hX9z1KUyWR4WO2bFK87RvUnXAIAAIQJX/OoZFwmze7eTYUnT3d4nDORSQEACE0UPbuohJgoJcQktHmOa7fNI9V1fr+Pq6+RUzIkrJpNjVf7z7wDAAikqoJCJedk+30cAAC05E0elYzLpNcPztT/2/y132M01dpdqQAAIHTQ0xOt6uhum037GrnCakekxkbrt5eP1NyxA5TTI5lwiU5x+M2/a+fDv9ThN//u13EAANAxRmXSIT2SO5xHJem24Wfrt1eMIpMCABDiKHqiTR3ZbbNpXyNXWO3IrvA1DXaV11r9fj3gq8Nv/l3Fr70uSSp+7fUWhc32jgMAAGMYkUk7Wjx1Wb2vRMdq6js0BgAACDyKnmiT0+mU2WzSuWne9yhy7bZ5XkZKs+fPSUlQSlyM33OxOZxavqNITmcAdkQCztC0oOnStLDZ3nEAAGAcozJpVmqi7h1zrjpyb2a93UEmBQAgDNDTE60qrqzRKzuKvO6fNLRHsq4b1LvVvkYFFdU60cE7NUuq61RYUa2cHskdGgdoi6eCpkvxa6+rcs9eWbblt3pckvpNnxaw+QEA0JUYnUmjoszqaLmSTAoAQOjjTk94tLf8lJbmFfrUMP6g5bTsDmerfY0+KS43ZG4bDBoH8KStgqdLawVPF+74BADAGGRSAADgL4qeaKG4skYvbjuoervDp9fV2x16cdtBFVfWtDhWa7Mrv8xiyPzyyyyqtdkNGQtoqqqgsN2Cp7eKX3tdVQWFhowFAEBXRCYFAAAdQdETzTidTr2yo8jncOnSWo+jk3VWOQxqe+RwSpZ6NjSC8ZJzspX1o9mGjJX1o9lKzsk2ZCwAALoaMikAAOgoip5opqCi2qflQ564ehw15W9gbU2RpeWVe8AI/aZP63DhM+tHs+npCQBAB4RLJq1rMHY8AABgHIqeaCZQPY7iooz9q/bu16XsmImA6Ujhk4InAAAdFy6ZNC6qI/vAAwCAQKLoCbdA9jjqHh8rs4GZ8FhNfYsr94CR+k2fprSxY3x6TdrYMRQ8AQDooHDKpMdrbMYNBgAADEXRE26B7HGUEBOlMb3SjBn839gxE4F0+M2/t7tL+5ks2/LZtR0AgA4Kp0y6seSEYWMBAABjUfSEW6B7HE3OyjB0fHbMRKAcfvPvfu/iXvza6xQ+AQDogEBn0gl90w0bmzwKAEDoougJN6N7HMVHNx8vJz1JGYmxho3PjpkIhI4UPF0ofAIA4L9AZ9KeCeRRAAC6AoqecDOyx1GUSUqLax4oTSaTrh+Uacwb/Bs7ZsJIVQWFHS54uhS/9rqqCgoNGQsAgK4k0JnUatTa+X8jjwIAEJooesLNyB5Ho3ulKSEmqsXz/VMTDRnf5cwr90BHJOdk+71r+5myfjRbyTnZhowFAEBXEuhMGug7SQEAQGjgNzSaMarv5pRWxgn0lXugo/pNn9Zu4bO9Xd2zfjSbXdwBAOiAQGZS8igAAF0DRU80k929m3rEx3RojL5J8cpOT/J4rDPuJgU6qq3CZ9aPZmvYokfbPE7BEwCAjglkJiWPAgDQNVD0hFtxZY2e+Hy/TtTZ/B4jLsqsO0b1l8nU+uXzQN9NChjBU+GzaUGzveMAAMA/nZFJyaMAAES+6GBPAKFhb/kpvbjtoOrt/jdij4sya97YAcpqpW+n0+lUQUW1Pi465vd7uGQkxLZ6NylgFFcBs/i11z0WNNs7DgAAfBPoTGpkHo02mwzvDwoAAIxD0RMqrqzpcLjsmxSvO0b1b7XgWVxZo1d2FOlIdZ3f79GUpd6mfcerdF5GiiHjAa3pN32aUkcMb3VTovaOAwAA7wQ6kxqdRxscTj276SvNGzuATAoAQAji0mQX53Q69cqOog6Fy54JsXr8oiGtFjz3lp/S0rxCwwKmJNkcTr247aCKK2sMGxNoTXsFTQqeAAB0TKAzaSDyqCTV2x1kUgAAQhRFzy6uoKK6w+HveK1VX5087fGYEVfsW1Nvd2j5jiI5nU7DxwYAAEDnCWQmDWQelcikAACEKoqeXdwnxeWGjLPBwzhGXLFvT0l1nQorqgM2PgAAAAIvUJm0M/KoRCYFACAUUfTswmptduWXWQwZK7/MolqbvdlzRlyx94angisAAADCQyAzaWflUYlMCgBAqKHo2YWdrLPKYdAqHIdTstRbmz1n1BX79ngquAIAACA8BDKTdlYelcikAACEGoqeXZjRy3zqGr4bz8gr9u3xVHAFAABAeAhUJu3MPCqRSQEACDUUPbuwuChjv/3x0d+NZ+QVe280LbgCAAAgfAQqk3Z2HpXIpAAAhJLoYE8Avqmvs+mUpU5Wa4NiY6OVkhavuPgYv8bqHh8rs0mGhEGzqfFqeq3NroSYqIA3iz9T04IrAAAAAqvWZtfJOqvq7Q7FRZnVPT5WCTFRfo0VqEza2XlUIpMCABBKKHqGAafTqaIDJ7TliyLt310mZ5NEaDKbNGR4b42blKX+A3vIZDJ5PW5CTJTG9ErT1lJLh+focEpLviyU2SSN6ZWmERkpHR7TW1EmKS0uttPeDwAAoCtyOp0qqKjWhuJybS+zNCtSujLg5KwM5aQnhUQmHdIjucPj+YJMCgBAaKHoGeKOHq7UWyu3q7y0yuNxp8OpfTuPat/Oo8ronawbZ41WZr9Ur8efnJVhSMB0cTilraUWQ8dsz+heaX7fWQAAAID2FVfW6JUdRa3uhN40A/ZJitecUf2VlZro9fiByKR7j3vOz4FCJgUAILSw/iKEHSgoV+4LG1steJ6pvLRKuS9s1IEC73epzElPUp+keH+nGBKmZGUEewoAAAARa2/5KS3NK2y14HmmI9V1WppXqL3lp7x+DzIpAAAwGkXPEHX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+      "text/plain": [
+       "<Figure size 800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 683,
+       "width": 670
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's plot K-means\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "for i in range(4):\n",
+    "    distances = cdist(X, cent_history[i])\n",
+    "    labels = distances.argmin(axis=1)\n",
+    "\n",
+    "    plt.subplot(2, 2, i + 1)\n",
+    "    plt.plot(X[labels == 0, 0], X[labels == 0, 1], \"bo\", label=\"cluster #1\")\n",
+    "    plt.plot(X[labels == 1, 0], X[labels == 1, 1], \"co\", label=\"cluster #2\")\n",
+    "    plt.plot(X[labels == 2, 0], X[labels == 2, 1], \"mo\", label=\"cluster #3\")\n",
+    "    plt.plot(cent_history[i][:, 0], cent_history[i][:, 1], \"rX\")\n",
+    "    plt.legend(loc=0)\n",
+    "    plt.title(\"Step {:}\".format(i + 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we used Euclidean distance, but the algorithm will converge with any other metric. You can not only vary the number of steps or the convergence criteria but also the distance measure between the points and cluster centroids.\n",
+    "\n",
+    "Another \"feature\" of this algorithm is its sensitivity to the initial positions of the cluster centroids. You can run the algorithm several times and then average all the centroid results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Choosing the number of clusters for K-means\n",
+    "\n",
+    "In contrast to the supervised learning tasks such as classification and regression, clustering requires more effort to choose the optimization criterion. Usually, when working with k-means, we optimize the sum of squared distances between the observations and their centroids.\n",
+    "\n",
+    "$$\\Large J(C) = \\sum_{k=1}^K\\sum_{i~\\in~C_k} ||x_i - \\mu_k|| \\rightarrow \\min\\limits_C,$$\n",
+    "\n",
+    "where $C$ – is a set of clusters with power $K$, $\\mu_k$ is a centroid of a cluster $C_k$.\n",
+    "\n",
+    "This definition seems reasonable -- we want our observations to be as close to their centroids as possible. But, there is a problem -- the optimum is reached when the number of centroids is equal to the number of observations, so you would end up with every single observation as its own separate cluster.\n",
+    "\n",
+    "In order to avoid that case, we should choose a number of clusters after which a function $J(C_k)$ is decreasing less rapidly. More formally,\n",
+    "$$\\Large D(k) = \\frac{|J(C_k) - J(C_{k+1})|}{|J(C_{k-1}) - J(C_k)|}  \\rightarrow \\min\\limits_k $$\n",
+    "\n",
+    "Let's look at an example.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\anaconda3\\envs\\myconda1\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1037: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
+      "  \"KMeans is known to have a memory leak on Windows \"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$J(C_k)$')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 437,
+       "width": 582
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "inertia = []\n",
+    "for k in range(1, 8):\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=1).fit(X)\n",
+    "    inertia.append(np.sqrt(kmeans.inertia_))\n",
+    "plt.plot(range(1, 8), inertia, marker=\"s\")\n",
+    "plt.xlabel(\"$k$\")\n",
+    "plt.ylabel(\"$J(C_k)$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that $J(C_k)$ decreases significantly until the number of clusters is 3 and then does not change as much anymore. This means that the optimal number of clusters is 3. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Issues\n",
+    "\n",
+    "Inherently, K-means is NP-hard. For $d$ dimensions, $k$ clusters, and $n$ observations, we will find a solution in $O(n^{d k+1})$ in time. There are some heuristics to deal with this; an example is MiniBatch K-means, which takes portions (batches) of data instead of fitting the whole dataset and then moves centroids by taking the average of the previous steps. Compare the implementation of K-means and MiniBatch K-means in the [sckit-learn documentation](http://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html).\n",
+    "\n",
+    "The [implemetation](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) of the algorithm using `scikit-learn` has its benefits such as the possibility to state the number of initializations with the `n_init` function parameter, which enables us to identify more robust centroids. Moreover, these runs can be done in parallel to decrease the computation time.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spectral clustering\n",
+    "\n",
+    "Spectral clustering combines some of the approaches described above to create a stronger clustering method.\n",
+    "\n",
+    "First of all, this algorithm requires us to define the similarity matrix for observations called the adjacency matrix. This can be done in a similar fashion as in the Affinity Propagation algorithm: $A_{i, j} = - ||x_i - x_j||^{2}$. This matrix describes a full graph with the observations as vertices and the estimated similarity value between a pair of observations as edge weights for that pair of vertices. For the metric defined above and two-dimensional observations, this is pretty intuitive - two observations are similar if the edge between them is shorter. \n",
+    "We'd like to split up the graph into two subgraphs in such a way that each observation in each subgraph would be similar to another observation in that subgraph. Formally, this is a Normalized cuts problem; for more details, we recommend reading [this paper](http://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Agglomerative clustering\n",
+    "\n",
+    "\n",
+    "The following algorithm is the simplest and easiest to understand among all the the clustering algorithms without a fixed number of clusters.\n",
+    "\n",
+    "\n",
+    "The algorithm is fairly simple:\n",
+    "1. We start by assigning each observation to its own cluster\n",
+    "2. Then sort the pairwise distances between the centers of clusters in descending order\n",
+    "3. Take the nearest two neigbor clusters and merge them together, and recompute the centers\n",
+    "4. Repeat steps 2 and 3 until all the data is merged into one cluster\n",
+    "\n",
+    "The process of searching for the nearest cluster can be conducted with different methods of bounding the observations:\n",
+    "1. Single linkage \n",
+    "$d(C_i, C_j) = min_{x_i \\in C_i, x_j \\in C_j} ||x_i - x_j||$\n",
+    "2. Complete linkage \n",
+    "$d(C_i, C_j) = max_{x_i \\in C_i, x_j \\in C_j} ||x_i - x_j||$\n",
+    "3. Average linkage \n",
+    "$d(C_i, C_j) = \\frac{1}{n_i n_j} \\sum_{x_i \\in C_i} \\sum_{x_j \\in C_j} ||x_i - x_j||$\n",
+    "4. Centroid linkage \n",
+    "$d(C_i, C_j) = ||\\mu_i - \\mu_j||$\n",
+    "\n",
+    "The 3rd one is the most effective in computation time since it does not require recomputing the distances every time the clusters are merged.\n",
+    "\n",
+    "The results can be visualized as a beautiful cluster tree (dendogram) to help recognize the moment the algorithm should be stopped to get optimal results. There are plenty of Python tools to build these dendograms for agglomerative clustering.\n",
+    "\n",
+    "Let's consider an example with the clusters we got from K-means:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 429,
+       "width": 829
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.zeros((150, 2))\n",
+    "\n",
+    "np.random.seed(seed=42)\n",
+    "X[:50, 0] = np.random.normal(loc=0.0, scale=0.3, size=50)\n",
+    "X[:50, 1] = np.random.normal(loc=0.0, scale=0.3, size=50)\n",
+    "\n",
+    "X[50:100, 0] = np.random.normal(loc=2.0, scale=0.5, size=50)\n",
+    "X[50:100, 1] = np.random.normal(loc=-1.0, scale=0.2, size=50)\n",
+    "\n",
+    "X[100:150, 0] = np.random.normal(loc=-1.0, scale=0.2, size=50)\n",
+    "X[100:150, 1] = np.random.normal(loc=2.0, scale=0.5, size=50)\n",
+    "\n",
+    "# pdist will calculate the upper triangle of the pairwise distance matrix\n",
+    "distance_mat = pdist(X)\n",
+    "# linkage — is an implementation if agglomerative algorithm\n",
+    "Z = hierarchy.linkage(distance_mat, \"single\")\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "dn = hierarchy.dendrogram(Z, color_threshold=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accuracy metrics\n",
+    "\n",
+    "As opposed to classfication, it is difficult to assess the quality of results from clustering. Here, a metric cannot depend on the labels but only on the goodness of split. Secondly, we do not usually have true labels of the observations when we use clustering.\n",
+    "\n",
+    "There are *internal* and *external* goodness metrics. External metrics use the information about the known true split while internal metrics do not use any external information and assess the goodness of clusters based only on the initial data. The optimal number of clusters is usually defined with respect to some internal metrics. \n",
+    "\n",
+    "All the metrics described below are implemented in `sklearn.metrics`.\n",
+    "\n",
+    "**Adjusted Rand Index (ARI)**\n",
+    "\n",
+    "Here, we assume that the true labels of objects are known. This metric does not depend on the labels' values but on the data cluster split. Let $N$ be the number of observations in a sample. Let $a$ to be the number of observation pairs with the same labels and located in the same cluster, and let $b$ to be the number of observations with different labels and located in different clusters. The Rand Index can be calculated using the following formula: \n",
+    "\n",
+    "$$\\Large \\text{RI} = \\frac{2(a + b)}{n(n-1)}.$$ \n",
+    "\n",
+    "In other words, it evaluates a share of observations for which these splits (initial and clustering result) are consistent. The Rand Index (RI) evaluates the similarity of the two splits of the same sample. In order for this index to be close to zero for any clustering outcomes with any $n$ and number of clusters, it is essential to scale it, hence the Adjusted Rand Index: \n",
+    "\n",
+    "$$\\Large \\text{ARI} = \\frac{\\text{RI} - E[\\text{RI}]}{\\max(\\text{RI}) - E[\\text{RI}]}.$$\n",
+    "\n",
+    "This metric is symmetric and does not depend in the label permutation. Therefore, this index is a measure of distances between different sample splits. $\\text{ARI}$ takes on values in the $[-1, 1]$ range. Negative values indicate the independence of splits, and positive values indicate that these splits are consistent (they match $\\text{ARI} = 1$).\n",
+    "\n",
+    "**Adjusted Mutual Information (AMI)**\n",
+    "\n",
+    "This metric is similar to $\\text{ARI}$. It is also symmetric and does not depend on the labels' values and permutation. It is defined by the [entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) function and interprets a sample split as a discrete distribution (likelihood of assigning to a cluster is equal to the percent of objects in it). The $MI$ index is defined as the [mutual information](https://en.wikipedia.org/wiki/Mutual_information) for two distributions, corresponding to the sample split into clusters. Intuitively, the mutual information measures the share of information common for both clustering splits i.e. how information about one of them decreases the uncertainty of the other one.\n",
+    "\n",
+    "Similarly to the $\\text{ARI}$, the $\\text{AMI}$ is defined. This allows us to get rid of the $MI$ index's increase with the number of clusters. The $\\text{AMI}$ lies in the $[0, 1]$ range. Values close to zero mean the splits are independent, and those close to 1 mean they are similar (with complete match at $\\text{AMI} = 1$).\n",
+    "\n",
+    "**Homogeneity, completeness, V-measure**\n",
+    "\n",
+    "Formally, these metrics are also defined based on the entropy function and the conditional entropy function, interpreting the sample splits as discrete distributions: \n",
+    "\n",
+    "$$\\Large h = 1 - \\frac{H(C\\mid K)}{H(C)}, c = 1 - \\frac{H(K\\mid C)}{H(K)},$$\n",
+    "\n",
+    "where $K$ is a clustering result and $C$ is the initial split. Therefore, $h$ evaluates whether each cluster is composed of same class objects, and $c$ measures how well the same class objects fit the clusters. These metrics are not symmetric. Both lie in the $[0, 1]$ range, and values closer to 1 indicate more accurate clustering results. These metrics' values are not scaled as the $\\text{ARI}$ or $\\text{AMI}$ metrics are and thus depend on the number of clusters. A random clustering result will not have metrics' values closer to zero when the number of clusters is big enough and the number of objects is small. In such a case, it would be more reasonable to use $\\text{ARI}$. However, with a large number of observations (more than 100) and the number of clusters less than 10, this issue is less critical and can be ignored.\n",
+    "\n",
+    "$V$-measure is a combination of $h$, and $c$ and is their harmonic mean:\n",
+    "$$\\Large v = 2\\frac{hc}{h+c}.$$\n",
+    "It is symmetric and measures how consistent two clustering results are.\n",
+    "\n",
+    "**Silhouette**\n",
+    "\n",
+    "In contrast to the metrics described above, this coefficient does not imply the knowledge about the true labels of the objects. It lets us estimate the quality of the clustering using only the initial, unlabeled sample and the clustering result. To start with, for each observation, the silhouette coefficient is computed. Let $a$ be the mean of the distance between an object and other objects within one cluster and $b$ be the mean distance from an object to an object from the nearest cluster (different from the one the object belongs to). Then the silhouette measure for this object is \n",
+    "\n",
+    "$$\\Large s = \\frac{b - a}{\\max(a, b)}.$$\n",
+    "\n",
+    "The silhouette of a sample is a mean value of silhouette values from this sample. Therefore, the silhouette distance shows to which extent the distance between the objects of the same class differ from the mean distance between the objects from different clusters. This coefficient takes values in the $[-1, 1]$ range. Values close to -1 correspond to bad clustering results while values closer to 1 correspond to dense, well-defined clusters. Therefore, the higher the silhouette value is, the better the results from clustering.\n",
+    "\n",
+    "With the help of silhouette, we can identify the optimal number of clusters $k$ (if we don't know it already from the data) by taking the number of clusters that maximizes the silhouette coefficient.\n",
+    "\n",
+    "To conclude, let's take a look at how these metrics perform with the MNIST handwritten numbers dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ARI</th>\n",
+       "      <th>AMI</th>\n",
+       "      <th>Homogenity</th>\n",
+       "      <th>Completeness</th>\n",
+       "      <th>V-measure</th>\n",
+       "      <th>Silhouette</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>K-means</th>\n",
+       "      <td>0.662295</td>\n",
+       "      <td>0.736567</td>\n",
+       "      <td>0.735448</td>\n",
+       "      <td>0.742972</td>\n",
+       "      <td>0.739191</td>\n",
+       "      <td>0.182097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Affinity</th>\n",
+       "      <td>0.174871</td>\n",
+       "      <td>0.612364</td>\n",
+       "      <td>0.958899</td>\n",
+       "      <td>0.486801</td>\n",
+       "      <td>0.645767</td>\n",
+       "      <td>0.115161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Spectral</th>\n",
+       "      <td>0.756461</td>\n",
+       "      <td>0.852040</td>\n",
+       "      <td>0.831691</td>\n",
+       "      <td>0.876614</td>\n",
+       "      <td>0.853562</td>\n",
+       "      <td>0.182729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Agglomerative</th>\n",
+       "      <td>0.794003</td>\n",
+       "      <td>0.866832</td>\n",
+       "      <td>0.857513</td>\n",
+       "      <td>0.879096</td>\n",
+       "      <td>0.868170</td>\n",
+       "      <td>0.178497</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    ARI       AMI  Homogenity  Completeness  V-measure  \\\n",
+       "K-means        0.662295  0.736567    0.735448      0.742972   0.739191   \n",
+       "Affinity       0.174871  0.612364    0.958899      0.486801   0.645767   \n",
+       "Spectral       0.756461  0.852040    0.831691      0.876614   0.853562   \n",
+       "Agglomerative  0.794003  0.866832    0.857513      0.879096   0.868170   \n",
+       "\n",
+       "               Silhouette  \n",
+       "K-means          0.182097  \n",
+       "Affinity         0.115161  \n",
+       "Spectral         0.182729  \n",
+       "Agglomerative    0.178497  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = datasets.load_digits()\n",
+    "X, y = data.data, data.target\n",
+    "\n",
+    "algorithms = []\n",
+    "algorithms.append(KMeans(n_clusters=10, random_state=1))\n",
+    "algorithms.append(AffinityPropagation())\n",
+    "algorithms.append(\n",
+    "    SpectralClustering(n_clusters=10, random_state=1, affinity=\"nearest_neighbors\")\n",
+    ")\n",
+    "algorithms.append(AgglomerativeClustering(n_clusters=10))\n",
+    "\n",
+    "data = []\n",
+    "for algo in algorithms:\n",
+    "    algo.fit(X)\n",
+    "    data.append(\n",
+    "        (\n",
+    "            {\n",
+    "                \"ARI\": metrics.adjusted_rand_score(y, algo.labels_),\n",
+    "                \"AMI\": metrics.adjusted_mutual_info_score(\n",
+    "                    y, algo.labels_, average_method=\"arithmetic\"\n",
+    "                ),\n",
+    "                \"Homogenity\": metrics.homogeneity_score(y, algo.labels_),\n",
+    "                \"Completeness\": metrics.completeness_score(y, algo.labels_),\n",
+    "                \"V-measure\": metrics.v_measure_score(y, algo.labels_),\n",
+    "                \"Silhouette\": metrics.silhouette_score(X, algo.labels_),\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "results = pd.DataFrame(\n",
+    "    data=data,\n",
+    "    columns=[\"ARI\", \"AMI\", \"Homogenity\", \"Completeness\", \"V-measure\", \"Silhouette\"],\n",
+    "    index=[\"K-means\", \"Affinity\", \"Spectral\", \"Agglomerative\"],\n",
+    ")\n",
+    "\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Demo assignment\n",
+    "\n",
+    "To practice with PCA and clustering, you can complete [this assignment](https://www.kaggle.com/kashnitsky/a7-demo-unsupervised-learning) where you'll be analyzing data from accelerometers and gyros of Samsung Galaxy S3 mobile phones. The assignment is just for you to practice, and goes with [solution](https://www.kaggle.com/kashnitsky/a7-demo-unsupervised-learning-solution)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Useful resources\n",
+    "- Medium [\"story\"](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-7-unsupervised-learning-pca-and-clustering-db7879568417) based on this notebook\n",
+    "- Main course [site](https://mlcourse.ai), [course repo](https://github.com/Yorko/mlcourse.ai), and YouTube [channel](https://www.youtube.com/watch?v=QKTuw4PNOsU&list=PLVlY_7IJCMJeRfZ68eVfEcu-UcN9BbwiX)\n",
+    "- Course materials as a [Kaggle Dataset](https://www.kaggle.com/kashnitsky/mlcourse)\n",
+    "- Overview of clustering methods in the [scikit-learn doc](http://scikit-learn.org/stable/modules/clustering.html).\n",
+    "- [Q&A](http://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues) for PCA with examples\n",
+    "- [Notebook](https://github.com/diefimov/MTH594_MachineLearning/blob/master/ipython/Lecture10.ipynb) on k-means and the EM-algorithm"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb b/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
index f03e19d996..37adb7c283 100644
--- a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
+++ b/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
@@ -1,12 +1,71 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython requests flask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from flask import Flask, request, render_template\n",
+    "import pickle\n",
+    "import sklearn\n",
+    "import requests\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build a web app to use a Machine Learning model"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "86574a90",
    "metadata": {},
    "source": [
-    "# Build a web app to use a Machine Learning model\n",
-    "\n",
     "In this section, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from NUFORC's database.\n",
     "\n",
     "You will learn:\n",
@@ -16,12 +75,22 @@
     "\n",
     "We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model 'in the wild', so to speak: in a web app.\n",
     "\n",
-    "To do this, you need to build a web app using Flask.\n",
-    "\n",
+    "To do this, you need to build a web app using Flask."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Building an app\n",
     "\n",
-    "There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app.\n",
-    "\n",
+    "There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "### Considerations\n",
     "\n",
     "There are many questions you need to ask:\n",
@@ -36,16 +105,26 @@
     "\n",
     "You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context.\n",
     "\n",
-    "For our purposes, since we have been working with Python-based notebooks, let's explore the steps you need to take to export a trained model from such a notebook to a format readable by a Python-built web app.\n",
-    "\n",
+    "For our purposes, since we have been working with Python-based notebooks, let's explore the steps you need to take to export a trained model from such a notebook to a format readable by a Python-built web app."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Tool\n",
     "\n",
     "For this task, you need two tools: Flask and Pickle, both of which run on Python.\n",
     "\n",
     "✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) to practice building with Flask.\n",
     "\n",
-    "✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.\n",
-    "\n",
+    "✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Exercise - clean your data\n",
     "\n",
     "In this section, you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:\n",
@@ -57,13 +136,12 @@
     "\n",
     "Create a blank `notebook` to continue the steps below:\n",
     "\n",
-    "Import `pandas`, `matplotlib`, and `numpy` as you did in the previous section and import the ufos spreadsheet. You can take a look at a sample data set:"
+    "Import `pandas`, `matplotlib`, and `numpy` as you did in the previous section and import the ufos spreadsheet. You can take a look at a sample data set:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ecc56479",
+   "execution_count": 3,
    "metadata": {
     "tags": [
      "output_scroll"
@@ -71,16 +149,14 @@
    },
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "    \n",
-    "ufos = pd.read_csv('../assets/data/ufos.csv')\n",
+    "ufos = pd.read_csv(\n",
+    "    \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ufos.csv\"\n",
+    ")\n",
     "ufos.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e135d535",
    "metadata": {},
    "source": [
     "Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the `Country` field."
@@ -89,23 +165,34 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b6aec34a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "ufos = pd.DataFrame({\n",
-    "    'Seconds': ufos['duration (seconds)'], \n",
-    "    'Country': ufos['country'],\n",
-    "    'Latitude': ufos['latitude'],\n",
-    "    'Longitude': ufos['longitude']\n",
-    "})\n",
+    "ufos = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Seconds\": ufos[\"duration (seconds)\"],\n",
+    "        \"Country\": ufos[\"country\"],\n",
+    "        \"Latitude\": ufos[\"latitude\"],\n",
+    "        \"Longitude\": ufos[\"longitude\"],\n",
+    "    }\n",
+    ")\n",
     "\n",
     "ufos.Country.unique()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1ceaa63b",
    "metadata": {},
    "source": [
     "Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:"
@@ -114,48 +201,137 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0061931b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 25863 entries, 2 to 80330\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Seconds    25863 non-null  float64\n",
+      " 1   Country    25863 non-null  object \n",
+      " 2   Latitude   25863 non-null  float64\n",
+      " 3   Longitude  25863 non-null  float64\n",
+      "dtypes: float64(3), object(1)\n",
+      "memory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
    "source": [
     "ufos.dropna(inplace=True)\n",
     "\n",
-    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "ufos = ufos[(ufos[\"Seconds\"] >= 1) & (ufos[\"Seconds\"] <= 60)]\n",
     "\n",
     "ufos.info()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fa5daa5b",
    "metadata": {},
    "source": [
-    "Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:"
+    "Use Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f69f768f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Seconds</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>53.200000</td>\n",
+       "      <td>-2.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>28.978333</td>\n",
+       "      <td>-96.645833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>35.823889</td>\n",
+       "      <td>-80.253611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>45.582778</td>\n",
+       "      <td>-122.352222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>51.783333</td>\n",
+       "      <td>-0.783333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "\n",
-    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "ufos[\"Country\"] = LabelEncoder().fit_transform(ufos[\"Country\"])\n",
     "\n",
     "ufos.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b7ea6d3",
    "metadata": {},
    "source": [
-    "```{note}\n",
-    "LabelEncoder encodes data alphabetically.\n",
-    "```\n",
-    "\n",
     "## Exercise - build your model\n",
     "\n",
     "Now you can get ready to train a model by dividing the data into the training and testing group.\n",
@@ -166,23 +342,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6ae5e8f5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "Selected_features = ['Seconds', 'Latitude', 'Longitude']\n",
+    "Selected_features = [\"Seconds\", \"Latitude\", \"Longitude\"]\n",
     "\n",
     "X = ufos[Selected_features].values\n",
-    "y = ufos['Country'].values\n",
+    "y = ufos[\"Country\"].values\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d37da3c3",
    "metadata": {},
    "source": [
     "Train your model using logistic regression:"
@@ -191,59 +363,95 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f1dfb39d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       0.86      0.31      0.45       250\n",
+      "           2       1.00      1.00      1.00         8\n",
+      "           3       1.00      1.00      1.00       131\n",
+      "           4       0.96      1.00      0.98      4743\n",
+      "\n",
+      "    accuracy                           0.96      5173\n",
+      "   macro avg       0.96      0.86      0.89      5173\n",
+      "weighted avg       0.96      0.96      0.96      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9640440750048328\n"
+     ]
+    }
+   ],
    "source": [
-    "from sklearn.metrics import accuracy_score, classification_report\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "\n",
-    "model = LogisticRegression(solver=\"sag\",max_iter=10000)\n",
+    "model = LogisticRegression(solver=\"sag\", max_iter=10000)\n",
     "model.fit(X_train, y_train)\n",
     "predictions = model.predict(X_test)\n",
     "\n",
     "print(classification_report(y_test, predictions))\n",
-    "print('Predicted labels: ', predictions)\n",
-    "print('Accuracy: ', accuracy_score(y_test, predictions))"
+    "print(\"Predicted labels: \", predictions)\n",
+    "print(\"Accuracy: \", accuracy_score(y_test, predictions))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "80ed331f",
    "metadata": {},
    "source": [
     "The accuracy isn't bad **(around 95%)**, unsurprisingly, as `Country` and `Latitude/Longitude` correlate.\n",
     "\n",
-    "The model you created isn't very revolutionary as you should be able to infer a `Country` from its `Latitude` and `Longitude`, but it's a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.\n",
-    "\n",
+    "The model you created isn't very revolutionary as you should be able to infer a `Country` from its `Latitude` and `Longitude`, but it's a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Exercise - 'pickle' your model\n",
     "\n",
-    "Now, it's time to _pickle_ your model! You can do that in a few lines of code. Once it's _pickled_, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,"
+    "Now, it's time to _pickle_ your model! You can do that in a few lines of code. Once it's _pickled_, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6afd0b36",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
    "source": [
-    "import pickle\n",
+    "# download .pkl file from the cloud\n",
+    "cloud_url = \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ufo-model.pkl\"\n",
+    "model_filename = \"ufo-model.pkl\"\n",
+    "response = requests.get(cloud_url)\n",
+    "with open(model_filename, \"wb\") as local_file:\n",
+    "    local_file.write(response.content)\n",
     "\n",
-    "model_filename = '../assets/pickle/ufo-model.pkl'\n",
-    "pickle.dump(model, open(model_filename, 'wb'))\n",
+    "pickle.dump(model, open(model_filename, \"wb\"))\n",
     "\n",
-    "model = pickle.load(open('../assets/pickle/ufo-model.pkl', 'rb'))\n",
-    "print(model.predict(pd.DataFrame([[50, 44, -12]])))"
+    "model = pickle.load(open(\"ufo-model.pkl\", \"rb\"))\n",
+    "print(model.predict([[50, 44, -12]]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model returns **'3'**, which is the country code for the UK. Wild! 👽"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "df00311f",
    "metadata": {},
    "source": [
-    "The model returns **'3'**, which is the country code for the UK. Wild! 👽\n",
-    "\n",
     "## Exercise - build a Flask app\n",
     "\n",
     "Now you can build a Flask app to call your model and return similar results but in a more visually pleasing way.\n",
@@ -252,7 +460,7 @@
     "\n",
     "In that folder create three more folders: `/static`, with a folder `/css` inside it, and `/templates`. You should now have the following files and directories:\n",
     "\n",
-    "```output\n",
+    ":::output\n",
     "web-app/\n",
     "    static/\n",
     "    css/\n",
@@ -262,30 +470,30 @@
     "    assets/\n",
     "        pickle/\n",
     "            ufo-model.pkl\n",
-    "```\n",
+    ":::\n",
     "\n",
     "✅ Refer to the solution folder for a view of the finished app\n",
     "\n",
     "The first file to create in `/web-app` folder is `requirements.txt` file. Like `package.json` in a JavaScript app, this file lists dependencies required by the app. In `requirements.txt` add the lines:\n",
     "\n",
-    "```text\n",
+    ":::text\n",
     "scikit-learn\n",
     "pandas\n",
     "numpy\n",
     "flask\n",
-    "```\n",
+    ":::\n",
     "\n",
     "Now, run this file by navigating to `/web-app`:\n",
     "\n",
-    "```bash\n",
+    ":::bash\n",
     "cd web-app\n",
-    "```\n",
+    ":::\n",
     "\n",
     "In your terminal type `pip install`, to install the libraries listed in `requirements.txt`:\n",
     "\n",
-    "```bash\n",
+    ":::bash\n",
     "pip install -r requirements.txt\n",
-    "```\n",
+    ":::\n",
     "\n",
     "Now, you're ready to create three more files to finish the app:\n",
     "\n",
@@ -295,7 +503,7 @@
     "\n",
     "Build out the _styles.css_ file with a few styles:\n",
     "\n",
-    "```css\n",
+    ":::css\n",
     "body {\n",
     "    width: 100%;\n",
     "    height: 100%;\n",
@@ -325,11 +533,11 @@
     "    padding: 12px;\n",
     "    display: inline-block;\n",
     "}\n",
-    "```\n",
+    ":::\n",
     "\n",
     "Next, build out the _index.html_ file:\n",
     "\n",
-    "```html\n",
+    ":::html\n",
     "<!DOCTYPE html>\n",
     "<html>\n",
     "    <head>\n",
@@ -360,24 +568,33 @@
     "\n",
     "    </body>\n",
     "</html>\n",
-    "```\n",
-    "\n",
-    "```{note}\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Take a look at the templating in this file. Notice the 'mustache' syntax around variables that will be provided by the app, like the prediction text: `{{}}`. There's also a form that posts a prediction to the `/predict` route.\n",
     "\n",
     "Finally, you're ready to build the python file that drives the consumption of the model and the display of predictions:\n",
-    "```\n",
-    "\n",
-    "In `app.py` add:\n",
-    "\n",
-    "```python\n",
-    "import numpy as np\n",
-    "from flask import Flask, request, render_template\n",
-    "import pickle\n",
     "\n",
+    "In `app.py` add:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::\n",
     "app = Flask(__name__)\n",
     "\n",
-    "model = pickle.load(open(\"../assets/pickle/ufo-model.pkl\", \"rb\"))\n",
+    "model = pickle.load(\n",
+    "    open(\n",
+    "        \"ufo-model.pkl\",\n",
+    "        \"rb\",\n",
+    "    )\n",
+    ")\n",
     "\n",
     "\n",
     "@app.route(\"/\")\n",
@@ -387,7 +604,6 @@
     "\n",
     "@app.route(\"/predict\", methods=[\"POST\"])\n",
     "def predict():\n",
-    "\n",
     "    int_features = [int(x) for x in request.form.values()]\n",
     "    final_features = [np.array(int_features)]\n",
     "    prediction = model.predict(final_features)\n",
@@ -402,13 +618,21 @@
     "\n",
     "\n",
     "if __name__ == \"__main__\":\n",
-    "    app.run(debug=True)\n",
-    "```\n",
-    "\n",
-    "```{note}\n",
-    "when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.\n",
-    "```\n",
-    "\n",
+    "    app.run(debug=False)\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!\n",
     "\n",
     "Before doing that, take a look at the parts of `app.py`:\n",
@@ -422,16 +646,31 @@
     "1. The form variables are gathered and converted to a NumPy array. They are then sent to the model and a prediction is returned.\n",
     "2. The Countries that we want to be displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.\n",
     "\n",
-    "Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction.\n",
-    "\n",
+    "Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Your turn! 🚀\n",
     "\n",
-    "Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `/train`. What are the pros and cons of pursuing this method? Practice it by following this [assignment](../assignments/ml-fundamentals/try-a-different-model.md)\n",
-    "\n",
+    "Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `/train`. What are the pros and cons of pursuing this method? Practice it by following this [assignment](../assignments/ml-fundamentals/try-a-different-model.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Self study\n",
     "\n",
-    "There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.\n",
-    "\n",
+    "There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Acknowledgements\n",
     "\n",
     "Thanks to Microsoft for creating the open-source course [ML-For-Beginners](https://github.com/microsoft/ML-For-Beginners). It inspires the majority of the content in this chapter."
@@ -439,39 +678,24 @@
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "Python",
-   "name": "Python3"
-  },
-  "source_map": [
-   14,
-   70,
-   77,
-   81,
-   90,
-   94,
-   100,
-   104,
-   110,
-   122,
-   131,
-   135,
-   146,
-   156,
-   164
-  ]
+   "display_name": "myconda1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.16"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}
diff --git a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.md b/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.md
deleted file mode 100644
index 478271b759..0000000000
--- a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.md
+++ /dev/null
@@ -1,358 +0,0 @@
----
-jupytext:
-  cell_metadata_filter: -all
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: Python
-  name: Python3
----
-
-# Build a web app to use a Machine Learning model
-
-In this section, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from NUFORC's database.
-
-You will learn:
-
-- How to 'pickle' a trained model
-- How to use that model in a Flask app
-
-We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model 'in the wild', so to speak: in a web app.
-
-To do this, you need to build a web app using Flask.
-
-## Building an app
-
-There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app.
-
-### Considerations
-
-There are many questions you need to ask:
-
-- **Is it a web app or a mobile app?** If you are building a mobile app or need to use the model in an IoT context, you could use [TensorFlow Lite](https://www.tensorflow.org/lite/) and use the model in an Android or iOS app.
-- **Where will the model reside?** In the cloud or locally?
-- **Offline support.** Does the app have to work offline?
-- **What technology was used to train the model?** The chosen technology may influence the tooling you need to use.
-  - **Using TensorFlow.** If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).
-  - **Using PyTorch.** If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future section for a Scikit-learn-trained model.
-  - **Using Lobe.ai or Azure Custom Vision.** If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
-
-You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context.
-
-For our purposes, since we have been working with Python-based notebooks, let's explore the steps you need to take to export a trained model from such a notebook to a format readable by a Python-built web app.
-
-## Tool
-
-For this task, you need two tools: Flask and Pickle, both of which run on Python.
-
-✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) to practice building with Flask.
-
-✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.
-
-## Exercise - clean your data
-
-In this section, you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:
-
-- **Long example description.** "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
-- **Short example description.** "the lights chased us".
-
-The `ufos.csv` spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`.
-
-Create a blank `notebook` to continue the steps below:
-
-Import `pandas`, `matplotlib`, and `numpy` as you did in the previous section and import the ufos spreadsheet. You can take a look at a sample data set:
-
-```{code-cell}
-:tags: ["output_scroll"]
-import pandas as pd
-import numpy as np
-    
-ufos = pd.read_csv('../assets/data/ufos.csv')
-ufos.head()
-```
-
-Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the `Country` field.
-
-```{code-cell}
-ufos = pd.DataFrame({
-    'Seconds': ufos['duration (seconds)'], 
-    'Country': ufos['country'],
-    'Latitude': ufos['latitude'],
-    'Longitude': ufos['longitude']
-})
-
-ufos.Country.unique()
-```
-
-Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:
-
-```{code-cell}
-ufos.dropna(inplace=True)
-
-ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]
-
-ufos.info()
-```
-
-Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:
-
-```{code-cell}
-from sklearn.preprocessing import LabelEncoder
-
-ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])
-
-ufos.head()
-```
-
-```{note}
-LabelEncoder encodes data alphabetically.
-```
-
-## Exercise - build your model
-
-Now you can get ready to train a model by dividing the data into the training and testing group.
-
- Select the three features you want to train on as your X vector, and the y vector will be the `Country`. You want to be able to input `Seconds`, `Latitude` and `Longitude` and get a country id to return.
-
-```{code-cell}
-from sklearn.model_selection import train_test_split
-
-Selected_features = ['Seconds', 'Latitude', 'Longitude']
-
-X = ufos[Selected_features].values
-y = ufos['Country'].values
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
-```
-
-Train your model using logistic regression:
-
-```{code-cell}
-from sklearn.metrics import accuracy_score, classification_report
-from sklearn.linear_model import LogisticRegression
-
-model = LogisticRegression(solver="sag",max_iter=10000)
-model.fit(X_train, y_train)
-predictions = model.predict(X_test)
-
-print(classification_report(y_test, predictions))
-print('Predicted labels: ', predictions)
-print('Accuracy: ', accuracy_score(y_test, predictions))
-```
-
-The accuracy isn't bad **(around 95%)**, unsurprisingly, as `Country` and `Latitude/Longitude` correlate.
-
-The model you created isn't very revolutionary as you should be able to infer a `Country` from its `Latitude` and `Longitude`, but it's a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.
-
-## Exercise - 'pickle' your model
-
-Now, it's time to _pickle_ your model! You can do that in a few lines of code. Once it's _pickled_, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,
-
-```{code-cell}
-import pickle
-
-model_filename = '../assets/pickle/ufo-model.pkl'
-pickle.dump(model, open(model_filename, 'wb'))
-
-model = pickle.load(open('../assets/pickle/ufo-model.pkl', 'rb'))
-print(model.predict(pd.DataFrame([[50, 44, -12]])))
-```
-
-The model returns **'3'**, which is the country code for the UK. Wild! 👽
-
-## Exercise - build a Flask app
-
-Now you can build a Flask app to call your model and return similar results but in a more visually pleasing way.
-
-Start by creating a folder called `/web-app` next to the `notebook.ipynb` file where your `ufo-model.pkl` file resides.
-
-In that folder create three more folders: `/static`, with a folder `/css` inside it, and `/templates`. You should now have the following files and directories:
-
-```output
-web-app/
-    static/
-    css/
-    templates/
-notebook.ipynb
-../
-    assets/
-        pickle/
-            ufo-model.pkl
-```
-
-✅ Refer to the solution folder for a view of the finished app
-
-The first file to create in `/web-app` folder is `requirements.txt` file. Like `package.json` in a JavaScript app, this file lists dependencies required by the app. In `requirements.txt` add the lines:
-
-```text
-scikit-learn
-pandas
-numpy
-flask
-```
-
-Now, run this file by navigating to `/web-app`:
-
-```bash
-cd web-app
-```
-
-In your terminal type `pip install`, to install the libraries listed in `requirements.txt`:
-
-```bash
-pip install -r requirements.txt
-```
-
-Now, you're ready to create three more files to finish the app:
-
-1. Create `app.py` in the root.
-2. Create `index.html` in `/templates` directory.
-3. Create `styles.css` in `/static/css` directory.
-
-Build out the _styles.css_ file with a few styles:
-
-```css
-body {
-    width: 100%;
-    height: 100%;
-    font-family: 'Helvetica';
-    background: black;
-    color: #fff;
-    text-align: center;
-    letter-spacing: 1.4px;
-    font-size: 30px;
-}
-
-input {
-    min-width: 150px;
-}
-
-.grid {
-    width: 300px;
-    border: 1px solid #2d2d2d;
-    display: grid;
-    justify-content: center;
-    margin: 20px auto;
-}
-
-.box {
-    color: #fff;
-    background: #2d2d2d;
-    padding: 12px;
-    display: inline-block;
-}
-```
-
-Next, build out the _index.html_ file:
-
-```html
-<!DOCTYPE html>
-<html>
-    <head>
-    <meta charset="UTF-8">
-    <title>🛸 UFO Appearance Prediction! 👽</title>
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
-    </head>
-
-    <body>
-    <div class="grid">
-
-        <div class="box">
-
-        <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
-
-        <form action="{{ url_for('predict')}}" method="post">
-            <input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
-            <input type="text" name="latitude" placeholder="Latitude" required="required" />
-            <input type="text" name="longitude" placeholder="Longitude" required="required" />
-            <button type="submit" class="btn">Predict country where the UFO is seen</button>
-        </form>
-
-        <p>{{ prediction_text }}</p>
-
-        </div>
-
-    </div>
-
-    </body>
-</html>
-```
-
-```{note}
-Take a look at the templating in this file. Notice the 'mustache' syntax around variables that will be provided by the app, like the prediction text: `{{}}`. There's also a form that posts a prediction to the `/predict` route.
-
-Finally, you're ready to build the python file that drives the consumption of the model and the display of predictions:
-```
-
-In `app.py` add:
-
-```python
-import numpy as np
-from flask import Flask, request, render_template
-import pickle
-
-app = Flask(__name__)
-
-model = pickle.load(open("../assets/pickle/ufo-model.pkl", "rb"))
-
-
-@app.route("/")
-def home():
-    return render_template("index.html")
-
-
-@app.route("/predict", methods=["POST"])
-def predict():
-
-    int_features = [int(x) for x in request.form.values()]
-    final_features = [np.array(int_features)]
-    prediction = model.predict(final_features)
-
-    output = prediction[0]
-
-    countries = ["Australia", "Canada", "Germany", "UK", "US"]
-
-    return render_template(
-        "index.html", prediction_text="Likely country: {}".format(countries[output])
-    )
-
-
-if __name__ == "__main__":
-    app.run(debug=True)
-```
-
-```{note}
-when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
-```
-
-If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!
-
-Before doing that, take a look at the parts of `app.py`:
-
-1. First, dependencies are loaded and the app starts.
-2. Then, the model is imported.
-3. Then, index.html is rendered on the home route.
-
-On the `/predict` route, several things happen when the form is posted:
-
-1. The form variables are gathered and converted to a NumPy array. They are then sent to the model and a prediction is returned.
-2. The Countries that we want to be displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.
-
-Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction.
-
-## Your turn! 🚀
-
-Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `/train`. What are the pros and cons of pursuing this method? Practice it by following this [assignment](../assignments/ml-fundamentals/try-a-different-model.md)
-
-## Self study
-
-There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.
-
-## Acknowledgements
-
-Thanks to Microsoft for creating the open-source course [ML-For-Beginners](https://github.com/microsoft/ML-For-Beginners). It inspires the majority of the content in this chapter.
diff --git a/_sources/ml-fundamentals/ml-overview.ipynb b/_sources/ml-fundamentals/ml-overview.ipynb
new file mode 100644
index 0000000000..c5e951ac2a
--- /dev/null
+++ b/_sources/ml-fundamentals/ml-overview.ipynb
@@ -0,0 +1,506 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Learning overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are living in the age of the intelligent machines. In the last decade, Machine Learning powered systems went from beating the world-class chess players to now influencing most of our daily decisions like what we watch on streaming services like Netflix and YouTube. What is Machine Learning? And why is it important to be studying it now?\n",
+    "\n",
+    "We hear that Machine Learning is transforming many industries, but what exactly can we use it for? And what are the set of problems that this technology is or not suited for?\n",
+    "\n",
+    "This introduction will give you the basics of Machine Learning or these things that you will always need to know. More concretely, it will cover the high level foundational knowledge such as:\n",
+    "\n",
+    "- What is Machine Learning?\n",
+    "- The difference between artificial intelligence, data science, Machine Learning and deep learning\n",
+    "- The difference between Machine Learning and ordinary programming\n",
+    "- Applications of Machine Learning\n",
+    "- When to use and when not to use Machine Learning\n",
+    "- Types of Machine Learning\n",
+    "- A typical Machine Learning project workflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is Machine Learning?\n",
+    "\n",
+    "Machine Learning is a new programming paradigm in which instead of explicitly programming computers to perform some tasks, we let them learn from data in order to find the underlying patterns in the data. In few words, Machine Learning is the science of giving the machine the ability to reason about the data.\n",
+    "\n",
+    "The term [Machine Learning](https://en.wikipedia.org/wiki/Machine_learning) was coined by Arthur Samuel in 1959. At that time, Arthur defined Machine Learning as a:\n",
+    "\n",
+    "> *Field of study that gives computers the ability to learn without being explicitly programmed.*\n",
+    "\n",
+    "A more technical definition of Machine Learning was provided by Tom M. Mitchell in 1997. Here is how Tom defined Machine Learning: \n",
+    "\n",
+    "> *A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.*\n",
+    "\n",
+    "Wikipedia provides a much clearer definition of Machine Learning: \n",
+    "\n",
+    "> *Machine Learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine Learning algorithms build a model based on sample data, known as \"training data\", in order to make predictions or decisions without being explicitly programmed to do so. - Wikipedia.*\n",
+    "\n",
+    "What does that mean?\n",
+    "\n",
+    "In simple words, Machine Learning algorithms are trained on data rather than being programmed explicitly. \n",
+    "\n",
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/ai-ml-dl.png\n",
+    "---\n",
+    "name: 'ai-ml-dl'\n",
+    "width: 90%\n",
+    "---\n",
+    "The difference between AI, Machine Learning and deep learning\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ordinary Programming vs Machine Learning\n",
+    "\n",
+    "In ordinary programming, the job of the programmer is to clearly write every single rule that makes up the task he/she is trying to accomplish. In order to get the results, she/he must write all rules that acts up on the data. \n",
+    "\n",
+    "Machine Learning flips that. Instead of having to write the rules that makes up a particular application, we can feed data and results(or labels) to the Machine Learning model, and its job can be to determine the set of rules that map the data and labels. \n",
+    "\n",
+    "Let's take a real world example. If you wanted to build an application that given a picture of person can determine if he/she is wearing or not wearing a facemask, you can just feed a bunch of images of people with and without facemasks to the Machine Learning model, and the model can learn the rules or patterns that map the images to whether they have a facemask or not. \n",
+    "\n",
+    "You can even extend that further and use those learned rules to recognize facemasks in the images that were never seen by the that model. \n",
+    "\n",
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/traditional-ml.png\n",
+    "---\n",
+    "name: 'traditional-ml.png'\n",
+    "width: 90%\n",
+    "---\n",
+    "Traditional Programming vs Deep Learning\n",
+    ":::\n",
+    "\n",
+    "Approaching a facemask recognition problem with rule based programming would really be a hard problem. You would have to write lots of code that would later turn out to not work typically because your program will be tested on different kinds of facemasks and people in various scenarios and it's almost impossible to express that in rules. Whereas with Machine Learning, all you need is a bunch of images of people with and without facemasks, and there you are few steps away from getting an effective facemask recognizer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key terminology\n",
+    "What is Machine Learning? Concisely put, it is the following:\n",
+    "\n",
+    "- ML systems learn how to combine input to produce useful predictions on never-before-seen data.\n",
+    "\n",
+    "Let's explore fundamental Machine Learning terminology."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Labels\n",
+    "\n",
+    "A `label` is the thing we're predicting—the `y` variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Features\n",
+    "\n",
+    "A `feature` is an input variable—the `x` variable in simple linear regression. A simple Machine Learning project might use a single feature, while a more sophisticated Machine Learning project could use millions of features, specified as:\n",
+    "\n",
+    "$$x_1, x_2, ..., x_N$$\n",
+    "\n",
+    "In the spam detector example, the features could include the following:\n",
+    "- words in the email text\n",
+    "- sender's address\n",
+    "- time of day the email was sent\n",
+    "- email contains the phrase \"one weird trick.\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examples\n",
+    "An `example` is a particular instance of data, **`x`**. (We put **`x`** in boldface to indicate that it is a vector.) We break examples into two categories:\n",
+    "\n",
+    "- labeled examples\n",
+    "- unlabeled examples\n",
+    "\n",
+    "A **`labeled example`** includes both feature(s) and the label. That is:\n",
+    "\n",
+    "```text\n",
+    "labeled examples: {features, label}: (x, y)\n",
+    "```\n",
+    "\n",
+    "Use labeled examples to **train** the model. In our spam detector example, the labeled examples would be individual emails that users have explicitly marked as \"spam\" or \"not spam.\"\n",
+    "\n",
+    "\n",
+    "An **`unlabeled example`** contains features but not the label. That is:\n",
+    "```text\n",
+    "unlabeled examples: {features, ?}: (x, ?)\n",
+    "```\n",
+    "\n",
+    "Once we've trained our model with labeled examples, we use that model to predict the label on unlabeled examples. In the spam detector, unlabeled examples are new emails that humans haven't yet labeled."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Models\n",
+    "A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with \"spam\". Let's highlight two phases of a model's life:\n",
+    "\n",
+    "- **Training** means creating or **learning** the model. That is, you show the model labeled examples and enable the model to gradually learn the relationships between features and label.\n",
+    "- **Inference** means applying the trained model to unlabeled examples. That is, you use the trained model to make useful predictions (`y'`). For example, during inference, you can predict if an email is a spam or not for new unlabeled examples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Regression vs classification\n",
+    "\n",
+    "A **regression** model predicts continuous values. For example, regression models make predictions that answer questions like the following:\n",
+    "- What is the value of a house in California?\n",
+    "- What is the probability that a user will click on this ad?\n",
+    "\n",
+    "A **classification** model predicts discrete values. For example, classification models make predictions that answer questions like the following:\n",
+    "- Is a given email message spam or not spam?\n",
+    "- Is this an image of a dog, a cat, or a hamster?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## When And When Not to Use Machine Learning\n",
+    "\n",
+    "Machine Learning is an incredible technology and it has shown a lot of successes in solving various real world problems. However, like any other technology, Machine Learning is not suitable for solving all kinds of problems. It is thus equally important to know when and when not to use Machine Learning.\n",
+    "\n",
+    "When to use Machine Learning? Machine Learning is preferred when approaching:\n",
+    "\n",
+    "* Problems that are too complex to be solved by ordinary programming. For these kinds of problems, it's probably safe to try Machine Learning. There is no way one can write all rules that can be used for recognizing facemasks or detect spam emails accurately for example.\n",
+    "\n",
+    "* Problems that involve visual reasoning and language understanding such as image recognition, speech recognition, machine translation, etc...As we will see later, large scale perception or visual and language problems are typically handled by deep learning systems. \n",
+    "\n",
+    "* Fast changing problems where the characteristics of the problems changes with time, and there is a need to keep the system functioning well. Machine Learning is suitable for these sorts of problems because Machine Learning algorithms can be retrained on new data. \n",
+    "\n",
+    "* Problems that are clear and have simple goals such as yes/no question or predicting a single continuous number such as the quantity of product likely to be consumed in a given time.  Andrew Ng., Founder of DeepLearning.AI and Adjunct Stanford Professor likes to say that Machine Learning (employed for automation purpose) is likely to succeed when solving a problem that takes a human a second or less to accomplish such as detecting defects in a product. Recognizing if there is a defect in a product is very simple yes/no question and take a second or less to complete. \n",
+    "\n",
+    "With that said, you may not use Machine Learning when:\n",
+    "\n",
+    "* You want the predictions made by your model to be fully explainable. This is because most Machine Learning models are considered to be `blackbox`. \n",
+    "* You do not have a reliable data for the problem you're trying to solve. A simple example here is trying to use Machine Learning for predicting stocks. Stock market data is unreliable and can change in a matter of seconds without any logical reasons, and so, it's pretty hard to for a model to learn some useful patterns from such unreliability. \n",
+    "* You can solve your problem with ordinary programming or a simple heuristic methods. \n",
+    "* You want a solution that will never need to be updated. The predictions made by Machine Learning models decay overtime, so if you are not ready to update data and retrain models frequently, you may have to consider non Machine Learning techniques. \n",
+    "\n",
+    "Machine Learning keeps transforming things that people never thought and with its vibrant online community, we will keep to be surprised but in the meantime, it's a safe belt to use it in problems in which the solution can be in your favor because Machine Learning systems are hard to maintain. \n",
+    "\n",
+    "We talked about when you should use Machine Learning and when you should not, but also, there are other areas where Machine Learning is being heavily used but with extra care and human in the loop. Example of such critical areas include medicine, self driving cars, etc...In some of those areas like self-driving cars(or driver assistants), Machine Learning is surely a big factor but also because the cost of error made by the model can be very high, human assistance becomes important.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Types of Machine Learning Systems\n",
+    "\n",
+    "In broad, there are 3 main types of Machine Learning systems that are:\n",
+    "\n",
+    "* Supervised learning\n",
+    "* Unsupervised learning\n",
+    "* Reinforcement learning\n",
+    "\n",
+    "Let's review all of these types to get a high level understanding of what's they really mean. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Supervised Learning\n",
+    "\n",
+    "Most Machine Learning tasks fall into supervised learning type. As the name implies, a supervised learning model is trained with input data along with some form of guidance that we can call labels. In other words, a supervised learning model maps the input data (or X in many textbooks) to output labels (y). Labels are also known as targets and they act as a description of the input data. \n",
+    "\n",
+    "The example of facemask recognition that we used in above sections is a good example of supervised learning. In broad, there are 2 main kinds of supervised learning problems that are:\n",
+    "\n",
+    "* **Classification problems** where the task is to identify a given category from numerous categories or simply make choice between a number of categories. Another example of classification task is to identify if the incoming email is either spam or not based on the email contents.\n",
+    "\n",
+    "* **Regression problems** where the goal is to predict a continuous value of something. A classical example for this category is to predict the price of the used car given its features such as brand, age, number of doors, number of sits, safety level, maintenance cost, etc...\n",
+    "\n",
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png\n",
+    "---\n",
+    "name: 'class-reg.png'\n",
+    "width: 90%\n",
+    "---\n",
+    "Graphical representation of classification and regression problems\n",
+    ":::\n",
+    "\n",
+    "Supervised learning algorithms includes shallow algorithms such as linear and logistic regression, decision trees, random forests, K-Nearest Neighbors (KNN), and support vector machines (SVM). Neural networks can be both supervised(like using them for image classification) and unsupervised. \n",
+    "\n",
+    "With that said, there are other advanced tasks that falls into supervised learning type such as:\n",
+    "\n",
+    "* Image captioning where the goal is to predict the caption of a given image. \n",
+    "* Object detection where the goal is to recognize the object in image and draw the bounding box around it. \n",
+    "* Image segmentation where the goal is to group the pixels that make up particular objects in the image. \n",
+    "\n",
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/vision.png\n",
+    "---\n",
+    "name: 'vision.png'\n",
+    "width: 90%\n",
+    "---\n",
+    "Advanced Tasks: Object detection, image segmentation, image captioning. You can test these advanced algorithms on your images using [vision-explorer](https://vision-explorer.allenai.org)\n",
+    ":::\n",
+    "\n",
+    "Some of those tasks can involve both classification and regression. Take an example for object detection, a task of recognizing and localizing an object in an image: it involves classification (recognizing the object among many other objects) and regression (predicting the coordinates of the objects in an image to make a bounding box).\n",
+    "\n",
+    "If any of the things we are talking about sounds unfamiliar, do not worry. There is no way to explain all things at once, but as we progress, things will get clear. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reinforcement Learning\n",
+    "\n",
+    "Reinforcement learning is a special type of Machine Learning that is most applicable in robotics and games. \n",
+    "\n",
+    "In reinforcement learning, a learning system called *an agent* can perceives the environment, performs some actions, and gets *rewarded* or *penalized* depending on how it is performing. The main goal of the agent is to accumulate as much as rewards as possible.\n",
+    "\n",
+    "The *agent* learns the best strategy(*policy*) necessary for getting the most reward itself. \n",
+    "\n",
+    "\n",
+    "Reinforcement learning holds some of the most historical AI moments. In 2016, DeepMind AlphaGo, a reinforcement learning system won Lee Sedol in [Google DeepMind Challenge Match](https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol). Go is a complex board game that requires intuition, creative and strategic thinking. And Lee was one of the world-class Go players. You can watch the movie of the game between AlphaGo and Lee [here](https://www.youtube.com/watch?v=WXuK6gekU1Y&list=PLqYmG7hTraZBy7J_4ynYPc0Ml1RUGcLmD&index=2&t=147s).\n",
+    "\n",
+    "For many of us, we may not get the most of reinforcement learning, typically because of limited resources and applicability, but is it a powerful thing for those who can afford it most notably employing it in robotics and games.\n",
+    "\n",
+    "Let's summarize the types of Machine Learning systems. By far, supervised and unsupervised learning are the two commonly used types of Machine Learning. Semi-supervised and self-supervised learning are also getting attractions in deep learning community, but they are still in research. We will focus more those two practical types: supervised and unsupervised."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Typical Machine Learning Workflow\n",
+    "\n",
+    "Although every Machine Learning problem is unique, they all follow a similar workflow. In this section, we will learn how to approach Machine Learning problems systematically.\n",
+    "\n",
+    "Overall, a typical Machine Learning project workflow consists of: \n",
+    "* Defining and formulating a problem\n",
+    "* Collecting data\n",
+    "* Establishing a baseline\n",
+    "* Exploratory data analysis(EDA)\n",
+    "* Preparing data\n",
+    "* Selecting and training a model\n",
+    "* Performing error analysis and improving a model\n",
+    "* Deploying a model\n",
+    "\n",
+    "Let's talk about each step in brief. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining a Problem\n",
+    "\n",
+    "Everything starts here. Problem definition is the important and the initial step in any Machine Learning project. This is where you make sure you understand the problem really well. Understanding the problem will give you proper intuitions about the next steps to follow such as right learning algorithms, etc. But wait, what does it mean to understand the problem?\n",
+    "\n",
+    "Understanding the problem is all about diving deep into the details of the problem at hand and asking the right questions. First, it's important to narrow down the problem until you can have a simple and a well-defined goal. Here are examples of simple goals: To *classify* products into different categories, to *predict* the price of a used car given its features (such as brand, age, etc...), to *recognize* if a person is wearing a facemask, to *divide* customers into different groups that share similar behaviors, etc...As you can see, the goal can tell whether the problem is classification, regression, or clustering...\n",
+    "\n",
+    "At this stage, avoid vague words. The simpler you can formulate the problem, the better things will be down the hall. It's also worth questioning if the project should be pursued. Most problems do not require Machine Learning. \n",
+    "\n",
+    "Problem definition also goes beyond determining the goal and objective of the project to thinking about the data that will be needed. Machine Learning models relies on the data, and the better models comes from better data. Do you have data that contains the things you want to predict? Models are not magical things, they are mathematical functions that takes data along with labels, and determine the patterns that can be used to make predictions on unseen data. If the data does not contain meaningful information relevant to what you want to predict, you will get poor predictions. \n",
+    "\n",
+    "After you understand the problem and have an idea of the data you want, the next step is to collect it. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Collecting Data\n",
+    "\n",
+    "This is usually the next stage after formulating a problem. Before we talk about collecting data, let's even understand the meaning of data. According to [wikipedia](https://en.wikipedia.org/wiki/Data), \"data are a set of values of qualitative or quantitative variables about one or more persons or objects.\"\n",
+    "\n",
+    "There are 2 main types of data that are:\n",
+    "* Structured data that are organized in tabular or spreadsheet format. Example of tabular data includes customer records, car sales, etc...\n",
+    "\n",
+    "* Unstructure data such as images, texts, sounds, and videos. Unstructured data are not organized as the former.\n",
+    "\n",
+    "Nowdays, there are lots of open-source datasets on platforms like Kaggle, Google datasets, UCL, and government websites. So, if you are solving a problem that someone solved before, it's very likely that you will find it somewhere in those platforms, or other public sources like this [repository](https://github.com/awesomedata/awesome-public-datasets). Your job as Machine Learning engineer is to find the relevant data that you can use to solve the presented problem.\n",
+    "\n",
+    "That said, there are times that you will have to collect your own data, especially if you are solving a problem that no one solved before. In this case, consider the time that you will have to spend collecting data and the cost. You also do not need to wait until you have your desired data points before you can start. Embrace ML development early on so that you can learn if you (really) need more data. This idea is inspired by Andrew Ng.\n",
+    "\n",
+    "Also, when collecting the data, quality is better than quantity. There are times when small data but good data can outwork big poor data. The amount of data you need is going to depend on the problem you're solving and its scope, but whatever the problem is, aiming to collect good data is the way to go."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Establishing a Baseline\n",
+    "\n",
+    "Without a benchmark, you won't know how to evaluate your results properly. A baseline is the simplest model that can solve your problem with minimal requirements. It does not have to be a model. It can be an existing open-source application, a statistical analysis or intuitions you get from data at a quick glance.\n",
+    "\n",
+    "The single most purpose of a baseline is to act as a reference point when comparing the actual model with the baseline. The ultimate goal is to beat a baseline, and sometime, if you can't beat it, it might mean the project is not worth pursuing, or the baseline can be all you need."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploratory data analysis(EDA)\n",
+    "\n",
+    "Before manipulating the data, it is quite important to learn about the dataset. This can be overlooked, but doing it well will help you know the effective strategies to be applied while cleaning the data.\n",
+    "\n",
+    "Go through some values, plot some features, and try to understand the correlation between them. If the work is vision-related, visualize some images, and spot what’s missing in the images. Are they diverse enough? What types of image scenarios can you add? Or what are images that can mislead the model?\n",
+    "\n",
+    "Here are other things that you would want to inspect in the data:\n",
+    "\n",
+    "* Duplicate values and samples\n",
+    "* Corrupted or data with unsupported formats (ex: having an image of .txt and with 0 Kilobytes)\n",
+    "* Class imbalances\n",
+    "* Biases\n",
+    "\n",
+    "Before performing EDA, split the data into the training set, validation and test sets to avoid data leakage. The training set is used for training the model, the validation set is used for evaluating the model performance during training to suggest improvements, and the test set is for evaluating the final and best model performance. Validation and test set should have the same statistical distribution.\n",
+    "\n",
+    "Also, to avoid data leakage, do not touch the test set in EDA or anywhere before the model training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Preprocessing\n",
+    "\n",
+    "Data processing is perhaps the biggest part of any Machine Learning project. There is a notion that Data Scientists and Machine Learning Engineers spend more than 80% of their time preparing the data and this makes sense because the real-world data are messy.\n",
+    "\n",
+    "In this step, it is where you convert the raw data to go in a format that can be accepted by the Machine Learning algorithms.\n",
+    "\n",
+    "Data preprocessing is hard because there are different types of data and the way you process one is different from the other. For example, in structured data, the way you process numerical features is going to be different from categorical features. Also in unstructured data, the way you manipulate images is going to be different from how you manipulate texts or sounds.\n",
+    "\n",
+    "As the next parts will cover the practical implementations of typical data preprocessing steps, let's be general about things you're likely going to deal with while manipulating the features:\n",
+    "\n",
+    "* **Imputing missing values:** Missing values can either be filled, removed or left as they are. There are various strategies for missing values such as mean, median, frequent imputations, backward and forward fill, and iterative imputations. The right imputation technique depends on the problem and the dataset. With the exception to tree-based models, most Machine Learning models do not accept missing values.\n",
+    "\n",
+    "* **Encoding categorical features:** Categorical features are all types of features that have categorical values. For example, A gender feature having the values male and female is a categorical feature. You will want to encode such types of features. The techniques for encoding them are label encoding where you can assign 0 to Male and 0 to Female, or one hot encoding where you can get the binary representations (0s and 1s) in one hot matrix. You will see this later in practice.\n",
+    "\n",
+    "* **Scaling the numeric features:** Most ML models work well when the input values are scaled to small values because they can train and converge faster than they would otherwise. There are two main scaling techniques that are normalization and standardization. Normalization rescales the features to the values between 0 and 1 whereas standardization rescales the features to have mean of 0 and a unit standard deviation. If you are aware that your data has normal or Gaussian distribution, normalization can be a good choice. Otherwise, standardization will work well in many cases.\n",
+    "\n",
+    "In many textbooks and courses, data preprocessing is also referred to as data cleaning or data preparation. Feature engineering is also a part of data preprocessing. Feature engineering is a creative task and requires some extra knowledge about the data and the problem as it involves creating new features from existing features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting and Training a Model\n",
+    "\n",
+    "Selecting, creating, and training a Machine Learning model is the tiniest part of any typical Machine Learning workflow. There are different types of models but in broad, most of them fall into these categories: linear models such as linear and logistic regression, tree-based models such as decision trees, ensemble models such as random forests, and finally neural networks. \n",
+    "\n",
+    "Depending on your problem, you can choose any relevant model from these categories or tries many of them. But, it is worth mentioning that getting a model that can ultimately solve a given problem is no *free lunch scenario*. You have to experiment with different models to get one that works for your problem and dataset.\n",
+    "\n",
+    "To reduce your modeling curve, here are a few things that you can consider while choosing a Machine Learning model:\n",
+    "\n",
+    "* **The scope of the problem:** The scope or type of your problem can give a strong signal on what learning algorithm to use.  Take an example: If you are going to build an image classifier, neural networks (Convolutional Neural Networks specifically) might be your go-to algorithm.\n",
+    "\n",
+    "* **The size of the dataset:** Linear models tend to work well in small data problems, whereas ensemble models and neural networks can work well when given huge amounts of data.\n",
+    "* **The level of interpretability:** If you want the predictions of your model to be explainable, neural networks may not help. Tree-based models such as decision trees can be explainable compared to other models.\n",
+    "* **Training time:** Complex models (neural networks and ensemble models) will take too long to train and thus draining the computation resources. On the other hand, linear models can train faster.\n",
+    "\n",
+    "As you can see, there is a trade-off during model selection. You want explainability, choose models that can provide that for you, most models don't. You have a small dataset or you care about the training time, same thing, a right model for you."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performing Error Analysis\n",
+    "\n",
+    "Performing error analysis will guide you throughout the process of improving the results of the model. The improvement can either be from the data or the model.\n",
+    "\n",
+    "One of the best ways to do error analysis is to plot the learning curve and to try noticing where the model is failing and what might be the reason and the right actions that you can take to reduce the errors.\n",
+    "\n",
+    "To improve the model part, you can try different model configuration values or hyperparameters. You can also try different models to see one that works well. But also, a good model comes from good data, so it's important to spend time examining the results of the model with respect to the input data.\n",
+    "\n",
+    "Here are questions that you can yourself iteratively in the process of error analysis:\n",
+    "\n",
+    "* Is the model doing poorly on all classes or is it one specific class?\n",
+    "* Is it because there are not enough data points for that particular class compared to other classes?\n",
+    "* There are trade-offs and limits on how much you can do to reduce the errors. Is there room for improvement?\n",
+    "\n",
+    "Often, the improvements will not come from tuning the model, but from spending time to increase the number of training samples and data quality.\n",
+    "\n",
+    "When improving the data, you can create artificial data (a.k.a data augmentation). This will work well most of the time. The whole error analysis is an iterative process, keep doing it and always aim to improve the data than the model. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploying a Model\n",
+    "\n",
+    "Model deployment is the last part of this workflow. When all the previous steps have gone right, and you are happy about the results of the model on the test set, the next step will be to deploy the model so that the users can start to make requests and get predictions or enhanced services."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Your turn! 🚀\n",
+    "\n",
+    "TBD."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acknowledgments\n",
+    "\n",
+    "Thanks to [Nyandwi](https://github.com/Nyandwi) for creating the open-source course [Complete Machine Learning Package](https://github.com/Nyandwi/machine_learning_complete). It inspires the majority of the content in this chapter. Some contents come from [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/framing/ml-terminology)."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/ml-fundamentals/ml-overview.md b/_sources/ml-fundamentals/ml-overview.md
deleted file mode 100644
index eed7b4d20e..0000000000
--- a/_sources/ml-fundamentals/ml-overview.md
+++ /dev/null
@@ -1,401 +0,0 @@
-# Machine Learning overview
-
-```{epigraph}
-Machine intelligence is the last invention that humanity will ever need to make.
-
--- Nick Bostrom
-```
-
-We are living in the age of the intelligent machines. In the last decade, Machine Learning powered systems went from beating the world-class chess players to now influencing most of our daily decisions like what we watch on streaming services like Netflix and YouTube. What is Machine Learning? And why is it important to be studying it now?
-
-We hear that Machine Learning is transforming many industries, but what exactly can we use it for? And what are the set of problems that this technology is or not suited for?
-
-This introduction will give you the basics of Machine Learning or these things that you will always need to know. More concretely, it will cover the high level foundational knowledge such as:
-
-- What is Machine Learning?
-- The difference between artificial intelligence, data science, Machine Learning and deep learning
-- The difference between Machine Learning and ordinary programming
-- Applications of Machine Learning
-- When to use and when not to use Machine Learning
-- Types of Machine Learning
-- A typical Machine Learning project workflow
-
-## What is Machine Learning?
-
-Machine Learning is a new programming paradigm in which instead of explicitly programming computers to perform some tasks, we let them learn from data in order to find the underlying patterns in the data. In few words, Machine Learning is the science of giving the machine the ability to reason about the data.
-
-The term [Machine Learning](https://en.wikipedia.org/wiki/Machine_learning) was coined by Arthur Samuel in 1959. At that time, Arthur defined Machine Learning as a:
-
-> *Field of study that gives computers the ability to learn without being explicitly programmed.*
-
-A more technical definition of Machine Learning was provided by Tom M. Mitchell in 1997. Here is how Tom defined Machine Learning: 
-
-> *A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.*
-
-Wikipedia provides a much clearer definition of Machine Learning: 
-
-> *Machine Learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine Learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. - Wikipedia.*
-
-What does that mean?
-
-In simple words, Machine Learning algorithms are trained on data rather than being programmed explicitly. 
-
-## Artificial Intelligence, Data Science, Machine Learning, and Deep Learning 
-
-AI or Artificial intelligence, data science, Machine Learning, and deep learning are used interchangeably, but they are quite different.
-
-AI is a branch of computer science concerned with building intelligent machines capable of performing tasks at the level of human. AI seeks to mimic human. AI is an interdisciplinary field that involves Machine Learning, programming, robotics, data science, etc...
-
-Machine Learning on the other hand is the branch of AI and as we saw, it is concerned with giving the machine the ability to learn from data. Machine Learning algorithms consists of shallow or classical algorithms such as decision trees and deep learning algorithms such as convolutional neural networks. We will learn more about these algorithms in the next parts. 
-
-Deep learning is a branch of Machine Learning that deals with the study of artificial neural networks and it was inspired by the human brain. Classical Machine Learning algorithms needs a lot of feature engineering, but deep learning algorithms can extract features in huge amount of data such as images themselves.  
-
-
-```{figure} ../../images/ml-fundamentals/ml-overview/ai-ml-dl.png
----
-name: 'ai-ml-dl'
-width: 90%
----
-The difference between AI, Machine Learning and deep learning
-```
-
-## Ordinary Programming vs Machine Learning
-
-In ordinary programming, the job of the programmer is to clearly write every single rule that makes up the task he/she is trying to accomplish. In order to get the results, she/he must write all rules that acts up on the data. 
-
-Machine Learning flips that. Instead of having to write the rules that makes up a particular application, we can feed data and results(or labels) to the Machine Learning model, and its job can be to determine the set of rules that map the data and labels. 
-
-Let's take a real world example. If you wanted to build an application that given a picture of person can determine if he/she is wearing or not wearing a facemask, you can just feed a bunch of images of people with and without facemasks to the Machine Learning model, and the model can learn the rules or patterns that map the images to whether they have a facemask or not. 
-
-You can even extend that further and use those learned rules to recognize facemasks in the images that were never seen by the that model. 
-
-
-```{figure} ../../images/ml-fundamentals/ml-overview/traditional-ml.png
----
-name: 'traditional-ml.png'
-width: 90%
----
-Traditional Programming vs Deep Learning
-```
-
-
-Approaching a facemask recognition problem with rule based programming would really be a hard problem. You would have to write lots of code that would later turn out to not work typically because your program will be tested on different kinds of facemasks and people in various scenarios and it's almost impossible to express that in rules. Whereas with Machine Learning, all you need is a bunch of images of people with and without facemasks, and there you are few steps away from getting an effective facemask recognizer.
-
-
-## Applications of Machine Learning
-
-Before we step into further Machine Learning foundations, let's look at some of its most exciting applications. 
-
-Machine Learning has transformed many industries, from banking, manufacturing, streaming, autonomous vehicles, agriculture, etc...In fact, most of the tech products and services we use daily possess some sorts of Machine Learning algorithms running in their backgrounds.  
-
-Here are the most commonly Machine Learning use cases:
-
-* **Fraud detection**: Banks and other financial organizations can use Machine Learning to detect frauds in real time. 
-
-* **Loan repayment prediction**: Banks can also use the historical data of their clients to predict if they will be able to repay back the loans before granting it to them. 
-
-* **diagnosing diseases and predicting the survival rate**: Machine Learning is increasingly finding its value in medicine. It can assist medical professionals in diagnosing diseases in handful of minutes. Medical professionals can also use Machine Learning to predict the likelihood or a course of disease or survival rate(prognosis).
-
-* **Detecting defects in industry**: Some manufacturing companies use machine vision to inspect defects in the products which ultimately result in speeding up the production process, automating the inspection task, reducing the cost and human workload. You can learn more about visual inspection in [2020 State of AI-Based Machine Vision by Landing AI](https://landing.ai/wp-content/uploads/2020/11/MachineVisionSurvey.pdf). 
-
-* **Churn prediction**:  Organizations that provides some kinds of services can use Machine Learning to predict if a given customer is likely to opt out from the service or cancel subscription. This can help the organization to improve the customer experiences in order to retain the customers. 
-
-* **Spam detection**: Almost all email providers such as Gmail or Outlook possess the ability to detect spams from all incoming emails to protect the users from fake promotions and scams. 
-
-* **Autonomous vehicles**: Today's autonomous vehicles such as self-driving cars use Machine Learning and deep learning systems to navigate in the roads. Using computer vision, they can be able to detect the pedestrian and traffic lights and signs and other surrounding objects. 
-
-There are many more applications of Machine Learning and the list could go on. For example, a given ads agency can use Machine Learning to learn the kinds of things that their visitors are interested in and they can use the results to place the relevant ads on the website. Same for streaming services like YouTube and Netflix. These services uses Machine Learning to suggest the best media to their clients based on their interests. 
-
-## Key terminology
-What is Machine Learning? Concisely put, it is the following:
-
-- ML systems learn how to combine input to produce useful predictions on never-before-seen data.
-
-Let's explore fundamental Machine Learning terminology.
-
-### Labels
-
-A `label` is the thing we're predicting—the `y` variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.
-
-### Features
-
-A `feature` is an input variable—the `x` variable in simple linear regression. A simple Machine Learning project might use a single feature, while a more sophisticated Machine Learning project could use millions of features, specified as:
-
-$$x_1, x_2, ..., x_N$$
-
-In the spam detector example, the features could include the following:
-- words in the email text
-- sender's address
-- time of day the email was sent
-- email contains the phrase "one weird trick."
-
-
-### Examples
-An `example` is a particular instance of data, **`x`**. (We put **`x`** in boldface to indicate that it is a vector.) We break examples into two categories:
-
-- labeled examples
-- unlabeled examples
-
-A **`labeled example`** includes both feature(s) and the label. That is:
-
-```text
-labeled examples: {features, label}: (x, y)
-```
-
-Use labeled examples to **train** the model. In our spam detector example, the labeled examples would be individual emails that users have explicitly marked as "spam" or "not spam."
-
-
-An **`unlabeled example`** contains features but not the label. That is:
-```text
-unlabeled examples: {features, ?}: (x, ?)
-```
-
-Once we've trained our model with labeled examples, we use that model to predict the label on unlabeled examples. In the spam detector, unlabeled examples are new emails that humans haven't yet labeled.
-
-### Models
-A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with "spam". Let's highlight two phases of a model's life:
-
-- **Training** means creating or **learning** the model. That is, you show the model labeled examples and enable the model to gradually learn the relationships between features and label.
-- **Inference** means applying the trained model to unlabeled examples. That is, you use the trained model to make useful predictions (`y'`). For example, during inference, you can predict if an email is a spam or not for new unlabeled examples.
-
-###  Regression vs classification
-
-A **regression** model predicts continuous values. For example, regression models make predictions that answer questions like the following:
-- What is the value of a house in California?
-- What is the probability that a user will click on this ad?
-
-
-A **classification** model predicts discrete values. For example, classification models make predictions that answer questions like the following:
-- Is a given email message spam or not spam?
-- Is this an image of a dog, a cat, or a hamster?
-
-
-## When And When Not to Use Machine Learning
-
-Machine Learning is an incredible technology and it has shown a lot of successes in solving various real world problems. However, like any other technology, Machine Learning is not suitable for solving all kinds of problems. It is thus equally important to know when and when not to use Machine Learning.
-
-When to use Machine Learning? Machine Learning is preferred when approaching:
-
-* Problems that are too complex to be solved by ordinary programming. For these kinds of problems, it's probably safe to try Machine Learning. There is no way one can write all rules that can be used for recognizing facemasks or detect spam emails accurately for example.
-
-* Problems that involve visual reasoning and language understanding such as image recognition, speech recognition, machine translation, etc...As we will see later, large scale perception or visual and language problems are typically handled by deep learning systems. 
-
-* Fast changing problems where the characteristics of the problems changes with time, and there is a need to keep the system functioning well. Machine Learning is suitable for these sorts of problems because Machine Learning algorithms can be retrained on new data. 
-
-* Problems that are clear and have simple goals such as yes/no question or predicting a single continuous number such as the quantity of product likely to be consumed in a given time.  Andrew Ng., Founder of DeepLearning.AI and Adjunct Stanford Professor likes to say that Machine Learning (employed for automation purpose) is likely to succeed when solving a problem that takes a human a second or less to accomplish such as detecting defects in a product. Recognizing if there is a defect in a product is very simple yes/no question and take a second or less to complete. 
-
-With that said, you may not use Machine Learning when:
-
-* You want the predictions made by your model to be fully explainable. This is because most Machine Learning models are considered to be `blackbox`. 
-* You do not have a reliable data for the problem you're trying to solve. A simple example here is trying to use Machine Learning for predicting stocks. Stock market data is unreliable and can change in a matter of seconds without any logical reasons, and so, it's pretty hard to for a model to learn some useful patterns from such unreliability. 
-* You can solve your problem with ordinary programming or a simple heuristic methods. 
-* You want a solution that will never need to be updated. The predictions made by Machine Learning models decay overtime, so if you are not ready to update data and retrain models frequently, you may have to consider non Machine Learning techniques. 
-
-Machine Learning keeps transforming things that people never thought and with its vibrant online community, we will keep to be surprised but in the meantime, it's a safe belt to use it in problems in which the solution can be in your favor because Machine Learning systems are hard to maintain. 
-
-We talked about when you should use Machine Learning and when you should not, but also, there are other areas where Machine Learning is being heavily used but with extra care and human in the loop. Example of such critical areas include medicine, self driving cars, etc...In some of those areas like self-driving cars(or driver assistants), Machine Learning is surely a big factor but also because the cost of error made by the model can be very high, human assistance becomes important.
-
-## Types of Machine Learning Systems
-
-In broad, there are 3 main types of Machine Learning systems that are:
-
-* Supervised learning
-* Unsupervised learning
-* Reinforcement learning
-
-Let's review all of these types to get a high level understanding of what's they really mean. 
-
-### Supervised Learning
-
-Most Machine Learning tasks fall into supervised learning type. As the name implies, a supervised learning model is trained with input data along with some form of guidance that we can call labels. In other words, a supervised learning model maps the input data (or X in many textbooks) to output labels (y). Labels are also known as targets and they act as a description of the input data. 
-
-The example of facemask recognition that we used in above sections is a good example of supervised learning. In broad, there are 2 main kinds of supervised learning problems that are:
-
-* **Classification problems** where the task is to identify a given category from numerous categories or simply make choice between a number of categories. Another example of classification task is to identify if the incoming email is either spam or not based on the email contents.
-
-* **Regression problems** where the goal is to predict a continuous value of something. A classical example for this category is to predict the price of the used car given its features such as brand, age, number of doors, number of sits, safety level, maintenance cost, etc...
-
-```{figure} ../../images/ml-fundamentals/ml-overview/class-reg.png
----
-name: 'class-reg.png'
-width: 90%
----
-Graphical representation of classification and regression problems
-```
-
-Supervised learning algorithms includes shallow algorithms such as linear and logistic regression, decision trees, random forests, K-Nearest Neighbors (KNN), and support vector machines (SVM). Neural networks can be both supervised(like using them for image classification) and unsupervised. 
-
-With that said, there are other advanced tasks that falls into supervised learning type such as:
-
-* Image captioning where the goal is to predict the caption of a given image. 
-* Object detection where the goal is to recognize the object in image and draw the bounding box around it. 
-* Image segmentation where the goal is to group the pixels that make up particular objects in the image. 
-
-
-```{figure} ../../images/ml-fundamentals/ml-overview/vision.png
----
-name: 'vision.png'
-width: 90%
----
-Advanced Tasks: Object detection, image segmentation, image captioning. You can test these advanced algorithms on your images using [vision-explorer](https://vision-explorer.allenai.org)
-```
-
-Some of those tasks can involve both classification and regression. Take an example for object detection, a task of recognizing and localizing an object in an image: it involves classification (recognizing the object among many other objects) and regression (predicting the coordinates of the objects in an image to make a bounding box).
-
-If any of the things we are talking about sounds unfamiliar, do not worry. There is no way to explain all things at once, but as we progress, things will get clear. 
-
-### Unsupervised Learning
-
-Supervised learning algorithms are trained with data and labels. Conversely, unsupervised learning algorithms are trained on unlabelled data. 
-
-Unsupervised learning algorithms are primarily used for:
-
-* Clustering: K-Means
-* Dimension reduction and data visualization: Principal Component Analysis(PCA), t-Distributed Stochastic Neighbor Embedding(t-SNE). 
-
-There are many more algorithms that could also be used for those 2 tasks, but, we will cover K-Means, PCA, and t-SNE because they are commonly used. 
-
-### Reinforcement Learning
-
-Reinforcement learning is a special type of Machine Learning that is most applicable in robotics and games. 
-
-In reinforcement learning, a learning system called *an agent* can perceives the environment, performs some actions, and gets *rewarded* or *penalized* depending on how it is performing. The main goal of the agent is to accumulate as much as rewards as possible.
-
-The *agent* learns the best strategy(*policy*) necessary for getting the most reward itself. 
-
-
-Reinforcement learning holds some of the most historical AI moments. In 2016, DeepMind AlphaGo, a reinforcement learning system won Lee Sedol in [Google DeepMind Challenge Match](https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol). Go is a complex board game that requires intuition, creative and strategic thinking. And Lee was one of the world-class Go players. You can watch the movie of the game between AlphaGo and Lee [here](https://www.youtube.com/watch?v=WXuK6gekU1Y&list=PLqYmG7hTraZBy7J_4ynYPc0Ml1RUGcLmD&index=2&t=147s).
-
-For many of us, we may not get the most of reinforcement learning, typically because of limited resources and applicability, but is it a powerful thing for those who can afford it most notably employing it in robotics and games.
-
-Let's summarize the types of Machine Learning systems. By far, supervised and unsupervised learning are the two commonly used types of Machine Learning. Semi-supervised and self-supervised learning are also getting attractions in deep learning community, but they are still in research. We will focus more those two practical types: supervised and unsupervised.
-
-## Typical Machine Learning Workflow
-
-Although every Machine Learning problem is unique, they all follow a similar workflow. In this section, we will learn how to approach Machine Learning problems systematically.
-
-Overall, a typical Machine Learning project workflow consists of: 
-* Defining and formulating a problem
-* Collecting data
-* Establishing a baseline
-* Exploratory data analysis(EDA)
-* Preparing data
-* Selecting and training a model
-* Performing error analysis and improving a model
-* Deploying a model
-
-Let's talk about each step in brief. 
-
-### Defining a Problem
-
-Everything starts here. Problem definition is the important and the initial step in any Machine Learning project. This is where you make sure you understand the problem really well. Understanding the problem will give you proper intuitions about the next steps to follow such as right learning algorithms, etc. But wait, what does it mean to understand the problem?
-
-Understanding the problem is all about diving deep into the details of the problem at hand and asking the right questions. First, it's important to narrow down the problem until you can have a simple and a well-defined goal. Here are examples of simple goals: To *classify* products into different categories, to *predict* the price of a used car given its features (such as brand, age, etc...), to *recognize* if a person is wearing a facemask, to *divide* customers into different groups that share similar behaviors, etc...As you can see, the goal can tell whether the problem is classification, regression, or clustering...
-
-At this stage, avoid vague words. The simpler you can formulate the problem, the better things will be down the hall. It's also worth questioning if the project should be pursued. Most problems do not require Machine Learning. 
-
-Problem definition also goes beyond determining the goal and objective of the project to thinking about the data that will be needed. Machine Learning models relies on the data, and the better models comes from better data. Do you have data that contains the things you want to predict? Models are not magical things, they are mathematical functions that takes data along with labels, and determine the patterns that can be used to make predictions on unseen data. If the data does not contain meaningful information relevant to what you want to predict, you will get poor predictions. 
-
-After you understand the problem and have an idea of the data you want, the next step is to collect it. 
-
-### Collecting Data
-
-This is usually the next stage after formulating a problem. Before we talk about collecting data, let's even understand the meaning of data. According to [wikipedia](https://en.wikipedia.org/wiki/Data), "data are a set of values of qualitative or quantitative variables about one or more persons or objects."
-
-There are 2 main types of data that are:
-* Structured data that are organized in tabular or spreadsheet format. Example of tabular data includes customer records, car sales, etc...
-
-* Unstructure data such as images, texts, sounds, and videos. Unstructured data are not organized as the former.
-
-Nowdays, there are lots of open-source datasets on platforms like Kaggle, Google datasets, UCL, and government websites. So, if you are solving a problem that someone solved before, it's very likely that you will find it somewhere in those platforms, or other public sources like this [repository](https://github.com/awesomedata/awesome-public-datasets). Your job as Machine Learning engineer is to find the relevant data that you can use to solve the presented problem.
-
-That said, there are times that you will have to collect your own data, especially if you are solving a problem that no one solved before. In this case, consider the time that you will have to spend collecting data and the cost. You also do not need to wait until you have your desired data points before you can start. Embrace ML development early on so that you can learn if you (really) need more data. This idea is inspired by Andrew Ng.
-
-Also, when collecting the data, quality is better than quantity. There are times when small data but good data can outwork big poor data. The amount of data you need is going to depend on the problem you're solving and its scope, but whatever the problem is, aiming to collect good data is the way to go.
-
-### Establishing a Baseline
-
-Without a benchmark, you won't know how to evaluate your results properly. A baseline is the simplest model that can solve your problem with minimal requirements. It does not have to be a model. It can be an existing open-source application, a statistical analysis or intuitions you get from data at a quick glance.
-
-The single most purpose of a baseline is to act as a reference point when comparing the actual model with the baseline. The ultimate goal is to beat a baseline, and sometime, if you can't beat it, it might mean the project is not worth pursuing, or the baseline can be all you need.
-
-### Exploratory data analysis(EDA)
-
-Before manipulating the data, it is quite important to learn about the dataset. This can be overlooked, but doing it well will help you know the effective strategies to be applied while cleaning the data.
-
-Go through some values, plot some features, and try to understand the correlation between them. If the work is vision-related, visualize some images, and spot what’s missing in the images. Are they diverse enough? What types of image scenarios can you add? Or what are images that can mislead the model?
-
-Here are other things that you would want to inspect in the data:
-
-* Duplicate values and samples
-* Corrupted or data with unsupported formats (ex: having an image of .txt and with 0 Kilobytes)
-* Class imbalances
-* Biases
-
-Before performing EDA, split the data into the training set, validation and test sets to avoid data leakage. The training set is used for training the model, the validation set is used for evaluating the model performance during training to suggest improvements, and the test set is for evaluating the final and best model performance. Validation and test set should have the same statistical distribution.
-
-Also, to avoid data leakage, do not touch the test set in EDA or anywhere before the model training.
-
-### Data Preprocessing
-
-Data processing is perhaps the biggest part of any Machine Learning project. There is a notion that Data Scientists and Machine Learning Engineers spend more than 80% of their time preparing the data and this makes sense because the real-world data are messy.
-
-In this step, it is where you convert the raw data to go in a format that can be accepted by the Machine Learning algorithms.
-
-Data preprocessing is hard because there are different types of data and the way you process one is different from the other. For example, in structured data, the way you process numerical features is going to be different from categorical features. Also in unstructured data, the way you manipulate images is going to be different from how you manipulate texts or sounds.
-
-As the next parts will cover the practical implementations of typical data preprocessing steps, let's be general about things you're likely going to deal with while manipulating the features:
-
-* **Imputing missing values:** Missing values can either be filled, removed or left as they are. There are various strategies for missing values such as mean, median, frequent imputations, backward and forward fill, and iterative imputations. The right imputation technique depends on the problem and the dataset. With the exception to tree-based models, most Machine Learning models do not accept missing values.
-
-* **Encoding categorical features:** Categorical features are all types of features that have categorical values. For example, A gender feature having the values male and female is a categorical feature. You will want to encode such types of features. The techniques for encoding them are label encoding where you can assign 0 to Male and 0 to Female, or one hot encoding where you can get the binary representations (0s and 1s) in one hot matrix. You will see this later in practice.
-
-* **Scaling the numeric features:** Most ML models work well when the input values are scaled to small values because they can train and converge faster than they would otherwise. There are two main scaling techniques that are normalization and standardization. Normalization rescales the features to the values between 0 and 1 whereas standardization rescales the features to have mean of 0 and a unit standard deviation. If you are aware that your data has normal or Gaussian distribution, normalization can be a good choice. Otherwise, standardization will work well in many cases.
-
-In many textbooks and courses, data preprocessing is also referred to as data cleaning or data preparation. Feature engineering is also a part of data preprocessing. Feature engineering is a creative task and requires some extra knowledge about the data and the problem as it involves creating new features from existing features.
-
-### Selecting and Training a Model
-
-Selecting, creating, and training a Machine Learning model is the tiniest part of any typical Machine Learning workflow. There are different types of models but in broad, most of them fall into these categories: linear models such as linear and logistic regression, tree-based models such as decision trees, ensemble models such as random forests, and finally neural networks. 
-
-Depending on your problem, you can choose any relevant model from these categories or tries many of them. But, it is worth mentioning that getting a model that can ultimately solve a given problem is no *free lunch scenario*. You have to experiment with different models to get one that works for your problem and dataset.
-
-To reduce your modeling curve, here are a few things that you can consider while choosing a Machine Learning model:
-
-* **The scope of the problem:** The scope or type of your problem can give a strong signal on what learning algorithm to use.  Take an example: If you are going to build an image classifier, neural networks (Convolutional Neural Networks specifically) might be your go-to algorithm.
-
-* **The size of the dataset:** Linear models tend to work well in small data problems, whereas ensemble models and neural networks can work well when given huge amounts of data.
-* **The level of interpretability:** If you want the predictions of your model to be explainable, neural networks may not help. Tree-based models such as decision trees can be explainable compared to other models.
-* **Training time:** Complex models (neural networks and ensemble models) will take too long to train and thus draining the computation resources. On the other hand, linear models can train faster.
-
-As you can see, there is a trade-off during model selection. You want explainability, choose models that can provide that for you, most models don't. You have a small dataset or you care about the training time, same thing, a right model for you.
-
-### Performing Error Analysis
-
-Performing error analysis will guide you throughout the process of improving the results of the model. The improvement can either be from the data or the model.
-
-One of the best ways to do error analysis is to plot the learning curve and to try noticing where the model is failing and what might be the reason and the right actions that you can take to reduce the errors.
-
-To improve the model part, you can try different model configuration values or hyperparameters. You can also try different models to see one that works well. But also, a good model comes from good data, so it's important to spend time examining the results of the model with respect to the input data.
-
-Here are questions that you can yourself iteratively in the process of error analysis:
-
-* Is the model doing poorly on all classes or is it one specific class?
-* Is it because there are not enough data points for that particular class compared to other classes?
-* There are trade-offs and limits on how much you can do to reduce the errors. Is there room for improvement?
-
-Often, the improvements will not come from tuning the model, but from spending time to increase the number of training samples and data quality.
-
-When improving the data, you can create artificial data (a.k.a data augmentation). This will work well most of the time. The whole error analysis is an iterative process, keep doing it and always aim to improve the data than the model. 
-
-### Deploying a Model
-
-Model deployment is the last part of this workflow. When all the previous steps have gone right, and you are happy about the results of the model on the test set, the next step will be to deploy the model so that the users can start to make requests and get predictions or enhanced services.
-
-## Your turn! 🚀
-
-TBD.
-
-## Acknowledgments
-
-Thanks to [Nyandwi](https://github.com/Nyandwi) for creating the open-source course [Complete Machine Learning Package](https://github.com/Nyandwi/machine_learning_complete). It inspires the majority of the content in this chapter. Some contents come from [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/framing/ml-terminology).
diff --git a/_sources/ml-fundamentals/ml-summary.ipynb b/_sources/ml-fundamentals/ml-summary.ipynb
new file mode 100644
index 0000000000..5b2213589b
--- /dev/null
+++ b/_sources/ml-fundamentals/ml-summary.ipynb
@@ -0,0 +1,142 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Summary of machine learning fundamentals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Machine Learning Landscape : Discriminative Models\n",
+    "\n",
+    "Most of supervised machine learning can be looked at using the following framework: \n",
+    "You have a set of training points $(x_i, y_i)$, and you want to find a function f that \"fits the data well\", \n",
+    "that is, $yi \\approx f(x_i)$ for most $i$.\n",
+    "\n",
+    "You will start by doing the following:\n",
+    "\n",
+    "- Define the form of $f$. For instance, we can define $f = wx + b$, for some constants $w$ and $b$. \n",
+    "Note that this is a set of functions — for different values of $w$ and $b$, \n",
+    "you will get different functions $f$, and you want to find an $f$\n",
+    "from this set that does the “best”.\n",
+    "- As you might have noticed, we have been talking about this notion of “best”, \n",
+    "which is ill-defined up to this point. So, we need to make this more concrete. \n",
+    "The goal here, as stated above, is to have $y_i \\approx f(x_i)$\n",
+    "for most $i$.\n",
+    "\n",
+    "The above two steps essentially define the **function class** and the **loss function** respectively.\n",
+    "\n",
+    "Depending on how you choose your function class and the loss function, \n",
+    "you get different supervised learning models or even unsupervised learning models:\n",
+    "\n",
+    "- Linear function class with squared-error loss function — Linear regression\n",
+    "- Linear function class with logistic loss function — Logistic regression\n",
+    "- Linear function class with hinge loss — SVM\n",
+    "- Function class containing a network of neurons with cross-entropy loss — Neural networks\n",
+    "\n",
+    "and so on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to conceive a \"new\" Machine Learning algorithm : Discriminative Models\n",
+    "\n",
+    "\n",
+    "- [How about perpendicular distance instead of vertical distance for Linear Regression?](https://math.stackexchange.com/questions/1530298/variant-of-linear-regression-using-perpendicular-distance-instead-of-vertical)\n",
+    "\n",
+    "- [How about Logistic Regression with Kernel Trick?](https://www.quora.com/How-can-one-use-kernels-utilizing-the-kernel-trick-in-logistic-regression)\n",
+    "\n",
+    "- [How about a Neural Network with Kernel Trick]\n",
+    "\n",
+    "- [How about a Neural Network not by Layers but inter-connected]\n",
+    "\n",
+    "- [How about horizontal or vertical lines - then we have Decision Tree]\n",
+    "\n",
+    "- [How about counting numbers - then we have KNN]\n",
+    "\n",
+    "- [How about counting numbers with Kernel trick](https://stats.stackexchange.com/questions/44166/kernelised-k-nearest-neighbour)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Machine Learning Landscape : Generative Models "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LDA "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Unsupervised learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Semi-supervised learning\n",
+    "\n",
+    "[Semi-supervised learning](https://www.baeldung.com/cs/svm-vs-neural-network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Your turn! 🚀\n",
+    "\n",
+    "TBD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div hidden>\n",
+    "    https://www.baeldung.com/cs/svm-vs-neural-network\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/ml-fundamentals/parameter-optimization/gradient-descent.ipynb b/_sources/ml-fundamentals/parameter-optimization/gradient-descent.ipynb
new file mode 100644
index 0000000000..cd24cf2389
--- /dev/null
+++ b/_sources/ml-fundamentals/parameter-optimization/gradient-descent.ipynb
@@ -0,0 +1,636 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: Could not find a version that satisfies the requirement skylearn (from versions: none)\n",
+      "ERROR: No matching distribution found for skylearn\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "!{sys.executable} -m pip install --quiet skylearn matplotlib numpy pandas sklearn ipython jupyterlab_myst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import sklearn\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from IPython.display import HTML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gradient descent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Objective of this session\n",
+    "\n",
+    "We have already learnt how to use Linear Regression and Logistic Regression models.\n",
+    "\n",
+    "The code might seem quite easy and intuitive for you. And you might naturally ask:\n",
+    "- What's behind the ```.fit()``` function?\n",
+    "- Why sometimes it takes quite a bit for this ```.fit()``` function to finish running?\n",
+    "\n",
+    "In this session, you will learn that the ```.fit()``` is the training of ML models, \n",
+    "i.e. tuning of parameters for ML models. And the technique behind is called \"Gradient Descent\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Video\n",
+    "\n",
+    "The corresponding video (in Chinese) for this notebook is [👉 available here on Bilibili](https://www.bilibili.com/video/BV1SY4y1G7o9/).\n",
+    "You can (and should) watch the video before diving into the details of gradient descent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://player.bilibili.com/player.html?aid=642485873&cid=764796592&page=1&high_quality=1&danmaku=0\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "video. <a href=\"https://player.bilibili.com/player.html?aid=642485873&cid=764796592&page=1&high_quality=1&danmaku=0\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://player.bilibili.com/player.html?aid=642485873&cid=764796592&page=1&high_quality=1&danmaku=0\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "video. <a href=\"https://player.bilibili.com/player.html?aid=642485873&cid=764796592&page=1&high_quality=1&danmaku=0\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's be playful ... to gain some intuition\n",
+    "\n",
+    "- [Tensorflow Playground](https://playground.tensorflow.org/#activation=sigmoid&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.00001&regularizationRate=0&noise=0&networkShape=&seed=0.71864&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=true&xSquared=true&ySquared=true&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false)\n",
+    "- [Gradient Descent Visualization](https://github.com/lilipads/gradient_descent_viz)\n",
+    "- [Optimization Algorithms Visualization](https://bl.ocks.org/EmilienDupont/aaf429be5705b219aaaf8d691e27ca87)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Some mathematics ... to gain more insight"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Abstract\n",
+    "\n",
+    "The idea behind gradient descent is simple - by gradually tuning parameters, such as slope ($m$) and the intercept ($b$) in our regression function $y = mx + b$, we minimize cost. \n",
+    "By cost, we usually mean some kind of a function that tells us how far off our model predicted result. For regression problems we often use `mean squared error` (MSE) cost function. If we use gradient descent for the classification problem, we will have a different set of parameters to tune.\n",
+    "\n",
+    "$$ MSE = \\frac{1}{n}\\sum_{i=1}^{n} (y_i - \\hat{y_i})^2 \\quad \\textrm{where} \\quad \\hat{y_i} = mx_i + b $$\n",
+    "\n",
+    "Now we have to figure out how to tweak parameters $m$ and $b$ to reduce MSE.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Partial derivatives\n",
+    "\n",
+    "We use partial derivatives to find how each individual parameter affects MSE, so that's where word _partial_ comes from. In simple words, we take the derivative with respect to $m$ and $b$ **separately**. Take a look at the formula below. It looks almost exactly the same as MSE, but this time we added f(m, b) to it. It essentially changes nothing, except now we can plug $m$ and $b$ numbers into it and calculate the result.\n",
+    "\n",
+    "$$𝑓(𝑚,𝑏)= \\frac{1}{n}\\sum_{i=1}^{n}(y_i - (mx_i+b))^2$$\n",
+    "\n",
+    "This formula (or better say function) is better representation for further calculations of partial derivatives. We can ignore sum for now and what comes before that and focus only on $y - (mx + b)^2$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Partial derivative with respect to $m$\n",
+    "\n",
+    "With respect to $m$ means we derive parameter $m$ and basically ignore what is going on with $b$, or we can say its 0. To derive with respect to $m$ we will use chain rule.\n",
+    "\n",
+    "$$ [f(g(x))]' = f'(g(x)) * g(x)' \\: - \\textrm{chain rule}$$\n",
+    "\n",
+    "Chain rule applies when one function sits inside of another. If you're new to this, you'd be surprised that $()^2$ is outside function, and $y-(\\boldsymbol{m}x+b)$ sits inside it. So, the chain rule says that we should take a derivative of outside function, keep inside function unchanged and then multiply by derivative of the inside function. Lets write these steps down:\n",
+    "\n",
+    "$$ (y - (mx + b))^2 $$\n",
+    "\n",
+    "1. Derivative of $()^2$ is $2()$, same as $x^2$ becomes $2x$\n",
+    "2. We do nothing with $y - (mx + b)$, so it stays the same\n",
+    "3. Derivative of $y - (mx + b)$ with respect to **_m_** is $(0 - (x + 0))$ or $-x$, because **_y_** and **_b_** are constants, they become 0, and derivative of **_mx_** is **_x_**\n",
+    " \n",
+    "Multiply all parts we get following: $2 * (y - (mx+b)) * -x$. \n",
+    "\n",
+    "Looks nicer if we move -x to the left: $-2x *(y-(mx+b))$. There we have it. The final version of our derivative is the following:\n",
+    "\n",
+    "$$\\frac{\\partial f}{\\partial m} = \\frac{1}{n}\\sum_{i=1}^{n}-2x_i(y_i - (mx_i+b))$$\n",
+    "\n",
+    "Here, $\\frac{df}{dm}$ means we find partial derivative of function f (we mentioned it earlier) with respect to m. We plug our derivative to the summation and we're done.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Partial derivative with respect to $b$\n",
+    "\n",
+    "Same rules apply to the derivative with respect to b.\n",
+    "\n",
+    "1. $()^2$ becomes $2()$, same as $x^2$ becomes $2x$\n",
+    "2. $y - (mx + b)$ stays the same\n",
+    "3. $y - (mx + b)$ becomes $(0 - (0 + 1))$ or $-1$, because **_y_** and **_mx_** are constants, they become 0, and derivative of **_b_** is 1\n",
+    "\n",
+    "Multiply all the parts together and we get $-2(y-(mx+b))$\n",
+    "\n",
+    "$$\\frac{\\partial f}{\\partial b} = \\frac{1}{n}\\sum_{i=1}^{n}-2(y_i - (mx_i+b))$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final function\n",
+    "\n",
+    "Few details we should discuss before jumping into code:\n",
+    "\n",
+    "1. Gradient descent is an iterative process and with each iteration ($epoch$) we slightly minimizing MSE, so each time we use our derived functions to update parameters $m$ and $b$.\n",
+    "2. Because it's iterative, we should choose how many iterations we take, or make algorithm stop when we approach minima of MSE. In other words when algorithm is no longer improving MSE, we know it reached minimum.\n",
+    "3. Gradient descent has an additional parameter learning rate ($lr$), which helps control how fast or slow algorithm going towards minima of MSE\n",
+    "\n",
+    "That's about it. So you can already understand that Gradient Descent for the most part is just process of taking derivatives and using them over and over to minimize function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time to code!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear regression With gradient descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LinearRegression:\n",
+    "    def __init__(self, learning_rate=0.0003, n_iters=3000):\n",
+    "        self.lr = learning_rate\n",
+    "        self.n_iters = n_iters\n",
+    "        self.weights = None\n",
+    "        self.bias = None\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "\n",
+    "        # init parameters\n",
+    "        self.weights = np.zeros(n_features)\n",
+    "        self.bias = 0\n",
+    "\n",
+    "        # gradient descent\n",
+    "        for _ in range(self.n_iters):\n",
+    "            # approximate y with linear combination of weights and x, plus bias\n",
+    "            y_predicted = np.dot(X, self.weights) + self.bias\n",
+    "\n",
+    "            # compute gradients\n",
+    "            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))\n",
+    "            db = (1 / n_samples) * np.sum(y_predicted - y)\n",
+    "            # update parameters\n",
+    "            self.weights -= self.lr * dw\n",
+    "            self.bias -= self.lr * db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        y_predicted = np.dot(X, self.weights) + self.bias\n",
+    "        return y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'lr': 0.0003, 'n_iters': 3000, 'weights': array([0.36114314, 0.15172482, 0.01138062, 0.07103796, 0.10143793,\n",
+      "       0.14812986, 0.09146885, 0.00270041]), 'bias': 0.014542612245156487}\n",
+      "0    -1.470137\n",
+      "1    -1.226722\n",
+      "2    -1.633534\n",
+      "3    -1.145394\n",
+      "4    -1.385705\n",
+      "        ...   \n",
+      "92    0.985388\n",
+      "93    1.125408\n",
+      "94    1.936285\n",
+      "95    1.776223\n",
+      "96    1.680470\n",
+      "Name: lpsa, Length: 97, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prostate = pd.read_table(\n",
+    "    \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-fundamental/parameter-optimization/gradient-descent/prostate.data\"\n",
+    ")\n",
+    "prostate.drop(prostate.columns[0], axis=1, inplace=True)\n",
+    "\n",
+    "X = prostate.drop([\"lpsa\", \"train\"], axis=1)\n",
+    "y = prostate[\"lpsa\"]\n",
+    "\n",
+    "regressor = LinearRegression()\n",
+    "\n",
+    "regressor.fit(X, y)\n",
+    "y_pred = regressor.predict(X)\n",
+    "\n",
+    "print(regressor.__dict__)\n",
+    "print(y - y_pred)\n",
+    "\n",
+    "plt.scatter(y, y_pred)\n",
+    "plt.plot([0, 5], [0, 5])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LinearRegressionWithSGD:\n",
+    "    def __init__(self, learning_rate=0.0003, n_iters=5000):\n",
+    "        self.lr = learning_rate\n",
+    "        self.n_iters = n_iters\n",
+    "        self.weights = None\n",
+    "        self.bias = None\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "\n",
+    "        # init parameters\n",
+    "        self.weights = np.zeros(n_features)\n",
+    "        self.bias = 0\n",
+    "\n",
+    "        batch_size = 5\n",
+    "        # stochastic gradient descent\n",
+    "        for _ in range(self.n_iters):\n",
+    "            # approximate y with linear combination of weights and x, plus bias\n",
+    "            y_predicted = np.dot(X, self.weights) + self.bias\n",
+    "\n",
+    "            indexes = np.random.randint(0, len(X), batch_size)  # random sample\n",
+    "\n",
+    "            Xs = np.take(X, indexes, axis=0)\n",
+    "            ys = np.take(y, indexes, axis=0)\n",
+    "            y_predicted_s = np.take(y_predicted, indexes)\n",
+    "\n",
+    "            # compute gradients\n",
+    "            dw = (1 / batch_size) * np.dot(Xs.T, (y_predicted_s - ys))\n",
+    "            db = (1 / batch_size) * np.sum(y_predicted_s - ys)\n",
+    "            # update parameters\n",
+    "            self.weights -= self.lr * dw\n",
+    "            self.bias -= self.lr * db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        y_predicted = np.dot(X, self.weights) + self.bias\n",
+    "        return y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'lr': 0.0003, 'n_iters': 5000, 'weights': array([4.46681695e-01, 2.15368365e-01, 2.42189689e-04, 9.50452753e-02,\n",
+      "       1.38774249e-01, 1.32810554e-01, 1.12508151e-01, 8.44556306e-03]), 'bias': 0.020240916078570047}\n",
+      "0    -1.159767\n",
+      "1    -0.826809\n",
+      "2    -1.192707\n",
+      "3    -0.725197\n",
+      "4    -1.097735\n",
+      "        ...   \n",
+      "92    0.772508\n",
+      "93    0.687037\n",
+      "94    1.873903\n",
+      "95    1.383421\n",
+      "96    1.616669\n",
+      "Name: lpsa, Length: 97, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prostate = pd.read_table(\n",
+    "    \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-fundamental/parameter-optimization/gradient-descent/prostate.data\"\n",
+    ")\n",
+    "prostate.drop(prostate.columns[0], axis=1, inplace=True)\n",
+    "\n",
+    "X = prostate.drop([\"lpsa\", \"train\"], axis=1)\n",
+    "y = prostate[\"lpsa\"]\n",
+    "\n",
+    "regressor = LinearRegressionWithSGD()\n",
+    "\n",
+    "regressor.fit(X, y)\n",
+    "y_pred = regressor.predict(X)\n",
+    "\n",
+    "print(regressor.__dict__)\n",
+    "print(y - y_pred)\n",
+    "\n",
+    "plt.scatter(y, y_pred)\n",
+    "plt.plot([0, 5], [0, 5])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logistic regression with gradient descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LogisticRegression:\n",
+    "    def __init__(self, learning_rate=0.001, n_iters=1000):\n",
+    "        self.lr = learning_rate\n",
+    "        self.n_iters = n_iters\n",
+    "        self.weights = None\n",
+    "        self.bias = None\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "\n",
+    "        # init parameters\n",
+    "        self.weights = np.zeros(n_features)\n",
+    "        self.bias = 0\n",
+    "\n",
+    "        # gradient descent\n",
+    "        for _ in range(self.n_iters):\n",
+    "            # approximate y with linear combination of weights and x, plus bias\n",
+    "            linear_model = np.dot(X, self.weights) + self.bias\n",
+    "            # apply sigmoid function\n",
+    "            y_predicted = self._sigmoid(linear_model)\n",
+    "\n",
+    "            # compute gradients\n",
+    "            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))\n",
+    "            db = (1 / n_samples) * np.sum(y_predicted - y)\n",
+    "            # update parameters\n",
+    "            self.weights -= self.lr * dw\n",
+    "            self.bias -= self.lr * db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        linear_model = np.dot(X, self.weights) + self.bias\n",
+    "        y_predicted = self._sigmoid(linear_model)\n",
+    "        y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]\n",
+    "        return np.array(y_predicted_cls)\n",
+    "\n",
+    "    def _sigmoid(self, x):\n",
+    "        return 1 / (1 + np.exp(-x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR classification perf:\n",
+      " [[88  9]\n",
+      " [40 16]]\n",
+      "LR classification error rate:\n",
+      " 0.3202614379084967\n"
+     ]
+    }
+   ],
+   "source": [
+    "heart = pd.read_csv(\n",
+    "    \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-fundamental/parameter-optimization/gradient-descent/SA_heart.csv\"\n",
+    ")\n",
+    "heart.famhist.replace(to_replace=[\"Present\", \"Absent\"], value=[1, 0], inplace=True)\n",
+    "heart.drop([\"row.names\"], axis=1, inplace=True)\n",
+    "X = heart.iloc[:, :-1]\n",
+    "y = heart.iloc[:, -1]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.33, random_state=42\n",
+    ")\n",
+    "\n",
+    "regressor = LogisticRegression(learning_rate=0.0001, n_iters=1000)\n",
+    "\n",
+    "regressor.fit(X_train, y_train)\n",
+    "y_pred = regressor.predict(X_test)\n",
+    "perf = sklearn.metrics.confusion_matrix(y_test, y_pred)\n",
+    "print(\"LR classification perf:\\n\", perf)\n",
+    "\n",
+    "error_rate = np.mean(y_test != y_pred)\n",
+    "print(\"LR classification error rate:\\n\", error_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Your turn 🚀\n",
+    "\n",
+    "Modify ```LogisticRegression``` so that the training will use SGD instead of GD."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [optional] At the frontier of Machine Learning Research "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://www.youtube.com/embed/mdKjMPmcWjY\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "video. <a href=\"https://www.youtube.com/embed/mdKjMPmcWjY\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://www.youtube.com/embed/mdKjMPmcWjY\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "video. <a href=\"https://www.youtube.com/embed/mdKjMPmcWjY\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bibliography\n",
+    "\n",
+    "- [Gradient Descent, Step-by-Step - StatQuest](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n",
+    "- [Stochastic Gradient Descent, Clearly Explained!!! - StatQuest](https://www.youtube.com/watch?v=vMh0zPT0tLI) \n",
+    "- http://43.142.12.204:12345/05-ML_04-Under-the-Hood.html\n",
+    "- http://43.142.12.204:9999/GradientDescentAnimation.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myconda1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/ml-fundamentals/parameter-optimization/loss-function.ipynb b/_sources/ml-fundamentals/parameter-optimization/loss-function.ipynb
new file mode 100644
index 0000000000..b661e41a6c
--- /dev/null
+++ b/_sources/ml-fundamentals/parameter-optimization/loss-function.ipynb
@@ -0,0 +1,291 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "!{sys.executable} -m pip install --quiet pandas numpy matplotlib jupyterlab_myst ipython "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Loss function\n",
+    "\n",
+    "The function we want to minimize or maximize is called the objective function or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function.\n",
+    "\n",
+    "— Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Objective of this section\n",
+    "\n",
+    "We have already learned math and code for \"Gradient Descent\", as well as other optimization techniques.\n",
+    "\n",
+    "In this section, we will learn more about loss functions for Linear Regression and Logistic Regression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What’s the Difference between a Loss Function and a Cost Function?\n",
+    "\n",
+    "A loss function is for a single training example. It is also sometimes called an error function. A cost function, on the other hand, is the average loss over the entire training dataset. The optimization strategies aim at minimizing the cost function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression Loss Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Squared Error Loss\n",
+    "\n",
+    "Squared Error loss for each training example, \n",
+    "also known as **L2 Loss**, is the square of the \n",
+    "difference between the actual and the predicted values:\n",
+    "\n",
+    "$$L = (y - f(x))^2$$\n",
+    "\n",
+    "The corresponding cost function is the \n",
+    "Mean of these Squared Errors (MSE)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Absolute Error Loss\n",
+    "\n",
+    "Absolute Error for each training example \n",
+    "is the distance between the predicted and the actual values, \n",
+    "irrespective of the sign. Absolute Error is also \n",
+    "known as the **L1 loss**:\n",
+    "\n",
+    "$$L = \\lvert y - f(x) \\rvert$$\n",
+    "\n",
+    "The corresponding cost function is the Mean of these Absolute Errors (MAE)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classification Loss Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Binary Cross Entropy Loss\n",
+    "\n",
+    "Cross-entropy is the default loss function to use for binary classification problems.\n",
+    "\n",
+    "It is intended for use with binary classification where the target values are in the set {0, 1}.\n",
+    "\n",
+    "Mathematically, it is the preferred loss function \n",
+    "under the inference framework of maximum likelihood. \n",
+    "It is the loss function to be evaluated first and only \n",
+    "changed if you have a good reason.\n",
+    "\n",
+    "Cross-entropy will calculate a score that summarizes \n",
+    "the average difference between the actual and predicted \n",
+    "probability distributions for predicting class 1. \n",
+    "The score is minimized and a perfect cross-entropy value is 0.\n",
+    "\n",
+    "This YouTube video by Andrew Ng explains very well Binary Cross Entropy Loss (make sure \n",
+    "that you have access to YouTube for this web page to render correctly):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://www.youtube.com/embed/SHEPb1JHw5o\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "video. <a href=\"https://www.youtube.com/embed/SHEPb1JHw5o\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://www.youtube.com/embed/SHEPb1JHw5o\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "video. <a href=\"https://www.youtube.com/embed/SHEPb1JHw5o\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi-Class Cross-Entropy Loss\n",
+    "\n",
+    "Cross-entropy is the default loss function to \n",
+    "use for multi-class classification problems.\n",
+    "\n",
+    "In this case, it is intended for use with \n",
+    "multi-class classification where the target values \n",
+    "are in the set {0, 1, 3, …, n}, where each class is \n",
+    "assigned a unique integer value.\n",
+    "\n",
+    "Mathematically, it is the preferred loss \n",
+    "function under the inference framework of \n",
+    "maximum likelihood. It is the loss function \n",
+    "to be evaluated first and only changed if you have a good reason.\n",
+    "\n",
+    "Cross-entropy will calculate a score that \n",
+    "summarizes the average difference between \n",
+    "the actual and predicted probability distributions \n",
+    "for all classes in the problem. The score is minimized \n",
+    "and a perfect cross-entropy value is 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  [optional] At the frontier of Machine Learning Research"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<p style=\"text-align: center;\">\n",
+       "<iframe src=\"https://www.youtube.com/embed/QBbC3Cjsnjg\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+       "video. <a href=\"https://www.youtube.com/embed/QBbC3Cjsnjg\">[source]</a>\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<p style=\"text-align: center;\">\n",
+    "<iframe src=\"https://www.youtube.com/embed/QBbC3Cjsnjg\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
+    "video. <a href=\"https://www.youtube.com/embed/QBbC3Cjsnjg\">[source]</a>\n",
+    "</p>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With its corresponding paper: [A General and Adaptive Robust Loss Function](https://arxiv.org/abs/1701.03077)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bibliography\n",
+    "\n",
+    "- [ML cheat sheet for loss functions](https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html)\n",
+    "- [A Short Introduction to Entropy, Cross-Entropy and KL-Divergence](https://www.youtube.com/watch?v=ErfnhcEV1O8)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myconda1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/ml-fundamentals/parameter-optimization/parameter-optimization.ipynb b/_sources/ml-fundamentals/parameter-optimization/parameter-optimization.ipynb
new file mode 100644
index 0000000000..4bdd8abd9c
--- /dev/null
+++ b/_sources/ml-fundamentals/parameter-optimization/parameter-optimization.ipynb
@@ -0,0 +1,60 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# parameter-optimization\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tableofcontents}\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myconda1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/prerequisites/python-programming-advanced.ipynb b/_sources/prerequisites/python-programming-advanced.ipynb
index e0b268fe81..37e680e95a 100644
--- a/_sources/prerequisites/python-programming-advanced.ipynb
+++ b/_sources/prerequisites/python-programming-advanced.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9a4c0122",
+   "id": "e44c15c2",
    "metadata": {},
    "source": [
     "<details>\n",
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "8ec6f3f2",
+   "id": "f9945030",
    "metadata": {
     "tags": [
      "hide-input"
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ce49663",
+   "id": "a8b09b83",
    "metadata": {},
    "source": [
     "## Control flow\n",
@@ -73,7 +73,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b4626c43",
+   "id": "d6eb69d8",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b796613",
+   "id": "7b13f21d",
    "metadata": {},
    "source": [
     "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"https://pythontutor.com/iframe-embed.html#code=def%20get_age_group%28age%29%3A%0A%20%20%20%20if%20age%20%3E%3D%200%20and%20age%20%3C%3D%209%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20a%20child!%22%29%0A%20%20%20%20elif%20age%20%3E%209%20and%20age%20%3C%3D%2018%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20an%20adolescent!%22%29%0A%20%20%20%20elif%20age%20%3E%2018%20and%20age%20%3C%3D%2065%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20an%20adult!%22%29%0A%20%20%20%20else%3A%20%23%20age%20%3E%2065%0A%20%20%20%20%20%20%20%20print%28%22Golden%20ages!%22%29%0A%0Aget_age_group%283%29%0Aget_age_group%2850%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=0&heapPrimitives=nevernest&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>\n",
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "e883cf8b",
+   "id": "cf987760",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1d05a03",
+   "id": "c05770a8",
    "metadata": {},
    "source": [
     "If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended that you first make a copy. Iterating over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:"
@@ -145,7 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "ae297d58",
+   "id": "f4f979ba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab1dd1b9",
+   "id": "3db2757b",
    "metadata": {},
    "source": [
     "Otherwise, with `for w in words`, the example would attempt to create an infinite list, inserting defenestrate over and over again."
@@ -165,7 +165,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8e1f978a",
+   "id": "c847f564",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c714915b",
+   "id": "10078c04",
    "metadata": {},
    "source": [
     "If you do need to iterate over a sequence of numbers, the built-in function `range()` comes in handy. It generates arithmetic progressions:"
@@ -183,7 +183,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "8a3c8b31",
+   "id": "5cbd9e05",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae17cbb9",
+   "id": "6b093bbf",
    "metadata": {},
    "source": [
     "Let's see a more complex example about how to find the prims by in an embedded `for` loops."
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "cd84d5ea",
+   "id": "44870e24",
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1ec2366",
+   "id": "e173c420",
    "metadata": {},
    "source": [
     "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"https://pythontutor.com/iframe-embed.html#code=%23%20find%20primes%20using%20a%20for-else%20construct%0Afor%20n%20in%20range%282,%2010%29%3A%0A%20%20%20%20x_range%20%3D%20range%282,%20n%29%0A%20%20%20%20for%20x%20in%20x_range%3A%0A%20%20%20%20%20%20%20%20if%20n%20%25%20x%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20break%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20%23%20loop%20fell%20through%20without%20finding%20a%20factor%0A%20%20%20%20%20%20%20%20print%28n%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=30&heapPrimitives=nevernest&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>\n",
@@ -245,7 +245,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "3a24cb7b",
+   "id": "91cb4d6a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5180f45",
+   "id": "8e29ef5c",
    "metadata": {},
    "source": [
     "Or simply use `enumerate()`."
@@ -270,7 +270,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7747aff9",
+   "id": "71b88762",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74061718",
+   "id": "579523d9",
    "metadata": {},
    "source": [
     "When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the `items()` method."
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "4c53ee3c",
+   "id": "bcc1152e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d48ba70",
+   "id": "0d903ae4",
    "metadata": {},
    "source": [
     "When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the `enumerate()` function"
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1602dcb7",
+   "id": "91c73a68",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aaeb424d",
+   "id": "a058883c",
    "metadata": {},
    "source": [
     "To loop over two or more sequences at the same time, the entries can be paired with the `zip()` function. In many ways, the object returned by `range()` behaves as if it is a list, but in fact, it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.\n",
@@ -349,7 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "0371e698",
+   "id": "88fdaf7a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf6bd956",
+   "id": "cf71298f",
    "metadata": {},
    "source": [
     "### The while statement\n",
@@ -381,7 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "00d804da",
+   "id": "6a37563b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d844568f",
+   "id": "2bc79c57",
    "metadata": {},
    "source": [
     "### The try statement\n",
@@ -423,7 +423,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "e39fe655",
+   "id": "dbfd0be7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c36f306",
+   "id": "de6602b1",
    "metadata": {},
    "source": [
     "You can define as many exception blocks as you want, e.g. if you want to execute a special block of code for a special kind of error:"
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "446f1755",
+   "id": "387d411f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a709efb7",
+   "id": "c52a6404",
    "metadata": {},
    "source": [
     "You can use the else keyword to define a block of code to be executed if no errors were raised."
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "e16c9a77",
+   "id": "1e1a5bbd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,7 +493,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec748f35",
+   "id": "d0bab8a7",
    "metadata": {},
    "source": [
     "The final block, if specified, will be executed regardless if the `try` block raises an error or not."
@@ -502,7 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "762fd716",
+   "id": "6fb6de31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "151e3be9",
+   "id": "e00c35d4",
    "metadata": {},
    "source": [
     "### The break statement\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "0f0bd473",
+   "id": "dd11cdb2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c334e67",
+   "id": "9750ae56",
    "metadata": {},
    "source": [
     "### The continue statement\n",
@@ -572,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "d9b2ee8e",
+   "id": "0f439c54",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -596,7 +596,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea5fdba9",
+   "id": "b6469580",
    "metadata": {},
    "source": [
     "## Functions\n",
@@ -614,7 +614,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "c52bb525",
+   "id": "2753e482",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "779cbb13",
+   "id": "611cdf3d",
    "metadata": {},
    "source": [
     "Now call the function we just defined."
@@ -663,7 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "e8d1a845",
+   "id": "5dbccc82",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23e75789",
+   "id": "f7f57e40",
    "metadata": {},
    "source": [
     "A function definition introduces the function name in the current symbol table. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function.\n",
@@ -683,7 +683,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "928af6d1",
+   "id": "5c7800e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -693,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "470a1b4b",
+   "id": "0bdbc291",
    "metadata": {},
    "source": [
     "In Python, functions are first-class citizens, they are objects and that means we can do a lot of useful stuff with them.\n",
@@ -704,7 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "bce093fb",
+   "id": "24cf0b28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +718,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8419e05b",
+   "id": "01c3a5e1",
    "metadata": {},
    "source": [
     "Define functions inside other functions."
@@ -727,7 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "0afe1812",
+   "id": "9021e6f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +743,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc839541",
+   "id": "e90187a0",
    "metadata": {},
    "source": [
     "Functions can be passed as parameters to other functions."
@@ -752,7 +752,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "6f75e896",
+   "id": "55d10400",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,7 +768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0eb9f4f0",
+   "id": "48b29daf",
    "metadata": {},
    "source": [
     "Functions can return other functions. In other words, functions generate other functions."
@@ -777,7 +777,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "6377f924",
+   "id": "b11c9d6e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -793,7 +793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14bd2ac6",
+   "id": "ae78ea94",
    "metadata": {},
    "source": [
     "Inner functions have access to the enclosing scope.\n",
@@ -804,7 +804,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "c29e7b10",
+   "id": "f3456215",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c962e12",
+   "id": "056314d9",
    "metadata": {},
    "source": [
     "### Scopes of variables inside functions (global and nonlocal statements)\n",
@@ -849,7 +849,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "fd336b0c",
+   "id": "5a9464f9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,7 +859,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8094a69d",
+   "id": "9fffe1bf",
    "metadata": {},
    "source": [
     "This is a scopes and namespaces example demonstrating how to reference the different scopes and namespaces, and how global and nonlocal affect variable binding:"
@@ -868,7 +868,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "d1275ae9",
+   "id": "596f29ca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -936,7 +936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c364d31",
+   "id": "3ae25819",
    "metadata": {},
    "source": [
     "On this example you may see how accessing and changing global variables from within inner functions might make debugging more difficult and code to be less predictable. Since you might have expected that `test_variable` should still be equal to 'initial global value' but it was changed by \"someone\" you need to know about the CONTEXT of who had changed that. So once again access global and non-local scope only if you know what you're doing otherwise it might be considered a bad practice.\n",
@@ -953,7 +953,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "d03642a2",
+   "id": "a2bb4642",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -966,7 +966,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d2c9bd8",
+   "id": "964caee8",
    "metadata": {},
    "source": [
     "This function `power_of` can be called in several ways because it has default value for the second argument. First, we may call it omitting the second argument at all."
@@ -975,7 +975,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "3718cd7f",
+   "id": "b0686d5b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -984,7 +984,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04b055d0",
+   "id": "ad5cfcfe",
    "metadata": {},
    "source": [
     "We may also want to override the second argument by using the following function calls."
@@ -993,7 +993,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "aeb0406d",
+   "id": "c0edd0f2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1003,7 +1003,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e66a1ce8",
+   "id": "9d39d3ef",
    "metadata": {},
    "source": [
     "### Keyword arguments\n",
@@ -1020,7 +1020,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "026a659d",
+   "id": "fac8cc71",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1075,7 +1075,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e24a1622",
+   "id": "76148141",
    "metadata": {},
    "source": [
     "But all the following calls would be invalid:\n",
@@ -1124,7 +1124,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "17c52316",
+   "id": "70dbdc7b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1137,7 +1137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40158ca7",
+   "id": "eb0234d2",
    "metadata": {},
    "source": [
     "Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the `*args` parameter are **keyword-only arguments**, meaning that they can only be used as keywords rather than positional arguments."
@@ -1146,7 +1146,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "b0b07349",
+   "id": "597b9521",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1159,7 +1159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b13b9e4b",
+   "id": "53891977",
    "metadata": {},
    "source": [
     "### Unpacking argument lists (* and ** statements)\n",
@@ -1176,7 +1176,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "c7066f86",
+   "id": "64b2e681",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1197,7 +1197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b88c44cc",
+   "id": "4b1eed11",
    "metadata": {},
    "source": [
     "### Lambda expressions (lambda statement)\n",
@@ -1214,7 +1214,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "22e43f46",
+   "id": "0e3315f7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "776dc0c1",
+   "id": "9aebe0db",
    "metadata": {},
    "source": [
     "Another use of lambda is to pass a small function as an argument."
@@ -1239,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "6d7d4e2b",
+   "id": "f814e4a8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1252,7 +1252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a9bb5a1",
+   "id": "f65da140",
    "metadata": {},
    "source": [
     "### Documentation strings\n",
@@ -1271,7 +1271,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "a787757d",
+   "id": "02b3100c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1285,7 +1285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3dcae566",
+   "id": "b0f5d3f7",
    "metadata": {},
    "source": [
     "The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).\n",
@@ -1313,7 +1313,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "03f3e34e",
+   "id": "66fab7c5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1325,7 +1325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfc51f4f",
+   "id": "6460bb92",
    "metadata": {},
    "source": [
     "### Function decorators\n",
@@ -1344,7 +1344,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "f5d112ef",
+   "id": "f82a87d2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1354,7 +1354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2de8fcd2",
+   "id": "7be90b5b",
    "metadata": {},
    "source": [
     "This function decorates another functions output with `<p>` tag."
@@ -1363,7 +1363,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "1fff9b9f",
+   "id": "c95f042d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1375,7 +1375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81faffab",
+   "id": "f60d7ec6",
    "metadata": {},
    "source": [
     "Now, let's call our decorator and pass the function we want to decorate to it."
@@ -1384,7 +1384,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "07081ac5",
+   "id": "92bc9a95",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1393,7 +1393,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69a88cf0",
+   "id": "4c8ac2ca",
    "metadata": {},
    "source": [
     "Here we go, we've just decorated the function output without changing the function itself."
@@ -1402,7 +1402,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "abe96bac",
+   "id": "13803605",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1412,7 +1412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7741c701",
+   "id": "2a93d8fa",
    "metadata": {},
    "source": [
     "Now, Python makes creating and using decorators a bit cleaner and nicer for the programmer through some syntactic sugar. There is a neat shortcut for that, which is to mention the name of the decorating function before the function to be decorated. The name of the decorator should be prepended with an `@` symbol."
@@ -1421,7 +1421,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "57cd2e00",
+   "id": "6b8eedb7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1434,7 +1434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6001aa3",
+   "id": "bf49cf52",
    "metadata": {},
    "source": [
     "Now let's consider we wanted to decorate our greeting function with one more function to wrap a `div` in the string output."
@@ -1443,7 +1443,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "adad75a8",
+   "id": "f055ca74",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04e91129",
+   "id": "1b9f38a5",
    "metadata": {},
    "source": [
     "With the basic approach, decorating get_text would be along the lines of `greeting_with_div_p = decorate_with_div(decorate_with_p(greeting_with_p))`\n",
@@ -1467,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "e1899ddf",
+   "id": "7ebcd7cb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1481,7 +1481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "827cca22",
+   "id": "98896aa1",
    "metadata": {},
    "source": [
     "One important thing to notice here is that the order of setting our decorators matters. If the order was different in the example above, the output would have been different.\n",
@@ -1494,7 +1494,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "f175c921",
+   "id": "dc053eb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1515,7 +1515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c24cd48",
+   "id": "59d5fd77",
    "metadata": {},
    "source": [
     "## Classes\n",
@@ -1534,7 +1534,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "762145d7",
+   "id": "14a3156c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1559,7 +1559,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28d65188",
+   "id": "f0e6f170",
    "metadata": {},
    "source": [
     "When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.\n",
@@ -1570,7 +1570,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "73ff7c86",
+   "id": "998a5b0f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1582,7 +1582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd7bb3c9",
+   "id": "400725a0",
    "metadata": {},
    "source": [
     "### Class objects\n",
@@ -1606,7 +1606,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "9fc69c2e",
+   "id": "4dc06929",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1627,7 +1627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16a3ec55",
+   "id": "b79ca8fc",
    "metadata": {},
    "source": [
     " `__doc__` is also a valid attribute, returning the docstring belonging to the class:\n",
@@ -1642,7 +1642,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "fc6d3325",
+   "id": "41e3d598",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1652,7 +1652,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ea77acf",
+   "id": "6af15ac0",
    "metadata": {},
    "source": [
     "CLASS INSTANTIATION uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):"
@@ -1661,7 +1661,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "c0cf7ccb",
+   "id": "e4462a63",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1673,7 +1673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f71a0d3",
+   "id": "3b7f29ae",
    "metadata": {},
    "source": [
     "The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named `__init__()`, like this:"
@@ -1682,7 +1682,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "66c94ee9",
+   "id": "d502efb5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1707,7 +1707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77a02dab",
+   "id": "75d3a359",
    "metadata": {},
    "source": [
     "### Instance objects\n",
@@ -1727,7 +1727,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "317006c5",
+   "id": "f5d1bf98",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1746,7 +1746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1137bde",
+   "id": "729c0452",
    "metadata": {},
    "source": [
     "### Methods objects\n",
@@ -1763,7 +1763,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "853ddedd",
+   "id": "bcc7c08f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1782,7 +1782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39335225",
+   "id": "15646509",
    "metadata": {},
    "source": [
     "A method is a function that “belongs to” an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called `append`, `insert`, `remove`, `sort`, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)\n",
@@ -1793,7 +1793,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "970bb033",
+   "id": "7b68ebdb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1803,7 +1803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2743328f",
+   "id": "ba4a321d",
    "metadata": {},
    "source": [
     "However, it is not necessary to call a method right away: `counter.get_counter()` is a method object, and can be stored away and called at a later time. For example:"
@@ -1812,7 +1812,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "0832dc1e",
+   "id": "a56caeaf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1822,7 +1822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32edb147",
+   "id": "1f0aa297",
    "metadata": {},
    "source": [
     "But be aware that `counter.get_counter()` is not the same thing as `MyCounter.get_counter()` — it is a method object, not a function object.\n",
@@ -1835,7 +1835,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "1205fe10",
+   "id": "4af0c94d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1845,7 +1845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9579cb14",
+   "id": "80e79c27",
    "metadata": {},
    "source": [
     "### Class and instance variables\n",
@@ -1862,7 +1862,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "65938cc3",
+   "id": "e199fc37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b04854a",
+   "id": "5693e655",
    "metadata": {},
    "source": [
     "Shared data can have possibly surprising effects involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances.\n",
@@ -1900,7 +1900,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "192dbf53",
+   "id": "852d825e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1933,7 +1933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f040cdb",
+   "id": "de51ff51",
    "metadata": {},
    "source": [
     "The correct design of the class should use an instance variable instead:"
@@ -1942,7 +1942,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "7dc93190",
+   "id": "606f2ec0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1971,7 +1971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a3e08e9",
+   "id": "47d7b6b6",
    "metadata": {},
    "source": [
     "### Inheritance\n",
@@ -1988,7 +1988,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "b3212816",
+   "id": "b3d56132",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2004,7 +2004,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f86a0ad2",
+   "id": "56cf2232",
    "metadata": {},
    "source": [
     "The base class (in our case `Person`) must be defined in a scope containing the derived class definition. In place of a base class name, other arbitrary expressions are also allowed.\n",
@@ -2019,7 +2019,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "c56a396f",
+   "id": "faee1610",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2038,7 +2038,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "218e912c",
+   "id": "7d687672",
    "metadata": {},
    "source": [
     "There’s nothing special about the instantiation of derived classes: `DerivedClassName()` creates a new instance of the class. Method references are resolved as follows: the corresponding class attribute is searched, descending the chain of base classes if necessary, and the method reference is valid if this yields a function object."
@@ -2047,7 +2047,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "b93a5411",
+   "id": "9c28e467",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2061,7 +2061,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0ea0abb",
+   "id": "69b3529e",
    "metadata": {},
    "source": [
     "Python has two built-in functions that work with inheritance:\n",
@@ -2073,7 +2073,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "1d9ad2bd",
+   "id": "c539e7f7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2089,7 +2089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bad3130b",
+   "id": "6f2c2aba",
    "metadata": {},
    "source": [
     "### Multiple inheritance\n",
@@ -2104,7 +2104,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "48d72c36",
+   "id": "acc1c59d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2137,7 +2137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5784457a",
+   "id": "42eab804",
    "metadata": {},
    "source": [
     "Python supports a form of multiple inheritances as well. A class definition with multiple base classes looks like this:"
@@ -2146,7 +2146,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "5a7cc8a1",
+   "id": "d4d3c75f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2161,7 +2161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "700eecbf",
+   "id": "61117420",
    "metadata": {},
    "source": [
     "For most purposes, in the simplest cases, you can think of the search for attributes inherited from a parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap in the hierarchy. Thus, if an attribute is not found in `CalendarClock`, it is searched for in `Clock`, then (recursively) in the base classes of `Clock`, and if it was not found there, it was searched for in `Calendar`, and so on.\n",
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "50c65416",
+   "id": "12054187",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2213,7 +2213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc6848c6",
+   "id": "c2fe8d48",
    "metadata": {},
    "source": [
     "There is a variant of the import statement that imports names from a module directly into the importing module’s symbol table. For example:"
@@ -2222,7 +2222,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "f82d2cf4",
+   "id": "a100091e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2232,7 +2232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4dc80350",
+   "id": "2cb09616",
    "metadata": {},
    "source": [
     "There is even a variant to import all names that a module defines. This imports all names except those beginning with an underscore `_`. In most cases Python programmers do not use this facility since it introduces an unknown set of names into the interpreter, possibly hiding some things you have already defined.\n",
@@ -2243,7 +2243,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "b851ac54",
+   "id": "ec6dec7d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2252,7 +2252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8599a251",
+   "id": "9bd2c061",
    "metadata": {},
    "source": [
     "It can also be used when utilizing from with similar effects:"
@@ -2261,7 +2261,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "77531d3c",
+   "id": "c1f43746",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2270,7 +2270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c12019e6",
+   "id": "6beec68c",
    "metadata": {},
    "source": [
     "When a module named spam is imported, the interpreter first searches for a built-in module with that name. If not found, it then searches for a file named spam.py in a list of directories given by the variable `sys.path`. `sys.path` is initialized from these locations:\n",
@@ -2283,7 +2283,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "d8a2e022",
+   "id": "5adc1c70",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2299,7 +2299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3137db2",
+   "id": "a7839c89",
    "metadata": {},
    "source": [
     "If you intend to use a function often you can assign it to a local name."
@@ -2308,7 +2308,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "30f0bd36",
+   "id": "5460c8ab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2318,7 +2318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "807e1619",
+   "id": "9e3ea635",
    "metadata": {},
    "source": [
     "The built-in function `dir()` is used to find out which names a module defines. It returns a sorted list of strings."
@@ -2327,7 +2327,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "ae1d775a",
+   "id": "f5d85a89",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2347,7 +2347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99a5e5db",
+   "id": "aae6231d",
    "metadata": {},
    "source": [
     "### Packages\n",
@@ -2362,7 +2362,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "febfc7f3",
+   "id": "d2f3a190",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2371,7 +2371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "017c17a0",
+   "id": "1fb731c8",
    "metadata": {},
    "source": [
     "An alternative way of importing the submodule is:"
@@ -2380,7 +2380,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "f1693555",
+   "id": "74d98bc0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2390,7 +2390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1b82884",
+   "id": "91f85a6e",
    "metadata": {},
    "source": [
     "Yet another variation is to import the desired function or variable directly:"
@@ -2399,7 +2399,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "80481189",
+   "id": "0b0ab33c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2408,7 +2408,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3322cb9a",
+   "id": "128982d3",
    "metadata": {},
    "source": [
     "Note that when using from package import item, the item can be either a submodule (or subpackage) of the package or some other name defined in the package, like a function, class, or variable.\n",
@@ -2421,7 +2421,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "1267939f",
+   "id": "3eddb791",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2432,7 +2432,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61f578f5",
+   "id": "1fd169cd",
    "metadata": {},
    "source": [
     "## Errors and exceptions\n",
@@ -2462,7 +2462,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "d588407e",
+   "id": "51b821d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2480,7 +2480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59fabbdd",
+   "id": "70be2caa",
    "metadata": {},
    "source": [
     "Let's simulate an undefined variable access exception."
@@ -2489,7 +2489,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "c2e98827",
+   "id": "93d0be89",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2508,7 +2508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27f26e43",
+   "id": "008189a3",
    "metadata": {},
    "source": [
     "A `try` statement may have more than one `except` clause, to specify handlers for different exceptions. At most one handler will be executed. Handlers only handle exceptions that occur in the corresponding `try` clause, not in other handlers of the same `try` statement. An `except` clause may name multiple exceptions as a parenthesized tuple, for example:"
@@ -2517,7 +2517,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "b4a8d350",
+   "id": "51d952b0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2535,7 +2535,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3658608",
+   "id": "52aab867",
    "metadata": {},
    "source": [
     "Exception handlers may be chained."
@@ -2544,7 +2544,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "e86b272e",
+   "id": "8356cbdb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2565,7 +2565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53ef4495",
+   "id": "1825b50b",
    "metadata": {},
    "source": [
     "The `try … except` statement has an optional `else` clause, which, when present, must follow all `except` clauses. It is useful for code that must be executed if the `try` clause does not raise an exception. For example:"
@@ -2574,7 +2574,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "385d745b",
+   "id": "17d06c3f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2597,7 +2597,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c6b3610",
+   "id": "6b857afa",
    "metadata": {},
    "source": [
     "### Raising exceptions (raise statement)\n",
@@ -2614,7 +2614,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "0f52f334",
+   "id": "4f3a9cde",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2634,7 +2634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e23dedf4",
+   "id": "5b4f8765",
    "metadata": {},
    "source": [
     "Programs may name their own exceptions by creating a new exception class. Exceptions should typically be derived from the `Exception` class, either directly or indirectly. Most exceptions are defined with names that end in `Error,` similar to the naming of the standard exceptions. Many standard modules define their own exceptions to report errors that may occur in functions they define.\n",
@@ -2645,7 +2645,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "f8a4a7ca",
+   "id": "63d9ab0f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2667,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d24b8631",
+   "id": "51f816c4",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/assets/code/prerequisites/__pycache__/fibonacci_module.cpython-39.pyc b/assets/code/prerequisites/__pycache__/fibonacci_module.cpython-39.pyc
index 09ec392a4c824f8a00d2a5b21ebabcdfe0c80311..0cc89a6819d527e5b46a8f8d9aaab1ee190bf692 100644
GIT binary patch
delta 19
Zcmdnaxt)_Mk(ZZ?0SJ1RY~)(Y0st*q1g!u7

delta 19
Zcmdnaxt)_Mk(ZZ?0SNXoZRA?Z0st#e1Umo#

diff --git a/assets/code/prerequisites/sound_package/__pycache__/__init__.cpython-39.pyc b/assets/code/prerequisites/sound_package/__pycache__/__init__.cpython-39.pyc
index b50e18c7e53fc613a3d423666a82205086426699..fae0d3f251f03db1a20f8d9756b309d60c9926b5 100644
GIT binary patch
delta 18
Ycmeyy_>GY(k(ZZ?0SJ1ROyqh4059qVHUIzs

delta 18
Ycmeyy_>GY(k(ZZ?0SNXoP2_q504`Dl!~g&Q

diff --git a/assets/code/prerequisites/sound_package/effects/__pycache__/__init__.cpython-39.pyc b/assets/code/prerequisites/sound_package/effects/__pycache__/__init__.cpython-39.pyc
index 7f64be58a3fb385cadae76caecc356a929960881..29fe4b6832502fae2f471c4423bececa8398b3b0 100644
GIT binary patch
delta 18
Ycmeyz_>Yk*k(ZZ?0SJ1ROyv3k05JjuP5=M^

delta 18
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index fbeffafae6662b0b98bc2bfec99a34f181a365eb..de86d1c3d626d40da64a6735d7b0a7c308787cca 100644
GIT binary patch
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diff --git a/assignments/README.html b/assignments/README.html
index 7b32a3121c..6e8fdb12ac 100644
--- a/assignments/README.html
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-    <title>37. Self-paced assignments &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40. Self-paced assignments &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="37.3. First assignment" href="set-up-env/first-assignment.html" />
-    <link rel="prev" title="36. Model deployment" href="../machine-learning-productionization/model-deployment.html" />
+    <link rel="next" title="40.3. First assignment" href="set-up-env/first-assignment.html" />
+    <link rel="prev" title="39. Model deployment" href="../machine-learning-productionization/model-deployment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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    </li>
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
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     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
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     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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-     16.2. Gradient boosting example
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-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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   <a class="reference internal" href="../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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  </li>
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   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
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  </li>
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   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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  </li>
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   <a class="reference internal" href="../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
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   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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  </li>
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
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+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
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-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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     <a class="reference internal" href="data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
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     <a class="reference internal" href="ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
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     <a class="reference internal" href="ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
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     <a class="reference internal" href="ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
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     <a class="reference internal" href="ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
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     <a class="reference internal" href="ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
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     <a class="reference internal" href="ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     </a>
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     <a class="reference internal" href="ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
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     <a class="reference internal" href="ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
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     </a>
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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+     40.1.3. GitHub
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-     37.1.4. How does this work?
+     40.1.4. How does this work?
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+     40.2.1. Environment
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-     37.1.2. [Optional]Local
+     40.1.2. [Optional]Local
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#github">
-     37.1.3. GitHub
+     40.1.3. GitHub
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-does-this-work">
-     37.1.4. How does this work?
+     40.1.4. How does this work?
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#development">
-   37.2. Development
+   40.2. Development
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#environment">
-     37.2.1. Environment
+     40.2.1. Environment
     </a>
    </li>
   </ul>
@@ -1567,18 +1599,18 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="self-paced-assignments">
-<h1><span class="section-number">37. </span>Self-paced assignments<a class="headerlink" href="#self-paced-assignments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40. </span>Self-paced assignments<a class="headerlink" href="#self-paced-assignments" title="Permalink to this headline">#</a></h1>
 <p><a class="reference external" href="https://mybinder.org/v2/gh/ocademy-ai/assignments-binder-requirements/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Focademy-ai%252Fmachine-learning%26urlpath%3Dlab%252Ftree%252Fmachine-learning%252F%26branch%3Dmain"><img alt="Binder" src="http://mybinder.org/badge_logo.svg" /></a></p>
 <p>Ocademy sets up the assignment as <a class="reference external" href="https://en.wikipedia.org/wiki/Test-driven_development">Test-driven Development</a> style. The questions are <a class="reference external" href="https://en.wikipedia.org/wiki/Unit_testing">unit testing</a> alike, implemented as a bunch of Jupyter Notebooks, and visualized by <a class="reference external" href="https://github.com/jupyterlab-contrib/jupyterlab-pytutor">jupyterlab-pytutor</a>. A real-world code quality assurance environment is simulated. You will learn how to handle software testing while working on the assignments.</p>
 <section id="try-it-out">
-<h2><span class="section-number">37.1. </span>Try it out<a class="headerlink" href="#try-it-out" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.1. </span>Try it out<a class="headerlink" href="#try-it-out" title="Permalink to this headline">#</a></h2>
 <p>There are multiple ways to do exercise with the assignment in self-paced mode. You MUST use Jupyter Lab as the runtime environment.</p>
 <section id="binder">
-<h3><span class="section-number">37.1.1. </span>Binder<a class="headerlink" href="#binder" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.1. </span>Binder<a class="headerlink" href="#binder" title="Permalink to this headline">#</a></h3>
 <p>Simply click on this <a class="reference external" href="https://mybinder.org/v2/gh/ocademy-ai/assignments-binder-requirements/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Focademy-ai%252Fmachine-learning%26urlpath%3Dlab%252Ftree%252Fmachine-learning%252F%26branch%3Dmain"><img alt="Binder" src="http://mybinder.org/badge_logo.svg" /></a>.</p>
 </section>
 <section id="optional-local">
-<h3><span class="section-number">37.1.2. </span>[Optional]Local<a class="headerlink" href="#optional-local" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.2. </span>[Optional]Local<a class="headerlink" href="#optional-local" title="Permalink to this headline">#</a></h3>
 <p>All the assignment notebooks can be executed independently without any prerequisite, and in any Jupyter Lab environment. Below steps will guide you to create a work environment if you don’t have yet, which consists of some necessary dependencies and helpful Jupyter Lab extensions.</p>
 <p>1. Install Python &amp; Conda by following <a class="reference external" href="https://github.com/ocademy-ai/machine-learning/blob/main/CONTRIBUTING.md#install-python--conda">this</a>.</p>
 <p>2. Install Jupyter Lab by following <a class="reference external" href="https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html">this</a>.</p>
@@ -1600,7 +1632,7 @@ <h3><span class="section-number">37.1.2. </span>[Optional]Local<a class="headerl
 <p><img alt="Screenshot 2023-04-23 at 4 54 20 PM" src="https://user-images.githubusercontent.com/5424267/233873630-de9c1b75-c635-45bd-b4d0-889f0c917301.png" /></p>
 </section>
 <section id="github">
-<h3><span class="section-number">37.1.3. </span>GitHub<a class="headerlink" href="#github" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.3. </span>GitHub<a class="headerlink" href="#github" title="Permalink to this headline">#</a></h3>
 <p>To start working on the assignments, you could follow the  steps below:</p>
 <ol>
 <li><p>[First time only] Fork the <a class="reference external" href="https://github.com/ocademy-ai/machine-learning">repo</a> under your own GitHub account.
@@ -1650,7 +1682,7 @@ <h3><span class="section-number">37.1.3. </span>GitHub<a class="headerlink" href
 </div>
 </section>
 <section id="how-does-this-work">
-<h3><span class="section-number">37.1.4. </span>How does this work?<a class="headerlink" href="#how-does-this-work" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.4. </span>How does this work?<a class="headerlink" href="#how-does-this-work" title="Permalink to this headline">#</a></h3>
 <p>The assignment is fully automated by leveraging GitHub Action. The action is defined by <a class="reference external" href="https://github.com/ocademy-ai/machine-learning/blob/main/.github/workflows/assignment.yml">assignment.yml</a>, which handles the continuous integration workflow post a code push. It listens to the code change under <code class="docutils literal notranslate"><span class="pre">open-machine-learning-jupyter-book/assignments</span></code> path, gets the changed files, and runs unit testing against them.</p>
 <p><code class="docutils literal notranslate"><span class="pre">pytest</span></code> is the testing framework used for the assignments. To support unit testing for the Jupyter Notebook, <code class="docutils literal notranslate"><span class="pre">nbmake</span></code> is used as a plugin together with <code class="docutils literal notranslate"><span class="pre">pytest</span></code>.</p>
 <div class="admonition seealso">
@@ -1663,9 +1695,9 @@ <h3><span class="section-number">37.1.4. </span>How does this work?<a class="hea
 </section>
 </section>
 <section id="development">
-<h2><span class="section-number">37.2. </span>Development<a class="headerlink" href="#development" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.2. </span>Development<a class="headerlink" href="#development" title="Permalink to this headline">#</a></h2>
 <section id="environment">
-<h3><span class="section-number">37.2.1. </span>Environment<a class="headerlink" href="#environment" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.2.1. </span>Environment<a class="headerlink" href="#environment" title="Permalink to this headline">#</a></h3>
 <p>The environment is managed at <a class="reference external" href="https://github.com/ocademy-ai/assignments-binder-requirements">assignments-binder-requirements</a> by following <a class="reference external" href="https://discourse.jupyter.org/t/tip-speed-up-binder-launches-by-pulling-github-content-in-a-binder-link-with-nbgitpuller/922">this</a>.</p>
 <p>To update the environment,</p>
 <ol class="simple">
@@ -1715,13 +1747,13 @@ <h3><span class="section-number">37.2.1. </span>Environment<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">36. </span>Model deployment</p>
+            <p class="prev-next-title"><span class="section-number">39. </span>Model deployment</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="set-up-env/first-assignment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.3. </span>First assignment</p>
+        <p class="prev-next-title"><span class="section-number">40.3. </span>First assignment</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/analyzing-COVID-19-papers.html b/assignments/data-science/analyzing-COVID-19-papers.html
index 4f94c81a98..90dd399b96 100644
--- a/assignments/data-science/analyzing-COVID-19-papers.html
+++ b/assignments/data-science/analyzing-COVID-19-papers.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.22. Analyzing COVID-19 papers &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.22. Analyzing COVID-19 papers &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
-    <link rel="prev" title="37.21. Soda profits" href="soda-profits.html" />
+    <link rel="next" title="40.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
+    <link rel="prev" title="40.21. Soda profits" href="soda-profits.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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    <li class="toctree-l2">
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-     37.3. First assignment
+     40.3. First assignment
     </a>
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     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
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-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
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    <li class="toctree-l2">
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-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
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    <li class="toctree-l2">
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-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
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-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
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-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
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     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
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     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
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-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
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     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
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-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
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    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
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-     37.26. Data preparation
+     40.26. Data preparation
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-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
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    <li class="toctree-l2">
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-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
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    <li class="toctree-l2">
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-     37.48. Gradient descent
+     40.48. Gradient descent
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    <li class="toctree-l2">
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-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
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-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
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-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
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-     37.53. Regression tools
+     40.53. Regression tools
     </a>
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    <li class="toctree-l2">
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-     37.54. Managing data
+     40.54. Managing data
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    <li class="toctree-l2">
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-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
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    <li class="toctree-l2">
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-     37.56. Try a different model
+     40.56. Try a different model
     </a>
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    <li class="toctree-l2">
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-     37.57. Create a regression model
+     40.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
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-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
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    <li class="toctree-l2">
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-     37.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
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-     37.62. Explore classification methods
+     40.62. Explore classification methods
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    <li class="toctree-l2">
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-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
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-     37.68. Model selection assignment 1
+     40.68. Model selection assignment 1
     </a>
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     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
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    <li class="toctree-l2">
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-     37.70. Dropout and Batch Normalization
+     40.70. Dropout and Batch Normalization
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    <li class="toctree-l2">
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-     37.71. Lasso and Ridge Regression
+     40.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
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-     37.72. Regularized Linear Models
+     40.72. Regularized Linear Models
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-     37.73. Random forests intro and regression
+     40.73. Random forests intro and regression
     </a>
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-     37.74. Random forests for classification
+     40.74. Random forests for classification
     </a>
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-     37.75. Beyond random forests: more ensemble models
+     40.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
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-     37.76. Decision trees
+     40.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
+     40.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
+     40.78. Gradient boosting
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    <li class="toctree-l2">
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-     37.79. Boosting with tuning
+     40.79. Boosting with tuning
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
+     40.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
+     40.82. Data engineering
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     <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
+     40.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
+     40.86. Study the solvers
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-     37.87. Build classification models
+     40.87. Build classification models
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-classification-model.html">
-     37.88. Build Classification Model
+     40.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
+     40.89. Parameter play
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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    </li>
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.8. Data Science lifecycle
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-     38.10. Data Science in real world
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-     38.11. Machine Learning overview
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-     38.12. Linear Regression
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-     38.13. Logistic Regression
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-     38.14. Logistic Regression
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-     38.15. Neural Network
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-     38.16. Build an machine learning web application
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+     41.17. Unsupervised learning
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+     41.18. Kernel method
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-     38.19. Model Selection
+     41.19. Model Selection
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    <li class="toctree-l2">
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1526,37 +1558,37 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-the-data">
-   37.22.1. Getting the data
+   40.22.1. Getting the data
   </a>
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-   37.22.2. Structured data extraction
+   40.22.2. Structured data extraction
   </a>
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   <a class="reference internal nav-link" href="#looking-for-trends-in-treatment-strategy">
-   37.22.3. Looking for trends in treatment strategy
+   40.22.3. Looking for trends in treatment strategy
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   <a class="reference internal nav-link" href="#computing-medicine-diagnosis-correspondence">
-   37.22.4. Computing medicine-diagnosis correspondence
+   40.22.4. Computing medicine-diagnosis correspondence
   </a>
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-   37.22.5. Conclusion
+   40.22.5. Conclusion
   </a>
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   <a class="reference internal nav-link" href="#challenge">
-   37.22.6. Challenge
+   40.22.6. Challenge
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.22.7. Acknowledgments
+   40.22.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1580,37 +1612,37 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-the-data">
-   37.22.1. Getting the data
+   40.22.1. Getting the data
   </a>
  </li>
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   <a class="reference internal nav-link" href="#structured-data-extraction">
-   37.22.2. Structured data extraction
+   40.22.2. Structured data extraction
   </a>
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   <a class="reference internal nav-link" href="#looking-for-trends-in-treatment-strategy">
-   37.22.3. Looking for trends in treatment strategy
+   40.22.3. Looking for trends in treatment strategy
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#computing-medicine-diagnosis-correspondence">
-   37.22.4. Computing medicine-diagnosis correspondence
+   40.22.4. Computing medicine-diagnosis correspondence
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.22.5. Conclusion
+   40.22.5. Conclusion
   </a>
  </li>
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   <a class="reference internal nav-link" href="#challenge">
-   37.22.6. Challenge
+   40.22.6. Challenge
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.22.7. Acknowledgments
+   40.22.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1624,7 +1656,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="analyzing-covid-19-papers">
-<h1><span class="section-number">37.22. </span>Analyzing COVID-19 papers<a class="headerlink" href="#analyzing-covid-19-papers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.22. </span>Analyzing COVID-19 papers<a class="headerlink" href="#analyzing-covid-19-papers" title="Permalink to this headline">#</a></h1>
 <p>In this challenge, we will continue with the topic of COVID pandemic, and focus on processing scientific papers on the subject. There is <a class="reference external" href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge">CORD-19 Dataset</a> with more than 7000 (at the time of writing) papers on COVID, available with metadata and abstracts (and for about half of them there is also full text provided).</p>
 <p>A full example of analyzing this dataset using <a class="reference external" href="https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum">Text Analytics for Health</a> cognitive service is described <a class="reference external" href="https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/">in this blog post</a>. We will discuss simplified version of this analysis.</p>
 <div class="cell docutils container">
@@ -1637,7 +1669,7 @@ <h1><span class="section-number">37.22. </span>Analyzing COVID-19 papers<a class
 </div>
 </div>
 <section id="getting-the-data">
-<h2><span class="section-number">37.22.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h2>
 <p>First, we need get the metadata for CORD papers that we will be working with.</p>
 <blockquote>
 <div><p><strong>NOTE</strong>: We do not provide a copy of the dataset as part of this repository. You may first need to download the <a class="reference external" href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv"><code class="docutils literal notranslate"><span class="pre">metadata.csv</span></code></a> file from <a class="reference external" href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge">this dataset on Kaggle</a>. Registration with Kaggle may be required. You may also download the dataset without registration <a class="reference external" href="https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html">from here</a>, but it will include all full texts in addition to metadata file.</p>
@@ -1667,7 +1699,7 @@ <h2><span class="section-number">37.22.1. </span>Getting the data<a class="heade
 <p>Interestingly, there are coronavirus-related papers that date back to 1880!</p>
 </section>
 <section id="structured-data-extraction">
-<h2><span class="section-number">37.22.2. </span>Structured data extraction<a class="headerlink" href="#structured-data-extraction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.2. </span>Structured data extraction<a class="headerlink" href="#structured-data-extraction" title="Permalink to this headline">#</a></h2>
 <p>Let’s see what kind of information we can easily extract from abstracts. One thing we might be interested in is to see which treatment strategies exist, and how they evolved over time. To begin with, we can manually compile the list of possible medications used to treat COVID, and also the list of diagnoses. We then go over them and search corresponding terms in the abstracts of papers.</p>
 <div class="cell docutils container">
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@@ -1713,7 +1745,7 @@ <h2><span class="section-number">37.22.2. </span>Structured data extraction<a cl
 </div>
 </section>
 <section id="looking-for-trends-in-treatment-strategy">
-<h2><span class="section-number">37.22.3. </span>Looking for trends in treatment strategy<a class="headerlink" href="#looking-for-trends-in-treatment-strategy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.3. </span>Looking for trends in treatment strategy<a class="headerlink" href="#looking-for-trends-in-treatment-strategy" title="Permalink to this headline">#</a></h2>
 <p>In the example above we have <code class="docutils literal notranslate"><span class="pre">sum</span></code>ed all values, but we can also do the same on a monthly basis:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1772,7 +1804,7 @@ <h2><span class="section-number">37.22.3. </span>Looking for trends in treatment
 </div>
 </section>
 <section id="computing-medicine-diagnosis-correspondence">
-<h2><span class="section-number">37.22.4. </span>Computing medicine-diagnosis correspondence<a class="headerlink" href="#computing-medicine-diagnosis-correspondence" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.4. </span>Computing medicine-diagnosis correspondence<a class="headerlink" href="#computing-medicine-diagnosis-correspondence" title="Permalink to this headline">#</a></h2>
 <p>One of the most interesting relationships we can look for is how different diagnoses are treated with different medicines. In order to visualize it, we need to compute <strong>co-occurence frequency map</strong>, which would show how many times two terms are mentioned in the same paper.</p>
 <p>Such a map is essentially a 2D matrix, which is best represented by <strong>numpy array</strong>. We will compute this map by walking through all abstracts, and marking entities that occur there:</p>
 <div class="cell docutils container">
@@ -1867,11 +1899,11 @@ <h2><span class="section-number">37.22.4. </span>Computing medicine-diagnosis co
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.22.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>You have seen that we can use quite simple methods to extract information from non-structured data sources, such as text. In this example, we have taken the existing list of medications, but it would be much more powerful to use natural language processing (NLP) techniques to perform entity extraction from text. In <a class="reference external" href="https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/">this blog post</a> we describe how to use cloud services for entity extraction. Another option would be using Python NLP libraries such as <a class="reference external" href="https://www.nltk.org/">NLTK</a> - an approach for extracting information from text using NLTK is described <a class="reference external" href="https://www.nltk.org/book/ch07.html">here</a>.</p>
 </section>
 <section id="challenge">
-<h2><span class="section-number">37.22.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
 <p>Continue to research the COVID paper data along the following lines:</p>
 <ol class="simple">
 <li><p>Build co-occurrence matrix of different medications, and see which medications often occur together (i.e. mentioned in one abstract). You can modify the code for building co-occurrence matrix for medications and diagnoses.</p></li>
@@ -1881,7 +1913,7 @@ <h2><span class="section-number">37.22.6. </span>Challenge<a class="headerlink"
 </ol>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.22.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1923,13 +1955,13 @@ <h2><span class="section-number">37.22.7. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.21. </span>Soda profits</p>
+            <p class="prev-next-title"><span class="section-number">40.21. </span>Soda profits</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="estimation-of-COVID-19-pandemic.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.23. </span>Estimation of COVID-19 pandemic</p>
+        <p class="prev-next-title"><span class="section-number">40.23. </span>Estimation of COVID-19 pandemic</p>
     </div>
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diff --git a/assignments/data-science/analyzing-data.html b/assignments/data-science/analyzing-data.html
index 5e994802c3..dc224026c1 100644
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-    <title>37.27. Analyzing data &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.27. Analyzing data &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.28. NYC taxi data in winter and summer" href="nyc-taxi-data-in-winter-and-summer.html" />
-    <link rel="prev" title="37.26. Data preparation" href="data-preparation.html" />
+    <link rel="next" title="40.28. NYC taxi data in winter and summer" href="nyc-taxi-data-in-winter-and-summer.html" />
+    <link rel="prev" title="40.26. Data preparation" href="data-preparation.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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+     18.4. XGBoost + k-fold CV + Feature Importance
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+   20. Kernel method
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+   30. Deep Q-learning
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+   32. Image segmentation
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+     40.3. First assignment
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-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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-     37.23. Estimation of COVID-19 pandemic
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-     37.24. Data processing in Python
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-     37.25. Evaluating data from a form
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-     37.26. Data preparation
+     40.26. Data preparation
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+     40.27. Analyzing data
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-     37.28. NYC taxi data in winter and summer
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-     37.29. Matplotlib applied
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-     37.35. Tell a story
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-     37.37. Exploring for answers
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-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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    </li>
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-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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-     37.47. Loss Function
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-     37.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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+     40.57. Create a regression model
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-     37.58. Linear and polynomial regression
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+     40.59. Retrying some regression
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
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+     40.67. Decision Trees - Classification
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-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.27.1. Acknowledgments
+   40.27.1. Acknowledgments
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@@ -1550,7 +1582,7 @@ <h2> Contents </h2>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.27.1. Acknowledgments
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+<h1><span class="section-number">40.27. </span>Analyzing data<a class="headerlink" href="#analyzing-data" title="Permalink to this headline">#</a></h1>
 <p>Examples of the Pandas functions mentioned in the <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html"><span class="doc std std-doc">section</span></a>.</p>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.27.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.27.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.26. </span>Data preparation</p>
+            <p class="prev-next-title"><span class="section-number">40.26. </span>Data preparation</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="nyc-taxi-data-in-winter-and-summer.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.28. </span>NYC taxi data in winter and summer</p>
+        <p class="prev-next-title"><span class="section-number">40.28. </span>NYC taxi data in winter and summer</p>
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diff --git a/assignments/data-science/analyzing-text-about-data-science.html b/assignments/data-science/analyzing-text-about-data-science.html
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-    <link rel="next" title="37.10. Data Science scenarios" href="data-science-scenarios.html" />
-    <link rel="prev" title="37.8. Python programming advanced" href="../prerequisites/python-programming-advanced.html" />
+    <link rel="next" title="40.10. Data Science scenarios" href="data-science-scenarios.html" />
+    <link rel="prev" title="40.8. Python programming advanced" href="../prerequisites/python-programming-advanced.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.5. Project Plan​ Template
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     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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-     37.29. Matplotlib applied
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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@@ -1526,32 +1558,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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 </ul>
@@ -1575,32 +1607,32 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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@@ -1614,10 +1646,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="analyzing-text-about-data-science">
-<h1><span class="section-number">37.9. </span>Analyzing text about Data Science<a class="headerlink" href="#analyzing-text-about-data-science" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.9. </span>Analyzing text about Data Science<a class="headerlink" href="#analyzing-text-about-data-science" title="Permalink to this headline">#</a></h1>
 <p>In this example, let’s do a simple exercise that covers all steps of a traditional data science process. You do not have to write any code, you can just click on the cells below to execute them and observe the result. As a challenge, you are encouraged to try this code out with different data.</p>
 <section id="goal">
-<h2><span class="section-number">37.9.1. </span>Goal<a class="headerlink" href="#goal" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.1. </span>Goal<a class="headerlink" href="#goal" title="Permalink to this headline">#</a></h2>
 <p>In this section, we have been discussing different concepts related to Data Science. Let’s try to discover more related concepts by doing some <strong>text mining</strong>. We will start with a text about Data Science, extract keywords from it, and then try to visualize the result.</p>
 <p>As a text, I will use the page on Data Science from Wikipedia:</p>
 <div class="cell docutils container">
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 </div>
 </section>
 <section id="step-1-getting-the-data">
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+<h2><span class="section-number">40.9.2. </span>Step 1: Getting the data<a class="headerlink" href="#step-1-getting-the-data" title="Permalink to this headline">#</a></h2>
 <p>First step in every data science process is getting the data. We will use <code class="docutils literal notranslate"><span class="pre">requests</span></code> library to do that as below.</p>
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@@ -1683,7 +1715,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
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-<h2><span class="section-number">37.9.3. </span>Step 2: Transforming the data<a class="headerlink" href="#step-2-transforming-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.3. </span>Step 2: Transforming the data<a class="headerlink" href="#step-2-transforming-the-data" title="Permalink to this headline">#</a></h2>
 <p>The next step is to convert the data into the form suitable for processing. In our case, we have downloaded HTML source code from the page, and we need to convert it into plain text.</p>
 <p>There are many ways this can be done. We will use the simplest built-in <a class="reference external" href="https://docs.python.org/3/library/html.parser.html">HTMLParser</a> object from Python. We need to subclass the <code class="docutils literal notranslate"><span class="pre">HTMLParser</span></code> class and define the code that will collect all text inside HTML tags, except <code class="docutils literal notranslate"><span class="pre">&lt;script&gt;</span></code> and <code class="docutils literal notranslate"><span class="pre">&lt;style&gt;</span></code> tags.</p>
 <div class="cell docutils container">
@@ -1822,7 +1854,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="step-3-getting-insights">
-<h2><span class="section-number">37.9.4. </span>Step 3: Getting insights<a class="headerlink" href="#step-3-getting-insights" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.4. </span>Step 3: Getting insights<a class="headerlink" href="#step-3-getting-insights" title="Permalink to this headline">#</a></h2>
 <p>The most important step is to turn our data into some form from which we can draw insights. In our case, we want to extract keywords from the text, and see which keywords are more meaningful.</p>
 <p>We will use Python library called <a class="reference external" href="https://github.com/aneesha/RAKE">RAKE</a> for keyword extraction. First, let’s install this library in case it is not present:</p>
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@@ -1854,7 +1886,7 @@ <h2><span class="section-number">37.9.4. </span>Step 3: Getting insights<a class
 </div><p>We obtained a list terms together with associated degree of importance. As you can see, the most relevant disciplines, such as machine learning and big data, are present in the list at top positions.</p>
 </section>
 <section id="step-4-visualizing-the-result">
-<h2><span class="section-number">37.9.5. </span>Step 4: Visualizing the result<a class="headerlink" href="#step-4-visualizing-the-result" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.5. </span>Step 4: Visualizing the result<a class="headerlink" href="#step-4-visualizing-the-result" title="Permalink to this headline">#</a></h2>
 <p>People can interpret the data best in the visual form. Thus it often makes sense to visualize the data in order to draw some insights. We can use <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> library in Python to plot simple distribution of the keywords with their relevance:</p>
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 <p>In this exercise we have gone through a simple process of extracting some meaning from Wikipedia text, in the form of keywords and word cloud. This example is quite simple, but it demonstrates well all typical steps a data scientist will take when working with data, starting from data acquisition, up to visualization.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.9.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1954,13 +1986,13 @@ <h2><span class="section-number">37.9.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.8. </span>Python programming advanced</p>
+            <p class="prev-next-title"><span class="section-number">40.8. </span>Python programming advanced</p>
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     </a>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.10. </span>Data Science scenarios</p>
+        <p class="prev-next-title"><span class="section-number">40.10. </span>Data Science scenarios</p>
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diff --git a/assignments/data-science/apply-your-skills.html b/assignments/data-science/apply-your-skills.html
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-    <link rel="prev" title="37.12. Lines, scatters and bars" href="lines-scatters-and-bars.html" />
+    <link rel="next" title="40.14. Try it in Excel" href="try-it-in-excel.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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-     13.4. Applied Machine Learning : build a web app
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
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     </a>
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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-<h1><span class="section-number">37.13. </span>Apply your skills<a class="headerlink" href="#apply-your-skills" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.13. </span>Apply your skills<a class="headerlink" href="#apply-your-skills" title="Permalink to this headline">#</a></h1>
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+<h2><span class="section-number">40.13.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>So far, you have worked with the Minnesota birds dataset to discover information about bird quantities and population density. Practice your application of these techniques by trying a different dataset, perhaps sourced from <a class="reference external" href="https://www.kaggle.com/">Kaggle</a>. Build a notebook to tell a story about this dataset, and make sure to use histograms when discussing it.</p>
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-<h2><span class="section-number">37.13.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.13.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1542,7 +1574,7 @@ <h2><span class="section-number">37.13.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.13.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.13.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1584,13 +1616,13 @@ <h2><span class="section-number">37.13.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.12. </span>Lines, scatters and bars</p>
+            <p class="prev-next-title"><span class="section-number">40.12. </span>Lines, scatters and bars</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="try-it-in-excel.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.14. </span>Try it in Excel</p>
+        <p class="prev-next-title"><span class="section-number">40.14. </span>Try it in Excel</p>
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     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/data-science/build-your-own-custom-vis.html b/assignments/data-science/build-your-own-custom-vis.html
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-    <link rel="prev" title="37.15. Dive into the beehive" href="dive-into-the-beehive.html" />
+    <link rel="next" title="40.17. Classifying datasets" href="classifying-datasets.html" />
+    <link rel="prev" title="40.15. Dive into the beehive" href="dive-into-the-beehive.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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  </li>
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+ <li class="toctree-l1 has-children">
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+     15.1. Loss function
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.9. Analyzing text about Data Science
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     <a class="reference internal" href="market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
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-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.16.3. Acknowledgments
+   40.16.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1519,13 +1551,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="build-your-own-custom-vis">
-<h1><span class="section-number">37.16. </span>Build your own custom vis<a class="headerlink" href="#build-your-own-custom-vis" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.16. </span>Build your own custom vis<a class="headerlink" href="#build-your-own-custom-vis" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.16.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.16.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Using the code sample in this project to create a social network, mock up data of your own social interactions. You could map your usage of social media or make a diagram of your family members. Create an interesting web app that shows a unique visualization of a social network.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.16.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.16.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1542,7 +1574,7 @@ <h2><span class="section-number">37.16.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.16.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.16.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1584,13 +1616,13 @@ <h2><span class="section-number">37.16.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.15. </span>Dive into the beehive</p>
+            <p class="prev-next-title"><span class="section-number">40.15. </span>Dive into the beehive</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="classifying-datasets.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.17. </span>Classifying datasets</p>
+        <p class="prev-next-title"><span class="section-number">40.17. </span>Classifying datasets</p>
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diff --git a/assignments/data-science/classifying-datasets.html b/assignments/data-science/classifying-datasets.html
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-    <link rel="next" title="37.18. Small diabetes study" href="small-diabetes-study.html" />
-    <link rel="prev" title="37.16. Build your own custom vis" href="build-your-own-custom-vis.html" />
+    <link rel="next" title="40.18. Small diabetes study" href="small-diabetes-study.html" />
+    <link rel="prev" title="40.16. Build your own custom vis" href="build-your-own-custom-vis.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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+     12.1. Tools of the trade
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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+     12.4. Logistic regression
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
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+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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    <li class="toctree-l2">
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
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    <li class="toctree-l2">
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-     37.9. Analyzing text about Data Science
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    <li class="toctree-l2">
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-     37.10. Data Science scenarios
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-     37.14. Try it in Excel
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-     37.21. Soda profits
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-     37.24. Data processing in Python
+     40.24. Data processing in Python
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     <a class="reference internal" href="matplotlib-applied.html">
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
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@@ -1461,12 +1493,12 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.17.1. Instructions
+   40.17.1. Instructions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.17.2. Rubric
+   40.17.2. Rubric
   </a>
  </li>
 </ul>
@@ -1490,12 +1522,12 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.17.1. Instructions
+   40.17.1. Instructions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.17.2. Rubric
+   40.17.2. Rubric
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@@ -1509,9 +1541,9 @@ <h2> Contents </h2>
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-<h1><span class="section-number">37.17. </span>Classifying datasets<a class="headerlink" href="#classifying-datasets" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.17. </span>Classifying datasets<a class="headerlink" href="#classifying-datasets" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.17.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.17.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Follow the prompts in this assignment to identify and classify the data with one of each of the following data types:</p>
 <p><strong>Structure Types</strong>: Structured, Semi-Structured, or Unstructured</p>
 <p><strong>Value Types</strong>: Qualitative or Quantitative</p>
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 <p>Source Type:</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.17.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.17.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1597,13 +1629,13 @@ <h2><span class="section-number">37.17.2. </span>Rubric<a class="headerlink" hre
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.16. </span>Build your own custom vis</p>
+            <p class="prev-next-title"><span class="section-number">40.16. </span>Build your own custom vis</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="small-diabetes-study.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.18. </span>Small diabetes study</p>
+        <p class="prev-next-title"><span class="section-number">40.18. </span>Small diabetes study</p>
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     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/data-preparation.html b/assignments/data-science/data-preparation.html
index b23e8348c8..2000ecbf1e 100644
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-    <title>37.26. Data preparation &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.26. Data preparation &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.27. Analyzing data" href="analyzing-data.html" />
-    <link rel="prev" title="37.25. Evaluating data from a form" href="evaluating-data-from-a-form.html" />
+    <link rel="next" title="40.27. Analyzing data" href="analyzing-data.html" />
+    <link rel="prev" title="40.25. Evaluating data from a form" href="evaluating-data-from-a-form.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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-  <label for="toctree-checkbox-11">
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   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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-   27. Deep Q-learning
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-   30. Diffusion Model
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-   31. Object detection
+   34. Object detection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
-   37. Self-paced assignments
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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    <li class="toctree-l2">
     <a class="reference internal" href="lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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-     37.15. Dive into the beehive
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-     37.16. Build your own custom vis
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     </a>
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     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="displaying-airport-data.html">
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     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
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-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="current reference internal" href="#">
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-     37.27. Analyzing data
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     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
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     <a class="reference internal" href="matplotlib-applied.html">
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     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
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     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
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-     37.53. Regression tools
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     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
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-     37.26.1.20. Categorical Data(Non-numeric)
+     40.26.1.20. Categorical Data(Non-numeric)
     </a>
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     </a>
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-     37.26.1.24. Encoding categorical data
+     40.26.1.24. Encoding categorical data
     </a>
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+   40.26.3. Acknowledgments
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@@ -1759,7 +1791,7 @@ <h2> Contents </h2>
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+     40.26.1.4. DataFrame.describe()
     </a>
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     </a>
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+     40.26.1.14. Detecting null values
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     </a>
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+     40.26.1.17. Dropping null values
     </a>
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+     40.26.1.18. Exercise:
     </a>
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     <a class="reference internal nav-link" href="#filling-null-values">
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     </a>
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     <a class="reference internal nav-link" href="#categorical-data-non-numeric">
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+     40.26.1.20. Categorical Data(Non-numeric)
     </a>
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     <a class="reference internal nav-link" href="#numeric-data">
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+     40.26.1.21. Numeric data
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     <a class="reference internal nav-link" href="#id6">
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     </a>
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     <a class="reference internal nav-link" href="#encoding-categorical-data">
-     37.26.1.24. Encoding categorical data
+     40.26.1.24. Encoding categorical data
     </a>
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+   40.26.2. Removing duplicate data
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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+     40.26.2.1. Identifying duplicates:
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-<h1><span class="section-number">37.26. </span>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.26. </span>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h1>
 <section id="exploring-dataframe-information">
-<h2><span class="section-number">37.26.1. </span>Exploring <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> information<a class="headerlink" href="#exploring-dataframe-information" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.26.1. </span>Exploring <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> information<a class="headerlink" href="#exploring-dataframe-information" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p><strong>Learning goal:</strong> By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.</p>
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+<h3><span class="section-number">40.26.1.1. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.shape</span></code><a class="headerlink" href="#dataframe-shape" title="Permalink to this headline">#</a></h3>
 <p>We have loaded the Iris Dataset in the variable <code class="docutils literal notranslate"><span class="pre">iris_df</span></code>. Before diving into the data, it would be valuable to know the number of datapoints we have and the overall size of the dataset. It is useful to look at the volume of data we are dealing with.</p>
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 <p><code class="docutils literal notranslate"><span class="pre">shape</span></code> here is an attribute of the dataframe and not a function, which is why it doesn’t end in a pair of parentheses.</p>
 </section>
 <section id="dataframe-columns">
-<h3><span class="section-number">37.26.1.2. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.columns</span></code><a class="headerlink" href="#dataframe-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.2. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.columns</span></code><a class="headerlink" href="#dataframe-columns" title="Permalink to this headline">#</a></h3>
 <p>Let us now move into the 4 columns of data. What does each of them exactly represent? The <code class="docutils literal notranslate"><span class="pre">columns</span></code> attribute will give us the name of the columns in the dataframe.</p>
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 <p>As we can see, there are four(4) columns. The <code class="docutils literal notranslate"><span class="pre">columns</span></code> attribute tells us the name of the columns and basically nothing else. This attribute assumes importance when we want to identify the features a dataset contains.</p>
 </section>
 <section id="dataframe-info">
-<h3><span class="section-number">37.26.1.3. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.info</span></code><a class="headerlink" href="#dataframe-info" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.3. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.info</span></code><a class="headerlink" href="#dataframe-info" title="Permalink to this headline">#</a></h3>
 <p>The amount of data(given by the <code class="docutils literal notranslate"><span class="pre">shape</span></code> attribute) and the name of the features or columns(given by the <code class="docutils literal notranslate"><span class="pre">columns</span></code> attribute) tell us something about the dataset. Now, we would want to dive deeper into the dataset. The <code class="docutils literal notranslate"><span class="pre">DataFrame.info()</span></code> function is quite useful for this.</p>
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-<h3><span class="section-number">37.26.1.4. </span>DataFrame.describe()<a class="headerlink" href="#dataframe-describe" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.4. </span>DataFrame.describe()<a class="headerlink" href="#dataframe-describe" title="Permalink to this headline">#</a></h3>
 <p>Say we have a lot of numerical data in our dataset. Univariate statistical calculations such as the mean, median, quartiles etc. can be done on each of the columns individually. The <code class="docutils literal notranslate"><span class="pre">DataFrame.describe()</span></code> function provides us with a statistical summary of the numerical columns of a dataset.</p>
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 <p>The output above shows the total number of data points, mean, standard deviation, minimum, lower quartile(25%), median(50%), upper quartile(75%) and the maximum value of each column.</p>
 </section>
 <section id="dataframe-head">
-<h3><span class="section-number">37.26.1.5. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code><a class="headerlink" href="#dataframe-head" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.5. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code><a class="headerlink" href="#dataframe-head" title="Permalink to this headline">#</a></h3>
 <p>With all the above functions and attributes, we have got a top level view of the dataset. We know how many data points are there, how many features are there, the data type of each feature and the number of non-null values for each feature.</p>
 <p>Now its time to look at the data itself. Let’s see what the first few rows(the first few datapoints) of our <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> look like:</p>
 <div class="cell docutils container">
@@ -2068,7 +2100,7 @@ <h3><span class="section-number">37.26.1.5. </span><code class="docutils literal
 <p>As the output here, we can see five(5) entries of the dataset. If we look at the index at the left, we find out that these are the first five rows.</p>
 </section>
 <section id="exercise">
-<h3><span class="section-number">37.26.1.6. </span>Exercise:<a class="headerlink" href="#exercise" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.6. </span>Exercise:<a class="headerlink" href="#exercise" title="Permalink to this headline">#</a></h3>
 <p>From the example given above, it is clear that, by default, <code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code> returns the first five rows of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. In the code cell below, can you figure out a way to display more than five rows?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2079,7 +2111,7 @@ <h3><span class="section-number">37.26.1.6. </span>Exercise:<a class="headerlink
 </div>
 </section>
 <section id="dataframe-tail">
-<h3><span class="section-number">37.26.1.7. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.tail</span></code><a class="headerlink" href="#dataframe-tail" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.7. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.tail</span></code><a class="headerlink" href="#dataframe-tail" title="Permalink to this headline">#</a></h3>
 <p>Another way of looking at the data can be from the end(instead of the beginning). The flipside of <code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code> is <code class="docutils literal notranslate"><span class="pre">DataFrame.tail</span></code>, which returns the last five rows of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2095,7 +2127,7 @@ <h3><span class="section-number">37.26.1.7. </span><code class="docutils literal
 </div></blockquote>
 </section>
 <section id="missing-data">
-<h3><span class="section-number">37.26.1.8. </span>Missing Data<a class="headerlink" href="#missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.8. </span>Missing Data<a class="headerlink" href="#missing-data" title="Permalink to this headline">#</a></h3>
 <p>Let us dive into missing data. Missing data occurs, when no value is stored in some of the columns.</p>
 <p>Let us take an example: say someone is conscious about his/her weight and doesn’t fill the weight field in a survey. Then, the weight value for that certain person will be missing.</p>
 <p>Most of the time, in real world datasets, missing values occur.</p>
@@ -2104,7 +2136,7 @@ <h3><span class="section-number">37.26.1.8. </span>Missing Data<a class="headerl
 <p>For missing values apart from floats, pandas uses the Python <code class="docutils literal notranslate"><span class="pre">None</span></code> object. While it might seem confusing that you will encounter two different kinds of values that say essentially the same thing, there are sound programmatic reasons for this design choice and, in practice, going this route enables pandas to deliver a good compromise for the vast majority of cases. Notwithstanding this, both <code class="docutils literal notranslate"><span class="pre">None</span></code> and <code class="docutils literal notranslate"><span class="pre">NaN</span></code> carry restrictions that you need to be mindful of with regards to how they can be used.</p>
 </section>
 <section id="none-non-float-missing-data">
-<h3><span class="section-number">37.26.1.9. </span><code class="docutils literal notranslate"><span class="pre">None</span></code>: non-float missing data<a class="headerlink" href="#none-non-float-missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.9. </span><code class="docutils literal notranslate"><span class="pre">None</span></code>: non-float missing data<a class="headerlink" href="#none-non-float-missing-data" title="Permalink to this headline">#</a></h3>
 <p>Because <code class="docutils literal notranslate"><span class="pre">None</span></code> comes from Python, it cannot be used in NumPy and pandas arrays that are not of data type <code class="docutils literal notranslate"><span class="pre">object</span></code>. Remember, NumPy arrays (and the data structures in pandas) can contain only one type of data. This is what gives them their tremendous power for large-scale data and computational work, but it also limits their flexibility. Such arrays have to upcast to the “lowest common denominator,” the data type that will encompass everything in the array. When <code class="docutils literal notranslate"><span class="pre">None</span></code> is in the array, it means you are working with Python objects.</p>
 <p>To see this in action, consider the following example array (note the <code class="docutils literal notranslate"><span class="pre">dtype</span></code> for it):</p>
 <div class="cell docutils container">
@@ -2129,7 +2161,7 @@ <h3><span class="section-number">37.26.1.9. </span><code class="docutils literal
 <p><strong>Key takeaway</strong>: Addition (and other operations) between integers and <code class="docutils literal notranslate"><span class="pre">None</span></code> values is undefined, which can limit what you can do with datasets that contain them.</p>
 </section>
 <section id="nan-missing-float-values">
-<h3><span class="section-number">37.26.1.10. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code>: missing float values<a class="headerlink" href="#nan-missing-float-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.10. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code>: missing float values<a class="headerlink" href="#nan-missing-float-values" title="Permalink to this headline">#</a></h3>
 <p>In contrast to <code class="docutils literal notranslate"><span class="pre">None</span></code>, NumPy (and therefore pandas) supports <code class="docutils literal notranslate"><span class="pre">NaN</span></code> for its fast, vectorized operations and ufuncs. The bad news is that any arithmetic performed on <code class="docutils literal notranslate"><span class="pre">NaN</span></code> always results in <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. For example:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2156,7 +2188,7 @@ <h3><span class="section-number">37.26.1.10. </span><code class="docutils litera
 </div>
 </section>
 <section id="id1">
-<h3><span class="section-number">37.26.1.11. </span>Exercise:<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.11. </span>Exercise:<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># What happens if you add np.nan and None together?</span>
@@ -2167,7 +2199,7 @@ <h3><span class="section-number">37.26.1.11. </span>Exercise:<a class="headerlin
 <p>Remember: <code class="docutils literal notranslate"><span class="pre">NaN</span></code> is just for missing floating-point values; there is no <code class="docutils literal notranslate"><span class="pre">NaN</span></code> equivalent for integers, strings, or Booleans.</p>
 </section>
 <section id="nan-and-none-null-values-in-pandas">
-<h3><span class="section-number">37.26.1.12. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code> and <code class="docutils literal notranslate"><span class="pre">None</span></code>: null values in pandas<a class="headerlink" href="#nan-and-none-null-values-in-pandas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.12. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code> and <code class="docutils literal notranslate"><span class="pre">None</span></code>: null values in pandas<a class="headerlink" href="#nan-and-none-null-values-in-pandas" title="Permalink to this headline">#</a></h3>
 <p>Even though <code class="docutils literal notranslate"><span class="pre">NaN</span></code> and <code class="docutils literal notranslate"><span class="pre">None</span></code> can behave somewhat differently, pandas is nevertheless built to handle them interchangeably. To see what we mean, consider a <code class="docutils literal notranslate"><span class="pre">Series</span></code> of integers:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2179,7 +2211,7 @@ <h3><span class="section-number">37.26.1.12. </span><code class="docutils litera
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.26.1.13. </span>Exercise:<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.13. </span>Exercise:<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now set an element of int_series equal to None.</span>
@@ -2199,7 +2231,7 @@ <h3><span class="section-number">37.26.1.13. </span>Exercise:<a class="headerlin
 <p>These are important methods to master and get comfortable with, so let’s go over them each in some depth.</p>
 </section>
 <section id="detecting-null-values">
-<h3><span class="section-number">37.26.1.14. </span>Detecting null values<a class="headerlink" href="#detecting-null-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.14. </span>Detecting null values<a class="headerlink" href="#detecting-null-values" title="Permalink to this headline">#</a></h3>
 <p>Now that we have understood the importance of missing values, we need to detect them in our dataset, before dealing with them.
 Both <code class="docutils literal notranslate"><span class="pre">isnull()</span></code> and <code class="docutils literal notranslate"><span class="pre">notnull()</span></code> are your primary methods for detecting null data. Both return Boolean masks over your data.</p>
 <div class="cell docutils container">
@@ -2228,7 +2260,7 @@ <h3><span class="section-number">37.26.1.14. </span>Detecting null values<a clas
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.26.1.15. </span>Exercise:<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.15. </span>Exercise:<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Try running example3[example3.notnull()].</span>
@@ -2240,7 +2272,7 @@ <h3><span class="section-number">37.26.1.15. </span>Exercise:<a class="headerlin
 <p><strong>Key takeaway</strong>: Both the <code class="docutils literal notranslate"><span class="pre">isnull()</span></code> and <code class="docutils literal notranslate"><span class="pre">notnull()</span></code> methods produce similar results when you use them in DataFrames: they show the results and the index of those results, which will help you enormously as you wrestle with your data.</p>
 </section>
 <section id="dealing-with-missing-data">
-<h3><span class="section-number">37.26.1.16. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.16. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p><strong>Learning goal:</strong> By the end of this subsection, you should know how and when to replace or remove null values from DataFrames.</p>
 </div></blockquote>
@@ -2254,7 +2286,7 @@ <h3><span class="section-number">37.26.1.16. </span>Dealing with missing data<a
 <p>We will discuss both these methods and their pros and cons in details.</p>
 </section>
 <section id="dropping-null-values">
-<h3><span class="section-number">37.26.1.17. </span>Dropping null values<a class="headerlink" href="#dropping-null-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.17. </span>Dropping null values<a class="headerlink" href="#dropping-null-values" title="Permalink to this headline">#</a></h3>
 <p>The amount of data we pass on to our model has a direct effect on its performance. Dropping null values means that we are reducing the number of datapoints, and hence reducing the size of the dataset. So, it is advisable to drop rows with null values when the dataset is quite large.</p>
 <p>Another instance maybe that a certain row or column has a lot of missing values. Then, they maybe dropped because they wouldn’t add much value to our analysis as most of the data is missing for that row/column.</p>
 <p>Beyond identifying missing values, pandas provides a convenient means to remove null values from <code class="docutils literal notranslate"><span class="pre">Series</span></code> and <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>s. To see this in action, let’s return to <code class="docutils literal notranslate"><span class="pre">example3</span></code>. The <code class="docutils literal notranslate"><span class="pre">DataFrame.dropna()</span></code> function helps in dropping the rows with null values.</p>
@@ -2316,7 +2348,7 @@ <h3><span class="section-number">37.26.1.17. </span>Dropping null values<a class
 </ol>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.26.1.18. </span>Exercise:<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.18. </span>Exercise:<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># How might you go about dropping just column 3?</span>
@@ -2336,12 +2368,12 @@ <h3><span class="section-number">37.26.1.18. </span>Exercise:<a class="headerlin
 <p>Here, the first and last row have been dropped, because they contain only two non-null values.</p>
 </section>
 <section id="filling-null-values">
-<h3><span class="section-number">37.26.1.19. </span>Filling null values<a class="headerlink" href="#filling-null-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.19. </span>Filling null values<a class="headerlink" href="#filling-null-values" title="Permalink to this headline">#</a></h3>
 <p>It sometimes makes sense to fill in missing values with ones which could be valid. There are a few techniques to fill null values. The first is using Domain Knowledge(knowledge of the subject on which the dataset is based) to somehow approximate the missing values.</p>
 <p>You could use <code class="docutils literal notranslate"><span class="pre">isnull</span></code> to do this in place, but that can be laborious, particularly if you have a lot of values to fill. Because this is such a common task in data science, pandas provides <code class="docutils literal notranslate"><span class="pre">fillna</span></code>, which returns a copy of the <code class="docutils literal notranslate"><span class="pre">Series</span></code> or <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> with the missing values replaced with one of your choosing. Let’s create another example <code class="docutils literal notranslate"><span class="pre">Series</span></code> to see how this works in practice.</p>
 </section>
 <section id="categorical-data-non-numeric">
-<h3><span class="section-number">37.26.1.20. </span>Categorical Data(Non-numeric)<a class="headerlink" href="#categorical-data-non-numeric" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.20. </span>Categorical Data(Non-numeric)<a class="headerlink" href="#categorical-data-non-numeric" title="Permalink to this headline">#</a></h3>
 <p>First let us consider non-numeric data. In datasets, we have columns with categorical data. Eg. Gender, True or False etc.</p>
 <p>In most of these cases, we replace missing values with the <code class="docutils literal notranslate"><span class="pre">mode</span></code> of the column. Say, we have 100 data points and 90 have said True, 8 have said False and 2 have not filled. Then, we can will the 2 with True, considering the full column.</p>
 <p>Again, here we can use domain knowledge here. Let us consider an example of filling with the mode.</p>
@@ -2384,7 +2416,7 @@ <h3><span class="section-number">37.26.1.20. </span>Categorical Data(Non-numeric
 <p>As we can see, the null value has been replaced. Needless to say, we could have written anything in place or <code class="docutils literal notranslate"><span class="pre">'True'</span></code> and it would have got substituted.</p>
 </section>
 <section id="numeric-data">
-<h3><span class="section-number">37.26.1.21. </span>Numeric data<a class="headerlink" href="#numeric-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.21. </span>Numeric data<a class="headerlink" href="#numeric-data" title="Permalink to this headline">#</a></h3>
 <p>Now, coming to numeric data. Here, we have a two common ways of replacing missing values:</p>
 <ol class="simple">
 <li><p>Replace with Median of the row</p></li>
@@ -2483,7 +2515,7 @@ <h3><span class="section-number">37.26.1.21. </span>Numeric data<a class="header
 </ol>
 </section>
 <section id="id5">
-<h3><span class="section-number">37.26.1.22. </span>Exercise:<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.22. </span>Exercise:<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># What happens if you try to fill null values with a string, like &#39;&#39;?</span>
@@ -2525,7 +2557,7 @@ <h3><span class="section-number">37.26.1.22. </span>Exercise:<a class="headerlin
 <p>Notice that when a previous value is not available for forward-filling, the null value remains.</p>
 </section>
 <section id="id6">
-<h3><span class="section-number">37.26.1.23. </span>Exercise:<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.23. </span>Exercise:<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># What output does example4.fillna(method=&#39;bfill&#39;, axis=1) produce?</span>
@@ -2549,7 +2581,7 @@ <h3><span class="section-number">37.26.1.23. </span>Exercise:<a class="headerlin
 </div></blockquote>
 </section>
 <section id="encoding-categorical-data">
-<h3><span class="section-number">37.26.1.24. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.24. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h3>
 <p>Machine learning models only deal with numbers and any form of numeric data. It won’t be able to tell the difference between a Yes and a No, but it would be able to distinguish between 0 and 1. So, after filling in the missing values, we need to do encode the categorical data to some numeric form for the model to understand.</p>
 <p>Encoding can be done in two ways. We will be discussing them next.</p>
 <p><strong>LABEL ENCODING</strong></p>
@@ -2633,13 +2665,13 @@ <h3><span class="section-number">37.26.1.24. </span>Encoding categorical data<a
 </section>
 </section>
 <section id="removing-duplicate-data">
-<h2><span class="section-number">37.26.2. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.26.2. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p><strong>Learning goal:</strong> By the end of this subsection, you should be comfortable identifying and removing duplicate values from DataFrames.</p>
 </div></blockquote>
 <p>In addition to missing data, you will often encounter duplicated data in real-world datasets. Fortunately, pandas provides an easy means of detecting and removing duplicate entries.</p>
 <section id="identifying-duplicates-duplicated">
-<h3><span class="section-number">37.26.2.1. </span>Identifying duplicates: <code class="docutils literal notranslate"><span class="pre">duplicated</span></code><a class="headerlink" href="#identifying-duplicates-duplicated" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.2.1. </span>Identifying duplicates: <code class="docutils literal notranslate"><span class="pre">duplicated</span></code><a class="headerlink" href="#identifying-duplicates-duplicated" title="Permalink to this headline">#</a></h3>
 <p>You can easily spot duplicate values using the <code class="docutils literal notranslate"><span class="pre">duplicated</span></code> method in pandas, which returns a Boolean mask indicating whether an entry in a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is a duplicate of an earlier one. Let’s create another example <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> to see this in action.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2659,7 +2691,7 @@ <h3><span class="section-number">37.26.2.1. </span>Identifying duplicates: <code
 </div>
 </section>
 <section id="dropping-duplicates-drop-duplicates">
-<h3><span class="section-number">37.26.2.2. </span>Dropping duplicates: <code class="docutils literal notranslate"><span class="pre">drop_duplicates</span></code><a class="headerlink" href="#dropping-duplicates-drop-duplicates" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.2.2. </span>Dropping duplicates: <code class="docutils literal notranslate"><span class="pre">drop_duplicates</span></code><a class="headerlink" href="#dropping-duplicates-drop-duplicates" title="Permalink to this headline">#</a></h3>
 <p><code class="docutils literal notranslate"><span class="pre">drop_duplicates</span></code> simply returns a copy of the data for which all of the <code class="docutils literal notranslate"><span class="pre">duplicated</span></code> values are <code class="docutils literal notranslate"><span class="pre">False</span></code>:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2682,7 +2714,7 @@ <h3><span class="section-number">37.26.2.2. </span>Dropping duplicates: <code cl
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.26.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.26.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter. Original Notebook source from <a class="reference external" href="https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb"><em>Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio</em></a> by Lee Stott</p>
 </section>
 </section>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.25. </span>Evaluating data from a form</p>
+            <p class="prev-next-title"><span class="section-number">40.25. </span>Evaluating data from a form</p>
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     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.27. </span>Analyzing data</p>
+        <p class="prev-next-title"><span class="section-number">40.27. </span>Analyzing data</p>
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-    <link rel="prev" title="37.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
+    <link rel="next" title="40.25. Evaluating data from a form" href="evaluating-data-from-a-form.html" />
+    <link rel="prev" title="40.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
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-     13.4. Applied Machine Learning : build a web app
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.103. Fun with Variational Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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    <li class="toctree-l2">
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-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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-     38.20. Convolutional Neural Network
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@@ -1463,22 +1495,22 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   40.24.2. COVID-19 papers analysis
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  <li class="toc-h2 nav-item toc-entry">
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@@ -1502,22 +1534,22 @@ <h2> Contents </h2>
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+   40.24.1. COVID-19 spread modeling
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+   40.24.2. COVID-19 papers analysis
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@@ -1531,10 +1563,10 @@ <h2> Contents </h2>
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   <section class="tex2jax_ignore mathjax_ignore" id="data-processing-in-python">
-<h1><span class="section-number">37.24. </span>Data processing in Python<a class="headerlink" href="#data-processing-in-python" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.24. </span>Data processing in Python<a class="headerlink" href="#data-processing-in-python" title="Permalink to this headline">#</a></h1>
 <p>In this assignment, we will ask you to elaborate on the code we have started developing in these <a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/numpy.html#your-turn">challenges</a>. The assignment consists of two parts:</p>
 <section id="covid-19-spread-modeling">
-<h2><span class="section-number">37.24.1. </span>COVID-19 spread modeling<a class="headerlink" href="#covid-19-spread-modeling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.1. </span>COVID-19 spread modeling<a class="headerlink" href="#covid-19-spread-modeling" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>[ ] Plot <span class="math notranslate nohighlight">\(R_t\)</span> graphs for 5-6 different countries on one plot for comparison, or using several plots side-by-side</p></li>
 <li><p>[ ] See how the number of deaths and recoveries correlate with a number of infected cases.</p></li>
@@ -1543,7 +1575,7 @@ <h2><span class="section-number">37.24.1. </span>COVID-19 spread modeling<a clas
 </ul>
 </section>
 <section id="covid-19-papers-analysis">
-<h2><span class="section-number">37.24.2. </span>COVID-19 papers analysis<a class="headerlink" href="#covid-19-papers-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.2. </span>COVID-19 papers analysis<a class="headerlink" href="#covid-19-papers-analysis" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>[ ] Build a co-occurrence matrix of different medications, and see which medications often occur together (i.e. mentioned in one abstract). You can modify the code for building a co-occurrence matrix for medications and diagnoses.</p></li>
 <li><p>[ ] Visualize this matrix using a heatmap.</p></li>
@@ -1552,7 +1584,7 @@ <h2><span class="section-number">37.24.2. </span>COVID-19 papers analysis<a clas
 </ul>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.24.3. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.3. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1569,7 +1601,7 @@ <h2><span class="section-number">37.24.3. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.24.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1611,13 +1643,13 @@ <h2><span class="section-number">37.24.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.23. </span>Estimation of COVID-19 pandemic</p>
+            <p class="prev-next-title"><span class="section-number">40.23. </span>Estimation of COVID-19 pandemic</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="evaluating-data-from-a-form.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.25. </span>Evaluating data from a form</p>
+        <p class="prev-next-title"><span class="section-number">40.25. </span>Evaluating data from a form</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html b/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html
index a88bdc4bd5..4464775a83 100644
--- a/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html
+++ b/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.41. Data Science in the cloud: The “Azure ML SDK” way &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.41. Data Science in the cloud: The “Azure ML SDK” way &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.42. Machine Learning overview - assignment 1" href="../ml-fundamentals/ml-overview-iris.html" />
-    <link rel="prev" title="37.40. Data Science project using Azure ML SDK" href="data-science-project-using-azure-ml-sdk.html" />
+    <link rel="next" title="40.42. Machine Learning overview - assignment 1" href="../ml-fundamentals/ml-overview-iris.html" />
+    <link rel="prev" title="40.40. Data Science project using Azure ML SDK" href="data-science-project-using-azure-ml-sdk.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
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     <a class="reference internal" href="data-preparation.html">
-     37.26. Data preparation
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     <a class="reference internal" href="analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
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     <a class="reference internal" href="market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
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-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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-     37.62. Explore classification methods
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-     37.63. Kernel method assignment 1
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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-     37.70. Dropout and Batch Normalization
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
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-     37.92. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,62 +1558,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.41.1. Introduction
+   40.41.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#azure-machine-learning-sdk-specific-imports">
-   37.41.2. Azure Machine Learning SDK-specific imports
+   40.41.2. Azure Machine Learning SDK-specific imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#initialize-workspace">
-   37.41.3. Initialize Workspace
+   40.41.3. Initialize Workspace
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#create-an-azure-ml-experiment">
-   37.41.4. Create an Azure ML experiment
+   40.41.4. Create an Azure ML experiment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#create-a-compute-cluster">
-   37.41.5. Create a compute cluster
+   40.41.5. Create a compute cluster
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data">
-   37.41.6. Data
+   40.41.6. Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#automl-configuration">
-   37.41.7. AutoML configuration
+   40.41.7. AutoML configuration
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#automl-run">
-   37.41.8. AutoML run
+   40.41.8. AutoML run
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#save-the-best-model">
-   37.41.9. Save the best model
+   40.41.9. Save the best model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#deploy-the-best-model">
-   37.41.10. Deploy the best model
+   40.41.10. Deploy the best model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#consume-the-endpoint">
-   37.41.11. Consume the endpoint
+   40.41.11. Consume the endpoint
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.41.12. Acknowledgments
+   40.41.12. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1605,62 +1637,62 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.41.1. Introduction
+   40.41.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#azure-machine-learning-sdk-specific-imports">
-   37.41.2. Azure Machine Learning SDK-specific imports
+   40.41.2. Azure Machine Learning SDK-specific imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#initialize-workspace">
-   37.41.3. Initialize Workspace
+   40.41.3. Initialize Workspace
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#create-an-azure-ml-experiment">
-   37.41.4. Create an Azure ML experiment
+   40.41.4. Create an Azure ML experiment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#create-a-compute-cluster">
-   37.41.5. Create a compute cluster
+   40.41.5. Create a compute cluster
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data">
-   37.41.6. Data
+   40.41.6. Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#automl-configuration">
-   37.41.7. AutoML configuration
+   40.41.7. AutoML configuration
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#automl-run">
-   37.41.8. AutoML run
+   40.41.8. AutoML run
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#save-the-best-model">
-   37.41.9. Save the best model
+   40.41.9. Save the best model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#deploy-the-best-model">
-   37.41.10. Deploy the best model
+   40.41.10. Deploy the best model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#consume-the-endpoint">
-   37.41.11. Consume the endpoint
+   40.41.11. Consume the endpoint
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.41.12. Acknowledgments
+   40.41.12. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1674,9 +1706,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-science-in-the-cloud-the-azure-ml-sdk-way">
-<h1><span class="section-number">37.41. </span>Data Science in the cloud: The “Azure ML SDK” way<a class="headerlink" href="#data-science-in-the-cloud-the-azure-ml-sdk-way" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.41. </span>Data Science in the cloud: The “Azure ML SDK” way<a class="headerlink" href="#data-science-in-the-cloud-the-azure-ml-sdk-way" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.41.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In this notebook, we will learn how to use the Azure ML SDK to train, deploy and consume a model through Azure ML.</p>
 <p>Pre-requisites:</p>
 <ol class="simple">
@@ -1698,7 +1730,7 @@ <h2><span class="section-number">37.41.1. </span>Introduction<a class="headerlin
 </ol>
 </section>
 <section id="azure-machine-learning-sdk-specific-imports">
-<h2><span class="section-number">37.41.2. </span>Azure Machine Learning SDK-specific imports<a class="headerlink" href="#azure-machine-learning-sdk-specific-imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.2. </span>Azure Machine Learning SDK-specific imports<a class="headerlink" href="#azure-machine-learning-sdk-specific-imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">azureml.core</span> <span class="kn">import</span> <span class="n">Workspace</span><span class="p">,</span> <span class="n">Experiment</span>
@@ -1713,7 +1745,7 @@ <h2><span class="section-number">37.41.2. </span>Azure Machine Learning SDK-spec
 </div>
 </section>
 <section id="initialize-workspace">
-<h2><span class="section-number">37.41.3. </span>Initialize Workspace<a class="headerlink" href="#initialize-workspace" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.3. </span>Initialize Workspace<a class="headerlink" href="#initialize-workspace" title="Permalink to this headline">#</a></h2>
 <p>Initialize a workspace object from persisted configuration. Make sure the config file is present at .\config.json</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1725,7 +1757,7 @@ <h2><span class="section-number">37.41.3. </span>Initialize Workspace<a class="h
 </div>
 </section>
 <section id="create-an-azure-ml-experiment">
-<h2><span class="section-number">37.41.4. </span>Create an Azure ML experiment<a class="headerlink" href="#create-an-azure-ml-experiment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.4. </span>Create an Azure ML experiment<a class="headerlink" href="#create-an-azure-ml-experiment" title="Permalink to this headline">#</a></h2>
 <p>Let’s create an experiment named ‘aml-experiment’ in the workspace we just initialized.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1738,7 +1770,7 @@ <h2><span class="section-number">37.41.4. </span>Create an Azure ML experiment<a
 </div>
 </section>
 <section id="create-a-compute-cluster">
-<h2><span class="section-number">37.41.5. </span>Create a compute cluster<a class="headerlink" href="#create-a-compute-cluster" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.5. </span>Create a compute cluster<a class="headerlink" href="#create-a-compute-cluster" title="Permalink to this headline">#</a></h2>
 <p>You will need to create a <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target">compute target</a> for your AutoML run.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1760,7 +1792,7 @@ <h2><span class="section-number">37.41.5. </span>Create a compute cluster<a clas
 </div>
 </section>
 <section id="data">
-<h2><span class="section-number">37.41.6. </span>Data<a class="headerlink" href="#data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.6. </span>Data<a class="headerlink" href="#data" title="Permalink to this headline">#</a></h2>
 <p>Make sure you have uploaded the dataset to Azure ML and that the key is the same name as the dataset.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1774,7 +1806,7 @@ <h2><span class="section-number">37.41.6. </span>Data<a class="headerlink" href=
 </div>
 </section>
 <section id="automl-configuration">
-<h2><span class="section-number">37.41.7. </span>AutoML configuration<a class="headerlink" href="#automl-configuration" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.7. </span>AutoML configuration<a class="headerlink" href="#automl-configuration" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">automl_settings</span> <span class="o">=</span> <span class="p">{</span>
@@ -1798,7 +1830,7 @@ <h2><span class="section-number">37.41.7. </span>AutoML configuration<a class="h
 </div>
 </section>
 <section id="automl-run">
-<h2><span class="section-number">37.41.8. </span>AutoML run<a class="headerlink" href="#automl-run" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.8. </span>AutoML run<a class="headerlink" href="#automl-run" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">remote_run</span> <span class="o">=</span> <span class="n">experiment</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">automl_config</span><span class="p">)</span>
@@ -1815,7 +1847,7 @@ <h2><span class="section-number">37.41.8. </span>AutoML run<a class="headerlink"
 </div>
 </section>
 <section id="save-the-best-model">
-<h2><span class="section-number">37.41.9. </span>Save the best model<a class="headerlink" href="#save-the-best-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.9. </span>Save the best model<a class="headerlink" href="#save-the-best-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">best_run</span><span class="p">,</span> <span class="n">fitted_model</span> <span class="o">=</span> <span class="n">remote_run</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
@@ -1845,7 +1877,7 @@ <h2><span class="section-number">37.41.9. </span>Save the best model<a class="he
 </div>
 </section>
 <section id="deploy-the-best-model">
-<h2><span class="section-number">37.41.10. </span>Deploy the best model<a class="headerlink" href="#deploy-the-best-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.10. </span>Deploy the best model<a class="headerlink" href="#deploy-the-best-model" title="Permalink to this headline">#</a></h2>
 <p>Run the following code to deploy the best model. You can see the state of the deployment in the Azure ML portal. This step can take a few minutes.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1866,7 +1898,7 @@ <h2><span class="section-number">37.41.10. </span>Deploy the best model<a class=
 </div>
 </section>
 <section id="consume-the-endpoint">
-<h2><span class="section-number">37.41.11. </span>Consume the endpoint<a class="headerlink" href="#consume-the-endpoint" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.11. </span>Consume the endpoint<a class="headerlink" href="#consume-the-endpoint" title="Permalink to this headline">#</a></h2>
 <p>You can add inputs to the following input sample.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1905,7 +1937,7 @@ <h2><span class="section-number">37.41.11. </span>Consume the endpoint<a class="
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.41.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1947,13 +1979,13 @@ <h2><span class="section-number">37.41.12. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.40. </span>Data Science project using Azure ML SDK</p>
+            <p class="prev-next-title"><span class="section-number">40.40. </span>Data Science project using Azure ML SDK</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ml-fundamentals/ml-overview-iris.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.42. </span>Machine Learning overview - assignment 1</p>
+        <p class="prev-next-title"><span class="section-number">40.42. </span>Machine Learning overview - assignment 1</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/data-science-project-using-azure-ml-sdk.html b/assignments/data-science/data-science-project-using-azure-ml-sdk.html
index 8851ec7c5f..12608d239d 100644
--- a/assignments/data-science/data-science-project-using-azure-ml-sdk.html
+++ b/assignments/data-science/data-science-project-using-azure-ml-sdk.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.40. Data Science project using Azure ML SDK &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.40. Data Science project using Azure ML SDK &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.41. Data Science in the cloud: The “Azure ML SDK” way" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html" />
-    <link rel="prev" title="37.39. Low code/no code Data Science project on Azure ML" href="low-code-no-code-data-science-project-on-azure-ml.html" />
+    <link rel="next" title="40.41. Data Science in the cloud: The “Azure ML SDK” way" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html" />
+    <link rel="prev" title="40.39. Low code/no code Data Science project on Azure ML" href="low-code-no-code-data-science-project-on-azure-ml.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     16.3. XGBoost
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.8. Python programming advanced
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     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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     <a class="reference internal" href="data-preparation.html">
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     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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-     37.45. Linear regression - California Housing
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.40.3. Acknowledgments
+   40.40.3. Acknowledgments
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@@ -1495,17 +1527,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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-   37.40.1. Instructions
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-   37.40.2. Rubric
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.40.3. Acknowledgments
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 </ul>
@@ -1519,13 +1551,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-science-project-using-azure-ml-sdk">
-<h1><span class="section-number">37.40. </span>Data Science project using Azure ML SDK<a class="headerlink" href="#data-science-project-using-azure-ml-sdk" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.40. </span>Data Science project using Azure ML SDK<a class="headerlink" href="#data-science-project-using-azure-ml-sdk" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.40.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.40.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>We saw how to use the Azure ML platform to train, deploy and consume a model with the Azure ML SDK. Now look around for some data that you could use to train another model, deploy it and consume it. You can look for datasets on <a class="reference external" href="https://kaggle.com">Kaggle</a> and <a class="reference external" href="https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Azure Open Datasets</a>.</p>
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 <section id="rubric">
-<h2><span class="section-number">37.40.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.40.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1542,7 +1574,7 @@ <h2><span class="section-number">37.40.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.40.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.40.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1584,13 +1616,13 @@ <h2><span class="section-number">37.40.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.39. </span>Low code/no code Data Science project on Azure ML</p>
+            <p class="prev-next-title"><span class="section-number">40.39. </span>Low code/no code Data Science project on Azure ML</p>
         </div>
     </a>
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     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.41. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
+        <p class="prev-next-title"><span class="section-number">40.41. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
     </div>
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diff --git a/assignments/data-science/data-science-scenarios.html b/assignments/data-science/data-science-scenarios.html
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-    <link rel="prev" title="37.9. Analyzing text about Data Science" href="analyzing-text-about-data-science.html" />
+    <link rel="next" title="40.11. Write a data ethics case study" href="write-a-data-ethics-case-study.html" />
+    <link rel="prev" title="40.9. Analyzing text about Data Science" href="analyzing-text-about-data-science.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
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   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/time-series.html">
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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     <a class="reference internal" href="write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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    <li class="toctree-l2">
     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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    <li class="toctree-l2">
     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    </li>
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     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="data-preparation.html">
-     37.26. Data preparation
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     <a class="reference internal" href="analyzing-data.html">
-     37.27. Analyzing data
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-     37.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
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-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
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-     37.58. Linear and polynomial regression
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     <a class="reference internal" href="../ml-fundamentals/retrying-some-regression.html">
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
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-     37.62. Explore classification methods
+     40.62. Explore classification methods
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-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
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-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
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-     37.68. Model selection assignment 1
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+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
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-     37.70. Dropout and Batch Normalization
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-     37.71. Lasso and Ridge Regression
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+     40.73. Random forests intro and regression
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+     40.74. Random forests for classification
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-     37.75. Beyond random forests: more ensemble models
+     40.75. Beyond random forests: more ensemble models
     </a>
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    <li class="toctree-l2">
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-     37.76. Decision trees
+     40.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
+     40.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
+     40.78. Gradient boosting
     </a>
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-     37.79. Boosting with tuning
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-     37.80. Random Forest Classifier with Feature Importance
+     40.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
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-     37.82. Data engineering
+     40.82. Data engineering
     </a>
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-     37.83. Counterintuitive Challenges in ML Debugging
+     40.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
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-     37.86. Study the solvers
+     40.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
+     40.87. Build classification models
     </a>
    </li>
    <li class="toctree-l2">
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-     37.88. Build Classification Model
+     40.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
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-     37.89. Parameter play
+     40.89. Parameter play
     </a>
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     <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
     </a>
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
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-     37.104. Time Series Forecasting Assignment
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     </a>
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-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
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-     37.113. Art by gan
+     40.113. Art by gan
     </a>
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../slides/introduction.html">
-   38. Slides
+   41. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
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   <ul>
    <li class="toctree-l2">
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-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
-     38.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1461,17 +1493,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.10.1. Instructions
+   40.10.1. Instructions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.10.2. Rubric
+   40.10.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.10.3. Acknowledgments
+   40.10.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1495,17 +1527,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.10.1. Instructions
+   40.10.1. Instructions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.10.2. Rubric
+   40.10.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.10.3. Acknowledgments
+   40.10.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1519,7 +1551,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-science-scenarios">
-<h1><span class="section-number">37.10. </span>Data Science scenarios<a class="headerlink" href="#data-science-scenarios" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.10. </span>Data Science scenarios<a class="headerlink" href="#data-science-scenarios" title="Permalink to this headline">#</a></h1>
 <p>In this first assignment, we ask you to think about some real-life process or problem in different problem domains, and how you can improve it using the Data Science process. Think about the following:</p>
 <ol class="simple">
 <li><p>Which data can you collect?</p></li>
@@ -1535,7 +1567,7 @@ <h1><span class="section-number">37.10. </span>Data Science scenarios<a class="h
 <li><p>How can you use data to make sure you are being productive at work?</p></li>
 </ol>
 <section id="instructions">
-<h2><span class="section-number">37.10.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.10.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Fill in the following table (substitute suggested problem domains for your own ones if needed):</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1569,7 +1601,7 @@ <h2><span class="section-number">37.10.1. </span>Instructions<a class="headerlin
 </table>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.10.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.10.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1586,7 +1618,7 @@ <h2><span class="section-number">37.10.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.10.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.10.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1628,13 +1660,13 @@ <h2><span class="section-number">37.10.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.9. </span>Analyzing text about Data Science</p>
+            <p class="prev-next-title"><span class="section-number">40.9. </span>Analyzing text about Data Science</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="write-a-data-ethics-case-study.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.11. </span>Write a data ethics case study</p>
+        <p class="prev-next-title"><span class="section-number">40.11. </span>Write a data ethics case study</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/displaying-airport-data.html b/assignments/data-science/displaying-airport-data.html
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@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.20. Displaying airport data &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.20. Displaying airport data &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.21. Soda profits" href="soda-profits.html" />
-    <link rel="prev" title="37.19. Introduction to probability and statistics" href="introduction-to-statistics-and-probability.html" />
+    <link rel="next" title="40.21. Soda profits" href="soda-profits.html" />
+    <link rel="prev" title="40.19. Introduction to probability and statistics" href="introduction-to-statistics-and-probability.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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    <li class="toctree-l2">
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-     11.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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    <li class="toctree-l2">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
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+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
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+  <ul>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.2. K-Means clustering
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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-     16.2. Gradient boosting example
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-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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     <a class="reference internal" href="../set-up-env/first-assignment.html">
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     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     <a class="reference internal" href="data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
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     <a class="reference internal" href="dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
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-     37.20. Displaying airport data
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     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="data-preparation.html">
-     37.26. Data preparation
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     <a class="reference internal" href="analyzing-data.html">
-     37.27. Analyzing data
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-     37.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
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-     37.38. Market research
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     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
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-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
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    </li>
    <li class="toctree-l2">
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     </a>
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-     37.55. Exploring visualizations
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     </a>
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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     </a>
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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     </a>
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-     37.61. Delicious asian and indian cuisines
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     </a>
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
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-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
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    <li class="toctree-l2">
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-     37.66. Decision Trees - Intro and Regression
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-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     </a>
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     </a>
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-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     </a>
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    <li class="toctree-l2">
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-     37.104. Time Series Forecasting Assignment
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     </a>
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-     37.106. Neural Networks for Classification with TensorFlow
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     </a>
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-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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     </a>
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    <li class="toctree-l2">
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-     37.113. Art by gan
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     </a>
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../slides/introduction.html">
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+   41. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
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     </a>
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-     38.2. Python programming basics
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-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
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-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
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-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
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-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
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     </a>
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    <li class="toctree-l2">
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-     38.8. Data Science lifecycle
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     </a>
    </li>
    <li class="toctree-l2">
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-     38.9. Data Science in the cloud
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     </a>
    </li>
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-     38.10. Data Science in real world
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     </a>
    </li>
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-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
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-     38.14. Logistic Regression
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     </a>
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
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-     38.18. Kernel method
+     41.18. Kernel method
     </a>
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-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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   </ul>
@@ -1526,22 +1558,22 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#database-schema">
-   37.20.1. Database schema
+   40.20.1. Database schema
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-data">
-   37.20.2. Loading data
+   40.20.2. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#assignment">
-   37.20.3. Assignment
+   40.20.3. Assignment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.20.4. Acknowledgments
+   40.20.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1565,22 +1597,22 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#database-schema">
-   37.20.1. Database schema
+   40.20.1. Database schema
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-data">
-   37.20.2. Loading data
+   40.20.2. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#assignment">
-   37.20.3. Assignment
+   40.20.3. Assignment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.20.4. Acknowledgments
+   40.20.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1594,10 +1626,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="displaying-airport-data">
-<h1><span class="section-number">37.20. </span>Displaying airport data<a class="headerlink" href="#displaying-airport-data" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.20. </span>Displaying airport data<a class="headerlink" href="#displaying-airport-data" title="Permalink to this headline">#</a></h1>
 <p>You have been provided a <a href="../../assets/data/airports.db">database</a> built on <a class="reference external" href="https://sqlite.org/index.html">SQLite</a> which contains information about airports.The schema is displayed below.</p>
 <section id="database-schema">
-<h2><span class="section-number">37.20.1. </span>Database schema<a class="headerlink" href="#database-schema" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.20.1. </span>Database schema<a class="headerlink" href="#database-schema" title="Permalink to this headline">#</a></h2>
 <p>A database’s schema is its table design and structure. The <strong>airports</strong> database has two tables, <code class="docutils literal notranslate"><span class="pre">cities</span></code>, which contain a list of cities in the United Kingdom and Ireland, and <code class="docutils literal notranslate"><span class="pre">airports</span></code>, which contains the list of all airports. Because some cities may have multiple airports, two tables were created to store the information. In this exercise, you will use joins to display information for different cities.</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1631,7 +1663,7 @@ <h2><span class="section-number">37.20.1. </span>Database schema<a class="header
 </table>
 </section>
 <section id="loading-data">
-<h2><span class="section-number">37.20.2. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.20.2. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install the necessary dependencies</span>
@@ -1659,7 +1691,7 @@ <h2><span class="section-number">37.20.2. </span>Loading data<a class="headerlin
 </div>
 </section>
 <section id="assignment">
-<h2><span class="section-number">37.20.3. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.20.3. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h2>
 <p>Create queries to return the following information:</p>
 <ol class="simple">
 <li><p>all city names in the Cities table</p></li>
@@ -1717,7 +1749,7 @@ <h2><span class="section-number">37.20.3. </span>Assignment<a class="headerlink"
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.20.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.20.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a> and author <a class="reference external" href="https://github.com/BethanyJep">Pikachú</a>. They inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1759,13 +1791,13 @@ <h2><span class="section-number">37.20.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.19. </span>Introduction to probability and statistics</p>
+            <p class="prev-next-title"><span class="section-number">40.19. </span>Introduction to probability and statistics</p>
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     </a>
     <a class='right-next' id="next-link" href="soda-profits.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.21. </span>Soda profits</p>
+        <p class="prev-next-title"><span class="section-number">40.21. </span>Soda profits</p>
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     <i class="fas fa-angle-right"></i>
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-    <link rel="prev" title="37.14. Try it in Excel" href="try-it-in-excel.html" />
+    <link rel="next" title="40.16. Build your own custom vis" href="build-your-own-custom-vis.html" />
+    <link rel="prev" title="40.14. Try it in Excel" href="try-it-in-excel.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
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-     13.4. Applied Machine Learning : build a web app
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     37.42. Machine Learning overview - assignment 1
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-<h2><span class="section-number">37.15.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.15.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section you started looking at a dataset around bees and their honey production over a period of time that saw losses in the bee colony population overall. Dig deeper into this dataset and build a notebook that can tell the story of the health of the bee population, state by state and year by year. Do you discover anything interesting about this dataset?</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.15.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.15.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1542,7 +1574,7 @@ <h2><span class="section-number">37.15.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.15.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.15.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1584,13 +1616,13 @@ <h2><span class="section-number">37.15.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.14. </span>Try it in Excel</p>
+            <p class="prev-next-title"><span class="section-number">40.14. </span>Try it in Excel</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-your-own-custom-vis.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.16. </span>Build your own custom vis</p>
+        <p class="prev-next-title"><span class="section-number">40.16. </span>Build your own custom vis</p>
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--- a/assignments/data-science/estimation-of-COVID-19-pandemic.html
+++ b/assignments/data-science/estimation-of-COVID-19-pandemic.html
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-    <title>37.23. Estimation of COVID-19 pandemic &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.23. Estimation of COVID-19 pandemic &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.24. Data processing in Python" href="data-processing-in-python.html" />
-    <link rel="prev" title="37.22. Analyzing COVID-19 papers" href="analyzing-COVID-19-papers.html" />
+    <link rel="next" title="40.24. Data processing in Python" href="data-processing-in-python.html" />
+    <link rel="prev" title="40.22. Analyzing COVID-19 papers" href="analyzing-COVID-19-papers.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
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+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
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+ </li>
+ <li class="toctree-l1 has-children">
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+  <label for="toctree-checkbox-9">
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-  <label for="toctree-checkbox-10">
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-  <label for="toctree-checkbox-11">
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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-   22. Generative adversarial networks
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
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-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
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-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science-scenarios.html">
-     37.10. Data Science scenarios
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    <li class="toctree-l2">
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-     37.11. Write a data ethics case study
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     <a class="reference internal" href="lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.15. Dive into the beehive
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-     37.16. Build your own custom vis
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-     37.17. Classifying datasets
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     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
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-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.23. Estimation of COVID-19 pandemic
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-     37.24. Data processing in Python
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-     37.25. Evaluating data from a form
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-     37.26. Data preparation
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     <a class="reference internal" href="matplotlib-applied.html">
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     <a class="reference internal" href="tell-a-story.html">
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     <a class="reference internal" href="market-research.html">
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-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pre-processing-the-data">
-   37.23.3. Pre-processing the data
+   40.23.3. Pre-processing the data
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#investigating-the-data">
-   37.23.4. Investigating the data
+   40.23.4. Investigating the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#computing-r-t">
-   37.23.5. Computing
+   40.23.5. Computing
    <span class="math notranslate nohighlight">
     \(R_t\)
    </span>
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#challenge">
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+   40.23.6. Challenge
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   37.23.7. References
+   40.23.7. References
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.23.8. Acknowledgments
+   40.23.8. Acknowledgments
   </a>
  </li>
 </ul>
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                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#loading-data">
-   37.23.1. Loading data
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#making-sense-of-the-data">
-   37.23.2. Making sense of the data
+   40.23.2. Making sense of the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pre-processing-the-data">
-   37.23.3. Pre-processing the data
+   40.23.3. Pre-processing the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#investigating-the-data">
-   37.23.4. Investigating the data
+   40.23.4. Investigating the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#computing-r-t">
-   37.23.5. Computing
+   40.23.5. Computing
    <span class="math notranslate nohighlight">
     \(R_t\)
    </span>
@@ -1618,17 +1650,17 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#challenge">
-   37.23.6. Challenge
+   40.23.6. Challenge
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   37.23.7. References
+   40.23.7. References
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.23.8. Acknowledgments
+   40.23.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1642,9 +1674,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="estimation-of-covid-19-pandemic">
-<h1><span class="section-number">37.23. </span>Estimation of COVID-19 pandemic<a class="headerlink" href="#estimation-of-covid-19-pandemic" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.23. </span>Estimation of COVID-19 pandemic<a class="headerlink" href="#estimation-of-covid-19-pandemic" title="Permalink to this headline">#</a></h1>
 <section id="loading-data">
-<h2><span class="section-number">37.23.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <p>We will use data on COVID-19 infected individuals, provided by the <a class="reference external" href="https://systems.jhu.edu/">Center for Systems Science and Engineering</a> (CSSE) at <a class="reference external" href="https://jhu.edu/">Johns Hopkins University</a>. Dataset is available in <a class="reference external" href="https://github.com/CSSEGISandData/COVID-19">this GitHub Repository</a>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1696,7 +1728,7 @@ <h2><span class="section-number">37.23.1. </span>Loading data<a class="headerlin
 </div>
 </section>
 <section id="making-sense-of-the-data">
-<h2><span class="section-number">37.23.2. </span>Making sense of the data<a class="headerlink" href="#making-sense-of-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.2. </span>Making sense of the data<a class="headerlink" href="#making-sense-of-the-data" title="Permalink to this headline">#</a></h2>
 <p>From the table above the role of province column is not clear. Let’s see the different values that are present in <code class="docutils literal notranslate"><span class="pre">Province/State</span></code> column:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1796,7 +1828,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="pre-processing-the-data">
-<h2><span class="section-number">37.23.3. </span>Pre-processing the data<a class="headerlink" href="#pre-processing-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.3. </span>Pre-processing the data<a class="headerlink" href="#pre-processing-the-data" title="Permalink to this headline">#</a></h2>
 <p>We are not interested in breaking countries down to further territories, thus we would first get rid of this breakdown and add information on all territories together, to get info for the whole country. This can be done using <code class="docutils literal notranslate"><span class="pre">groupby</span></code>:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2035,7 +2067,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="investigating-the-data">
-<h2><span class="section-number">37.23.4. </span>Investigating the data<a class="headerlink" href="#investigating-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.4. </span>Investigating the data<a class="headerlink" href="#investigating-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s now switch to investigating a specific country. Let’s create a frame that contains the data on infections indexed by date:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2893,7 +2925,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="computing-r-t">
-<h2><span class="section-number">37.23.5. </span>Computing <span class="math notranslate nohighlight">\(R_t\)</span><a class="headerlink" href="#computing-r-t" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.5. </span>Computing <span class="math notranslate nohighlight">\(R_t\)</span><a class="headerlink" href="#computing-r-t" title="Permalink to this headline">#</a></h2>
 <p>To see how infectious is the disease, we look at the <strong>basic reproduction number</strong> <span class="math notranslate nohighlight">\(R_0\)</span>, which indicated the number of people that an infected person would further infect. When <span class="math notranslate nohighlight">\(R_0\)</span> is more than 1, the epidemic is likely to spread.</p>
 <p><span class="math notranslate nohighlight">\(R_0\)</span> is a property of the disease itself, and does not take into account some protective measures that people may take to slow down the pandemic. During the pandemic progression, we can estimate the reproduction number <span class="math notranslate nohighlight">\(R_t\)</span> at any given time <span class="math notranslate nohighlight">\(t\)</span>. It has been shown that this number can be roughly estimated by taking a window of 8 days, and computing $<span class="math notranslate nohighlight">\(R_t=\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}\)</span><span class="math notranslate nohighlight">\(
 where \)</span>I_t<span class="math notranslate nohighlight">\( is the number of newly infected individuals on day \)</span>t$.</p>
@@ -3412,7 +3444,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="challenge">
-<h2><span class="section-number">37.23.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
 <p>Now it is time for you to play more with the code and data! Here are a few suggestions you can experiment with:</p>
 <ul class="simple">
 <li><p>See the spread of the pandemic in different countries.</p></li>
@@ -3423,7 +3455,7 @@ <h2><span class="section-number">37.23.6. </span>Challenge<a class="headerlink"
 </ul>
 </section>
 <section id="references">
-<h2><span class="section-number">37.23.7. </span>References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.7. </span>References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <p>You may look at further studies of COVID epidemic spread in the following publications:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/">Sliding SIR Model for Rt Estimation during COVID Pandemic</a>, blog post by <a class="reference external" href="http://soshnikov.com">Dmitry Soshnikov</a></p></li>
@@ -3432,7 +3464,7 @@ <h2><span class="section-number">37.23.7. </span>References<a class="headerlink"
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.23.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3474,13 +3506,13 @@ <h2><span class="section-number">37.23.8. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.22. </span>Analyzing COVID-19 papers</p>
+            <p class="prev-next-title"><span class="section-number">40.22. </span>Analyzing COVID-19 papers</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-processing-in-python.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.24. </span>Data processing in Python</p>
+        <p class="prev-next-title"><span class="section-number">40.24. </span>Data processing in Python</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
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+    <title>40.25. Evaluating data from a form &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.26. Data preparation" href="data-preparation.html" />
-    <link rel="prev" title="37.24. Data processing in Python" href="data-processing-in-python.html" />
+    <link rel="next" title="40.26. Data preparation" href="data-preparation.html" />
+    <link rel="prev" title="40.24. Data processing in Python" href="data-processing-in-python.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <ul>
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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-  <label for="toctree-checkbox-10">
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-   18. Kernel method
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+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   30. Deep Q-learning
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+   32. Image segmentation
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+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.3. First assignment
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
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-     37.17. Classifying datasets
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-     37.21. Soda profits
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-     37.37. Exploring for answers
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    <li class="toctree-l2">
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-     37.39. Low code/no code Data Science project on Azure ML
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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-     37.104. Time Series Forecasting Assignment
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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+     40.116. Basic classification: Classify images of clothing
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     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
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+   40.25.1. Instructions
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   <a class="reference internal nav-link" href="#instructions">
-   37.25.1. Instructions
+   40.25.1. Instructions
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-   37.25.2. Acknowledgments
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+<h1><span class="section-number">40.25. </span>Evaluating data from a form<a class="headerlink" href="#evaluating-data-from-a-form" title="Permalink to this headline">#</a></h1>
 <p>A client has been testing a small form as below to gather some basic data about their client-base. They have brought their findings to you to validate the data they have gathered.</p>
 <p>You have been provided a <a href="../../assets/data/simple-form.csv">dataset of csv records</a> that contain entries from the form as well as some basic visualizations.The client pointed out that some of the visualizations look incorrect but they’re unsure about how to resolve them.</p>
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+<h2><span class="section-number">40.25.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
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 <div class="cell docutils container">
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 <section id="acknowledgments">
-<h2><span class="section-number">37.25.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.25.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1710,13 +1742,13 @@ <h2><span class="section-number">37.25.2. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.24. </span>Data processing in Python</p>
+            <p class="prev-next-title"><span class="section-number">40.24. </span>Data processing in Python</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-preparation.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.26. </span>Data preparation</p>
+        <p class="prev-next-title"><span class="section-number">40.26. </span>Data preparation</p>
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     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/explore-a-planetary-computer-dataset.html b/assignments/data-science/explore-a-planetary-computer-dataset.html
index fa6a15745a..ad3797d446 100644
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-    <link rel="prev" title="37.35. Tell a story" href="tell-a-story.html" />
+    <link rel="next" title="40.37. Exploring for answers" href="exploring-for-anwser.html" />
+    <link rel="prev" title="40.35. Tell a story" href="tell-a-story.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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+     12.4. Logistic regression
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  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+   <i class="fas fa-chevron-down">
+   </i>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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-   18. Kernel method
+   20. Kernel method
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.14. Try it in Excel
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-     37.35. Tell a story
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-     37.36. Explore a planetary computer dataset
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-     37.39. Low code/no code Data Science project on Azure ML
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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-     37.49. Linear Regression Implementation from Scratch
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.103. Fun with Variational Autoencoders
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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+     40.116. Basic classification: Classify images of clothing
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+     41.5. Relational vs. non-relational database
     </a>
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+     41.6. NumPy and Pandas
     </a>
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     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
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-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
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     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
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+     41.16. Build an machine learning web application
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.36.1. Instructions
+   40.36.1. Instructions
   </a>
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-   37.36.2. Rubric
+   40.36.2. Rubric
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.36.3. Acknowledgments
+   40.36.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1495,17 +1527,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#instructions">
-   37.36.1. Instructions
+   40.36.1. Instructions
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   <a class="reference internal nav-link" href="#rubric">
-   37.36.2. Rubric
+   40.36.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.36.3. Acknowledgments
+   40.36.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1519,9 +1551,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="explore-a-planetary-computer-dataset">
-<h1><span class="section-number">37.36. </span>Explore a planetary computer dataset<a class="headerlink" href="#explore-a-planetary-computer-dataset" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.36. </span>Explore a planetary computer dataset<a class="headerlink" href="#explore-a-planetary-computer-dataset" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.36.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.36.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section, we talked about various data science application domains - with deep dives into examples related to research, sustainability, and digital humanities. In this assignment, you’ll explore one of these examples in more detail, and apply some of your learnings around data visualizations and analysis to derive insights about sustainability data.</p>
 <p>The <a class="reference external" href="https://planetarycomputer.microsoft.com/">Planetary Computer</a> project has datasets and APIs that can be accessed with an account - request one for access if you want to try the bonus step of the assignment. The site also provides an <a class="reference external" href="https://planetarycomputer.microsoft.com/explore">Explorer</a> feature that you can use without creating an account.</p>
 <p><code class="docutils literal notranslate"><span class="pre">Steps:</span></code>
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 <section id="rubric">
-<h2><span class="section-number">37.36.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.36.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1567,7 +1599,7 @@ <h2><span class="section-number">37.36.2. </span>Rubric<a class="headerlink" hre
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 <section id="acknowledgments">
-<h2><span class="section-number">37.36.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.36.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1609,13 +1641,13 @@ <h2><span class="section-number">37.36.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.35. </span>Tell a story</p>
+            <p class="prev-next-title"><span class="section-number">40.35. </span>Tell a story</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="exploring-for-anwser.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.37. </span>Exploring for answers</p>
+        <p class="prev-next-title"><span class="section-number">40.37. </span>Exploring for answers</p>
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     </a>
diff --git a/assignments/data-science/exploring-for-anwser.html b/assignments/data-science/exploring-for-anwser.html
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+    <title>40.37. Exploring for answers &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.38. Market research" href="market-research.html" />
-    <link rel="prev" title="37.36. Explore a planetary computer dataset" href="explore-a-planetary-computer-dataset.html" />
+    <link rel="next" title="40.38. Market research" href="market-research.html" />
+    <link rel="prev" title="40.36. Explore a planetary computer dataset" href="explore-a-planetary-computer-dataset.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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+     14.1. Introduction to classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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+     16.1. Introduction to clustering
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-     14.2. K-Means clustering
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   30. Deep Q-learning
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.14. Try it in Excel
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-     37.21. Soda profits
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-     37.29. Matplotlib applied
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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-     37.45. Linear regression - California Housing
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-     37.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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-     37.85. Case Study: Debugging in Regression
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-     37.86. Study the solvers
+     40.86. Study the solvers
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-     37.87. Build classification models
+     40.87. Build classification models
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-     37.88. Build Classification Model
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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+     40.116. Basic classification: Classify images of clothing
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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+     41.1. Python programming introduction
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+     41.4. Data Science introduction
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     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
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+     41.5. Relational vs. non-relational database
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
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     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
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-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
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+     41.10. Data Science in real world
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+     41.11. Machine Learning overview
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-     38.14. Logistic Regression
+     41.14. Logistic Regression
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     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
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+     41.16. Build an machine learning web application
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     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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    </li>
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     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
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    </li>
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     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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    </li>
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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   <a class="reference internal nav-link" href="#instructions">
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   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.37.3. Acknowledgments
+   40.37.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1560,17 +1592,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.37.1. Instructions
+   40.37.1. Instructions
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.37.2. Rubric
+   40.37.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.37.3. Acknowledgments
+   40.37.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1584,12 +1616,12 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="exploring-for-answers">
-<h1><span class="section-number">37.37. </span>Exploring for answers<a class="headerlink" href="#exploring-for-answers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.37. </span>Exploring for answers<a class="headerlink" href="#exploring-for-answers" title="Permalink to this headline">#</a></h1>
 <p>This is a continuation of the previous section’s <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html"><span class="doc std std-doc">assignment</span></a>, where we briefly took a look at the data set. Now we will be taking a deeper look at the data.</p>
 <p>Again, the question the client wants to know: <strong>Do yellow taxi passengers in New York City tip drivers more in the winter or summer?</strong></p>
 <p>Your team is in the <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html"><span class="doc std std-doc">Analyzing</span></a> stage of the Data Science Lifecycle, where you are responsible for doing exploratory data analysis on the dataset. You have been provided a notebook and dataset that contains 200 taxi transactions from January and July 2019.</p>
 <section id="instructions">
-<h2><span class="section-number">37.37.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.37.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Below data is from the <a class="reference external" href="https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets">Taxi &amp; Limousine Commission</a>. Refer to the <a class="reference external" href="https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf">dataset’s dictionary</a> and <a class="reference external" href="https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf">user guide</a> for more information about the data.</p>
 <p>Use some the techniques in this section to do your own EDA in the notebook (add cells if you’d like) and answer the following questions:</p>
 <ul class="simple">
@@ -1615,7 +1647,7 @@ <h2><span class="section-number">37.37.1. </span>Instructions<a class="headerlin
 </div>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.37.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.37.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1632,7 +1664,7 @@ <h2><span class="section-number">37.37.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.37.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.37.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1674,13 +1706,13 @@ <h2><span class="section-number">37.37.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.36. </span>Explore a planetary computer dataset</p>
+            <p class="prev-next-title"><span class="section-number">40.36. </span>Explore a planetary computer dataset</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="market-research.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.38. </span>Market research</p>
+        <p class="prev-next-title"><span class="section-number">40.38. </span>Market research</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/introduction-to-statistics-and-probability.html b/assignments/data-science/introduction-to-statistics-and-probability.html
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-    <title>37.19. Introduction to probability and statistics &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.19. Introduction to probability and statistics &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.20. Displaying airport data" href="displaying-airport-data.html" />
-    <link rel="prev" title="37.18. Small diabetes study" href="small-diabetes-study.html" />
+    <link rel="next" title="40.20. Displaying airport data" href="displaying-airport-data.html" />
+    <link rel="prev" title="40.18. Small diabetes study" href="small-diabetes-study.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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+     14.1. Introduction to classification
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.5. Project Plan​ Template
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-     37.116. Basic classification: Classify images of clothing
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.16. Build an machine learning web application
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-     38.20. Convolutional Neural Network
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+   40.19.4. Confidence intervals
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+   40.19.6. Simulating a normal distribution with the central limit theorem
   </a>
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+   40.19.6. Simulating a normal distribution with the central limit theorem
   </a>
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@@ -1646,7 +1678,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-probability-and-statistics">
-<h1><span class="section-number">37.19. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.19. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h1>
 <p>In this notebook, we will play around with some of the concepts we have previously discussed. Many concepts from probability and statistics are well-represented in major libraries for data processing in Python, such as <code class="docutils literal notranslate"><span class="pre">numpy</span></code> and <code class="docutils literal notranslate"><span class="pre">pandas</span></code>.</p>
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-<h2><span class="section-number">37.19.1. </span>Random Variables and Distributions<a class="headerlink" href="#random-variables-and-distributions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.1. </span>Random Variables and Distributions<a class="headerlink" href="#random-variables-and-distributions" title="Permalink to this headline">#</a></h2>
 <p>Let’s start with drawing a sample of 30 values from a uniform distribution from <span class="math notranslate nohighlight">\(0\)</span> to <span class="math notranslate nohighlight">\(9\)</span>. We will also compute mean and variance.</p>
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-<h2><span class="section-number">37.19.2. </span>Analyzing Real Data<a class="headerlink" href="#analyzing-real-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.2. </span>Analyzing Real Data<a class="headerlink" href="#analyzing-real-data" title="Permalink to this headline">#</a></h2>
 <p>Mean and variance are very important when analyzing real-world data. Let’s load the data about baseball players from <a class="reference external" href="http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights">SOCR MLB Height/Weight Data</a></p>
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-<h2><span class="section-number">37.19.3. </span>Normal distribution<a class="headerlink" href="#normal-distribution" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.3. </span>Normal distribution<a class="headerlink" href="#normal-distribution" title="Permalink to this headline">#</a></h2>
 <p>Let’s create an artificial sample of weights that follows a normal distribution with the same mean and variance as our real data:</p>
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-<h2><span class="section-number">37.19.4. </span>Confidence intervals<a class="headerlink" href="#confidence-intervals" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.4. </span>Confidence intervals<a class="headerlink" href="#confidence-intervals" title="Permalink to this headline">#</a></h2>
 <p>Let’s now calculate confidence intervals for the weights and heights of baseball players. We will use the code <a class="reference external" href="https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data">from this stackoverflow discussion</a>:</p>
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+<h2><span class="section-number">40.19.5. </span>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Permalink to this headline">#</a></h2>
 <p>Let’s explore different roles in our baseball players dataset:</p>
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-<h2><span class="section-number">37.19.6. </span>Simulating a normal distribution with the central limit theorem<a class="headerlink" href="#simulating-a-normal-distribution-with-the-central-limit-theorem" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.6. </span>Simulating a normal distribution with the central limit theorem<a class="headerlink" href="#simulating-a-normal-distribution-with-the-central-limit-theorem" title="Permalink to this headline">#</a></h2>
 <p>The pseudo-random generator in Python is designed to give us a uniform distribution. If we want to create a generator for normal distribution, we can use the central limit theorem. To get a normally distributed value we will just compute a mean of a uniform-generated sample.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1910,7 +1942,7 @@ <h2><span class="section-number">37.19.6. </span>Simulating a normal distributio
 </div>
 </section>
 <section id="correlation-and-evil-baseball-corp">
-<h2><span class="section-number">37.19.7. </span>Correlation and evil baseball corp<a class="headerlink" href="#correlation-and-evil-baseball-corp" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.7. </span>Correlation and evil baseball corp<a class="headerlink" href="#correlation-and-evil-baseball-corp" title="Permalink to this headline">#</a></h2>
 <p>Correlation allows us to find relations between data sequences. In our toy example, let’s pretend there is an evil baseball corporation that pays its players according to their height - the taller the player is, the more money he/she gets. Suppose there is a base salary of <span class="math notranslate nohighlight">\(1000, and an additional bonus from \)</span>0 to $100, depending on height. We will take the real players from MLB, and compute their imaginary salaries:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2011,11 +2043,11 @@ <h2><span class="section-number">37.19.7. </span>Correlation and evil baseball c
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.19.8. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.8. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>In this notebook we have learnt how to perform basic operations on data to compute statistical functions. We now know how to use a sound apparatus of math and statistics in order to prove some hypotheses, and how to compute confidence intervals for arbitrary variables given a data sample.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.19.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2057,13 +2089,13 @@ <h2><span class="section-number">37.19.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.18. </span>Small diabetes study</p>
+            <p class="prev-next-title"><span class="section-number">40.18. </span>Small diabetes study</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="displaying-airport-data.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.20. </span>Displaying airport data</p>
+        <p class="prev-next-title"><span class="section-number">40.20. </span>Displaying airport data</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/lines-scatters-and-bars.html b/assignments/data-science/lines-scatters-and-bars.html
index 7e8378f45d..efd6bc258e 100644
--- a/assignments/data-science/lines-scatters-and-bars.html
+++ b/assignments/data-science/lines-scatters-and-bars.html
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-    <title>37.12. Lines, scatters and bars &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.12. Lines, scatters and bars &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.13. Apply your skills" href="apply-your-skills.html" />
-    <link rel="prev" title="37.11. Write a data ethics case study" href="write-a-data-ethics-case-study.html" />
+    <link rel="next" title="40.13. Apply your skills" href="apply-your-skills.html" />
+    <link rel="prev" title="40.11. Write a data ethics case study" href="write-a-data-ethics-case-study.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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+  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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-     15.1. Bagging
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    </li>
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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-   23. Recurrent Neural Networks
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-   24. Autoencoder
+   27. Autoencoder
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-   25. Long-short term memory
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  </li>
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+   30. Deep Q-learning
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-   28. Image classification
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-   29. Image segmentation
+   32. Image segmentation
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  </li>
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
  <li class="toctree-l1">
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-   31. Object detection
+   34. Object detection
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    <li class="toctree-l2">
     <a class="reference internal" href="write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="data-preparation.html">
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     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="matplotlib-applied.html">
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     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
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     <a class="reference internal" href="exploring-for-anwser.html">
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     <a class="reference internal" href="market-research.html">
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     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
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+     41.21. Generative Adversarial Network
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@@ -1461,17 +1493,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal nav-link" href="#instructions">
-   37.12.1. Instructions
+   40.12.1. Instructions
   </a>
  </li>
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-   37.12.2. Rubric
+   40.12.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.12.3. Acknowledgments
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   </a>
  </li>
 </ul>
@@ -1495,17 +1527,17 @@ <h2> Contents </h2>
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   <a class="reference internal nav-link" href="#instructions">
-   37.12.1. Instructions
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   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.12.3. Acknowledgments
+   40.12.3. Acknowledgments
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  </li>
 </ul>
@@ -1519,13 +1551,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="lines-scatters-and-bars">
-<h1><span class="section-number">37.12. </span>Lines, scatters and bars<a class="headerlink" href="#lines-scatters-and-bars" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.12. </span>Lines, scatters and bars<a class="headerlink" href="#lines-scatters-and-bars" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.12.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.12.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section, you worked with line charts, scatterplots, and bar charts to show interesting facts about this dataset. In this assignment, dig deeper into the dataset to discover a fact about a given type of bird. For example, create a notebook visualizing all the interesting data you can uncover about Snow Geese. Use the three plots mentioned above to tell a story in your notebook.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.12.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.12.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1542,7 +1574,7 @@ <h2><span class="section-number">37.12.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.12.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.12.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1584,13 +1616,13 @@ <h2><span class="section-number">37.12.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.11. </span>Write a data ethics case study</p>
+            <p class="prev-next-title"><span class="section-number">40.11. </span>Write a data ethics case study</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="apply-your-skills.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.13. </span>Apply your skills</p>
+        <p class="prev-next-title"><span class="section-number">40.13. </span>Apply your skills</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
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-    <link rel="prev" title="37.38. Market research" href="market-research.html" />
+    <link rel="next" title="40.40. Data Science project using Azure ML SDK" href="data-science-project-using-azure-ml-sdk.html" />
+    <link rel="prev" title="40.38. Market research" href="market-research.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
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+   </i>
+  </label>
+  <ul>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.14. Try it in Excel
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     <a class="reference internal" href="dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-preparation.html">
-     37.26. Data preparation
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-data.html">
-     37.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="market-research.html">
-     37.38. Market research
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    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
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    <li class="toctree-l2">
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-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
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-     37.59. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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-     37.62. Explore classification methods
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     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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-     37.70. Dropout and Batch Normalization
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
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     <a class="reference internal" href="../ml-fundamentals/build-classification-models.html">
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
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    </li>
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
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+   40.39.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.39.3. Acknowledgments
+   40.39.3. Acknowledgments
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 </ul>
@@ -1495,17 +1527,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#instructions">
-   37.39.1. Instructions
+   40.39.1. Instructions
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-   37.39.2. Rubric
+   40.39.2. Rubric
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.39.3. Acknowledgments
+   40.39.3. Acknowledgments
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  </li>
 </ul>
@@ -1519,13 +1551,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="low-code-no-code-data-science-project-on-azure-ml">
-<h1><span class="section-number">37.39. </span>Low code/no code Data Science project on Azure ML<a class="headerlink" href="#low-code-no-code-data-science-project-on-azure-ml" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.39. </span>Low code/no code Data Science project on Azure ML<a class="headerlink" href="#low-code-no-code-data-science-project-on-azure-ml" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.39.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.39.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. Now, look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on <a class="reference external" href="https://kaggle.com">Kaggle</a> and <a class="reference external" href="https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Azure Open Datasets</a>.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.39.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.39.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1542,7 +1574,7 @@ <h2><span class="section-number">37.39.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.39.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.39.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1584,13 +1616,13 @@ <h2><span class="section-number">37.39.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.38. </span>Market research</p>
+            <p class="prev-next-title"><span class="section-number">40.38. </span>Market research</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-science-project-using-azure-ml-sdk.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.40. </span>Data Science project using Azure ML SDK</p>
+        <p class="prev-next-title"><span class="section-number">40.40. </span>Data Science project using Azure ML SDK</p>
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     </a>
diff --git a/assignments/data-science/market-research.html b/assignments/data-science/market-research.html
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-    <link rel="prev" title="37.37. Exploring for answers" href="exploring-for-anwser.html" />
+    <link rel="next" title="40.39. Low code/no code Data Science project on Azure ML" href="low-code-no-code-data-science-project-on-azure-ml.html" />
+    <link rel="prev" title="40.37. Exploring for answers" href="exploring-for-anwser.html" />
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    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
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+ <li class="toctree-l1 has-children">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-preparation.html">
-     37.26. Data preparation
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-data.html">
-     37.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
-     37.54. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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-     37.74. Random forests for classification
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-     37.75. Beyond random forests: more ensemble models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
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-     37.83. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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-     37.85. Case Study: Debugging in Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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     <a class="reference internal" href="../ml-fundamentals/build-classification-models.html">
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-     37.88. Build Classification Model
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-     37.89. Parameter play
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-     38.1. Python programming introduction
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     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
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     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
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     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
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+     41.17. Unsupervised learning
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
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+     41.18. Kernel method
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
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     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
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    </li>
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
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+<h1><span class="section-number">40.38. </span>Market research<a class="headerlink" href="#market-research" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.38.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.38.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In the <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html"><span class="doc std std-doc">Data science in the cloud</span></a> section, you learned that there are several important cloud providers. Do some market research to discover what each can offer to the Data Scientist. Are the offerings comparable? Write a paper to describe the offerings of three or more of these cloud providers.</p>
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 <section id="rubric">
-<h2><span class="section-number">37.38.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.38.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.37. </span>Exploring for answers</p>
+            <p class="prev-next-title"><span class="section-number">40.37. </span>Exploring for answers</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="low-code-no-code-data-science-project-on-azure-ml.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.39. </span>Low code/no code Data Science project on Azure ML</p>
+        <p class="prev-next-title"><span class="section-number">40.39. </span>Low code/no code Data Science project on Azure ML</p>
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diff --git a/assignments/data-science/matplotlib-applied.html b/assignments/data-science/matplotlib-applied.html
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-    <link rel="prev" title="37.28. NYC taxi data in winter and summer" href="nyc-taxi-data-in-winter-and-summer.html" />
+    <link rel="next" title="40.35. Tell a story" href="tell-a-story.html" />
+    <link rel="prev" title="40.28. NYC taxi data in winter and summer" href="nyc-taxi-data-in-winter-and-summer.html" />
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    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+    </a>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-   22. Generative adversarial networks
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     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-preparation.html">
-     37.26. Data preparation
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-data.html">
-     37.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
-     37.54. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
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-     37.74. Random forests for classification
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
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     <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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-     37.85. Case Study: Debugging in Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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     <a class="reference internal" href="../ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
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-     37.88. Build Classification Model
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     <a class="reference internal" href="../ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
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-     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
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-     38.1. Python programming introduction
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-     38.2. Python programming basics
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+     41.11. Machine Learning overview
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    </li>
    <li class="toctree-l2">
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    </li>
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+     41.19. Model Selection
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+   40.33. Scatter Plots, subplots
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+   40.34. Kernel Density Estimation Plots
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#acknowledgments">
-     37.34.1. Acknowledgments
+     40.34.1. Acknowledgments
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    </li>
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-   37.29. Matplotlib applied
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     <a class="reference internal nav-link" href="#applying-matplotlib-visualizations-to-kaggle-titanic">
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+     40.30.1. Applying Matplotlib Visualizations to Kaggle: Titanic
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-   37.33. Scatter Plots, subplots
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-   37.34. Kernel Density Estimation Plots
+   40.34. Kernel Density Estimation Plots
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#acknowledgments">
-     37.34.1. Acknowledgments
+     40.34.1. Acknowledgments
     </a>
    </li>
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@@ -1653,7 +1685,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
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-<h1><span class="section-number">37.29. </span>Matplotlib applied<a class="headerlink" href="#matplotlib-applied" title="Permalink to this headline">#</a></h1>
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 <li><p>Applying Matplotlib Visualizations to Kaggle: Titanic</p></li>
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+<h1><span class="section-number">40.30. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h1>
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 <section id="applying-matplotlib-visualizations-to-kaggle-titanic">
-<h2><span class="section-number">37.30.1. </span>Applying Matplotlib Visualizations to Kaggle: Titanic<a class="headerlink" href="#applying-matplotlib-visualizations-to-kaggle-titanic" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.30.1. </span>Applying Matplotlib Visualizations to Kaggle: Titanic<a class="headerlink" href="#applying-matplotlib-visualizations-to-kaggle-titanic" title="Permalink to this headline">#</a></h2>
 <p>Prepare the titanic data to plot:</p>
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+<h1><span class="section-number">40.31. </span>Bar Plots, Histograms, subplot2grid<a class="headerlink" href="#bar-plots-histograms-subplot2grid" title="Permalink to this headline">#</a></h1>
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+<h1><span class="section-number">40.32. </span>Normalized Plots<a class="headerlink" href="#normalized-plots" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pclass_xt</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">df_train</span><span class="p">[</span><span class="s2">&quot;Pclass&quot;</span><span class="p">],</span> <span class="n">df_train</span><span class="p">[</span><span class="s2">&quot;Survived&quot;</span><span class="p">])</span>
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-<h1><span class="section-number">37.33. </span>Scatter Plots, subplots<a class="headerlink" href="#scatter-plots-subplots" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.33. </span>Scatter Plots, subplots<a class="headerlink" href="#scatter-plots-subplots" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
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 <div class="cell docutils container">
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 You can consider to use <code>numpy.sort</code> and <code>pandas.DataFrame.plot</code>.<br>
 </details>
 </div><section id="acknowledgments">
-<h2><span class="section-number">37.34.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.34.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Donne Martin for creating the open-source project <a href="https://github.com/donnemartin/data-science-ipython-notebooks">data-science-ipython-notebooks</a>, which inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2353,13 +2385,13 @@ <h2><span class="section-number">37.34.1. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.28. </span>NYC taxi data in winter and summer</p>
+            <p class="prev-next-title"><span class="section-number">40.28. </span>NYC taxi data in winter and summer</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="tell-a-story.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.35. </span>Tell a story</p>
+        <p class="prev-next-title"><span class="section-number">40.35. </span>Tell a story</p>
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-    <link rel="next" title="37.29. Matplotlib applied" href="matplotlib-applied.html" />
-    <link rel="prev" title="37.27. Analyzing data" href="analyzing-data.html" />
+    <link rel="next" title="40.29. Matplotlib applied" href="matplotlib-applied.html" />
+    <link rel="prev" title="40.27. Analyzing data" href="analyzing-data.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    <li class="toctree-l2">
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+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
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  </li>
  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
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+     14.3. Yet other classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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    </li>
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  </li>
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-     16.4. XGBoost + k-fold CV + Feature Importance
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   39. Model deployment
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.14. Try it in Excel
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-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="data-processing-in-python.html">
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-     37.27. Analyzing data
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-     37.28. NYC taxi data in winter and summer
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-     37.29. Matplotlib applied
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-     37.35. Tell a story
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     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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-     37.37. Exploring for answers
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     <a class="reference internal" href="market-research.html">
-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
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-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
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-     37.116. Basic classification: Classify images of clothing
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.14. Logistic Regression
+     41.14. Logistic Regression
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-     38.15. Neural Network
+     41.15. Neural Network
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+     41.16. Build an machine learning web application
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     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
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-     38.19. Model Selection
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
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   <a class="reference internal nav-link" href="#instructions">
-   37.28.1. Instructions
+   40.28.1. Instructions
   </a>
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   <a class="reference internal nav-link" href="#rubric">
-   37.28.2. Rubric
+   40.28.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.28.3. Acknowledgments
+   40.28.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1560,17 +1592,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#instructions">
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-   37.28.2. Rubric
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.28.3. Acknowledgments
+   40.28.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1584,13 +1616,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="nyc-taxi-data-in-winter-and-summer">
-<h1><span class="section-number">37.28. </span>NYC taxi data in winter and summer<a class="headerlink" href="#nyc-taxi-data-in-winter-and-summer" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.28. </span>NYC taxi data in winter and summer<a class="headerlink" href="#nyc-taxi-data-in-winter-and-summer" title="Permalink to this headline">#</a></h1>
 <p>A client has approached your team for help in investigating a taxi customer’s seasonal spending habits in New York City. They want to know: <strong>Do yellow taxi passengers in New York City tip drivers more in the winter or summer?</strong></p>
 <p>Your team is in the <a class="reference external" href="../../data-science/data-science-lifecycle/introduction.md#Capturing">capturing</a> stage of the Data Science Lifecycle and you are in charge of handling the dataset. You have been provided a notebook and <a href="../../assets/data/taxi.csv">data</a> to explore.</p>
 <p>We will use Python to load yellow taxi trip data from the <a class="reference external" href="https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets">NYC Taxi &amp; Limousine Commission</a>.
 You can also open the taxi data file in a text editor or spreadsheet software like Excel.</p>
 <section id="instructions">
-<h2><span class="section-number">37.28.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.28.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Assess whether or not the data in this dataset can help answer the question.</p></li>
 <li><p>Explore the <a class="reference external" href="https://data.cityofnewyork.us/browse?sortBy=most_accessed&amp;utf8=%E2%9C%93">NYC Open Data catalog</a>. Identify an additional dataset that could potentially be helpful in answering the client’s question.</p></li>
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 </div>
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 <section id="rubric">
-<h2><span class="section-number">37.28.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.28.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1631,7 +1663,7 @@ <h2><span class="section-number">37.28.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.28.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.28.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1673,13 +1705,13 @@ <h2><span class="section-number">37.28.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.27. </span>Analyzing data</p>
+            <p class="prev-next-title"><span class="section-number">40.27. </span>Analyzing data</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="matplotlib-applied.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.29. </span>Matplotlib applied</p>
+        <p class="prev-next-title"><span class="section-number">40.29. </span>Matplotlib applied</p>
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     </a>
diff --git a/assignments/data-science/small-diabetes-study.html b/assignments/data-science/small-diabetes-study.html
index 8b2f1d0bd1..d3475f850c 100644
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.18. Small diabetes study &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.18. Small diabetes study &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.19. Introduction to probability and statistics" href="introduction-to-statistics-and-probability.html" />
-    <link rel="prev" title="37.17. Classifying datasets" href="classifying-datasets.html" />
+    <link rel="next" title="40.19. Introduction to probability and statistics" href="introduction-to-statistics-and-probability.html" />
+    <link rel="prev" title="40.17. Classifying datasets" href="classifying-datasets.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    <li class="toctree-l2">
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-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
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-   35. Model training &amp; evaluation
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-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
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     <a class="reference internal" href="displaying-airport-data.html">
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     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="analyzing-data.html">
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     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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-     37.75. Beyond random forests: more ensemble models
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-     37.80. Random Forest Classifier with Feature Importance
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     </a>
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-     37.84. Case Study: Debugging in Classification
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-     37.86. Study the solvers
+     40.86. Study the solvers
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-     37.90. How to choose cnn architecture mnist
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-     37.95. Intro to TensorFlow for Deep Learning
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     </a>
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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-     37.104. Time Series Forecasting Assignment
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     </a>
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-     37.106. Neural Networks for Classification with TensorFlow
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     </a>
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
+     40.113. Art by gan
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../slides/introduction.html">
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
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+     41.1. Python programming introduction
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-     38.2. Python programming basics
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-     38.3. Python programming advanced
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    </li>
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-     38.4. Data Science introduction
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-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
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-     38.6. NumPy and Pandas
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     </a>
    </li>
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-     38.7. Data visualization
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     </a>
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-     38.10. Data Science in real world
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+     41.11. Machine Learning overview
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-     38.13. Logistic Regression
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-     38.14. Logistic Regression
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-     38.15. Neural Network
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
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     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
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-     38.19. Model Selection
+     41.19. Model Selection
     </a>
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     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1526,56 +1558,56 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-probability-and-statistics">
-   37.18.1. Introduction to probability and statistics
+   40.18.1. Introduction to probability and statistics
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-compute-mean-values-and-variance-for-all-values">
-     37.18.1.1. Task 1: Compute mean values and variance for all values
+     40.18.1.1. Task 1: Compute mean values and variance for all values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender">
-     37.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
+     40.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-1-get-the-data-ready">
-       37.18.1.2.1. Part 1: get the data ready
+       40.18.1.2.1. Part 1: get the data ready
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-2-plot-the-data">
-       37.18.1.2.2. Part 2: plot the data
+       40.18.1.2.2. Part 2: plot the data
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables">
-     37.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
+     40.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y">
-     37.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
+     40.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women">
-     37.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
+     40.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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+   40.18.2. Rubric
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.18.3. Acknowledgments
+   40.18.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1599,56 +1631,56 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-probability-and-statistics">
-   37.18.1. Introduction to probability and statistics
+   40.18.1. Introduction to probability and statistics
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-compute-mean-values-and-variance-for-all-values">
-     37.18.1.1. Task 1: Compute mean values and variance for all values
+     40.18.1.1. Task 1: Compute mean values and variance for all values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender">
-     37.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
+     40.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-1-get-the-data-ready">
-       37.18.1.2.1. Part 1: get the data ready
+       40.18.1.2.1. Part 1: get the data ready
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-2-plot-the-data">
-       37.18.1.2.2. Part 2: plot the data
+       40.18.1.2.2. Part 2: plot the data
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables">
-     37.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
+     40.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y">
-     37.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
+     40.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women">
-     37.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
+     40.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.18.2. Rubric
+   40.18.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.18.3. Acknowledgments
+   40.18.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1662,10 +1694,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="small-diabetes-study">
-<h1><span class="section-number">37.18. </span>Small diabetes study<a class="headerlink" href="#small-diabetes-study" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.18. </span>Small diabetes study<a class="headerlink" href="#small-diabetes-study" title="Permalink to this headline">#</a></h1>
 <p>In this assignment, we will work with a small dataset of diabetes patients taken from <a class="reference external" href="https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html">here</a>.</p>
 <section id="introduction-to-probability-and-statistics">
-<h2><span class="section-number">37.18.1. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.18.1. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -1693,7 +1725,7 @@ <h2><span class="section-number">37.18.1. </span>Introduction to probability and
 </ul>
 <p>Let’s study this dataset using methods of probability and statistics.</p>
 <section id="task-1-compute-mean-values-and-variance-for-all-values">
-<h3><span class="section-number">37.18.1.1. </span>Task 1: Compute mean values and variance for all values<a class="headerlink" href="#task-1-compute-mean-values-and-variance-for-all-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.1. </span>Task 1: Compute mean values and variance for all values<a class="headerlink" href="#task-1-compute-mean-values-and-variance-for-all-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_df_mean</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1786,9 +1818,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender">
-<h3><span class="section-number">37.18.1.2. </span>Task 2: Plot boxplots for BMI, BP and Y depending on gender<a class="headerlink" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.2. </span>Task 2: Plot boxplots for BMI, BP and Y depending on gender<a class="headerlink" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender" title="Permalink to this headline">#</a></h3>
 <section id="part-1-get-the-data-ready">
-<h4><span class="section-number">37.18.1.2.1. </span>Part 1: get the data ready<a class="headerlink" href="#part-1-get-the-data-ready" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.18.1.2.1. </span>Part 1: get the data ready<a class="headerlink" href="#part-1-get-the-data-ready" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns_to_plot</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;BMI&#39;</span><span class="p">,</span> <span class="s1">&#39;BP&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">]</span>
@@ -1876,7 +1908,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-2-plot-the-data">
-<h4><span class="section-number">37.18.1.2.2. </span>Part 2: plot the data<a class="headerlink" href="#part-2-plot-the-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.18.1.2.2. </span>Part 2: plot the data<a class="headerlink" href="#part-2-plot-the-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_boxplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">column</span><span class="p">):</span>
@@ -1956,7 +1988,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables">
-<h3><span class="section-number">37.18.1.3. </span>Task 3: What is the distribution of Age, Sex, BMI and Y variables?<a class="headerlink" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.3. </span>Task 3: What is the distribution of Age, Sex, BMI and Y variables?<a class="headerlink" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_plot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">column</span><span class="p">):</span>
@@ -2049,7 +2081,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="task-4-test-the-correlation-between-different-variables-and-disease-progression-y">
-<h3><span class="section-number">37.18.1.4. </span>Task 4: Test the correlation between different variables and disease progression (Y)<a class="headerlink" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.4. </span>Task 4: Test the correlation between different variables and disease progression (Y)<a class="headerlink" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p><strong>Hint</strong> Correlation matrix would give you the most useful information on which values are dependent.</p>
 </div></blockquote>
@@ -2119,7 +2151,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women">
-<h3><span class="section-number">37.18.1.5. </span>Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women<a class="headerlink" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.5. </span>Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women<a class="headerlink" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># get the correlation between &#39;SEX&#39; and &#39;Y&#39;</span>
@@ -2210,7 +2242,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.18.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.18.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -2227,7 +2259,7 @@ <h2><span class="section-number">37.18.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.18.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.18.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2269,13 +2301,13 @@ <h2><span class="section-number">37.18.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.17. </span>Classifying datasets</p>
+            <p class="prev-next-title"><span class="section-number">40.17. </span>Classifying datasets</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="introduction-to-statistics-and-probability.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.19. </span>Introduction to probability and statistics</p>
+        <p class="prev-next-title"><span class="section-number">40.19. </span>Introduction to probability and statistics</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/soda-profits.html b/assignments/data-science/soda-profits.html
index b4a1639fee..8caea2adf5 100644
--- a/assignments/data-science/soda-profits.html
+++ b/assignments/data-science/soda-profits.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.21. Soda profits &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.21. Soda profits &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.22. Analyzing COVID-19 papers" href="analyzing-COVID-19-papers.html" />
-    <link rel="prev" title="37.20. Displaying airport data" href="displaying-airport-data.html" />
+    <link rel="next" title="40.22. Analyzing COVID-19 papers" href="analyzing-COVID-19-papers.html" />
+    <link rel="prev" title="40.20. Displaying airport data" href="displaying-airport-data.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
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-     15.3. Feature importance
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
  <li class="toctree-l1">
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-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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+   30. Deep Q-learning
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-   28. Image classification
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-     37.3. First assignment
+     40.3. First assignment
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-     37.5. Project Plan​ Template
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.14. Try it in Excel
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-     37.19. Introduction to probability and statistics
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-     37.24. Data processing in Python
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-     37.35. Tell a story
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-     37.36. Explore a planetary computer dataset
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-     37.37. Exploring for answers
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-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     37.42. Machine Learning overview - assignment 1
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-     37.45. Linear regression - California Housing
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-     37.49. Linear Regression Implementation from Scratch
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-     37.63. Kernel method assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.65. Support Vector Machines (SVM) - Classification
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-     37.66. Decision Trees - Intro and Regression
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-     37.67. Decision Trees - Classification
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-     37.68. Model selection assignment 1
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-     37.73. Random forests intro and regression
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-     37.75. Beyond random forests: more ensemble models
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.83. Counterintuitive Challenges in ML Debugging
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-     37.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
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+     40.104. Time Series Forecasting Assignment
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-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
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-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
+     40.113. Art by gan
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-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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 <li><p>Calculate the Gross profits of FY ‘15, ‘16, ‘17, and ‘18.</p>
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a> and the data author <a class="reference external" href="https://github.com/paladique">Jasmine Greenaway</a>. They inspires the majority of the content in this chapter.</p>
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 <p>[1]. CocaCola excel. (n.d.). Retrieved 29 May 2023, from <a class="reference external" href="https://www.kaggle.com/datasets/yiyiwang0826/cocacola-excel">https://www.kaggle.com/datasets/yiyiwang0826/cocacola-excel</a></p>
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             <p class="prev-next-subtitle">previous</p>
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+            <p class="prev-next-title"><span class="section-number">40.20. </span>Displaying airport data</p>
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-        <p class="prev-next-title"><span class="section-number">37.22. </span>Analyzing COVID-19 papers</p>
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+   32. Image segmentation
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.3. First assignment
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-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.14. Try it in Excel
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-     37.15. Dive into the beehive
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-     37.16. Build your own custom vis
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-     37.17. Classifying datasets
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-     37.21. Soda profits
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-     37.39. Low code/no code Data Science project on Azure ML
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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 <section id="instructions">
-<h2><span class="section-number">37.35.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
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 <p>Data Science is all about storytelling. Pick any dataset and write a short paper about a story you could tell about it. What do you hope your dataset can reveal? What will you do if its revelations prove problematic? What if your data doesn’t easily unlock its secrets? Think of the scenarios your dataset might present and write them down.</p>
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 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
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 <p>A one-page essay is presented in .doc format with the dataset explained, documented, credited, and a coherent story is presented about it with detailed examples from the data. A shorter essay is presented in a less detailed format | The essay is found lacking in one of the above details.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.35.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.35.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.29. </span>Matplotlib applied</p>
+            <p class="prev-next-title"><span class="section-number">40.29. </span>Matplotlib applied</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="explore-a-planetary-computer-dataset.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.36. </span>Explore a planetary computer dataset</p>
+        <p class="prev-next-title"><span class="section-number">40.36. </span>Explore a planetary computer dataset</p>
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-    <link rel="prev" title="37.13. Apply your skills" href="apply-your-skills.html" />
+    <link rel="next" title="40.15. Dive into the beehive" href="dive-into-the-beehive.html" />
+    <link rel="prev" title="40.13. Apply your skills" href="apply-your-skills.html" />
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    10. Machine Learning overview
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+     14.4. Applied Machine Learning : build a web app
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   22. Model selection
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+   23. Intro to Deep Learning
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-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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+   27. Autoencoder
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-   25. Long-short term memory
+   28. Long-short term memory
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-   26. Time series
+   29. Time series
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-   27. Deep Q-learning
+   30. Deep Q-learning
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-   28. Image classification
+   31. Image classification
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-   29. Image segmentation
+   32. Image segmentation
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-   30. Diffusion Model
+   33. Diffusion Model
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-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   36. Problem framing
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+   37. Data engineering
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-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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-   36. Model deployment
+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
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-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
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-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
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-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    <li class="toctree-l2">
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-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
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-     37.11. Write a data ethics case study
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-     37.12. Lines, scatters and bars
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
+     40.14. Try it in Excel
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-     37.15. Dive into the beehive
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     <a class="reference internal" href="build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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-     37.17. Classifying datasets
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     <a class="reference internal" href="small-diabetes-study.html">
-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    </li>
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     <a class="reference internal" href="displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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     <a class="reference internal" href="data-processing-in-python.html">
-     37.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
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-     37.25. Evaluating data from a form
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    </li>
    <li class="toctree-l2">
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-     37.26. Data preparation
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    </li>
    <li class="toctree-l2">
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-     37.27. Analyzing data
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     <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="matplotlib-applied.html">
-     37.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="tell-a-story.html">
-     37.35. Tell a story
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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+     40.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Intro to Autoencoders
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     </a>
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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-     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
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+   40.14.1. Instructions
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.14.3. Acknowledgments
+   40.14.3. Acknowledgments
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.14.1. Instructions
+   40.14.1. Instructions
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.14.3. Acknowledgments
+   40.14.3. Acknowledgments
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 </ul>
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+<h1><span class="section-number">40.14. </span>Try it in Excel<a class="headerlink" href="#try-it-in-excel" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.14.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.14.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
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 <section id="rubric">
-<h2><span class="section-number">37.14.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.14.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.14.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.14.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1584,13 +1616,13 @@ <h2><span class="section-number">37.14.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.13. </span>Apply your skills</p>
+            <p class="prev-next-title"><span class="section-number">40.13. </span>Apply your skills</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="dive-into-the-beehive.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.15. </span>Dive into the beehive</p>
+        <p class="prev-next-title"><span class="section-number">40.15. </span>Dive into the beehive</p>
     </div>
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     </a>
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-    <title>37.11. Write a data ethics case study &#8212; Ocademy Open Machine Learning Book</title>
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-    <link rel="prev" title="37.10. Data Science scenarios" href="data-science-scenarios.html" />
+    <link rel="next" title="40.12. Lines, scatters and bars" href="lines-scatters-and-bars.html" />
+    <link rel="prev" title="40.10. Data Science scenarios" href="data-science-scenarios.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   30. Deep Q-learning
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+   39. Model deployment
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.14. Try it in Excel
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-     37.19. Introduction to probability and statistics
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-     37.21. Soda profits
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-     37.24. Data processing in Python
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
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+<h1><span class="section-number">40.11. </span>Write a data ethics case study<a class="headerlink" href="#write-a-data-ethics-case-study" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.11.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.11.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>You’ve learned about various <a class="reference external" href="../../data-science/introduction/data-science-ethics.md#ethics-challenges">data ethics challenges</a> and seen some examples of <a class="reference external" href="../../data-science/introduction/data-science-ethics.md#case-studies">case studies</a> reflecting data ethics challenges in real-world contexts.</p>
 <p>In this assignment, you’ll write your own case study reflecting a data ethics challenge from your own experience, or from a relevant real-world context you are familiar with. Just follow these steps:</p>
 <ol class="simple">
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 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.11.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.11.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1590,13 +1622,13 @@ <h2><span class="section-number">37.11.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.10. </span>Data Science scenarios</p>
+            <p class="prev-next-title"><span class="section-number">40.10. </span>Data Science scenarios</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="lines-scatters-and-bars.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.12. </span>Lines, scatters and bars</p>
+        <p class="prev-next-title"><span class="section-number">40.12. </span>Lines, scatters and bars</p>
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    10. Machine Learning overview
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+     14.4. Applied Machine Learning : build a web app
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+   40.101.5. Second example: Image denoising
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+     40.101.5.1. Define a convolutional autoencoder
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+     40.101.7.1. Load ECG data
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+     40.101.7.2. Build the model
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-     37.101.7.3. Detect anomalies
+     40.101.7.3. Detect anomalies
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="intro-to-autoencoders">
-<h1><span class="section-number">37.101. </span>Intro to Autoencoders<a class="headerlink" href="#intro-to-autoencoders" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.101. </span>Intro to Autoencoders<a class="headerlink" href="#intro-to-autoencoders" title="Permalink to this headline">#</a></h1>
 <p>This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.</p>
 <p>An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error.</p>
 <p>To learn more about autoencoders, please consider reading chapter 14 from <a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.</p>
 <section id="import-tensorflow-and-other-libraries">
-<h2><span class="section-number">37.101.1. </span>Import TensorFlow and other libraries<a class="headerlink" href="#import-tensorflow-and-other-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.101.1. </span>Import TensorFlow and other libraries<a class="headerlink" href="#import-tensorflow-and-other-libraries" title="Permalink to this headline">#</a></h2>
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 </div>
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+<h2><span class="section-number">40.101.3. </span>Load the dataset<a class="headerlink" href="#load-the-dataset" title="Permalink to this headline">#</a></h2>
 <p>To start, you will train the basic autoencoder using the Fashion MNIST dataset. Each image in this dataset is 28x28 pixels.</p>
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+<h2><span class="section-number">40.101.4. </span>First example: Basic autoencoder<a class="headerlink" href="#first-example-basic-autoencoder" title="Permalink to this headline">#</a></h2>
 <p><img alt="Basic autoencoder results" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/autoencoder/intro_autoencoder_result.png" /></p>
 <p>Define an autoencoder with two Dense layers: an <code class="docutils literal notranslate"><span class="pre">encoder</span></code>, which compresses the images into a 64 dimensional latent vector, and a <code class="docutils literal notranslate"><span class="pre">decoder</span></code>, that reconstructs the original image from the latent space.</p>
 <p>To define your model, use the <a class="reference external" href="https://www.tensorflow.org/guide/keras/custom_layers_and_models">Keras Model Subclassing API</a>.</p>
@@ -1951,7 +1983,7 @@ <h2><span class="section-number">37.101.4. </span>First example: Basic autoencod
 </div>
 </section>
 <section id="second-example-image-denoising">
-<h2><span class="section-number">37.101.5. </span>Second example: Image denoising<a class="headerlink" href="#second-example-image-denoising" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.101.5. </span>Second example: Image denoising<a class="headerlink" href="#second-example-image-denoising" title="Permalink to this headline">#</a></h2>
 <p><img alt="Image denoising results" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/autoencoder/image_denoise_fmnist_results.png" /></p>
 <p>An autoencoder can also be trained to remove noise from images. In the following section, you will create a noisy version of the Fashion MNIST dataset by applying random noise to each image. You will then train an autoencoder using the noisy image as input, and the original image as the target.</p>
 <p>Let’s reimport the dataset to omit the modifications made earlier.</p>
@@ -2012,7 +2044,7 @@ <h2><span class="section-number">37.101.5. </span>Second example: Image denoisin
 </div>
 </div>
 <section id="define-a-convolutional-autoencoder">
-<h3><span class="section-number">37.101.5.1. </span>Define a convolutional autoencoder<a class="headerlink" href="#define-a-convolutional-autoencoder" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.101.5.1. </span>Define a convolutional autoencoder<a class="headerlink" href="#define-a-convolutional-autoencoder" title="Permalink to this headline">#</a></h3>
 <p>In this example, you will train a convolutional autoencoder using  <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D">Conv2D</a> layers in the <code class="docutils literal notranslate"><span class="pre">encoder</span></code>, and <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2DTranspose">Conv2DTranspose</a> layers in the <code class="docutils literal notranslate"><span class="pre">decoder</span></code>.</p>
 <div class="cell docutils container">
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@@ -2168,15 +2200,15 @@ <h3><span class="section-number">37.101.5.1. </span>Define a convolutional autoe
 </section>
 </section>
 <section id="third-example-anomaly-detection">
-<h2><span class="section-number">37.101.6. </span>Third example: Anomaly detection<a class="headerlink" href="#third-example-anomaly-detection" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.101.6. </span>Third example: Anomaly detection<a class="headerlink" href="#third-example-anomaly-detection" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="overview">
-<h2><span class="section-number">37.101.7. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.101.7. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>In this example, you will train an autoencoder to detect anomalies on the <a class="reference external" href="http://www.timeseriesclassification.com/description.php?Dataset=ECG5000">ECG5000 dataset</a>. This dataset contains 5,000 <a class="reference external" href="https://en.wikipedia.org/wiki/Electrocardiography">Electrocardiograms</a>, each with 140 data points. You will use a simplified version of the dataset, where each example has been labeled either <code class="docutils literal notranslate"><span class="pre">0</span></code> (corresponding to an abnormal rhythm), or <code class="docutils literal notranslate"><span class="pre">1</span></code> (corresponding to a normal rhythm). You are interested in identifying the abnormal rhythms.</p>
 <p>Note: This is a labeled dataset, so you could phrase this as a supervised learning problem. The goal of this example is to illustrate anomaly detection concepts you can apply to larger datasets, where you do not have labels available (for example, if you had many thousands of normal rhythms, and only a small number of abnormal rhythms).</p>
 <p>How will you detect anomalies using an autoencoder? Recall that an autoencoder is trained to minimize reconstruction error. You will train an autoencoder on the normal rhythms only, then use it to reconstruct all the data. Our hypothesis is that the abnormal rhythms will have higher reconstruction error. You will then classify a rhythm as an anomaly if the reconstruction error surpasses a fixed threshold.</p>
 <section id="load-ecg-data">
-<h3><span class="section-number">37.101.7.1. </span>Load ECG data<a class="headerlink" href="#load-ecg-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.101.7.1. </span>Load ECG data<a class="headerlink" href="#load-ecg-data" title="Permalink to this headline">#</a></h3>
 <p>The dataset you will use is based on one from <a class="reference external" href="http://www.timeseriesclassification.com/description.php?Dataset=ECG5000">timeseriesclassification.com</a>.</p>
 <div class="cell docutils container">
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@@ -2474,7 +2506,7 @@ <h3><span class="section-number">37.101.7.1. </span>Load ECG data<a class="heade
 </div>
 </section>
 <section id="build-the-model">
-<h3><span class="section-number">37.101.7.2. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.101.7.2. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">AnomalyDetector</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
@@ -2566,7 +2598,7 @@ <h3><span class="section-number">37.101.7.2. </span>Build the model<a class="hea
 </div>
 </section>
 <section id="detect-anomalies">
-<h3><span class="section-number">37.101.7.3. </span>Detect anomalies<a class="headerlink" href="#detect-anomalies" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.101.7.3. </span>Detect anomalies<a class="headerlink" href="#detect-anomalies" title="Permalink to this headline">#</a></h3>
 <p>Detect anomalies by calculating whether the reconstruction loss is greater than a fixed threshold. In this tutorial, you will calculate the mean average error for normal examples from the training set, then classify future examples as anomalous if the reconstruction error is higher than one standard deviation from the training set.</p>
 <p>Plot the reconstruction error on normal ECGs from the training set</p>
 <div class="cell docutils container">
@@ -2657,11 +2689,11 @@ <h3><span class="section-number">37.101.7.3. </span>Detect anomalies<a class="he
 </section>
 </section>
 <section id="next-steps">
-<h2><span class="section-number">37.101.8. </span>Next steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.101.8. </span>Next steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">#</a></h2>
 <p>To learn more about anomaly detection with autoencoders, check out this excellent <a class="reference external" href="https://anomagram.fastforwardlabs.com/#/">interactive example</a> built with TensorFlow.js by Victor Dibia. For a real-world use case, you can learn how <a class="reference external" href="https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html">Airbus Detects Anomalies in ISS Telemetry Data</a> using TensorFlow. To learn more about the basics, consider reading this <a class="reference external" href="https://blog.keras.io/building-autoencoders-in-keras.html">blog post</a> by François Chollet. For more details, check out chapter 14 from <a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.101.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.101.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TensorFlow Core for creating the open-source course <a class="reference external" href="https://www.tensorflow.org/tutorials/generative/autoencoder">autoencoder</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2703,13 +2735,13 @@ <h2><span class="section-number">37.101.9. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.99. </span>Google Stock Price Prediction RNN</p>
+            <p class="prev-next-title"><span class="section-number">40.99. </span>Google Stock Price Prediction RNN</p>
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-    <title>37.102. Base/Denoising Autoencoder &amp; Dimension Reduction &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.102. Base/Denoising Autoencoder &amp; Dimension Reduction &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.103. Fun with Variational Autoencoders" href="variational-autoencoder-and-faces-generation.html" />
-    <link rel="prev" title="37.101. Intro to Autoencoders" href="autoencoder.html" />
+    <link rel="next" title="40.103. Fun with Variational Autoencoders" href="variational-autoencoder-and-faces-generation.html" />
+    <link rel="prev" title="40.101. Intro to Autoencoders" href="autoencoder.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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-     11.1. Tools of the trade
+     12.1. Tools of the trade
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     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
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-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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-     11.4. Logistic regression
+     12.4. Logistic regression
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-     13.1. Introduction to classification
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-     13.2. More classifiers
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-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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-     14.2. K-Means clustering
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-   19. Model selection
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+   38. Model training &amp; evaluation
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@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../nn-for-classification-assignment.html">
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     </a>
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-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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-     37.112. DQN On Foreign Exchange Market
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     </a>
    </li>
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-     37.113. Art by gan
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     </a>
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     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
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     </a>
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     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
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-     38.4. Data Science introduction
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     </a>
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+     41.11. Machine Learning overview
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-     38.14. Logistic Regression
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     38.18. Kernel method
+     41.18. Kernel method
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     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1528,110 +1560,110 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.102.1. Introduction
+   40.102.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-and-scaling-datasets">
-   37.102.2. Loading and Scaling Datasets
+   40.102.2. Loading and Scaling Datasets
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   37.102.3. Load model
+   40.102.3. Load model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-2d-plot">
-     37.102.3.1. Checking dataset by 2D plot
+     40.102.3.1. Checking dataset by 2D plot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-3d-plot">
-     37.102.3.2. Checking dataset by 3D plot
+     40.102.3.2. Checking dataset by 3D plot
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#base-autoencoder">
-   37.102.4. Base Autoencoder
+   40.102.4. Base Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#modeling">
-     37.102.4.1. Modeling
+     40.102.4.1. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training">
-     37.102.4.2. Training
+     40.102.4.2. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-the-latent-space-after-dimension-reduction">
-     37.102.4.3. Plotting the latent space after Dimension Reduction
+     40.102.4.3. Plotting the latent space after Dimension Reduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-results">
-     37.102.4.4. Checking results
+     40.102.4.4. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#denoising-autoencoder">
-   37.102.5. Denoising Autoencoder
+   40.102.5. Denoising Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-random-noise-to-the-image">
-     37.102.5.1. Adding random noise to the image.
+     40.102.5.1. Adding random noise to the image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-a-noisy-image">
-     37.102.5.2. Plotting a noisy image.
+     40.102.5.2. Plotting a noisy image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-noisy-dataset-using-demension-reduction">
-     37.102.5.3. Checking Noisy Dataset using Demension Reduction
+     40.102.5.3. Checking Noisy Dataset using Demension Reduction
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#noisy-dataset">
-       37.102.5.3.1. 1) Noisy Dataset
+       40.102.5.3.1. 1) Noisy Dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#orignal-dataset">
-       37.102.5.3.2. 2) Orignal Dataset
+       40.102.5.3.2. 2) Orignal Dataset
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.102.5.4. Modeling
+     40.102.5.4. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.102.5.5. Training
+     40.102.5.5. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.102.5.6. Checking results
+     40.102.5.6. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.102.6. Acknowledgments
+   40.102.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1655,110 +1687,110 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.102.1. Introduction
+   40.102.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-and-scaling-datasets">
-   37.102.2. Loading and Scaling Datasets
+   40.102.2. Loading and Scaling Datasets
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   37.102.3. Load model
+   40.102.3. Load model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-2d-plot">
-     37.102.3.1. Checking dataset by 2D plot
+     40.102.3.1. Checking dataset by 2D plot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-3d-plot">
-     37.102.3.2. Checking dataset by 3D plot
+     40.102.3.2. Checking dataset by 3D plot
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#base-autoencoder">
-   37.102.4. Base Autoencoder
+   40.102.4. Base Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#modeling">
-     37.102.4.1. Modeling
+     40.102.4.1. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training">
-     37.102.4.2. Training
+     40.102.4.2. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-the-latent-space-after-dimension-reduction">
-     37.102.4.3. Plotting the latent space after Dimension Reduction
+     40.102.4.3. Plotting the latent space after Dimension Reduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-results">
-     37.102.4.4. Checking results
+     40.102.4.4. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#denoising-autoencoder">
-   37.102.5. Denoising Autoencoder
+   40.102.5. Denoising Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-random-noise-to-the-image">
-     37.102.5.1. Adding random noise to the image.
+     40.102.5.1. Adding random noise to the image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-a-noisy-image">
-     37.102.5.2. Plotting a noisy image.
+     40.102.5.2. Plotting a noisy image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-noisy-dataset-using-demension-reduction">
-     37.102.5.3. Checking Noisy Dataset using Demension Reduction
+     40.102.5.3. Checking Noisy Dataset using Demension Reduction
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#noisy-dataset">
-       37.102.5.3.1. 1) Noisy Dataset
+       40.102.5.3.1. 1) Noisy Dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#orignal-dataset">
-       37.102.5.3.2. 2) Orignal Dataset
+       40.102.5.3.2. 2) Orignal Dataset
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.102.5.4. Modeling
+     40.102.5.4. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.102.5.5. Training
+     40.102.5.5. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.102.5.6. Checking results
+     40.102.5.6. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.102.6. Acknowledgments
+   40.102.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1772,9 +1804,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="base-denoising-autoencoder-dimension-reduction">
-<h1><span class="section-number">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction<a class="headerlink" href="#base-denoising-autoencoder-dimension-reduction" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction<a class="headerlink" href="#base-denoising-autoencoder-dimension-reduction" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.102.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction/base_noise.gif" /></p>
 <p>Autoencoder is a neural network that simply copies input to output. In some ways, it looks like a simple neural network, but it makes a difficult neural network by constraining the network in various ways. For example, the number of neurons in the hidden layer is smaller than that of the input layer to compress the data (reduce the dimension), or add noise to the input data and then restore the original input. There are various autoencoders, such as learning These constraints prevent the autoencoder from simply copying the input directly to the output, and control it to learn how to represent the data efficiently.</p>
 <p>In this notebook, we will cover two autoencoders:</p>
@@ -1801,7 +1833,7 @@ <h2><span class="section-number">37.102.1. </span>Introduction<a class="headerli
 </div>
 </section>
 <section id="loading-and-scaling-datasets">
-<h2><span class="section-number">37.102.2. </span>Loading and Scaling Datasets<a class="headerlink" href="#loading-and-scaling-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.2. </span>Loading and Scaling Datasets<a class="headerlink" href="#loading-and-scaling-datasets" title="Permalink to this headline">#</a></h2>
 <p>Train a basic autoencoder using the Fashon MNIST dataset. Each image in this dataset is 28x28 pixels.
 Inputs are scaled for training.</p>
 <div class="cell docutils container">
@@ -1819,7 +1851,7 @@ <h2><span class="section-number">37.102.2. </span>Loading and Scaling Datasets<a
 </div>
 </section>
 <section id="load-model">
-<h2><span class="section-number">37.102.3. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.3. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -1875,7 +1907,7 @@ <h2><span class="section-number">37.102.3. </span>Load model<a class="headerlink
 </div>
 </div>
 <section id="checking-dataset-by-2d-plot">
-<h3><span class="section-number">37.102.3.1. </span>Checking dataset by 2D plot<a class="headerlink" href="#checking-dataset-by-2d-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.3.1. </span>Checking dataset by 2D plot<a class="headerlink" href="#checking-dataset-by-2d-plot" title="Permalink to this headline">#</a></h3>
 <p>Autoencoding can be thought of as a kind of dimensionality reduction process. Therefore, after compressing the fashion MNIST dataset through UMAP in two dimensions, let’s check how it is mapped for each label.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1923,7 +1955,7 @@ <h3><span class="section-number">37.102.3.1. </span>Checking dataset by 2D plot<
 <p>Ref: <a class="reference external" href="https://umap-learn.readthedocs.io/en">https://umap-learn.readthedocs.io/en</a></p>
 </section>
 <section id="checking-dataset-by-3d-plot">
-<h3><span class="section-number">37.102.3.2. </span>Checking dataset by 3D plot<a class="headerlink" href="#checking-dataset-by-3d-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.3.2. </span>Checking dataset by 3D plot<a class="headerlink" href="#checking-dataset-by-3d-plot" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">plotly</span>
@@ -1949,9 +1981,9 @@ <h3><span class="section-number">37.102.3.2. </span>Checking dataset by 3D plot<
 </section>
 </section>
 <section id="base-autoencoder">
-<h2><span class="section-number">37.102.4. </span>Base Autoencoder<a class="headerlink" href="#base-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.4. </span>Base Autoencoder<a class="headerlink" href="#base-autoencoder" title="Permalink to this headline">#</a></h2>
 <section id="modeling">
-<h3><span class="section-number">37.102.4.1. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.1. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
 <p>An autoencoder always consists of two parts: an encoder and a decoder.</p>
 <ul class="simple">
 <li><p>Encoder (Recognition network): it transforms an input into an internal representation.</p></li>
@@ -1995,7 +2027,7 @@ <h3><span class="section-number">37.102.4.1. </span>Modeling<a class="headerlink
 </div>
 </section>
 <section id="training">
-<h3><span class="section-number">37.102.4.2. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.2. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h3>
 <p>Train the model using x_train as input and target. The encoder learns to compress the dataset into a latent space in 784 dimensions, and the decoder learns to reconstruct the original image.</p>
 <div class="cell docutils container">
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@@ -2026,7 +2058,7 @@ <h3><span class="section-number">37.102.4.2. </span>Training<a class="headerlink
 </div>
 </section>
 <section id="plotting-the-latent-space-after-dimension-reduction">
-<h3><span class="section-number">37.102.4.3. </span>Plotting the latent space after Dimension Reduction<a class="headerlink" href="#plotting-the-latent-space-after-dimension-reduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.3. </span>Plotting the latent space after Dimension Reduction<a class="headerlink" href="#plotting-the-latent-space-after-dimension-reduction" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;class&#39;</span><span class="p">])</span>
@@ -2046,7 +2078,7 @@ <h3><span class="section-number">37.102.4.3. </span>Plotting the latent space af
 <p>The <span class="math notranslate nohighlight">\(28*28\)</span> dimension input is compressed into the <span class="math notranslate nohighlight">\(7*7\)</span> latent space by the encoder. The latent space is compressed into 2D using Dimension Reduction. Although it is an approximate expression, it can be seen that each class is well clustered in the compressed latent space.</p>
 </section>
 <section id="checking-results">
-<h3><span class="section-number">37.102.4.4. </span>Checking results<a class="headerlink" href="#checking-results" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.4. </span>Checking results<a class="headerlink" href="#checking-results" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">4</span>
@@ -2076,7 +2108,7 @@ <h3><span class="section-number">37.102.4.4. </span>Checking results<a class="he
 </section>
 </section>
 <section id="denoising-autoencoder">
-<h2><span class="section-number">37.102.5. </span>Denoising Autoencoder<a class="headerlink" href="#denoising-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.5. </span>Denoising Autoencoder<a class="headerlink" href="#denoising-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>Another way to constrain the autoencoder to learn meaningful features is to add noise to the input and train it to reconstruct the original noise-free input. Noise can be generated by adding Gaussian noise to the input as shown in the figure below, or by randomly turning off the input unit (node) like a dropout.</p>
 <div class="cell docutils container">
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@@ -2099,7 +2131,7 @@ <h2><span class="section-number">37.102.5. </span>Denoising Autoencoder<a class=
 </div>
 </div>
 <section id="adding-random-noise-to-the-image">
-<h3><span class="section-number">37.102.5.1. </span>Adding random noise to the image.<a class="headerlink" href="#adding-random-noise-to-the-image" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.1. </span>Adding random noise to the image.<a class="headerlink" href="#adding-random-noise-to-the-image" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">noise_factor</span> <span class="o">=</span> <span class="mf">0.2</span>
@@ -2114,7 +2146,7 @@ <h3><span class="section-number">37.102.5.1. </span>Adding random noise to the i
 </div>
 </section>
 <section id="plotting-a-noisy-image">
-<h3><span class="section-number">37.102.5.2. </span>Plotting a noisy image.<a class="headerlink" href="#plotting-a-noisy-image" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.2. </span>Plotting a noisy image.<a class="headerlink" href="#plotting-a-noisy-image" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">4</span>
@@ -2131,9 +2163,9 @@ <h3><span class="section-number">37.102.5.2. </span>Plotting a noisy image.<a cl
 </div>
 </section>
 <section id="checking-noisy-dataset-using-demension-reduction">
-<h3><span class="section-number">37.102.5.3. </span>Checking Noisy Dataset using Demension Reduction<a class="headerlink" href="#checking-noisy-dataset-using-demension-reduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.3. </span>Checking Noisy Dataset using Demension Reduction<a class="headerlink" href="#checking-noisy-dataset-using-demension-reduction" title="Permalink to this headline">#</a></h3>
 <section id="noisy-dataset">
-<h4><span class="section-number">37.102.5.3.1. </span>1) Noisy Dataset<a class="headerlink" href="#noisy-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.102.5.3.1. </span>1) Noisy Dataset<a class="headerlink" href="#noisy-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train_noisy_flat</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_train_noisy</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
@@ -2159,7 +2191,7 @@ <h4><span class="section-number">37.102.5.3.1. </span>1) Noisy Dataset<a class="
 </div>
 </section>
 <section id="orignal-dataset">
-<h4><span class="section-number">37.102.5.3.2. </span>2) Orignal Dataset<a class="headerlink" href="#orignal-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.102.5.3.2. </span>2) Orignal Dataset<a class="headerlink" href="#orignal-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train_flat</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
@@ -2182,7 +2214,7 @@ <h4><span class="section-number">37.102.5.3.2. </span>2) Orignal Dataset<a class
 </section>
 </section>
 <section id="id1">
-<h3><span class="section-number">37.102.5.4. </span>Modeling<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.4. </span>Modeling<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Denoise</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
@@ -2217,7 +2249,7 @@ <h3><span class="section-number">37.102.5.4. </span>Modeling<a class="headerlink
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.102.5.5. </span>Training<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.5. </span>Training<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">autoencoder</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train_noisy</span><span class="p">,</span> <span class="n">x_train</span><span class="p">,</span>
@@ -2246,7 +2278,7 @@ <h3><span class="section-number">37.102.5.5. </span>Training<a class="headerlink
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.102.5.6. </span>Checking results<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.6. </span>Checking results<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Plots both the noisy and denoised images generated by the autoencoder.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2285,7 +2317,7 @@ <h3><span class="section-number">37.102.5.6. </span>Checking results<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.102.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TOH SEOK KIM for creating the Kaggle open-source project <a class="reference external" href="https://www.kaggle.com/code/ohseokkim/base-denoising-autoencoder-dimension-reduction">Base/Denoising Autoencoder + Dimension Reduction</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2327,13 +2359,13 @@ <h2><span class="section-number">37.102.6. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.101. </span>Intro to Autoencoders</p>
+            <p class="prev-next-title"><span class="section-number">40.101. </span>Intro to Autoencoders</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="variational-autoencoder-and-faces-generation.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.103. </span>Fun with Variational Autoencoders</p>
+        <p class="prev-next-title"><span class="section-number">40.103. </span>Fun with Variational Autoencoders</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html b/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
index f11cf8fd8e..dd01b49d85 100644
--- a/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
+++ b/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.103. Fun with Variational Autoencoders &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.103. Fun with Variational Autoencoders &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.104. Time Series Forecasting Assignment" href="../time-series-forecasting-assignment.html" />
-    <link rel="prev" title="37.102. Base/Denoising Autoencoder &amp; Dimension Reduction" href="base-denoising-autoencoder-dimension-reduction.html" />
+    <link rel="next" title="40.104. Time Series Forecasting Assignment" href="../time-series-forecasting-assignment.html" />
+    <link rel="prev" title="40.102. Base/Denoising Autoencoder &amp; Dimension Reduction" href="base-denoising-autoencoder-dimension-reduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  </li>
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   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   25. Long-short term memory
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     <a class="reference internal" href="../../project-plan-template.html">
-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
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-     37.8. Python programming advanced
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     <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
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     <a class="reference internal" href="../../data-science/data-science-scenarios.html">
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     <a class="reference internal" href="../../data-science/write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="../cnn/object-recognition-in-images-using-cnn.html">
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.16. Build an machine learning web application
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-     38.18. Kernel method
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-     38.20. Convolutional Neural Network
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         <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#fork-it-and-give-it-an-upvote">
-   37.103.1. Fork it and give it an upvote.
+   40.103.1. Fork it and give it an upvote.
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#a-bit-of-theory">
-   37.103.2. A bit of theory
+   40.103.2. A bit of theory
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-datasets">
-   37.103.3. Load datasets
+   40.103.3. Load datasets
   </a>
  </li>
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   <a class="reference internal nav-link" href="#explore-the-data">
-   37.103.4. Explore the data
+   40.103.4. Explore the data
   </a>
  </li>
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   <a class="reference internal nav-link" href="#prepare-the-dataset">
-   37.103.5. Prepare the dataset
+   40.103.5. Prepare the dataset
   </a>
  </li>
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   <a class="reference internal nav-link" href="#building-simple-autoencoder">
-   37.103.6. Building simple autoencoder
+   40.103.6. Building simple autoencoder
   </a>
  </li>
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   <a class="reference internal nav-link" href="#train-autoencoder">
-   37.103.7. Train autoencoder
+   40.103.7. Train autoencoder
   </a>
  </li>
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-   37.103.8. Sampling
+   40.103.8. Sampling
   </a>
  </li>
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   <a class="reference internal nav-link" href="#adding-smile-and-glasses">
-   37.103.9. Adding smile and glasses
+   40.103.9. Adding smile and glasses
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variational-autoencoder">
-   37.103.10. Variational autoencoder
+   40.103.10. Variational autoencoder
   </a>
  </li>
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+   40.103.11. Conclusion
   </a>
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-   37.103.12. Acknowledgments
+   40.103.12. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1605,62 +1637,62 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#fork-it-and-give-it-an-upvote">
-   37.103.1. Fork it and give it an upvote.
+   40.103.1. Fork it and give it an upvote.
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   37.103.2. A bit of theory
+   40.103.2. A bit of theory
   </a>
  </li>
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   <a class="reference internal nav-link" href="#load-datasets">
-   37.103.3. Load datasets
+   40.103.3. Load datasets
   </a>
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   <a class="reference internal nav-link" href="#explore-the-data">
-   37.103.4. Explore the data
+   40.103.4. Explore the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prepare-the-dataset">
-   37.103.5. Prepare the dataset
+   40.103.5. Prepare the dataset
   </a>
  </li>
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   <a class="reference internal nav-link" href="#building-simple-autoencoder">
-   37.103.6. Building simple autoencoder
+   40.103.6. Building simple autoencoder
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-autoencoder">
-   37.103.7. Train autoencoder
+   40.103.7. Train autoencoder
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sampling">
-   37.103.8. Sampling
+   40.103.8. Sampling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-smile-and-glasses">
-   37.103.9. Adding smile and glasses
+   40.103.9. Adding smile and glasses
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variational-autoencoder">
-   37.103.10. Variational autoencoder
+   40.103.10. Variational autoencoder
   </a>
  </li>
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   <a class="reference internal nav-link" href="#conclusion">
-   37.103.11. Conclusion
+   40.103.11. Conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.103.12. Acknowledgments
+   40.103.12. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1674,10 +1706,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="fun-with-variational-autoencoders">
-<h1><span class="section-number">37.103. </span>Fun with Variational Autoencoders<a class="headerlink" href="#fun-with-variational-autoencoders" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.103. </span>Fun with Variational Autoencoders<a class="headerlink" href="#fun-with-variational-autoencoders" title="Permalink to this headline">#</a></h1>
 <p>This is a starter kernel to use <strong>Labelled Faces in the Wild (LFW) Dataset</strong> in order to maintain knowledge about main Autoencoder principles. PyTorch will be used for modelling.</p>
 <section id="fork-it-and-give-it-an-upvote">
-<h2><span class="section-number">37.103.1. </span>Fork it and give it an upvote.<a class="headerlink" href="#fork-it-and-give-it-an-upvote" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.1. </span>Fork it and give it an upvote.<a class="headerlink" href="#fork-it-and-give-it-an-upvote" title="Permalink to this headline">#</a></h2>
 <p><img alt="architecture" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/variational-autoencoder-and-faces-generation/architecture.png" /></p>
 <p>Useful links:</p>
 <ul class="simple">
@@ -1690,7 +1722,7 @@ <h2><span class="section-number">37.103.1. </span>Fork it and give it an upvote.
 </ul>
 </section>
 <section id="a-bit-of-theory">
-<h2><span class="section-number">37.103.2. </span>A bit of theory<a class="headerlink" href="#a-bit-of-theory" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.2. </span>A bit of theory<a class="headerlink" href="#a-bit-of-theory" title="Permalink to this headline">#</a></h2>
 <p>“Autoencoding” is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) learned automatically from examples rather than engineered by a human. Additionally, in almost all contexts where the term “autoencoder” is used, the compression and decompression functions are implemented with neural networks.</p>
 <ol class="simple">
 <li><p>Autoencoders are data-specific, which means that they will only be able to compress data similar to what they have been trained on. This is different from, say, the MPEG-2 Audio Layer III (MP3) compression algorithm, which only holds assumptions about “sound” in general, but not about specific types of sounds. An autoencoder trained on pictures of faces would do a rather poor job of compressing pictures of trees, because the features it would learn would be face-specific.</p></li>
@@ -1732,7 +1764,7 @@ <h2><span class="section-number">37.103.2. </span>A bit of theory<a class="heade
 </div>
 </section>
 <section id="load-datasets">
-<h2><span class="section-number">37.103.3. </span>Load datasets<a class="headerlink" href="#load-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.3. </span>Load datasets<a class="headerlink" href="#load-datasets" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datasets_url</span> <span class="o">=</span> <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/variational-autoencoder-and-faces-generation/lfw-deepfunneled.zip&quot;</span>
@@ -1808,7 +1840,7 @@ <h2><span class="section-number">37.103.3. </span>Load datasets<a class="headerl
 </div>
 </section>
 <section id="explore-the-data">
-<h2><span class="section-number">37.103.4. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.4. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
 <p>Image data is collected from DATASET_PATH and a dataset is created in which the person information for each image is extracted and used for subsequent data analysis or processing. Finally, the filter() function is used to limit the size of the dataset by retaining only information about people who appear less than 25 times.</p>
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@@ -1853,7 +1885,7 @@ <h2><span class="section-number">37.103.4. </span>Explore the data<a class="head
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 </section>
 <section id="prepare-the-dataset">
-<h2><span class="section-number">37.103.5. </span>Prepare the dataset<a class="headerlink" href="#prepare-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.5. </span>Prepare the dataset<a class="headerlink" href="#prepare-the-dataset" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><ul class="simple">
 <li><p>Reads the attribute data from the txt file at the specified path and stores it in a DataFrame object called df_attrs. The txt file is tab delimited, skipping the first line.</p></li>
@@ -1936,7 +1968,7 @@ <h2><span class="section-number">37.103.5. </span>Prepare the dataset<a class="h
 </div>
 </section>
 <section id="building-simple-autoencoder">
-<h2><span class="section-number">37.103.6. </span>Building simple autoencoder<a class="headerlink" href="#building-simple-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.6. </span>Building simple autoencoder<a class="headerlink" href="#building-simple-autoencoder" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dim_z</span><span class="o">=</span><span class="mi">100</span>
@@ -2042,7 +2074,7 @@ <h2><span class="section-number">37.103.6. </span>Building simple autoencoder<a
 </div>
 </section>
 <section id="train-autoencoder">
-<h2><span class="section-number">37.103.7. </span>Train autoencoder<a class="headerlink" href="#train-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.7. </span>Train autoencoder<a class="headerlink" href="#train-autoencoder" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><ul class="simple">
 <li><p>get_batch: It uses the Generator method to generate batches of a specified size by iterating over them. The amount of data generated is batch_size each time, until the entire data set is traversed.</p></li>
@@ -2208,7 +2240,7 @@ <h2><span class="section-number">37.103.7. </span>Train autoencoder<a class="hea
 </div>
 </section>
 <section id="sampling">
-<h2><span class="section-number">37.103.8. </span>Sampling<a class="headerlink" href="#sampling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.8. </span>Sampling<a class="headerlink" href="#sampling" title="Permalink to this headline">#</a></h2>
 <p>Let’s generate some samples from random vectors</p>
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@@ -2227,7 +2259,7 @@ <h2><span class="section-number">37.103.8. </span>Sampling<a class="headerlink"
 </div>
 </section>
 <section id="adding-smile-and-glasses">
-<h2><span class="section-number">37.103.9. </span>Adding smile and glasses<a class="headerlink" href="#adding-smile-and-glasses" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.9. </span>Adding smile and glasses<a class="headerlink" href="#adding-smile-and-glasses" title="Permalink to this headline">#</a></h2>
 <p>Let’s find some attributes like smiles or glasses on the photo and try to add it to the photos which don’t have it. We will use the second dataset for it. It contains a bunch of such attributes.</p>
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@@ -2357,7 +2389,7 @@ <h2><span class="section-number">37.103.9. </span>Adding smile and glasses<a cla
 <p>While the concept is pretty straightforward the simple autoencoder have some disadvantages. Let’s explore them and try to do better.</p>
 </section>
 <section id="variational-autoencoder">
-<h2><span class="section-number">37.103.10. </span>Variational autoencoder<a class="headerlink" href="#variational-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.10. </span>Variational autoencoder<a class="headerlink" href="#variational-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>So far we have trained our encoder to reconstruct the very same image that we’ve transfered to latent space. That means that when we’re trying to <strong>generate</strong> new image from the point decoder never met we’re getting <em>the best image it can produce</em>, but the quelity is not good enough.</p>
 <blockquote>
 <div><p><strong>In other words the encoded vectors may not be continuous in the latent space.</strong></p>
@@ -2533,12 +2565,12 @@ <h2><span class="section-number">37.103.10. </span>Variational autoencoder<a cla
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.103.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Variational autoencoders are cool. Although models in this particular notebook are simple they let us design complex generative models of data, and fit them to large datasets. They can generate images of fictional celebrity faces and high-resolution digital artwork.
 These models also yield state-of-the-art machine learning results in image generation and reinforcement learning. Variational autoencoders (VAEs) were defined in 2013 by Kingma et al. and Rezende et al.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.103.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to SERGEI AVERKIEV for creating the Kaggle open-source project <a class="reference external" href="https://www.kaggle.com/code/averkij/variational-autoencoder-and-faces-generation">Variational Autoencoder and Faces Generation</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2580,13 +2612,13 @@ <h2><span class="section-number">37.103.12. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction</p>
+            <p class="prev-next-title"><span class="section-number">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction</p>
         </div>
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     <div class="prev-next-info">
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-        <p class="prev-next-title"><span class="section-number">37.104. </span>Time Series Forecasting Assignment</p>
+        <p class="prev-next-title"><span class="section-number">40.104. </span>Time Series Forecasting Assignment</p>
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     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html b/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html
index 19f788ebad..a0703015e4 100644
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+++ b/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html
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-    <title>37.90. How to choose cnn architecture mnist &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.90. How to choose cnn architecture mnist &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="prev" title="37.89. Parameter play" href="../../ml-fundamentals/parameter-play.html" />
+    <link rel="next" title="40.92. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
+    <link rel="prev" title="40.89. Parameter play" href="../../ml-fundamentals/parameter-play.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
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   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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-     13.2. More classifiers
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-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
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+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.1. Introduction to clustering
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-     14.2. K-Means clustering
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-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
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   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
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-   22. Generative adversarial networks
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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+  <label for="toctree-checkbox-13">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../set-up-env/first-assignment.html">
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+     40.3. First assignment
     </a>
    </li>
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     <a class="reference internal" href="../../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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     <a class="reference internal" href="../../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
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     <a class="reference internal" href="../../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
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     <a class="reference internal" href="../../data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
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-     37.27. Analyzing data
+     40.27. Analyzing data
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     <a class="reference internal nav-link" href="#basic-cnn-structure">
-     37.90.2. Basic CNN structure
+     40.90.2. Basic CNN structure
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     37.90.3. Load libraries
+     40.90.3. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-the-data">
-     37.90.4. Load the data
+     40.90.4. Load the data
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prepare-data-for-neural-network">
-     37.90.5. Prepare data for neural network
+     40.90.5. Prepare data for neural network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#global-variables">
-     37.90.6. Global variables
+     40.90.6. Global variables
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-convolution-subsambling-pairs">
-     37.90.7. 1. How many convolution-subsambling pairs?
+     40.90.7. 1. How many convolution-subsambling pairs?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-1">
-     37.90.8. Experiment 1
+     40.90.8. Experiment 1
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#build-convolutional-neural-networks">
-       37.90.8.1. Build convolutional neural networks
+       40.90.8.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-validation-set-and-train-networks">
-       37.90.8.2. Create validation set and train networks
+       40.90.8.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plot-accuracies">
-       37.90.8.3. Plot accuracies
+       40.90.8.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary">
-       37.90.8.4. Summary
+       40.90.8.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-feature-maps">
-     37.90.9. 2. How many feature maps?
+     40.90.9. 2. How many feature maps?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-2">
-     37.90.10. Experiment 2
+     40.90.10. Experiment 2
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.90.10.1. Build convolutional neural networks
+       40.90.10.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       37.90.10.2. Create validation set and train networks
+       40.90.10.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.90.10.3. Plot accuracies
+       40.90.10.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       37.90.10.4. Summary
+       40.90.10.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-large-a-dense-layer">
-     37.90.11. 3. How large a dense layer?
+     40.90.11. 3. How large a dense layer?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-3">
-     37.90.12. Experiment 3
+     40.90.12. Experiment 3
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.90.13. Build convolutional neural networks
+     40.90.13. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.90.14. Create validation set and train networks
+     40.90.14. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.90.15. Plot accuracies
+     40.90.15. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id8">
-       37.90.15.1. Summary
+       40.90.15.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-much-dropout">
-     37.90.16. 4. How much dropout?
+     40.90.16. 4. How much dropout?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-4">
-     37.90.17. Experiment 4
+     40.90.17. Experiment 4
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     37.90.18. Build convolutional neural networks
+     40.90.18. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     37.90.19. Create validation set and train networks
+     40.90.19. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     37.90.20. Plot accuracies
+     40.90.20. Plot accuracies
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id12">
-     37.90.21. Summary
+     40.90.21. Summary
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#advanced-features">
-     37.90.22. 5. Advanced features
+     40.90.22. 5. Advanced features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id13">
-     37.90.23. Build convolutional neural networks
+     40.90.23. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id14">
-     37.90.24. Create validation set and train networks
+     40.90.24. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-more-training-images-via-data-augmentation-and-train-network">
-     37.90.25. Create more training images via data augmentation and train network
+     40.90.25. Create more training images via data augmentation and train network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id15">
-     37.90.26. Plot accuracies
+     40.90.26. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id16">
-       37.90.26.1. Summary
+       40.90.26.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#conclusion">
-     37.90.27. Conclusion
+     40.90.27. Conclusion
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.91. Acknowledgments
+   40.91. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1750,207 +1782,207 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.90. How to choose cnn architecture mnist
+   40.90. How to choose cnn architecture mnist
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-the-best-cnn-architecture-for-mnist">
-     37.90.1. What is the best CNN architecture for MNIST?
+     40.90.1. What is the best CNN architecture for MNIST?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basic-cnn-structure">
-     37.90.2. Basic CNN structure
+     40.90.2. Basic CNN structure
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     37.90.3. Load libraries
+     40.90.3. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-the-data">
-     37.90.4. Load the data
+     40.90.4. Load the data
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prepare-data-for-neural-network">
-     37.90.5. Prepare data for neural network
+     40.90.5. Prepare data for neural network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#global-variables">
-     37.90.6. Global variables
+     40.90.6. Global variables
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-convolution-subsambling-pairs">
-     37.90.7. 1. How many convolution-subsambling pairs?
+     40.90.7. 1. How many convolution-subsambling pairs?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-1">
-     37.90.8. Experiment 1
+     40.90.8. Experiment 1
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#build-convolutional-neural-networks">
-       37.90.8.1. Build convolutional neural networks
+       40.90.8.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-validation-set-and-train-networks">
-       37.90.8.2. Create validation set and train networks
+       40.90.8.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plot-accuracies">
-       37.90.8.3. Plot accuracies
+       40.90.8.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary">
-       37.90.8.4. Summary
+       40.90.8.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-feature-maps">
-     37.90.9. 2. How many feature maps?
+     40.90.9. 2. How many feature maps?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-2">
-     37.90.10. Experiment 2
+     40.90.10. Experiment 2
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.90.10.1. Build convolutional neural networks
+       40.90.10.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       37.90.10.2. Create validation set and train networks
+       40.90.10.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.90.10.3. Plot accuracies
+       40.90.10.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       37.90.10.4. Summary
+       40.90.10.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-large-a-dense-layer">
-     37.90.11. 3. How large a dense layer?
+     40.90.11. 3. How large a dense layer?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-3">
-     37.90.12. Experiment 3
+     40.90.12. Experiment 3
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.90.13. Build convolutional neural networks
+     40.90.13. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.90.14. Create validation set and train networks
+     40.90.14. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.90.15. Plot accuracies
+     40.90.15. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id8">
-       37.90.15.1. Summary
+       40.90.15.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-much-dropout">
-     37.90.16. 4. How much dropout?
+     40.90.16. 4. How much dropout?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-4">
-     37.90.17. Experiment 4
+     40.90.17. Experiment 4
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     37.90.18. Build convolutional neural networks
+     40.90.18. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     37.90.19. Create validation set and train networks
+     40.90.19. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     37.90.20. Plot accuracies
+     40.90.20. Plot accuracies
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id12">
-     37.90.21. Summary
+     40.90.21. Summary
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#advanced-features">
-     37.90.22. 5. Advanced features
+     40.90.22. 5. Advanced features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id13">
-     37.90.23. Build convolutional neural networks
+     40.90.23. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id14">
-     37.90.24. Create validation set and train networks
+     40.90.24. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-more-training-images-via-data-augmentation-and-train-network">
-     37.90.25. Create more training images via data augmentation and train network
+     40.90.25. Create more training images via data augmentation and train network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id15">
-     37.90.26. Plot accuracies
+     40.90.26. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id16">
-       37.90.26.1. Summary
+       40.90.26.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#conclusion">
-     37.90.27. Conclusion
+     40.90.27. Conclusion
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.91. Acknowledgments
+   40.91. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1964,19 +1996,19 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="how-to-choose-cnn-architecture-mnist">
-<h1><span class="section-number">37.90. </span>How to choose cnn architecture mnist<a class="headerlink" href="#how-to-choose-cnn-architecture-mnist" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.90. </span>How to choose cnn architecture mnist<a class="headerlink" href="#how-to-choose-cnn-architecture-mnist" title="Permalink to this headline">#</a></h1>
 <section id="what-is-the-best-cnn-architecture-for-mnist">
-<h2><span class="section-number">37.90.1. </span>What is the best CNN architecture for MNIST?<a class="headerlink" href="#what-is-the-best-cnn-architecture-for-mnist" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.1. </span>What is the best CNN architecture for MNIST?<a class="headerlink" href="#what-is-the-best-cnn-architecture-for-mnist" title="Permalink to this headline">#</a></h2>
 <p>There are so many choices for CNN architecture. How do we choose the best one? First we must define what <strong>best</strong> means. The best may be the simplest, or it may be the most efficient at producing accuracy while minimizing computational complexity. In this kernel, we will run experiments to find the most accurate and efficient CNN architecture for classifying MNIST handwritten digits.</p>
 <p>The best known MNIST classifier found on the internet achieves 99.8% accuracy!! That’s amazing. The best Kaggle kernel MNIST classifier achieves 99.75% [posted here][<a class="reference external" href="https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist">https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist</a>]. This kernel demostrates the experiments used to determine that kernel’s CNN architecture.</p>
 </section>
 <section id="basic-cnn-structure">
-<h2><span class="section-number">37.90.2. </span>Basic CNN structure<a class="headerlink" href="#basic-cnn-structure" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.2. </span>Basic CNN structure<a class="headerlink" href="#basic-cnn-structure" title="Permalink to this headline">#</a></h2>
 <p>A typical CNN design begins with feature extraction and finishes with classification. Feature extraction is performed by alternating convolution layers with subsambling layers. Classification is performed with dense layers followed by a final softmax layer. For image classification, this architecture performs better than an entirely fully connected feed forward neural network.
 <img alt="extract" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/how-to-choose-cnn-architecture-mnist/extract.png" /></p>
 </section>
 <section id="load-libraries">
-<h2><span class="section-number">37.90.3. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.3. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2003,7 +2035,7 @@ <h2><span class="section-number">37.90.3. </span>Load libraries<a class="headerl
 </div>
 </section>
 <section id="load-the-data">
-<h2><span class="section-number">37.90.4. </span>Load the data<a class="headerlink" href="#load-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.4. </span>Load the data<a class="headerlink" href="#load-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/mnist_train.csv&quot;</span><span class="p">)</span>
@@ -2014,7 +2046,7 @@ <h2><span class="section-number">37.90.4. </span>Load the data<a class="headerli
 </div>
 </section>
 <section id="prepare-data-for-neural-network">
-<h2><span class="section-number">37.90.5. </span>Prepare data for neural network<a class="headerlink" href="#prepare-data-for-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.5. </span>Prepare data for neural network<a class="headerlink" href="#prepare-data-for-neural-network" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span>
@@ -2035,7 +2067,7 @@ <h2><span class="section-number">37.90.5. </span>Prepare data for neural network
 </div>
 </section>
 <section id="global-variables">
-<h2><span class="section-number">37.90.6. </span>Global variables<a class="headerlink" href="#global-variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.6. </span>Global variables<a class="headerlink" href="#global-variables" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">annealer</span> <span class="o">=</span> <span class="n">LearningRateScheduler</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mf">1e-3</span> <span class="o">*</span> <span class="mf">0.95</span><span class="o">**</span><span class="n">x</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -2046,7 +2078,7 @@ <h2><span class="section-number">37.90.6. </span>Global variables<a class="heade
 </div>
 </section>
 <section id="how-many-convolution-subsambling-pairs">
-<h2><span class="section-number">37.90.7. </span>1. How many convolution-subsambling pairs?<a class="headerlink" href="#how-many-convolution-subsambling-pairs" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.7. </span>1. How many convolution-subsambling pairs?<a class="headerlink" href="#how-many-convolution-subsambling-pairs" title="Permalink to this headline">#</a></h2>
 <p>First question, how many pairs of convolution-subsampling should we use? For example, our network could have 1, 2, or 3:</p>
 <ul class="simple">
 <li><p>784 - <strong>[24C5-P2]</strong> - 256 - 10</p></li>
@@ -2056,10 +2088,10 @@ <h2><span class="section-number">37.90.7. </span>1. How many convolution-subsamb
 <p>It’s typical to increase the number of feature maps for each subsequent pair as shown here.</p>
 </section>
 <section id="experiment-1">
-<h2><span class="section-number">37.90.8. </span>Experiment 1<a class="headerlink" href="#experiment-1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.8. </span>Experiment 1<a class="headerlink" href="#experiment-1" title="Permalink to this headline">#</a></h2>
 <p>Let’s see whether one, two, or three pairs is best. We are not doing four pairs since the image will be reduced too small before then. The input image is 28x28. After one pair, it’s 14x14. After two, it’s 7x7. After three it’s 4x4 (or 3x3 if we don’t use padding=‘same’). It doesn’t make sense to do a fourth convolution.</p>
 <section id="build-convolutional-neural-networks">
-<h3><span class="section-number">37.90.8.1. </span>Build convolutional neural networks<a class="headerlink" href="#build-convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.1. </span>Build convolutional neural networks<a class="headerlink" href="#build-convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">3</span>
@@ -2095,7 +2127,7 @@ <h3><span class="section-number">37.90.8.1. </span>Build convolutional neural ne
 </div>
 </section>
 <section id="create-validation-set-and-train-networks">
-<h3><span class="section-number">37.90.8.2. </span>Create validation set and train networks<a class="headerlink" href="#create-validation-set-and-train-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.2. </span>Create validation set and train networks<a class="headerlink" href="#create-validation-set-and-train-networks" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2126,7 +2158,7 @@ <h3><span class="section-number">37.90.8.2. </span>Create validation set and tra
 </div>
 </section>
 <section id="plot-accuracies">
-<h3><span class="section-number">37.90.8.3. </span>Plot accuracies<a class="headerlink" href="#plot-accuracies" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.3. </span>Plot accuracies<a class="headerlink" href="#plot-accuracies" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2145,12 +2177,12 @@ <h3><span class="section-number">37.90.8.3. </span>Plot accuracies<a class="head
 </div>
 </section>
 <section id="summary">
-<h3><span class="section-number">37.90.8.4. </span>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.4. </span>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">#</a></h3>
 <p>From the above experiment, it seems that 3 pairs of convolution-subsambling is slightly better than 2 pairs. However for efficiency, the improvement doesn’t warrant the additional computional cost, so let’s use 2.</p>
 </section>
 </section>
 <section id="how-many-feature-maps">
-<h2><span class="section-number">37.90.9. </span>2. How many feature maps?<a class="headerlink" href="#how-many-feature-maps" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.9. </span>2. How many feature maps?<a class="headerlink" href="#how-many-feature-maps" title="Permalink to this headline">#</a></h2>
 <p>In the previous experiement, we decided that two pairs is sufficient. How many feature maps should we include? For example, we could do</p>
 <ul class="simple">
 <li><p>784 - [<strong>8</strong>C5-P2] - [<strong>16</strong>C5-P2] - 256 - 10</p></li>
@@ -2162,9 +2194,9 @@ <h2><span class="section-number">37.90.9. </span>2. How many feature maps?<a cla
 </ul>
 </section>
 <section id="experiment-2">
-<h2><span class="section-number">37.90.10. </span>Experiment 2<a class="headerlink" href="#experiment-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.10. </span>Experiment 2<a class="headerlink" href="#experiment-2" title="Permalink to this headline">#</a></h2>
 <section id="id1">
-<h3><span class="section-number">37.90.10.1. </span>Build convolutional neural networks<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.1. </span>Build convolutional neural networks<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">6</span>
@@ -2189,7 +2221,7 @@ <h3><span class="section-number">37.90.10.1. </span>Build convolutional neural n
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.90.10.2. </span>Create validation set and train networks<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.2. </span>Create validation set and train networks<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2220,7 +2252,7 @@ <h3><span class="section-number">37.90.10.2. </span>Create validation set and tr
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.90.10.3. </span>Plot accuracies<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.3. </span>Plot accuracies<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2239,12 +2271,12 @@ <h3><span class="section-number">37.90.10.3. </span>Plot accuracies<a class="hea
 </div>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.90.10.4. </span>Summary<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.4. </span>Summary<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>From the above experiement, it appears that 32 maps in the first convolutional layer and 64 maps in the second convolutional layer is the best. Architectures with more maps only perform slightly better and are not worth the additonal computation cost.</p>
 </section>
 </section>
 <section id="how-large-a-dense-layer">
-<h2><span class="section-number">37.90.11. </span>3. How large a dense layer?<a class="headerlink" href="#how-large-a-dense-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.11. </span>3. How large a dense layer?<a class="headerlink" href="#how-large-a-dense-layer" title="Permalink to this headline">#</a></h2>
 <p>In our previous experiment, we decided on 32 and 64 maps in our convolutional layers. How many dense units should we use? For example we could use</p>
 <ul class="simple">
 <li><p>784 - [32C5-P2] - [64C5-P2] - <strong>0</strong> - 10</p></li>
@@ -2258,10 +2290,10 @@ <h2><span class="section-number">37.90.11. </span>3. How large a dense layer?<a
 </ul>
 </section>
 <section id="experiment-3">
-<h2><span class="section-number">37.90.12. </span>Experiment 3<a class="headerlink" href="#experiment-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.12. </span>Experiment 3<a class="headerlink" href="#experiment-3" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="id5">
-<h2><span class="section-number">37.90.13. </span>Build convolutional neural networks<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.13. </span>Build convolutional neural networks<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">8</span>
@@ -2286,7 +2318,7 @@ <h2><span class="section-number">37.90.13. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id6">
-<h2><span class="section-number">37.90.14. </span>Create validation set and train networks<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.14. </span>Create validation set and train networks<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2318,7 +2350,7 @@ <h2><span class="section-number">37.90.14. </span>Create validation set and trai
 </div>
 </section>
 <section id="id7">
-<h2><span class="section-number">37.90.15. </span>Plot accuracies<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.15. </span>Plot accuracies<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2336,22 +2368,22 @@ <h2><span class="section-number">37.90.15. </span>Plot accuracies<a class="heade
 </div>
 </div>
 <section id="id8">
-<h3><span class="section-number">37.90.15.1. </span>Summary<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.15.1. </span>Summary<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
 <p>From this experiment, it appears that 128 units is the best. Dense layers with more units only perform slightly better and are not worth the additional computational cost. (We also tested using two consecutive dense layers instead of one, but that showed no benefit over a single dense layer.)</p>
 </section>
 </section>
 <section id="how-much-dropout">
-<h2><span class="section-number">37.90.16. </span>4. How much dropout?<a class="headerlink" href="#how-much-dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.16. </span>4. How much dropout?<a class="headerlink" href="#how-much-dropout" title="Permalink to this headline">#</a></h2>
 <p>Dropout will prevent our network from overfitting thus helping our network generalize better. How much dropout should we add after each layer?</p>
 <ul class="simple">
 <li><p>0%, 10%, 20%, 30%, 40%, 50%, 60%, or 70%</p></li>
 </ul>
 </section>
 <section id="experiment-4">
-<h2><span class="section-number">37.90.17. </span>Experiment 4<a class="headerlink" href="#experiment-4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.17. </span>Experiment 4<a class="headerlink" href="#experiment-4" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="id9">
-<h2><span class="section-number">37.90.18. </span>Build convolutional neural networks<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.18. </span>Build convolutional neural networks<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">8</span>
@@ -2378,7 +2410,7 @@ <h2><span class="section-number">37.90.18. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id10">
-<h2><span class="section-number">37.90.19. </span>Create validation set and train networks<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.19. </span>Create validation set and train networks<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2410,7 +2442,7 @@ <h2><span class="section-number">37.90.19. </span>Create validation set and trai
 </div>
 </section>
 <section id="id11">
-<h2><span class="section-number">37.90.20. </span>Plot accuracies<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.20. </span>Plot accuracies<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2429,11 +2461,11 @@ <h2><span class="section-number">37.90.20. </span>Plot accuracies<a class="heade
 </div>
 </section>
 <section id="id12">
-<h2><span class="section-number">37.90.21. </span>Summary<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.21. </span>Summary<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <p>From this experiment, it appears that 40% dropout is the best.</p>
 </section>
 <section id="advanced-features">
-<h2><span class="section-number">37.90.22. </span>5. Advanced features<a class="headerlink" href="#advanced-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.22. </span>5. Advanced features<a class="headerlink" href="#advanced-features" title="Permalink to this headline">#</a></h2>
 <p>Instead of using one convolution layer of size 5x5, you can mimic 5x5 by using two consecutive 3x3 layers and it will be more nonlinear. Instead of using a max pooling layer, you can subsample by using a convolution layer with strides=2 and it will be learnable. Lastly, does batch normalization help? And does data augmentation help? Let’s test all four of these</p>
 <ul class="simple">
 <li><p>replace ‘32C5’ with ‘32C3-32C3’</p></li>
@@ -2443,7 +2475,7 @@ <h2><span class="section-number">37.90.22. </span>5. Advanced features<a class="
 </ul>
 </section>
 <section id="id13">
-<h2><span class="section-number">37.90.23. </span>Build convolutional neural networks<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.23. </span>Build convolutional neural networks<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">5</span>
@@ -2562,7 +2594,7 @@ <h2><span class="section-number">37.90.23. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id14">
-<h2><span class="section-number">37.90.24. </span>Create validation set and train networks<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.24. </span>Create validation set and train networks<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
@@ -2594,7 +2626,7 @@ <h2><span class="section-number">37.90.24. </span>Create validation set and trai
 </div>
 </section>
 <section id="create-more-training-images-via-data-augmentation-and-train-network">
-<h2><span class="section-number">37.90.25. </span>Create more training images via data augmentation and train network<a class="headerlink" href="#create-more-training-images-via-data-augmentation-and-train-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.25. </span>Create more training images via data augmentation and train network<a class="headerlink" href="#create-more-training-images-via-data-augmentation-and-train-network" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span>
@@ -2624,7 +2656,7 @@ <h2><span class="section-number">37.90.25. </span>Create more training images vi
 </div>
 </section>
 <section id="id15">
-<h2><span class="section-number">37.90.26. </span>Plot accuracies<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.26. </span>Plot accuracies<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2642,12 +2674,12 @@ <h2><span class="section-number">37.90.26. </span>Plot accuracies<a class="heade
 </div>
 </div>
 <section id="id16">
-<h3><span class="section-number">37.90.26.1. </span>Summary<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.26.1. </span>Summary<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h3>
 <p>From this experiment, we see that each of the four advanced features improve accuracy. The first model uses no advanced features. The second uses only the double convolution layer trick. The third uses only the learnable subsambling layer trick. The third model uses both of those techniques plus batch normalization. The last model employs all three of those techniques plus data augmentation and achieves the best accuracy of 99.5%! (Or more if we train longer.) (Experiments determing the best data augmentation hyper-parameters are posted at the end of the kernel <a class="reference external" href="https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist">here</a>.)</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.90.27. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.27. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Training convolutional neural networks is a random process. This makes experiments difficult because each time you run the same experiment, you get different results. Therefore, you must run your experiments dozens of times and take an average. This kernel was run dozens of times and it seems that the best CNN architecture for classifying MNIST handwritten digits is 784 - [32C5-P2] - [64C5-P2] - 128 - 10 with 40% dropout. Afterward, more experiments show that replacing ‘32C5’ with ‘32C3-32C3’ improves accuracy. And replacing ‘P2’ with ‘32C5S2’ improves accuracy. And adding batch normalizaiton and data augmentation improve the CNN. The best CNN found from the experiments here becomes</p>
 <ul class="simple">
 <li><p><strong>784 - [32C3-32C3-32C5S2] - [64C3-64C3-64C5S2] - 128 - 10</strong></p></li>
@@ -2656,7 +2688,7 @@ <h2><span class="section-number">37.90.27. </span>Conclusion<a class="headerlink
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.91. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.91. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/cdeotte">Chris Deotte</a> for creating <a class="reference external" href="https://www.kaggle.com/code/cdeotte/how-to-choose-cnn-architecture-mnist">how-to-choose-cnn-architecture-mnist</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2697,13 +2729,13 @@ <h1><span class="section-number">37.91. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.89. </span>Parameter play</p>
+            <p class="prev-next-title"><span class="section-number">40.89. </span>Parameter play</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="sign-language-digits-classification-with-cnn.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.92. </span>Sign Language Digits Classification with CNN</p>
+        <p class="prev-next-title"><span class="section-number">40.92. </span>Sign Language Digits Classification with CNN</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html b/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html
index 14393d8d11..3e3800bedc 100644
--- a/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html
+++ b/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html
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-    <title>37.94. Object Recognition in Images using CNN &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.94. Object Recognition in Images using CNN &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.95. Intro to TensorFlow for Deep Learning" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html" />
-    <link rel="prev" title="37.92. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
+    <link rel="next" title="40.95. Intro to TensorFlow for Deep Learning" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html" />
+    <link rel="prev" title="40.92. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
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     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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-  <label for="toctree-checkbox-10">
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-  <label for="toctree-checkbox-11">
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-   20. Intro to Deep Learning
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-   21. Convolutional Neural Networks
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-   22. Generative adversarial networks
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-   23. Recurrent Neural Networks
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-   24. Autoencoder
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-   31. Object detection
+   34. Object detection
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   <a class="reference internal" href="../../../machine-learning-productionization/overview.html">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.3. First assignment
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     <a class="reference internal" href="../../project-plan-template.html">
-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.14. Try it in Excel
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-     37.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,63 +1558,63 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-dataset">
-   37.94.1. About Dataset
+   40.94.1. About Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#table-of-contents">
-   37.94.2. Table of Contents
+   40.94.2. Table of Contents
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libaries">
-     37.94.2.1. Import Libaries
+     40.94.2.1. Import Libaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-the-data">
-     37.94.2.2. Exploring the Data
+     40.94.2.2. Exploring the Data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-and-validation-datasets">
-   37.94.3. Training and Validation Datasets
+   40.94.3. Training and Validation Datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-data-single-batch-images">
-     37.94.3.1. training data single batch images
+     40.94.3.1. training data single batch images
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#convolutional-neural-network">
-   37.94.4. Convolutional Neural Network
+   40.94.4. Convolutional Neural Network
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-model-convolutional-neural-network">
-     37.94.4.1. Defining the Model (Convolutional Neural Network)
+     40.94.4.1. Defining the Model (Convolutional Neural Network)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-model">
-   37.94.5. Training the Model
+   40.94.5. Training the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-with-individual-images">
-   37.94.6. Testing with individual images
+   40.94.6. Testing with individual images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.94.7. Acknowledgments
+   40.94.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1606,63 +1638,63 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-dataset">
-   37.94.1. About Dataset
+   40.94.1. About Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#table-of-contents">
-   37.94.2. Table of Contents
+   40.94.2. Table of Contents
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libaries">
-     37.94.2.1. Import Libaries
+     40.94.2.1. Import Libaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-the-data">
-     37.94.2.2. Exploring the Data
+     40.94.2.2. Exploring the Data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-and-validation-datasets">
-   37.94.3. Training and Validation Datasets
+   40.94.3. Training and Validation Datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-data-single-batch-images">
-     37.94.3.1. training data single batch images
+     40.94.3.1. training data single batch images
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#convolutional-neural-network">
-   37.94.4. Convolutional Neural Network
+   40.94.4. Convolutional Neural Network
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-model-convolutional-neural-network">
-     37.94.4.1. Defining the Model (Convolutional Neural Network)
+     40.94.4.1. Defining the Model (Convolutional Neural Network)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-model">
-   37.94.5. Training the Model
+   40.94.5. Training the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-with-individual-images">
-   37.94.6. Testing with individual images
+   40.94.6. Testing with individual images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.94.7. Acknowledgments
+   40.94.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1676,18 +1708,18 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="object-recognition-in-images-using-cnn">
-<h1><span class="section-number">37.94. </span>Object Recognition in Images using CNN<a class="headerlink" href="#object-recognition-in-images-using-cnn" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.94. </span>Object Recognition in Images using CNN<a class="headerlink" href="#object-recognition-in-images-using-cnn" title="Permalink to this headline">#</a></h1>
 <section id="about-dataset">
-<h2><span class="section-number">37.94.1. </span>About Dataset<a class="headerlink" href="#about-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.1. </span>About Dataset<a class="headerlink" href="#about-dataset" title="Permalink to this headline">#</a></h2>
 <p>CIFAR-10 is an established computer-vision dataset used for object recognition. It is a subset of the 80
 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes,
 with 6000 images per class. It was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.</p>
 <p><img alt="cifar10" src="https://miro.medium.com/max/709/1*LyV7_xga4jUHdx4_jHk1PQ.png" /></p>
 </section>
 <section id="table-of-contents">
-<h2><span class="section-number">37.94.2. </span>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.2. </span>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline">#</a></h2>
 <section id="import-libaries">
-<h3><span class="section-number">37.94.2.1. </span>Import Libaries<a class="headerlink" href="#import-libaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.2.1. </span>Import Libaries<a class="headerlink" href="#import-libaries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -1710,7 +1742,7 @@ <h3><span class="section-number">37.94.2.1. </span>Import Libaries<a class="head
 </div>
 </section>
 <section id="exploring-the-data">
-<h3><span class="section-number">37.94.2.2. </span>Exploring the Data<a class="headerlink" href="#exploring-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.2.2. </span>Exploring the Data<a class="headerlink" href="#exploring-the-data" title="Permalink to this headline">#</a></h3>
 <p>Extract from tar archive file</p>
 <p>The dataset is extracted to the directory <code class="docutils literal notranslate"><span class="pre">tmp/object-recognition-in-images-using-cnn/cifar10</span></code>. It contains 2 folders <code class="docutils literal notranslate"><span class="pre">train</span></code> and <code class="docutils literal notranslate"><span class="pre">test</span></code>, containing the training set (50000 images) and test set (10000 images) respectively. Each of them contains 10 folders, one for each class of images. Let’s verify this using <code class="docutils literal notranslate"><span class="pre">os.listdir</span></code>.</p>
 <div class="cell docutils container">
@@ -1881,7 +1913,7 @@ <h3><span class="section-number">37.94.2.2. </span>Exploring the Data<a class="h
 </section>
 </section>
 <section id="training-and-validation-datasets">
-<h2><span class="section-number">37.94.3. </span>Training and Validation Datasets<a class="headerlink" href="#training-and-validation-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.3. </span>Training and Validation Datasets<a class="headerlink" href="#training-and-validation-datasets" title="Permalink to this headline">#</a></h2>
 <p>While building real world machine learning models, it is quite common to split the dataset into 3 parts:</p>
 <ol class="simple">
 <li><p><strong>Training set</strong> - used to train the model i.e. compute the loss and adjust the weights of the model using gradient descent.</p></li>
@@ -1941,7 +1973,7 @@ <h2><span class="section-number">37.94.3. </span>Training and Validation Dataset
 </div>
 </div>
 <section id="training-data-single-batch-images">
-<h3><span class="section-number">37.94.3.1. </span>training data single batch images<a class="headerlink" href="#training-data-single-batch-images" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.3.1. </span>training data single batch images<a class="headerlink" href="#training-data-single-batch-images" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">show_images_batch</span><span class="p">(</span><span class="n">train_dl</span><span class="p">)</span>
@@ -1959,9 +1991,9 @@ <h3><span class="section-number">37.94.3.1. </span>training data single batch im
 </section>
 </section>
 <section id="convolutional-neural-network">
-<h2><span class="section-number">37.94.4. </span>Convolutional Neural Network<a class="headerlink" href="#convolutional-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.4. </span>Convolutional Neural Network<a class="headerlink" href="#convolutional-neural-network" title="Permalink to this headline">#</a></h2>
 <section id="defining-the-model-convolutional-neural-network">
-<h3><span class="section-number">37.94.4.1. </span>Defining the Model (Convolutional Neural Network)<a class="headerlink" href="#defining-the-model-convolutional-neural-network" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.4.1. </span>Defining the Model (Convolutional Neural Network)<a class="headerlink" href="#defining-the-model-convolutional-neural-network" title="Permalink to this headline">#</a></h3>
 <p>The 2D convolution is a fairly simple operation at heart: you start with a kernel, which is simply a small matrix of weights. This kernel “slides” over the 2D input data, performing an elementwise multiplication with the part of the input it is currently on, and then summing up the results into a single output pixel.</p>
 <img src="https://miro.medium.com/max/1070/1*Zx-ZMLKab7VOCQTxdZ1OAw.gif" style="max-width:400px;">
 <p>Let us implement a convolution operation on a 1 channel image with a 3x3 kernel.</p>
@@ -2170,7 +2202,7 @@ <h3><span class="section-number">37.94.4.1. </span>Defining the Model (Convoluti
 </section>
 </section>
 <section id="training-the-model">
-<h2><span class="section-number">37.94.5. </span>Training the Model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.5. </span>Training the Model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
 <p>In this mode, the result of every computation will have requires_grad=False, even when the inputs have requires_grad=True.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2286,7 +2318,7 @@ <h2><span class="section-number">37.94.5. </span>Training the Model<a class="hea
 <p>Initialy, both the training and validation losses seem to decrease over time. However, if we train the model for long enough,training loss continues to decrease, while the validation loss stops decreasing, and even starts to increase after a certain point(overfitting).</p>
 </section>
 <section id="testing-with-individual-images">
-<h2><span class="section-number">37.94.6. </span>Testing with individual images<a class="headerlink" href="#testing-with-individual-images" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.6. </span>Testing with individual images<a class="headerlink" href="#testing-with-individual-images" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">test_dataset</span> <span class="o">=</span> <span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span><span class="o">+</span><span class="s1">&#39;/test&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ToTensor</span><span class="p">())</span>
@@ -2391,7 +2423,7 @@ <h2><span class="section-number">37.94.6. </span>Testing with individual images<
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.94.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to datajameson for creating the Kaggle notebook <a class="reference external" href="https://www.kaggle.com/code/datajameson/cifar-10-object-recognition-cnn-explained">Cifar-10 Object Recognition(CNN) Explained</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2433,13 +2465,13 @@ <h2><span class="section-number">37.94.7. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.92. </span>Sign Language Digits Classification with CNN</p>
+            <p class="prev-next-title"><span class="section-number">40.92. </span>Sign Language Digits Classification with CNN</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.95. </span>Intro to TensorFlow for Deep Learning</p>
+        <p class="prev-next-title"><span class="section-number">40.95. </span>Intro to TensorFlow for Deep Learning</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html b/assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html
index c524e1cf76..374b7da912 100644
--- a/assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html
+++ b/assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.92. Sign Language Digits Classification with CNN &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.92. Sign Language Digits Classification with CNN &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.94. Object Recognition in Images using CNN" href="object-recognition-in-images-using-cnn.html" />
-    <link rel="prev" title="37.90. How to choose cnn architecture mnist" href="how-to-choose-cnn-architecture-mnist.html" />
+    <link rel="next" title="40.94. Object Recognition in Images using CNN" href="object-recognition-in-images-using-cnn.html" />
+    <link rel="prev" title="40.90. How to choose cnn architecture mnist" href="how-to-choose-cnn-architecture-mnist.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
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@@ -1526,69 +1558,69 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+     40.92.2. Load data from numpy file
     </a>
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    <li class="toc-h2 nav-item toc-entry">
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+     40.92.3. Preparing Data
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-test-split">
-     37.92.4. Train Test Split
+     40.92.4. Train Test Split
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+     40.92.5. Implementation of CNN
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    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-augmentation-with-keras-api">
-     37.92.6. Data Augmentation With Keras API
+     40.92.6. Data Augmentation With Keras API
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    </li>
    <li class="toc-h2 nav-item toc-entry">
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@@ -1612,69 +1644,69 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+     40.92.1. Load libraries
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+     40.92.2. Load data from numpy file
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+     40.92.3. Preparing Data
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-     37.92.4. Train Test Split
+     40.92.4. Train Test Split
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    </li>
    <li class="toc-h2 nav-item toc-entry">
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-     37.92.5. Implementation of CNN
+     40.92.5. Implementation of CNN
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    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-augmentation-with-keras-api">
-     37.92.6. Data Augmentation With Keras API
+     40.92.6. Data Augmentation With Keras API
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#changin-zoom-level">
-     37.92.7. Changin zoom level
+     40.92.7. Changin zoom level
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+<h1><span class="section-number">40.92. </span>Sign Language Digits Classification with CNN<a class="headerlink" href="#sign-language-digits-classification-with-cnn" title="Permalink to this headline">#</a></h1>
 <section id="load-libraries">
-<h2><span class="section-number">37.92.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">40.92.2. </span>Load data from numpy file<a class="headerlink" href="#load-data-from-numpy-file" title="Permalink to this headline">#</a></h2>
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@@ -1774,7 +1806,7 @@ <h2><span class="section-number">37.92.2. </span>Load data from numpy file<a cla
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+<h2><span class="section-number">40.92.3. </span>Preparing Data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h2>
 <p>We will re-organize data to match labels and images correctly.</p>
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 </ul>
 </section>
 <section id="train-test-split">
-<h2><span class="section-number">37.92.4. </span>Train Test Split<a class="headerlink" href="#train-test-split" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.4. </span>Train Test Split<a class="headerlink" href="#train-test-split" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
@@ -1847,10 +1879,10 @@ <h2><span class="section-number">37.92.4. </span>Train Test Split<a class="heade
 </ul>
 </section>
 <section id="implementation-of-cnn">
-<h2><span class="section-number">37.92.5. </span>Implementation of CNN<a class="headerlink" href="#implementation-of-cnn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.5. </span>Implementation of CNN<a class="headerlink" href="#implementation-of-cnn" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="data-augmentation-with-keras-api">
-<h2><span class="section-number">37.92.6. </span>Data Augmentation With Keras API<a class="headerlink" href="#data-augmentation-with-keras-api" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.6. </span>Data Augmentation With Keras API<a class="headerlink" href="#data-augmentation-with-keras-api" title="Permalink to this headline">#</a></h2>
 <p>Data augmentation is a technique which generates new training samples without changing labels of images. To generate new samples, some features of images are changed like brightness, rotation or zoom level. To apply it, ImageDataGenerator class is used in KERAS API. This class refers parameters and changes images. After complete the changing process, it returns new samples. This is important! ImageDataGenerator returns only new images. It means that out training dataset consists of different from original dataset. It provides more generalizaton for model anf of course it is desirable.</p>
 <p>So, in implementation of CNN part, we will use data augmentation and we will change rotation and zoom level of images. we chose these parameters with a simple logic. Think of test data that we might encounter in real life. we don’t always hold our hand at 90 degrees. So it is quite possible that we have a rotational change when using sign language. Likewise, the zoom level of the photo to be taken may also change. So we thought we could train my model better by creating a more general data set with these two parameters.  Let’s take a closer look at these parameters.</p>
 <ul class="simple">
@@ -1887,7 +1919,7 @@ <h2><span class="section-number">37.92.6. </span>Data Augmentation With Keras AP
 </div>
 </section>
 <section id="changin-zoom-level">
-<h2><span class="section-number">37.92.7. </span>Changin zoom level<a class="headerlink" href="#changin-zoom-level" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.7. </span>Changin zoom level<a class="headerlink" href="#changin-zoom-level" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">zoom_range</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
@@ -1899,7 +1931,7 @@ <h2><span class="section-number">37.92.7. </span>Changin zoom level<a class="hea
 </div>
 </section>
 <section id="changing-rotaion">
-<h2><span class="section-number">37.92.8. </span>Changing rotaion<a class="headerlink" href="#changing-rotaion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.8. </span>Changing rotaion<a class="headerlink" href="#changing-rotaion" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">rotation_range</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
@@ -1911,7 +1943,7 @@ <h2><span class="section-number">37.92.8. </span>Changing rotaion<a class="heade
 </div>
 </section>
 <section id="changing-rotaion-zoom">
-<h2><span class="section-number">37.92.9. </span>Changing rotaion, zoom<a class="headerlink" href="#changing-rotaion-zoom" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.9. </span>Changing rotaion, zoom<a class="headerlink" href="#changing-rotaion-zoom" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">zoom_range</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">rotation_range</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
@@ -1923,7 +1955,7 @@ <h2><span class="section-number">37.92.9. </span>Changing rotaion, zoom<a class=
 </div>
 </section>
 <section id="model-implementation">
-<h2><span class="section-number">37.92.10. </span>Model Implementation<a class="headerlink" href="#model-implementation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.10. </span>Model Implementation<a class="headerlink" href="#model-implementation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
@@ -1973,7 +2005,7 @@ <h2><span class="section-number">37.92.10. </span>Model Implementation<a class="
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.92.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2006,7 +2038,7 @@ <h2><span class="section-number">37.92.11. </span>Conclusion<a class="headerlink
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.93. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.93. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/gorkemgunay">Görkem Günay</a> for creating <a class="reference external" href="https://www.kaggle.com/code/gorkemgunay/sign-language-digits-classification-with-cnn">sign-language-digits-classification-with-cnn</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2047,13 +2079,13 @@ <h1><span class="section-number">37.93. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.90. </span>How to choose cnn architecture mnist</p>
+            <p class="prev-next-title"><span class="section-number">40.90. </span>How to choose cnn architecture mnist</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="object-recognition-in-images-using-cnn.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.94. </span>Object Recognition in Images using CNN</p>
+        <p class="prev-next-title"><span class="section-number">40.94. </span>Object Recognition in Images using CNN</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html b/assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html
index 09a88b8115..4668a5154d 100644
--- a/assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html
+++ b/assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.112. DQN On Foreign Exchange Market &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.112. DQN On Foreign Exchange Market &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.113. Art by gan" href="../gan/art-by-gan.html" />
-    <link rel="prev" title="37.107. NN Classify 15 Fruits Assignment" href="../nn-classify-15-fruits-assignment.html" />
+    <link rel="next" title="40.113. Art by gan" href="../gan/art-by-gan.html" />
+    <link rel="prev" title="40.107. NN Classify 15 Fruits Assignment" href="../nn-classify-15-fruits-assignment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
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     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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   <a class="reference internal" href="../../../deep-learning/cnn.html">
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-   22. Generative adversarial networks
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   <a class="reference internal" href="../../../machine-learning-productionization/overview.html">
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    <li class="toctree-l2">
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     <a class="reference internal" href="../../set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../../project-plan-template.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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    <li class="toctree-l2">
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-     37.7. Python programming basics
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     <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     <a class="reference internal" href="../../data-science/data-science-scenarios.html">
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     <a class="reference internal" href="../../data-science/write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="../../data-science/displaying-airport-data.html">
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     <a class="reference internal" href="../../data-science/soda-profits.html">
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+     40.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
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+     40.116. Basic classification: Classify images of clothing
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-   37.112.2. Define envireonment
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+   40.112.5. Acknowledgement
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   <section class="tex2jax_ignore mathjax_ignore" id="dqn-on-foreign-exchange-market">
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-<h2><span class="section-number">37.112.1. </span>Load dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.112.1. </span>Load dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">40.112.2. </span>Define envireonment<a class="headerlink" href="#define-envireonment" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">37.112.3. </span>Agent class<a class="headerlink" href="#agent-class" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.112.3. </span>Agent class<a class="headerlink" href="#agent-class" title="Permalink to this headline">#</a></h2>
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 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Deep Q-learning Agent</span>
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+<h2><span class="section-number">40.112.4. </span>Train the DQN<a class="headerlink" href="#train-the-dqn" title="Permalink to this headline">#</a></h2>
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 <section id="acknowledgement">
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+<h2><span class="section-number">40.112.5. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/emrebulbul23">emrebulbul23</a> for creating <a class="reference external" href="https://www.kaggle.com/code/emrebulbul23/dqn-on-foreign-exchange-market">DQN on foreign exchange market</a>. It inspired the majority of the content in this article.</p>
 </section>
 </section>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.107. </span>NN Classify 15 Fruits Assignment</p>
+            <p class="prev-next-title"><span class="section-number">40.107. </span>NN Classify 15 Fruits Assignment</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../gan/art-by-gan.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.113. </span>Art by gan</p>
+        <p class="prev-next-title"><span class="section-number">40.113. </span>Art by gan</p>
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-    <link rel="prev" title="37.112. DQN On Foreign Exchange Market" href="../dqn/dqn-on-foreign-exchange-market.html" />
+    <link rel="next" title="40.115. Generative Adversarial Networks (GANs)" href="gan-introduction.html" />
+    <link rel="prev" title="40.112. DQN On Foreign Exchange Market" href="../dqn/dqn-on-foreign-exchange-market.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
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+     40.113.1. Data loading &amp; Prepreprocessing
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+     40.113.1. Data loading &amp; Prepreprocessing
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+     40.113.2. Loading the data
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+     40.113.3. Preprocessing the data
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+     40.113.7. The discriminator
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-     37.113.9. GAN compilation
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-     37.113.10. Training the model
+     40.113.10. Training the model
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+     40.113.11. Ploting the Learning Curves
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+     40.113.12. AI makes artwork
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@@ -1708,7 +1740,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="art-by-gan">
-<h1><span class="section-number">37.113. </span>Art by gan<a class="headerlink" href="#art-by-gan" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.113. </span>Art by gan<a class="headerlink" href="#art-by-gan" title="Permalink to this headline">#</a></h1>
 <p>In this Notebook, we will build a Generative Adversarial Network  (GAN) to illustrate the workings of a Generative Adversarial Network and to generate images. Generative modelling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data. As GANs work by identifying the patterns in the data, we will be using oil painted portraits. However, glancing over the dataset gives me an idea that it is going to be a long shot. The orientation and poses in the dataset vary vastly. Keeping that in mind we are still willing to give it a try. Only because portraits are our jam. We basically love oil painted portraits.</p>
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-<h2><span class="section-number">37.113.1. </span>Data loading &amp; Prepreprocessing<a class="headerlink" href="#data-loading-prepreprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.1. </span>Data loading &amp; Prepreprocessing<a class="headerlink" href="#data-loading-prepreprocessing" title="Permalink to this headline">#</a></h2>
 <p>For this project, We are using .jpg files of images of portraits. The dataset includes various artists. We are loading data as TensorFlow.Dataset, with a batch size of 64. We have reduced the image size to (64,64), presuming, it will be computationally less taxing on the GPU.</p>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.113.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
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 <p>Most of the images are portraits. A portrait is a painting representation of a person, The face is predominantly depicted portraits along with expressions and postures. To represent the personality of the subject. Since our model is relative a smaller GAN we have reduced the size of the image.</p>
 </section>
 <section id="preprocessing-the-data">
-<h2><span class="section-number">37.113.3. </span>Preprocessing the data<a class="headerlink" href="#preprocessing-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.3. </span>Preprocessing the data<a class="headerlink" href="#preprocessing-the-data" title="Permalink to this headline">#</a></h2>
 <p><strong>Normalization:</strong> For the data normalization, we will convert the data in the range between 0 to 1. This helps in fast convergence and makes it easy for the computer to do calculations faster.
 Each of the three RGB channels in the image can take pixel values ranging from 0 to 256. Dividing it by 255 converts it to a range between 0 to 1.</p>
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 <p>Now the data is up as a tensorflow Dataset object and is Prepocessed. Next up is building the GAN.</p>
 </section>
 <section id="building-gan">
-<h2><span class="section-number">37.113.4. </span>Building GAN<a class="headerlink" href="#building-gan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.4. </span>Building GAN<a class="headerlink" href="#building-gan" title="Permalink to this headline">#</a></h2>
 <p>GANs employs deep learning methods. It is a dexterous way of posing the problem as a supervised learning problem. It is composed of two models namely Generator and a Discriminator.</p>
 <p>Two models are trained simultaneously by an adversarial process. A generator (“the artist”) learns to create images that look like the dataset while a discriminator (“the art critic”) learns to tell real images apart from fakes.</p>
 <p>During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes.</p>
@@ -1837,7 +1869,7 @@ <h2><span class="section-number">37.113.4. </span>Building GAN<a class="headerli
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+<h2><span class="section-number">40.113.5. </span>The generator<a class="headerlink" href="#the-generator" title="Permalink to this headline">#</a></h2>
 <p>The Generator is a neural network that generates the images. It takes in a random noise as seed and outputs sample data. As the GAN’s training progresses the Generator output becomes more and more like the training set, as the Generator tries to improve the output so that the discrimination passes the output as a real image.</p>
 <p><strong>Following steps are involved in the models building</strong></p>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">latent_dim</span> <span class="o">=</span> <span class="mi">100</span>
@@ -1896,12 +1928,12 @@ <h2><span class="section-number">37.113.6. </span>Building a generator<a class="
 <p>Clearly, the output is a random seed containing noise as the Generator is not trained yet.</p>
 </section>
 <section id="the-discriminator">
-<h2><span class="section-number">37.113.7. </span>The discriminator<a class="headerlink" href="#the-discriminator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.7. </span>The discriminator<a class="headerlink" href="#the-discriminator" title="Permalink to this headline">#</a></h2>
 <p>In GANs the Generator works along with the Discriminator.</p>
 <p>The Discriminator network decided whether the data is fake aka created by the Generator or real i.e. from the original input data. To do so it applies a binary classification method using a sigmoid function to get an output in the range of 0 to 1.</p>
 </section>
 <section id="building-a-discriminator">
-<h2><span class="section-number">37.113.8. </span>Building a discriminator<a class="headerlink" href="#building-a-discriminator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.8. </span>Building a discriminator<a class="headerlink" href="#building-a-discriminator" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">discriminator</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
@@ -1950,7 +1982,7 @@ <h2><span class="section-number">37.113.8. </span>Building a discriminator<a cla
 <p>Let us proceed and build the GAN architecture to train.</p>
 </section>
 <section id="gan-compilation">
-<h2><span class="section-number">37.113.9. </span>GAN compilation<a class="headerlink" href="#gan-compilation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.9. </span>GAN compilation<a class="headerlink" href="#gan-compilation" title="Permalink to this headline">#</a></h2>
 <p>GAN training has two sections:</p>
 <p><strong>Section 1</strong>: The Discriminator is trained while the Generator is idle.
 The discriminator is trained real images and random noise (from an untrained generator). This trains it to tell between fake and real. This accommodates the discriminator to predict as fakes.</p>
@@ -2027,7 +2059,7 @@ <h2><span class="section-number">37.113.9. </span>GAN compilation<a class="heade
 </div>
 </section>
 <section id="training-the-model">
-<h2><span class="section-number">37.113.10. </span>Training the model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.10. </span>Training the model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
 <p>Calling the above created GAN function trains the generator and discriminator simultaneously.
 To implement the GAN we must define:</p>
 <ul class="simple">
@@ -2060,7 +2092,7 @@ <h2><span class="section-number">37.113.10. </span>Training the model<a class="h
 </div>
 </section>
 <section id="ploting-the-learning-curves">
-<h2><span class="section-number">37.113.11. </span>Ploting the Learning Curves<a class="headerlink" href="#ploting-the-learning-curves" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.11. </span>Ploting the Learning Curves<a class="headerlink" href="#ploting-the-learning-curves" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2077,7 +2109,7 @@ <h2><span class="section-number">37.113.11. </span>Ploting the Learning Curves<a
 To get the art output we will create a function that saves the output portraits generated. We will be plotting the generated Portraits</p>
 </section>
 <section id="ai-makes-artwork">
-<h2><span class="section-number">37.113.12. </span>AI makes artwork<a class="headerlink" href="#ai-makes-artwork" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.12. </span>AI makes artwork<a class="headerlink" href="#ai-makes-artwork" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Number of images to be generate</span>
@@ -2117,12 +2149,12 @@ <h2><span class="section-number">37.113.12. </span>AI makes artwork<a class="hea
 </div>
 </section>
 <section id="conculsion">
-<h2><span class="section-number">37.113.13. </span>Conculsion<a class="headerlink" href="#conculsion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.13. </span>Conculsion<a class="headerlink" href="#conculsion" title="Permalink to this headline">#</a></h2>
 <p>In the evaluation of the model: We can see that the GAN picked up the patterns in the portraits. It worked quite well. For further improvement,  as GANs are notorious for being data-hungry, I would consider increasing the dataset. There are many inconsistencies in the data which is rather complicated for the GAN to learn. Cleaning the data with some consistencies in the portrait styles would certainly help. Training it longer i.e. for more epochs would also help. Lastly, one can always strive to make a  more robust architecture for the Neural Networks.</p>
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.114. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.114. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/karnikakapoor">Karnika Kapoor</a> for creating <a class="reference external" href="https://www.kaggle.com/code/karnikakapoor/art-by-gan">art-by-gan</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2163,13 +2195,13 @@ <h1><span class="section-number">37.114. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.112. </span>DQN On Foreign Exchange Market</p>
+            <p class="prev-next-title"><span class="section-number">40.112. </span>DQN On Foreign Exchange Market</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gan-introduction.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.115. </span>Generative Adversarial Networks (GANs)</p>
+        <p class="prev-next-title"><span class="section-number">40.115. </span>Generative Adversarial Networks (GANs)</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/gan/gan-introduction.html b/assignments/deep-learning/gan/gan-introduction.html
index 6db550cc39..426b05a50f 100644
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-    <title>37.115. Generative Adversarial Networks (GANs) &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.115. Generative Adversarial Networks (GANs) &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.116. Basic classification: Classify images of clothing" href="../overview/basic-classification-classify-images-of-clothing.html" />
-    <link rel="prev" title="37.113. Art by gan" href="art-by-gan.html" />
+    <link rel="next" title="40.116. Basic classification: Classify images of clothing" href="../overview/basic-classification-classify-images-of-clothing.html" />
+    <link rel="prev" title="40.113. Art by gan" href="art-by-gan.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
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+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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     <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
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     <a class="reference internal" href="../../data-science/data-science-scenarios.html">
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     <a class="reference internal" href="../../data-science/write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="../../data-science/lines-scatters-and-bars.html">
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     <a class="reference internal" href="../../data-science/apply-your-skills.html">
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     <a class="reference internal" href="../../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/dive-into-the-beehive.html">
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     <a class="reference internal" href="../../data-science/build-your-own-custom-vis.html">
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     <a class="reference internal" href="../../data-science/classifying-datasets.html">
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     <a class="reference internal" href="../../data-science/small-diabetes-study.html">
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     <a class="reference internal" href="../../data-science/introduction-to-statistics-and-probability.html">
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     <a class="reference internal" href="../../data-science/displaying-airport-data.html">
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     <a class="reference internal" href="../../data-science/soda-profits.html">
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     <a class="reference internal" href="../../data-science/analyzing-COVID-19-papers.html">
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     <a class="reference internal" href="../../data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     <a class="reference internal" href="../../data-science/evaluating-data-from-a-form.html">
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     <a class="reference internal" href="../../data-science/nyc-taxi-data-in-winter-and-summer.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,91 +1558,91 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-data">
-   37.115.1. Loading data
+   40.115.1. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   37.115.2. Importing the libraries
+   40.115.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#some-dogs">
-   37.115.3. Some dogs
+   40.115.3. Some dogs
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#image-preprocessing">
-   37.115.4. Image Preprocessing
+   40.115.4. Image Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#weights">
-   37.115.5. Weights
+   40.115.5. Weights
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generator">
-   37.115.6. Generator
+   40.115.6. Generator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#discriminator">
-   37.115.7. Discriminator
+   40.115.7. Discriminator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#another-setup">
-   37.115.8. Another setup
+   40.115.8. Another setup
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training">
-   37.115.9. Training
+   40.115.9. Training
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#best-public-training">
-   37.115.10. Best public training
+   40.115.10. Best public training
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#parameters">
-     37.115.10.1. Parameters
+     40.115.10.1. Parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#initialize-models-and-optimizers">
-     37.115.10.2. Initialize models and optimizers
+     40.115.10.2. Initialize models and optimizers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-generated-images">
-   37.115.11. Show generated images
+   40.115.11. Show generated images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-loop">
-   37.115.12. Training Loop
+   40.115.12. Training Loop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generation-example">
-   37.115.13. Generation example
+   40.115.13. Generation example
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#save-models">
-     37.115.13.1. Save models
+     40.115.13.1. Save models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   37.115.14. Acknowledgement
+   40.115.14. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1634,91 +1666,91 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-data">
-   37.115.1. Loading data
+   40.115.1. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   37.115.2. Importing the libraries
+   40.115.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#some-dogs">
-   37.115.3. Some dogs
+   40.115.3. Some dogs
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#image-preprocessing">
-   37.115.4. Image Preprocessing
+   40.115.4. Image Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#weights">
-   37.115.5. Weights
+   40.115.5. Weights
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generator">
-   37.115.6. Generator
+   40.115.6. Generator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#discriminator">
-   37.115.7. Discriminator
+   40.115.7. Discriminator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#another-setup">
-   37.115.8. Another setup
+   40.115.8. Another setup
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training">
-   37.115.9. Training
+   40.115.9. Training
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#best-public-training">
-   37.115.10. Best public training
+   40.115.10. Best public training
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#parameters">
-     37.115.10.1. Parameters
+     40.115.10.1. Parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#initialize-models-and-optimizers">
-     37.115.10.2. Initialize models and optimizers
+     40.115.10.2. Initialize models and optimizers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-generated-images">
-   37.115.11. Show generated images
+   40.115.11. Show generated images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-loop">
-   37.115.12. Training Loop
+   40.115.12. Training Loop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generation-example">
-   37.115.13. Generation example
+   40.115.13. Generation example
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#save-models">
-     37.115.13.1. Save models
+     40.115.13.1. Save models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   37.115.14. Acknowledgement
+   40.115.14. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1732,9 +1764,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="generative-adversarial-networks-gans">
-<h1><span class="section-number">37.115. </span>Generative Adversarial Networks (GANs)<a class="headerlink" href="#generative-adversarial-networks-gans" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.115. </span>Generative Adversarial Networks (GANs)<a class="headerlink" href="#generative-adversarial-networks-gans" title="Permalink to this headline">#</a></h1>
 <section id="loading-data">
-<h2><span class="section-number">37.115.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -1769,7 +1801,7 @@ <h2><span class="section-number">37.115.1. </span>Loading data<a class="headerli
 </div>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">37.115.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
@@ -1798,7 +1830,7 @@ <h2><span class="section-number">37.115.2. </span>Importing the libraries<a clas
 </div>
 </section>
 <section id="some-dogs">
-<h2><span class="section-number">37.115.3. </span>Some dogs<a class="headerlink" href="#some-dogs" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.3. </span>Some dogs<a class="headerlink" href="#some-dogs" title="Permalink to this headline">#</a></h2>
 <p>The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world.</p>
 <div class="cell docutils container">
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@@ -1821,7 +1853,7 @@ <h2><span class="section-number">37.115.3. </span>Some dogs<a class="headerlink"
 </div>
 </section>
 <section id="image-preprocessing">
-<h2><span class="section-number">37.115.4. </span>Image Preprocessing<a class="headerlink" href="#image-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.4. </span>Image Preprocessing<a class="headerlink" href="#image-preprocessing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
@@ -1856,7 +1888,7 @@ <h2><span class="section-number">37.115.4. </span>Image Preprocessing<a class="h
 </div>
 </section>
 <section id="weights">
-<h2><span class="section-number">37.115.5. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.5. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">weights_init</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
@@ -1872,7 +1904,7 @@ <h2><span class="section-number">37.115.5. </span>Weights<a class="headerlink" h
 </div>
 </section>
 <section id="generator">
-<h2><span class="section-number">37.115.6. </span>Generator<a class="headerlink" href="#generator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.6. </span>Generator<a class="headerlink" href="#generator" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">G</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
@@ -1910,7 +1942,7 @@ <h2><span class="section-number">37.115.6. </span>Generator<a class="headerlink"
 </div>
 </section>
 <section id="discriminator">
-<h2><span class="section-number">37.115.7. </span>Discriminator<a class="headerlink" href="#discriminator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.7. </span>Discriminator<a class="headerlink" href="#discriminator" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">D</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
@@ -1943,7 +1975,7 @@ <h2><span class="section-number">37.115.7. </span>Discriminator<a class="headerl
 </div>
 </section>
 <section id="another-setup">
-<h2><span class="section-number">37.115.8. </span>Another setup<a class="headerlink" href="#another-setup" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.8. </span>Another setup<a class="headerlink" href="#another-setup" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
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@@ -2009,7 +2041,7 @@ <h2><span class="section-number">37.115.8. </span>Another setup<a class="headerl
 </div>
 </section>
 <section id="training">
-<h2><span class="section-number">37.115.9. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.9. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">EPOCH</span> <span class="o">=</span> <span class="mi">0</span>
@@ -2067,9 +2099,9 @@ <h2><span class="section-number">37.115.9. </span>Training<a class="headerlink"
 </div>
 </section>
 <section id="best-public-training">
-<h2><span class="section-number">37.115.10. </span>Best public training<a class="headerlink" href="#best-public-training" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.10. </span>Best public training<a class="headerlink" href="#best-public-training" title="Permalink to this headline">#</a></h2>
 <section id="parameters">
-<h3><span class="section-number">37.115.10.1. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.115.10.1. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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@@ -2090,7 +2122,7 @@ <h3><span class="section-number">37.115.10.1. </span>Parameters<a class="headerl
 </div>
 </section>
 <section id="initialize-models-and-optimizers">
-<h3><span class="section-number">37.115.10.2. </span>Initialize models and optimizers<a class="headerlink" href="#initialize-models-and-optimizers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.115.10.2. </span>Initialize models and optimizers<a class="headerlink" href="#initialize-models-and-optimizers" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">netG</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">nz</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
@@ -2128,7 +2160,7 @@ <h3><span class="section-number">37.115.10.2. </span>Initialize models and optim
 </section>
 </section>
 <section id="show-generated-images">
-<h2><span class="section-number">37.115.11. </span>Show generated images<a class="headerlink" href="#show-generated-images" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.11. </span>Show generated images<a class="headerlink" href="#show-generated-images" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">show_generated_img</span><span class="p">(</span><span class="n">n_images</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
@@ -2153,7 +2185,7 @@ <h2><span class="section-number">37.115.11. </span>Show generated images<a class
 </div>
 </section>
 <section id="training-loop">
-<h2><span class="section-number">37.115.12. </span>Training Loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.12. </span>Training Loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -2225,7 +2257,7 @@ <h2><span class="section-number">37.115.12. </span>Training Loop<a class="header
 </div>
 </section>
 <section id="generation-example">
-<h2><span class="section-number">37.115.13. </span>Generation example<a class="headerlink" href="#generation-example" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.13. </span>Generation example<a class="headerlink" href="#generation-example" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">40.115.14. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/jesucristo">jesucristo</a> for creating <a class="reference external" href="https://www.kaggle.com/code/jesucristo/gan-introduction/notebook">GAN Introduction</a>. It inspired the majority of the content in this article.</p>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.116. </span>Basic classification: Classify images of clothing</p>
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@@ -1526,34 +1558,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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-   37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+   40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     37.97.1. Load libraries
+     40.97.1. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-pre-processing">
-     37.97.2. Data Pre-processing
+     40.97.2. Data Pre-processing
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-analysis">
-     37.97.3. Exploratory Analysis
+     40.97.3. Exploratory Analysis
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lstm-model">
-     37.97.4. LSTM Model
+     40.97.4. LSTM Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgements">
-   37.98. Acknowledgements
+   40.98. Acknowledgements
   </a>
  </li>
 </ul>
@@ -1577,34 +1609,34 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+   40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     37.97.1. Load libraries
+     40.97.1. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-pre-processing">
-     37.97.2. Data Pre-processing
+     40.97.2. Data Pre-processing
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-analysis">
-     37.97.3. Exploratory Analysis
+     40.97.3. Exploratory Analysis
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lstm-model">
-     37.97.4. LSTM Model
+     40.97.4. LSTM Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgements">
-   37.98. Acknowledgements
+   40.98. Acknowledgements
   </a>
  </li>
 </ul>
@@ -1618,9 +1650,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="bitcoin-lstm-model-with-tweet-volume-and-sentiment">
-<h1><span class="section-number">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment<a class="headerlink" href="#bitcoin-lstm-model-with-tweet-volume-and-sentiment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment<a class="headerlink" href="#bitcoin-lstm-model-with-tweet-volume-and-sentiment" title="Permalink to this headline">#</a></h1>
 <section id="load-libraries">
-<h2><span class="section-number">37.97.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.97.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -1651,7 +1683,7 @@ <h2><span class="section-number">37.97.1. </span>Load libraries<a class="headerl
 </div>
 </section>
 <section id="data-pre-processing">
-<h2><span class="section-number">37.97.2. </span>Data Pre-processing<a class="headerlink" href="#data-pre-processing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.97.2. </span>Data Pre-processing<a class="headerlink" href="#data-pre-processing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">notclean</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
@@ -3350,7 +3382,7 @@ <h2><span class="section-number">37.97.2. </span>Data Pre-processing<a class="he
 </div>
 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">37.97.3. </span>Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.97.3. </span>Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># --------------Analysis----------------------------#</span>
@@ -4690,7 +4722,7 @@ <h2><span class="section-number">37.97.3. </span>Exploratory Analysis<a class="h
 </div>
 </section>
 <section id="lstm-model">
-<h2><span class="section-number">37.97.4. </span>LSTM Model<a class="headerlink" href="#lstm-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.97.4. </span>LSTM Model<a class="headerlink" href="#lstm-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span>
@@ -5511,7 +5543,7 @@ <h2><span class="section-number">37.97.4. </span>LSTM Model<a class="headerlink"
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgements">
-<h1><span class="section-number">37.98. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.98. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/paul92s">Paul Simpson</a> for creating <a class="reference external" href="https://www.kaggle.com/code/paul92s/bitcoin-lstm-model-with-tweet-volume-and-sentiment">Bitcoin Lstm Model with Tweet Volume and Sentiment</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -5552,13 +5584,13 @@ <h1><span class="section-number">37.98. </span>Acknowledgements<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.95. </span>Intro to TensorFlow for Deep Learning</p>
+            <p class="prev-next-title"><span class="section-number">40.95. </span>Intro to TensorFlow for Deep Learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../rnn/google-stock-price-prediction-rnn.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.99. </span>Google Stock Price Prediction RNN</p>
+        <p class="prev-next-title"><span class="section-number">40.99. </span>Google Stock Price Prediction RNN</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/nn-classify-15-fruits-assignment.html b/assignments/deep-learning/nn-classify-15-fruits-assignment.html
index 3acdbe39fa..ceff70c0dd 100644
--- a/assignments/deep-learning/nn-classify-15-fruits-assignment.html
+++ b/assignments/deep-learning/nn-classify-15-fruits-assignment.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.107. NN Classify 15 Fruits Assignment &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.107. NN Classify 15 Fruits Assignment &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.112. DQN On Foreign Exchange Market" href="dqn/dqn-on-foreign-exchange-market.html" />
-    <link rel="prev" title="37.106. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
+    <link rel="next" title="40.112. DQN On Foreign Exchange Market" href="dqn/dqn-on-foreign-exchange-market.html" />
+    <link rel="prev" title="40.106. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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    </li>
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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    <li class="toctree-l2">
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-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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+  <label for="toctree-checkbox-10">
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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+     17.1. Bagging
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-     15.2. Random forest
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-     15.3. Feature importance
+     17.3. Feature importance
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-   16. Introduction to Gradient Boosting
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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-     16.3. XGBoost
+     18.3. XGBoost
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
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-   19. Model selection
+   22. Model selection
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-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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-   24. Autoencoder
+   27. Autoencoder
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-   25. Long-short term memory
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-   26. Time series
+   29. Time series
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-   27. Deep Q-learning
+   30. Deep Q-learning
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+   32. Image segmentation
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-   30. Diffusion Model
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-   31. Object detection
+   34. Object detection
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+   35. Overview
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-   33. Problem framing
+   36. Problem framing
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-   34. Data engineering
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-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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-     37.3. First assignment
+     40.3. First assignment
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-     37.4. Second assignment
+     40.4. Second assignment
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.11. Write a data ethics case study
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.16. Build your own custom vis
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-     37.19. Introduction to probability and statistics
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-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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-     37.23. Estimation of COVID-19 pandemic
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-     37.24. Data processing in Python
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-     37.25. Evaluating data from a form
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-     37.28. NYC taxi data in winter and summer
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-     37.29. Matplotlib applied
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-     37.35. Tell a story
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-     37.36. Explore a planetary computer dataset
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-     37.37. Exploring for answers
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-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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-     37.47. Loss Function
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-     37.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.61. Delicious asian and indian cuisines
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-     37.62. Explore classification methods
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-     37.63. Kernel method assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.65. Support Vector Machines (SVM) - Classification
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-     37.66. Decision Trees - Intro and Regression
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-     37.67. Decision Trees - Classification
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-     37.68. Model selection assignment 1
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-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.70. Dropout and Batch Normalization
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-     37.71. Lasso and Ridge Regression
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-     37.72. Regularized Linear Models
+     40.72. Regularized Linear Models
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-     37.73. Random forests intro and regression
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-     37.74. Random forests for classification
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-     37.75. Beyond random forests: more ensemble models
+     40.75. Beyond random forests: more ensemble models
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-     37.76. Decision trees
+     40.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
+     40.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
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-     37.79. Boosting with tuning
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-     37.80. Random Forest Classifier with Feature Importance
+     40.80. Random Forest Classifier with Feature Importance
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-     37.82. Data engineering
+     40.82. Data engineering
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-     37.83. Counterintuitive Challenges in ML Debugging
+     40.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
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-     37.86. Study the solvers
+     40.86. Study the solvers
     </a>
    </li>
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-     37.87. Build classification models
+     40.87. Build classification models
     </a>
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    <li class="toctree-l2">
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-     37.88. Build Classification Model
+     40.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
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-     37.89. Parameter play
+     40.89. Parameter play
     </a>
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     <a class="reference internal" href="cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
     </a>
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     <a class="reference internal" href="cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
     </a>
    </li>
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     <a class="reference internal" href="tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
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     <a class="reference internal" href="lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
     </a>
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     <a class="reference internal" href="autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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     <a class="reference internal" href="autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
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     <a class="reference internal" href="dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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    </li>
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-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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-   37.107. NN Classify 15 Fruits Assignment
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   </a>
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+   40.108. Data collection
   </a>
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-   37.109. Load and visualize the dataset
+   40.109. Load and visualize the dataset
   </a>
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   <a class="reference internal nav-link" href="#train-the-neural-network-from-scratch-with-keras-and-w-o-generator">
-   37.110. Train the neural network from scratch with Keras and w/o generator
+   40.110. Train the neural network from scratch with Keras and w/o generator
   </a>
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-and-train-the-nn-model">
-     37.110.1. Create and train the NN Model
+     40.110.1. Create and train the NN Model
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#predictions-a-class-anchor-id-3-a">
-     37.110.2. Predictions
+     40.110.2. Predictions
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      </a>
     </a>
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-     37.110.3. Visualize the result with pictures of fruits
+     40.110.3. Visualize the result with pictures of fruits
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.111. Acknowledgments
+   40.111. Acknowledgments
   </a>
  </li>
 </ul>
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                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#">
-   37.107. NN Classify 15 Fruits Assignment
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   </a>
  </li>
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-   37.108. Data collection
+   40.108. Data collection
   </a>
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  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-and-visualize-the-dataset">
-   37.109. Load and visualize the dataset
+   40.109. Load and visualize the dataset
   </a>
  </li>
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   <a class="reference internal nav-link" href="#train-the-neural-network-from-scratch-with-keras-and-w-o-generator">
-   37.110. Train the neural network from scratch with Keras and w/o generator
+   40.110. Train the neural network from scratch with Keras and w/o generator
   </a>
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-and-train-the-nn-model">
-     37.110.1. Create and train the NN Model
+     40.110.1. Create and train the NN Model
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#predictions-a-class-anchor-id-3-a">
-     37.110.2. Predictions
+     40.110.2. Predictions
      <a class="anchor" id="3">
      </a>
     </a>
    </li>
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     <a class="reference internal nav-link" href="#visualize-the-result-with-pictures-of-fruits">
-     37.110.3. Visualize the result with pictures of fruits
+     40.110.3. Visualize the result with pictures of fruits
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.111. Acknowledgments
+   40.111. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1642,11 +1674,11 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="nn-classify-15-fruits-assignment">
-<h1><span class="section-number">37.107. </span>NN Classify 15 Fruits Assignment<a class="headerlink" href="#nn-classify-15-fruits-assignment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.107. </span>NN Classify 15 Fruits Assignment<a class="headerlink" href="#nn-classify-15-fruits-assignment" title="Permalink to this headline">#</a></h1>
 <p><img alt="Fruit Example" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/assignment/deep-learning/nn/examplefruit.png" /></p>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="data-collection">
-<h1><span class="section-number">37.108. </span>Data collection<a class="headerlink" href="#data-collection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.108. </span>Data collection<a class="headerlink" href="#data-collection" title="Permalink to this headline">#</a></h1>
 <p>The database used in this study is comprising of 44406 fruit images, which we collected
 in a period of 6 months. The images where made with in our lab’s environment under different
 scenarios which we mention below. We captured all the images on a clear background with
@@ -1685,7 +1717,7 @@ <h1><span class="section-number">37.108. </span>Data collection<a class="headerl
 </ul>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="load-and-visualize-the-dataset">
-<h1><span class="section-number">37.109. </span>Load and visualize the dataset<a class="headerlink" href="#load-and-visualize-the-dataset" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.109. </span>Load and visualize the dataset<a class="headerlink" href="#load-and-visualize-the-dataset" title="Permalink to this headline">#</a></h1>
 <p>The database used in this study is comprising of 70549 fruit images, which were collected in a period of 6 months. The images where made with in a lab’s environment under different scenarios which we mention below. All the images were captured on a clear background with resolution of 320×258 pixels.</p>
 <p>Type of fruits in the dataset:</p>
 <ul class="simple">
@@ -1861,7 +1893,7 @@ <h1><span class="section-number">37.109. </span>Load and visualize the dataset<a
 </div>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="train-the-neural-network-from-scratch-with-keras-and-w-o-generator">
-<h1><span class="section-number">37.110. </span>Train the neural network from scratch with Keras and w/o generator<a class="headerlink" href="#train-the-neural-network-from-scratch-with-keras-and-w-o-generator" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.110. </span>Train the neural network from scratch with Keras and w/o generator<a class="headerlink" href="#train-the-neural-network-from-scratch-with-keras-and-w-o-generator" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The pictures will be resized to have the same size for the neural network</span>
@@ -1878,7 +1910,7 @@ <h1><span class="section-number">37.110. </span>Train the neural network from sc
 </div>
 </div>
 <section id="create-and-train-the-nn-model">
-<h2><span class="section-number">37.110.1. </span>Create and train the NN Model<a class="headerlink" href="#create-and-train-the-nn-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.110.1. </span>Create and train the NN Model<a class="headerlink" href="#create-and-train-the-nn-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cut_df</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">number_of_parts</span><span class="p">,</span> <span class="n">part</span><span class="p">):</span>
@@ -2057,7 +2089,7 @@ <h2><span class="section-number">37.110.1. </span>Create and train the NN Model<
 </div>
 </section>
 <section id="predictions-a-class-anchor-id-3-a">
-<h2><span class="section-number">37.110.2. </span>Predictions<a class="anchor" id="3"></a><a class="headerlink" href="#predictions-a-class-anchor-id-3-a" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.110.2. </span>Predictions<a class="anchor" id="3"></a><a class="headerlink" href="#predictions-a-class-anchor-id-3-a" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
@@ -2108,7 +2140,7 @@ <h2><span class="section-number">37.110.2. </span>Predictions<a class="anchor" i
 </div>
 </section>
 <section id="visualize-the-result-with-pictures-of-fruits">
-<h2><span class="section-number">37.110.3. </span>Visualize the result with pictures of fruits<a class="headerlink" href="#visualize-the-result-with-pictures-of-fruits" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.110.3. </span>Visualize the result with pictures of fruits<a class="headerlink" href="#visualize-the-result-with-pictures-of-fruits" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
@@ -2127,7 +2159,7 @@ <h2><span class="section-number">37.110.3. </span>Visualize the result with pict
 <!-- <strong>The predictions are very good with around 97% accuracy using only 1/5 of the dataset to train the model.</strong> --></section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.111. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.111. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/databeru"> DATALIRA </a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/databeru/classify-15-fruits-with-tensorflow-acc-99-6"> Classify 15 Fruits with TensorFlow </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2168,13 +2200,13 @@ <h1><span class="section-number">37.111. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.106. </span>Neural Networks for Classification with TensorFlow</p>
+            <p class="prev-next-title"><span class="section-number">40.106. </span>Neural Networks for Classification with TensorFlow</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="dqn/dqn-on-foreign-exchange-market.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.112. </span>DQN On Foreign Exchange Market</p>
+        <p class="prev-next-title"><span class="section-number">40.112. </span>DQN On Foreign Exchange Market</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/nn-for-classification-assignment.html b/assignments/deep-learning/nn-for-classification-assignment.html
index ee6c404b02..4092f94d41 100644
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+++ b/assignments/deep-learning/nn-for-classification-assignment.html
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-    <title>37.106. Neural Networks for Classification with TensorFlow &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.106. Neural Networks for Classification with TensorFlow &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.107. NN Classify 15 Fruits Assignment" href="nn-classify-15-fruits-assignment.html" />
-    <link rel="prev" title="37.104. Time Series Forecasting Assignment" href="time-series-forecasting-assignment.html" />
+    <link rel="next" title="40.107. NN Classify 15 Fruits Assignment" href="nn-classify-15-fruits-assignment.html" />
+    <link rel="prev" title="40.104. Time Series Forecasting Assignment" href="time-series-forecasting-assignment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-   35. Model training &amp; evaluation
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-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
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-     37.24. Data processing in Python
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-     37.29. Matplotlib applied
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     <a class="reference internal" href="../data-science/market-research.html">
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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+     40.106.1.1. Getting the data
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+   40.106.3. Preparing the Data
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+   40.106.4. Creating, Compiling and Training a Model
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+   40.106.5. Visualizing the Results
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+     40.106.14.1. Implementing Callbacks
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+     40.106.14.2. Custom Callback
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+   40.106.16. Acknowledgments
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+   40.106.1. Getting Started: Binary Classifier
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+     40.106.1.1. Getting the data
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+   40.106.2. Taking a look in the data
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+   40.106.3. Preparing the Data
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+   40.106.4. Creating, Compiling and Training a Model
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+   40.106.5. Visualizing the Results
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-   37.106.6. Evaluating the Model
+   40.106.6. Evaluating the Model
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-   37.106.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
+   40.106.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
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+   40.106.8. Getting the Fashion data
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+   40.106.9. Looking in the Fashion Data
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-   37.106.10. Preparing the Data
+   40.106.10. Preparing the Data
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+   40.106.11. Creating, Compiling, and Training a Model
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-   37.106.12. Visualizing the Model Results
+   40.106.12. Visualizing the Model Results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   37.106.13. Model Evaluation
+   40.106.13. Model Evaluation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#controlling-training-with-callbacks">
-   37.106.14. Controlling Training with Callbacks
+   40.106.14. Controlling Training with Callbacks
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implementing-callbacks">
-     37.106.14.1. Implementing Callbacks
+     40.106.14.1. Implementing Callbacks
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#custom-callback">
-     37.106.14.2. Custom Callback
+     40.106.14.2. Custom Callback
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-tensorboard-for-model-visualization">
-   37.106.15. Using TensorBoard for Model Visualization
+   40.106.15. Using TensorBoard for Model Visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.106.16. Acknowledgments
+   40.106.16. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1752,14 +1784,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="neural-networks-for-classification-with-tensorflow">
-<h1><span class="section-number">37.106. </span>Neural Networks for Classification with TensorFlow<a class="headerlink" href="#neural-networks-for-classification-with-tensorflow" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.106. </span>Neural Networks for Classification with TensorFlow<a class="headerlink" href="#neural-networks-for-classification-with-tensorflow" title="Permalink to this headline">#</a></h1>
 <section id="getting-started-binary-classifier">
-<h2><span class="section-number">37.106.1. </span>Getting Started: Binary Classifier<a class="headerlink" href="#getting-started-binary-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.1. </span>Getting Started: Binary Classifier<a class="headerlink" href="#getting-started-binary-classifier" title="Permalink to this headline">#</a></h2>
 <p>We will first practice building neural networks for binary classifier. In binary classification, we have two classes.</p>
 <p>We will use a classical cancer dataset to predict if a given patient has a malignant or benign based on their medical information. We will get it from sklearn datasets. You can read more about the dataset.</p>
 <p>The dataset contains two labels: malignant, benign.</p>
 <section id="getting-the-data">
-<h3><span class="section-number">37.106.1.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.106.1.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h3>
 <p>We will get the data from sklearn datasets.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1838,7 +1870,7 @@ <h3><span class="section-number">37.106.1.1. </span>Getting the data<a class="he
 </section>
 </section>
 <section id="taking-a-look-in-the-data">
-<h2><span class="section-number">37.106.2. </span>Taking a look in the data<a class="headerlink" href="#taking-a-look-in-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.2. </span>Taking a look in the data<a class="headerlink" href="#taking-a-look-in-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Looking from the head </span>
@@ -1868,7 +1900,7 @@ <h2><span class="section-number">37.106.2. </span>Taking a look in the data<a cl
 </div>
 </section>
 <section id="preparing-the-data">
-<h2><span class="section-number">37.106.3. </span>Preparing the Data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.3. </span>Preparing the Data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data from sklearn is reasonably cleaned. Let’s split the data into train and test sets, and we will follow with scaling the values to be between 0 and 1.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1911,7 +1943,7 @@ <h2><span class="section-number">37.106.3. </span>Preparing the Data<a class="he
 <p>We are now ready to create, compile and train the model.</p>
 </section>
 <section id="creating-compiling-and-training-a-model">
-<h2><span class="section-number">37.106.4. </span>Creating, Compiling and Training a Model<a class="headerlink" href="#creating-compiling-and-training-a-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.4. </span>Creating, Compiling and Training a Model<a class="headerlink" href="#creating-compiling-and-training-a-model" title="Permalink to this headline">#</a></h2>
 <p>In TensorFlow, creating a model is only putting together an empty graphs. We are going to use Sequential API to stack the layers, from the input to output.</p>
 <p>In model compilaton, it’s where we specify the optimizer and loss function. Loss function is there for calculating the difference between the predictions and the actual output, and optimizer is there for reducing the loss.</p>
 <p>Also, if we are interested in tracking other metrics during training, we can specify them in <code class="docutils literal notranslate"><span class="pre">metric</span></code>.</p>
@@ -1964,7 +1996,7 @@ <h2><span class="section-number">37.106.4. </span>Creating, Compiling and Traini
 <p>‼️ If you retrain again, it will continue where it left. So, for example, if you train for 30 epochs, and you rerun the cell, it will train for same more epochs again.</p>
 </section>
 <section id="visualizing-the-results">
-<h2><span class="section-number">37.106.5. </span>Visualizing the Results<a class="headerlink" href="#visualizing-the-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.5. </span>Visualizing the Results<a class="headerlink" href="#visualizing-the-results" title="Permalink to this headline">#</a></h2>
 <p>Visualizing the model results after training is always a good way to learn what you can do to improve the performance.</p>
 <p>Let’s get a Pandas dataframe containing training loss and accuracy, and validation loss and accuracy.</p>
 <div class="cell docutils container">
@@ -1982,7 +2014,7 @@ <h2><span class="section-number">37.106.5. </span>Visualizing the Results<a clas
 <p>Let’s evaluate the model on the test set.</p>
 </section>
 <section id="evaluating-the-model">
-<h2><span class="section-number">37.106.6. </span>Evaluating the Model<a class="headerlink" href="#evaluating-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.6. </span>Evaluating the Model<a class="headerlink" href="#evaluating-the-model" title="Permalink to this headline">#</a></h2>
 <p>Quite often, you will want to test your model on the data that it never saw. This data is normally called <code class="docutils literal notranslate"><span class="pre">test</span> <span class="pre">set</span></code> and in more applied practice, you will only feed the test to the model after you have done your best to improve it.</p>
 <p>Let’s now evaluate the model on the test set. One thing to note here is that the test set must be preprocessed the same way we preprocessed the training set. The training set was rescaled and that was applied to the test set.</p>
 <p>If this is not obeyed, you would not know why you’re having poor results. Just look up on the next next cell how poor the accuracy will be if I evaluate the model on unscaled data when I trained it on scaled data.</p>
@@ -2100,13 +2132,13 @@ <h2><span class="section-number">37.106.6. </span>Evaluating the Model<a class="
 F1 score is the harmonic mean of precision and recall.</p>
 </section>
 <section id="going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier">
-<h2><span class="section-number">37.106.7. </span>Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier<a class="headerlink" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.7. </span>Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier<a class="headerlink" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier" title="Permalink to this headline">#</a></h2>
 <p>So far, we have built a neural network for regression(in previous labs) and binary classification. And we have only been working with structured datasets(datasets in tabular format).</p>
 <p>Can the same neural networks we used be able to recognize images? In this next practice, we will turn the page to image classification. We will build a neural network for recognizing 10 different fashions and along the way, we will learn other things such as stopping the training upon a given condition is met, and using TensorBoard to visualize model.</p>
 <p>That is going to be cool! Let’s get started!</p>
 </section>
 <section id="getting-the-fashion-data">
-<h2><span class="section-number">37.106.8. </span>Getting the Fashion data<a class="headerlink" href="#getting-the-fashion-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.8. </span>Getting the Fashion data<a class="headerlink" href="#getting-the-fashion-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s get the dataset from Keras.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2121,7 +2153,7 @@ <h2><span class="section-number">37.106.8. </span>Getting the Fashion data<a cla
 </div>
 </section>
 <section id="looking-in-the-fashion-data">
-<h2><span class="section-number">37.106.9. </span>Looking in the Fashion Data<a class="headerlink" href="#looking-in-the-fashion-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.9. </span>Looking in the Fashion Data<a class="headerlink" href="#looking-in-the-fashion-data" title="Permalink to this headline">#</a></h2>
 <p>As always, it is a best practice to peep into the images to see how they like.</p>
 <p>Let’s display the pixels values of a given image, image, and its corresponding label.</p>
 <div class="cell docutils container">
@@ -2192,7 +2224,7 @@ <h2><span class="section-number">37.106.9. </span>Looking in the Fashion Data<a
 <p>Now that we know the dataset that we are working with, let us do some few proprocessing before building a model.</p>
 </section>
 <section id="id1">
-<h2><span class="section-number">37.106.10. </span>Preparing the Data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.10. </span>Preparing the Data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <p>In many cases, real world images datasets are not that clean like fashion mnist.</p>
 <p>You may have to correct images that were incorrectly labeled, or you have labels in texts that need to be converted to numbers(most machine learning models accept numeric input), or scale the pixels values.</p>
 <p>The latter is what we are going to do. It is inarguable that scaling the images pixels to value between 0 and 1 increase the performance of the neural network, and hence the results. Let’s do it!!</p>
@@ -2211,7 +2243,7 @@ <h2><span class="section-number">37.106.10. </span>Preparing the Data<a class="h
 <p>We are now ready to build a neural network.</p>
 </section>
 <section id="id2">
-<h2><span class="section-number">37.106.11. </span>Creating, Compiling, and Training a Model<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.11. </span>Creating, Compiling, and Training a Model<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <p>There are few points to note before creating a model:</p>
 <ul class="simple">
 <li><p>When working with images, the shape of the input images has to be correctly provided. This is a common error done by many people, including me (before I learned it).</p></li>
@@ -2261,7 +2293,7 @@ <h2><span class="section-number">37.106.11. </span>Creating, Compiling, and Trai
 <p>But also, training mnist for 20 epochs is not slow that we would need to activate GPU. We will take an advantage of GPU in later labs.</p>
 </section>
 <section id="visualizing-the-model-results">
-<h2><span class="section-number">37.106.12. </span>Visualizing the Model Results<a class="headerlink" href="#visualizing-the-model-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.12. </span>Visualizing the Model Results<a class="headerlink" href="#visualizing-the-model-results" title="Permalink to this headline">#</a></h2>
 <p>Let’s visualize the model results to see how training went.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2280,7 +2312,7 @@ <h2><span class="section-number">37.106.12. </span>Visualizing the Model Results
 <p>Let’s see how the model performs on unseed data: test set.</p>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.106.13. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.13. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># if you need a model trained, you can use this cell</span>
@@ -2311,11 +2343,11 @@ <h2><span class="section-number">37.106.13. </span>Model Evaluation<a class="hea
 <p>We could also find other Classification metrics based on True/False positives &amp; negatives such as precision, recall, but since we already saw how to compute them in the binary classifier, let’s see other interesting things: Controlling the training using callbacks and using TensorBoard.</p>
 </section>
 <section id="controlling-training-with-callbacks">
-<h2><span class="section-number">37.106.14. </span>Controlling Training with Callbacks<a class="headerlink" href="#controlling-training-with-callbacks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.14. </span>Controlling Training with Callbacks<a class="headerlink" href="#controlling-training-with-callbacks" title="Permalink to this headline">#</a></h2>
 <p>We can use Callbacks functions to control the training.</p>
 <p>Take an example: we can stop training when the model is lo longer showing significant improvements on validation set. Or we can terminate training when a certain condition is met.</p>
 <section id="implementing-callbacks">
-<h3><span class="section-number">37.106.14.1. </span>Implementing Callbacks<a class="headerlink" href="#implementing-callbacks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.106.14.1. </span>Implementing Callbacks<a class="headerlink" href="#implementing-callbacks" title="Permalink to this headline">#</a></h3>
 <p>There are various functionalities available in Keras Callbacks.</p>
 <p>Let’s start with how to use ModelCheckpoint to save the model when the performance on the validation set is best so far. By saving the best model on the validation set, we avoid things like overfitting which is a common issue in machine learning model training, neural network specifically. We also train for less time.</p>
 <p>I will rebuild a same model again.</p>
@@ -2491,7 +2523,7 @@ <h3><span class="section-number">37.106.14.1. </span>Implementing Callbacks<a cl
 </div>
 </section>
 <section id="custom-callback">
-<h3><span class="section-number">37.106.14.2. </span>Custom Callback<a class="headerlink" href="#custom-callback" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.106.14.2. </span>Custom Callback<a class="headerlink" href="#custom-callback" title="Permalink to this headline">#</a></h3>
 <p>Keras offers various functions for implementing custom callbacks that are very handy when you want to control the model training with a little bit of customization.</p>
 <p>You can do certain actions on almost every step of the training. Let’s stop the training when the accuracy is 95%.</p>
 <div class="cell docutils container">
@@ -2556,7 +2588,7 @@ <h3><span class="section-number">37.106.14.2. </span>Custom Callback<a class="he
 </section>
 </section>
 <section id="using-tensorboard-for-model-visualization">
-<h2><span class="section-number">37.106.15. </span>Using TensorBoard for Model Visualization<a class="headerlink" href="#using-tensorboard-for-model-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.15. </span>Using TensorBoard for Model Visualization<a class="headerlink" href="#using-tensorboard-for-model-visualization" title="Permalink to this headline">#</a></h2>
 <p>Tensorboard is incredible tool used by many people (and not just only TensorFlow developers) to experiment with machine learning.</p>
 <p>With TensorBoard, you can:</p>
 <ul class="simple">
@@ -2647,7 +2679,7 @@ <h2><span class="section-number">37.106.15. </span>Using TensorBoard for Model V
 <p>As you can see, TensorBoard is very useful. The fact that you can use it to visualize the performance metrics, model graphs, and datasets as well.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.106.16. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.16. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jean de Dieu Nyandwi for creating the open-source course <a class="reference external" href="https://nyandwi.com/machine_learning_complete/"> machine learning complete </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2689,13 +2721,13 @@ <h2><span class="section-number">37.106.16. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.104. </span>Time Series Forecasting Assignment</p>
+            <p class="prev-next-title"><span class="section-number">40.104. </span>Time Series Forecasting Assignment</p>
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-        <p class="prev-next-title"><span class="section-number">37.107. </span>NN Classify 15 Fruits Assignment</p>
+        <p class="prev-next-title"><span class="section-number">40.107. </span>NN Classify 15 Fruits Assignment</p>
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diff --git a/assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html b/assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html
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--- a/assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.116. Basic classification: Classify images of clothing &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.116. Basic classification: Classify images of clothing &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="38. Slides" href="../../../slides/introduction.html" />
-    <link rel="prev" title="37.115. Generative Adversarial Networks (GANs)" href="../gan/gan-introduction.html" />
+    <link rel="next" title="41. Slides" href="../../../slides/introduction.html" />
+    <link rel="prev" title="40.115. Generative Adversarial Networks (GANs)" href="../gan/gan-introduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
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-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.104. Time Series Forecasting Assignment
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.113. Art by gan
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   37.116.1. Load model
+   40.116.1. Load model
   </a>
  </li>
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   <a class="reference internal nav-link" href="#overview-deep-learning-machine-learning-and-ai">
-   37.116.2. Overview: Deep learning, machine learning, and AI
+   40.116.2. Overview: Deep learning, machine learning, and AI
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview-of-this-guide">
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+   40.116.3. Overview of this guide
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  </li>
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   <a class="reference internal nav-link" href="#import-the-fashion-mnist-dataset">
-   37.116.4. Import the Fashion MNIST dataset
+   40.116.4. Import the Fashion MNIST dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#explore-the-data">
-   37.116.5. Explore the data
+   40.116.5. Explore the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preprocess-the-data">
-   37.116.6. Preprocess the data
+   40.116.6. Preprocess the data
   </a>
  </li>
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-   37.116.7. Build the model
+   40.116.7. Build the model
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#set-up-the-layers">
-     37.116.7.1. Set up the layers
+     40.116.7.1. Set up the layers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compile-the-model">
-     37.116.7.2. Compile the model
+     40.116.7.2. Compile the model
     </a>
    </li>
   </ul>
  </li>
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-   37.116.8. Train the model
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   </a>
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-     37.116.8.1. Feed the model
+     40.116.8.1. Feed the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluate-accuracy">
-     37.116.8.2. Evaluate accuracy
+     40.116.8.2. Evaluate accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-predictions">
-     37.116.8.3. Make predictions
+     40.116.8.3. Make predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#verify-predictions">
-     37.116.8.4. Verify predictions
+     40.116.8.4. Verify predictions
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#use-the-trained-model">
-   37.116.9. Use the trained model
+   40.116.9. Use the trained model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.116.10. Acknowledgments
+   40.116.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1629,86 +1661,86 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   37.116.1. Load model
+   40.116.1. Load model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview-deep-learning-machine-learning-and-ai">
-   37.116.2. Overview: Deep learning, machine learning, and AI
+   40.116.2. Overview: Deep learning, machine learning, and AI
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview-of-this-guide">
-   37.116.3. Overview of this guide
+   40.116.3. Overview of this guide
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-the-fashion-mnist-dataset">
-   37.116.4. Import the Fashion MNIST dataset
+   40.116.4. Import the Fashion MNIST dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#explore-the-data">
-   37.116.5. Explore the data
+   40.116.5. Explore the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preprocess-the-data">
-   37.116.6. Preprocess the data
+   40.116.6. Preprocess the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-the-model">
-   37.116.7. Build the model
+   40.116.7. Build the model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#set-up-the-layers">
-     37.116.7.1. Set up the layers
+     40.116.7.1. Set up the layers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compile-the-model">
-     37.116.7.2. Compile the model
+     40.116.7.2. Compile the model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-the-model">
-   37.116.8. Train the model
+   40.116.8. Train the model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feed-the-model">
-     37.116.8.1. Feed the model
+     40.116.8.1. Feed the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluate-accuracy">
-     37.116.8.2. Evaluate accuracy
+     40.116.8.2. Evaluate accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-predictions">
-     37.116.8.3. Make predictions
+     40.116.8.3. Make predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#verify-predictions">
-     37.116.8.4. Verify predictions
+     40.116.8.4. Verify predictions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#use-the-trained-model">
-   37.116.9. Use the trained model
+   40.116.9. Use the trained model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.116.10. Acknowledgments
+   40.116.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1722,9 +1754,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="basic-classification-classify-images-of-clothing">
-<h1><span class="section-number">37.116. </span>Basic classification: Classify images of clothing<a class="headerlink" href="#basic-classification-classify-images-of-clothing" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.116. </span>Basic classification: Classify images of clothing<a class="headerlink" href="#basic-classification-classify-images-of-clothing" title="Permalink to this headline">#</a></h1>
 <section id="load-model">
-<h2><span class="section-number">37.116.1. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.1. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
 <p>The following code has nothing to do with machine learning and can be skipped.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1769,7 +1801,7 @@ <h2><span class="section-number">37.116.1. </span>Load model<a class="headerlink
 </div>
 </section>
 <section id="overview-deep-learning-machine-learning-and-ai">
-<h2><span class="section-number">37.116.2. </span>Overview: Deep learning, machine learning, and AI<a class="headerlink" href="#overview-deep-learning-machine-learning-and-ai" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.2. </span>Overview: Deep learning, machine learning, and AI<a class="headerlink" href="#overview-deep-learning-machine-learning-and-ai" title="Permalink to this headline">#</a></h2>
 <p><img alt="ai-vs-machine-learning-vs-deep-learning" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/overview/ai-vs-machine-learning-vs-deep-learning.png" /></p>
 <p>Consider the following definitions to understand deep learning vs. machine learning vs. AI:</p>
 <ul class="simple">
@@ -1787,7 +1819,7 @@ <h2><span class="section-number">37.116.2. </span>Overview: Deep learning, machi
 <p>By using machine learning and deep learning techniques, you can build computer systems and applications that do tasks that are commonly associated with human intelligence. These tasks include image recognition, speech recognition, and language translation.</p>
 </section>
 <section id="overview-of-this-guide">
-<h2><span class="section-number">37.116.3. </span>Overview of this guide<a class="headerlink" href="#overview-of-this-guide" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.3. </span>Overview of this guide<a class="headerlink" href="#overview-of-this-guide" title="Permalink to this headline">#</a></h2>
 <p>This guide provide a  simple sample of deep learning. It will help you understand the model framework for deep learning.</p>
 <p>This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It’s okay if you don’t understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go.</p>
 <p>This guide uses <a class="reference external" href="https://www.tensorflow.org/guide/keras">tf.keras</a>, a high-level API to build and train models in TensorFlow.</p>
@@ -1807,7 +1839,7 @@ <h2><span class="section-number">37.116.3. </span>Overview of this guide<a class
 </div>
 </section>
 <section id="import-the-fashion-mnist-dataset">
-<h2><span class="section-number">37.116.4. </span>Import the Fashion MNIST dataset<a class="headerlink" href="#import-the-fashion-mnist-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.4. </span>Import the Fashion MNIST dataset<a class="headerlink" href="#import-the-fashion-mnist-dataset" title="Permalink to this headline">#</a></h2>
 <p>This guide uses the <a class="reference external" href="https://github.com/zalandoresearch/fashion-mnist">Fashion MNIST</a> dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:</p>
 <table>
   <tr><td>
@@ -1893,7 +1925,7 @@ <h2><span class="section-number">37.116.4. </span>Import the Fashion MNIST datas
 </div>
 </section>
 <section id="explore-the-data">
-<h2><span class="section-number">37.116.5. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.5. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1936,7 +1968,7 @@ <h2><span class="section-number">37.116.5. </span>Explore the data<a class="head
 </div>
 </section>
 <section id="preprocess-the-data">
-<h2><span class="section-number">37.116.6. </span>Preprocess the data<a class="headerlink" href="#preprocess-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.6. </span>Preprocess the data<a class="headerlink" href="#preprocess-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1977,10 +2009,10 @@ <h2><span class="section-number">37.116.6. </span>Preprocess the data<a class="h
 </div>
 </section>
 <section id="build-the-model">
-<h2><span class="section-number">37.116.7. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.7. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h2>
 <p>Building the neural network requires configuring the layers of the model, then compiling the model.</p>
 <section id="set-up-the-layers">
-<h3><span class="section-number">37.116.7.1. </span>Set up the layers<a class="headerlink" href="#set-up-the-layers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.7.1. </span>Set up the layers<a class="headerlink" href="#set-up-the-layers" title="Permalink to this headline">#</a></h3>
 <p>The basic building block of a neural network is the <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers"><em>layer</em></a>. Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.</p>
 <p>Most of deep learning consists of chaining together simple layers. Most layers, such as <code class="docutils literal notranslate"><span class="pre">tf.keras.layers.Dense</span></code>, have parameters that are learned during training.</p>
 <div class="cell docutils container">
@@ -1998,7 +2030,7 @@ <h3><span class="section-number">37.116.7.1. </span>Set up the layers<a class="h
 <p>After the pixels are flattened, the network consists of a sequence of two <code class="docutils literal notranslate"><span class="pre">tf.keras.layers.Dense</span></code> layers. These are densely connected, or fully connected, neural layers. The first <code class="docutils literal notranslate"><span class="pre">Dense</span></code> layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.</p>
 </section>
 <section id="compile-the-model">
-<h3><span class="section-number">37.116.7.2. </span>Compile the model<a class="headerlink" href="#compile-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.7.2. </span>Compile the model<a class="headerlink" href="#compile-the-model" title="Permalink to this headline">#</a></h3>
 <p>Before the model is ready for training, it needs a few more settings. These are added during the model’s <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile"><em>compile</em></a> step:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/losses"><em>Loss function</em></a> —This measures how accurate the model is during training. You want to minimize this function to “steer” the model in the right direction.</p></li>
@@ -2017,7 +2049,7 @@ <h3><span class="section-number">37.116.7.2. </span>Compile the model<a class="h
 </section>
 </section>
 <section id="train-the-model">
-<h2><span class="section-number">37.116.8. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.8. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h2>
 <p>Training the neural network model requires the following steps:</p>
 <ol class="simple">
 <li><p>Feed the training data to the model. In this example, the training data is in the <code class="docutils literal notranslate"><span class="pre">train_images</span></code> and <code class="docutils literal notranslate"><span class="pre">train_labels</span></code> arrays.</p></li>
@@ -2026,7 +2058,7 @@ <h2><span class="section-number">37.116.8. </span>Train the model<a class="heade
 <li><p>Verify that the predictions match the labels from the <code class="docutils literal notranslate"><span class="pre">test_labels</span></code> array.</p></li>
 </ol>
 <section id="feed-the-model">
-<h3><span class="section-number">37.116.8.1. </span>Feed the model<a class="headerlink" href="#feed-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.1. </span>Feed the model<a class="headerlink" href="#feed-the-model" title="Permalink to this headline">#</a></h3>
 <p>To start training,  call the <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit"><code class="docutils literal notranslate"><span class="pre">model.fit</span></code></a> method—so called because it “fits” the model to the training data:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2046,7 +2078,7 @@ <h3><span class="section-number">37.116.8.1. </span>Feed the model<a class="head
 <p>As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data.</p>
 </section>
 <section id="evaluate-accuracy">
-<h3><span class="section-number">37.116.8.2. </span>Evaluate accuracy<a class="headerlink" href="#evaluate-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.2. </span>Evaluate accuracy<a class="headerlink" href="#evaluate-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Next, compare how the model performs on the test dataset:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2064,7 +2096,7 @@ <h3><span class="section-number">37.116.8.2. </span>Evaluate accuracy<a class="h
 </ul>
 </section>
 <section id="make-predictions">
-<h3><span class="section-number">37.116.8.3. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.3. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
 <p>With the model trained, you can use it to make predictions about some images.
 Attach a softmax layer to convert the model’s linear outputs—<a class="reference external" href="https://developers.google.com/machine-learning/glossary#logits">logits</a>—to probabilities, which should be easier to interpret.</p>
 <div class="cell docutils container">
@@ -2145,7 +2177,7 @@ <h3><span class="section-number">37.116.8.3. </span>Make predictions<a class="he
 </div>
 </section>
 <section id="verify-predictions">
-<h3><span class="section-number">37.116.8.4. </span>Verify predictions<a class="headerlink" href="#verify-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.4. </span>Verify predictions<a class="headerlink" href="#verify-predictions" title="Permalink to this headline">#</a></h3>
 <p>With the model trained, you can use it to make predictions about some images.</p>
 <p>Let’s look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label.</p>
 <div class="cell docutils container">
@@ -2197,7 +2229,7 @@ <h3><span class="section-number">37.116.8.4. </span>Verify predictions<a class="
 </section>
 </section>
 <section id="use-the-trained-model">
-<h2><span class="section-number">37.116.9. </span>Use the trained model<a class="headerlink" href="#use-the-trained-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.9. </span>Use the trained model<a class="headerlink" href="#use-the-trained-model" title="Permalink to this headline">#</a></h2>
 <p>Finally, use the trained model to make a prediction about a single image.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2250,7 +2282,7 @@ <h2><span class="section-number">37.116.9. </span>Use the trained model<a class=
 <p>And the model predicts a label as expected.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.116.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Tensorflow docs for creating the Tensorflow notebook <a class="reference external" href="https://www.tensorflow.org/tutorials/keras/classification">Basic classification: Classify images of clothing</a>. It inspires the majority of the content in this chapter.</p>
 <p>Thanks to Microsoft for providing <a class="reference external" href="https://learn.microsoft.com/en-us/azure/machine-learning/concept-deep-learning-vs-machine-learning?view=azureml-api-2">overview of deep learning, machine learning, and AI</a>.</p>
 </section>
@@ -2293,13 +2325,13 @@ <h2><span class="section-number">37.116.10. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.115. </span>Generative Adversarial Networks (GANs)</p>
+            <p class="prev-next-title"><span class="section-number">40.115. </span>Generative Adversarial Networks (GANs)</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">38. </span>Slides</p>
+        <p class="prev-next-title"><span class="section-number">41. </span>Slides</p>
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.99. Google Stock Price Prediction RNN &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.99. Google Stock Price Prediction RNN &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.101. Intro to Autoencoders" href="../autoencoder/autoencoder.html" />
-    <link rel="prev" title="37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html" />
+    <link rel="next" title="40.101. Intro to Autoencoders" href="../autoencoder/autoencoder.html" />
+    <link rel="prev" title="40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
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   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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-     14.2. K-Means clustering
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 <section id="load-libraries">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>  <span class="c1"># linear algebra</span>
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 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ptheru">Priya</a> for creating <a class="reference external" href="https://www.kaggle.com/code/ptheru/google-stock-price-prediction-rnn">Google stock price prediction - RNN</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
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-            <p class="prev-next-title"><span class="section-number">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
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-        <p class="prev-next-title"><span class="section-number">37.101. </span>Intro to Autoencoders</p>
+        <p class="prev-next-title"><span class="section-number">40.101. </span>Intro to Autoencoders</p>
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+     40.95.1. 1. What is TensorFlow
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+     40.95.3. Model SubClassing
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+       40.95.4.2. 3.2 Creating a Tensor with tf.constant()
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+   40.96. Acknowledgments
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   </a>
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-     37.95.1. 1. What is TensorFlow
+     40.95.1. 1. What is TensorFlow
     </a>
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+     40.95.2. 2. TensorFlow Model APIs
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+       40.95.2.1. 2.1 Sequential API
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     <a class="reference internal nav-link" href="#model-subclassing">
-     37.95.3. Model SubClassing
+     40.95.3. Model SubClassing
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-basics-of-tensors">
-     37.95.4. 3. The Basics of Tensors
+     40.95.4. 3. The Basics of Tensors
     </a>
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      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#intro-to-tensors">
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+       40.95.4.1. 3.1 Intro to Tensors
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      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#creating-a-tensor-with-tf-constant">
-       37.95.4.2. 3.2 Creating a Tensor with tf.constant()
+       40.95.4.2. 3.2 Creating a Tensor with tf.constant()
       </a>
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      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#creating-a-tensor-with-tf-variable">
-       37.95.4.3. 3.3 Creating a Tensor with tf.Variable()
+       40.95.4.3. 3.3 Creating a Tensor with tf.Variable()
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#creating-a-tensor-from-existing-functions">
-       37.95.4.4. 3.4 Creating a Tensor from Existing Functions
+       40.95.4.4. 3.4 Creating a Tensor from Existing Functions
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#selecting-data-in-tensor">
-       37.95.4.5. 3.5 Selecting Data in Tensor
+       40.95.4.5. 3.5 Selecting Data in Tensor
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      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#performing-operations-on-tensors">
-       37.95.4.6. 3.6 Performing Operations on Tensors
+       40.95.4.6. 3.6 Performing Operations on Tensors
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#manipulating-the-shape-of-tensor">
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+       40.95.4.7. 3.7 Manipulating the Shape of Tensor
       </a>
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     </ul>
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 <section class="tex2jax_ignore mathjax_ignore" id="intro-to-tensorflow-for-deep-learning">
-<h1><span class="section-number">37.95. </span>Intro to TensorFlow for Deep Learning<a class="headerlink" href="#intro-to-tensorflow-for-deep-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.95. </span>Intro to TensorFlow for Deep Learning<a class="headerlink" href="#intro-to-tensorflow-for-deep-learning" title="Permalink to this headline">#</a></h1>
 <section id="what-is-tensorflow">
-<h2><span class="section-number">37.95.1. </span>1. What is TensorFlow<a class="headerlink" href="#what-is-tensorflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.1. </span>1. What is TensorFlow<a class="headerlink" href="#what-is-tensorflow" title="Permalink to this headline">#</a></h2>
 <p>TensorFlow is an open source and an end to end platform used for building machine learning models. Being end to end, you can prepare data, build models, diagnose, improve, and deploy them.</p>
 <p>TensorFlow uses <a class="reference external" href="https://keras.io">Keras</a> at its backend. Keras is a well beautifully designed API for building deep learning models in popular fields such as Computer Vision and Natural Language Processing.</p>
 <p>TensorFlow has got a strong community, from users, learning resources and whole range of technical supports. Not only it powers majority of Google apps such as YouTube, Maps and Google Photos, it is also widely used across startups and other big techs. If you would like to know who is using TensorFlow, <a class="reference external" href="https://www.tensorflow.org/about/case-studies">here you go!</a>.</p>
 </section>
 <section id="tensorflow-model-apis">
-<h2><span class="section-number">37.95.2. </span>2. TensorFlow Model APIs<a class="headerlink" href="#tensorflow-model-apis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.2. </span>2. TensorFlow Model APIs<a class="headerlink" href="#tensorflow-model-apis" title="Permalink to this headline">#</a></h2>
 <p>TensorFlow being suited for variety of tasks, there are 3 ways to build deep learning models.</p>
 <section id="sequential-api">
-<h3><span class="section-number">37.95.2.1. </span>2.1 Sequential API<a class="headerlink" href="#sequential-api" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.2.1. </span>2.1 Sequential API<a class="headerlink" href="#sequential-api" title="Permalink to this headline">#</a></h3>
 <p>This is the simplest model building option. When building a model, you start from the input to the output, no other way around. This API is suited for tasks that don’t require multiple inputs or outputs, or skip connections.</p>
 <p>Sequential API can be a good API to use for things like image classification or regression tasks. With things like object detection and segmentation, we need another API, which is Functional API.</p>
 <p>Below is how a sequential model looks like.</p>
@@ -1749,7 +1781,7 @@ <h3><span class="section-number">37.95.2.1. </span>2.1 Sequential API<a class="h
 </div>
 </section>
 <section id="functional-api">
-<h3><span class="section-number">37.95.2.2. </span>Functional API<a class="headerlink" href="#functional-api" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.2.2. </span>Functional API<a class="headerlink" href="#functional-api" title="Permalink to this headline">#</a></h3>
 <p>This type of API makes it easy to build models that can take multiple inputs/outputs, or skip connections.</p>
 <p>It is well suited in advanced things like object detection and segmentation. In object detection, there are two main involved things.</p>
 <p>One is recognizing the object(classification) and other is localizing the object(regression: predicting the bounding boxes coordinates).</p>
@@ -1771,7 +1803,7 @@ <h3><span class="section-number">37.95.2.2. </span>Functional API<a class="heade
 </section>
 </section>
 <section id="model-subclassing">
-<h2><span class="section-number">37.95.3. </span>Model SubClassing<a class="headerlink" href="#model-subclassing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.3. </span>Model SubClassing<a class="headerlink" href="#model-subclassing" title="Permalink to this headline">#</a></h2>
 <p>SubClassing API is for building custom models and having full control of every step in model building and training.</p>
 <p>In most cases, Sequential and Functional API will be all you need to build almost anything.</p>
 <p>Below is how a subclassing model looks like.</p>
@@ -1801,9 +1833,9 @@ <h2><span class="section-number">37.95.3. </span>Model SubClassing<a class="head
 </div>
 </section>
 <section id="the-basics-of-tensors">
-<h2><span class="section-number">37.95.4. </span>3. The Basics of Tensors<a class="headerlink" href="#the-basics-of-tensors" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.4. </span>3. The Basics of Tensors<a class="headerlink" href="#the-basics-of-tensors" title="Permalink to this headline">#</a></h2>
 <section id="intro-to-tensors">
-<h3><span class="section-number">37.95.4.1. </span>3.1 Intro to Tensors<a class="headerlink" href="#intro-to-tensors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.1. </span>3.1 Intro to Tensors<a class="headerlink" href="#intro-to-tensors" title="Permalink to this headline">#</a></h3>
 <p>A tensor is a multidimensional array of the same data type. A tensor can be a scalar (single number), a vector, or a matrix.</p>
 <p>If you have used NumPy, tensors are like NumPy arrays, except that tensors have GPU(Graphical Processing Unit) support.</p>
 <p>A typical tensor has the following information:</p>
@@ -1833,7 +1865,7 @@ <h3><span class="section-number">37.95.4.1. </span>3.1 Intro to Tensors<a class=
 </ul>
 </section>
 <section id="creating-a-tensor-with-tf-constant">
-<h3><span class="section-number">37.95.4.2. </span>3.2 Creating a Tensor with tf.constant()<a class="headerlink" href="#creating-a-tensor-with-tf-constant" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.2. </span>3.2 Creating a Tensor with tf.constant()<a class="headerlink" href="#creating-a-tensor-with-tf-constant" title="Permalink to this headline">#</a></h3>
 <p>A Tensor can be a scalar, a vector or a matrix.
 Let’s use <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> to create these tensor types.</p>
 <p>A tensor created with <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> is immutable.</p>
@@ -1978,7 +2010,7 @@ <h3><span class="section-number">37.95.4.2. </span>3.2 Creating a Tensor with tf
 </div>
 </section>
 <section id="creating-a-tensor-with-tf-variable">
-<h3><span class="section-number">37.95.4.3. </span>3.3 Creating a Tensor with tf.Variable()<a class="headerlink" href="#creating-a-tensor-with-tf-variable" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.3. </span>3.3 Creating a Tensor with tf.Variable()<a class="headerlink" href="#creating-a-tensor-with-tf-variable" title="Permalink to this headline">#</a></h3>
 <p>A tensor created with <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> is immutable, it can not be changed. Such kind of tensor can not be used as weights in neural networks because they need to be changed/updated in backpropogation for example.</p>
 <p>With <code class="docutils literal notranslate"><span class="pre">tf.Variable()</span></code>, we can create tensors that can be mutable and thus can be used in things like updating the weights of neural networks like said above.</p>
 <p>Creating variable tensor is as simple as the former.</p>
@@ -2009,7 +2041,7 @@ <h3><span class="section-number">37.95.4.3. </span>3.3 Creating a Tensor with tf
 </div>
 </section>
 <section id="creating-a-tensor-from-existing-functions">
-<h3><span class="section-number">37.95.4.4. </span>3.4 Creating a Tensor from Existing Functions<a class="headerlink" href="#creating-a-tensor-from-existing-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.4. </span>3.4 Creating a Tensor from Existing Functions<a class="headerlink" href="#creating-a-tensor-from-existing-functions" title="Permalink to this headline">#</a></h3>
 <p>There some types of uniform tensors that you would not want to create from scratch, when in fact, they are already built.</p>
 <p>Take an example of 1’s tensor, 0’s, and random tensors. Let’s create them.</p>
 <div class="cell docutils container">
@@ -2123,7 +2155,7 @@ <h3><span class="section-number">37.95.4.4. </span>3.4 Creating a Tensor from Ex
 <p>You can learn more about Random number generation at <a class="reference external" href="https://www.tensorflow.org/guide/random_numbers#the_tfrandomgenerator_class">TensorFlow docs</a>.</p>
 </section>
 <section id="selecting-data-in-tensor">
-<h3><span class="section-number">37.95.4.5. </span>3.5 Selecting Data in Tensor<a class="headerlink" href="#selecting-data-in-tensor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.5. </span>3.5 Selecting Data in Tensor<a class="headerlink" href="#selecting-data-in-tensor" title="Permalink to this headline">#</a></h3>
 <p>We can also select values in any tensor, both single dimensional tensor and multi dimensional tensor.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2172,7 +2204,7 @@ <h3><span class="section-number">37.95.4.5. </span>3.5 Selecting Data in Tensor<
 </div>
 </section>
 <section id="performing-operations-on-tensors">
-<h3><span class="section-number">37.95.4.6. </span>3.6 Performing Operations on Tensors<a class="headerlink" href="#performing-operations-on-tensors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.6. </span>3.6 Performing Operations on Tensors<a class="headerlink" href="#performing-operations-on-tensors" title="Permalink to this headline">#</a></h3>
 <p>All numeric operations can be performed on tensor. Let’s see few of them.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2223,7 +2255,7 @@ <h3><span class="section-number">37.95.4.6. </span>3.6 Performing Operations on
 <p>You can learn more at official docs, <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/math"><code class="docutils literal notranslate"><span class="pre">tf.math()</span></code></a> specifically. Almost all maths operations can be done on tensors.</p>
 </section>
 <section id="manipulating-the-shape-of-tensor">
-<h3><span class="section-number">37.95.4.7. </span>3.7 Manipulating the Shape of Tensor<a class="headerlink" href="#manipulating-the-shape-of-tensor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.7. </span>3.7 Manipulating the Shape of Tensor<a class="headerlink" href="#manipulating-the-shape-of-tensor" title="Permalink to this headline">#</a></h3>
 <p>There are times you would want to reshape a tensor. Here is how to go about it.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2290,7 +2322,7 @@ <h3><span class="section-number">37.95.4.7. </span>3.7 Manipulating the Shape of
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.96. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.96. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://github.com/Nyandwi">Jean de Dieu Nyandwi</a> for creating <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/7_intro_to_artificial%20_neural_networks_and_tensorflow/2_intro_to_tensorflow_for_deeplearning.ipynb">2_intro_to_tensorflow_for_deeplearning</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2331,13 +2363,13 @@ <h1><span class="section-number">37.96. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.94. </span>Object Recognition in Images using CNN</p>
+            <p class="prev-next-title"><span class="section-number">40.94. </span>Object Recognition in Images using CNN</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
+        <p class="prev-next-title"><span class="section-number">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/deep-learning/time-series-forecasting-assignment.html b/assignments/deep-learning/time-series-forecasting-assignment.html
index 10efbaa042..855b95617c 100644
--- a/assignments/deep-learning/time-series-forecasting-assignment.html
+++ b/assignments/deep-learning/time-series-forecasting-assignment.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.104. Time Series Forecasting Assignment &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.104. Time Series Forecasting Assignment &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.106. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
-    <link rel="prev" title="37.103. Fun with Variational Autoencoders" href="autoencoder/variational-autoencoder-and-faces-generation.html" />
+    <link rel="next" title="40.106. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
+    <link rel="prev" title="40.103. Fun with Variational Autoencoders" href="autoencoder/variational-autoencoder-and-faces-generation.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.14. Try it in Excel
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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+       40.104.3.1. CSV Data Path
       </a>
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-       37.104.3.2. Reading CSV file with Pandas
+       40.104.3.2. Reading CSV file with Pandas
       </a>
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       </a>
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         </a>
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+         40.104.4.2.4. LSTM
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    </li>
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       </a>
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+       40.104.5.3. CNN
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  </li>
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+   40.105. Acknowledgments
   </a>
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
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+   40.104. Time Series Forecasting Assignment
   </a>
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-     37.104.1. Tasks
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      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task1">
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       </a>
      </li>
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       <a class="reference internal nav-link" href="#task2">
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     </ul>
    </li>
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     <a class="reference internal nav-link" href="#imports">
-     37.104.2. Imports
+     40.104.2. Imports
     </a>
    </li>
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     <a class="reference internal nav-link" href="#loading-data">
-     37.104.3. Loading Data
+     40.104.3. Loading Data
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#csv-data-path">
-       37.104.3.1. CSV Data Path
+       40.104.3.1. CSV Data Path
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#reading-csv-file-with-pandas">
-       37.104.3.2. Reading CSV file with Pandas
+       40.104.3.2. Reading CSV file with Pandas
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#normalizing-data">
-       37.104.3.3. Normalizing Data
+       40.104.3.3. Normalizing Data
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.104.4. Task1
+     40.104.4. Task1
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-preparation">
-       37.104.4.1. Data Preparation
+       40.104.4.1. Data Preparation
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#modeling">
-       37.104.4.2. Modeling
+       40.104.4.2. Modeling
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#ann">
-         37.104.4.2.1. ANN
+         40.104.4.2.1. ANN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#cnn">
-         37.104.4.2.2. CNN
+         40.104.4.2.2. CNN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#simple-rnn">
-         37.104.4.2.3. Simple RNN
+         40.104.4.2.3. Simple RNN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#lstm">
-         37.104.4.2.4. LSTM
+         40.104.4.2.4. LSTM
         </a>
        </li>
       </ul>
@@ -1755,27 +1787,27 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h2 nav-item toc-entry">
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-     37.104.5. Task 2
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       <a class="reference internal nav-link" href="#id2">
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+       40.104.5.1. ANN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rnn">
-       37.104.5.2. RNN
+       40.104.5.2. RNN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.104.5.3. CNN
+       40.104.5.3. CNN
       </a>
      </li>
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       <a class="reference internal nav-link" href="#id4">
-       37.104.5.4. LSTM
+       40.104.5.4. LSTM
       </a>
      </li>
     </ul>
@@ -1784,7 +1816,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.105. Acknowledgments
+   40.105. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1798,21 +1830,21 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="time-series-forecasting-assignment">
-<h1><span class="section-number">37.104. </span>Time Series Forecasting Assignment<a class="headerlink" href="#time-series-forecasting-assignment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.104. </span>Time Series Forecasting Assignment<a class="headerlink" href="#time-series-forecasting-assignment" title="Permalink to this headline">#</a></h1>
 <section id="tasks">
-<h2><span class="section-number">37.104.1. </span>Tasks<a class="headerlink" href="#tasks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.1. </span>Tasks<a class="headerlink" href="#tasks" title="Permalink to this headline">#</a></h2>
 <p><img alt="types" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/assignment/deep-learning/time-series/time-series.jpg" /></p>
 <section id="task1">
-<h3><span class="section-number">37.104.1.1. </span>Task1<a class="headerlink" href="#task1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.1.1. </span>Task1<a class="headerlink" href="#task1" title="Permalink to this headline">#</a></h3>
 <p>Given 10 timesteps of features, predict the USDT of the next timestep (many to one)</p>
 </section>
 <section id="task2">
-<h3><span class="section-number">37.104.1.2. </span>Task2<a class="headerlink" href="#task2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.1.2. </span>Task2<a class="headerlink" href="#task2" title="Permalink to this headline">#</a></h3>
 <p>Given 10 timestaps of features, predict the USDT of those timesteps (many to many)</p>
 </section>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.104.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1838,9 +1870,9 @@ <h2><span class="section-number">37.104.2. </span>Imports<a class="headerlink" h
 </div>
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-<h2><span class="section-number">37.104.3. </span>Loading Data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.3. </span>Loading Data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <section id="csv-data-path">
-<h3><span class="section-number">37.104.3.1. </span>CSV Data Path<a class="headerlink" href="#csv-data-path" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.3.1. </span>CSV Data Path<a class="headerlink" href="#csv-data-path" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/time-series/timeseries.csv&#39;</span>
@@ -1850,7 +1882,7 @@ <h3><span class="section-number">37.104.3.1. </span>CSV Data Path<a class="heade
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 </section>
 <section id="reading-csv-file-with-pandas">
-<h3><span class="section-number">37.104.3.2. </span>Reading CSV file with Pandas<a class="headerlink" href="#reading-csv-file-with-pandas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.3.2. </span>Reading CSV file with Pandas<a class="headerlink" href="#reading-csv-file-with-pandas" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
@@ -1867,7 +1899,7 @@ <h3><span class="section-number">37.104.3.2. </span>Reading CSV file with Pandas
 </div>
 </section>
 <section id="normalizing-data">
-<h3><span class="section-number">37.104.3.3. </span>Normalizing Data<a class="headerlink" href="#normalizing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.3.3. </span>Normalizing Data<a class="headerlink" href="#normalizing-data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
@@ -1894,9 +1926,9 @@ <h3><span class="section-number">37.104.3.3. </span>Normalizing Data<a class="he
 </section>
 </section>
 <section id="id1">
-<h2><span class="section-number">37.104.4. </span>Task1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.4. </span>Task1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <section id="data-preparation">
-<h3><span class="section-number">37.104.4.1. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.4.1. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h3>
 <p>Preparing the data for the first task</p>
 <p>We will try to predict the value of USDT on the 11th day using the data of other features from day 1 to 10</p>
 <div class="cell docutils container">
@@ -1999,9 +2031,9 @@ <h3><span class="section-number">37.104.4.1. </span>Data Preparation<a class="he
 </div>
 </section>
 <section id="modeling">
-<h3><span class="section-number">37.104.4.2. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.4.2. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
 <section id="ann">
-<h4><span class="section-number">37.104.4.2.1. </span>ANN<a class="headerlink" href="#ann" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.104.4.2.1. </span>ANN<a class="headerlink" href="#ann" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="kn">import</span> <span class="n">EarlyStopping</span><span class="p">,</span> <span class="n">ModelCheckpoint</span>
@@ -2068,7 +2100,7 @@ <h4><span class="section-number">37.104.4.2.1. </span>ANN<a class="headerlink" h
 </div>
 </section>
 <section id="cnn">
-<h4><span class="section-number">37.104.4.2.2. </span>CNN<a class="headerlink" href="#cnn" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.104.4.2.2. </span>CNN<a class="headerlink" href="#cnn" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2128,7 +2160,7 @@ <h4><span class="section-number">37.104.4.2.2. </span>CNN<a class="headerlink" h
 </div>
 </section>
 <section id="simple-rnn">
-<h4><span class="section-number">37.104.4.2.3. </span>Simple RNN<a class="headerlink" href="#simple-rnn" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.104.4.2.3. </span>Simple RNN<a class="headerlink" href="#simple-rnn" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2188,7 +2220,7 @@ <h4><span class="section-number">37.104.4.2.3. </span>Simple RNN<a class="header
 </div>
 </section>
 <section id="lstm">
-<h4><span class="section-number">37.104.4.2.4. </span>LSTM<a class="headerlink" href="#lstm" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.104.4.2.4. </span>LSTM<a class="headerlink" href="#lstm" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">lstm_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2243,7 +2275,7 @@ <h4><span class="section-number">37.104.4.2.4. </span>LSTM<a class="headerlink"
 </section>
 </section>
 <section id="task-2">
-<h2><span class="section-number">37.104.5. </span>Task 2<a class="headerlink" href="#task-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.5. </span>Task 2<a class="headerlink" href="#task-2" title="Permalink to this headline">#</a></h2>
 <p>Just setting future to 10 and the window to 10</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2268,7 +2300,7 @@ <h2><span class="section-number">37.104.5. </span>Task 2<a class="headerlink" hr
 </div>
 <p>Same models</p>
 <section id="id2">
-<h3><span class="section-number">37.104.5.1. </span>ANN<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.5.1. </span>ANN<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">ann_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2321,7 +2353,7 @@ <h3><span class="section-number">37.104.5.1. </span>ANN<a class="headerlink" hre
 </div>
 </section>
 <section id="rnn">
-<h3><span class="section-number">37.104.5.2. </span>RNN<a class="headerlink" href="#rnn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.5.2. </span>RNN<a class="headerlink" href="#rnn" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2381,7 +2413,7 @@ <h3><span class="section-number">37.104.5.2. </span>RNN<a class="headerlink" hre
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.104.5.3. </span>CNN<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.5.3. </span>CNN<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2441,7 +2473,7 @@ <h3><span class="section-number">37.104.5.3. </span>CNN<a class="headerlink" hre
 </div>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.104.5.4. </span>LSTM<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.5.4. </span>LSTM<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">lstm_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2503,7 +2535,7 @@ <h3><span class="section-number">37.104.5.4. </span>LSTM<a class="headerlink" hr
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.105. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.105. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to Taha Bouhsine for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/skywolfmo/wts2-time-series-forecasting-with-tf"> WTS2: Time Series Forecasting with TF </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2544,13 +2576,13 @@ <h1><span class="section-number">37.105. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.103. </span>Fun with Variational Autoencoders</p>
+            <p class="prev-next-title"><span class="section-number">40.103. </span>Fun with Variational Autoencoders</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="nn-for-classification-assignment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.106. </span>Neural Networks for Classification with TensorFlow</p>
+        <p class="prev-next-title"><span class="section-number">40.106. </span>Neural Networks for Classification with TensorFlow</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html b/assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html
index 9a8d18402c..e105ad4094 100644
--- a/assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html
+++ b/assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.83. Counterintuitive Challenges in ML Debugging &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.83. Counterintuitive Challenges in ML Debugging &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.84. Case Study: Debugging in Classification" href="debugging-in-classification.html" />
-    <link rel="prev" title="37.82. Data engineering" href="data-engineering.html" />
+    <link rel="next" title="40.84. Case Study: Debugging in Classification" href="debugging-in-classification.html" />
+    <link rel="prev" title="40.82. Data engineering" href="data-engineering.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.3. First assignment
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    <li class="toctree-l2">
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-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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     </a>
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    <li class="toctree-l2">
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.12. Lines, scatters and bars
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-     37.14. Try it in Excel
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-     37.17. Classifying datasets
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-     37.24. Data processing in Python
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-     37.29. Matplotlib applied
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-     37.36. Explore a planetary computer dataset
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
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-     37.103. Fun with Variational Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
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-     37.116. Basic classification: Classify images of clothing
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.11. Machine Learning overview
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-     38.12. Linear Regression
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-     38.15. Neural Network
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-     38.16. Build an machine learning web application
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     38.18. Kernel method
+     41.18. Kernel method
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-     38.19. Model Selection
+     41.19. Model Selection
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-     38.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
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+   40.83.1. Case Study: Debugging a Simple Model
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-the-dataset">
-     37.83.1.1. Create the Dataset
+     40.83.1.1. Create the Dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fit-simple-data-with-simple-model">
-     37.83.1.2. Fit Simple Data with Simple Model
+     40.83.1.2. Fit Simple Data with Simple Model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution-getting-loss-to-decrease">
-     37.83.1.3. Solution: Getting Loss to Decrease
+     40.83.1.3. Solution: Getting Loss to Decrease
     </a>
    </li>
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     <a class="reference internal nav-link" href="#solution-reaching-convergence">
-     37.83.1.4. Solution: Reaching Convergence
+     40.83.1.4. Solution: Reaching Convergence
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-case-study">
-     37.83.1.5. Summary of Case Study
+     40.83.1.5. Summary of Case Study
     </a>
    </li>
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-   37.83.2. Exploding Gradients
+   40.83.2. Exploding Gradients
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cause-high-learning-rate">
-     37.83.2.1. Cause: High Learning Rate
+     40.83.2.1. Cause: High Learning Rate
     </a>
    </li>
   </ul>
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-   37.83.3. Conclusion
+   40.83.3. Conclusion
   </a>
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
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+   40.83.1. Case Study: Debugging a Simple Model
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-     37.83.1.1. Create the Dataset
+     40.83.1.1. Create the Dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     37.83.1.2. Fit Simple Data with Simple Model
+     40.83.1.2. Fit Simple Data with Simple Model
     </a>
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+     40.83.1.3. Solution: Getting Loss to Decrease
     </a>
    </li>
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-     37.83.1.4. Solution: Reaching Convergence
+     40.83.1.4. Solution: Reaching Convergence
     </a>
    </li>
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-     37.83.1.5. Summary of Case Study
+     40.83.1.5. Summary of Case Study
     </a>
    </li>
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-   37.83.2. Exploding Gradients
+   40.83.2. Exploding Gradients
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cause-high-learning-rate">
-     37.83.2.1. Cause: High Learning Rate
+     40.83.2.1. Cause: High Learning Rate
     </a>
    </li>
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  </li>
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-   37.83.3. Conclusion
+   40.83.3. Conclusion
   </a>
  </li>
 </ul>
@@ -1663,7 +1695,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="counterintuitive-challenges-in-ml-debugging">
-<h1><span class="section-number">37.83. </span>Counterintuitive Challenges in ML Debugging<a class="headerlink" href="#counterintuitive-challenges-in-ml-debugging" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.83. </span>Counterintuitive Challenges in ML Debugging<a class="headerlink" href="#counterintuitive-challenges-in-ml-debugging" title="Permalink to this headline">#</a></h1>
 <p>In this Colab, you will explore why ML debugging is harder than traditional debugging by debugging a simple regression problem, with one feature and one label. You will:</p>
 <ul class="simple">
 <li><p>Create the dataset.</p></li>
@@ -1673,9 +1705,9 @@ <h1><span class="section-number">37.83. </span>Counterintuitive Challenges in ML
 </ul>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <section id="case-study-debugging-a-simple-model">
-<h2><span class="section-number">37.83.1. </span>Case Study: Debugging a Simple Model<a class="headerlink" href="#case-study-debugging-a-simple-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.83.1. </span>Case Study: Debugging a Simple Model<a class="headerlink" href="#case-study-debugging-a-simple-model" title="Permalink to this headline">#</a></h2>
 <section id="create-the-dataset">
-<h3><span class="section-number">37.83.1.1. </span>Create the Dataset<a class="headerlink" href="#create-the-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.1. </span>Create the Dataset<a class="headerlink" href="#create-the-dataset" title="Permalink to this headline">#</a></h3>
 <p>Run the cells below to load libraries.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1719,7 +1751,7 @@ <h3><span class="section-number">37.83.1.1. </span>Create the Dataset<a class="h
 </div>
 </section>
 <section id="fit-simple-data-with-simple-model">
-<h3><span class="section-number">37.83.1.2. </span>Fit Simple Data with Simple Model<a class="headerlink" href="#fit-simple-data-with-simple-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.2. </span>Fit Simple Data with Simple Model<a class="headerlink" href="#fit-simple-data-with-simple-model" title="Permalink to this headline">#</a></h3>
 <p>TensorFlow provides several different APIs. This Colab only demonstrates the Keras API since Keras lets you quickly train models in a few lines of code using high-level APIs. In Keras, the typical neural network is a <code class="docutils literal notranslate"><span class="pre">sequential</span></code> model with fully-connected, or <code class="docutils literal notranslate"><span class="pre">dense</span></code>, layers.</p>
 <p>Your dataset is simple. A neural network with just 1 neuron should learn your dataset. Define a neural network with 1 layer having 1 neuron using the model type <code class="docutils literal notranslate"><span class="pre">keras.Sequential</span></code> with the layer type <code class="docutils literal notranslate"><span class="pre">keras.layers.Dense</span></code>. To understand the Keras code, read the code comments. Then run the cell. The code prints the model summary to show a model with 1 layer and 2 parameters (weight and bias).</p>
 <div class="cell docutils container">
@@ -1770,7 +1802,7 @@ <h3><span class="section-number">37.83.1.2. </span>Fit Simple Data with Simple M
 <p>Consider these actions and experiment where necessary. Then read the following section for the solution.</p>
 </section>
 <section id="solution-getting-loss-to-decrease">
-<h3><span class="section-number">37.83.1.3. </span>Solution: Getting Loss to Decrease<a class="headerlink" href="#solution-getting-loss-to-decrease" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.3. </span>Solution: Getting Loss to Decrease<a class="headerlink" href="#solution-getting-loss-to-decrease" title="Permalink to this headline">#</a></h3>
 <p>Before trying to adjust specific model parameters, such as the hyperparameter values, you should first check for good development practices. Here, you should start with a linear model because of these two best practices:</p>
 <ul class="simple">
 <li><p>Regression: In a regression problem, the last layer must always be linear.</p></li>
@@ -1794,7 +1826,7 @@ <h3><span class="section-number">37.83.1.3. </span>Solution: Getting Loss to Dec
 <p>Your loss decreases, albeit slowly! You’re on the right track. How can you get your loss to converge? Experiment with the code above. For the solution, read the following section.</p>
 </section>
 <section id="solution-reaching-convergence">
-<h3><span class="section-number">37.83.1.4. </span>Solution: Reaching Convergence<a class="headerlink" href="#solution-reaching-convergence" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.4. </span>Solution: Reaching Convergence<a class="headerlink" href="#solution-reaching-convergence" title="Permalink to this headline">#</a></h3>
 <p>Your loss isn’t decreasing fast enough. From the guidance on <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/reducing-loss/learning-rate">Learning Rate</a>, you know that you can increase the learning rate to train faster. Run the following code to increase the learning rate to 0.1. The the model reaches convergence quickly.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1824,7 +1856,7 @@ <h3><span class="section-number">37.83.1.4. </span>Solution: Reaching Convergenc
 <p>Yes, the predictions match the features very well.</p>
 </section>
 <section id="summary-of-case-study">
-<h3><span class="section-number">37.83.1.5. </span>Summary of Case Study<a class="headerlink" href="#summary-of-case-study" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.5. </span>Summary of Case Study<a class="headerlink" href="#summary-of-case-study" title="Permalink to this headline">#</a></h3>
 <p>When debugging ML models, you should first attempt to diagnose the problem and apply the appropriate fix. For example, if you had changed your optimizer using <code class="docutils literal notranslate"><span class="pre">optimizer='sgd'</span></code>, then your model also converges faster. However, the problem was not with the optimizer but with the learning rate. Changing the optimizer only helps because <code class="docutils literal notranslate"><span class="pre">optimizer='sgd'</span></code> has a higher default learning rate than <code class="docutils literal notranslate"><span class="pre">optimizer='adam'</span></code>.</p>
 <p>Alternatively, you could train the model for longer with the default learning rate. However, in real-world ML, models take long to train. You should keep your training cycles as short as possible. Therefore, increasing the learning rate is the correct fix.</p>
 <p>These options demonstrate how debugging in ML is n-dimensional, and therefore you must use your understanding of model mechanics to narrow down your options. Because running experiments in ML is time consuming, requires careful setup, and can be subject to reproducibility issues, it’s important to use your understanding of model mechanics to  narrow down options without having to experiment.</p>
@@ -1832,10 +1864,10 @@ <h3><span class="section-number">37.83.1.5. </span>Summary of Case Study<a class
 </section>
 </section>
 <section id="exploding-gradients">
-<h2><span class="section-number">37.83.2. </span>Exploding Gradients<a class="headerlink" href="#exploding-gradients" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.83.2. </span>Exploding Gradients<a class="headerlink" href="#exploding-gradients" title="Permalink to this headline">#</a></h2>
 <p>A common problem in model training is a loss that “explodes” or becomes <code class="docutils literal notranslate"><span class="pre">nan</span></code>. A common cause is anomalous feature data, such as outliers and <code class="docutils literal notranslate"><span class="pre">nan</span></code> values, or a high learning rate. The following sections demonstrate these causes.</p>
 <section id="cause-high-learning-rate">
-<h3><span class="section-number">37.83.2.1. </span>Cause: High Learning Rate<a class="headerlink" href="#cause-high-learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.2.1. </span>Cause: High Learning Rate<a class="headerlink" href="#cause-high-learning-rate" title="Permalink to this headline">#</a></h3>
 <p>In this section, you will create data in the range [0,50] and show that the gradient explodes when you train the model using a learning rate of 0.01. Then you’ll reduce the learning rate to make the model converge.</p>
 <p>Create and visualize the data by running the following code.</p>
 <div class="cell docutils container">
@@ -1870,7 +1902,7 @@ <h3><span class="section-number">37.83.2.1. </span>Cause: High Learning Rate<a c
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.83.3. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.83.3. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>This Colab demonstrated the following principles.</p>
 <ul class="simple">
 <li><p>The n-dimensional nature of debugging in ML makes ML debugging hard.</p></li>
@@ -1918,13 +1950,13 @@ <h2><span class="section-number">37.83.3. </span>Conclusion<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.82. </span>Data engineering</p>
+            <p class="prev-next-title"><span class="section-number">40.82. </span>Data engineering</p>
         </div>
     </a>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.84. </span>Case Study: Debugging in Classification</p>
+        <p class="prev-next-title"><span class="section-number">40.84. </span>Case Study: Debugging in Classification</p>
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diff --git a/assignments/machine-learning-productionization/data-engineering.html b/assignments/machine-learning-productionization/data-engineering.html
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-    <link rel="prev" title="37.80. Random Forest Classifier with Feature Importance" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html" />
+    <link rel="next" title="40.83. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
+    <link rel="prev" title="40.80. Random Forest Classifier with Feature Importance" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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-     37.54. Managing data
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.101. Intro to Autoencoders
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-     37.103. Fun with Variational Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
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+   40.82.1. Exploring dataset
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+   40.82.3. Removing duplicate data
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+   40.82.2. Dealing with missing data
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+   40.82.3. Removing duplicate data
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.82.6. Acknowledgments
+   40.82.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1614,7 +1646,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-engineering">
-<h1><span class="section-number">37.82. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.82. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
 <p>This assignment focuses on techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. Please refer to <a class="reference external" href="#data-engineering">Machine Learning productionization - Data engineering</a> to learn more.</p>
 <p>Fill <code class="docutils literal notranslate"><span class="pre">____</span></code> pieces of the below implementation in order to pass the assertions.</p>
 <!-- 
@@ -1626,7 +1658,7 @@ <h1><span class="section-number">37.82. </span>Data engineering<a class="headerl
 
 Identifying the group of attributes that uniquely identify a single record is very important and needs validation from a domain expert. Removing duplicates on this group leaves you with distinct records necessary for model training. This group acts as a key to performing multiple aggregate and transformations operations on the dataset like calculating rolling mean, backfilling null values, missing value imputation (details on this in next point), etc.
 True duplicates, i.e., instances of the same data point, are usually removed. In this way, the increase of the sample weight on these points is eliminated, and the risk of any artificial inflation in the performance metrics is reduced --><section id="exploring-dataset">
-<h2><span class="section-number">37.82.1. </span>Exploring dataset<a class="headerlink" href="#exploring-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.1. </span>Exploring dataset<a class="headerlink" href="#exploring-dataset" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.</p>
 </div></blockquote>
@@ -1686,7 +1718,7 @@ <h2><span class="section-number">37.82.1. </span>Exploring dataset<a class="head
 <p><strong>Takeaway</strong>: Even just by looking at the metadata about the information in a DataFrame or the first and last few values in one, you can get an immediate idea about the size, shape, and content of the data you are dealing with.</p>
 </section>
 <section id="dealing-with-missing-data">
-<h2><span class="section-number">37.82.2. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.2. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
 <p>Missing data can cause inaccuracies as well as weak or biased results. Sometimes these can be resolved by a “reload” of the data, filling in the missing values with computation and code like Python, or simply just removing the value and corresponding data. There are numerous reasons for why data may be missing and the actions that are taken to resolve these missing values can be dependent on how and why they went missing in the first place.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should know how to replace or remove null values from DataFrames.</p>
@@ -1796,7 +1828,7 @@ <h2><span class="section-number">37.82.2. </span>Dealing with missing data<a cla
 </div>
 </section>
 <section id="removing-duplicate-data">
-<h2><span class="section-number">37.82.3. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.3. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
 <p>Data that has more than one occurrence can produce inaccurate results and usually should be removed. This can be a common occurrence when joining two or more datasets together. However, there are instances where duplication in joined datasets contain pieces that can provide additional information and may need to be preserved.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should be comfortable identifying and removing duplicate values from DataFrames.</p>
@@ -1839,7 +1871,7 @@ <h2><span class="section-number">37.82.3. </span>Removing duplicate data<a class
 </div>
 </section>
 <section id="handle-inconsistent-data">
-<h2><span class="section-number">37.82.4. </span>Handle inconsistent data<a class="headerlink" href="#handle-inconsistent-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.4. </span>Handle inconsistent data<a class="headerlink" href="#handle-inconsistent-data" title="Permalink to this headline">#</a></h2>
 <p>Depending on the source, data can have inconsistencies in how it’s presented. This can cause problems in searching for and representing the value, where it’s seen within the dataset but is not properly represented in visualizations or query results. Common formatting problems involve resolving whitespace, dates, and data types. Resolving formatting issues is typically up to the people who are using the data. For example, standards on how dates and numbers are presented can differ by country.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should know how to handle the inconsistent data format in the DataFrame.</p>
@@ -1887,7 +1919,7 @@ <h2><span class="section-number">37.82.4. </span>Handle inconsistent data<a clas
 </div>
 </section>
 <section id="at-last">
-<h2><span class="section-number">37.82.5. </span>At last<a class="headerlink" href="#at-last" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.5. </span>At last<a class="headerlink" href="#at-last" title="Permalink to this headline">#</a></h2>
 <p>Let’s apply all the methods above to make the data to be clean.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1920,7 +1952,7 @@ <h2><span class="section-number">37.82.5. </span>At last<a class="headerlink" hr
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.82.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It contributes some of the content in this chapter.</p>
 </section>
 </section>
@@ -1962,13 +1994,13 @@ <h2><span class="section-number">37.82.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.80. </span>Random Forest Classifier with Feature Importance</p>
+            <p class="prev-next-title"><span class="section-number">40.80. </span>Random Forest Classifier with Feature Importance</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="counterintuitive-challenges-in-ml-debugging.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.83. </span>Counterintuitive Challenges in ML Debugging</p>
+        <p class="prev-next-title"><span class="section-number">40.83. </span>Counterintuitive Challenges in ML Debugging</p>
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     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/machine-learning-productionization/debugging-in-classification.html b/assignments/machine-learning-productionization/debugging-in-classification.html
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-    <title>37.84. Case Study: Debugging in Classification &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.84. Case Study: Debugging in Classification &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.85. Case Study: Debugging in Regression" href="debugging-in-regression.html" />
-    <link rel="prev" title="37.83. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
+    <link rel="next" title="40.85. Case Study: Debugging in Regression" href="debugging-in-regression.html" />
+    <link rel="prev" title="40.83. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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+     16.1. Introduction to clustering
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    <li class="toctree-l2">
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-     14.2. K-Means clustering
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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    <li class="toctree-l2">
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-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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   </ul>
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  <li class="toctree-l1">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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  </li>
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  </li>
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
-     37.54. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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    <li class="toctree-l2">
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-     37.56. Try a different model
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    <li class="toctree-l2">
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-     37.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
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-     37.59. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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-     37.74. Random forests for classification
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-     37.75. Beyond random forests: more ensemble models
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.83. Counterintuitive Challenges in ML Debugging
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-     37.84. Case Study: Debugging in Classification
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     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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-     37.88. Build Classification Model
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     <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,101 +1558,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-mnist-data">
-   37.84.1. Load MNIST Data
+   40.84.1. Load MNIST Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-the-data-format">
-   37.84.2. Understanding the Data Format
+   40.84.2. Understanding the Data Format
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#do-you-have-imbalanced-classes">
-   37.84.3. Do you have Imbalanced Classes?
+   40.84.3. Do you have Imbalanced Classes?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#shuffle-and-split-dataset">
-   37.84.4. Shuffle and Split Dataset
+   40.84.4. Shuffle and Split Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#process-data">
-   37.84.5. Process Data
+   40.84.5. Process Data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     37.84.5.1. Solution
+     40.84.5.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#remove-all-zero-features">
-   37.84.6. Remove All-Zero Features?
+   40.84.6. Remove All-Zero Features?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-baseline">
-   37.84.7. Establish Baseline
+   40.84.7. Establish Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-linear-model">
-   37.84.8. Train a Linear Model
+   40.84.8. Train a Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.84.8.1. Solution
+     40.84.8.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fixing-loss-calculation">
-   37.84.9. Fixing Loss Calculation
+   40.84.9. Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-nonlinear-model">
-   37.84.10. Train a Nonlinear Model
+   40.84.10. Train a Nonlinear Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-a-second-layer">
-   37.84.11. Adding a Second Layer
+   40.84.11. Adding a Second Layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-training-validation-data-skew">
-   37.84.12. Check for Training/Validation Data Skew
+   40.84.12. Check for Training/Validation Data Skew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#apply-dropout-regularization">
-   37.84.13. Apply Dropout Regularization
+   40.84.13. Apply Dropout Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-accuracy-for-data-slices">
-   37.84.14. Check Accuracy for Data Slices
+   40.84.14. Check Accuracy for Data Slices
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-for-anomalous-values">
-   37.84.15. Testing for Anomalous Values
+   40.84.15. Testing for Anomalous Values
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-pass-tests-by-fixing-loss-calculation">
-   37.84.16. Optional: Pass Tests by Fixing Loss Calculation
+   40.84.16. Optional: Pass Tests by Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.84.17. Conclusion
+   40.84.17. Conclusion
   </a>
  </li>
 </ul>
@@ -1644,101 +1676,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-mnist-data">
-   37.84.1. Load MNIST Data
+   40.84.1. Load MNIST Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-the-data-format">
-   37.84.2. Understanding the Data Format
+   40.84.2. Understanding the Data Format
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#do-you-have-imbalanced-classes">
-   37.84.3. Do you have Imbalanced Classes?
+   40.84.3. Do you have Imbalanced Classes?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#shuffle-and-split-dataset">
-   37.84.4. Shuffle and Split Dataset
+   40.84.4. Shuffle and Split Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#process-data">
-   37.84.5. Process Data
+   40.84.5. Process Data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     37.84.5.1. Solution
+     40.84.5.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#remove-all-zero-features">
-   37.84.6. Remove All-Zero Features?
+   40.84.6. Remove All-Zero Features?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-baseline">
-   37.84.7. Establish Baseline
+   40.84.7. Establish Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-linear-model">
-   37.84.8. Train a Linear Model
+   40.84.8. Train a Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.84.8.1. Solution
+     40.84.8.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fixing-loss-calculation">
-   37.84.9. Fixing Loss Calculation
+   40.84.9. Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-nonlinear-model">
-   37.84.10. Train a Nonlinear Model
+   40.84.10. Train a Nonlinear Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-a-second-layer">
-   37.84.11. Adding a Second Layer
+   40.84.11. Adding a Second Layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-training-validation-data-skew">
-   37.84.12. Check for Training/Validation Data Skew
+   40.84.12. Check for Training/Validation Data Skew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#apply-dropout-regularization">
-   37.84.13. Apply Dropout Regularization
+   40.84.13. Apply Dropout Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-accuracy-for-data-slices">
-   37.84.14. Check Accuracy for Data Slices
+   40.84.14. Check Accuracy for Data Slices
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-for-anomalous-values">
-   37.84.15. Testing for Anomalous Values
+   40.84.15. Testing for Anomalous Values
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-pass-tests-by-fixing-loss-calculation">
-   37.84.16. Optional: Pass Tests by Fixing Loss Calculation
+   40.84.16. Optional: Pass Tests by Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.84.17. Conclusion
+   40.84.17. Conclusion
   </a>
  </li>
 </ul>
@@ -1763,7 +1795,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="case-study-debugging-in-classification">
-<h1><span class="section-number">37.84. </span>Case Study: Debugging in Classification<a class="headerlink" href="#case-study-debugging-in-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.84. </span>Case Study: Debugging in Classification<a class="headerlink" href="#case-study-debugging-in-classification" title="Permalink to this headline">#</a></h1>
 <p>This Colab quickly demonstrates a few concepts related to debugging classification models. You will explore potential problems in implementing these tasks:</p>
 <ul class="simple">
 <li><p>Calculating loss for classification problems.</p></li>
@@ -1773,7 +1805,7 @@ <h1><span class="section-number">37.84. </span>Case Study: Debugging in Classifi
 </ul>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <section id="load-mnist-data">
-<h2><span class="section-number">37.84.1. </span>Load MNIST Data<a class="headerlink" href="#load-mnist-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.1. </span>Load MNIST Data<a class="headerlink" href="#load-mnist-data" title="Permalink to this headline">#</a></h2>
 <p>MNIST is a dataset of images of the numbers 0 to 9. The problem is to classify the images as numbers. Setup libraries and load the MNIST dataset. Display the first few rows to verify that the data loaded. You’ll explore the data format after the data loads.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1811,7 +1843,7 @@ <h2><span class="section-number">37.84.1. </span>Load MNIST Data<a class="header
 </div>
 </section>
 <section id="understanding-the-data-format">
-<h2><span class="section-number">37.84.2. </span>Understanding the Data Format<a class="headerlink" href="#understanding-the-data-format" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.2. </span>Understanding the Data Format<a class="headerlink" href="#understanding-the-data-format" title="Permalink to this headline">#</a></h2>
 <p>Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains ‘6’, then a human rater interpreted the handwritten character as the digit ‘6’.  The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes.</p>
 <p><img alt="img" src="https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png" /></p>
 <p>Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren’t all 0.  Modify the form below and run the code to view data for a given example.</p>
@@ -1826,7 +1858,7 @@ <h2><span class="section-number">37.84.2. </span>Understanding the Data Format<a
 </div>
 </section>
 <section id="do-you-have-imbalanced-classes">
-<h2><span class="section-number">37.84.3. </span>Do you have Imbalanced Classes?<a class="headerlink" href="#do-you-have-imbalanced-classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.3. </span>Do you have Imbalanced Classes?<a class="headerlink" href="#do-you-have-imbalanced-classes" title="Permalink to this headline">#</a></h2>
 <p>As we read in the course, imbalanced classes make classification harder. Let’s look at the distribution of classes. Do you have imbalanced classes?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1843,7 +1875,7 @@ <h2><span class="section-number">37.84.3. </span>Do you have Imbalanced Classes?
 <p>The preceding graph shows that the 10 classes are roughly equally represented.</p>
 </section>
 <section id="shuffle-and-split-dataset">
-<h2><span class="section-number">37.84.4. </span>Shuffle and Split Dataset<a class="headerlink" href="#shuffle-and-split-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.4. </span>Shuffle and Split Dataset<a class="headerlink" href="#shuffle-and-split-dataset" title="Permalink to this headline">#</a></h2>
 <p>As part of <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data Debugging</a> best practices, ensure your splits are statistically equivalent by shuffling your data to remove any pre-existing order.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1859,7 +1891,7 @@ <h2><span class="section-number">37.84.4. </span>Shuffle and Split Dataset<a cla
 </div>
 </section>
 <section id="process-data">
-<h2><span class="section-number">37.84.5. </span>Process Data<a class="headerlink" href="#process-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.5. </span>Process Data<a class="headerlink" href="#process-data" title="Permalink to this headline">#</a></h2>
 <p>Scale the data values to <code class="docutils literal notranslate"><span class="pre">[0,1]</span></code> since the values are bounded to <code class="docutils literal notranslate"><span class="pre">[0,255]</span></code> and do not contain outliers. Then check that the scaled data values are as expected by generating summary statistics using the <code class="docutils literal notranslate"><span class="pre">DataFrame.describe()</span></code> function.</p>
 <p>Run the following cell to scale data and generate statistics. This cell takes a few minutes to run.</p>
 <div class="cell docutils container">
@@ -1895,7 +1927,7 @@ <h2><span class="section-number">37.84.5. </span>Process Data<a class="headerlin
 </div>
 </div>
 <section id="solution">
-<h3><span class="section-number">37.84.5.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.84.5.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
 <p>Start exploring your data by generating a high-level summary using <code class="docutils literal notranslate"><span class="pre">Dataframe.describe()</span></code>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1931,11 +1963,11 @@ <h3><span class="section-number">37.84.5.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="remove-all-zero-features">
-<h2><span class="section-number">37.84.6. </span>Remove All-Zero Features?<a class="headerlink" href="#remove-all-zero-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.6. </span>Remove All-Zero Features?<a class="headerlink" href="#remove-all-zero-features" title="Permalink to this headline">#</a></h2>
 <p>You might think that getting NaNs and discovering that some features were all-zero is good luck because those features can be discarded. However, your training data and validation data might have different all-zero features. Since you should not use validation data to make modeling decisions, you cannot remove only those features that are all-zero in both. Furthermore, data in the future might have different characteristics. There are pros and cons in either case. This Colab keeps the features since reducing the feature set is not a concern.</p>
 </section>
 <section id="establish-baseline">
-<h2><span class="section-number">37.84.7. </span>Establish Baseline<a class="headerlink" href="#establish-baseline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.7. </span>Establish Baseline<a class="headerlink" href="#establish-baseline" title="Permalink to this headline">#</a></h2>
 <p>Following development best practices, you should establish a baseline. The simplest baseline is predicting the most common class. You saw that the most common class is 1. Let’s check the accuracy when always predicting 1.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1947,7 +1979,7 @@ <h2><span class="section-number">37.84.7. </span>Establish Baseline<a class="hea
 <p>Your baseline accuracy is about 11%. Should be easy to beat, right?</p>
 </section>
 <section id="train-a-linear-model">
-<h2><span class="section-number">37.84.8. </span>Train a Linear Model<a class="headerlink" href="#train-a-linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.8. </span>Train a Linear Model<a class="headerlink" href="#train-a-linear-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s start nice and easy with a linear model. All we need is an accuracy &gt; 11%.</p>
 <p>First, let’s define a function to plot our loss and accuracy curves. The function will also print the final loss and accuracy. Instead of using <code class="docutils literal notranslate"><span class="pre">verbose=1</span></code>, you can call the function.</p>
 <div class="cell docutils container">
@@ -2009,12 +2041,12 @@ <h2><span class="section-number">37.84.8. </span>Train a Linear Model<a class="h
 <p>Wow, that accuracy is terrible! What could the cause be?</p>
 <p>Hint: You followed the same procedure as for the previous regression problem. Do you need an adaptation for a classification problem? Experiment with the code above or skip to the solution below.</p>
 <section id="id1">
-<h3><span class="section-number">37.84.8.1. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.84.8.1. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>In regression, the last layer uses a linear activation function. In classification, the last layer cannot use a linear transform. Instead, one option is a softmax transform. Furthermore, in regression, the loss is calculated using MSE while in classification, loss is calculated using crossentropy. Before running your model, if you wrote a test to validate the output values, your test would detect the anomalous output. You’ll look at such a test later. Move onto the next section to fix the loss calculation.</p>
 </section>
 </section>
 <section id="fixing-loss-calculation">
-<h2><span class="section-number">37.84.9. </span>Fixing Loss Calculation<a class="headerlink" href="#fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.9. </span>Fixing Loss Calculation<a class="headerlink" href="#fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
 <p>Since your labels are integers instead of one-hot encodings, use <code class="docutils literal notranslate"><span class="pre">sparse_categorical_crossentropy</span></code> instead of <code class="docutils literal notranslate"><span class="pre">categorical_crossentropy</span></code> so that you avoid converting the integers to one-hot encoding.</p>
 <p>Retrain the model with the new loss calculation by running the following cell. Look through the code to note the changes. What do you think of the result?</p>
 <div class="cell docutils container">
@@ -2041,7 +2073,7 @@ <h2><span class="section-number">37.84.9. </span>Fixing Loss Calculation<a class
 <p>Your loss curves are much better. Your accuracy has improved too. You’re on the right track.</p>
 </section>
 <section id="train-a-nonlinear-model">
-<h2><span class="section-number">37.84.10. </span>Train a Nonlinear Model<a class="headerlink" href="#train-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.10. </span>Train a Nonlinear Model<a class="headerlink" href="#train-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
 <p>Switch to a nonlinear model by modifying the code below to use relu activation functions instead of linear activation functions. Run the code. What do you observe?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2066,7 +2098,7 @@ <h2><span class="section-number">37.84.10. </span>Train a Nonlinear Model<a clas
 <p>The quality of the nonlinear model is significantly better than of the linear model. Progress! Move onto the next section.</p>
 </section>
 <section id="adding-a-second-layer">
-<h2><span class="section-number">37.84.11. </span>Adding a Second Layer<a class="headerlink" href="#adding-a-second-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.11. </span>Adding a Second Layer<a class="headerlink" href="#adding-a-second-layer" title="Permalink to this headline">#</a></h2>
 <p>Increasing the model’s capacity significantly improved your results. Perhaps you can continue this strategy by adding a second relu layer. Run the following code cell to train the model with another relu layer.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2093,7 +2125,7 @@ <h2><span class="section-number">37.84.11. </span>Adding a Second Layer<a class=
 <p>Does your model begin to overfit the training data if you train for long enough? (Your model starts overfitting training data at the point when your validation loss starts increasing.)</p>
 </section>
 <section id="check-for-training-validation-data-skew">
-<h2><span class="section-number">37.84.12. </span>Check for Training/Validation Data Skew<a class="headerlink" href="#check-for-training-validation-data-skew" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.12. </span>Check for Training/Validation Data Skew<a class="headerlink" href="#check-for-training-validation-data-skew" title="Permalink to this headline">#</a></h2>
 <p>Our validation accuracy is a little worse than our training accuracy. While this result is always expected, you should check for typical errors. The commonest cause is having different distributions of data and labels in training and validation. Confirm that the distribution of classes in training and validation data is similar.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2112,7 +2144,7 @@ <h2><span class="section-number">37.84.12. </span>Check for Training/Validation
 </div>
 </section>
 <section id="apply-dropout-regularization">
-<h2><span class="section-number">37.84.13. </span>Apply Dropout Regularization<a class="headerlink" href="#apply-dropout-regularization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.13. </span>Apply Dropout Regularization<a class="headerlink" href="#apply-dropout-regularization" title="Permalink to this headline">#</a></h2>
 <p>Dropout regularization is a common regularization method that removes a random selection of a fixed number of units in a network layer for a single gradient step. Typically, dropout will improve generalization at a dropout rate of between 10% and 50% of neurons.</p>
 <p>Try to reduce the divergence between training and validation loss by using dropout regularization with values between 0.1 and 0.5. Dropout does not improve the results in this case. However, at a dropout of 0.5, the difference in loss decreases, though both training and validation loss decrease in absolute terms.</p>
 <div class="cell docutils container">
@@ -2155,7 +2187,7 @@ <h2><span class="section-number">37.84.13. </span>Apply Dropout Regularization<a
 0.5 | 0.97 | 0.94</p>
 </section>
 <section id="check-accuracy-for-data-slices">
-<h2><span class="section-number">37.84.14. </span>Check Accuracy for Data Slices<a class="headerlink" href="#check-accuracy-for-data-slices" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.14. </span>Check Accuracy for Data Slices<a class="headerlink" href="#check-accuracy-for-data-slices" title="Permalink to this headline">#</a></h2>
 <p>For classification problems, you should always check the metrics by class to ensure your model predicts well across all classes. Check accuracy on the 10 classes by running the next cell by using the function <code class="docutils literal notranslate"><span class="pre">sklearn.metrics.classification_report</span></code> from the scikit-learn library. In the output, the rows with indices 0 to 9 correspond to the classes for the labels 0 to 9. The columns “Precision”, “Recall”, and “<a class="reference external" href="https://en.wikipedia.org/wiki/F1_score">F1-Score</a>” correspond to the respective classification metrics for each class. “Support” is the number of examples for the class in question. For example, for the label “4”, when predicting on 464 examples labelled “4”, the model has a precision of 0.98, a recall of 0.97, and a F1 score of 0.98.</p>
 <p>The classification metrics are very uniform across all classes, which is perfect. In your classification problem, in case any metric is lower for a class, then you should investigate why the model has lower-quality predictions for that class.</p>
 <div class="cell docutils container">
@@ -2169,7 +2201,7 @@ <h2><span class="section-number">37.84.14. </span>Check Accuracy for Data Slices
 </div>
 </section>
 <section id="testing-for-anomalous-values">
-<h2><span class="section-number">37.84.15. </span>Testing for Anomalous Values<a class="headerlink" href="#testing-for-anomalous-values" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.15. </span>Testing for Anomalous Values<a class="headerlink" href="#testing-for-anomalous-values" title="Permalink to this headline">#</a></h2>
 <p>In the section <a class="reference external" href="https://colab.corp.google.com/google_src/cloud/karangill/mlcc/google3/engedu/ml/capitalg/colab/testing_debugging_classification.ipynb#scrollTo=B6AOgLcC5nwp">Train a Linear Model</a>, you debugged an incorrect calculation of loss. Before running your model, if you wrote a test to validate the output values, your test would detect the anomalous output. For example, you could test whether the distribution of predicted labels on the training dataset is similar to the actual distribution of training labels. A simple statistical implementation of this concept is to compare the standard deviation and mean of the predicted and actual labels.</p>
 <p>First, check the standard deviation and mean of the actual labels.</p>
 <div class="cell docutils container">
@@ -2248,7 +2280,7 @@ <h2><span class="section-number">37.84.15. </span>Testing for Anomalous Values<a
 <p>Given equally represented classes, the predicted labels are clearly skewed. From this plot, a possible explanation is that your loss calculation does not appear to be weighting all classes equally. This anomaly is a hint that your loss calculation is incorrect.</p>
 </section>
 <section id="optional-pass-tests-by-fixing-loss-calculation">
-<h2><span class="section-number">37.84.16. </span>Optional: Pass Tests by Fixing Loss Calculation<a class="headerlink" href="#optional-pass-tests-by-fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.16. </span>Optional: Pass Tests by Fixing Loss Calculation<a class="headerlink" href="#optional-pass-tests-by-fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
 <p>As an optional exercise, you can attempt to pass the tests by fixing the loss calculation. You will need to:</p>
 <ol class="simple">
 <li><p>Fix the loss calculation by setting the output layer’s activation to <code class="docutils literal notranslate"><span class="pre">softmax</span></code>.</p></li>
@@ -2258,7 +2290,7 @@ <h2><span class="section-number">37.84.16. </span>Optional: Pass Tests by Fixing
 </ol>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.84.17. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.17. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>The Colab demonstrated the following principles:</p>
 <ul class="simple">
 <li><p>Calculate loss correctly for your problem.</p></li>
@@ -2307,13 +2339,13 @@ <h2><span class="section-number">37.84.17. </span>Conclusion<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.83. </span>Counterintuitive Challenges in ML Debugging</p>
+            <p class="prev-next-title"><span class="section-number">40.83. </span>Counterintuitive Challenges in ML Debugging</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">37.85. </span>Case Study: Debugging in Regression</p>
+        <p class="prev-next-title"><span class="section-number">40.85. </span>Case Study: Debugging in Regression</p>
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diff --git a/assignments/machine-learning-productionization/debugging-in-regression.html b/assignments/machine-learning-productionization/debugging-in-regression.html
index 5f9be0284b..51e193777a 100644
--- a/assignments/machine-learning-productionization/debugging-in-regression.html
+++ b/assignments/machine-learning-productionization/debugging-in-regression.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.85. Case Study: Debugging in Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.85. Case Study: Debugging in Regression &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.86. Study the solvers" href="../ml-fundamentals/study-the-solvers.html" />
-    <link rel="prev" title="37.84. Case Study: Debugging in Classification" href="debugging-in-classification.html" />
+    <link rel="next" title="40.86. Study the solvers" href="../ml-fundamentals/study-the-solvers.html" />
+    <link rel="prev" title="40.84. Case Study: Debugging in Classification" href="debugging-in-classification.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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-     15.1. Bagging
+     17.1. Bagging
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-     15.2. Random forest
+     17.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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  </li>
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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-   24. Autoencoder
+   27. Autoencoder
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-   25. Long-short term memory
+   28. Long-short term memory
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-   26. Time series
+   29. Time series
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-   27. Deep Q-learning
+   30. Deep Q-learning
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-   29. Image segmentation
+   32. Image segmentation
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-   30. Diffusion Model
+   33. Diffusion Model
   </a>
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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  </li>
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../slides/introduction.html">
-   38. Slides
+   41. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
-     38.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,131 +1558,131 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#setup">
-   37.85.1. Setup
+   40.85.1. Setup
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-correlation-matrix">
-   37.85.2. Check Correlation Matrix
+   40.85.2. Check Correlation Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#validate-input-data-against-data-schema">
-   37.85.3. Validate Input Data against Data Schema
+   40.85.3. Validate Input Data against Data Schema
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     37.85.3.1. Solution
+     40.85.3.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-and-normalize-data">
-   37.85.4. Split and Normalize Data
+   40.85.4. Split and Normalize Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#test-engineered-data">
-   37.85.5. Test Engineered Data
+   40.85.5. Test Engineered Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-splits-for-statistical-equivalence">
-   37.85.6. Check Splits for Statistical Equivalence
+   40.85.6. Check Splits for Statistical Equivalence
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-a-baseline">
-   37.85.7. Establish a Baseline
+   40.85.7. Establish a Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-model">
-   37.85.8. Linear Model
+   40.85.8. Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hint">
-     37.85.8.1. Hint
+     40.85.8.1. Hint
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.85.8.2. Solution
+     40.85.8.2. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#add-feature-to-linear-model">
-   37.85.9. Add Feature to Linear Model
+   40.85.9. Add Feature to Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.85.9.1. Solution
+     40.85.9.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#use-a-nonlinear-model">
-   37.85.10. Use a Nonlinear Model
+   40.85.10. Use a Nonlinear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.85.10.1. Solution
+     40.85.10.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optimize-your-model">
-   37.85.11. Optimize Your Model
+   40.85.11. Optimize Your Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.85.11.1. Hint
+     40.85.11.1. Hint
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.85.11.2. Solution
+     40.85.11.2. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-implementation-bugs-using-reduced-dataset">
-   37.85.12. Check for Implementation Bugs using Reduced Dataset
+   40.85.12. Check for Implementation Bugs using Reduced Dataset
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.85.12.1. Solution
+     40.85.12.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#trying-a-very-complex-model">
-   37.85.13. Trying a Very Complex Model
+   40.85.13. Trying a Very Complex Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#results">
-     37.85.13.1. Results
+     40.85.13.1. Results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.85.14. Conclusion
+   40.85.14. Conclusion
   </a>
  </li>
 </ul>
@@ -1674,131 +1706,131 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#setup">
-   37.85.1. Setup
+   40.85.1. Setup
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-correlation-matrix">
-   37.85.2. Check Correlation Matrix
+   40.85.2. Check Correlation Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#validate-input-data-against-data-schema">
-   37.85.3. Validate Input Data against Data Schema
+   40.85.3. Validate Input Data against Data Schema
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     37.85.3.1. Solution
+     40.85.3.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-and-normalize-data">
-   37.85.4. Split and Normalize Data
+   40.85.4. Split and Normalize Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#test-engineered-data">
-   37.85.5. Test Engineered Data
+   40.85.5. Test Engineered Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-splits-for-statistical-equivalence">
-   37.85.6. Check Splits for Statistical Equivalence
+   40.85.6. Check Splits for Statistical Equivalence
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-a-baseline">
-   37.85.7. Establish a Baseline
+   40.85.7. Establish a Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-model">
-   37.85.8. Linear Model
+   40.85.8. Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hint">
-     37.85.8.1. Hint
+     40.85.8.1. Hint
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.85.8.2. Solution
+     40.85.8.2. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#add-feature-to-linear-model">
-   37.85.9. Add Feature to Linear Model
+   40.85.9. Add Feature to Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.85.9.1. Solution
+     40.85.9.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#use-a-nonlinear-model">
-   37.85.10. Use a Nonlinear Model
+   40.85.10. Use a Nonlinear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.85.10.1. Solution
+     40.85.10.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optimize-your-model">
-   37.85.11. Optimize Your Model
+   40.85.11. Optimize Your Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.85.11.1. Hint
+     40.85.11.1. Hint
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.85.11.2. Solution
+     40.85.11.2. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-implementation-bugs-using-reduced-dataset">
-   37.85.12. Check for Implementation Bugs using Reduced Dataset
+   40.85.12. Check for Implementation Bugs using Reduced Dataset
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.85.12.1. Solution
+     40.85.12.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#trying-a-very-complex-model">
-   37.85.13. Trying a Very Complex Model
+   40.85.13. Trying a Very Complex Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#results">
-     37.85.13.1. Results
+     40.85.13.1. Results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.85.14. Conclusion
+   40.85.14. Conclusion
   </a>
  </li>
 </ul>
@@ -1823,7 +1855,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="case-study-debugging-in-regression">
-<h1><span class="section-number">37.85. </span>Case Study: Debugging in Regression<a class="headerlink" href="#case-study-debugging-in-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.85. </span>Case Study: Debugging in Regression<a class="headerlink" href="#case-study-debugging-in-regression" title="Permalink to this headline">#</a></h1>
 <p>In this Colab, you will learn how to debug a regression problem through a case study. You will:</p>
 <ol class="simple">
 <li><p>Set up the problem.</p></li>
@@ -1834,7 +1866,7 @@ <h1><span class="section-number">37.85. </span>Case Study: Debugging in Regressi
 <li><p>Debug your chosen model.</p></li>
 </ol>
 <section id="setup">
-<h2><span class="section-number">37.85.1. </span>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.1. </span>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">#</a></h2>
 <p>This Colab uses the wine quality dataset&lt;sup#[1]&lt;/sup#, which is hosted at <a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/wine+quality">UCI</a>. This dataset contains data on the physicochemical properties of wine along with wine quality ratings. The problem is to predict wine quality (0-10) from physicochemical properties.</p>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <p>&lt;small#[1] Modeling wine preferences by data mining from physicochemical properties. P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Decision Support Systems, Elsevier, 47(4):547-553, 2009.&lt;/small#</p>
@@ -1871,7 +1903,7 @@ <h2><span class="section-number">37.85.1. </span>Setup<a class="headerlink" href
 </div>
 </section>
 <section id="check-correlation-matrix">
-<h2><span class="section-number">37.85.2. </span>Check Correlation Matrix<a class="headerlink" href="#check-correlation-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.2. </span>Check Correlation Matrix<a class="headerlink" href="#check-correlation-matrix" title="Permalink to this headline">#</a></h2>
 <p>Before developing your ML model, you need to select features. To find informative features, check the correlation matrix by running the following cell. Which features are informative?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1885,7 +1917,7 @@ <h2><span class="section-number">37.85.2. </span>Check Correlation Matrix<a clas
 <p><code class="docutils literal notranslate"><span class="pre">alcohol</span></code> is most highly correlated with <code class="docutils literal notranslate"><span class="pre">quality</span></code>. Looking for other informative features, notice that <code class="docutils literal notranslate"><span class="pre">volatile</span> <span class="pre">acidity</span></code> correlates with <code class="docutils literal notranslate"><span class="pre">quality</span></code> but not with <code class="docutils literal notranslate"><span class="pre">alcohol</span></code>, making it a good second feature. Remember that a correlation matrix is not helpful if predictive signals are encoded in combinations of features.</p>
 </section>
 <section id="validate-input-data-against-data-schema">
-<h2><span class="section-number">37.85.3. </span>Validate Input Data against Data Schema<a class="headerlink" href="#validate-input-data-against-data-schema" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.3. </span>Validate Input Data against Data Schema<a class="headerlink" href="#validate-input-data-against-data-schema" title="Permalink to this headline">#</a></h2>
 <p>Before processing your data, you should validate the data against a data schema as described in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data and Feature Debugging</a>.</p>
 <p>First, define a function that validates data against a schema.</p>
 <div class="cell docutils container">
@@ -1976,7 +2008,7 @@ <h2><span class="section-number">37.85.3. </span>Validate Input Data against Dat
 </div>
 </div>
 <section id="solution">
-<h3><span class="section-number">37.85.3.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.3.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">wine_schema</span> <span class="o">=</span> <span class="p">{</span>
@@ -2012,7 +2044,7 @@ <h3><span class="section-number">37.85.3.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="split-and-normalize-data">
-<h2><span class="section-number">37.85.4. </span>Split and Normalize Data<a class="headerlink" href="#split-and-normalize-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.4. </span>Split and Normalize Data<a class="headerlink" href="#split-and-normalize-data" title="Permalink to this headline">#</a></h2>
 <p>Split the dataset into data and labels.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2040,7 +2072,7 @@ <h2><span class="section-number">37.85.4. </span>Split and Normalize Data<a clas
 </div>
 </section>
 <section id="test-engineered-data">
-<h2><span class="section-number">37.85.5. </span>Test Engineered Data<a class="headerlink" href="#test-engineered-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.5. </span>Test Engineered Data<a class="headerlink" href="#test-engineered-data" title="Permalink to this headline">#</a></h2>
 <p>After normalizing your data, you should test your engineered data for errors as described in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data and Feature Debugging</a>. In this section, you will test that engineering data:</p>
 <ul class="simple">
 <li><p>Has the expected number of rows and columns.</p></li>
@@ -2087,7 +2119,7 @@ <h2><span class="section-number">37.85.5. </span>Test Engineered Data<a class="h
 </div>
 </section>
 <section id="check-splits-for-statistical-equivalence">
-<h2><span class="section-number">37.85.6. </span>Check Splits for Statistical Equivalence<a class="headerlink" href="#check-splits-for-statistical-equivalence" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.6. </span>Check Splits for Statistical Equivalence<a class="headerlink" href="#check-splits-for-statistical-equivalence" title="Permalink to this headline">#</a></h2>
 <p>As described in the <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data Debugging</a> guidelines, before developing your model, you should check that your training and validation splits are equally representative. Assuming a training:validation split of 80:20, compare the mean and the standard deviation of the splits by running the next two code cells. Note that this comparison is not a rigorous test for statistical equivalence but simply a quick and dirty comparison of the splits.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2122,7 +2154,7 @@ <h2><span class="section-number">37.85.6. </span>Check Splits for Statistical Eq
 <p>Ensuring your splits are statistically equivalent is a good development practice. In general, following good development practices will simplify your model debugging. To learn about testing for statistical equivalence, see <a class="reference external" href="https://journals.sagepub.com/doi/10.1177/1948550617697177">Equivalence Tests Lakens, D.</a>.</p>
 </section>
 <section id="establish-a-baseline">
-<h2><span class="section-number">37.85.7. </span>Establish a Baseline<a class="headerlink" href="#establish-a-baseline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.7. </span>Establish a Baseline<a class="headerlink" href="#establish-a-baseline" title="Permalink to this headline">#</a></h2>
 <p>For a regression problem, the simplest baseline to predict the average value. Run the following code to calculate the mean-squared error (MSE) loss on the training split using the average value as a baseline. Your loss is approximately 0.75. Any model should beat this loss to justify its use.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2135,7 +2167,7 @@ <h2><span class="section-number">37.85.7. </span>Establish a Baseline<a class="h
 </div>
 </section>
 <section id="linear-model">
-<h2><span class="section-number">37.85.8. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.8. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h2>
 <p>Following good ML dev practice, let’s start with a linear model that uses the most informative feature from the correlation matrix: <code class="docutils literal notranslate"><span class="pre">alcohol</span></code>. Even if this model performs badly, we can still use it as a baseline. This model should beat our previous baseline’s MSE of 0.75.</p>
 <p>First, let’s define a function to plot our loss and accuracy curves. The function will also print the final loss and accuracy. Instead of using <code class="docutils literal notranslate"><span class="pre">verbose=1</span></code>, you can call the function.</p>
 <div class="cell docutils container">
@@ -2189,7 +2221,7 @@ <h2><span class="section-number">37.85.8. </span>Linear Model<a class="headerlin
 </div>
 </div>
 <section id="hint">
-<h3><span class="section-number">37.85.8.1. </span>Hint<a class="headerlink" href="#hint" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.8.1. </span>Hint<a class="headerlink" href="#hint" title="Permalink to this headline">#</a></h3>
 <p>The loss decreases but very slowly. Possible fixes are:</p>
 <ul class="simple">
 <li><p>Increase number of epochs.</p></li>
@@ -2199,7 +2231,7 @@ <h3><span class="section-number">37.85.8.1. </span>Hint<a class="headerlink" hre
 <p>Play with these three parameters in the code above to decrease the loss.</p>
 </section>
 <section id="id1">
-<h3><span class="section-number">37.85.8.2. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.8.2. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Run the following code cell to train the model using a reduced batch size of 100. Reducing the batch size leads to a greater decrease in loss per epoch. The minimum achievable loss is about 0.64. This is a significant increase over our baseline of 0.75.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2228,7 +2260,7 @@ <h3><span class="section-number">37.85.8.2. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="add-feature-to-linear-model">
-<h2><span class="section-number">37.85.9. </span>Add Feature to Linear Model<a class="headerlink" href="#add-feature-to-linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.9. </span>Add Feature to Linear Model<a class="headerlink" href="#add-feature-to-linear-model" title="Permalink to this headline">#</a></h2>
 <p>Try adding a feature to the linear model. Since you need to combine the two features into one prediction for regression, you’ll also need to add a second layer. Modify the code below to implement the following changes:</p>
 <ol class="simple">
 <li><p>Add <code class="docutils literal notranslate"><span class="pre">'volatile</span> <span class="pre">acidity'</span></code> to the features in <code class="docutils literal notranslate"><span class="pre">wineFeaturesSimple</span></code>.</p></li>
@@ -2263,7 +2295,7 @@ <h2><span class="section-number">37.85.9. </span>Add Feature to Linear Model<a c
 </div>
 </div>
 <section id="id2">
-<h3><span class="section-number">37.85.9.1. </span>Solution<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.9.1. </span>Solution<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p>Run the following code to add the second feature and the second layer. The training loss is about 0.59, a small decrease from the previous loss of 0.64.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2294,7 +2326,7 @@ <h3><span class="section-number">37.85.9.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="use-a-nonlinear-model">
-<h2><span class="section-number">37.85.10. </span>Use a Nonlinear Model<a class="headerlink" href="#use-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.10. </span>Use a Nonlinear Model<a class="headerlink" href="#use-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s try a nonlinear model. Modify the code below to make the following changes:</p>
 <ol class="simple">
 <li><p>Change the first layer to use <code class="docutils literal notranslate"><span class="pre">relu</span></code>. (Output layer stays linear since this is a regression problem.)</p></li>
@@ -2326,7 +2358,7 @@ <h2><span class="section-number">37.85.10. </span>Use a Nonlinear Model<a class=
 </div>
 </div>
 <section id="id3">
-<h3><span class="section-number">37.85.10.1. </span>Solution<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.10.1. </span>Solution<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Run the following cell to use a <code class="docutils literal notranslate"><span class="pre">relu</span></code> activation in your first hidden layer. Your loss stays about the same, perhaps declining negligibly to 0.58.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2355,7 +2387,7 @@ <h3><span class="section-number">37.85.10.1. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="optimize-your-model">
-<h2><span class="section-number">37.85.11. </span>Optimize Your Model<a class="headerlink" href="#optimize-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.11. </span>Optimize Your Model<a class="headerlink" href="#optimize-your-model" title="Permalink to this headline">#</a></h2>
 <p>We have two features with one hidden layer but didn’t see an improvement. At this point, it’s tempting to use all your features with a high-capacity network. However, you must resist the temptation. Instead, follow the guidance in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/optimization">Model Optimization</a> to improve model performance. For a hint and for a discussion, see the following text sections.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2385,11 +2417,11 @@ <h2><span class="section-number">37.85.11. </span>Optimize Your Model<a class="h
 </div>
 </div>
 <section id="id4">
-<h3><span class="section-number">37.85.11.1. </span>Hint<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.11.1. </span>Hint<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>You can try to reduce loss by adding features, adding layers, or playing with the hyperparameters. Before adding more features, check the correlation matrix. Don’t expect your loss to decrease by much. Sadly, that is a common experience in machine learning!</p>
 </section>
 <section id="id5">
-<h3><span class="section-number">37.85.11.2. </span>Solution<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.11.2. </span>Solution<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <p>Run the following code to:</p>
 <ul class="simple">
 <li><p>Add the features chlorides and density.</p></li>
@@ -2427,7 +2459,7 @@ <h3><span class="section-number">37.85.11.2. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="check-for-implementation-bugs-using-reduced-dataset">
-<h2><span class="section-number">37.85.12. </span>Check for Implementation Bugs using Reduced Dataset<a class="headerlink" href="#check-for-implementation-bugs-using-reduced-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.12. </span>Check for Implementation Bugs using Reduced Dataset<a class="headerlink" href="#check-for-implementation-bugs-using-reduced-dataset" title="Permalink to this headline">#</a></h2>
 <p>Your loss isn’t decreasing by much. Perhaps your model has an implementation bug. From the <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/model-errors">Model Debugging</a> guidelines, a quick test for implementation bugs is to obtain a low loss on a reduced dataset of, say, 10 examples. Remember, passing this test does not validate your modeling approach but only checks for basic implementation bugs. In your ML problem, if your model passes this test, then continue debugging your model to train on your full dataset.</p>
 <p>In the following code, experiment with the learning rate, batch size, and number of epochs. Can you reach a low loss? Choose hyperparameter values that let you iterate quickly.</p>
 <div class="cell docutils container">
@@ -2459,7 +2491,7 @@ <h2><span class="section-number">37.85.12. </span>Check for Implementation Bugs
 </div>
 </div>
 <section id="id6">
-<h3><span class="section-number">37.85.12.1. </span>Solution<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.12.1. </span>Solution<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>Run the following code cell to train the model using these hyperparameter values:</p>
 <ul class="simple">
 <li><p>learning rate = 0.01</p></li>
@@ -2497,7 +2529,7 @@ <h3><span class="section-number">37.85.12.1. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="trying-a-very-complex-model">
-<h2><span class="section-number">37.85.13. </span>Trying a Very Complex Model<a class="headerlink" href="#trying-a-very-complex-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.13. </span>Trying a Very Complex Model<a class="headerlink" href="#trying-a-very-complex-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s go all in and use a very complex model with all the features. For science! And to satisfy ourselves that a simple model is indeed better. Let’s use all 11 features with 3 fully-connected relu layers and a final linear layer. The next cell takes a while to run. Skip to the results in the cell after if you like.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2522,14 +2554,14 @@ <h2><span class="section-number">37.85.13. </span>Trying a Very Complex Model<a
 </div>
 </div>
 <section id="results">
-<h3><span class="section-number">37.85.13.1. </span>Results<a class="headerlink" href="#results" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.13.1. </span>Results<a class="headerlink" href="#results" title="Permalink to this headline">#</a></h3>
 <p>If you train for long enough, the minimum achievable MSE is around 0.52, which is a decrease of 0.04 from the previous best loss of 0.56. This decrease probably isn’t worth the performance and complexity cost of using all your features and a deeper network. However, that decision depends on the objectives you’re optimizing for.</p>
 <p>If you train the model long enough, the validation loss actually starts increasing while training loss continues to decrease. This divergence in loss curves means your model is overfitting. The overfitting results from the closer fit that your very complex model can learn. Stick with the simpler model. You’ll be happier and live longer!</p>
 <p>If you do want to optimize your loss, then play with the model to find the minimum achievable training loss before overfitting sets in. Try playing with the network parameters in the code cell above to achieve a loss of 0.51. But be warned—optimizing your loss could take a lot of trial and error…</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.85.14. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.14. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>This Colab demonstrated the following principles:</p>
 <ul class="simple">
 <li><p>The most important step in machine learning is understanding your data.</p></li>
@@ -2578,13 +2610,13 @@ <h2><span class="section-number">37.85.14. </span>Conclusion<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.84. </span>Case Study: Debugging in Classification</p>
+            <p class="prev-next-title"><span class="section-number">40.84. </span>Case Study: Debugging in Classification</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ml-fundamentals/study-the-solvers.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.86. </span>Study the solvers</p>
+        <p class="prev-next-title"><span class="section-number">40.86. </span>Study the solvers</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html b/assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html
index 51bbfda259..1485d0e7d7 100644
--- a/assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html
+++ b/assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.75. Beyond random forests: more ensemble models &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.75. Beyond random forests: more ensemble models &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.76. Decision trees" href="decision-trees.html" />
-    <link rel="prev" title="37.74. Random forests for classification" href="random-forests-for-classification.html" />
+    <link rel="next" title="40.76. Decision trees" href="decision-trees.html" />
+    <link rel="prev" title="40.74. Random forests for classification" href="random-forests-for-classification.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
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+   40.75.4. Tasks and roles
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     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
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+     40.75.4.1. Task 1: Exploratory data analysis
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-     37.75.4.3. Task 3: Data preprocessing
+     40.75.4.3. Task 3: Data preprocessing
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-     37.75.4.4. Task 4: Training ensemble classifiers
+     40.75.4.4. Task 4: Training ensemble classifiers
     </a>
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-       37.75.4.4.1. 4.1: Voting classifier
+       40.75.4.4.1. 4.1: Voting classifier
       </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#bagging-classifier">
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+       40.75.4.4.2. 4.2: Bagging classifier
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+       40.75.4.4.3. 4.3: Gradient boosting classifier
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       <a class="reference internal nav-link" href="#adaboost-classifier">
-       37.75.4.4.4. 4.4: AdaBoost classifier
+       40.75.4.4.4. 4.4: AdaBoost classifier
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+       40.75.4.4.5. 4.5: Stacking classifier
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-     37.75.4.5. Task 5: Evaluating the ensemble model on the test set
+     40.75.4.5. Task 5: Evaluating the ensemble model on the test set
     </a>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="beyond-random-forests-more-ensemble-models">
-<h1><span class="section-number">37.75. </span>Beyond random forests: more ensemble models<a class="headerlink" href="#beyond-random-forests-more-ensemble-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.75. </span>Beyond random forests: more ensemble models<a class="headerlink" href="#beyond-random-forests-more-ensemble-models" title="Permalink to this headline">#</a></h1>
 <p>Ensembles are the combination of different machine learning models, where by averaging the results of all models trained together on the same dataset, the result can be away good than any individual model. These types of models can be used for all kinds of tasks, including classification, regression and detecting anomalies.</p>
 <p>In his book <em><a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow</a></em>, <em>Aurelion Geron</em> gave a perfect analogy describing the ensemble learning: “Suppose you pose a complex question to thousands of random people, then aggregate their answers. In many cases you will find that this aggregated answer is better than an expert’s answer. This is called the <em>wisdom of the crowd</em>. Similarly, if you aggregate the predictions of a group of predictors (such as classifiers or regressors), you will often get better predictions than with the best individual predictor. A group of predictors is called an ensemble; thus, this technique is called Ensemble Learning, and an Ensemble Learning algorithm is called an Ensemble method.”</p>
 <p>Random Forests that was used in the previous assignment is an example of ensemble models. It combined different decision trees. Usually, these types of models are classified into three groups that will be covered in this assignment:</p>
@@ -1734,10 +1766,10 @@ <h1><span class="section-number">37.75. </span>Beyond random forests: more ensem
 <p>Most of the above algorithms are implemented in Sklearn, except XGBoost and Stacking which is implemented indirectly.</p>
 <p>Like said in the beginning, these types of models can be used for both regression and classification, but this assignment will consider classification domain and for giving focus on the ensemble models, we will use the same dataset we used previouly, in random forests.</p>
 <section id="ensemble-methods-for-classification">
-<h2><span class="section-number">37.75.1. </span>Ensemble methods for classification<a class="headerlink" href="#ensemble-methods-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.1. </span>Ensemble methods for classification<a class="headerlink" href="#ensemble-methods-for-classification" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.75.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1752,7 +1784,7 @@ <h2><span class="section-number">37.75.2. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.75.3. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.3. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will use Random forests to build a classifier that identify the increase or decrease of the electricity using “the data that was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.”</p>
 <p>“The dataset contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). Source: <a class="reference external" href="https://www.openml.org/d/151">Open ML electricity</a>.</p>
 <p>Here are the information about the features:</p>
@@ -1807,9 +1839,9 @@ <h2><span class="section-number">37.75.3. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="tasks-and-roles">
-<h2><span class="section-number">37.75.4. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.4. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">37.75.4.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, as always, let’s split the data into training and test sets.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1912,7 +1944,7 @@ <h3><span class="section-number">37.75.4.1. </span>Task 1: Exploratory data anal
 <p>In the above correlation matrix, you can see that class feature is not there and this is because it still has categorical values.</p>
 </section>
 <section id="task-2-more-data-exploration">
-<h3><span class="section-number">37.75.4.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Before preprocessing the data, let’s take a look into specific features.</p>
 <p>Let’s see how many Ups/Downs are in the class feature.</p>
 <div class="cell docutils container">
@@ -1998,7 +2030,7 @@ <h3><span class="section-number">37.75.4.2. </span>Task 2: More data exploration
 </div>
 </section>
 <section id="task-3-data-preprocessing">
-<h3><span class="section-number">37.75.4.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Let’s encode the categorical feature <code class="docutils literal notranslate"><span class="pre">class</span></code>. But before that, let’s take training input data and labels.</p>
 <div class="cell docutils container">
@@ -2052,9 +2084,9 @@ <h3><span class="section-number">37.75.4.3. </span>Task 3: Data preprocessing<a
 <p>But again if you look at the dat, the <code class="docutils literal notranslate"><span class="pre">day</span></code> feature is not normalized as other features. We can normalize it or leave it but for now let’s go ahead and train the random forests classifier.</p>
 </section>
 <section id="task-4-training-ensemble-classifiers">
-<h3><span class="section-number">37.75.4.4. </span>Task 4: Training ensemble classifiers<a class="headerlink" href="#task-4-training-ensemble-classifiers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.4. </span>Task 4: Training ensemble classifiers<a class="headerlink" href="#task-4-training-ensemble-classifiers" title="Permalink to this headline">#</a></h3>
 <section id="voting-classifier">
-<h4><span class="section-number">37.75.4.4.1. </span>4.1: Voting classifier<a class="headerlink" href="#voting-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.1. </span>4.1: Voting classifier<a class="headerlink" href="#voting-classifier" title="Permalink to this headline">#</a></h4>
 <p>Let’s assume that you have trained 3 different classifiers on the training data but none of these classifiers had an oustanding results.</p>
 <p>The idea of Voting ensemble technique is fairly simple. We can aggregate the results of all those 3 classifiers and the result will be good than any single classifier.</p>
 <p>Let’s train 3 classifiers on the training data and then we will go ahead and aggregate their results.</p>
@@ -2155,7 +2187,7 @@ <h4><span class="section-number">37.75.4.4.1. </span>4.1: Voting classifier<a cl
 <p>As we can see, it slightly outperformed all individual classifiers. It’s small increments in the accuracy but most of the time, it will be always better than any individual classifier.</p>
 </section>
 <section id="bagging-classifier">
-<h4><span class="section-number">37.75.4.4.2. </span>4.2: Bagging classifier<a class="headerlink" href="#bagging-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.2. </span>4.2: Bagging classifier<a class="headerlink" href="#bagging-classifier" title="Permalink to this headline">#</a></h4>
 <p>Instead of training different algorithms on the same data and average their results as Voting does, Bagging ensemble method train a single or multiple classifier/regressors on different subsets of the training data and aggregate the results on all subsets.</p>
 <p>When this is used with complex models (that overfit data easily) like decision trees, the overfitting can be reduced.</p>
 <p>Let’s use bagging to train 500 decision trees on different subsets of data and then average the predictions on those subsets.</p>
@@ -2188,7 +2220,7 @@ <h4><span class="section-number">37.75.4.4.2. </span>4.2: Bagging classifier<a c
 <p>Wow, this is much better. Bagging ensembles work well and they outperform Voting ensembles a lot. Other remarkable thing about them is that they are able to overcome the overfitting especially when used with decision trees (decision trees tend to overfit easily). Let’s now see other type of ensemble models: Gradient Boosting Classifier.</p>
 </section>
 <section id="gradient-boosting-classifier">
-<h4><span class="section-number">37.75.4.4.3. </span>4.3: Gradient boosting classifier<a class="headerlink" href="#gradient-boosting-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.3. </span>4.3: Gradient boosting classifier<a class="headerlink" href="#gradient-boosting-classifier" title="Permalink to this headline">#</a></h4>
 <p>Gradient boosting works like bagging methods. The only difference is that instead of training models on subsets of training data, models(decision trees) are trained in a sequence where each tree model takes the error of the previous tree to correct it and the sequence goes on…</p>
 <p>Simply, the initial model is trained on the full data, but then the next models minimizes the previous errors.</p>
 <p>Just like other models, gradient boosting classifier has hyperparameters but the most important ones are number of estimators or trees (<code class="docutils literal notranslate"><span class="pre">n_estimators</span></code>) and learning rate (<code class="docutils literal notranslate"><span class="pre">learning_rate</span></code>).</p>
@@ -2219,7 +2251,7 @@ <h4><span class="section-number">37.75.4.4.3. </span>4.3: Gradient boosting clas
 <p>For classification problems beyond two classes, Scikit-Learn recommend using <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier">Histogram Gradient Boosting Classifier</a> which is a faster version of the Gradient Boosting Classifier. It is inspired by <a class="reference external" href="https://github.com/Microsoft/LightGBM">Microsoft Light Gradient Boosting Machine (LightGBM)</a>. LightGBM is fater, low memory usage, better performance, support distributed training and GPU, and can handle large datasets.</p>
 </section>
 <section id="adaboost-classifier">
-<h4><span class="section-number">37.75.4.4.4. </span>4.4: AdaBoost classifier<a class="headerlink" href="#adaboost-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.4. </span>4.4: AdaBoost classifier<a class="headerlink" href="#adaboost-classifier" title="Permalink to this headline">#</a></h4>
 <p>AdaBoost is another ensemble model in class of boosting methods. It’s very much like gradient boost but instead of minimizing the error of the consecutive models, it updates the weights.</p>
 <p>So, the first model (decision tree) is trained on the full training data, the next model weights are updated based off the previous weights and so forth.</p>
 <p>The main parameters to tune to make AdaBoost work well are number of estimators and the maximum depth of each estimator.</p>
@@ -2251,7 +2283,7 @@ <h4><span class="section-number">37.75.4.4.4. </span>4.4: AdaBoost classifier<a
 <p>Again, you can tune the number of estimators and the depth of the base estimator. The base estimator is Decision Trees by default.</p>
 </section>
 <section id="stacking-classifier">
-<h4><span class="section-number">37.75.4.4.5. </span>4.5: Stacking classifier<a class="headerlink" href="#stacking-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.5. </span>4.5: Stacking classifier<a class="headerlink" href="#stacking-classifier" title="Permalink to this headline">#</a></h4>
 <p>Also referred to as Stacked Generalization, it is an ensemble method employed to reduce the biases and the errors rate made by multiple predictors(individual models/estimators).</p>
 <p>Instead of averaging the predictions made by individual models or predictors, in stacking, multiple models are trained on the full training data and then the final model that is trained on different subsets of the training data takes the predictions of the former models and find the final predictions.</p>
 <p>From its <a class="reference external" href="http://machine-learning.martinsewell.com/ensembles/stacking/Wolpert1992.pdf">original paper</a>, it is noted that it can be seen as the sophisticated version of the cross validation (per the reason why the final estimator is trained on the subsets or folds of the training data).</p>
@@ -2291,7 +2323,7 @@ <h4><span class="section-number">37.75.4.4.5. </span>4.5: Stacking classifier<a
 </section>
 </section>
 <section id="task-5-evaluating-the-ensemble-model-on-the-test-set">
-<h3><span class="section-number">37.75.4.5. </span>Task 5: Evaluating the ensemble model on the test set<a class="headerlink" href="#task-5-evaluating-the-ensemble-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.5. </span>Task 5: Evaluating the ensemble model on the test set<a class="headerlink" href="#task-5-evaluating-the-ensemble-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let’s say that all we have been doing was trying to find an ensemble model that can fit well the data and so we want to test it on the test set before shipping it into production.</p>
 <p>To narrow the choice down, we will use A Gradient Boosting method.</p>
 <p>As always, we will prepare the test set in the same way that we prepared the training set. Let’s go!</p>
@@ -2334,14 +2366,14 @@ <h3><span class="section-number">37.75.4.5. </span>Task 5: Evaluating the ensemb
 </section>
 </section>
 <section id="final-note">
-<h2><span class="section-number">37.75.5. </span>Final note<a class="headerlink" href="#final-note" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.5. </span>Final note<a class="headerlink" href="#final-note" title="Permalink to this headline">#</a></h2>
 <p>This notebook was all about ensemble learning methods. There is truly a <code class="docutils literal notranslate"><span class="pre">wisdom</span> <span class="pre">in</span> <span class="pre">the</span> <span class="pre">crowd</span></code>(CC: Aurelion Geron). By averaging the results of different models, we are able to improve the overall prediction.</p>
 <p>There is a notion that ensemble models are slow and expensive to run in production. It’s true, but with the advent of computation power and how incredible these types of ML models work well, that’s no longer issue for someone. They are complex algorithms but they can also reduce complexity. How?</p>
 <p>Think about it: Instead of building a single complex model (typically a neural network), you can build small models that will train and compute predictions faster and then average their results using a given ensemble method. By that way, you are leveraging ensemble methods to reduce complexity.</p>
 <p>To learn more about the ensemble models, you can refer to this chapter <code class="docutils literal notranslate"><span class="pre">Chapter</span> <span class="pre">7</span> <span class="pre">Ensemble</span> <span class="pre">Learning</span> <span class="pre">and</span> <span class="pre">Random</span> <span class="pre">Forests</span></code> of the book <a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow</a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.75.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2383,13 +2415,13 @@ <h2><span class="section-number">37.75.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.74. </span>Random forests for classification</p>
+            <p class="prev-next-title"><span class="section-number">40.74. </span>Random forests for classification</p>
         </div>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.76. </span>Decision trees</p>
+        <p class="prev-next-title"><span class="section-number">40.76. </span>Decision trees</p>
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diff --git a/assignments/ml-advanced/ensemble-learning/decision-trees.html b/assignments/ml-advanced/ensemble-learning/decision-trees.html
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-    <title>37.76. Decision trees &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.76. Decision trees &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.77. Hyperparameter tuning gradient boosting" href="../gradient-boosting/hyperparameter-tuning-gradient-boosting.html" />
-    <link rel="prev" title="37.75. Beyond random forests: more ensemble models" href="beyond-random-forests-more-ensemble-models.html" />
+    <link rel="next" title="40.77. Hyperparameter tuning gradient boosting" href="../gradient-boosting/hyperparameter-tuning-gradient-boosting.html" />
+    <link rel="prev" title="40.75. Beyond random forests: more ensemble models" href="beyond-random-forests-more-ensemble-models.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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-     14.2. K-Means clustering
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   <a class="reference internal nav-link" href="#decision-tree">
-   37.76.2. Decision tree
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   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-how-to-build-a-decision-tree">
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     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#entropy">
-       37.76.2.1.1. Entropy
+       40.76.2.1.1. Entropy
       </a>
      </li>
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       <a class="reference internal nav-link" href="#toy-example">
-       37.76.2.1.2. Toy Example
+       40.76.2.1.2. Toy Example
       </a>
      </li>
     </ul>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-tree-building-algorithm">
-     37.76.2.2. Task 2: Tree-building algorithm
+     40.76.2.2. Task 2: Tree-building algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-other-quality-criteria-for-splits-in-classification-problems">
-     37.76.2.3. Task 3: Other quality criteria for splits in classification problems
+     40.76.2.3. Task 3: Other quality criteria for splits in classification problems
     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.76.2.3.1. Example
+       40.76.2.3.1. Example
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-can-we-read-such-a-tree">
-       37.76.2.3.2. How can we “read” such a tree?
+       40.76.2.3.2. How can we “read” such a tree?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-how-a-decision-tree-works-with-numerical-features">
-     37.76.2.4. Task 4: How a decision tree works with numerical features
+     40.76.2.4. Task 4: How a decision tree works with numerical features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-crucial-tree-parameters">
-     37.76.2.5. Task 5: Crucial tree parameters
+     40.76.2.5. Task 5: Crucial tree parameters
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     <a class="reference internal nav-link" href="#task-6-class-decisiontreeclassifier-in-scikit-learn">
-     37.76.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
+     40.76.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-decision-tree-in-a-regression-problem">
-     37.76.2.7. Task 7: Decision tree in a regression problem
+     40.76.2.7. Task 7: Decision tree in a regression problem
     </a>
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       <a class="reference internal nav-link" href="#id2">
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+       40.76.2.7.1. Example
       </a>
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     </ul>
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-   37.76.3. Choosing model parameters and cross-validation
+   40.76.3. Choosing model parameters and cross-validation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#application-examples-and-complex-cases">
-   37.76.4. Application examples and complex cases
+   40.76.4. Application examples and complex cases
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-decision-trees-in-a-customer-churn-prediction-task">
-     37.76.4.1. Task 1: Decision trees in a customer churn prediction task
+     40.76.4.1. Task 1: Decision trees in a customer churn prediction task
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-complex-case-for-decision-trees">
-     37.76.4.2. Task 2: Complex case for decision trees
+     40.76.4.2. Task 2: Complex case for decision trees
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition">
-     37.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
+     40.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pros-and-cons-of-decision-tree">
-   37.76.5. Pros and cons of decision tree
+   40.76.5. Pros and cons of decision tree
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.76.6. Acknowledgments
+   40.76.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1800,9 +1832,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees">
-<h1><span class="section-number">37.76. </span>Decision trees<a class="headerlink" href="#decision-trees" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.76. </span>Decision trees<a class="headerlink" href="#decision-trees" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.76.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Before we dive into the material for this week’s article, let’s talk about the kind of problem that we are going to solve and its place in the exciting field of machine learning. T. Mitchell’s book “Machine Learning” (1997) gives a classic, general definition of machine learning as follows:</p>
 <blockquote>
 <div><p>A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.</p>
@@ -1818,14 +1850,14 @@ <h2><span class="section-number">37.76.1. </span>Introduction<a class="headerlin
 <p>A good overview is provided in the “Machine Learning basics” chapter of <a class="reference external" href="http://www.deeplearningbook.org">“Deep Learning”</a> (by Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016).</p>
 <p>Experience E  refers to data (we can’t go anywhere without it). Machine learning algorithms can be divided into those that are trained in supervised or unsupervised manner. In unsupervised learning tasks, one has a set consisting of instances described by a set of features. In supervised learning problems, there’s also a target variable, which is what we would like to be able to predict, known for each instance in a training set.</p>
 <section id="example">
-<h3><span class="section-number">37.76.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
 <p>Classification and regression are supervised learning problems. For example, as a credit institution, we may want to predict loan defaults based on the data accumulated about our clients. Here, the experience E is the available training data: a set of instances (clients), a collection of features (such as age, salary, type of loan, past loan defaults, etc.) for each, and a target variable (whether they defaulted on the loan). This target variable is just a fact of loan default (1 or 0), so recall that this is a (binary) classification problem. If you were instead predicting by how much time the loan payment is overdue, this would become a regression problem.</p>
 <p>Finally, the third term used in the definition of machine learning is a metric of the algorithm’s performance evaluation P. Such metrics differ for various problems and algorithms, and we’ll discuss them as we study new algorithms. For now, we’ll refer to a simple metric for classification algorithms, the proportion of correct answers – accuracy – on the test set.</p>
 <p>Let’s take a look at two supervised learning problems: classification and regression.</p>
 </section>
 </section>
 <section id="decision-tree">
-<h2><span class="section-number">37.76.2. </span>Decision tree<a class="headerlink" href="#decision-tree" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.2. </span>Decision tree<a class="headerlink" href="#decision-tree" title="Permalink to this headline">#</a></h2>
 <p>We begin our overview of classification and regression methods with one of the most popular ones – a decision tree. Decision trees are used in everyday life decisions, not just in machine learning. Flow diagrams are actually visual representations of decision trees. For example, Higher School of Economics publishes information diagrams to make the lives of its employees easier. Here is a snippet of instructions for publishing a paper on the Institution portal.</p>
 <p><img align='center' src='https://habrastorage.org/webt/wh/ty/tf/whtytf2hxotbnhymqno6vmcivr4.png' width=60%><br></p>
 <p>In terms of machine learning, one can see it as a simple classifier that determines the appropriate form of publication (book, article, chapter of the book, preprint, publication in the “Higher School of Economics and the Media”) based on the content (book, pamphlet, paper), type of journal, original publication type (scientific journal, proceedings), etc.</p>
@@ -1835,18 +1867,18 @@ <h2><span class="section-number">37.76.2. </span>Decision tree<a class="headerli
 <p>The decision tree as a machine learning algorithm is essentially the same thing as the diagram shown above; we incorporate a stream of logical rules of the form “feature <span class="math notranslate nohighlight">\(a\)</span> value is less than <span class="math notranslate nohighlight">\(x\)</span> and feature <span class="math notranslate nohighlight">\(b\)</span> value is less than <span class="math notranslate nohighlight">\(y\)</span> … =&gt; Category 1” into a tree-like data structure. The advantage of this algorithm is that they are easily interpretable. For example, using the above scheme, the bank can explain to the client why they were denied for a loan: e.g the client does not own a house and her income is less than 5,000.</p>
 <p>As we’ll see later, many other models, although more accurate, do not have this property and can be regarded as more of a “black box” approach, where it is harder to interpret how the input data was transformed into the output. Due to this “understandability” and similarity to human decision-making (you can easily explain your model to your boss), decision trees have gained immense popularity. C4.5, a representative of this group of classification methods, is even the first in the list of 10 best data mining algorithms (“Top 10 Algorithms in Data Mining”, Knowledge and Information Systems, 2008. <a class="reference external" href="https://www.researchgate.net/publication/29467751_Top_10_algorithms_in_data_mining">ResearchGate</a>).</p>
 <section id="task-1-how-to-build-a-decision-tree">
-<h3><span class="section-number">37.76.2.1. </span>Task 1: How to build a decision tree<a class="headerlink" href="#task-1-how-to-build-a-decision-tree" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.1. </span>Task 1: How to build a decision tree<a class="headerlink" href="#task-1-how-to-build-a-decision-tree" title="Permalink to this headline">#</a></h3>
 <p>Earlier, we saw that the decision to grant a loan is made based on age, assets, income, and other variables. But what variable to look at first? Let’s discuss a simple example where all the variables are binary.</p>
 <p>Recall the game of “20 Questions”, which is often referenced when introducing decision trees. You’ve probably played this game – one person thinks of a celebrity while the other tries to guess by asking only “Yes” or “No” questions. What question will the guesser ask first? Of course, they will ask the one that narrows down the number of the remaining options the most. Asking “Is it Angelina Jolie?” would, in the case of a negative response, leave all but one celebrity in the realm of possibility. In contrast, asking “Is the celebrity a woman?” would reduce the possibilities to roughly half. That is to say, the “gender” feature separates the celebrity dataset much better than other features like “Angelina Jolie”, “Spanish”, or “loves football.” This reasoning corresponds to the concept of information gain based on entropy.</p>
 <section id="entropy">
-<h4><span class="section-number">37.76.2.1.1. </span>Entropy<a class="headerlink" href="#entropy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.1.1. </span>Entropy<a class="headerlink" href="#entropy" title="Permalink to this headline">#</a></h4>
 <p>Shannon’s entropy is defined for a system with N possible states as follows:</p>
 <div class="math notranslate nohighlight">
 \[\Large S = -\sum_{i=1}^{N}p_i \log_2{p_i},\]</div>
 <p>where <span class="math notranslate nohighlight">\(p_i\)</span> is the probability of finding the system in the <span class="math notranslate nohighlight">\(i\)</span>-th state. This is a very important concept used in physics, information theory, and other areas. Entropy can be described as the degree of chaos in the system. The higher the entropy, the less ordered the system and vice versa. This will help us formalize “effective data splitting”, which we alluded to in the context of “20 Questions”.</p>
 </section>
 <section id="toy-example">
-<h4><span class="section-number">37.76.2.1.2. </span>Toy Example<a class="headerlink" href="#toy-example" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.1.2. </span>Toy Example<a class="headerlink" href="#toy-example" title="Permalink to this headline">#</a></h4>
 <p>To illustrate how entropy can help us identify good features for building a decision tree, let’s look at a toy example. We will predict the color of the ball based on its position.</p>
 <p><img align='center' src='https://habrastorage.org/webt/mu/vl/mt/muvlmtd2njeqf18trbldenpqvnm.png'><br></p>
 <p>There are 9 blue balls and 11 yellow balls. If we randomly pull out a ball, then it will be blue with probability <span class="math notranslate nohighlight">\(p_1=\frac{9}{20}\)</span> and yellow with probability <span class="math notranslate nohighlight">\(p_2=\frac{11}{20}\)</span>, which gives us an entropy <span class="math notranslate nohighlight">\(S_0 = -\frac{9}{20}\log_2{\frac{9}{20}}-\frac{11}{20}\log_2{\frac{11}{20}} \approx 1\)</span>. This value by itself may not tell us much, but let’s see how the value changes if we were to break the balls into two groups: with the position less than or equal to 12 and greater than 12.</p>
@@ -1864,7 +1896,7 @@ <h4><span class="section-number">37.76.2.1.2. </span>Toy Example<a class="header
 </section>
 </section>
 <section id="task-2-tree-building-algorithm">
-<h3><span class="section-number">37.76.2.2. </span>Task 2: Tree-building algorithm<a class="headerlink" href="#task-2-tree-building-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.2. </span>Task 2: Tree-building algorithm<a class="headerlink" href="#task-2-tree-building-algorithm" title="Permalink to this headline">#</a></h3>
 <p>We can make sure that the tree built in the previous example is optimal: it took only 5 “questions” (conditioned on the variable <span class="math notranslate nohighlight">\(x\)</span>) to perfectly fit a decision tree to the training set. Under other split conditions, the resulting tree would be deeper, i.e. take more “questions” to reach an answer.</p>
 <p>At the heart of the popular algorithms for decision tree construction, such as ID3 or C4.5, lies the principle of greedy maximization of information gain: at each step, the algorithm chooses the variable that gives the greatest information gain upon splitting. Then the procedure is repeated recursively until the entropy is zero (or some small value to account for overfitting). Different algorithms use different heuristics for “early stopping” or “cut-off” to avoid constructing an overfitted tree.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="n">L</span><span class="p">):</span>
@@ -1880,7 +1912,7 @@ <h3><span class="section-number">37.76.2.2. </span>Task 2: Tree-building algorit
 </div>
 </section>
 <section id="task-3-other-quality-criteria-for-splits-in-classification-problems">
-<h3><span class="section-number">37.76.2.3. </span>Task 3: Other quality criteria for splits in classification problems<a class="headerlink" href="#task-3-other-quality-criteria-for-splits-in-classification-problems" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.3. </span>Task 3: Other quality criteria for splits in classification problems<a class="headerlink" href="#task-3-other-quality-criteria-for-splits-in-classification-problems" title="Permalink to this headline">#</a></h3>
 <p>We discussed how entropy allows us to formalize partitions in a tree. But this is only one heuristic; there exists others:</p>
 <ul class="simple">
 <li><p>Gini uncertainty (Gini impurity): <span class="math notranslate nohighlight">\(G = 1 - \sum\limits_k (p_k)^2\)</span>. Maximizing this criterion can be interpreted as the maximization of the number of pairs of objects of the same class that are in the same subtree (not to be confused with the Gini index).</p></li>
@@ -1932,7 +1964,7 @@ <h3><span class="section-number">37.76.2.3. </span>Task 3: Other quality criteri
 </div>
 </div>
 <section id="id1">
-<h4><span class="section-number">37.76.2.3.1. </span>Example<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.3.1. </span>Example<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>Let’s consider fitting a decision tree to some synthetic data. We will generate samples from two classes, both normal distributions but with different means.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2010,12 +2042,12 @@ <h4><span class="section-number">37.76.2.3.1. </span>Example<a class="headerlink
 <p>And how does the tree itself look? We see that the tree “cuts” the space into 8 rectangles, i.e. the tree has 8 leaves. Within each rectangle, the tree will make the prediction according to the majority label of the objects inside it.</p>
 <img src="https://habrastorage.org/webt/z0/yp/wm/z0ypwmvjko4r2mymcohy7ovevwe.png" /></section>
 <section id="how-can-we-read-such-a-tree">
-<h4><span class="section-number">37.76.2.3.2. </span>How can we “read” such a tree?<a class="headerlink" href="#how-can-we-read-such-a-tree" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.3.2. </span>How can we “read” such a tree?<a class="headerlink" href="#how-can-we-read-such-a-tree" title="Permalink to this headline">#</a></h4>
 <p>In the beginning, there were 200 samples (instances), 100 of each class. The entropy of the initial state was maximal, <span class="math notranslate nohighlight">\(S=1\)</span>. Then, the first partition of the samples into 2 groups was made by comparing the value of <span class="math notranslate nohighlight">\(x_2\)</span> with <span class="math notranslate nohighlight">\(1.211\)</span> (find this part of the border in the picture above). With that, the entropy of both left and right groups decreased. The process continues up to depth 3. In this visualization, the more samples of the first class, the darker the orange color of the vertex; the more samples of the second class, the darker the blue. At the beginning, the number of samples from two classes is equal, so the root node of the tree is white.</p>
 </section>
 </section>
 <section id="task-4-how-a-decision-tree-works-with-numerical-features">
-<h3><span class="section-number">37.76.2.4. </span>Task 4: How a decision tree works with numerical features<a class="headerlink" href="#task-4-how-a-decision-tree-works-with-numerical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.4. </span>Task 4: How a decision tree works with numerical features<a class="headerlink" href="#task-4-how-a-decision-tree-works-with-numerical-features" title="Permalink to this headline">#</a></h3>
 <p>Suppose we have a numeric feature “Age” that has a lot of unique values. A decision tree will look for the best (according to some criterion of information gain) split by checking binary attributes such as “Age &lt;17”, “Age &lt; 22.87”, and so on. But what if the age range is large? Or what if another quantitative variable, “salary”, can also be “cut” in many ways? There will be too many binary attributes to select from at each step during tree construction. To resolve this problem, heuristics are usually used to limit the number of thresholds to which we compare the quantitative variable.</p>
 <p>Let’s consider an example. Suppose we have the following dataset:</p>
 <div class="cell docutils container">
@@ -2446,7 +2478,7 @@ <h3><span class="section-number">37.76.2.4. </span>Task 4: How a decision tree w
 <p>More examples of numeric feature discretization can be found in posts like <a class="reference external" href="http://kevinmeurer.com/a-simple-guide-to-entropy-based-discretization/">“A Simple Guide to Entropy-Based Discretization”</a> or <a class="reference external" href="http://clear-lines.com/blog/post/Discretizing-a-continuous-variable-using-Entropy.aspx">“Discretizing a continuous variable using Entropy”</a>. One of the most prominent scientific papers on this subject is “On the handling of continuous-valued attributes in decision tree generation” (UM Fayyad. KB Irani, “Machine Learning”, 1992).</p>
 </section>
 <section id="task-5-crucial-tree-parameters">
-<h3><span class="section-number">37.76.2.5. </span>Task 5: Crucial tree parameters<a class="headerlink" href="#task-5-crucial-tree-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.5. </span>Task 5: Crucial tree parameters<a class="headerlink" href="#task-5-crucial-tree-parameters" title="Permalink to this headline">#</a></h3>
 <p>Technically, you can build a decision tree until each leaf has exactly one instance, but this is not common in practice when building a single tree because it will be <em>overfitted</em>, or too tuned to the training set, and will not predict labels for new data well. At the bottom of the tree, at some great depth, there will be partitions on less important features (e.g. whether a client came from Leeds or New York). We can exaggerate this story further and find that all four clients who came to the bank for a loan in green trousers did not return the loan. Even if that were true in training, we do not want our classification model to generate such specific rules.</p>
 <p>There are two exceptions where the trees are built to the maximum depth:</p>
 <ul class="simple">
@@ -2462,7 +2494,7 @@ <h3><span class="section-number">37.76.2.5. </span>Task 5: Crucial tree paramete
 </ul>
 </section>
 <section id="task-6-class-decisiontreeclassifier-in-scikit-learn">
-<h3><span class="section-number">37.76.2.6. </span>Task 6: Class DecisionTreeClassifier in Scikit-learn<a class="headerlink" href="#task-6-class-decisiontreeclassifier-in-scikit-learn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.6. </span>Task 6: Class DecisionTreeClassifier in Scikit-learn<a class="headerlink" href="#task-6-class-decisiontreeclassifier-in-scikit-learn" title="Permalink to this headline">#</a></h3>
 <p>The main parameters of the <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html"><code class="docutils literal notranslate"><span class="pre">sklearn.tree.DecisionTreeClassifier</span></code></a> class are:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">max_depth</span></code> – the maximum depth of the tree;</p></li>
@@ -2472,7 +2504,7 @@ <h3><span class="section-number">37.76.2.6. </span>Task 6: Class DecisionTreeCla
 <p>The parameters of the tree need to be set depending on input data, and it is usually done by means of <em>cross-validation</em>, more on this below.</p>
 </section>
 <section id="task-7-decision-tree-in-a-regression-problem">
-<h3><span class="section-number">37.76.2.7. </span>Task 7: Decision tree in a regression problem<a class="headerlink" href="#task-7-decision-tree-in-a-regression-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.7. </span>Task 7: Decision tree in a regression problem<a class="headerlink" href="#task-7-decision-tree-in-a-regression-problem" title="Permalink to this headline">#</a></h3>
 <p>When predicting a numeric variable, the idea of a tree construction remains the same, but the quality criteria changes:</p>
 <ul class="simple">
 <li><p>Variance:</p></li>
@@ -2481,7 +2513,7 @@ <h3><span class="section-number">37.76.2.7. </span>Task 7: Decision tree in a re
 \[\Large D = \frac{1}{\ell} \sum\limits_{i =1}^{\ell} (y_i - \frac{1}{\ell} \sum\limits_{j=1}^{\ell} y_j)^2, \]</div>
 <p>where <span class="math notranslate nohighlight">\(\ell\)</span> is the number of samples in a leaf, <span class="math notranslate nohighlight">\(y_i\)</span> is the value of the target variable. Simply put, by minimizing the variance, we look for features that divide the training set in such a way that the values of the target feature in each leaf are roughly equal.</p>
 <section id="id2">
-<h4><span class="section-number">37.76.2.7.1. </span>Example<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.7.1. </span>Example<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <p>Let’s generate some data distributed by the function <span class="math notranslate nohighlight">\(f(x) = e^{-x ^ 2} + 1.5 * e^{-(x - 2) ^ 2}\)</span> with some noise. Then we will train a tree with this data and predictions that the tree makes.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2539,7 +2571,7 @@ <h4><span class="section-number">37.76.2.7.1. </span>Example<a class="headerlink
 </section>
 </section>
 <section id="choosing-model-parameters-and-cross-validation">
-<h2><span class="section-number">37.76.3. </span>Choosing model parameters and cross-validation<a class="headerlink" href="#choosing-model-parameters-and-cross-validation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.3. </span>Choosing model parameters and cross-validation<a class="headerlink" href="#choosing-model-parameters-and-cross-validation" title="Permalink to this headline">#</a></h2>
 <p>The main task of learning algorithms is to be able to <em>generalize</em> to unseen data. Since we cannot immediately check the model performance on new, incoming data (because we do not know the true values of the target variable yet), it is necessary to sacrifice a small portion of the data to check the quality of the model on it.</p>
 <p>This is often done in one of two ways:</p>
 <ul class="simple">
@@ -2552,9 +2584,9 @@ <h2><span class="section-number">37.76.3. </span>Choosing model parameters and c
 <p>Cross-validation is a very important technique in machine learning and can also be applied in statistics and econometrics. It helps with hyperparameter tuning, model comparison, feature evaluation, etc. More details can be found <a class="reference external" href="https://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html">here</a> (blog post by Sebastian Raschka) or in any classic textbook on machine (statistical) learning.</p>
 </section>
 <section id="application-examples-and-complex-cases">
-<h2><span class="section-number">37.76.4. </span>Application examples and complex cases<a class="headerlink" href="#application-examples-and-complex-cases" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.4. </span>Application examples and complex cases<a class="headerlink" href="#application-examples-and-complex-cases" title="Permalink to this headline">#</a></h2>
 <section id="task-1-decision-trees-in-a-customer-churn-prediction-task">
-<h3><span class="section-number">37.76.4.1. </span>Task 1: Decision trees in a customer churn prediction task<a class="headerlink" href="#task-1-decision-trees-in-a-customer-churn-prediction-task" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.4.1. </span>Task 1: Decision trees in a customer churn prediction task<a class="headerlink" href="#task-1-decision-trees-in-a-customer-churn-prediction-task" title="Permalink to this headline">#</a></h3>
 <p>Let’s read data into a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and preprocess it. Store <em>State</em> in a separate <code class="docutils literal notranslate"><span class="pre">Series</span></code> object for now and remove it from the dataframe. We will
 train the first model without the <em>State</em> feature, and then we will see if it helps.</p>
 <div class="cell docutils container">
@@ -2879,7 +2911,7 @@ <h3><span class="section-number">37.76.4.1. </span>Task 1: Decision trees in a c
 </div>
 </section>
 <section id="task-2-complex-case-for-decision-trees">
-<h3><span class="section-number">37.76.4.2. </span>Task 2: Complex case for decision trees<a class="headerlink" href="#task-2-complex-case-for-decision-trees" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.4.2. </span>Task 2: Complex case for decision trees<a class="headerlink" href="#task-2-complex-case-for-decision-trees" title="Permalink to this headline">#</a></h3>
 <p>To continue the discussion of the pros and cons of the methods in question, let’s consider a simple classification task, where a tree would perform well but does it in an “overly complicated” manner. Let’s create a set of points on a plane (2 features), each point will be one of two classes (+1 for red, or -1 for yellow). If you look at it as a classification problem, it seems very simple: the classes are separated by a line.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2927,7 +2959,7 @@ <h3><span class="section-number">37.76.4.2. </span>Task 2: Complex case for deci
 <img src="https://habrastorage.org/webt/az/ld/yj/azldyj0ija_sxkk438m959qpf0w.png" /><p>Obviously, decision trees don’t work as well as linear classifiers in this case</p>
 </section>
 <section id="task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition">
-<h3><span class="section-number">37.76.4.3. </span>Task 3: Decision trees in a task of MNIST handwritten digits recognition<a class="headerlink" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.4.3. </span>Task 3: Decision trees in a task of MNIST handwritten digits recognition<a class="headerlink" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition" title="Permalink to this headline">#</a></h3>
 <p>Now let’s have a look at how decision trees performs on a real-world task. We will use the <code class="docutils literal notranslate"><span class="pre">sklearn</span></code> built-in dataset on handwritten digits.</p>
 <p>Pictures here are 8x8 matrices (intensity of white color for each pixel). Then each such matrix is “unfolded” into a vector of length 64, and we obtain a feature description of an object.</p>
 <p>Let’s draw some handwritten digits. We see that they are distinguishable.</p>
@@ -3070,7 +3102,7 @@ <h3><span class="section-number">37.76.4.3. </span>Task 3: Decision trees in a t
 </section>
 </section>
 <section id="pros-and-cons-of-decision-tree">
-<h2><span class="section-number">37.76.5. </span>Pros and cons of decision tree<a class="headerlink" href="#pros-and-cons-of-decision-tree" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.5. </span>Pros and cons of decision tree<a class="headerlink" href="#pros-and-cons-of-decision-tree" title="Permalink to this headline">#</a></h2>
 <p>Pros:</p>
 <ul class="simple">
 <li><p>Generation of clear human-understandable classification rules, e.g. “if age &lt;25 and is interested in motorcycles, deny the loan”. This property is called interpretability of the model.</p></li>
@@ -3091,7 +3123,7 @@ <h2><span class="section-number">37.76.5. </span>Pros and cons of decision tree<
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.76.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-3-decision-trees-and-knn/notebook">Decision Trees and kNN</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3133,13 +3165,13 @@ <h2><span class="section-number">37.76.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.75. </span>Beyond random forests: more ensemble models</p>
+            <p class="prev-next-title"><span class="section-number">40.75. </span>Beyond random forests: more ensemble models</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../gradient-boosting/hyperparameter-tuning-gradient-boosting.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.77. </span>Hyperparameter tuning gradient boosting</p>
+        <p class="prev-next-title"><span class="section-number">40.77. </span>Hyperparameter tuning gradient boosting</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html b/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
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+++ b/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.80. Random Forest Classifier with Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.80. Random Forest Classifier with Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.82. Data engineering" href="../../machine-learning-productionization/data-engineering.html" />
-    <link rel="prev" title="37.79. Boosting with tuning" href="../gradient-boosting/boosting-with-tuning.html" />
+    <link rel="next" title="40.82. Data engineering" href="../../machine-learning-productionization/data-engineering.html" />
+    <link rel="prev" title="40.79. Boosting with tuning" href="../gradient-boosting/boosting-with-tuning.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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    <li class="toctree-l2">
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     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
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   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-  <label for="toctree-checkbox-11">
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-     16.3. XGBoost
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     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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+     18.4. XGBoost + k-fold CV + Feature Importance
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   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
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-   19. Model selection
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   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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-   22. Generative adversarial networks
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-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../../machine-learning-productionization/overview.html">
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-   35. Model training &amp; evaluation
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-  <label for="toctree-checkbox-12">
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+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     37.14. Try it in Excel
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-     37.15. Dive into the beehive
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     <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,187 +1558,187 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.80. Random Forest Classifier with Feature Importance
+   40.80. Random Forest Classifier with Feature Importance
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-problem-statement">
-     37.80.1. The problem statement
+     40.80.1. The problem statement
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     37.80.2. Import libraries
+     40.80.2. Import libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-dataset">
-     37.80.3. Import dataset
+     40.80.3. Import dataset
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis">
-     37.80.4. Exploratory data analysis
+     40.80.4. Exploratory data analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-dimensions-of-dataset">
-       37.80.4.1. View dimensions of dataset
+       40.80.4.1. View dimensions of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-the-dataset-a-class-anchor-id-4-2-a">
-       37.80.4.2. Preview the dataset
+       40.80.4.2. Preview the dataset
        <a class="anchor" id="4.2">
        </a>
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rename-column-names">
-       37.80.4.3. Rename column names
+       40.80.4.3. Rename column names
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-summary-of-dataset">
-       37.80.4.4. View summary of dataset
+       40.80.4.4. View summary of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-the-data-types-of-columns">
-       37.80.4.5. Check the data types of columns
+       40.80.4.5. Check the data types of columns
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-statistical-properties-of-dataset">
-       37.80.4.6. View statistical properties of dataset
+       40.80.4.6. View statistical properties of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-with-assert-statement">
-       37.80.4.7. Check with ASSERT statement
+       40.80.4.7. Check with ASSERT statement
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#functional-approach-to-eda">
-       37.80.4.8. Functional approach to EDA
+       40.80.4.8. Functional approach to EDA
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-categorical-variables">
-     37.80.5. Explore Categorical Variables
+     40.80.5. Explore Categorical Variables
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#find-categorical-variables">
-       37.80.5.1. Find categorical variables
+       40.80.5.1. Find categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-categorical-variables">
-       37.80.5.2. Preview categorical variables
+       40.80.5.2. Preview categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-categorical-variables">
-       37.80.5.3. Summary of categorical variables
+       40.80.5.3. Summary of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#frequency-distribution-of-categorical-variables">
-       37.80.5.4. Frequency distribution of categorical variables
+       40.80.5.4. Frequency distribution of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#percentage-of-frequency-distribution-of-values">
-       37.80.5.5. Percentage of frequency distribution of values
+       40.80.5.5. Percentage of frequency distribution of values
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#explore-the-variables">
-       37.80.5.6. Explore the variables
+       40.80.5.6. Explore the variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-of-labels-cardinality">
-       37.80.5.7. Number of labels: Cardinality
+       40.80.5.7. Number of labels: Cardinality
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     37.80.6. Declare feature vector and target variable
+     40.80.6. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     37.80.7. Split data into separate training and test set
+     40.80.7. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-engineering">
-     37.80.8. Feature Engineering
+     40.80.8. Feature Engineering
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-categorical-variables-in-training-set">
-       37.80.8.1. Display categorical variables in training set
+       40.80.8.1. Display categorical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-numerical-variables-in-training-set">
-       37.80.8.2. Display numerical variables in training set
+       40.80.8.2. Display numerical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#engineering-missing-values-in-categorical-variables">
-       37.80.8.3. Engineering missing values in categorical variables
+       40.80.8.3. Engineering missing values in categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encode-categorical-variables">
-       37.80.8.4. Encode categorical variables
+       40.80.8.4. Encode categorical variables
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.80.9. Feature Scaling
+     40.80.9. Feature Scaling
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-default-parameters">
-     37.80.10. Random Forest Classifier model with default parameters
+     40.80.10. Random Forest Classifier model with default parameters
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-100-decision-trees">
-     37.80.11. Random Forest Classifier model with 100 Decision Trees
+     40.80.11. Random Forest Classifier model with 100 Decision Trees
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-the-random-forest-model-on-selected-features">
-     37.80.12. Build the Random Forest model on selected features
+     40.80.12. Build the Random Forest model on selected features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#confusion-matrix">
-     37.80.13. Confusion matrix
+     40.80.13. Confusion matrix
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     37.80.14. Classification Report
+     40.80.14. Classification Report
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.81. Acknowledgments
+   40.81. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1730,187 +1762,187 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.80. Random Forest Classifier with Feature Importance
+   40.80. Random Forest Classifier with Feature Importance
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-problem-statement">
-     37.80.1. The problem statement
+     40.80.1. The problem statement
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     37.80.2. Import libraries
+     40.80.2. Import libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-dataset">
-     37.80.3. Import dataset
+     40.80.3. Import dataset
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis">
-     37.80.4. Exploratory data analysis
+     40.80.4. Exploratory data analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-dimensions-of-dataset">
-       37.80.4.1. View dimensions of dataset
+       40.80.4.1. View dimensions of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-the-dataset-a-class-anchor-id-4-2-a">
-       37.80.4.2. Preview the dataset
+       40.80.4.2. Preview the dataset
        <a class="anchor" id="4.2">
        </a>
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rename-column-names">
-       37.80.4.3. Rename column names
+       40.80.4.3. Rename column names
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-summary-of-dataset">
-       37.80.4.4. View summary of dataset
+       40.80.4.4. View summary of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-the-data-types-of-columns">
-       37.80.4.5. Check the data types of columns
+       40.80.4.5. Check the data types of columns
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-statistical-properties-of-dataset">
-       37.80.4.6. View statistical properties of dataset
+       40.80.4.6. View statistical properties of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-with-assert-statement">
-       37.80.4.7. Check with ASSERT statement
+       40.80.4.7. Check with ASSERT statement
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#functional-approach-to-eda">
-       37.80.4.8. Functional approach to EDA
+       40.80.4.8. Functional approach to EDA
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-categorical-variables">
-     37.80.5. Explore Categorical Variables
+     40.80.5. Explore Categorical Variables
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#find-categorical-variables">
-       37.80.5.1. Find categorical variables
+       40.80.5.1. Find categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-categorical-variables">
-       37.80.5.2. Preview categorical variables
+       40.80.5.2. Preview categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-categorical-variables">
-       37.80.5.3. Summary of categorical variables
+       40.80.5.3. Summary of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#frequency-distribution-of-categorical-variables">
-       37.80.5.4. Frequency distribution of categorical variables
+       40.80.5.4. Frequency distribution of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#percentage-of-frequency-distribution-of-values">
-       37.80.5.5. Percentage of frequency distribution of values
+       40.80.5.5. Percentage of frequency distribution of values
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#explore-the-variables">
-       37.80.5.6. Explore the variables
+       40.80.5.6. Explore the variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-of-labels-cardinality">
-       37.80.5.7. Number of labels: Cardinality
+       40.80.5.7. Number of labels: Cardinality
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     37.80.6. Declare feature vector and target variable
+     40.80.6. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     37.80.7. Split data into separate training and test set
+     40.80.7. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-engineering">
-     37.80.8. Feature Engineering
+     40.80.8. Feature Engineering
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-categorical-variables-in-training-set">
-       37.80.8.1. Display categorical variables in training set
+       40.80.8.1. Display categorical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-numerical-variables-in-training-set">
-       37.80.8.2. Display numerical variables in training set
+       40.80.8.2. Display numerical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#engineering-missing-values-in-categorical-variables">
-       37.80.8.3. Engineering missing values in categorical variables
+       40.80.8.3. Engineering missing values in categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encode-categorical-variables">
-       37.80.8.4. Encode categorical variables
+       40.80.8.4. Encode categorical variables
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.80.9. Feature Scaling
+     40.80.9. Feature Scaling
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-default-parameters">
-     37.80.10. Random Forest Classifier model with default parameters
+     40.80.10. Random Forest Classifier model with default parameters
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-100-decision-trees">
-     37.80.11. Random Forest Classifier model with 100 Decision Trees
+     40.80.11. Random Forest Classifier model with 100 Decision Trees
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-the-random-forest-model-on-selected-features">
-     37.80.12. Build the Random Forest model on selected features
+     40.80.12. Build the Random Forest model on selected features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#confusion-matrix">
-     37.80.13. Confusion matrix
+     40.80.13. Confusion matrix
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     37.80.14. Classification Report
+     40.80.14. Classification Report
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.81. Acknowledgments
+   40.81. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1924,13 +1956,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forest-classifier-with-feature-importance">
-<h1><span class="section-number">37.80. </span>Random Forest Classifier with Feature Importance<a class="headerlink" href="#random-forest-classifier-with-feature-importance" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.80. </span>Random Forest Classifier with Feature Importance<a class="headerlink" href="#random-forest-classifier-with-feature-importance" title="Permalink to this headline">#</a></h1>
 <section id="the-problem-statement">
-<h2><span class="section-number">37.80.1. </span>The problem statement<a class="headerlink" href="#the-problem-statement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.1. </span>The problem statement<a class="headerlink" href="#the-problem-statement" title="Permalink to this headline">#</a></h2>
 <p>We try to make predictions where the prediction task is to determine whether a person makes over 50K a year. We implement Random Forest Classification with Python and Scikit-Learn. So, to answer the question, we build a Random Forest classifier to predict whether a person makes over 50K a year.</p>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">37.80.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This Python 3 environment comes with many helpful analytics libraries installed</span>
@@ -1975,7 +2007,7 @@ <h2><span class="section-number">37.80.2. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="import-dataset">
-<h2><span class="section-number">37.80.3. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.3. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/random-forest-income_evaluation.csv&#39;</span>
@@ -1987,9 +2019,9 @@ <h2><span class="section-number">37.80.3. </span>Import dataset<a class="headerl
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">37.80.4. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.4. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <section id="view-dimensions-of-dataset">
-<h3><span class="section-number">37.80.4.1. </span>View dimensions of dataset<a class="headerlink" href="#view-dimensions-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.1. </span>View dimensions of dataset<a class="headerlink" href="#view-dimensions-of-dataset" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print the shape</span>
@@ -2001,7 +2033,7 @@ <h3><span class="section-number">37.80.4.1. </span>View dimensions of dataset<a
 <p>We can see that there are 32561 instances and 15 attributes in the data set.</p>
 </section>
 <section id="preview-the-dataset-a-class-anchor-id-4-2-a">
-<h3><span class="section-number">37.80.4.2. </span>Preview the dataset <a class="anchor" id="4.2"></a><a class="headerlink" href="#preview-the-dataset-a-class-anchor-id-4-2-a" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.2. </span>Preview the dataset <a class="anchor" id="4.2"></a><a class="headerlink" href="#preview-the-dataset-a-class-anchor-id-4-2-a" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -2011,7 +2043,7 @@ <h3><span class="section-number">37.80.4.2. </span>Preview the dataset <a class=
 </div>
 </section>
 <section id="rename-column-names">
-<h3><span class="section-number">37.80.4.3. </span>Rename column names<a class="headerlink" href="#rename-column-names" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.3. </span>Rename column names<a class="headerlink" href="#rename-column-names" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;workclass&#39;</span><span class="p">,</span> <span class="s1">&#39;fnlwgt&#39;</span><span class="p">,</span> <span class="s1">&#39;education&#39;</span><span class="p">,</span> <span class="s1">&#39;education_num&#39;</span><span class="p">,</span> <span class="s1">&#39;marital_status&#39;</span><span class="p">,</span> <span class="s1">&#39;occupation&#39;</span><span class="p">,</span> <span class="s1">&#39;relationship&#39;</span><span class="p">,</span>
@@ -2026,7 +2058,7 @@ <h3><span class="section-number">37.80.4.3. </span>Rename column names<a class="
 </div>
 </section>
 <section id="view-summary-of-dataset">
-<h3><span class="section-number">37.80.4.4. </span>View summary of dataset<a class="headerlink" href="#view-summary-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.4. </span>View summary of dataset<a class="headerlink" href="#view-summary-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
@@ -2036,7 +2068,7 @@ <h3><span class="section-number">37.80.4.4. </span>View summary of dataset<a cla
 </div>
 </section>
 <section id="check-the-data-types-of-columns">
-<h3><span class="section-number">37.80.4.5. </span>Check the data types of columns<a class="headerlink" href="#check-the-data-types-of-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.5. </span>Check the data types of columns<a class="headerlink" href="#check-the-data-types-of-columns" title="Permalink to this headline">#</a></h3>
 <p>The above <a class="reference external" href="http://df.info">df.info</a>() command gives us the number of filled values along with the data types of columns.</p>
 <p>If we simply want to check the data type of a particular column, we can use the following command.</p>
 <div class="cell docutils container">
@@ -2048,7 +2080,7 @@ <h3><span class="section-number">37.80.4.5. </span>Check the data types of colum
 </div>
 </section>
 <section id="view-statistical-properties-of-dataset">
-<h3><span class="section-number">37.80.4.6. </span>View statistical properties of dataset<a class="headerlink" href="#view-statistical-properties-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.6. </span>View statistical properties of dataset<a class="headerlink" href="#view-statistical-properties-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -2082,7 +2114,7 @@ <h3><span class="section-number">37.80.4.6. </span>View statistical properties o
 </div>
 </section>
 <section id="check-with-assert-statement">
-<h3><span class="section-number">37.80.4.7. </span>Check with ASSERT statement<a class="headerlink" href="#check-with-assert-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.7. </span>Check with ASSERT statement<a class="headerlink" href="#check-with-assert-statement" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
@@ -2092,7 +2124,7 @@ <h3><span class="section-number">37.80.4.7. </span>Check with ASSERT statement<a
 </div>
 </section>
 <section id="functional-approach-to-eda">
-<h3><span class="section-number">37.80.4.8. </span>Functional approach to EDA<a class="headerlink" href="#functional-approach-to-eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.8. </span>Functional approach to EDA<a class="headerlink" href="#functional-approach-to-eda" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">initial_eda</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -2124,9 +2156,9 @@ <h3><span class="section-number">37.80.4.8. </span>Functional approach to EDA<a
 </section>
 </section>
 <section id="explore-categorical-variables">
-<h2><span class="section-number">37.80.5. </span>Explore Categorical Variables<a class="headerlink" href="#explore-categorical-variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.5. </span>Explore Categorical Variables<a class="headerlink" href="#explore-categorical-variables" title="Permalink to this headline">#</a></h2>
 <section id="find-categorical-variables">
-<h3><span class="section-number">37.80.5.1. </span>Find categorical variables<a class="headerlink" href="#find-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.1. </span>Find categorical variables<a class="headerlink" href="#find-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">categorical</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="o">==</span><span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2140,7 +2172,7 @@ <h3><span class="section-number">37.80.5.1. </span>Find categorical variables<a
 </div>
 </section>
 <section id="preview-categorical-variables">
-<h3><span class="section-number">37.80.5.2. </span>Preview categorical variables<a class="headerlink" href="#preview-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.2. </span>Preview categorical variables<a class="headerlink" href="#preview-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">categorical</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -2150,10 +2182,10 @@ <h3><span class="section-number">37.80.5.2. </span>Preview categorical variables
 </div>
 </section>
 <section id="summary-of-categorical-variables">
-<h3><span class="section-number">37.80.5.3. </span>Summary of categorical variables<a class="headerlink" href="#summary-of-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.3. </span>Summary of categorical variables<a class="headerlink" href="#summary-of-categorical-variables" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="frequency-distribution-of-categorical-variables">
-<h3><span class="section-number">37.80.5.4. </span>Frequency distribution of categorical variables<a class="headerlink" href="#frequency-distribution-of-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.4. </span>Frequency distribution of categorical variables<a class="headerlink" href="#frequency-distribution-of-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span> 
@@ -2165,7 +2197,7 @@ <h3><span class="section-number">37.80.5.4. </span>Frequency distribution of cat
 </div>
 </section>
 <section id="percentage-of-frequency-distribution-of-values">
-<h3><span class="section-number">37.80.5.5. </span>Percentage of frequency distribution of values<a class="headerlink" href="#percentage-of-frequency-distribution-of-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.5. </span>Percentage of frequency distribution of values<a class="headerlink" href="#percentage-of-frequency-distribution-of-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span>
@@ -2176,7 +2208,7 @@ <h3><span class="section-number">37.80.5.5. </span>Percentage of frequency distr
 </div>
 </section>
 <section id="explore-the-variables">
-<h3><span class="section-number">37.80.5.6. </span>Explore the variables<a class="headerlink" href="#explore-the-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.6. </span>Explore the variables<a class="headerlink" href="#explore-the-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for missing values</span>
@@ -2465,7 +2497,7 @@ <h3><span class="section-number">37.80.5.6. </span>Explore the variables<a class
 </div>
 </section>
 <section id="number-of-labels-cardinality">
-<h3><span class="section-number">37.80.5.7. </span>Number of labels: Cardinality<a class="headerlink" href="#number-of-labels-cardinality" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.7. </span>Number of labels: Cardinality<a class="headerlink" href="#number-of-labels-cardinality" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for cardinality in categorical variables</span>
@@ -2481,7 +2513,7 @@ <h3><span class="section-number">37.80.5.7. </span>Number of labels: Cardinality
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h2><span class="section-number">37.80.6. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.6. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;income&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -2493,7 +2525,7 @@ <h2><span class="section-number">37.80.6. </span>Declare feature vector and targ
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h2><span class="section-number">37.80.7. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.7. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2514,9 +2546,9 @@ <h2><span class="section-number">37.80.7. </span>Split data into separate traini
 </div>
 </section>
 <section id="feature-engineering">
-<h2><span class="section-number">37.80.8. </span>Feature Engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.8. </span>Feature Engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
 <section id="display-categorical-variables-in-training-set">
-<h3><span class="section-number">37.80.8.1. </span>Display categorical variables in training set<a class="headerlink" href="#display-categorical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.1. </span>Display categorical variables in training set<a class="headerlink" href="#display-categorical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">categorical</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">X_train</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2528,7 +2560,7 @@ <h3><span class="section-number">37.80.8.1. </span>Display categorical variables
 </div>
 </section>
 <section id="display-numerical-variables-in-training-set">
-<h3><span class="section-number">37.80.8.2. </span>Display numerical variables in training set<a class="headerlink" href="#display-numerical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.2. </span>Display numerical variables in training set<a class="headerlink" href="#display-numerical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numerical</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">X_train</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">!=</span> <span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2540,7 +2572,7 @@ <h3><span class="section-number">37.80.8.2. </span>Display numerical variables i
 </div>
 </section>
 <section id="engineering-missing-values-in-categorical-variables">
-<h3><span class="section-number">37.80.8.3. </span>Engineering missing values in categorical variables<a class="headerlink" href="#engineering-missing-values-in-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.3. </span>Engineering missing values in categorical variables<a class="headerlink" href="#engineering-missing-values-in-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print percentage of missing values in the categorical variables in training set</span>
@@ -2613,7 +2645,7 @@ <h3><span class="section-number">37.80.8.3. </span>Engineering missing values in
 <p>We can see that there are no missing values in X_train and X_test.</p>
 </section>
 <section id="encode-categorical-variables">
-<h3><span class="section-number">37.80.8.4. </span>Encode categorical variables<a class="headerlink" href="#encode-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.4. </span>Encode categorical variables<a class="headerlink" href="#encode-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># preview categorical variables in X_train</span>
@@ -2680,7 +2712,7 @@ <h3><span class="section-number">37.80.8.4. </span>Encode categorical variables<
 </section>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">37.80.9. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.9. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span>
@@ -2718,7 +2750,7 @@ <h2><span class="section-number">37.80.9. </span>Feature Scaling<a class="header
 <p>We now have X_train dataset ready to be fed into the Random Forest classifier. We will do it as follows.</p>
 </section>
 <section id="random-forest-classifier-model-with-default-parameters">
-<h2><span class="section-number">37.80.10. </span>Random Forest Classifier model with default parameters<a class="headerlink" href="#random-forest-classifier-model-with-default-parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.10. </span>Random Forest Classifier model with default parameters<a class="headerlink" href="#random-forest-classifier-model-with-default-parameters" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import Random Forest classifier</span>
@@ -2756,7 +2788,7 @@ <h2><span class="section-number">37.80.10. </span>Random Forest Classifier model
 </div>
 </section>
 <section id="random-forest-classifier-model-with-100-decision-trees">
-<h2><span class="section-number">37.80.11. </span>Random Forest Classifier model with 100 Decision Trees<a class="headerlink" href="#random-forest-classifier-model-with-100-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.11. </span>Random Forest Classifier model with 100 Decision Trees<a class="headerlink" href="#random-forest-classifier-model-with-100-decision-trees" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate the classifier with n_estimators = 100</span>
@@ -2812,7 +2844,7 @@ <h2><span class="section-number">37.80.11. </span>Random Forest Classifier model
 </div>
 </section>
 <section id="build-the-random-forest-model-on-selected-features">
-<h2><span class="section-number">37.80.12. </span>Build the Random Forest model on selected features<a class="headerlink" href="#build-the-random-forest-model-on-selected-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.12. </span>Build the Random Forest model on selected features<a class="headerlink" href="#build-the-random-forest-model-on-selected-features" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># drop the least important feature from X_train and X_test</span>
@@ -2852,7 +2884,7 @@ <h2><span class="section-number">37.80.12. </span>Build the Random Forest model
 </div>
 </section>
 <section id="confusion-matrix">
-<h2><span class="section-number">37.80.13. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.13. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>A confusion matrix is a tool for summarizing the performance of a classification algorithm. A confusion matrix will give us a clear picture of classification model performance and the types of errors produced by the model. It gives us a summary of correct and incorrect predictions broken down by each category. The summary is represented in a tabular form.</p>
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-<h2><span class="section-number">37.80.14. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.14. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
@@ -2894,7 +2926,7 @@ <h2><span class="section-number">37.80.14. </span>Classification Report<a class=
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-<h1><span class="section-number">37.81. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.81. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/prashant111">prashant111</a> for creating <a class="reference external" href="https://www.kaggle.com/code/prashant111/random-forest-classifier-feature-importance">random-forest-classifier-feature-importance</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2935,13 +2967,13 @@ <h1><span class="section-number">37.81. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.79. </span>Boosting with tuning</p>
+            <p class="prev-next-title"><span class="section-number">40.79. </span>Boosting with tuning</p>
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-        <p class="prev-next-title"><span class="section-number">37.82. </span>Data engineering</p>
+        <p class="prev-next-title"><span class="section-number">40.82. </span>Data engineering</p>
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-    <link rel="prev" title="37.73. Random forests intro and regression" href="random-forests-intro-and-regression.html" />
+    <link rel="next" title="40.75. Beyond random forests: more ensemble models" href="beyond-random-forests-more-ensemble-models.html" />
+    <link rel="prev" title="40.73. Random forests intro and regression" href="random-forests-intro-and-regression.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
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+   40.74.1. Imports
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-   37.74.2. Loading the data
+   40.74.2. Loading the data
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
-     37.74.2.1. Task 1: Exploratory data analysis
+     40.74.2.1. Task 1: Exploratory data analysis
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-more-data-exploration">
-     37.74.2.2. Task 2: More data exploration
+     40.74.2.2. Task 2: More data exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-data-preprocessing">
-     37.74.2.3. Task 3: Data preprocessing
+     40.74.2.3. Task 3: Data preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-training-random-forests-classifier">
-     37.74.2.4. Task 4: Training random forests classifier
+     40.74.2.4. Task 4: Training random forests classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task5-evaluating-random-forests-classifier">
-     37.74.2.5. Task5: Evaluating random forests classifier
+     40.74.2.5. Task5: Evaluating random forests classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-improving-random-forests">
-     37.74.2.6. Task 6: Improving random forests
+     40.74.2.6. Task 6: Improving random forests
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-evaluating-the-model-on-the-test-set">
-     37.74.2.7. Task 7: Evaluating the model on the test set
+     40.74.2.7. Task 7: Evaluating the model on the test set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-feature-importance">
-     37.74.2.8. Task 8: Feature importance
+     40.74.2.8. Task 8: Feature importance
     </a>
    </li>
   </ul>
  </li>
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-   37.74.3. Acknowledgments
+   40.74.3. Acknowledgments
   </a>
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 </ul>
@@ -1602,59 +1634,59 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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-   37.74.1. Imports
+   40.74.1. Imports
   </a>
  </li>
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   <a class="reference internal nav-link" href="#loading-the-data">
-   37.74.2. Loading the data
+   40.74.2. Loading the data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
-     37.74.2.1. Task 1: Exploratory data analysis
+     40.74.2.1. Task 1: Exploratory data analysis
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-more-data-exploration">
-     37.74.2.2. Task 2: More data exploration
+     40.74.2.2. Task 2: More data exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-data-preprocessing">
-     37.74.2.3. Task 3: Data preprocessing
+     40.74.2.3. Task 3: Data preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-training-random-forests-classifier">
-     37.74.2.4. Task 4: Training random forests classifier
+     40.74.2.4. Task 4: Training random forests classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task5-evaluating-random-forests-classifier">
-     37.74.2.5. Task5: Evaluating random forests classifier
+     40.74.2.5. Task5: Evaluating random forests classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-improving-random-forests">
-     37.74.2.6. Task 6: Improving random forests
+     40.74.2.6. Task 6: Improving random forests
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-evaluating-the-model-on-the-test-set">
-     37.74.2.7. Task 7: Evaluating the model on the test set
+     40.74.2.7. Task 7: Evaluating the model on the test set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-feature-importance">
-     37.74.2.8. Task 8: Feature importance
+     40.74.2.8. Task 8: Feature importance
     </a>
    </li>
   </ul>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.74.3. Acknowledgments
+   40.74.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1668,12 +1700,12 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forests-for-classification">
-<h1><span class="section-number">37.74. </span>Random forests for classification<a class="headerlink" href="#random-forests-for-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.74. </span>Random forests for classification<a class="headerlink" href="#random-forests-for-classification" title="Permalink to this headline">#</a></h1>
 <p>Random Forests are powerful machine learning algorithms used for supervised classification and regression. Random forests works by averaging the predictions of the multiple and randomized decision trees. Decision trees tends to overfit and so by combining multiple decision trees, the effect of overfitting can be minimized.</p>
 <p>Random Forests are type of ensemble models. More about ensembles models in the next assignment.</p>
 <p>Different to other learning algorithms, random forests provide a way to find the importance of each feature and this is implemented in Sklearn.</p>
 <section id="imports">
-<h2><span class="section-number">37.74.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.74.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1688,7 +1720,7 @@ <h2><span class="section-number">37.74.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.74.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.74.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will use Random forests to build a classifier that identify the increase or decrease of the electricity using “the data that was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.”</p>
 <p>“The dataset contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). Source: <a class="reference external" href="https://www.openml.org/d/151">Open ML electricity</a>.</p>
 <p>Here are the information about the features:</p>
@@ -1742,7 +1774,7 @@ <h2><span class="section-number">37.74.2. </span>Loading the data<a class="heade
 </div>
 </div>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">37.74.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, as always, let’s split the data into training and test sets.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1838,7 +1870,7 @@ <h3><span class="section-number">37.74.2.1. </span>Task 1: Exploratory data anal
 <p>In the above correlation matrix, you can see that class feature is not there and this is because it still has categorical values.</p>
 </section>
 <section id="task-2-more-data-exploration">
-<h3><span class="section-number">37.74.2.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Before preprocessing the data, let’s take a look into specific features.</p>
 <p>Let’s see how many Ups/Downs are in the class feature.</p>
 <div class="cell docutils container">
@@ -1924,7 +1956,7 @@ <h3><span class="section-number">37.74.2.2. </span>Task 2: More data exploration
 </div>
 </section>
 <section id="task-3-data-preprocessing">
-<h3><span class="section-number">37.74.2.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Let’s encode the categorical feature <code class="docutils literal notranslate"><span class="pre">class</span></code>. But before that, let’s take training input data and labels.</p>
 <div class="cell docutils container">
@@ -1963,7 +1995,7 @@ <h3><span class="section-number">37.74.2.3. </span>Task 3: Data preprocessing<a
 <p>But again if you look at the data, the <code class="docutils literal notranslate"><span class="pre">day</span></code> feature is not normalized as other features. We can normalize it or leave it but for now let’s go ahead and train the random forests classifier.</p>
 </section>
 <section id="task-4-training-random-forests-classifier">
-<h3><span class="section-number">37.74.2.4. </span>Task 4: Training random forests classifier<a class="headerlink" href="#task-4-training-random-forests-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.4. </span>Task 4: Training random forests classifier<a class="headerlink" href="#task-4-training-random-forests-classifier" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
@@ -1984,7 +2016,7 @@ <h3><span class="section-number">37.74.2.4. </span>Task 4: Training random fores
 </div>
 </section>
 <section id="task5-evaluating-random-forests-classifier">
-<h3><span class="section-number">37.74.2.5. </span>Task5: Evaluating random forests classifier<a class="headerlink" href="#task5-evaluating-random-forests-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.5. </span>Task5: Evaluating random forests classifier<a class="headerlink" href="#task5-evaluating-random-forests-classifier" title="Permalink to this headline">#</a></h3>
 <p>Let’s build 3 functions to display accuracy, confusion matrix, and classification report.</p>
 <ul class="simple">
 <li><p>Accuracy provide a percentage score of the model’s ability to make correct predictions.</p></li>
@@ -2075,7 +2107,7 @@ <h3><span class="section-number">37.74.2.5. </span>Task5: Evaluating random fore
 <p>The model clearly overfitted the data. Let’s see how we can regularize it.</p>
 </section>
 <section id="task-6-improving-random-forests">
-<h3><span class="section-number">37.74.2.6. </span>Task 6: Improving random forests<a class="headerlink" href="#task-6-improving-random-forests" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.6. </span>Task 6: Improving random forests<a class="headerlink" href="#task-6-improving-random-forests" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Random forest model parameters</span>
@@ -2177,7 +2209,7 @@ <h3><span class="section-number">37.74.2.6. </span>Task 6: Improving random fore
 <p>One way to improve the model can be to search more hyperparameters or adding more good data is always the best cure.</p>
 </section>
 <section id="task-7-evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">37.74.2.7. </span>Task 7: Evaluating the model on the test set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.7. </span>Task 7: Evaluating the model on the test set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let us evaluate the model on the test set. But we need first run the label_encoder on the class feature as we did in the training labels. Note that we only transform (not fit_transform).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2213,7 +2245,7 @@ <h3><span class="section-number">37.74.2.7. </span>Task 7: Evaluating the model
 <p>As you can see the model is no longer overfitting. On the training set, the accuracy was 79%, which is a figure very similar to the test set. And the model never saw the test data. To improve the model in the case like this, if is often best to add more data if possible.</p>
 </section>
 <section id="task-8-feature-importance">
-<h3><span class="section-number">37.74.2.8. </span>Task 8: Feature importance<a class="headerlink" href="#task-8-feature-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.8. </span>Task 8: Feature importance<a class="headerlink" href="#task-8-feature-importance" title="Permalink to this headline">#</a></h3>
 <p>Different to other machine learning models, random forests can show how each feature contributed to the model generalization. Let’s find it.</p>
 <p>The results are values between 0 and 1. The closer to 1, the good the feature was to the model.</p>
 <div class="cell docutils container">
@@ -2233,7 +2265,7 @@ <h3><span class="section-number">37.74.2.8. </span>Task 8: Feature importance<a
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.74.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.74.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2275,13 +2307,13 @@ <h2><span class="section-number">37.74.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.73. </span>Random forests intro and regression</p>
+            <p class="prev-next-title"><span class="section-number">40.73. </span>Random forests intro and regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="beyond-random-forests-more-ensemble-models.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.75. </span>Beyond random forests: more ensemble models</p>
+        <p class="prev-next-title"><span class="section-number">40.75. </span>Beyond random forests: more ensemble models</p>
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     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html b/assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.73. Random forests intro and regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.73. Random forests intro and regression &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.74. Random forests for classification" href="random-forests-for-classification.html" />
-    <link rel="prev" title="37.72. Regularized Linear Models" href="../model-selection/regularized-linear-models.html" />
+    <link rel="next" title="40.74. Random forests for classification" href="random-forests-for-classification.html" />
+    <link rel="prev" title="40.72. Regularized Linear Models" href="../model-selection/regularized-linear-models.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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+   40.73.3. Tasks and roles
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+     40.73.3.1. Task 1: Exploratory analysis
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      <li class="toc-h4 nav-item toc-entry">
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+       40.73.3.1.1. Part 1: The histogram
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+       40.73.3.1.3. Part 3: Check the train data
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-       37.73.3.1.4. Part 4: Look the correlation
+       40.73.3.1.4. Part 4: Look the correlation
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    </li>
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+     40.73.3.2. Task 2: Data preprocessing
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+     40.73.3.3. Task 3: Training random forests regressor
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-     37.73.3.4. Task 4: Evaluating random forests regressor
+     40.73.3.4. Task 4: Evaluating random forests regressor
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-     37.73.3.5. Task 5: Improving random forests
+     40.73.3.5. Task 5: Improving random forests
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               <div>
                 
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-<h1><span class="section-number">37.73. </span>Random forests intro and regression<a class="headerlink" href="#random-forests-intro-and-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.73. </span>Random forests intro and regression<a class="headerlink" href="#random-forests-intro-and-regression" title="Permalink to this headline">#</a></h1>
 <p>Random Forests are powerful machine learning algorithms used for supervised classification and regression. Random forests works by averaging the predictions of the multiple and randomized decision trees. Decision trees tends to overfit and so by combining multiple decision trees, the effect of overfitting can be minimized.</p>
 <p>Random Forests are type of ensemble models. More about ensembles models in the next notebook.</p>
 <p>Different to other learning algorithms, random forests provide a way to find the importance of each feature and this is implemented in Sklearn.</p>
 <section id="imports">
-<h2><span class="section-number">37.73.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1737,7 +1769,7 @@ <h2><span class="section-number">37.73.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.73.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this regression task with random forests, we will use the Machine CPU (Central Processing Unit) dataset which is available at <a class="reference external" href="https://www.openml.org/t/5492">OpenML</a>.</p>
 <p>If you are reading this, it’s very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data:</p>
 <ul class="simple">
@@ -1790,9 +1822,9 @@ <h2><span class="section-number">37.73.2. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="tasks-and-roles">
-<h2><span class="section-number">37.73.3. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.3. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-analysis">
-<h3><span class="section-number">37.73.3.1. </span>Task 1: Exploratory analysis<a class="headerlink" href="#task-1-exploratory-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.1. </span>Task 1: Exploratory analysis<a class="headerlink" href="#task-1-exploratory-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1809,7 +1841,7 @@ <h3><span class="section-number">37.73.3.1. </span>Task 1: Exploratory analysis<
 </div>
 </div>
 <section id="part-1-the-histogram">
-<h4><span class="section-number">37.73.3.1.1. </span>Part 1: The histogram<a class="headerlink" href="#part-1-the-histogram" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.1. </span>Part 1: The histogram<a class="headerlink" href="#part-1-the-histogram" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_hist</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1882,7 +1914,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-2-the-pairplot">
-<h4><span class="section-number">37.73.3.1.2. </span>Part 2: The pairplot<a class="headerlink" href="#part-2-the-pairplot" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.2. </span>Part 2: The pairplot<a class="headerlink" href="#part-2-the-pairplot" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_pairplot</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1955,7 +1987,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-3-check-the-train-data">
-<h4><span class="section-number">37.73.3.1.3. </span>Part 3: Check the train data<a class="headerlink" href="#part-3-check-the-train-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.3. </span>Part 3: Check the train data<a class="headerlink" href="#part-3-check-the-train-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_desc</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1995,7 +2027,7 @@ <h4><span class="section-number">37.73.3.1.3. </span>Part 3: Check the train dat
 <p>Great! We don’t have any missing values.</p>
 </section>
 <section id="part-4-look-the-correlation">
-<h4><span class="section-number">37.73.3.1.4. </span>Part 4: Look the correlation<a class="headerlink" href="#part-4-look-the-correlation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.4. </span>Part 4: Look the correlation<a class="headerlink" href="#part-4-look-the-correlation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_corr</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -2036,7 +2068,7 @@ <h4><span class="section-number">37.73.3.1.4. </span>Part 4: Look the correlatio
 </section>
 </section>
 <section id="task-2-data-preprocessing">
-<h3><span class="section-number">37.73.3.2. </span>Task 2: Data preprocessing<a class="headerlink" href="#task-2-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.2. </span>Task 2: Data preprocessing<a class="headerlink" href="#task-2-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.
 Let’s set up a pipeline to scale features but before that, let’s take training input data and labels.</p>
 <div class="cell docutils container">
@@ -2060,7 +2092,7 @@ <h3><span class="section-number">37.73.3.2. </span>Task 2: Data preprocessing<a
 </div>
 </section>
 <section id="task-3-training-random-forests-regressor">
-<h3><span class="section-number">37.73.3.3. </span>Task 3: Training random forests regressor<a class="headerlink" href="#task-3-training-random-forests-regressor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.3. </span>Task 3: Training random forests regressor<a class="headerlink" href="#task-3-training-random-forests-regressor" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
@@ -2076,7 +2108,7 @@ <h3><span class="section-number">37.73.3.3. </span>Task 3: Training random fores
 </div>
 </section>
 <section id="task-4-evaluating-random-forests-regressor">
-<h3><span class="section-number">37.73.3.4. </span>Task 4: Evaluating random forests regressor<a class="headerlink" href="#task-4-evaluating-random-forests-regressor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.4. </span>Task 4: Evaluating random forests regressor<a class="headerlink" href="#task-4-evaluating-random-forests-regressor" title="Permalink to this headline">#</a></h3>
 <p>Let’s first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven’t improved it yet. we will make a function to make it easier and to avoid repetitions.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2107,7 +2139,7 @@ <h3><span class="section-number">37.73.3.4. </span>Task 4: Evaluating random for
 </div>
 </section>
 <section id="task-5-improving-random-forests">
-<h3><span class="section-number">37.73.3.5. </span>Task 5: Improving random forests<a class="headerlink" href="#task-5-improving-random-forests" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.5. </span>Task 5: Improving random forests<a class="headerlink" href="#task-5-improving-random-forests" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">forest_reg</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
@@ -2172,7 +2204,7 @@ <h3><span class="section-number">37.73.3.5. </span>Task 5: Improving random fore
 <p>Surprisingly, by searching model hyperparameters, the model did not improve. Can you guess why? We can observe many things while running Grid Search and reading about the <a class="reference external" href="https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees">random forests</a>. If you can’t get good results, set the <code class="docutils literal notranslate"><span class="pre">bootstrap</span></code> to False. It is true by default, and that means that you are training on samples of the training set instead of the whole training set. Try going back to the orginal model and change it to True and note how the prediction changes. Also learn more about the other hyperparameters.</p>
 </section>
 <section id="task-6-feature-importance">
-<h3><span class="section-number">37.73.3.6. </span>Task 6: Feature importance<a class="headerlink" href="#task-6-feature-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.6. </span>Task 6: Feature importance<a class="headerlink" href="#task-6-feature-importance" title="Permalink to this headline">#</a></h3>
 <p>Different to other machine learning models, random forests can show how each feature contributed to the model generalization. Let’s find it.
 The results are values between 0 and 1. The closer to 1, the good the feature was to the model.</p>
 <div class="cell docutils container">
@@ -2190,7 +2222,7 @@ <h3><span class="section-number">37.73.3.6. </span>Task 6: Feature importance<a
 <p>It makes sense that the model was able to find that out. Main memory (RAM, Read Only Memory) and cache memory (which stores frequently used information thus facilitating faster processing and quick retrieval of information) are the two most factors of the CPU performance and if you are going to buy a new computer, you want to have high RAM and cache memory in order to have a powerful machine that can process/compute and retrieve things faster.</p>
 </section>
 <section id="task-7-evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">37.73.3.7. </span>Task 7: Evaluating the Model on the Test Set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.7. </span>Task 7: Evaluating the Model on the Test Set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let us evaluate the model on the test set. But we need first run the pipeline on the test data. Note that we only transform (not fit_transform).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2221,7 +2253,7 @@ <h3><span class="section-number">37.73.3.7. </span>Task 7: Evaluating the Model
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.73.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2263,13 +2295,13 @@ <h2><span class="section-number">37.73.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.72. </span>Regularized Linear Models</p>
+            <p class="prev-next-title"><span class="section-number">40.72. </span>Regularized Linear Models</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="random-forests-for-classification.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.74. </span>Random forests for classification</p>
+        <p class="prev-next-title"><span class="section-number">40.74. </span>Random forests for classification</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html b/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html
index fb2de0bdcd..5d5cc2728b 100644
--- a/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html
+++ b/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.79. Boosting with tuning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.79. Boosting with tuning &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.80. Random Forest Classifier with Feature Importance" href="../ensemble-learning/random-forest-classifier-feature-importance.html" />
-    <link rel="prev" title="37.78. Gradient boosting" href="gradient-boosting-assignment.html" />
+    <link rel="next" title="40.80. Random Forest Classifier with Feature Importance" href="../ensemble-learning/random-forest-classifier-feature-importance.html" />
+    <link rel="prev" title="40.78. Gradient boosting" href="gradient-boosting-assignment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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-       37.79.2.5.2. Task 2: Identifying relationship between Numerical Predictor and Target (
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        <em>
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-       37.79.3.1.3. Task 3: Useless features in predicting SalePrice
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       </a>
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+     40.79.3.3. Filling Missing Values
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       <a class="reference internal nav-link" href="#task-1-ordinal-features">
-       37.79.3.3.1. Task 1: Ordinal features
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       <a class="reference internal nav-link" href="#id1">
-       37.79.3.3.2. Task 2: Categorical features
+       40.79.3.3.2. Task 2: Categorical features
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       <a class="reference internal nav-link" href="#task-3-numerical-features">
-       37.79.3.3.3. Task 3: Numerical features
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       <a class="reference internal nav-link" href="#task-4-changing-data-type">
-       37.79.3.3.4. Task 4: Changing Data Type
+       40.79.3.3.4. Task 4: Changing Data Type
       </a>
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       <a class="reference internal nav-link" href="#task-5-mapping-ordinal-features">
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     <a class="reference internal nav-link" href="#task-2-converting-categorical-to-numerical">
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     <a class="reference internal nav-link" href="#task-3-saleprice-distribution">
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    </li>
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     <a class="reference internal nav-link" href="#scaling-of-data">
-     37.79.5.2. Scaling of Data
+     40.79.5.2. Scaling of Data
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     <a class="reference internal nav-link" href="#ensemble-algorithms">
-     37.79.5.3. Ensemble Algorithms
+     40.79.5.3. Ensemble Algorithms
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       <a class="reference internal nav-link" href="#task-1-ensembling-models">
-       37.79.5.3.1. Task 1: Ensembling models
+       40.79.5.3.1. Task 1: Ensembling models
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       <a class="reference internal nav-link" href="#task-2-xgboost">
-       37.79.5.3.2. Task 2: XGBoost
+       40.79.5.3.2. Task 2: XGBoost
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       <a class="reference internal nav-link" href="#task-3-lightgbm">
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       <a class="reference internal nav-link" href="#task-4-catboost">
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       <a class="reference internal nav-link" href="#task-5-training-and-evaluation">
-       37.79.5.3.5. Task 5: Training and evaluation
+       40.79.5.3.5. Task 5: Training and evaluation
       </a>
      </li>
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       <a class="reference internal nav-link" href="#task-6-blending">
-       37.79.5.3.6. Task 6 Blending
+       40.79.5.3.6. Task 6 Blending
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+   40.79.6. Acknowledgments
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+   40.79.1. Introduction
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-   37.79.2. Understanding data
+   40.79.2. Understanding data
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-     37.79.2.1. Task 1: Imports and load data
+     40.79.2.1. Task 1: Imports and load data
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     <a class="reference internal nav-link" href="#task-2-concatenate-the-train-and-test-together">
-     37.79.2.2. Task 2: Concatenate the train and test together
+     40.79.2.2. Task 2: Concatenate the train and test together
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-isolate-numerical-and-categorical-columns">
-     37.79.2.3. Task 3: Isolate numerical and categorical columns
+     40.79.2.3. Task 3: Isolate numerical and categorical columns
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#univariate-analysis">
-     37.79.2.4. Univariate Analysis
+     40.79.2.4. Univariate Analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-numeric-features">
-       37.79.2.4.1. Task 1: Numeric features
+       40.79.2.4.1. Task 1: Numeric features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-categorical-features">
-       37.79.2.4.2. Task 2: Categorical features
+       40.79.2.4.2. Task 2: Categorical features
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#bi-variate-analysis">
-     37.79.2.5. Bi-Variate Analysis
+     40.79.2.5. Bi-Variate Analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-correlation-matrix">
-       37.79.2.5.1. Task 1: Correlation matrix
+       40.79.2.5.1. Task 1: Correlation matrix
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice">
-       37.79.2.5.2. Task 2: Identifying relationship between Numerical Predictor and Target (
+       40.79.2.5.2. Task 2: Identifying relationship between Numerical Predictor and Target (
        <em>
         SalePrice
        </em>
@@ -1828,7 +1860,7 @@ <h2> Contents </h2>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-scatterplot">
-       37.79.2.5.3. Task 3: Scatterplot
+       40.79.2.5.3. Task 3: Scatterplot
       </a>
      </li>
     </ul>
@@ -1837,69 +1869,69 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-processing">
-   37.79.3. Data Processing
+   40.79.3. Data Processing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-redundant-features">
-     37.79.3.1. Removing redundant features
+     40.79.3.1. Removing redundant features
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-features-with-multicollinearity">
-       37.79.3.1.1. Task 1：Features with multicollinearity
+       40.79.3.1.1. Task 1：Features with multicollinearity
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-features-with-a-lot-of-missing-values">
-       37.79.3.1.2. Task 2: Features with a lot of missing values
+       40.79.3.1.2. Task 2: Features with a lot of missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-useless-features-in-predicting-saleprice">
-       37.79.3.1.3. Task 3: Useless features in predicting SalePrice
+       40.79.3.1.3. Task 3: Useless features in predicting SalePrice
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-removing-features-that-have-mostly-just-1-value">
-       37.79.3.1.4. Task 4: Removing features that have mostly just 1 value
+       40.79.3.1.4. Task 4: Removing features that have mostly just 1 value
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-outliers">
-     37.79.3.2. Dealing with outliers
+     40.79.3.2. Dealing with outliers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#filling-missing-values">
-     37.79.3.3. Filling Missing Values
+     40.79.3.3. Filling Missing Values
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ordinal-features">
-       37.79.3.3.1. Task 1: Ordinal features
+       40.79.3.3.1. Task 1: Ordinal features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.79.3.3.2. Task 2: Categorical features
+       40.79.3.3.2. Task 2: Categorical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-numerical-features">
-       37.79.3.3.3. Task 3: Numerical features
+       40.79.3.3.3. Task 3: Numerical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-changing-data-type">
-       37.79.3.3.4. Task 4: Changing Data Type
+       40.79.3.3.4. Task 4: Changing Data Type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-mapping-ordinal-features">
-       37.79.3.3.5. Task 5: Mapping ordinal features
+       40.79.3.3.5. Task 5: Mapping ordinal features
       </a>
      </li>
     </ul>
@@ -1908,74 +1940,74 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-engineering">
-   37.79.4. Feature engineering
+   40.79.4. Feature engineering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-binay-columns">
-     37.79.4.1. Task 1: Binay columns
+     40.79.4.1. Task 1: Binay columns
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-converting-categorical-to-numerical">
-     37.79.4.2. Task 2: Converting categorical to numerical
+     40.79.4.2. Task 2: Converting categorical to numerical
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-saleprice-distribution">
-     37.79.4.3. Task 3: SalePrice distribution
+     40.79.4.3. Task 3: SalePrice distribution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modeling">
-   37.79.5. Modeling
+   40.79.5. Modeling
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-into-train-validation-set">
-     37.79.5.1. Split into train-validation set
+     40.79.5.1. Split into train-validation set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scaling-of-data">
-     37.79.5.2. Scaling of Data
+     40.79.5.2. Scaling of Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ensemble-algorithms">
-     37.79.5.3. Ensemble Algorithms
+     40.79.5.3. Ensemble Algorithms
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ensembling-models">
-       37.79.5.3.1. Task 1: Ensembling models
+       40.79.5.3.1. Task 1: Ensembling models
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-xgboost">
-       37.79.5.3.2. Task 2: XGBoost
+       40.79.5.3.2. Task 2: XGBoost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-lightgbm">
-       37.79.5.3.3. Task 3: LightGBM
+       40.79.5.3.3. Task 3: LightGBM
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-catboost">
-       37.79.5.3.4. Task 4: Catboost
+       40.79.5.3.4. Task 4: Catboost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-training-and-evaluation">
-       37.79.5.3.5. Task 5: Training and evaluation
+       40.79.5.3.5. Task 5: Training and evaluation
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-6-blending">
-       37.79.5.3.6. Task 6 Blending
+       40.79.5.3.6. Task 6 Blending
       </a>
      </li>
     </ul>
@@ -1984,7 +2016,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.79.6. Acknowledgments
+   40.79.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1998,9 +2030,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="boosting-with-tuning">
-<h1><span class="section-number">37.79. </span>Boosting with tuning<a class="headerlink" href="#boosting-with-tuning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.79. </span>Boosting with tuning<a class="headerlink" href="#boosting-with-tuning" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.79.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In this assginment, we will take you through the step by step approach in solving a House Pricing regression problem. This notebook aims to:</p>
 <ol class="simple">
 <li><p>Provide insights on Housing Data</p></li>
@@ -2011,9 +2043,9 @@ <h2><span class="section-number">37.79.1. </span>Introduction<a class="headerlin
 <p>We hope that after reading this notebook, beginners will be more comfortable in tackling any learning problems and able to use the taught techniques to solve any problems from start to end. For non-beginners, hopefully you are able to get something out of it from this assignment and gain new insights and knowledge along the way.</p>
 </section>
 <section id="understanding-data">
-<h2><span class="section-number">37.79.2. </span>Understanding data<a class="headerlink" href="#understanding-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.2. </span>Understanding data<a class="headerlink" href="#understanding-data" title="Permalink to this headline">#</a></h2>
 <section id="task-1-imports-and-load-data">
-<h3><span class="section-number">37.79.2.1. </span>Task 1: Imports and load data<a class="headerlink" href="#task-1-imports-and-load-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.1. </span>Task 1: Imports and load data<a class="headerlink" href="#task-1-imports-and-load-data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="c1"># linear algebra</span>
@@ -2046,7 +2078,7 @@ <h3><span class="section-number">37.79.2.1. </span>Task 1: Imports and load data
 <p>Just taking a quick glance at the top rows of the dataframe, we can see that there are some columns that are filled with <strong>NAN (Not a Number)</strong>. We will investigate this later on.</p>
 </section>
 <section id="task-2-concatenate-the-train-and-test-together">
-<h3><span class="section-number">37.79.2.2. </span>Task 2: Concatenate the train and test together<a class="headerlink" href="#task-2-concatenate-the-train-and-test-together" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.2. </span>Task 2: Concatenate the train and test together<a class="headerlink" href="#task-2-concatenate-the-train-and-test-together" title="Permalink to this headline">#</a></h3>
 <p>What we did here is first to concatenate the train and test together for extracting insights into the Housing Price data as a whole. It will also be more convenient for our preprocessing steps later on as we will only have 1 data reference</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2065,7 +2097,7 @@ <h3><span class="section-number">37.79.2.2. </span>Task 2: Concatenate the train
 </div>
 </section>
 <section id="task-3-isolate-numerical-and-categorical-columns">
-<h3><span class="section-number">37.79.2.3. </span>Task 3: Isolate numerical and categorical columns<a class="headerlink" href="#task-3-isolate-numerical-and-categorical-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.3. </span>Task 3: Isolate numerical and categorical columns<a class="headerlink" href="#task-3-isolate-numerical-and-categorical-columns" title="Permalink to this headline">#</a></h3>
 <p>Lets isolate both the numerical and categorical columns since we will be applying different visualization techniques on them</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2099,9 +2131,9 @@ <h3><span class="section-number">37.79.2.3. </span>Task 3: Isolate numerical and
 </div>
 </section>
 <section id="univariate-analysis">
-<h3><span class="section-number">37.79.2.4. </span>Univariate Analysis<a class="headerlink" href="#univariate-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.4. </span>Univariate Analysis<a class="headerlink" href="#univariate-analysis" title="Permalink to this headline">#</a></h3>
 <section id="task-1-numeric-features">
-<h4><span class="section-number">37.79.2.4.1. </span>Task 1: Numeric features<a class="headerlink" href="#task-1-numeric-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.4.1. </span>Task 1: Numeric features<a class="headerlink" href="#task-1-numeric-features" title="Permalink to this headline">#</a></h4>
 <p>For numerical features, we are always concerned about the <strong>distribution</strong> of these features, including the <strong>statistical characteristics</strong> of these columns e.g mean, median, mode. Hence  we will usually use <strong>Distribution plot</strong> to visualize their data distribution. <strong>Boxplots</strong> are also commonly used to unearth the statistical characteristics of each feature. More often than not, we use it to look for any outliers that we might need to filter out later on during the preprocessing step.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2149,7 +2181,7 @@ <h4><span class="section-number">37.79.2.4.1. </span>Task 1: Numeric features<a
 </div>
 </section>
 <section id="task-2-categorical-features">
-<h4><span class="section-number">37.79.2.4.2. </span>Task 2: Categorical features<a class="headerlink" href="#task-2-categorical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.4.2. </span>Task 2: Categorical features<a class="headerlink" href="#task-2-categorical-features" title="Permalink to this headline">#</a></h4>
 <p>In the case of categorical features, we will often use countplots to visualize the count of each distinct value within each features. We can see that some categorical features like <strong>Utilities, Condition2</strong> consist of mainly just one value, which does not add any useful information. Thus, we will also remove them later on.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2167,11 +2199,11 @@ <h4><span class="section-number">37.79.2.4.2. </span>Task 2: Categorical feature
 </section>
 </section>
 <section id="bi-variate-analysis">
-<h3><span class="section-number">37.79.2.5. </span>Bi-Variate Analysis<a class="headerlink" href="#bi-variate-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.5. </span>Bi-Variate Analysis<a class="headerlink" href="#bi-variate-analysis" title="Permalink to this headline">#</a></h3>
 <p>Bi-variate analysis looks at 2 different features to identify any possible relationship or distinctive patterns between the 2 features. One of the commonly used technique is through the  <strong>Correlation Matrix</strong>. Correlation matrix is an effective tool to uncover linear relationship (Correlation) between any 2 continuous features. Correlation not only allow us to determine which features are important to Saleprice, but also as a mean to investigate any <strong>multicollinearity</strong> between our independent predictors.<br />
 Multicollinearity happens when 2 or more independent variables are highly correlated with one another. In such situation, it causes precision loss in our regression coefficients, affecting our ability to identify the most important features that are most useful to our model</p>
 <section id="task-1-correlation-matrix">
-<h4><span class="section-number">37.79.2.5.1. </span>Task 1: Correlation matrix<a class="headerlink" href="#task-1-correlation-matrix" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.5.1. </span>Task 1: Correlation matrix<a class="headerlink" href="#task-1-correlation-matrix" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">12</span><span class="p">))</span>
@@ -2191,7 +2223,7 @@ <h4><span class="section-number">37.79.2.5.1. </span>Task 1: Correlation matrix<
 <p>From the correlation matrix we have identified the above variables which are highly correlated with each other. This finding will guide us in our preprocessing steps later on as we aim to remove highly correlated features to avoid performance loss in our model</p>
 </section>
 <section id="task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice">
-<h4><span class="section-number">37.79.2.5.2. </span>Task 2: Identifying relationship between Numerical Predictor and Target (<em>SalePrice</em>)<a class="headerlink" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.5.2. </span>Task 2: Identifying relationship between Numerical Predictor and Target (<em>SalePrice</em>)<a class="headerlink" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice" title="Permalink to this headline">#</a></h4>
 <p>Below, we sorted the strength of linear relationship between Saleprice and other features. OverallQual and GrLivArea has the strongest linear relationship with SalePrice. Hence, these 2 features will be important factor in predicting Housing Price</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2204,7 +2236,7 @@ <h4><span class="section-number">37.79.2.5.2. </span>Task 2: Identifying relatio
 </div>
 </section>
 <section id="task-3-scatterplot">
-<h4><span class="section-number">37.79.2.5.3. </span>Task 3: Scatterplot<a class="headerlink" href="#task-3-scatterplot" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.5.3. </span>Task 3: Scatterplot<a class="headerlink" href="#task-3-scatterplot" title="Permalink to this headline">#</a></h4>
 <p>Using scatterplot can also help us to identify potential linear relationship between Numerical features. Although scatterplot does not provide quantitative evidence on the strength of linear relationship between our features, it is useful in helping us to visualize any sort of relationship that correlation matrix could not calculate. E.g Quadratic, Exponential relationships.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2221,7 +2253,7 @@ <h4><span class="section-number">37.79.2.5.3. </span>Task 3: Scatterplot<a class
 </section>
 </section>
 <section id="data-processing">
-<h2><span class="section-number">37.79.3. </span>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.3. </span>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h2>
 <p>Now that we have more or less finished analysing our data and gaining insights through the various analysis and visualization, we will have to leverage on these insights to guide our preprocessing decision, so as to provide a clean and error-free data for our model to train on later on.</p>
 <p>We do not perform visualization and analysis just to create pretty graphs or for the sake of doing it, IT IS VITAL TO OUR PREPROCESSING!</p>
 <p>This section outlines the steps for Data Processing:</p>
@@ -2231,9 +2263,9 @@ <h2><span class="section-number">37.79.3. </span>Data Processing<a class="header
 <li><p>Filling in missing values</p></li>
 </ol>
 <section id="removing-redundant-features">
-<h3><span class="section-number">37.79.3.1. </span>Removing redundant features<a class="headerlink" href="#removing-redundant-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.3.1. </span>Removing redundant features<a class="headerlink" href="#removing-redundant-features" title="Permalink to this headline">#</a></h3>
 <section id="task-1-features-with-multicollinearity">
-<h4><span class="section-number">37.79.3.1.1. </span>Task 1：Features with multicollinearity<a class="headerlink" href="#task-1-features-with-multicollinearity" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.1. </span>Task 1：Features with multicollinearity<a class="headerlink" href="#task-1-features-with-multicollinearity" title="Permalink to this headline">#</a></h4>
 <p>From the above correlation matrix, we have pinpointed certain features that are highly correlated</p>
 <ul class="simple">
 <li><p>GarageYrBlt and YearBuilt</p></li>
@@ -2251,7 +2283,7 @@ <h4><span class="section-number">37.79.3.1.1. </span>Task 1：Features with mult
 </div>
 </section>
 <section id="task-2-features-with-a-lot-of-missing-values">
-<h4><span class="section-number">37.79.3.1.2. </span>Task 2: Features with a lot of missing values<a class="headerlink" href="#task-2-features-with-a-lot-of-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.2. </span>Task 2: Features with a lot of missing values<a class="headerlink" href="#task-2-features-with-a-lot-of-missing-values" title="Permalink to this headline">#</a></h4>
 <p>Apart from these highly correlated features, we will also remove features that is not very useful in prediction due to many missing values. PoolQC, MiscFeature, Alley has too many missing values to provide any useful information</p>
 <div class="cell docutils container">
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@@ -2273,7 +2305,7 @@ <h4><span class="section-number">37.79.3.1.2. </span>Task 2: Features with a lot
 </div>
 </section>
 <section id="task-3-useless-features-in-predicting-saleprice">
-<h4><span class="section-number">37.79.3.1.3. </span>Task 3: Useless features in predicting SalePrice<a class="headerlink" href="#task-3-useless-features-in-predicting-saleprice" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.3. </span>Task 3: Useless features in predicting SalePrice<a class="headerlink" href="#task-3-useless-features-in-predicting-saleprice" title="Permalink to this headline">#</a></h4>
 <p>We will also remove features that does not have any linear relationship with target <em>SalePrice</em>. We can see from the plot below that the MoSold and YrSold does not have any impact on the price of the house sold.</p>
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@@ -2296,7 +2328,7 @@ <h4><span class="section-number">37.79.3.1.3. </span>Task 3: Useless features in
 </div>
 </section>
 <section id="task-4-removing-features-that-have-mostly-just-1-value">
-<h4><span class="section-number">37.79.3.1.4. </span>Task 4: Removing features that have mostly just 1 value<a class="headerlink" href="#task-4-removing-features-that-have-mostly-just-1-value" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.4. </span>Task 4: Removing features that have mostly just 1 value<a class="headerlink" href="#task-4-removing-features-that-have-mostly-just-1-value" title="Permalink to this headline">#</a></h4>
 <p>Earlier during our Univariate analysis, we found that some features mostly consist of just a single value or 0s, which is not useful to us. Therefore, we set a user defined threshold at 96%. If a column has more than 96% of the same value, we will render the features to be useless and remove it, since there isnt much information we can extract from</p>
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@@ -2341,7 +2373,7 @@ <h4><span class="section-number">37.79.3.1.4. </span>Task 4: Removing features t
 </section>
 </section>
 <section id="dealing-with-outliers">
-<h3><span class="section-number">37.79.3.2. </span>Dealing with outliers<a class="headerlink" href="#dealing-with-outliers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.3.2. </span>Dealing with outliers<a class="headerlink" href="#dealing-with-outliers" title="Permalink to this headline">#</a></h3>
 <p>Removing outliers will prevent our models performance from being affected by extreme values.<br />
 From our boxplot earlier, we have pinpointed the following features with extreme outliers:</p>
 <ul class="simple">
@@ -2384,7 +2416,7 @@ <h3><span class="section-number">37.79.3.2. </span>Dealing with outliers<a class
 </div>
 </section>
 <section id="filling-missing-values">
-<h3><span class="section-number">37.79.3.3. </span>Filling Missing Values<a class="headerlink" href="#filling-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.3.3. </span>Filling Missing Values<a class="headerlink" href="#filling-missing-values" title="Permalink to this headline">#</a></h3>
 <p>Our machine learning model is unable to deal with missing values, thus we need to deal with them based on our understanding of the features. These missing values are denoted <strong>NAN</strong> as we have seen earlier during our data exploration.</p>
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@@ -2394,7 +2426,7 @@ <h3><span class="section-number">37.79.3.3. </span>Filling Missing Values<a clas
 </div>
 </div>
 <section id="task-1-ordinal-features">
-<h4><span class="section-number">37.79.3.3.1. </span>Task 1: Ordinal features<a class="headerlink" href="#task-1-ordinal-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.1. </span>Task 1: Ordinal features<a class="headerlink" href="#task-1-ordinal-features" title="Permalink to this headline">#</a></h4>
 <p>We will replace the ordinal missing values with NA, which will be mapped later on when we encode them into an ordered arrangement</p>
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@@ -2409,7 +2441,7 @@ <h4><span class="section-number">37.79.3.3.1. </span>Task 1: Ordinal features<a
 </div>
 </section>
 <section id="id1">
-<h4><span class="section-number">37.79.3.3.2. </span>Task 2: Categorical features<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.2. </span>Task 2: Categorical features<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>We will replace the missing value of our categorical features with the most frequent occurrence (mode) of the individual features.</p>
 <div class="cell docutils container">
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@@ -2422,7 +2454,7 @@ <h4><span class="section-number">37.79.3.3.2. </span>Task 2: Categorical feature
 </div>
 </section>
 <section id="task-3-numerical-features">
-<h4><span class="section-number">37.79.3.3.3. </span>Task 3: Numerical features<a class="headerlink" href="#task-3-numerical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.3. </span>Task 3: Numerical features<a class="headerlink" href="#task-3-numerical-features" title="Permalink to this headline">#</a></h4>
 <p>For <strong>Numerical Features</strong>, the common approach will be to replace the missing value with the mean of the feature distribution.<br />
 However, certain features like <em>LotFrontage</em> and <em>GarageArea</em> have wide variance in their distribution. Taking mean values across <em>Neighborhoods</em>, we will see that the mean varies alot from just taking the mean value of these individual column, since each neightborhood have different LotFrontage and GarageArea mean value. Hence, i decided to group these features by Neighborhoods to impute the respective mean values.</p>
 <p><strong>Note</strong>: My initial approach was based on the means of both train and test set. This exposed us to the issue of data leakage, where information from the test set is used to compute mean values. The right way to do it will be to impute solely based on the mean of the train data.</p>
@@ -2464,7 +2496,7 @@ <h4><span class="section-number">37.79.3.3.3. </span>Task 3: Numerical features<
 </div>
 </section>
 <section id="task-4-changing-data-type">
-<h4><span class="section-number">37.79.3.3.4. </span>Task 4: Changing Data Type<a class="headerlink" href="#task-4-changing-data-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.4. </span>Task 4: Changing Data Type<a class="headerlink" href="#task-4-changing-data-type" title="Permalink to this headline">#</a></h4>
 <p>Since <strong>MSSubClass</strong> is an integer column based on some mapped values in string notation, we change its data type to string value instead</p>
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@@ -2475,7 +2507,7 @@ <h4><span class="section-number">37.79.3.3.4. </span>Task 4: Changing Data Type<
 </div>
 </section>
 <section id="task-5-mapping-ordinal-features">
-<h4><span class="section-number">37.79.3.3.5. </span>Task 5: Mapping ordinal features<a class="headerlink" href="#task-5-mapping-ordinal-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.5. </span>Task 5: Mapping ordinal features<a class="headerlink" href="#task-5-mapping-ordinal-features" title="Permalink to this headline">#</a></h4>
 <p>There are some columns which are ordinal by nature, which represents the quality or condition of certain housing features. In this case, we will map the respective strings to a value. The better the quality, the higher the value</p>
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@@ -2508,7 +2540,7 @@ <h4><span class="section-number">37.79.3.3.5. </span>Task 5: Mapping ordinal fea
 </section>
 </section>
 <section id="feature-engineering">
-<h2><span class="section-number">37.79.4. </span>Feature engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.4. </span>Feature engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
 <p>Feature Engineering is a technique by which we create new features that could potentially aid in predicting our target variable, which in this case, is <em>SalePrice</em>. In this notebook, we will create additional features based on our <strong>Domain Knowledge</strong> of the housing features</p>
 <p>Based on the current feature we have, the first additional featuire we can add would be <strong>TotalLot</strong>, which sums up both the <em>LotFrontage</em> and <em>LotArea</em> to identify the total area of land available as lot. We can also calculate the total number of surface area of the house, <strong>TotalSF</strong> by adding the area from basement and 2nd floor. <strong>TotalBath</strong> can also be used to tell us in total how many bathrooms are there in the house. We can also add all the different types of porches around the house and generalise into a total porch area, <strong>TotalPorch</strong>.</p>
 <ul class="simple">
@@ -2530,7 +2562,7 @@ <h2><span class="section-number">37.79.4. </span>Feature engineering<a class="he
 </div>
 </div>
 <section id="task-1-binay-columns">
-<h3><span class="section-number">37.79.4.1. </span>Task 1: Binay columns<a class="headerlink" href="#task-1-binay-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.4.1. </span>Task 1: Binay columns<a class="headerlink" href="#task-1-binay-columns" title="Permalink to this headline">#</a></h3>
 <p>We also include simple feature engineering by creating binary columns for some features that can indicate the <strong>presence(1) / absence(0)</strong> of some features of the house</p>
 <div class="cell docutils container">
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@@ -2545,7 +2577,7 @@ <h3><span class="section-number">37.79.4.1. </span>Task 1: Binay columns<a class
 </div>
 </section>
 <section id="task-2-converting-categorical-to-numerical">
-<h3><span class="section-number">37.79.4.2. </span>Task 2: Converting categorical to numerical<a class="headerlink" href="#task-2-converting-categorical-to-numerical" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.4.2. </span>Task 2: Converting categorical to numerical<a class="headerlink" href="#task-2-converting-categorical-to-numerical" title="Permalink to this headline">#</a></h3>
 <p>Lastly, because machine learning only learns from data that is numerical in nature, we will convert the remaining categorical columns into one-hot features using the <em>get_dummies()</em> method into numerical columns that is suitable for feeding into our machine learning algorithm.</p>
 <div class="cell docutils container">
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@@ -2556,7 +2588,7 @@ <h3><span class="section-number">37.79.4.2. </span>Task 2: Converting categorica
 </div>
 </section>
 <section id="task-3-saleprice-distribution">
-<h3><span class="section-number">37.79.4.3. </span>Task 3: SalePrice distribution<a class="headerlink" href="#task-3-saleprice-distribution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.4.3. </span>Task 3: SalePrice distribution<a class="headerlink" href="#task-3-saleprice-distribution" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
@@ -2588,10 +2620,10 @@ <h3><span class="section-number">37.79.4.3. </span>Task 3: SalePrice distributio
 </section>
 </section>
 <section id="modeling">
-<h2><span class="section-number">37.79.5. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.5. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h2>
 <p>This section will consist of scaling the data for better optimization in our training, and also introducing the varieties of ensembling methods that are used in this notebook for predicting the Housing price. We also try out hyperparameter tuning briefly, as i will be dedicating a new notebook that will explain more in details on the process of Hyperparameter Tuning as well as the mathematical aspect of the ensemble algorithms.</p>
 <section id="split-into-train-validation-set">
-<h3><span class="section-number">37.79.5.1. </span>Split into train-validation set<a class="headerlink" href="#split-into-train-validation-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.5.1. </span>Split into train-validation set<a class="headerlink" href="#split-into-train-validation-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">train</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
@@ -2603,7 +2635,7 @@ <h3><span class="section-number">37.79.5.1. </span>Split into train-validation s
 </div>
 </section>
 <section id="scaling-of-data">
-<h3><span class="section-number">37.79.5.2. </span>Scaling of Data<a class="headerlink" href="#scaling-of-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.5.2. </span>Scaling of Data<a class="headerlink" href="#scaling-of-data" title="Permalink to this headline">#</a></h3>
 <p><strong>RobustScaler</strong> is a transformation technique that removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). It is also robust to outliers, which makes it ideal for data where there are too many outliers that will drastically reduce the number of training data.</p>
 <p>Previously i fitted the RobustScaler on both Train and Test set, and that is a mistake on my side. By fitting the scaler on both train and testset, we exposed ourselves to the problem of <strong>Data Leakage</strong>. Data Leakage is a problem when information from outside the training dataset is used to create the model. If we fit the scaler on both training and test data, our training data characteristics will contain the distribution of our testset. As such, we are unknowningly passing in information about our test data into the final training data for training, which will not give us the opportunity to truly test our model on data it has never seen before.<br />
 <strong>Lesson Learnt</strong>: Fit the scaler just on training data, and then transforming it on both training and test data</p>
@@ -2621,7 +2653,7 @@ <h3><span class="section-number">37.79.5.2. </span>Scaling of Data<a class="head
 </div>
 </section>
 <section id="ensemble-algorithms">
-<h3><span class="section-number">37.79.5.3. </span>Ensemble Algorithms<a class="headerlink" href="#ensemble-algorithms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.5.3. </span>Ensemble Algorithms<a class="headerlink" href="#ensemble-algorithms" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2632,7 +2664,7 @@ <h3><span class="section-number">37.79.5.3. </span>Ensemble Algorithms<a class="
 </div>
 </div>
 <section id="task-1-ensembling-models">
-<h4><span class="section-number">37.79.5.3.1. </span>Task 1: Ensembling models<a class="headerlink" href="#task-1-ensembling-models" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.1. </span>Task 1: Ensembling models<a class="headerlink" href="#task-1-ensembling-models" title="Permalink to this headline">#</a></h4>
 <p>Ensembling methods are meta-algorithms which involves combining several machine learning models into one predictive model, aim at decreasing variance(reduce overfitting) and improving bias(improve accuracy).<br />
 The 3 main ensembling methods are <strong>Bagging, Boosting and Stacking</strong>.<br />
 In this notebook, we will focus mainly on <strong>Boosting</strong>, which is what we will be using for our prediction.</p>
@@ -2653,7 +2685,7 @@ <h4><span class="section-number">37.79.5.3.1. </span>Task 1: Ensembling models<a
 </div>
 </section>
 <section id="task-2-xgboost">
-<h4><span class="section-number">37.79.5.3.2. </span>Task 2: XGBoost<a class="headerlink" href="#task-2-xgboost" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.2. </span>Task 2: XGBoost<a class="headerlink" href="#task-2-xgboost" title="Permalink to this headline">#</a></h4>
 <p>Extreme Gradient Boost (XGB) is a boosting algorithm that uses the gradient boosting framework; where gradient descent algorithm is employed to minimize the errors in the sequential model. It improves on the gradient boosting framework with faster execution speed and improved performance.<br />
 <br/>
 <img src="https://miro.medium.com/max/1400/1*QJZ6W-Pck_W7RlIDwUIN9Q.jpeg" width=700/></p>
@@ -2693,7 +2725,7 @@ <h4><span class="section-number">37.79.5.3.2. </span>Task 2: XGBoost<a class="he
 </div>
 </section>
 <section id="task-3-lightgbm">
-<h4><span class="section-number">37.79.5.3.3. </span>Task 3: LightGBM<a class="headerlink" href="#task-3-lightgbm" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.3. </span>Task 3: LightGBM<a class="headerlink" href="#task-3-lightgbm" title="Permalink to this headline">#</a></h4>
 <p>LightBGM is another gradient boosting framework developed by Microsoft that is based on decision tree algorithm, designed to be efficient and distributed. Some of the advantages of implementing LightBGM compared to other boosting frameworks include:</p>
 <ol class="simple">
 <li><p>Faster training speed and higher efficiency (use histogram based algorithm i.e it buckets continuous feature values into discrete bins which fasten the training procedure)</p></li>
@@ -2745,7 +2777,7 @@ <h4><span class="section-number">37.79.5.3.3. </span>Task 3: LightGBM<a class="h
 </div>
 </section>
 <section id="task-4-catboost">
-<h4><span class="section-number">37.79.5.3.4. </span>Task 4: Catboost<a class="headerlink" href="#task-4-catboost" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.4. </span>Task 4: Catboost<a class="headerlink" href="#task-4-catboost" title="Permalink to this headline">#</a></h4>
 <p>Catboost is another alternative gradient boosting framework developed by Yandex. The word “Catboost” is derived from two words; “Category” and “Boosting”. This means that Catboost itself can deal with categorical features which usually has to be converted to numerical encodings in order to feed into traditional gradient boost frameworks and machine learning models.<br />
 The 2 critical features in Catboost algorithm is the use of <strong>ordered boosting</strong> and <strong>innovative algorithm for processing categorical features</strong>, which fight the prediction shift caused by a special kind of target leakage present in all existing implementations of gradient boosting algorithms.<br />
 Find out more <a class="reference external" href="https://towardsdatascience.com/https-medium-com-talperetz24-mastering-the-new-generation-of-gradient-boosting-db04062a7ea2">here</a></p>
@@ -2784,7 +2816,7 @@ <h4><span class="section-number">37.79.5.3.4. </span>Task 4: Catboost<a class="h
 </div>
 </section>
 <section id="task-5-training-and-evaluation">
-<h4><span class="section-number">37.79.5.3.5. </span>Task 5: Training and evaluation<a class="headerlink" href="#task-5-training-and-evaluation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.5. </span>Task 5: Training and evaluation<a class="headerlink" href="#task-5-training-and-evaluation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mean_cross_val</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -2839,7 +2871,7 @@ <h4><span class="section-number">37.79.5.3.5. </span>Task 5: Training and evalua
 </div>
 </section>
 <section id="task-6-blending">
-<h4><span class="section-number">37.79.5.3.6. </span>Task 6 Blending<a class="headerlink" href="#task-6-blending" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.6. </span>Task 6 Blending<a class="headerlink" href="#task-6-blending" title="Permalink to this headline">#</a></h4>
 <p><strong>Blending</strong> is a technique that give different weightage to different algorithms that will affect their influence of the predictions. Such techniques can help to improve performance since it uses a variety of models as predictors. Special thanks to <a class="reference external" href="https://www.kaggle.com/itslek">&#64;itslek</a> for the implementation of blending. I have randomly chosen the weights for each models in this case, however, you can improve on this by futher tuning the weightage to be given to each model to achieve a better accuracy!</p>
 <div class="cell docutils container">
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@@ -2863,7 +2895,7 @@ <h4><span class="section-number">37.79.5.3.6. </span>Task 6 Blending<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.79.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/angqx95">aqx</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/angqx95/data-science-workflow-top-2-with-tuning">Data Science Workflow TOP 2% (with Tuning)</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2905,13 +2937,13 @@ <h2><span class="section-number">37.79.6. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">37.78. </span>Gradient boosting</p>
+            <p class="prev-next-title"><span class="section-number">40.78. </span>Gradient boosting</p>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.80. </span>Random Forest Classifier with Feature Importance</p>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="gradient-boosting">
-<h1><span class="section-number">37.78. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.78. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
 <p>The logic of gradient boosting is very simple.
 One of the very basic assumption of linear regression is that it’s sum of residuals is 0. Although, tree based models are not based on any of such assumptions, but if we think logic behind these assumptions, we might argue that, if sum of residuals is not 0, then most probably there is some pattern in the residuals of our model which can be leveraged to make our model better.
 So, the intuition behind gradient boosting algorithm is to leverage the pattern in residuals and strenghten a weak prediction model, until our residuals become randomly distributed. Once we reach a stage that residuals do not have any pattern that could be modeled, we can stop modeling residuals. Algorithmically, we are minimizing our loss function, such that test loss reach it’s minima.</p>
@@ -1695,7 +1727,7 @@ <h1><span class="section-number">37.78. </span>Gradient boosting<a class="header
 </div>
 </div>
 <section id="data-simulation">
-<h2><span class="section-number">37.78.1. </span>Data simulation<a class="headerlink" href="#data-simulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.78.1. </span>Data simulation<a class="headerlink" href="#data-simulation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">50</span><span class="p">)</span>
@@ -1722,7 +1754,7 @@ <h2><span class="section-number">37.78.1. </span>Data simulation<a class="header
 </div>
 </section>
 <section id="scatter-plot-of-data">
-<h2><span class="section-number">37.78.2. </span>Scatter plot of data<a class="headerlink" href="#scatter-plot-of-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.78.2. </span>Scatter plot of data<a class="headerlink" href="#scatter-plot-of-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span>
@@ -1744,7 +1776,7 @@ <h2><span class="section-number">37.78.2. </span>Scatter plot of data<a class="h
 </div>
 </section>
 <section id="gradient-boosting-decisiontrees-in-a-loop">
-<h2><span class="section-number">37.78.3. </span>Gradient Boosting (DecisionTrees in a loop)<a class="headerlink" href="#gradient-boosting-decisiontrees-in-a-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.78.3. </span>Gradient Boosting (DecisionTrees in a loop)<a class="headerlink" href="#gradient-boosting-decisiontrees-in-a-loop" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">std_agg</span><span class="p">(</span><span class="n">cnt</span><span class="p">,</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">):</span> <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">s2</span><span class="o">/</span><span class="n">cnt</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">s1</span><span class="o">/</span><span class="n">cnt</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
@@ -1809,7 +1841,7 @@ <h2><span class="section-number">37.78.3. </span>Gradient Boosting (DecisionTree
 </div>
 <p>Errors are not changing much after <code class="docutils literal notranslate"><span class="pre">20th</span> <span class="pre">iteration</span></code> and pattern in residuals is also removed. Residuals look distributed around the mean</p>
 <section id="maths-behind-this-logic">
-<h3><span class="section-number">37.78.3.1. </span>Maths behind this logic<a class="headerlink" href="#maths-behind-this-logic" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.78.3.1. </span>Maths behind this logic<a class="headerlink" href="#maths-behind-this-logic" title="Permalink to this headline">#</a></h3>
 <p><span class="math notranslate nohighlight">\( Predictions = y_i^p \)</span><br />
 <span class="math notranslate nohighlight">\( Loss = L(y_i, y_i^p) \)</span><br />
 <span class="math notranslate nohighlight">\( Loss = MSE = \sum {(y_i - y_i^p)}^2 \)</span><br />
@@ -1820,7 +1852,7 @@ <h3><span class="section-number">37.78.3.1. </span>Maths behind this logic<a cla
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.78.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.78.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/grroverpr">Prince Grover</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/grroverpr/gradient-boosting-simplified/notebook">gradient-boosting-simplified</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1862,13 +1894,13 @@ <h2><span class="section-number">37.78.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.77. </span>Hyperparameter tuning gradient boosting</p>
+            <p class="prev-next-title"><span class="section-number">40.77. </span>Hyperparameter tuning gradient boosting</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="boosting-with-tuning.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.79. </span>Boosting with tuning</p>
+        <p class="prev-next-title"><span class="section-number">40.79. </span>Boosting with tuning</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html b/assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html
index 977d7f2c02..0e20f84a60 100644
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-    <title>37.77. Hyperparameter tuning gradient boosting &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.77. Hyperparameter tuning gradient boosting &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.78. Gradient boosting" href="gradient-boosting-assignment.html" />
-    <link rel="prev" title="37.76. Decision trees" href="../ensemble-learning/decision-trees.html" />
+    <link rel="next" title="40.78. Gradient boosting" href="gradient-boosting-assignment.html" />
+    <link rel="prev" title="40.76. Decision trees" href="../ensemble-learning/decision-trees.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
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   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <label for="toctree-checkbox-9">
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     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <label for="toctree-checkbox-10">
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-  <label for="toctree-checkbox-11">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../../deep-learning/gan.html">
-   22. Generative adversarial networks
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../../data-science/write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="../../data-science/lines-scatters-and-bars.html">
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     <a class="reference internal" href="../../data-science/apply-your-skills.html">
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     <a class="reference internal" href="../../data-science/try-it-in-excel.html">
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     <a class="reference internal" href="../../data-science/dive-into-the-beehive.html">
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     <a class="reference internal" href="../../data-science/displaying-airport-data.html">
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     <a class="reference internal" href="../../data-science/analyzing-COVID-19-papers.html">
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     <a class="reference internal" href="../../data-science/data-processing-in-python.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,227 +1558,227 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#context">
-   37.77.1. Context:
+   40.77.1. Context:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#features">
-   37.77.2. Features
+   40.77.2. Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#plan">
-   37.77.3. Plan:
+   40.77.3. Plan:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     37.77.3.1. Data preprocessing:
+     40.77.3.1. Data preprocessing:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-visualization">
-     37.77.3.2. Data Visualization
+     40.77.3.2. Data Visualization
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-creation">
-     37.77.3.3. Model Creation:
+     40.77.3.3. Model Creation:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.77.4. Imports¶
+   40.77.4. Imports¶
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.77.5. Loading the data
+   40.77.5. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   37.77.6. Exploratory data analysis
+   40.77.6. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-city">
-     37.77.6.1. Task 1: City
+     40.77.6.1. Task 1: City
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task2-gender">
-     37.77.6.2. Task2: Gender
+     40.77.6.2. Task2: Gender
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-relevant-experience">
-     37.77.6.3. Task 3：Relevant experience
+     40.77.6.3. Task 3：Relevant experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding">
-     37.77.6.4. Encoding:
+     40.77.6.4. Encoding:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-enrolled-university">
-     37.77.6.5. Task 4: Enrolled university
+     40.77.6.5. Task 4: Enrolled university
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-education-level">
-     37.77.6.6. Task 5: Education level
+     40.77.6.6. Task 5: Education level
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-major-discipline">
-     37.77.6.7. Task 6: Major discipline
+     40.77.6.7. Task 6: Major discipline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-experience">
-     37.77.6.8. Task 7: Experience
+     40.77.6.8. Task 7: Experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-company-size">
-     37.77.6.9. Task 8:Company size
+     40.77.6.9. Task 8:Company size
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-       37.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
+       40.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-company-type">
-     37.77.6.10. Task 9:Company type
+     40.77.6.10. Task 9:Company type
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-last-new-job">
-     37.77.6.11. Task 10: Last new job
+     40.77.6.11. Task 10: Last new job
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-pre-modeling-steps">
-     37.77.6.12. Task 11: Pre modeling steps
+     40.77.6.12. Task 11: Pre modeling steps
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#models-testing">
-   37.77.7. Models testing
+   40.77.7. Models testing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#boosting">
-   37.77.8. Boosting:
+   40.77.8. Boosting:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting">
-   37.77.9. Gradient Boosting:
+   40.77.9. Gradient Boosting:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tree-specific-parameters">
-     37.77.9.1. Tree-specific parameters
+     40.77.9.1. Tree-specific parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting-parameters">
-     37.77.9.2. Boosting parameters:
+     40.77.9.2. Boosting parameters:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#miscellaneous-parameters">
-     37.77.9.3. Miscellaneous parameters
+     40.77.9.3. Miscellaneous parameters
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-tuning-using-gridsearchcv">
-   37.77.10. Hyperparameter Tuning using GridSearchCV
+   40.77.10. Hyperparameter Tuning using GridSearchCV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-initialize-model">
-     37.77.10.1. Task 1: Initialize model
+     40.77.10.1. Task 1: Initialize model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-remove-enrollee-id">
-     37.77.10.2. Task 2: Remove enrollee_id
+     40.77.10.2. Task 2: Remove enrollee_id
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-look-for-optimal-number">
-     37.77.10.3. Task 3: Look for optimal number
+     40.77.10.3. Task 3: Look for optimal number
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-find-learning-rate">
-     37.77.10.4. Task 4: Find learning rate
+     40.77.10.4. Task 4: Find learning rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-find-max-depth-and-min-samples-split">
-     37.77.10.5. Task 5: Find max_depth and min_samples_split
+     40.77.10.5. Task 5: Find max_depth and min_samples_split
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-find-min-samples-leaf">
-     37.77.10.6. Task 6: Find min_samples_leaf
+     40.77.10.6. Task 6: Find min_samples_leaf
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-auc">
-     37.77.10.7. Task 7: AUC
+     40.77.10.7. Task 7: AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-find-max-features">
-     37.77.10.8. Task 8: Find max_features
+     40.77.10.8. Task 8: Find max_features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-find-subsample-value">
-     37.77.10.9. Task 9: Find subsample value
+     40.77.10.9. Task 9: Find subsample value
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-improvement">
-     37.77.10.10. Task 10: Improvement
+     40.77.10.10. Task 10: Improvement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-model-fitting">
-     37.77.10.11. Task 11: Model fitting
+     40.77.10.11. Task 11: Model fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-12-prediction">
-     37.77.10.12. Task 12: Prediction
+     40.77.10.12. Task 12: Prediction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-13-result">
-     37.77.10.13. Task 13: Result
+     40.77.10.13. Task 13: Result
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.77.11. Acknowledgments
+   40.77.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1770,227 +1802,227 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#context">
-   37.77.1. Context:
+   40.77.1. Context:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#features">
-   37.77.2. Features
+   40.77.2. Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#plan">
-   37.77.3. Plan:
+   40.77.3. Plan:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     37.77.3.1. Data preprocessing:
+     40.77.3.1. Data preprocessing:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-visualization">
-     37.77.3.2. Data Visualization
+     40.77.3.2. Data Visualization
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-creation">
-     37.77.3.3. Model Creation:
+     40.77.3.3. Model Creation:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.77.4. Imports¶
+   40.77.4. Imports¶
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.77.5. Loading the data
+   40.77.5. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   37.77.6. Exploratory data analysis
+   40.77.6. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-city">
-     37.77.6.1. Task 1: City
+     40.77.6.1. Task 1: City
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task2-gender">
-     37.77.6.2. Task2: Gender
+     40.77.6.2. Task2: Gender
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-relevant-experience">
-     37.77.6.3. Task 3：Relevant experience
+     40.77.6.3. Task 3：Relevant experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding">
-     37.77.6.4. Encoding:
+     40.77.6.4. Encoding:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-enrolled-university">
-     37.77.6.5. Task 4: Enrolled university
+     40.77.6.5. Task 4: Enrolled university
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-education-level">
-     37.77.6.6. Task 5: Education level
+     40.77.6.6. Task 5: Education level
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-major-discipline">
-     37.77.6.7. Task 6: Major discipline
+     40.77.6.7. Task 6: Major discipline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-experience">
-     37.77.6.8. Task 7: Experience
+     40.77.6.8. Task 7: Experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-company-size">
-     37.77.6.9. Task 8:Company size
+     40.77.6.9. Task 8:Company size
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-       37.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
+       40.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-company-type">
-     37.77.6.10. Task 9:Company type
+     40.77.6.10. Task 9:Company type
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-last-new-job">
-     37.77.6.11. Task 10: Last new job
+     40.77.6.11. Task 10: Last new job
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-pre-modeling-steps">
-     37.77.6.12. Task 11: Pre modeling steps
+     40.77.6.12. Task 11: Pre modeling steps
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#models-testing">
-   37.77.7. Models testing
+   40.77.7. Models testing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#boosting">
-   37.77.8. Boosting:
+   40.77.8. Boosting:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting">
-   37.77.9. Gradient Boosting:
+   40.77.9. Gradient Boosting:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tree-specific-parameters">
-     37.77.9.1. Tree-specific parameters
+     40.77.9.1. Tree-specific parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting-parameters">
-     37.77.9.2. Boosting parameters:
+     40.77.9.2. Boosting parameters:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#miscellaneous-parameters">
-     37.77.9.3. Miscellaneous parameters
+     40.77.9.3. Miscellaneous parameters
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-tuning-using-gridsearchcv">
-   37.77.10. Hyperparameter Tuning using GridSearchCV
+   40.77.10. Hyperparameter Tuning using GridSearchCV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-initialize-model">
-     37.77.10.1. Task 1: Initialize model
+     40.77.10.1. Task 1: Initialize model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-remove-enrollee-id">
-     37.77.10.2. Task 2: Remove enrollee_id
+     40.77.10.2. Task 2: Remove enrollee_id
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-look-for-optimal-number">
-     37.77.10.3. Task 3: Look for optimal number
+     40.77.10.3. Task 3: Look for optimal number
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-find-learning-rate">
-     37.77.10.4. Task 4: Find learning rate
+     40.77.10.4. Task 4: Find learning rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-find-max-depth-and-min-samples-split">
-     37.77.10.5. Task 5: Find max_depth and min_samples_split
+     40.77.10.5. Task 5: Find max_depth and min_samples_split
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-find-min-samples-leaf">
-     37.77.10.6. Task 6: Find min_samples_leaf
+     40.77.10.6. Task 6: Find min_samples_leaf
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-auc">
-     37.77.10.7. Task 7: AUC
+     40.77.10.7. Task 7: AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-find-max-features">
-     37.77.10.8. Task 8: Find max_features
+     40.77.10.8. Task 8: Find max_features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-find-subsample-value">
-     37.77.10.9. Task 9: Find subsample value
+     40.77.10.9. Task 9: Find subsample value
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-improvement">
-     37.77.10.10. Task 10: Improvement
+     40.77.10.10. Task 10: Improvement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-model-fitting">
-     37.77.10.11. Task 11: Model fitting
+     40.77.10.11. Task 11: Model fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-12-prediction">
-     37.77.10.12. Task 12: Prediction
+     40.77.10.12. Task 12: Prediction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-13-result">
-     37.77.10.13. Task 13: Result
+     40.77.10.13. Task 13: Result
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.77.11. Acknowledgments
+   40.77.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2004,13 +2036,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="hyperparameter-tuning-gradient-boosting">
-<h1><span class="section-number">37.77. </span>Hyperparameter tuning gradient boosting<a class="headerlink" href="#hyperparameter-tuning-gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.77. </span>Hyperparameter tuning gradient boosting<a class="headerlink" href="#hyperparameter-tuning-gradient-boosting" title="Permalink to this headline">#</a></h1>
 <section id="context">
-<h2><span class="section-number">37.77.1. </span>Context:<a class="headerlink" href="#context" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.1. </span>Context:<a class="headerlink" href="#context" title="Permalink to this headline">#</a></h2>
 <p>A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which are conducted by the company. Many people sign up for their training. Company wants to know which of these candidates really want to work for the company after training, because it helps reducing the cost and time as well as increasing the quality of training and the planning of the courses. Information related to demographics, education, experience are in hands from candidates signup and enrollment.</p>
 </section>
 <section id="features">
-<h2><span class="section-number">37.77.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>enrollee_id : Unique ID for candidate</p></li>
 <li><p>city: City code</p></li>
@@ -2029,20 +2061,20 @@ <h2><span class="section-number">37.77.2. </span>Features<a class="headerlink" h
 </ul>
 </section>
 <section id="plan">
-<h2><span class="section-number">37.77.3. </span>Plan:<a class="headerlink" href="#plan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.3. </span>Plan:<a class="headerlink" href="#plan" title="Permalink to this headline">#</a></h2>
 <p>We will treat each column in two phases:</p>
 <section id="data-preprocessing">
-<h3><span class="section-number">37.77.3.1. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.3.1. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Imputing missing values.</p></li>
 <li><p>Handling categorical data.</p></li>
 </ul>
 </section>
 <section id="data-visualization">
-<h3><span class="section-number">37.77.3.2. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.3.2. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="model-creation">
-<h3><span class="section-number">37.77.3.3. </span>Model Creation:<a class="headerlink" href="#model-creation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.3.3. </span>Model Creation:<a class="headerlink" href="#model-creation" title="Permalink to this headline">#</a></h3>
 <p>Then we will test several model and choose the one with the best performance:</p>
 <ul class="simple">
 <li><p>Models: Logistic Regression-SVM-Gradient Boosting-KNN-RandomForest-XGBoost classifier.</p></li>
@@ -2052,7 +2084,7 @@ <h3><span class="section-number">37.77.3.3. </span>Model Creation:<a class="head
 </section>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.77.4. </span>Imports¶<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.4. </span>Imports¶<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <p>Lets get started, first lets import our typical libraries:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2075,7 +2107,7 @@ <h2><span class="section-number">37.77.4. </span>Imports¶<a class="headerlink"
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.77.5. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.5. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s import our train and test data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2114,9 +2146,9 @@ <h2><span class="section-number">37.77.5. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">37.77.6. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.6. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <section id="task-1-city">
-<h3><span class="section-number">37.77.6.1. </span>Task 1: City<a class="headerlink" href="#task-1-city" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.1. </span>Task 1: City<a class="headerlink" href="#task-1-city" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s2">&quot;city&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2209,7 +2241,7 @@ <h3><span class="section-number">37.77.6.1. </span>Task 1: City<a class="headerl
 </div>
 </section>
 <section id="task2-gender">
-<h3><span class="section-number">37.77.6.2. </span>Task2: Gender<a class="headerlink" href="#task2-gender" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.2. </span>Task2: Gender<a class="headerlink" href="#task2-gender" title="Permalink to this headline">#</a></h3>
 <p>It is obvious we have to encode the caregorical data and take care of missing data, there are a lot of ways to handle gender missing values such as replacing them with most common gender in the dataset, deleting those rows…etc.
 But I prefer to fill the gender missing values with “Other” since we may have candidates identify as non-binary.
 First lets take a look at our gender column:</p>
@@ -2277,7 +2309,7 @@ <h3><span class="section-number">37.77.6.2. </span>Task2: Gender<a class="header
 </div>
 </section>
 <section id="task-3-relevant-experience">
-<h3><span class="section-number">37.77.6.3. </span>Task 3：Relevant experience<a class="headerlink" href="#task-3-relevant-experience" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.3. </span>Task 3：Relevant experience<a class="headerlink" href="#task-3-relevant-experience" title="Permalink to this headline">#</a></h3>
 <p>Before making any decision we have to look at the values of this column:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2297,7 +2329,7 @@ <h3><span class="section-number">37.77.6.3. </span>Task 3：Relevant experience<
 </div>
 </section>
 <section id="encoding">
-<h3><span class="section-number">37.77.6.4. </span>Encoding:<a class="headerlink" href="#encoding" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.4. </span>Encoding:<a class="headerlink" href="#encoding" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s2">&quot;relevent_experience&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s2">&quot;relevent_experience&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">({</span><span class="s2">&quot;Has relevent experience&quot;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;No relevent experience&quot;</span><span class="p">:</span><span class="mi">0</span><span class="p">})</span>
@@ -2307,7 +2339,7 @@ <h3><span class="section-number">37.77.6.4. </span>Encoding:<a class="headerlink
 </div>
 </section>
 <section id="task-4-enrolled-university">
-<h3><span class="section-number">37.77.6.5. </span>Task 4: Enrolled university<a class="headerlink" href="#task-4-enrolled-university" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.5. </span>Task 4: Enrolled university<a class="headerlink" href="#task-4-enrolled-university" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;enrolled_university&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2353,7 +2385,7 @@ <h3><span class="section-number">37.77.6.5. </span>Task 4: Enrolled university<a
 </div>
 </section>
 <section id="task-5-education-level">
-<h3><span class="section-number">37.77.6.6. </span>Task 5: Education level<a class="headerlink" href="#task-5-education-level" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.6. </span>Task 5: Education level<a class="headerlink" href="#task-5-education-level" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;education_level&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2395,7 +2427,7 @@ <h3><span class="section-number">37.77.6.6. </span>Task 5: Education level<a cla
 </div>
 </section>
 <section id="task-6-major-discipline">
-<h3><span class="section-number">37.77.6.7. </span>Task 6: Major discipline<a class="headerlink" href="#task-6-major-discipline" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.7. </span>Task 6: Major discipline<a class="headerlink" href="#task-6-major-discipline" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;major_discipline&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2435,7 +2467,7 @@ <h3><span class="section-number">37.77.6.7. </span>Task 6: Major discipline<a cl
 </div>
 </section>
 <section id="task-7-experience">
-<h3><span class="section-number">37.77.6.8. </span>Task 7: Experience<a class="headerlink" href="#task-7-experience" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.8. </span>Task 7: Experience<a class="headerlink" href="#task-7-experience" title="Permalink to this headline">#</a></h3>
 <p>The experience variable is an object indicating the minimum or maximum years of experience a candidate had, so deleting the operators won’t make a big difference.</p>
 <p>First we have to convert the column to string.</p>
 <div class="cell docutils container">
@@ -2479,7 +2511,7 @@ <h3><span class="section-number">37.77.6.8. </span>Task 7: Experience<a class="h
 </div>
 </section>
 <section id="task-8-company-size">
-<h3><span class="section-number">37.77.6.9. </span>Task 8:Company size<a class="headerlink" href="#task-8-company-size" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.9. </span>Task 8:Company size<a class="headerlink" href="#task-8-company-size" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;company_size&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2497,7 +2529,7 @@ <h3><span class="section-number">37.77.6.9. </span>Task 8:Company size<a class="
 </div>
 </div>
 <section id="we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-<h4><span class="section-number">37.77.6.9.1. </span>We can see that most candidates work in small companies (between 50-500)<a class="headerlink" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.77.6.9.1. </span>We can see that most candidates work in small companies (between 50-500)<a class="headerlink" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500" title="Permalink to this headline">#</a></h4>
 <p>Identify each interval with a number:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2524,7 +2556,7 @@ <h4><span class="section-number">37.77.6.9.1. </span>We can see that most candid
 </section>
 </section>
 <section id="task-9-company-type">
-<h3><span class="section-number">37.77.6.10. </span>Task 9:Company type<a class="headerlink" href="#task-9-company-type" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.10. </span>Task 9:Company type<a class="headerlink" href="#task-9-company-type" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s1">&#39;company_type&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
@@ -2561,7 +2593,7 @@ <h3><span class="section-number">37.77.6.10. </span>Task 9:Company type<a class=
 </div>
 </section>
 <section id="task-10-last-new-job">
-<h3><span class="section-number">37.77.6.11. </span>Task 10: Last new job<a class="headerlink" href="#task-10-last-new-job" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.11. </span>Task 10: Last new job<a class="headerlink" href="#task-10-last-new-job" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;last_new_job&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2608,7 +2640,7 @@ <h3><span class="section-number">37.77.6.11. </span>Task 10: Last new job<a clas
 </div>
 </section>
 <section id="task-11-pre-modeling-steps">
-<h3><span class="section-number">37.77.6.12. </span>Task 11: Pre modeling steps<a class="headerlink" href="#task-11-pre-modeling-steps" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.12. </span>Task 11: Pre modeling steps<a class="headerlink" href="#task-11-pre-modeling-steps" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pred</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span>
@@ -2633,7 +2665,7 @@ <h3><span class="section-number">37.77.6.12. </span>Task 11: Pre modeling steps<
 </section>
 </section>
 <section id="models-testing">
-<h2><span class="section-number">37.77.7. </span>Models testing<a class="headerlink" href="#models-testing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.7. </span>Models testing<a class="headerlink" href="#models-testing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
@@ -2689,7 +2721,7 @@ <h2><span class="section-number">37.77.7. </span>Models testing<a class="headerl
 <p>Obviously,Gradient Boosting gives the best result.</p>
 </section>
 <section id="boosting">
-<h2><span class="section-number">37.77.8. </span>Boosting:<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.8. </span>Boosting:<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h2>
 <p><strong>Ensemble learning techniques:</strong> They combine the predictions of multiple machine learning models.</p>
 <p><strong>Boosting:</strong> Boosting algorithms play a crucial role in dealing with bias variance trade-off.  Unlike bagging algorithms, which only controls for high variance in a model, boosting controls both the aspects (bias &amp; variance), and is considered to be more effective.
 Boosting is an ensemble learning technique.</p>
@@ -2699,7 +2731,7 @@ <h2><span class="section-number">37.77.8. </span>Boosting:<a class="headerlink"
 <p><a href="https://ibb.co/PgstW8k"><img src="https://i.ibb.co/WfNsz1C/boosting.png" alt="boosting" border="0"></a></p>
 </section>
 <section id="gradient-boosting">
-<h2><span class="section-number">37.77.9. </span>Gradient Boosting:<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.9. </span>Gradient Boosting:<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h2>
 <p><img alt="image.png" src="../../../_images/boosting_visual_3.png" /></p>
 <p>The overall parameters of gradient boosting can be devided into three categories:</p>
 <ul class="simple">
@@ -2708,10 +2740,10 @@ <h2><span class="section-number">37.77.9. </span>Gradient Boosting:<a class="hea
 <li><p>Miscellaneous Parameters: Other parameters for overall functioning.</p></li>
 </ul>
 <section id="tree-specific-parameters">
-<h3><span class="section-number">37.77.9.1. </span>Tree-specific parameters<a class="headerlink" href="#tree-specific-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.9.1. </span>Tree-specific parameters<a class="headerlink" href="#tree-specific-parameters" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="boosting-parameters">
-<h3><span class="section-number">37.77.9.2. </span>Boosting parameters:<a class="headerlink" href="#boosting-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.9.2. </span>Boosting parameters:<a class="headerlink" href="#boosting-parameters" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>learning_rate</p></li>
 <li><p>n_estimators</p></li>
@@ -2719,7 +2751,7 @@ <h3><span class="section-number">37.77.9.2. </span>Boosting parameters:<a class=
 </ul>
 </section>
 <section id="miscellaneous-parameters">
-<h3><span class="section-number">37.77.9.3. </span>Miscellaneous parameters<a class="headerlink" href="#miscellaneous-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.9.3. </span>Miscellaneous parameters<a class="headerlink" href="#miscellaneous-parameters" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>loss</p></li>
 <li><p>init</p></li>
@@ -2731,7 +2763,7 @@ <h3><span class="section-number">37.77.9.3. </span>Miscellaneous parameters<a cl
 </section>
 </section>
 <section id="hyperparameter-tuning-using-gridsearchcv">
-<h2><span class="section-number">37.77.10. </span>Hyperparameter Tuning using GridSearchCV<a class="headerlink" href="#hyperparameter-tuning-using-gridsearchcv" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.10. </span>Hyperparameter Tuning using GridSearchCV<a class="headerlink" href="#hyperparameter-tuning-using-gridsearchcv" title="Permalink to this headline">#</a></h2>
 <p><strong>GradientBoostingClassifier</strong>(*, loss=‘deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=‘friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)</p>
 <p>Grid search is best described as exhuastive guess and check. We have a problem: find the hyperparameters that result in the best cross validation score, and a set of values to try in the hyperparameter grid - the domain. The grid search method for finding the answer is to try all combinations of values in the domain and hope that the best combination is in the grid (in reality, we will never know if we found the best settings unless we have an infinite hyperparameter grid which would then require an infinite amount of time to run).</p>
 <p>Grid search suffers from one limiting problem: it is extremely computationally expensive because we have to perform cross validation with every single combination of hyperparameters in the grid!</p>
@@ -2747,7 +2779,7 @@ <h2><span class="section-number">37.77.10. </span>Hyperparameter Tuning using Gr
 <li><p>It is ‘uninformed’. Results of one model don’t help creating the next model.</p></li>
 </ul>
 <section id="task-1-initialize-model">
-<h3><span class="section-number">37.77.10.1. </span>Task 1: Initialize model<a class="headerlink" href="#task-1-initialize-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.1. </span>Task 1: Initialize model<a class="headerlink" href="#task-1-initialize-model" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
@@ -2772,7 +2804,7 @@ <h3><span class="section-number">37.77.10.1. </span>Task 1: Initialize model<a c
 </div>
 </section>
 <section id="task-2-remove-enrollee-id">
-<h3><span class="section-number">37.77.10.2. </span>Task 2: Remove enrollee_id<a class="headerlink" href="#task-2-remove-enrollee-id" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.2. </span>Task 2: Remove enrollee_id<a class="headerlink" href="#task-2-remove-enrollee-id" title="Permalink to this headline">#</a></h3>
 <p>We shouldn’t use the enrollee_id since it is unique for each candidate.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2790,7 +2822,7 @@ <h3><span class="section-number">37.77.10.2. </span>Task 2: Remove enrollee_id<a
 </div>
 </section>
 <section id="task-3-look-for-optimal-number">
-<h3><span class="section-number">37.77.10.3. </span>Task 3: Look for optimal number<a class="headerlink" href="#task-3-look-for-optimal-number" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.3. </span>Task 3: Look for optimal number<a class="headerlink" href="#task-3-look-for-optimal-number" title="Permalink to this headline">#</a></h3>
 <p>First we have to look for an optimal number of estimators:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2826,7 +2858,7 @@ <h3><span class="section-number">37.77.10.3. </span>Task 3: Look for optimal num
 </div>
 </section>
 <section id="task-4-find-learning-rate">
-<h3><span class="section-number">37.77.10.4. </span>Task 4: Find learning rate<a class="headerlink" href="#task-4-find-learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.4. </span>Task 4: Find learning rate<a class="headerlink" href="#task-4-find-learning-rate" title="Permalink to this headline">#</a></h3>
 <p>As you can see that here we got 90 as the optimal estimators for 0.1 learning rate, it is close to 100 so we will increase the learning rate to 0.2. (I tried working with number of estimators as 90 but when I increased the learning rate I got slightly better results, I won’t include the whole process because it will take a lot of time to run)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2847,7 +2879,7 @@ <h3><span class="section-number">37.77.10.4. </span>Task 4: Find learning rate<a
 </div>
 </section>
 <section id="task-5-find-max-depth-and-min-samples-split">
-<h3><span class="section-number">37.77.10.5. </span>Task 5: Find max_depth and min_samples_split<a class="headerlink" href="#task-5-find-max-depth-and-min-samples-split" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.5. </span>Task 5: Find max_depth and min_samples_split<a class="headerlink" href="#task-5-find-max-depth-and-min-samples-split" title="Permalink to this headline">#</a></h3>
 <p>The next step is to find the max_depth and min_samples_split.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2863,7 +2895,7 @@ <h3><span class="section-number">37.77.10.5. </span>Task 5: Find max_depth and m
 <p>So we got a maximum depth of 7 and minimum samples split is 700.</p>
 </section>
 <section id="task-6-find-min-samples-leaf">
-<h3><span class="section-number">37.77.10.6. </span>Task 6: Find min_samples_leaf<a class="headerlink" href="#task-6-find-min-samples-leaf" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.6. </span>Task 6: Find min_samples_leaf<a class="headerlink" href="#task-6-find-min-samples-leaf" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">param_test3</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;min_samples_split&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span><span class="mi">1200</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span> <span class="s1">&#39;min_samples_leaf&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">71</span><span class="p">,</span><span class="mi">10</span><span class="p">)}</span>
@@ -2877,7 +2909,7 @@ <h3><span class="section-number">37.77.10.6. </span>Task 6: Find min_samples_lea
 </div>
 </section>
 <section id="task-7-auc">
-<h3><span class="section-number">37.77.10.7. </span>Task 7: AUC<a class="headerlink" href="#task-7-auc" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.7. </span>Task 7: AUC<a class="headerlink" href="#task-7-auc" title="Permalink to this headline">#</a></h3>
 <p>Lets write a function that returns the accuracy, auc score and the importance of each variable:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2926,7 +2958,7 @@ <h3><span class="section-number">37.77.10.7. </span>Task 7: AUC<a class="headerl
 </div>
 </section>
 <section id="task-8-find-max-features">
-<h3><span class="section-number">37.77.10.8. </span>Task 8: Find max_features<a class="headerlink" href="#task-8-find-max-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.8. </span>Task 8: Find max_features<a class="headerlink" href="#task-8-find-max-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">param_test4</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;max_features&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">2</span><span class="p">)}</span>
@@ -2947,7 +2979,7 @@ <h3><span class="section-number">37.77.10.8. </span>Task 8: Find max_features<a
 </div>
 </section>
 <section id="task-9-find-subsample-value">
-<h3><span class="section-number">37.77.10.9. </span>Task 9: Find subsample value<a class="headerlink" href="#task-9-find-subsample-value" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.9. </span>Task 9: Find subsample value<a class="headerlink" href="#task-9-find-subsample-value" title="Permalink to this headline">#</a></h3>
 <p>The next step would be try different subsample values.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2963,7 +2995,7 @@ <h3><span class="section-number">37.77.10.9. </span>Task 9: Find subsample value
 <p>We got 0.85 as the optimum subsample value.</p>
 </section>
 <section id="task-10-improvement">
-<h3><span class="section-number">37.77.10.10. </span>Task 10: Improvement<a class="headerlink" href="#task-10-improvement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.10. </span>Task 10: Improvement<a class="headerlink" href="#task-10-improvement" title="Permalink to this headline">#</a></h3>
 <p>Now, we need to lower the learning rate and increase the number of estimators to see if we get better results.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2993,7 +3025,7 @@ <h3><span class="section-number">37.77.10.10. </span>Task 10: Improvement<a clas
 <p>increasing the number of estimators got us a slightly better model.</p>
 </section>
 <section id="task-11-model-fitting">
-<h3><span class="section-number">37.77.10.11. </span>Task 11: Model fitting<a class="headerlink" href="#task-11-model-fitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.11. </span>Task 11: Model fitting<a class="headerlink" href="#task-11-model-fitting" title="Permalink to this headline">#</a></h3>
 <p>Lets fit the model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3011,7 +3043,7 @@ <h3><span class="section-number">37.77.10.11. </span>Task 11: Model fitting<a cl
 </div>
 </section>
 <section id="task-12-prediction">
-<h3><span class="section-number">37.77.10.12. </span>Task 12: Prediction<a class="headerlink" href="#task-12-prediction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.12. </span>Task 12: Prediction<a class="headerlink" href="#task-12-prediction" title="Permalink to this headline">#</a></h3>
 <p>Make predictions on test and training set:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3044,7 +3076,7 @@ <h3><span class="section-number">37.77.10.12. </span>Task 12: Prediction<a class
 </div>
 </section>
 <section id="task-13-result">
-<h3><span class="section-number">37.77.10.13. </span>Task 13: Result<a class="headerlink" href="#task-13-result" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.13. </span>Task 13: Result<a class="headerlink" href="#task-13-result" title="Permalink to this headline">#</a></h3>
 <p>Save results to a dataframe</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3063,7 +3095,7 @@ <h3><span class="section-number">37.77.10.13. </span>Task 13: Result<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.77.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/elyousfiomar">Omar El Yousfi</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/elyousfiomar/hyperparameter-tuning-gradient-boosting/notebook#Relevant-experience:">Hyperparameter tuning - Gradient boosting</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3105,13 +3137,13 @@ <h2><span class="section-number">37.77.11. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.76. </span>Decision trees</p>
+            <p class="prev-next-title"><span class="section-number">40.76. </span>Decision trees</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gradient-boosting-assignment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.78. </span>Gradient boosting</p>
+        <p class="prev-next-title"><span class="section-number">40.78. </span>Gradient boosting</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html b/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html
index da5f870fb3..81342495b9 100644
--- a/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html
+++ b/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.67. Decision Trees - Classification &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.67. Decision Trees - Classification &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.68. Model selection assignment 1" href="../model-selection/model-selection-assignment-1.html" />
-    <link rel="prev" title="37.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
+    <link rel="next" title="40.68. Model selection assignment 1" href="../model-selection/model-selection-assignment-1.html" />
+    <link rel="prev" title="40.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
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-     37.67.4.2. Checking Summary Statistics
+     40.67.4.2. Checking Summary Statistics
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+     40.67.8.1. Evaluating the model on the test set
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+   40.67.2. 1 - Imports
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+     40.67.4.1. Splitting Data into Training and Test sets
     </a>
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+     40.67.4.2. Checking Summary Statistics
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-     37.67.4.3. Checking Missing Values
+     40.67.4.3. Checking Missing Values
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+   40.67.5. 4 - Data Preprocessing
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     <a class="reference internal nav-link" href="#handling-categorical-features">
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+     40.67.5.1. Handling Categorical Features
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+   40.67.6. 5 - Training Decision Tree Classifier
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+   40.67.7. 6 - Evaluating Decision Trees
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   <a class="reference internal nav-link" href="#improving-decision-trees">
-   37.67.8. 7 - Improving Decision Trees
+   40.67.8. 7 - Improving Decision Trees
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-the-model-on-the-test-set">
-     37.67.8.1. Evaluating the model on the test set
+     40.67.8.1. Evaluating the model on the test set
     </a>
    </li>
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  </li>
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+   40.67.9. Acknowledgments
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@@ -1730,7 +1762,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees-classification">
-<h1><span class="section-number">37.67. </span>Decision Trees - Classification<a class="headerlink" href="#decision-trees-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.67. </span>Decision Trees - Classification<a class="headerlink" href="#decision-trees-classification" title="Permalink to this headline">#</a></h1>
 <p>Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets.</p>
 <p>In the previous notebook, we learnt to predict the continous values with decision trees (regression). This notebook is for classification.</p>
 <p>Different to other algorithms, here are two things that trees are very particular about:</p>
@@ -1749,9 +1781,9 @@ <h1><span class="section-number">37.67. </span>Decision Trees - Classification<a
 <p><img alt="Decision Trees.png" src="../../../_images/4XijLSXib.png" /></p>
 <p>A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as <code class="docutils literal notranslate"><span class="pre">max_depth</span></code>hyperparameter) in decision trees. We will see other techniques to avoid overfitting.</p>
 <section id="decision-trees-for-classification">
-<h2><span class="section-number">37.67.1. </span>Decision Trees for Classification<a class="headerlink" href="#decision-trees-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.1. </span>Decision Trees for Classification<a class="headerlink" href="#decision-trees-for-classification" title="Permalink to this headline">#</a></h2>
 <section id="contents">
-<h3><span class="section-number">37.67.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>[1 - Imports]</p></li>
 <li><p>[2 - Loading the data]</p></li>
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 </section>
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-<h2><span class="section-number">37.67.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1779,7 +1811,7 @@ <h2><span class="section-number">37.67.2. </span>1 - Imports<a class="headerlink
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.67.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this classification task with decision trees, we will use a car dataset that is avilable at <a class="reference external" href="https://www.openml.org/d/21">OpenML</a> to predict the car acceptability given the information about the car. We will load it with Sklearn <code class="docutils literal notranslate"><span class="pre">fetch_openml</span></code> function.</p>
 <p>The version of the data we are fetching is 2. In the version 1 of the dataset, the target class had 4 classes (unacc, acc, good, vgood) but in the second version, the majority class is Positive§ whereas the rest are Negative§. If you want to see version 1, you can change version parameter in a cell below.</p>
 <p>Here are the informations about the features:</p>
@@ -1999,10 +2031,10 @@ <h2><span class="section-number">37.67.3. </span>2 - Loading the data<a class="h
 </div>
 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">37.67.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
 <section id="splitting-data-into-training-and-test-sets">
-<h3><span class="section-number">37.67.4.1. </span>Splitting Data into Training and Test sets<a class="headerlink" href="#splitting-data-into-training-and-test-sets" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.1. </span>Splitting Data into Training and Test sets<a class="headerlink" href="#splitting-data-into-training-and-test-sets" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2022,7 +2054,7 @@ <h3><span class="section-number">37.67.4.1. </span>Splitting Data into Training
 </div>
 </section>
 <section id="checking-summary-statistics">
-<h3><span class="section-number">37.67.4.2. </span>Checking Summary Statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.2. </span>Checking Summary Statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Checking summary statistics</span>
@@ -2106,7 +2138,7 @@ <h3><span class="section-number">37.67.4.2. </span>Checking Summary Statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">37.67.4.3. </span>Checking Missing Values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.3. </span>Checking Missing Values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Checking missing values</span>
@@ -2130,7 +2162,7 @@ <h3><span class="section-number">37.67.4.3. </span>Checking Missing Values<a cla
 <p>We don’t have any missing values.</p>
 </section>
 <section id="checking-categorical-features">
-<h3><span class="section-number">37.67.4.4. </span>Checking Categorical Features<a class="headerlink" href="#checking-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.4. </span>Checking Categorical Features<a class="headerlink" href="#checking-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Let’s inspect some categorical features that are in the dataset, almost all re. Let’s see that!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2387,10 +2419,10 @@ <h3><span class="section-number">37.67.4.4. </span>Checking Categorical Features
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.67.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <section id="handling-categorical-features">
-<h3><span class="section-number">37.67.5.1. </span>Handling Categorical Features<a class="headerlink" href="#handling-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.5.1. </span>Handling Categorical Features<a class="headerlink" href="#handling-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Decision trees don’t care if the features are scaled or not, and they can handle the categorical features. There is a <a class="reference external" href="https://scikit-learn.org/stable/modules/tree.html#tree">note on documentation</a> that the sklearn tree implementation doesn’t support categorical features, so I will go ahead and handle them. It’s fun anyways :)</p>
 <p>Before handling categorical features, let’s create a training input data and labels.</p>
 <div class="cell docutils container">
@@ -2440,7 +2472,7 @@ <h3><span class="section-number">37.67.5.1. </span>Handling Categorical Features
 </section>
 </section>
 <section id="training-decision-tree-classifier">
-<h2><span class="section-number">37.67.6. </span>5 - Training Decision Tree Classifier<a class="headerlink" href="#training-decision-tree-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.6. </span>5 - Training Decision Tree Classifier<a class="headerlink" href="#training-decision-tree-classifier" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
@@ -2471,7 +2503,7 @@ <h2><span class="section-number">37.67.6. </span>5 - Training Decision Tree Clas
 </div>
 </section>
 <section id="evaluating-decision-trees">
-<h2><span class="section-number">37.67.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
 <p>Let’s build 3 functions to display accuracy, confusion matrix, and classification report. Classification report contains all useful metrics such as precision, recall, and f1 score.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2577,7 +2609,7 @@ <h2><span class="section-number">37.67.7. </span>6 - Evaluating Decision Trees<a
 <p>The model clearly overfitted the data. Let’s see how we can regularize it.</p>
 </section>
 <section id="improving-decision-trees">
-<h2><span class="section-number">37.67.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tree_clf</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
@@ -2780,7 +2812,7 @@ <h2><span class="section-number">37.67.8. </span>7 - Improving Decision Trees<a
 </ul>
 <p>The higher the precision and recall are, the higher the F1 score. But there is a tradeoff between them. Increasing precision will reduce recall, and vice versa. So it’s fair to say that it depends on the problem you’re trying to solve and the metrics you want to optimize for.</p>
 <section id="evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">37.67.8.1. </span>Evaluating the model on the test set<a class="headerlink" href="#evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.8.1. </span>Evaluating the model on the test set<a class="headerlink" href="#evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>It is only after we have improved the model that we can feed it to the test set. If we try to show the test set to the model while we are still training, we may cause a potential leak thus producing misleading predictions.</p>
 <p>Also, we will apply the same processing functions that we applied to the training set.</p>
 <div class="cell docutils container">
@@ -2860,7 +2892,7 @@ <h3><span class="section-number">37.67.8.1. </span>Evaluating the model on the t
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.67.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2902,13 +2934,13 @@ <h2><span class="section-number">37.67.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.66. </span>Decision Trees - Intro and Regression</p>
+            <p class="prev-next-title"><span class="section-number">40.66. </span>Decision Trees - Intro and Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../model-selection/model-selection-assignment-1.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.68. </span>Model selection assignment 1</p>
+        <p class="prev-next-title"><span class="section-number">40.68. </span>Model selection assignment 1</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html b/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html
index 02d3fbf5a7..906fae357b 100644
--- a/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html
+++ b/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.66. Decision Trees - Intro and Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.66. Decision Trees - Intro and Regression &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.67. Decision Trees - Classification" href="decision_trees_for_classification.html" />
-    <link rel="prev" title="37.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
+    <link rel="next" title="40.67. Decision Trees - Classification" href="decision_trees_for_classification.html" />
+    <link rel="prev" title="40.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees-intro-and-regression">
-<h1><span class="section-number">37.66. </span>Decision Trees - Intro and Regression<a class="headerlink" href="#decision-trees-intro-and-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.66. </span>Decision Trees - Intro and Regression<a class="headerlink" href="#decision-trees-intro-and-regression" title="Permalink to this headline">#</a></h1>
 <p>Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets.</p>
 <p>Here are 7 interesting facts about decision trees:</p>
 <ul class="simple">
@@ -1676,9 +1708,9 @@ <h1><span class="section-number">37.66. </span>Decision Trees - Intro and Regres
 <p>A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as <code class="docutils literal notranslate"><span class="pre">max_depth</span></code>hyperparameter) in decision trees. We will see other techniques to avoid overfitting.</p>
 <p>To motivate the superpower of decision trees, let’s use it for a regression task where instead of predicting class, we are predicting a continous value. In the next lab, we will use them for classification.</p>
 <section id="decision-trees-for-regression">
-<h2><span class="section-number">37.66.1. </span>Decision Trees for Regression<a class="headerlink" href="#decision-trees-for-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.1. </span>Decision Trees for Regression<a class="headerlink" href="#decision-trees-for-regression" title="Permalink to this headline">#</a></h2>
 <section id="contents">
-<h3><span class="section-number">37.66.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.66.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>[1 - Imports]</p></li>
 <li><p>[2 - Loading the data]</p></li>
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 </section>
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-<h2><span class="section-number">37.66.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1706,7 +1738,7 @@ <h2><span class="section-number">37.66.2. </span>1 - Imports<a class="headerlink
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.66.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this regression task with decision trees, we will use the Machine CPU (Central Processing Unit) data which is avilable at <a class="reference external" href="https://www.openml.org/t/5492">OpenML</a>. We will load it with Sklearn <code class="docutils literal notranslate"><span class="pre">fetch_openml</span></code> function.</p>
 <p>If you are reading this, it’s very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data:</p>
 <ul class="simple">
@@ -1994,7 +2026,7 @@ <h2><span class="section-number">37.66.3. </span>2 - Loading the data<a class="h
 </div>
 </section>
 <section id="exploratory-analysis">
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+<h2><span class="section-number">40.66.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
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@@ -2174,7 +2206,7 @@ <h2><span class="section-number">37.66.4. </span>3 - Exploratory Analysis<a clas
 <p>We don’t have any missing values.</p>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.66.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Decision trees don’t care if the features are scaled or not. Let’s set up a pipeline to scale features, and we will use it to verify that notion.</p>
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+<h2><span class="section-number">40.66.6. </span>5 - Training Decision Tree Regressor<a class="headerlink" href="#training-decision-tree-regressor" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
@@ -2222,7 +2254,7 @@ <h2><span class="section-number">37.66.6. </span>5 - Training Decision Tree Regr
 </div>
 </section>
 <section id="evaluating-decision-trees">
-<h2><span class="section-number">37.66.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
 <p>Let’s first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven’t improved it yet. I will make a function to make it easier and to avoid repetitions.</p>
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 <p>Let’s try to improve the model.</p>
 </section>
 <section id="improving-decision-trees">
-<h2><span class="section-number">37.66.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tree_reg</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
@@ -2399,7 +2431,7 @@ <h2><span class="section-number">37.66.8. </span>7 - Improving Decision Trees<a
 <p>This is the end of the notebook. We have learned the fundamental idea behind the decision trees, and used it to predict the CPU performance. In the next lab, we will use it for classification task and we will use a real world dataset so that we can practically improve the decision trees model.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.66.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2441,13 +2473,13 @@ <h2><span class="section-number">37.66.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.65. </span>Support Vector Machines (SVM) - Classification</p>
+            <p class="prev-next-title"><span class="section-number">40.65. </span>Support Vector Machines (SVM) - Classification</p>
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-        <p class="prev-next-title"><span class="section-number">37.67. </span>Decision Trees - Classification</p>
+        <p class="prev-next-title"><span class="section-number">40.67. </span>Decision Trees - Classification</p>
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-    <link rel="prev" title="37.62. Explore classification methods" href="../../ml-fundamentals/explore-classification-methods.html" />
+    <link rel="next" title="40.64. Support Vector Machines (SVM) - Intro and SVM for Regression" href="support_vector_machines_for_regression.html" />
+    <link rel="prev" title="40.62. Explore classification methods" href="../../ml-fundamentals/explore-classification-methods.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-   18. Kernel method
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+ </li>
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     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
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-     37.9. Analyzing text about Data Science
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       </a>
      </li>
     </ul>
@@ -1602,27 +1634,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-kernel">
-   37.63.4. Polynomial Kernel
+   40.63.4. Polynomial Kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.63.4.1. Polynomial Kernel
+     40.63.4.1. Polynomial Kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#radial-basis-function-kernel">
-     37.63.4.2. Radial Basis Function Kernel
+     40.63.4.2. Radial Basis Function Kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.63.4.2.1. Radial Basis Function kernel
+       40.63.4.2.1. Radial Basis Function kernel
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#svm-classification-with-rbf-kernel">
-       37.63.4.2.2. SVM Classification with rbf kernel
+       40.63.4.2.2. SVM Classification with rbf kernel
       </a>
      </li>
     </ul>
@@ -1631,297 +1663,297 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-scikit-learn-libraries">
-   37.63.5. SVM Scikit-Learn libraries
+   40.63.5. SVM Scikit-Learn libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dataset-description">
-   37.63.6. Dataset description
+   40.63.6. Dataset description
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#attribute-information">
-     37.63.6.1. Attribute Information:
+     40.63.6.1. Attribute Information:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   37.63.7. Import libraries
+   40.63.7. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-dataset">
-   37.63.8. Import dataset
+   40.63.8. Import dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   37.63.9. Exploratory data analysis
+   40.63.9. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-missing-values-in-variables">
-     37.63.9.1. Explore missing values in variables
+     40.63.9.1. Explore missing values in variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-numerical-variables">
-     37.63.9.2. Summary of numerical variables
+     40.63.9.2. Summary of numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#outliers-in-numerical-variables">
-     37.63.9.3. Outliers in numerical variables
+     40.63.9.3. Outliers in numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handle-outliers-with-svms">
-     37.63.9.4. Handle outliers with SVMs
+     40.63.9.4. Handle outliers with SVMs
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-the-distribution-of-variables">
-     37.63.9.5. Check the distribution of variables
+     40.63.9.5. Check the distribution of variables
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-   37.63.10. Declare feature vector and target variable
+   40.63.10. Declare feature vector and target variable
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-   37.63.11. Split data into separate training and test set
+   40.63.11. Split data into separate training and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   37.63.12. Feature Scaling
+   40.63.12. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-default-hyperparameters">
-   37.63.13. Run SVM with default hyperparameters
+   40.63.13. Run SVM with default hyperparameters
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-100-0">
-     37.63.13.1. Run SVM with rbf kernel and C=100.0
+     40.63.13.1. Run SVM with rbf kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-1000-0">
-     37.63.13.2. Run SVM with rbf kernel and C=1000.0
+     40.63.13.2. Run SVM with rbf kernel and C=1000.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-linear-kernel">
-   37.63.14. Run SVM with linear kernel
+   40.63.14. Run SVM with linear kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1-0">
-     37.63.14.1. Run SVM with linear kernel and C=1.0
+     40.63.14.1. Run SVM with linear kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-100-0">
-     37.63.14.2. Run SVM with linear kernel and C=100.0
+     40.63.14.2. Run SVM with linear kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1000-0">
-     37.63.14.3. Run SVM with linear kernel and C=1000.0
+     40.63.14.3. Run SVM with linear kernel and C=1000.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-the-train-set-and-test-set-accuracy">
-     37.63.14.4. Compare the train-set and test-set accuracy
+     40.63.14.4. Compare the train-set and test-set accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-for-overfitting-and-underfitting">
-     37.63.14.5. Check for overfitting and underfitting
+     40.63.14.5. Check for overfitting and underfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-model-accuracy-with-null-accuracy">
-     37.63.14.6. Compare model accuracy with null accuracy
+     40.63.14.6. Compare model accuracy with null accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel">
-   37.63.15. Run SVM with polynomial kernel
+   40.63.15. Run SVM with polynomial kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-1-0">
-     37.63.15.1. Run SVM with polynomial kernel and C=1.0
+     40.63.15.1. Run SVM with polynomial kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-100-0">
-     37.63.15.2. Run SVM with polynomial kernel and C=100.0
+     40.63.15.2. Run SVM with polynomial kernel and C=100.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel">
-   37.63.16. Run SVM with sigmoid kernel
+   40.63.16. Run SVM with sigmoid kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-1-0">
-     37.63.16.1. Run SVM with sigmoid kernel and C=1.0
+     40.63.16.1. Run SVM with sigmoid kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-100-0">
-     37.63.16.2. Run SVM with sigmoid kernel and C=100.0
+     40.63.16.2. Run SVM with sigmoid kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     37.63.16.3. Comments
+     40.63.16.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#confusion-matrix">
-   37.63.17. Confusion matrix
+   40.63.17. Confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-metrices">
-   37.63.18. Classification metrices
+   40.63.18. Classification metrices
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     37.63.18.1. Classification Report
+     40.63.18.1. Classification Report
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-accuracy">
-     37.63.18.2. Classification accuracy
+     40.63.18.2. Classification accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-error">
-     37.63.18.3. Classification error
+     40.63.18.3. Classification error
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#precision">
-     37.63.18.4. Precision
+     40.63.18.4. Precision
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#recall">
-     37.63.18.5. Recall
+     40.63.18.5. Recall
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#true-positive-rate">
-     37.63.18.6. True Positive Rate
+     40.63.18.6. True Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#false-positive-rate">
-     37.63.18.7. False Positive Rate
+     40.63.18.7. False Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#specificity">
-     37.63.18.8. Specificity
+     40.63.18.8. Specificity
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#f1-score">
-     37.63.18.9. f1-score
+     40.63.18.9. f1-score
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support">
-     37.63.18.10. Support
+     40.63.18.10. Support
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#roc-auc">
-   37.63.19. ROC - AUC
+   40.63.19. ROC - AUC
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#roc-curve">
-     37.63.19.1. ROC Curve
+     40.63.19.1. ROC Curve
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.63.19.2. ROC  AUC
+     40.63.19.2. ROC  AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.63.19.3. Comments
+     40.63.19.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split">
-   37.63.20. Stratified k-fold Cross Validation with shuffle split
+   40.63.20. Stratified k-fold Cross Validation with shuffle split
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-     37.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
+     40.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-     37.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
+     40.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.63.20.3. Comments
+     40.63.20.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-optimization-using-gridsearch-cv">
-   37.63.21. Hyperparameter Optimization using GridSearch CV
+   40.63.21. Hyperparameter Optimization using GridSearch CV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.63.21.1. Comments
+     40.63.21.1. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   37.63.22. Results and conclusion
+   40.63.22. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.63.23. Acknowledgments
+   40.63.23. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1945,74 +1977,74 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-support-vector-machines">
-   37.63.1. Introduction to Support Vector Machines
+   40.63.1. Introduction to Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-intuition">
-   37.63.2. Support Vector Machines intuition
+   40.63.2. Support Vector Machines intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hyperplane">
-     37.63.2.1. Hyperplane
+     40.63.2.1. Hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support-vectors">
-     37.63.2.2. Support Vectors
+     40.63.2.2. Support Vectors
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin">
-     37.63.2.3. Margin
+     40.63.2.3. Margin
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin-in-svm">
-     37.63.2.4. Margin in SVM
+     40.63.2.4. Margin in SVM
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#svm-under-the-hood">
-     37.63.2.5. SVM Under the hood
+     40.63.2.5. SVM Under the hood
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#maximum-margin-hyperplane">
-     37.63.2.6. Maximum margin hyperplane
+     40.63.2.6. Maximum margin hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#problem-with-dispersed-datasets">
-     37.63.2.7. Problem with dispersed datasets
+     40.63.2.7. Problem with dispersed datasets
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space">
-     37.63.2.8. Kernel trick - transformation of input space to higher dimensional space
+     40.63.2.8. Kernel trick - transformation of input space to higher dimensional space
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#kernel-trick">
-   37.63.3. Kernel trick
+   40.63.3. Kernel trick
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-function">
-     37.63.3.1. Kernel function
+     40.63.3.1. Kernel function
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-kernel">
-     37.63.3.2. Linear kernel
+     40.63.3.2. Linear kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.63.3.2.1. Linear Kernel
+       40.63.3.2.1. Linear Kernel
       </a>
      </li>
     </ul>
@@ -2021,27 +2053,27 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-kernel">
-   37.63.4. Polynomial Kernel
+   40.63.4. Polynomial Kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.63.4.1. Polynomial Kernel
+     40.63.4.1. Polynomial Kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#radial-basis-function-kernel">
-     37.63.4.2. Radial Basis Function Kernel
+     40.63.4.2. Radial Basis Function Kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.63.4.2.1. Radial Basis Function kernel
+       40.63.4.2.1. Radial Basis Function kernel
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#svm-classification-with-rbf-kernel">
-       37.63.4.2.2. SVM Classification with rbf kernel
+       40.63.4.2.2. SVM Classification with rbf kernel
       </a>
      </li>
     </ul>
@@ -2050,297 +2082,297 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-scikit-learn-libraries">
-   37.63.5. SVM Scikit-Learn libraries
+   40.63.5. SVM Scikit-Learn libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dataset-description">
-   37.63.6. Dataset description
+   40.63.6. Dataset description
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#attribute-information">
-     37.63.6.1. Attribute Information:
+     40.63.6.1. Attribute Information:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   37.63.7. Import libraries
+   40.63.7. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-dataset">
-   37.63.8. Import dataset
+   40.63.8. Import dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   37.63.9. Exploratory data analysis
+   40.63.9. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-missing-values-in-variables">
-     37.63.9.1. Explore missing values in variables
+     40.63.9.1. Explore missing values in variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-numerical-variables">
-     37.63.9.2. Summary of numerical variables
+     40.63.9.2. Summary of numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#outliers-in-numerical-variables">
-     37.63.9.3. Outliers in numerical variables
+     40.63.9.3. Outliers in numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handle-outliers-with-svms">
-     37.63.9.4. Handle outliers with SVMs
+     40.63.9.4. Handle outliers with SVMs
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-the-distribution-of-variables">
-     37.63.9.5. Check the distribution of variables
+     40.63.9.5. Check the distribution of variables
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-   37.63.10. Declare feature vector and target variable
+   40.63.10. Declare feature vector and target variable
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-   37.63.11. Split data into separate training and test set
+   40.63.11. Split data into separate training and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   37.63.12. Feature Scaling
+   40.63.12. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-default-hyperparameters">
-   37.63.13. Run SVM with default hyperparameters
+   40.63.13. Run SVM with default hyperparameters
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-100-0">
-     37.63.13.1. Run SVM with rbf kernel and C=100.0
+     40.63.13.1. Run SVM with rbf kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-1000-0">
-     37.63.13.2. Run SVM with rbf kernel and C=1000.0
+     40.63.13.2. Run SVM with rbf kernel and C=1000.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-linear-kernel">
-   37.63.14. Run SVM with linear kernel
+   40.63.14. Run SVM with linear kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1-0">
-     37.63.14.1. Run SVM with linear kernel and C=1.0
+     40.63.14.1. Run SVM with linear kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-100-0">
-     37.63.14.2. Run SVM with linear kernel and C=100.0
+     40.63.14.2. Run SVM with linear kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1000-0">
-     37.63.14.3. Run SVM with linear kernel and C=1000.0
+     40.63.14.3. Run SVM with linear kernel and C=1000.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-the-train-set-and-test-set-accuracy">
-     37.63.14.4. Compare the train-set and test-set accuracy
+     40.63.14.4. Compare the train-set and test-set accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-for-overfitting-and-underfitting">
-     37.63.14.5. Check for overfitting and underfitting
+     40.63.14.5. Check for overfitting and underfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-model-accuracy-with-null-accuracy">
-     37.63.14.6. Compare model accuracy with null accuracy
+     40.63.14.6. Compare model accuracy with null accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel">
-   37.63.15. Run SVM with polynomial kernel
+   40.63.15. Run SVM with polynomial kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-1-0">
-     37.63.15.1. Run SVM with polynomial kernel and C=1.0
+     40.63.15.1. Run SVM with polynomial kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-100-0">
-     37.63.15.2. Run SVM with polynomial kernel and C=100.0
+     40.63.15.2. Run SVM with polynomial kernel and C=100.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel">
-   37.63.16. Run SVM with sigmoid kernel
+   40.63.16. Run SVM with sigmoid kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-1-0">
-     37.63.16.1. Run SVM with sigmoid kernel and C=1.0
+     40.63.16.1. Run SVM with sigmoid kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-100-0">
-     37.63.16.2. Run SVM with sigmoid kernel and C=100.0
+     40.63.16.2. Run SVM with sigmoid kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     37.63.16.3. Comments
+     40.63.16.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#confusion-matrix">
-   37.63.17. Confusion matrix
+   40.63.17. Confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-metrices">
-   37.63.18. Classification metrices
+   40.63.18. Classification metrices
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     37.63.18.1. Classification Report
+     40.63.18.1. Classification Report
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-accuracy">
-     37.63.18.2. Classification accuracy
+     40.63.18.2. Classification accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-error">
-     37.63.18.3. Classification error
+     40.63.18.3. Classification error
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#precision">
-     37.63.18.4. Precision
+     40.63.18.4. Precision
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#recall">
-     37.63.18.5. Recall
+     40.63.18.5. Recall
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#true-positive-rate">
-     37.63.18.6. True Positive Rate
+     40.63.18.6. True Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#false-positive-rate">
-     37.63.18.7. False Positive Rate
+     40.63.18.7. False Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#specificity">
-     37.63.18.8. Specificity
+     40.63.18.8. Specificity
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#f1-score">
-     37.63.18.9. f1-score
+     40.63.18.9. f1-score
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support">
-     37.63.18.10. Support
+     40.63.18.10. Support
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#roc-auc">
-   37.63.19. ROC - AUC
+   40.63.19. ROC - AUC
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#roc-curve">
-     37.63.19.1. ROC Curve
+     40.63.19.1. ROC Curve
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.63.19.2. ROC  AUC
+     40.63.19.2. ROC  AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.63.19.3. Comments
+     40.63.19.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split">
-   37.63.20. Stratified k-fold Cross Validation with shuffle split
+   40.63.20. Stratified k-fold Cross Validation with shuffle split
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-     37.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
+     40.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-     37.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
+     40.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.63.20.3. Comments
+     40.63.20.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-optimization-using-gridsearch-cv">
-   37.63.21. Hyperparameter Optimization using GridSearch CV
+   40.63.21. Hyperparameter Optimization using GridSearch CV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.63.21.1. Comments
+     40.63.21.1. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   37.63.22. Results and conclusion
+   40.63.22. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.63.23. Acknowledgments
+   40.63.23. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2354,36 +2386,36 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="kernel-method-assignment-1">
-<h1><span class="section-number">37.63. </span>Kernel method assignment 1<a class="headerlink" href="#kernel-method-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.63. </span>Kernel method assignment 1<a class="headerlink" href="#kernel-method-assignment-1" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines (SVMs in short) are supervised machine learning algorithms that are used for classification and regression purposes. In this kernel, We build a Support Vector Machines classifier to classify a Pulsar star. We have used the <strong>Predicting a Pulsar Star</strong> dataset for this project.</p>
 <section id="introduction-to-support-vector-machines">
-<h2><span class="section-number">37.63.1. </span>Introduction to Support Vector Machines<a class="headerlink" href="#introduction-to-support-vector-machines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.1. </span>Introduction to Support Vector Machines<a class="headerlink" href="#introduction-to-support-vector-machines" title="Permalink to this headline">#</a></h2>
 <p><strong>Support Vector Machines</strong> (SVMs in short) are machine learning algorithms that are used for classification and regression purposes. SVMs are one of the powerful machine learning algorithms for classification, regression and outlier detection purposes. An SVM classifier builds a model that assigns new data points to one of the given categories. Thus, it can be viewed as a non-probabilistic binary linear classifier.</p>
 <p>The original SVM algorithm was developed by Vladimir N Vapnik and Alexey Ya. Chervonenkis in 1963. At that time, the algorithm was in early stages. The only possibility is to draw hyperplanes for linear classifier. In 1992, Bernhard E. Boser, Isabelle M Guyon and Vladimir N Vapnik suggested a way to create non-linear classifiers by applying the kernel trick to maximum-margin hyperplanes. The current standard was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995.</p>
 <p>SVMs can be used for linear classification purposes. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using the <strong>kernel trick</strong>. It enable us to implicitly map the inputs into high dimensional feature spaces.</p>
 </section>
 <section id="support-vector-machines-intuition">
-<h2><span class="section-number">37.63.2. </span>Support Vector Machines intuition<a class="headerlink" href="#support-vector-machines-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.2. </span>Support Vector Machines intuition<a class="headerlink" href="#support-vector-machines-intuition" title="Permalink to this headline">#</a></h2>
 <p>Now, we should be familiar with some SVM terminology.</p>
 <section id="hyperplane">
-<h3><span class="section-number">37.63.2.1. </span>Hyperplane<a class="headerlink" href="#hyperplane" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.1. </span>Hyperplane<a class="headerlink" href="#hyperplane" title="Permalink to this headline">#</a></h3>
 <p>A hyperplane is a decision boundary which separates between given set of data points having different class labels. The SVM classifier separates data points using a hyperplane with the maximum amount of margin. This hyperplane is known as the <code class="docutils literal notranslate"><span class="pre">maximum</span> <span class="pre">margin</span> <span class="pre">hyperplane</span></code> and the linear classifier it defines is known as the <code class="docutils literal notranslate"><span class="pre">maximum</span> <span class="pre">margin</span> <span class="pre">classifier</span></code>.</p>
 </section>
 <section id="support-vectors">
-<h3><span class="section-number">37.63.2.2. </span>Support Vectors<a class="headerlink" href="#support-vectors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.2. </span>Support Vectors<a class="headerlink" href="#support-vectors" title="Permalink to this headline">#</a></h3>
 <p>Support vectors are the sample data points, which are closest to the hyperplane.  These data points will define the separating line or hyperplane better by calculating margins.</p>
 </section>
 <section id="margin">
-<h3><span class="section-number">37.63.2.3. </span>Margin<a class="headerlink" href="#margin" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.3. </span>Margin<a class="headerlink" href="#margin" title="Permalink to this headline">#</a></h3>
 <p>A margin is a separation gap between the two lines on the closest data points. It is calculated as the perpendicular distance from the line to support vectors or closest data points. In SVMs, we try to maximize this separation gap so that we get maximum margin.</p>
 <p>The following diagram illustrates these concepts visually.</p>
 </section>
 <section id="margin-in-svm">
-<h3><span class="section-number">37.63.2.4. </span>Margin in SVM<a class="headerlink" href="#margin-in-svm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.4. </span>Margin in SVM<a class="headerlink" href="#margin-in-svm" title="Permalink to this headline">#</a></h3>
 <p><img alt="Margin in SVM" src="https://static.wixstatic.com/media/8f929f_7ecacdcf69d2450087cb4a898ef90837~mv2.png" /></p>
 </section>
 <section id="svm-under-the-hood">
-<h3><span class="section-number">37.63.2.5. </span>SVM Under the hood<a class="headerlink" href="#svm-under-the-hood" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.5. </span>SVM Under the hood<a class="headerlink" href="#svm-under-the-hood" title="Permalink to this headline">#</a></h3>
 <p>In SVMs, our main objective is to select a hyperplane with the maximum possible margin between support vectors in the given dataset. SVM searches for the maximum margin hyperplane in the following 2 step process –</p>
 <ol class="simple">
 <li><p>Generate hyperplanes which segregates the classes in the best possible way. There are many hyperplanes that might classify the data. We should look for the best hyperplane that represents the largest separation, or margin, between the two classes.</p></li>
@@ -2392,78 +2424,78 @@ <h3><span class="section-number">37.63.2.5. </span>SVM Under the hood<a class="h
 <p>The following diagram illustrates the concept of <strong>maximum margin</strong> and <strong>maximum margin hyperplane</strong> in a clear manner.</p>
 </section>
 <section id="maximum-margin-hyperplane">
-<h3><span class="section-number">37.63.2.6. </span>Maximum margin hyperplane<a class="headerlink" href="#maximum-margin-hyperplane" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.6. </span>Maximum margin hyperplane<a class="headerlink" href="#maximum-margin-hyperplane" title="Permalink to this headline">#</a></h3>
 <p><img alt="Maximum margin hyperplane" src="https://static.packt-cdn.com/products/9781783555130/graphics/3547_03_07.jpg" /></p>
 </section>
 <section id="problem-with-dispersed-datasets">
-<h3><span class="section-number">37.63.2.7. </span>Problem with dispersed datasets<a class="headerlink" href="#problem-with-dispersed-datasets" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.7. </span>Problem with dispersed datasets<a class="headerlink" href="#problem-with-dispersed-datasets" title="Permalink to this headline">#</a></h3>
 <p>Sometimes, the sample data points are so dispersed that it is not possible to separate them using a linear hyperplane.
 In such a situation, SVMs uses a <code class="docutils literal notranslate"><span class="pre">kernel</span> <span class="pre">trick</span></code> to transform the input space to a higher dimensional space as shown in the diagram below. It uses a mapping function to transform the 2-D input space into the 3-D input space. Now, we can easily segregate the data points using linear separation.</p>
 </section>
 <section id="kernel-trick-transformation-of-input-space-to-higher-dimensional-space">
-<h3><span class="section-number">37.63.2.8. </span>Kernel trick - transformation of input space to higher dimensional space<a class="headerlink" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.8. </span>Kernel trick - transformation of input space to higher dimensional space<a class="headerlink" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space" title="Permalink to this headline">#</a></h3>
 <p><img alt="Kernel trick" src="http://www.aionlinecourse.com/uploads/tutorials/2019/07/11_21_kernel_svm_3.png" /></p>
 </section>
 </section>
 <section id="kernel-trick">
-<h2><span class="section-number">37.63.3. </span>Kernel trick<a class="headerlink" href="#kernel-trick" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.3. </span>Kernel trick<a class="headerlink" href="#kernel-trick" title="Permalink to this headline">#</a></h2>
 <p>In practice, SVM algorithm is implemented using a <code class="docutils literal notranslate"><span class="pre">kernel</span></code>. It uses a technique called the <code class="docutils literal notranslate"><span class="pre">kernel</span> <span class="pre">trick</span></code>. In simple words, a <code class="docutils literal notranslate"><span class="pre">kernel</span></code> is just a function that maps the data to a higher dimension where data is separable. A kernel transforms a low-dimensional input data space into a higher dimensional space. So, it converts non-linear separable problems to linear separable problems by adding more dimensions to it. Thus, the kernel trick helps us to build a more accurate classifier. Hence, it is useful in non-linear separation problems.</p>
 <p>We can define a kernel function as follows-</p>
 <section id="kernel-function">
-<h3><span class="section-number">37.63.3.1. </span>Kernel function<a class="headerlink" href="#kernel-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.3.1. </span>Kernel function<a class="headerlink" href="#kernel-function" title="Permalink to this headline">#</a></h3>
 <p><img alt="Kernel function" src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTodZptqcRor0LGo8Qn7_kJB9n9BACMt6jgIPZ4C3g_rgh_uSRZLQ&amp;s" /></p>
 <p>In the context of SVMs, there are 4 popular kernels – <code class="docutils literal notranslate"><span class="pre">Linear</span> <span class="pre">kernel</span></code>,<code class="docutils literal notranslate"><span class="pre">Polynomial</span> <span class="pre">kernel</span></code>,<code class="docutils literal notranslate"><span class="pre">Radial</span> <span class="pre">Basis</span> <span class="pre">Function</span> <span class="pre">(RBF)</span> <span class="pre">kernel</span></code> (also called Gaussian kernel) and <code class="docutils literal notranslate"><span class="pre">Sigmoid</span> <span class="pre">kernel</span></code>. These are described below -</p>
 </section>
 <section id="linear-kernel">
-<h3><span class="section-number">37.63.3.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.3.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">#</a></h3>
 <p>In linear kernel, the kernel function takes the form of a linear function as follows-</p>
 <p><strong>linear kernel : K(xi , xj ) = xiT xj</strong></p>
 <p>Linear kernel is used when the data is linearly separable. It means that data can be separated using a single line. It is one of the most common kernels to be used. It is mostly used when there are large number of features in a dataset. Linear kernel is often used for text classification purposes.</p>
 <p>Training with a linear kernel is usually faster, because we only need to optimize the C regularization parameter. When training with other kernels, we also need to optimize the γ parameter. So, performing a grid search will usually take more time.</p>
 <p>Linear kernel can be visualized with the following figure.</p>
 <section id="id1">
-<h4><span class="section-number">37.63.3.2.1. </span>Linear Kernel<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.63.3.2.1. </span>Linear Kernel<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p><img alt="Linear Kernel" src="https://scikit-learn.org/stable/_images/sphx_glr_plot_svm_kernels_thumb.png" /></p>
 </section>
 </section>
 </section>
 <section id="polynomial-kernel">
-<h2><span class="section-number">37.63.4. </span>Polynomial Kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.4. </span>Polynomial Kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">#</a></h2>
 <p>Polynomial kernel represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables. The polynomial kernel looks not only at the given features of input samples to determine their similarity, but also combinations of the input samples.</p>
 <p>For degree-d polynomials, the polynomial kernel is defined as follows –</p>
 <p><strong>Polynomial kernel : K(xi , xj ) = (γxiT xj + r)d , γ &gt; 0</strong></p>
 <p>Polynomial kernel is very popular in Natural Language Processing. The most common degree is d = 2 (quadratic), since larger degrees tend to overfit on NLP problems. It can be visualized with the following diagram.</p>
 <section id="id2">
-<h3><span class="section-number">37.63.4.1. </span>Polynomial Kernel<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.4.1. </span>Polynomial Kernel<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p><img alt="Polynomial Kernel" src="https://www.researchgate.net/profile/Cheng_Soon_Ong/publication/23442384/figure/fig12/AS:341444054274063&#64;1458418014823/The-effect-of-the-degree-of-a-polynomial-kernel-The-polynomial-kernel-of-degree-1-leads.png" /></p>
 </section>
 <section id="radial-basis-function-kernel">
-<h3><span class="section-number">37.63.4.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.4.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Permalink to this headline">#</a></h3>
 <p>Radial basis function kernel is a general purpose kernel. It is used when we have no prior knowledge about the data. The RBF kernel on two samples x and y is defined by the following equation –</p>
 <section id="id3">
-<h4><span class="section-number">37.63.4.2.1. </span>Radial Basis Function kernel<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.63.4.2.1. </span>Radial Basis Function kernel<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p>![RBK Kernel](data:image/png;base64,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)</p>
 <p>The following diagram demonstrates the SVM classification with rbf kernel.</p>
 </section>
 <section id="svm-classification-with-rbf-kernel">
-<h4><span class="section-number">37.63.4.2.2. </span>SVM Classification with rbf kernel<a class="headerlink" href="#svm-classification-with-rbf-kernel" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.63.4.2.2. </span>SVM Classification with rbf kernel<a class="headerlink" href="#svm-classification-with-rbf-kernel" title="Permalink to this headline">#</a></h4>
 <p><img alt="SVM Classification with rbf kernel" src="https://www.researchgate.net/profile/Periklis_Gogas/publication/286180566/figure/fig5/AS:304327777374210&#64;1449568804246/An-example-of-an-SVM-classification-using-the-RBF-kernel-The-two-classes-are-separated.png" /></p>
 </section>
 </section>
 </section>
 <section id="svm-scikit-learn-libraries">
-<h2><span class="section-number">37.63.5. </span>SVM Scikit-Learn libraries<a class="headerlink" href="#svm-scikit-learn-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.5. </span>SVM Scikit-Learn libraries<a class="headerlink" href="#svm-scikit-learn-libraries" title="Permalink to this headline">#</a></h2>
 <p>Scikit-Learn provides useful libraries to implement Support Vector Machine algorithm on a dataset. There are many libraries that can help us to implement SVM smoothly. We just need to call the library with parameters that suit to our needs. In this project, I am dealing with a classification task. So, I will mention the Scikit-Learn libraries for SVM classification purposes.</p>
 <p>First, there is a <strong>LinearSVC()</strong> classifier. As the name suggests, this classifier uses only linear kernel. In LinearSVC() classifier, we don’t pass the value of kernel since it is used only for linear classification purposes.</p>
 <p>Scikit-Learn provides two other classifiers - <strong>SVC()</strong> and <strong>NuSVC()</strong> which are used for classification purposes. These classifiers are mostly similar with some difference in parameters. <strong>NuSVC()</strong> is similar to <strong>SVC()</strong> but uses a parameter to control the number of support vectors. We pass the values of kernel, gamma and C along with other parameters. By default kernel parameter uses rbf as its value but we can pass values like poly, linear, sigmoid or callable function.</p>
 </section>
 <section id="dataset-description">
-<h2><span class="section-number">37.63.6. </span>Dataset description<a class="headerlink" href="#dataset-description" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.6. </span>Dataset description<a class="headerlink" href="#dataset-description" title="Permalink to this headline">#</a></h2>
 <p>I have used the <strong>Predicting a Pulsar Star</strong> dataset for this project.</p>
 <p>Pulsars are a rare type of Neutron star that produce radio emission detectable here on Earth. They are of considerable scientific interest as probes of space-time, the inter-stellar medium, and states of matter. Classification algorithms in particular are being adopted, which treat the data sets as binary classification problems. Here the legitimate pulsar examples form  minority positive class and spurious examples form the majority negative class.</p>
 <p>The data set shared here contains 16,259 spurious examples caused by RFI/noise, and 1,639 real pulsar examples. Each row lists the variables first, and the class label is the final entry. The class labels used are 0 (negative) and 1 (positive).</p>
 <section id="attribute-information">
-<h3><span class="section-number">37.63.6.1. </span>Attribute Information:<a class="headerlink" href="#attribute-information" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.6.1. </span>Attribute Information:<a class="headerlink" href="#attribute-information" title="Permalink to this headline">#</a></h3>
 <p>Each candidate is described by 8 continuous variables, and a single class variable. The first four are simple statistics obtained from the integrated pulse profile. The remaining four variables are similarly obtained from the DM-SNR curve . These are summarised below:</p>
 <ol class="simple">
 <li><p>Mean of the integrated profile.</p></li>
@@ -2479,7 +2511,7 @@ <h3><span class="section-number">37.63.6.1. </span>Attribute Information:<a clas
 </section>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">37.63.7. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.7. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <p>I will start off by importing the required Python libraries.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2497,7 +2529,7 @@ <h2><span class="section-number">37.63.7. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="import-dataset">
-<h2><span class="section-number">37.63.8. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.8. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;../../../assets/data/pulsar_stars.csv&#39;</span>
@@ -2509,7 +2541,7 @@ <h2><span class="section-number">37.63.8. </span>Import dataset<a class="headerl
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">37.63.9. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.9. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <p>Now, we will explore the data to gain insights about the data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2779,7 +2811,7 @@ <h2><span class="section-number">37.63.9. </span>Exploratory data analysis<a cla
 </div>
 <p>We can see that there are no missing values in the dataset and all the variables are numerical variables.</p>
 <section id="explore-missing-values-in-variables">
-<h3><span class="section-number">37.63.9.1. </span>Explore missing values in variables<a class="headerlink" href="#explore-missing-values-in-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.1. </span>Explore missing values in variables<a class="headerlink" href="#explore-missing-values-in-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for missing values in variables</span>
@@ -2806,7 +2838,7 @@ <h3><span class="section-number">37.63.9.1. </span>Explore missing values in var
 <p>We can see that there are no missing values in the dataset.</p>
 </section>
 <section id="summary-of-numerical-variables">
-<h3><span class="section-number">37.63.9.2. </span>Summary of numerical variables<a class="headerlink" href="#summary-of-numerical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.2. </span>Summary of numerical variables<a class="headerlink" href="#summary-of-numerical-variables" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>There are 9 numerical variables in the dataset.</p></li>
 <li><p>8 are continuous variables and 1 is discrete variable.</p></li>
@@ -2815,7 +2847,7 @@ <h3><span class="section-number">37.63.9.2. </span>Summary of numerical variable
 </ul>
 </section>
 <section id="outliers-in-numerical-variables">
-<h3><span class="section-number">37.63.9.3. </span>Outliers in numerical variables<a class="headerlink" href="#outliers-in-numerical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.3. </span>Outliers in numerical variables<a class="headerlink" href="#outliers-in-numerical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># view summary statistics in numerical variables</span>
@@ -3023,7 +3055,7 @@ <h3><span class="section-number">37.63.9.3. </span>Outliers in numerical variabl
 <p>The above boxplots confirm that there are lot of outliers in these variables.</p>
 </section>
 <section id="handle-outliers-with-svms">
-<h3><span class="section-number">37.63.9.4. </span>Handle outliers with SVMs<a class="headerlink" href="#handle-outliers-with-svms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.4. </span>Handle outliers with SVMs<a class="headerlink" href="#handle-outliers-with-svms" title="Permalink to this headline">#</a></h3>
 <p>There are 2 variants of SVMs. They are <code class="docutils literal notranslate"><span class="pre">hard-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code> and <code class="docutils literal notranslate"><span class="pre">soft-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code>.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">hard-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code> does not deal with outliers. In this case, we want to find the hyperplane with maximum margin such that every training point is correctly classified with margin at least 1. This technique does not handle outliers well.</p>
 <p>Another version of SVM is called <code class="docutils literal notranslate"><span class="pre">soft-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code>. In this case, we can have a few points incorrectly classified or
@@ -3031,7 +3063,7 @@ <h3><span class="section-number">37.63.9.4. </span>Handle outliers with SVMs<a c
 <p>The message is that since the dataset contains outliers, so the value of C should be high while training the model.</p>
 </section>
 <section id="check-the-distribution-of-variables">
-<h3><span class="section-number">37.63.9.5. </span>Check the distribution of variables<a class="headerlink" href="#check-the-distribution-of-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.5. </span>Check the distribution of variables<a class="headerlink" href="#check-the-distribution-of-variables" title="Permalink to this headline">#</a></h3>
 <p>Now, I will plot the histograms to check distributions to find out if they are normal or skewed.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3105,7 +3137,7 @@ <h3><span class="section-number">37.63.9.5. </span>Check the distribution of var
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h2><span class="section-number">37.63.10. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.10. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;target_class&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -3117,7 +3149,7 @@ <h2><span class="section-number">37.63.10. </span>Declare feature vector and tar
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h2><span class="section-number">37.63.11. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.11. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># split X and y into training and testing sets</span>
@@ -3145,7 +3177,7 @@ <h2><span class="section-number">37.63.11. </span>Split data into separate train
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">37.63.12. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.12. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span>
@@ -3311,7 +3343,7 @@ <h2><span class="section-number">37.63.12. </span>Feature Scaling<a class="heade
 <p>We now have <code class="docutils literal notranslate"><span class="pre">X_train</span></code> dataset ready to be fed into the Logistic Regression classifier. I will do it as follows.</p>
 </section>
 <section id="run-svm-with-default-hyperparameters">
-<h2><span class="section-number">37.63.13. </span>Run SVM with default hyperparameters<a class="headerlink" href="#run-svm-with-default-hyperparameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.13. </span>Run SVM with default hyperparameters<a class="headerlink" href="#run-svm-with-default-hyperparameters" title="Permalink to this headline">#</a></h2>
 <p>Default hyperparameter means C=1.0,  kernel=<code class="docutils literal notranslate"><span class="pre">rbf</span></code> and gamma=<code class="docutils literal notranslate"><span class="pre">auto</span></code> among other parameters.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3347,7 +3379,7 @@ <h2><span class="section-number">37.63.13. </span>Run SVM with default hyperpara
 </div>
 </div>
 <section id="run-svm-with-rbf-kernel-and-c-100-0">
-<h3><span class="section-number">37.63.13.1. </span>Run SVM with rbf kernel and C=100.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.13.1. </span>Run SVM with rbf kernel and C=100.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <p>We have seen that there are outliers in our dataset. So, we should increase the value of C as higher C means fewer outliers.
 So, I will run SVM with kernel=<code class="docutils literal notranslate"><span class="pre">rbf</span></code> and C=100.0.</p>
 <div class="cell docutils container">
@@ -3379,7 +3411,7 @@ <h3><span class="section-number">37.63.13.1. </span>Run SVM with rbf kernel and
 <p>Now, I will further increase the value of C=1000.0 and check accuracy.</p>
 </section>
 <section id="run-svm-with-rbf-kernel-and-c-1000-0">
-<h3><span class="section-number">37.63.13.2. </span>Run SVM with rbf kernel and C=1000.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.13.2. </span>Run SVM with rbf kernel and C=1000.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with rbf kernel and C=1000</span>
@@ -3409,9 +3441,9 @@ <h3><span class="section-number">37.63.13.2. </span>Run SVM with rbf kernel and
 </section>
 </section>
 <section id="run-svm-with-linear-kernel">
-<h2><span class="section-number">37.63.14. </span>Run SVM with linear kernel<a class="headerlink" href="#run-svm-with-linear-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.14. </span>Run SVM with linear kernel<a class="headerlink" href="#run-svm-with-linear-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-linear-kernel-and-c-1-0">
-<h3><span class="section-number">37.63.14.1. </span>Run SVM with linear kernel and C=1.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.1. </span>Run SVM with linear kernel and C=1.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=1.0</span>
@@ -3439,7 +3471,7 @@ <h3><span class="section-number">37.63.14.1. </span>Run SVM with linear kernel a
 </div>
 </section>
 <section id="run-svm-with-linear-kernel-and-c-100-0">
-<h3><span class="section-number">37.63.14.2. </span>Run SVM with linear kernel and C=100.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.2. </span>Run SVM with linear kernel and C=100.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=100.0</span>
@@ -3467,7 +3499,7 @@ <h3><span class="section-number">37.63.14.2. </span>Run SVM with linear kernel a
 </div>
 </section>
 <section id="run-svm-with-linear-kernel-and-c-1000-0">
-<h3><span class="section-number">37.63.14.3. </span>Run SVM with linear kernel and C=1000.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.3. </span>Run SVM with linear kernel and C=1000.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=1000.0</span>
@@ -3497,7 +3529,7 @@ <h3><span class="section-number">37.63.14.3. </span>Run SVM with linear kernel a
 <p>Here, <strong>y_test</strong> are the true class labels and <strong>y_pred</strong> are the predicted class labels in the test-set.</p>
 </section>
 <section id="compare-the-train-set-and-test-set-accuracy">
-<h3><span class="section-number">37.63.14.4. </span>Compare the train-set and test-set accuracy<a class="headerlink" href="#compare-the-train-set-and-test-set-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.4. </span>Compare the train-set and test-set accuracy<a class="headerlink" href="#compare-the-train-set-and-test-set-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Now, I will compare the train-set and test-set accuracy to check for overfitting.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3528,7 +3560,7 @@ <h3><span class="section-number">37.63.14.4. </span>Compare the train-set and te
 <p>We can see that the training set and test-set accuracy are very much comparable.</p>
 </section>
 <section id="check-for-overfitting-and-underfitting">
-<h3><span class="section-number">37.63.14.5. </span>Check for overfitting and underfitting<a class="headerlink" href="#check-for-overfitting-and-underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.5. </span>Check for overfitting and underfitting<a class="headerlink" href="#check-for-overfitting-and-underfitting" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print the scores on training and test set</span>
@@ -3549,7 +3581,7 @@ <h3><span class="section-number">37.63.14.5. </span>Check for overfitting and un
 <p>The training-set accuracy score is 0.9783 while the test-set accuracy to be 0.9830. These two values are quite comparable. So, there is no question of overfitting.</p>
 </section>
 <section id="compare-model-accuracy-with-null-accuracy">
-<h3><span class="section-number">37.63.14.6. </span>Compare model accuracy with null accuracy<a class="headerlink" href="#compare-model-accuracy-with-null-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.6. </span>Compare model accuracy with null accuracy<a class="headerlink" href="#compare-model-accuracy-with-null-accuracy" title="Permalink to this headline">#</a></h3>
 <p>So, the model accuracy is 0.9832. But, we cannot say that our model is very good based on the above accuracy. We must compare it with the <strong>null accuracy</strong>. Null accuracy is the accuracy that could be achieved by always predicting the most frequent class.</p>
 <p>So, we should first check the class distribution in the test set.</p>
 <div class="cell docutils container">
@@ -3589,9 +3621,9 @@ <h3><span class="section-number">37.63.14.6. </span>Compare model accuracy with
 </section>
 </section>
 <section id="run-svm-with-polynomial-kernel">
-<h2><span class="section-number">37.63.15. </span>Run SVM with polynomial kernel<a class="headerlink" href="#run-svm-with-polynomial-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.15. </span>Run SVM with polynomial kernel<a class="headerlink" href="#run-svm-with-polynomial-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-polynomial-kernel-and-c-1-0">
-<h3><span class="section-number">37.63.15.1. </span>Run SVM with polynomial kernel and C=1.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.15.1. </span>Run SVM with polynomial kernel and C=1.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with polynomial kernel and C=1.0</span>
@@ -3619,7 +3651,7 @@ <h3><span class="section-number">37.63.15.1. </span>Run SVM with polynomial kern
 </div>
 </section>
 <section id="run-svm-with-polynomial-kernel-and-c-100-0">
-<h3><span class="section-number">37.63.15.2. </span>Run SVM with polynomial kernel and C=100.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.15.2. </span>Run SVM with polynomial kernel and C=100.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with polynomial kernel and C=100.0</span>
@@ -3649,9 +3681,9 @@ <h3><span class="section-number">37.63.15.2. </span>Run SVM with polynomial kern
 </section>
 </section>
 <section id="run-svm-with-sigmoid-kernel">
-<h2><span class="section-number">37.63.16. </span>Run SVM with sigmoid kernel<a class="headerlink" href="#run-svm-with-sigmoid-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.16. </span>Run SVM with sigmoid kernel<a class="headerlink" href="#run-svm-with-sigmoid-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-sigmoid-kernel-and-c-1-0">
-<h3><span class="section-number">37.63.16.1. </span>Run SVM with sigmoid kernel and C=1.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.16.1. </span>Run SVM with sigmoid kernel and C=1.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with sigmoid kernel and C=1.0</span>
@@ -3679,7 +3711,7 @@ <h3><span class="section-number">37.63.16.1. </span>Run SVM with sigmoid kernel
 </div>
 </section>
 <section id="run-svm-with-sigmoid-kernel-and-c-100-0">
-<h3><span class="section-number">37.63.16.2. </span>Run SVM with sigmoid kernel and C=100.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.16.2. </span>Run SVM with sigmoid kernel and C=100.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with sigmoid kernel and C=100.0</span>
@@ -3708,7 +3740,7 @@ <h3><span class="section-number">37.63.16.2. </span>Run SVM with sigmoid kernel
 <p>We can see that sigmoid kernel is also performing poorly just like with polynomial kernel.</p>
 </section>
 <section id="comments">
-<h3><span class="section-number">37.63.16.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.16.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>We get maximum accuracy with <code class="docutils literal notranslate"><span class="pre">rbf</span></code> and <code class="docutils literal notranslate"><span class="pre">linear</span></code> kernel with C=100.0. and the accuracy is 0.9832. Based on the above analysis we can conclude that our classification model accuracy is very good. Our model is doing a very good job in terms of predicting the class labels.</p>
 <p>But, this is not true. Here, we have an imbalanced dataset. The problem is that accuracy is an inadequate measure for quantifying predictive performance in the imbalanced dataset problem.</p>
 <p>So, we must explore alternative metrices that provide better guidance in selecting models. In particular, we would like to know the underlying distribution of values and the type of errors our classifer is making.</p>
@@ -3716,7 +3748,7 @@ <h3><span class="section-number">37.63.16.3. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="confusion-matrix">
-<h2><span class="section-number">37.63.17. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.17. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>A confusion matrix is a tool for summarizing the performance of a classification algorithm. A confusion matrix will give us a clear picture of classification model performance and the types of errors produced by the model. It gives us a summary of correct and incorrect predictions broken down by each category. The summary is represented in a tabular form.</p>
 <p>Four types of outcomes are possible while evaluating a classification model performance. These four outcomes are described below:-</p>
 <p><strong>True Positives (TP)</strong> – True Positives occur when we predict an observation belongs to a certain class and the observation actually belongs to that class.</p>
@@ -3789,9 +3821,9 @@ <h2><span class="section-number">37.63.17. </span>Confusion matrix<a class="head
 </div>
 </section>
 <section id="classification-metrices">
-<h2><span class="section-number">37.63.18. </span>Classification metrices<a class="headerlink" href="#classification-metrices" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.18. </span>Classification metrices<a class="headerlink" href="#classification-metrices" title="Permalink to this headline">#</a></h2>
 <section id="classification-report">
-<h3><span class="section-number">37.63.18.1. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.1. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h3>
 <p><strong>Classification report</strong> is another way to evaluate the classification model performance. It displays the  <strong>precision</strong>, <strong>recall</strong>, <strong>f1</strong> and <strong>support</strong> scores for the model. I have described these terms in later.</p>
 <p>We can print a classification report as follows:-</p>
 <div class="cell docutils container">
@@ -3817,7 +3849,7 @@ <h3><span class="section-number">37.63.18.1. </span>Classification Report<a clas
 </div>
 </section>
 <section id="classification-accuracy">
-<h3><span class="section-number">37.63.18.2. </span>Classification accuracy<a class="headerlink" href="#classification-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.2. </span>Classification accuracy<a class="headerlink" href="#classification-accuracy" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TP</span> <span class="o">=</span> <span class="n">cm</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
@@ -3846,7 +3878,7 @@ <h3><span class="section-number">37.63.18.2. </span>Classification accuracy<a cl
 </div>
 </section>
 <section id="classification-error">
-<h3><span class="section-number">37.63.18.3. </span>Classification error<a class="headerlink" href="#classification-error" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.3. </span>Classification error<a class="headerlink" href="#classification-error" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print classification error</span>
@@ -3865,7 +3897,7 @@ <h3><span class="section-number">37.63.18.3. </span>Classification error<a class
 </div>
 </section>
 <section id="precision">
-<h3><span class="section-number">37.63.18.4. </span>Precision<a class="headerlink" href="#precision" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.4. </span>Precision<a class="headerlink" href="#precision" title="Permalink to this headline">#</a></h3>
 <p><strong>Precision</strong> can be defined as the percentage of correctly predicted positive outcomes out of all the predicted positive outcomes. It can be given as the ratio of true positives (TP) to the sum of true and false positives (TP + FP).</p>
 <p>So, <strong>Precision</strong> identifies the proportion of correctly predicted positive outcome. It is more concerned with the positive class than the negative class.</p>
 <p>Mathematically, precision can be defined as the ratio of <code class="docutils literal notranslate"><span class="pre">TP</span> <span class="pre">to</span> <span class="pre">(TP</span> <span class="pre">+</span> <span class="pre">FP)</span></code>.</p>
@@ -3888,7 +3920,7 @@ <h3><span class="section-number">37.63.18.4. </span>Precision<a class="headerlin
 </div>
 </section>
 <section id="recall">
-<h3><span class="section-number">37.63.18.5. </span>Recall<a class="headerlink" href="#recall" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.5. </span>Recall<a class="headerlink" href="#recall" title="Permalink to this headline">#</a></h3>
 <p>Recall can be defined as the percentage of correctly predicted positive outcomes out of all the actual positive outcomes.
 It can be given as the ratio of true positives (TP) to the sum of true positives and false negatives (TP + FN). <strong>Recall</strong> is also called <strong>Sensitivity</strong>.</p>
 <p><strong>Recall</strong> identifies the proportion of correctly predicted actual positives.</p>
@@ -3909,7 +3941,7 @@ <h3><span class="section-number">37.63.18.5. </span>Recall<a class="headerlink"
 </div>
 </section>
 <section id="true-positive-rate">
-<h3><span class="section-number">37.63.18.6. </span>True Positive Rate<a class="headerlink" href="#true-positive-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.6. </span>True Positive Rate<a class="headerlink" href="#true-positive-rate" title="Permalink to this headline">#</a></h3>
 <p><strong>True Positive Rate</strong> is synonymous with <strong>Recall</strong>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3928,7 +3960,7 @@ <h3><span class="section-number">37.63.18.6. </span>True Positive Rate<a class="
 </div>
 </section>
 <section id="false-positive-rate">
-<h3><span class="section-number">37.63.18.7. </span>False Positive Rate<a class="headerlink" href="#false-positive-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.7. </span>False Positive Rate<a class="headerlink" href="#false-positive-rate" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">false_positive_rate</span> <span class="o">=</span> <span class="n">FP</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span>
@@ -3946,7 +3978,7 @@ <h3><span class="section-number">37.63.18.7. </span>False Positive Rate<a class=
 </div>
 </section>
 <section id="specificity">
-<h3><span class="section-number">37.63.18.8. </span>Specificity<a class="headerlink" href="#specificity" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.8. </span>Specificity<a class="headerlink" href="#specificity" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">specificity</span> <span class="o">=</span> <span class="n">TN</span> <span class="o">/</span> <span class="p">(</span><span class="n">TN</span> <span class="o">+</span> <span class="n">FP</span><span class="p">)</span>
@@ -3963,20 +3995,20 @@ <h3><span class="section-number">37.63.18.8. </span>Specificity<a class="headerl
 </div>
 </section>
 <section id="f1-score">
-<h3><span class="section-number">37.63.18.9. </span>f1-score<a class="headerlink" href="#f1-score" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.9. </span>f1-score<a class="headerlink" href="#f1-score" title="Permalink to this headline">#</a></h3>
 <p><strong>f1-score</strong> is the weighted harmonic mean of precision and recall. The best possible <strong>f1-score</strong> would be 1.0 and the worst
 would be 0.0.  <strong>f1-score</strong> is the harmonic mean of precision and recall. So, <strong>f1-score</strong> is always lower than accuracy measures as they embed precision and recall into their computation. The weighted average of <code class="docutils literal notranslate"><span class="pre">f1-score</span></code> should be used to
 compare classifier models, not global accuracy.</p>
 </section>
 <section id="support">
-<h3><span class="section-number">37.63.18.10. </span>Support<a class="headerlink" href="#support" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.10. </span>Support<a class="headerlink" href="#support" title="Permalink to this headline">#</a></h3>
 <p><strong>Support</strong> is the actual number of occurrences of the class in our dataset.</p>
 </section>
 </section>
 <section id="roc-auc">
-<h2><span class="section-number">37.63.19. </span>ROC - AUC<a class="headerlink" href="#roc-auc" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.19. </span>ROC - AUC<a class="headerlink" href="#roc-auc" title="Permalink to this headline">#</a></h2>
 <section id="roc-curve">
-<h3><span class="section-number">37.63.19.1. </span>ROC Curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.19.1. </span>ROC Curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">#</a></h3>
 <p>Another tool to measure the classification model performance visually is <strong>ROC Curve</strong>. ROC Curve stands for <strong>Receiver Operating Characteristic Curve</strong>. An <strong>ROC Curve</strong> is a plot which shows the performance of a classification model at various
 classification threshold levels.</p>
 <p>The <strong>ROC Curve</strong> plots the <strong>True Positive Rate (TPR)</strong> against the <strong>False Positive Rate (FPR)</strong> at various threshold levels.</p>
@@ -4016,7 +4048,7 @@ <h3><span class="section-number">37.63.19.1. </span>ROC Curve<a class="headerlin
 <p>ROC curve help us to choose a threshold level that balances sensitivity and specificity for a particular context.</p>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.63.19.2. </span>ROC  AUC<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.19.2. </span>ROC  AUC<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p><strong>ROC AUC</strong> stands for <strong>Receiver Operating Characteristic - Area Under Curve</strong>. It is a technique to compare classifier performance. In this technique, we measure the <code class="docutils literal notranslate"><span class="pre">area</span> <span class="pre">under</span> <span class="pre">the</span> <span class="pre">curve</span> <span class="pre">(AUC)</span></code>. A perfect classifier will have a ROC AUC equal to 1, whereas a purely random classifier will have a ROC AUC equal to 0.5.</p>
 <p>So, <strong>ROC AUC</strong> is the percentage of the ROC plot that is underneath the curve.</p>
 <div class="cell docutils container">
@@ -4039,7 +4071,7 @@ <h3><span class="section-number">37.63.19.2. </span>ROC  AUC<a class="headerlink
 </div>
 </section>
 <section id="id5">
-<h3><span class="section-number">37.63.19.3. </span>Comments<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.19.3. </span>Comments<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>ROC AUC is a single number summary of classifier performance. The higher the value, the better the classifier.</p></li>
 <li><p>ROC AUC of our model approaches towards 1. So, we can conclude that our classifier does a good job in classifying the pulsar star.</p></li>
@@ -4065,12 +4097,12 @@ <h3><span class="section-number">37.63.19.3. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split">
-<h2><span class="section-number">37.63.20. </span>Stratified k-fold Cross Validation with shuffle split<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.20. </span>Stratified k-fold Cross Validation with shuffle split<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split" title="Permalink to this headline">#</a></h2>
 <p>k-fold cross-validation is a very useful technique to evaluate model performance. But, it fails here because we have a imbalnced dataset. So, in the case of imbalanced dataset, I will use another technique to evaluate model performance. It is called <code class="docutils literal notranslate"><span class="pre">stratified</span> <span class="pre">k-fold</span> <span class="pre">cross-validation</span></code>.</p>
 <p>In <code class="docutils literal notranslate"><span class="pre">stratified</span> <span class="pre">k-fold</span> <span class="pre">cross-validation</span></code>, we split the data such that the proportions between classes are the same in each fold as they are in the whole dataset.</p>
 <p>Moreover, I will shuffle the data before splitting because shuffling yields much better result.</p>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-<h3><span class="section-number">37.63.20.1. </span>Stratified k-Fold Cross Validation with shuffle split with  linear kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.20.1. </span>Stratified k-Fold Cross Validation with shuffle split with  linear kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">KFold</span>
@@ -4119,7 +4151,7 @@ <h3><span class="section-number">37.63.20.1. </span>Stratified k-Fold Cross Vali
 </div>
 </section>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-<h3><span class="section-number">37.63.20.2. </span>Stratified k-Fold Cross Validation with shuffle split with rbf kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.20.2. </span>Stratified k-Fold Cross Validation with shuffle split with rbf kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rbf_svc</span><span class="o">=</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">)</span>
@@ -4162,13 +4194,13 @@ <h3><span class="section-number">37.63.20.2. </span>Stratified k-Fold Cross Vali
 </div>
 </section>
 <section id="id6">
-<h3><span class="section-number">37.63.20.3. </span>Comments<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.20.3. </span>Comments<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>I obtain higher average stratified k-fold cross-validation score of 0.9789 with linear kernel but the model accuracy is 0.9832.
 So, stratified cross-validation technique does not help to improve the model performance.</p>
 </section>
 </section>
 <section id="hyperparameter-optimization-using-gridsearch-cv">
-<h2><span class="section-number">37.63.21. </span>Hyperparameter Optimization using GridSearch CV<a class="headerlink" href="#hyperparameter-optimization-using-gridsearch-cv" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.21. </span>Hyperparameter Optimization using GridSearch CV<a class="headerlink" href="#hyperparameter-optimization-using-gridsearch-cv" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import GridSearchCV</span>
@@ -4234,7 +4266,7 @@ <h2><span class="section-number">37.63.21. </span>Hyperparameter Optimization us
 </div>
 </div>
 <section id="id7">
-<h3><span class="section-number">37.63.21.1. </span>Comments<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.21.1. </span>Comments<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Our original model test accuracy is 0.9832 while GridSearch CV score on test-set is 0.9835.</p></li>
 <li><p>So, GridSearch CV helps to identify the parameters that will improve the performance for this particular model.</p></li>
@@ -4245,7 +4277,7 @@ <h3><span class="section-number">37.63.21.1. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="results-and-conclusion">
-<h2><span class="section-number">37.63.22. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.22. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p>There are outliers in our dataset. So, as we increase the value of C to limit fewer outliers, the accuracy increased. This is true with different kinds of kernels.</p></li>
 <li><p>We get maximum accuracy with <code class="docutils literal notranslate"><span class="pre">rbf</span></code> and <code class="docutils literal notranslate"><span class="pre">linear</span></code> kernel with C=100.0 and the accuracy is 0.9832. So, we can conclude that our model is doing a very good job in terms of predicting the class labels. But, this is not true. Here, we have an imbalanced dataset. Accuracy is an inadequate measure for quantifying predictive performance in the imbalanced dataset problem. So, we must explore <code class="docutils literal notranslate"><span class="pre">confusion</span> <span class="pre">matrix</span></code> that provide better guidance in selecting models.</p></li>
@@ -4255,7 +4287,7 @@ <h2><span class="section-number">37.63.22. </span>Results and conclusion<a class
 </ol>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.63.23. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.23. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to PRASHANT BANERJEE for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/prashant111/svm-classifier-tutorial"> SVM Classifier Tutorial </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -4297,13 +4329,13 @@ <h2><span class="section-number">37.63.23. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.62. </span>Explore classification methods</p>
+            <p class="prev-next-title"><span class="section-number">40.62. </span>Explore classification methods</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="support_vector_machines_for_regression.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
+        <p class="prev-next-title"><span class="section-number">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
     </div>
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diff --git a/assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html b/assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html
index e33d88a894..db3da54b69 100644
--- a/assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html
+++ b/assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.65. Support Vector Machines (SVM) - Classification &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.65. Support Vector Machines (SVM) - Classification &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
-    <link rel="prev" title="37.64. Support Vector Machines (SVM) - Intro and SVM for Regression" href="support_vector_machines_for_regression.html" />
+    <link rel="next" title="40.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
+    <link rel="prev" title="40.64. Support Vector Machines (SVM) - Intro and SVM for Regression" href="support_vector_machines_for_regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
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+   40.65.7. 6 - Evaluating Support Vector Classifier
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-   37.65.8. 7 - Improving Support Vector Classifier
+   40.65.8. 7 - Improving Support Vector Classifier
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-   37.65.9. Acknowledgments
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 </ul>
@@ -1645,7 +1677,7 @@ <h2> Contents </h2>
                 
   <p><a name='0'></a></p>
 <section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-svm-classification">
-<h1><span class="section-number">37.65. </span>Support Vector Machines (SVM) - Classification<a class="headerlink" href="#support-vector-machines-svm-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.65. </span>Support Vector Machines (SVM) - Classification<a class="headerlink" href="#support-vector-machines-svm-classification" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines are the type of supervised learning algorithms used for regression, classification and detecting outliers. SVMs are remarkably one of the powerful models in classical machine learning suited for handling complex and high dimensional datasets.</p>
 <p>With SVM supporting different kernels (linear, polynomial, Radial Basis Function(rbf), and sigmoid), SVM can tackle different kinds of datasets, both linear and non linear.</p>
 <p>While the maths behind the SVMs are beyond the scope of this notebook, here is the idea behind SVMs:</p>
@@ -1707,7 +1739,7 @@ <h1><span class="section-number">37.65. </span>Support Vector Machines (SVM) - C
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-<h2><span class="section-number">37.65.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="#1">1 - Imports</a></p></li>
 <li><p><a class="reference external" href="#2">2 - Loading the data</a></p></li>
@@ -1720,7 +1752,7 @@ <h2><span class="section-number">37.65.1. </span>Contents<a class="headerlink" h
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-<h2><span class="section-number">37.65.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1736,7 +1768,7 @@ <h2><span class="section-number">37.65.2. </span>1 - Imports<a class="headerlink
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 </section>
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-<h2><span class="section-number">37.65.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>We will get Iris data from Sklearn datasets. Setting <code class="docutils literal notranslate"><span class="pre">as_frame</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code> will return data as a Pandas Dataframe.</p>
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-<h2><span class="section-number">37.65.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before exploring some insight in data, let’s split it into test and train set.</p>
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-<h2><span class="section-number">37.65.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>The features already have small values but let’s scale them to be between 0 and 1. SVM work well with scaled values. I will set up a pipeline to handle that.</p>
 <div class="cell docutils container">
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@@ -1989,7 +2021,7 @@ <h2><span class="section-number">37.65.5. </span>4 - Data Preprocessing<a class=
 <p><a name='5'></a></p>
 </section>
 <section id="training-support-vector-classifier">
-<h2><span class="section-number">37.65.6. </span>5 - Training Support Vector Classifier<a class="headerlink" href="#training-support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.6. </span>5 - Training Support Vector Classifier<a class="headerlink" href="#training-support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <p>We are going to train two classifiers: Linear SVC and SVC that we can use different kernels. SVM supports <code class="docutils literal notranslate"><span class="pre">linear</span></code>, <code class="docutils literal notranslate"><span class="pre">polynomial</span></code>, <code class="docutils literal notranslate"><span class="pre">sigmoid</span></code> and <code class="docutils literal notranslate"><span class="pre">rbf</span></code> kernels.</p>
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@@ -2017,7 +2049,7 @@ <h2><span class="section-number">37.65.6. </span>5 - Training Support Vector Cla
 <p><a name='6'></a></p>
 </section>
 <section id="evaluating-support-vector-classifier">
-<h2><span class="section-number">37.65.7. </span>6 - Evaluating Support Vector Classifier<a class="headerlink" href="#evaluating-support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.7. </span>6 - Evaluating Support Vector Classifier<a class="headerlink" href="#evaluating-support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <p>Let’s first check the accuracy on the training. For this step since we are trying to find model to improve further, we won’t touch test set yet.</p>
 <div class="cell docutils container">
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@@ -2093,7 +2125,7 @@ <h2><span class="section-number">37.65.7. </span>6 - Evaluating Support Vector C
 <p><a name='7'></a></p>
 </section>
 <section id="improving-support-vector-classifier">
-<h2><span class="section-number">37.65.8. </span>7 - Improving Support Vector Classifier<a class="headerlink" href="#improving-support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.8. </span>7 - Improving Support Vector Classifier<a class="headerlink" href="#improving-support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
@@ -3572,7 +3604,7 @@ <h2><span class="section-number">37.65.8. </span>7 - Improving Support Vector Cl
 <p>This is the end of the lab which was all about using Support Vector Machines for classification task. As you can see, SVM is a robust algorithm given how it supports different kernels. These kernels are what make it suitable for both linear and non linear problems. In real world, many datasets are not linear. So when you can’t get good results with linear models, try things like SVM with polynomial kernel.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.65.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3614,13 +3646,13 @@ <h2><span class="section-number">37.65.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
+            <p class="prev-next-title"><span class="section-number">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="decision_trees_for_regression.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.66. </span>Decision Trees - Intro and Regression</p>
+        <p class="prev-next-title"><span class="section-number">40.66. </span>Decision Trees - Intro and Regression</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
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-    <title>37.64. Support Vector Machines (SVM) - Intro and SVM for Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.64. Support Vector Machines (SVM) - Intro and SVM for Regression &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="prev" title="37.63. Kernel method assignment 1" href="kernel-method-assignment-1.html" />
+    <link rel="next" title="40.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
+    <link rel="prev" title="40.63. Kernel method assignment 1" href="kernel-method-assignment-1.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
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     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-  <label for="toctree-checkbox-10">
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-  <label for="toctree-checkbox-11">
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   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../project-plan-template.html">
-     37.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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-     37.7. Python programming basics
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    </li>
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-     37.8. Python programming advanced
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    <li class="toctree-l2">
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-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,47 +1558,47 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#contents">
-   37.64.1. Contents
+   40.64.1. Contents
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.64.2. 1 - Imports
+   40.64.2. 1 - Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.64.3. 2 - Loading the data
+   40.64.3. 2 - Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-analysis">
-   37.64.4. 3 - Exploratory Analysis
+   40.64.4. 3 - Exploratory Analysis
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   37.64.5. 4 - Data Preprocessing
+   40.64.5. 4 - Data Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-support-vector-regressor">
-   37.64.6. 5 - Training Support Vector Regressor
+   40.64.6. 5 - Training Support Vector Regressor
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-support-vector-regressor">
-   37.64.7. 6 - Evaluating Support Vector Regressor
+   40.64.7. 6 - Evaluating Support Vector Regressor
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#improving-support-vector-regressor">
-   37.64.8. 7 - Improving Support Vector Regressor
+   40.64.8. 7 - Improving Support Vector Regressor
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.64.9. Acknowledgments
+   40.64.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1590,47 +1622,47 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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-   37.64.1. Contents
+   40.64.1. Contents
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.64.2. 1 - Imports
+   40.64.2. 1 - Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.64.3. 2 - Loading the data
+   40.64.3. 2 - Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-analysis">
-   37.64.4. 3 - Exploratory Analysis
+   40.64.4. 3 - Exploratory Analysis
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   37.64.5. 4 - Data Preprocessing
+   40.64.5. 4 - Data Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-support-vector-regressor">
-   37.64.6. 5 - Training Support Vector Regressor
+   40.64.6. 5 - Training Support Vector Regressor
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-support-vector-regressor">
-   37.64.7. 6 - Evaluating Support Vector Regressor
+   40.64.7. 6 - Evaluating Support Vector Regressor
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#improving-support-vector-regressor">
-   37.64.8. 7 - Improving Support Vector Regressor
+   40.64.8. 7 - Improving Support Vector Regressor
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.64.9. Acknowledgments
+   40.64.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1645,7 +1677,7 @@ <h2> Contents </h2>
                 
   <p><a name='0'></a></p>
 <section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-svm-intro-and-svm-for-regression">
-<h1><span class="section-number">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression<a class="headerlink" href="#support-vector-machines-svm-intro-and-svm-for-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression<a class="headerlink" href="#support-vector-machines-svm-intro-and-svm-for-regression" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines are the type of supervised learning algorithms used for regression, classification and detecting outliers. SVMs are remarkably one of the powerful models in classical machine learning suited for handling complex and high dimensional datasets.</p>
 <p>With SVM supporting different kernels (linear, polynomial, Radial Basis Function(rbf), and sigmoid), SVM can tackle different kinds of datasets, both linear and non linear.</p>
 <p>While the maths behind the SVMs are beyond the scope of this notebook, here is the idea behind SVMs:</p>
@@ -1653,7 +1685,7 @@ <h1><span class="section-number">37.64. </span>Support Vector Machines (SVM) - I
 <p><img alt="SVM" src="../../../_images/1200px-Kernel_Machine.svg.png" /></p>
 <p>SMVs are widely used for classification. But to motivate that, let’s start with regression. For the purpose of examining how powerful this algorithm is, we will use the same dataset that we used in linear regression notebook, and hopefully the difference in performance will be notable.</p>
 <section id="contents">
-<h2><span class="section-number">37.64.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="#1">1 - Imports</a></p></li>
 <li><p><a class="reference external" href="#2">2 - Loading the data</a></p></li>
@@ -1666,7 +1698,7 @@ <h2><span class="section-number">37.64.1. </span>Contents<a class="headerlink" h
 <p><a name='1'></a></p>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.64.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1683,7 +1715,7 @@ <h2><span class="section-number">37.64.2. </span>1 - Imports<a class="headerlink
 <p><a name='2'></a></p>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.64.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cal_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../../assets/data/housing.csv&quot;</span><span class="p">)</span>
@@ -1806,7 +1838,7 @@ <h2><span class="section-number">37.64.3. </span>2 - Loading the data<a class="h
 </div>
 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">37.64.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>This is going to be a quick glance through the dataset. The full exploratory data analysis was done in the linear regression notebook[ADD LINK]. Before anything, let’s split the data into training and test set.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2057,7 +2089,7 @@ <h2><span class="section-number">37.64.4. </span>3 - Exploratory Analysis<a clas
 <p><a name='4'></a></p>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.64.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>To do:</p>
 <ul class="simple">
 <li><p>Handle missing values</p></li>
@@ -2149,7 +2181,7 @@ <h2><span class="section-number">37.64.5. </span>4 - Data Preprocessing<a class=
 <p><a name='5'></a></p>
 </section>
 <section id="training-support-vector-regressor">
-<h2><span class="section-number">37.64.6. </span>5 - Training Support Vector Regressor<a class="headerlink" href="#training-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.6. </span>5 - Training Support Vector Regressor<a class="headerlink" href="#training-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>In regression, instead of separating the classes with decision boundary  like in classification, SVR fits the training data points on the boundary margin but keep them off.</p>
 <p>We can implement it quite easily with Scikit-Learn.</p>
 <div class="cell docutils container">
@@ -2183,7 +2215,7 @@ <h2><span class="section-number">37.64.6. </span>5 - Training Support Vector Reg
 <p><a name='6'></a></p>
 </section>
 <section id="evaluating-support-vector-regressor">
-<h2><span class="section-number">37.64.7. </span>6 - Evaluating Support Vector Regressor<a class="headerlink" href="#evaluating-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.7. </span>6 - Evaluating Support Vector Regressor<a class="headerlink" href="#evaluating-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>Let us evaluate the two models we created on the training set before evaluating on the test set. This is a good practice. Since finding a good model can take many iterations of improvements, it’s not advised to touch the test set until the model is good enough. Otherwise it would fail to make predictions on the new data.</p>
 <p>As always, we evaluate regression models with mean squarred error, but the commonly used one is root mean squarred error.</p>
 <div class="cell docutils container">
@@ -2223,7 +2255,7 @@ <h2><span class="section-number">37.64.7. </span>6 - Evaluating Support Vector R
 <p><a name='7'></a></p>
 </section>
 <section id="improving-support-vector-regressor">
-<h2><span class="section-number">37.64.8. </span>7 - Improving Support Vector Regressor<a class="headerlink" href="#improving-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.8. </span>7 - Improving Support Vector Regressor<a class="headerlink" href="#improving-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>Let use Randomized Search to improve the SVR model. Few notes about the parameters:</p>
 <ul class="simple">
 <li><p><em>Gamma(y)</em>: This is a regularization hyperparameter. When gamma is small, the model can underfit. It is too high, model can overfit.</p></li>
@@ -2356,7 +2388,7 @@ <h2><span class="section-number">37.64.8. </span>7 - Improving Support Vector Re
 <p>This is the end of the notebook. It was a practical introduction to using Support Vector Machines for regression. In the next lab, we will take a further step, where we will do classification with SVM.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.64.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2398,13 +2430,13 @@ <h2><span class="section-number">37.64.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.63. </span>Kernel method assignment 1</p>
+            <p class="prev-next-title"><span class="section-number">40.63. </span>Kernel method assignment 1</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="support_vector_machines_for_classification.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.65. </span>Support Vector Machines (SVM) - Classification</p>
+        <p class="prev-next-title"><span class="section-number">40.65. </span>Support Vector Machines (SVM) - Classification</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html b/assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html
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+++ b/assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html
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-    <title>37.70. Dropout and Batch Normalization &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.70. Dropout and Batch Normalization &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.71. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
-    <link rel="prev" title="37.69. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
+    <link rel="next" title="40.71. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
+    <link rel="prev" title="40.69. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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+   17. Getting started with ensemble learning
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-   37.70.4. Example - Using Dropout and Batch Normalization
+   40.70.4. Example - Using Dropout and Batch Normalization
   </a>
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@@ -1632,14 +1664,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="dropout-and-batch-normalization">
-<h1><span class="section-number">37.70. </span>Dropout and Batch Normalization<a class="headerlink" href="#dropout-and-batch-normalization" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.70. </span>Dropout and Batch Normalization<a class="headerlink" href="#dropout-and-batch-normalization" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.70.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>There’s more to the world of deep learning than just dense layers. There are dozens of kinds of layers you might add to a model. (Try browsing through the Keras docs for a sample!) Some are like dense layers and define connections between neurons, and others can do preprocessing or transformations of other sorts.</p>
 <p>In this lesson, we’ll learn about a two kinds of special layers, not containing any neurons themselves, but that add some functionality that can sometimes benefit a model in various ways. Both are commonly used in modern architectures.</p>
 </section>
 <section id="dropout">
-<h2><span class="section-number">37.70.2. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.2. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
 <p>The first of these is the “dropout layer”, which can help correct overfitting.</p>
 <p>In the last lesson we talked about how overfitting is caused by the network learning spurious patterns in the training data. To recognize these spurious patterns a network will often rely on very a specific combinations of weight, a kind of “conspiracy” of weights. Being so specific, they tend to be fragile: remove one and the conspiracy falls apart.</p>
 <p>This is the idea behind <strong>dropout</strong>. To break up these conspiracies, we randomly drop out some fraction of a layer’s input units every step of training, making it much harder for the network to learn those spurious patterns in the training data. Instead, it has to search for broad, general patterns, whose weight patterns tend to be more robust.</p>
@@ -1647,7 +1679,7 @@ <h2><span class="section-number">37.70.2. </span>Dropout<a class="headerlink" hr
 <em>Here, 50% dropout has been added between the two hidden layers.</em></p>
 <p>You could also think about dropout as creating a kind of ensemble of networks. The predictions will no longer be made by one big network, but instead by a committee of smaller networks. Individuals in the committee tend to make different kinds of mistakes, but be right at the same time, making the committee as a whole better than any individual. (If you’re familiar with random forests as an ensemble of decision trees, it’s the same idea.)</p>
 <section id="adding-dropout">
-<h3><span class="section-number">37.70.2.1. </span>Adding Dropout<a class="headerlink" href="#adding-dropout" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.70.2.1. </span>Adding Dropout<a class="headerlink" href="#adding-dropout" title="Permalink to this headline">#</a></h3>
 <p>In Keras, the dropout rate argument <em>rate</em> defines what percentage of the input units to shut off. Put the <em>Dropout</em> layer just before the layer you want the dropout applied to:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
     <span class="c1"># ...</span>
@@ -1660,13 +1692,13 @@ <h3><span class="section-number">37.70.2.1. </span>Adding Dropout<a class="heade
 </section>
 </section>
 <section id="batch-normalization">
-<h2><span class="section-number">37.70.3. </span>Batch Normalization<a class="headerlink" href="#batch-normalization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.3. </span>Batch Normalization<a class="headerlink" href="#batch-normalization" title="Permalink to this headline">#</a></h2>
 <p>The next special layer we’ll look at performs “batch normalization” (or “batchnorm”), which can help correct training that is slow or unstable.</p>
 <p>With neural networks, it’s generally a good idea to put all of your data on a common scale, perhaps with something like scikit-learn’s <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> or <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html">MinMaxScaler</a>. The reason is that SGD will shift the network weights in proportion to how large an activation the data produces. Features that tend to produce activations of very different sizes can make for unstable training behavior.</p>
 <p>Now, if it’s good to normalize the data before it goes into the network, maybe also normalizing inside the network would be better! In fact, we have a special kind of layer that can do this, the <strong>batch normalization layer</strong>. A batch normalization layer looks at each batch as it comes in, first normalizing the batch with its own mean and standard deviation, and then also putting the data on a new scale with two trainable rescaling parameters. Batchnorm, in effect, performs a kind of coordinated rescaling of its inputs.</p>
 <p>Most often, batchnorm is added as an aid to the optimization process (though it can sometimes also help prediction performance). Models with batchnorm tend to need fewer epochs to complete training. Moreover, batchnorm can also fix various problems that can cause the training to get “stuck”. Consider adding batch normalization to your models, especially if you’re having trouble during training.</p>
 <section id="adding-batch-normalization">
-<h3><span class="section-number">37.70.3.1. </span>Adding Batch Normalization<a class="headerlink" href="#adding-batch-normalization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.70.3.1. </span>Adding Batch Normalization<a class="headerlink" href="#adding-batch-normalization" title="Permalink to this headline">#</a></h3>
 <p>It seems that batch normalization can be used at almost any point in a network. You can put it after a layer…</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
 <span class="n">layers</span><span class="o">.</span><span class="n">BatchNormalization</span><span class="p">(),</span>
@@ -1682,7 +1714,7 @@ <h3><span class="section-number">37.70.3.1. </span>Adding Batch Normalization<a
 </section>
 </section>
 <section id="example-using-dropout-and-batch-normalization">
-<h2><span class="section-number">37.70.4. </span>Example - Using Dropout and Batch Normalization<a class="headerlink" href="#example-using-dropout-and-batch-normalization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.4. </span>Example - Using Dropout and Batch Normalization<a class="headerlink" href="#example-using-dropout-and-batch-normalization" title="Permalink to this headline">#</a></h2>
 <p>Let’s continue developing the Red Wine model. Now we’ll increase the capacity even more, but add dropout to control overfitting and batch normalization to speed up optimization. This time, we’ll also leave off standardizing the data, to demonstrate how batch normalization can stabalize the training.</p>
 <div class="cell docutils container">
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@@ -1769,7 +1801,7 @@ <h2><span class="section-number">37.70.4. </span>Example - Using Dropout and Bat
 <p>You’ll typically get better performance if you standardize your data before using it for training. That we were able to use the raw data at all, however, shows how effective batch normalization can be on more difficult datasets.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.70.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ryanholbrook">Ryan Holbrook</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/ryanholbrook/dropout-and-batch-normalization">Learning Curve To Identify Overfit &amp; Underfit</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1811,13 +1843,13 @@ <h2><span class="section-number">37.70.5. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
+            <p class="prev-next-title"><span class="section-number">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
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-        <p class="prev-next-title"><span class="section-number">37.71. </span>Lasso and Ridge Regression</p>
+        <p class="prev-next-title"><span class="section-number">40.71. </span>Lasso and Ridge Regression</p>
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-    <link rel="prev" title="37.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
+    <link rel="next" title="40.72. Regularized Linear Models" href="regularized-linear-models.html" />
+    <link rel="prev" title="40.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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-   18. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.14. Try it in Excel
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+   40.71.8. Ridge Regression using scikit-learn
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
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+   40.71.9. Acknowledgments
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                             <ul class="visible nav section-nav flex-column">
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+   40.71.1. Common Regression class
   </a>
  </li>
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   <a class="reference internal nav-link" href="#regularization-classes">
-   37.71.2. Regularization classes
+   40.71.2. Regularization classes
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-creation">
-   37.71.3. Data creation
+   40.71.3. Data creation
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lasson-regression-from-scratch">
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+   40.71.4. Lasson Regression from scratch
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   <a class="reference internal nav-link" href="#lasso-regression-using-skicit-learn">
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   </a>
  </li>
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-   37.71.8. Ridge Regression using scikit-learn
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.71.9. Acknowledgments
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   </a>
  </li>
 </ul>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="lasso-and-ridge-regression">
-<h1><span class="section-number">37.71. </span>Lasso and Ridge Regression<a class="headerlink" href="#lasso-and-ridge-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.71. </span>Lasso and Ridge Regression<a class="headerlink" href="#lasso-and-ridge-regression" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
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 <section id="common-regression-class">
-<h2><span class="section-number">37.71.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
 <p>The most of things common in Lasson and Ridge Regression. <br/>
 The only different between two regression is, what regularization it is using. <br/></p>
 <ul class="simple">
@@ -1763,7 +1795,7 @@ <h2><span class="section-number">37.71.1. </span>Common Regression class<a class
 </div>
 </section>
 <section id="regularization-classes">
-<h2><span class="section-number">37.71.2. </span>Regularization classes<a class="headerlink" href="#regularization-classes" title="Permalink to this headline">#</a></h2>
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 <div class="cell docutils container">
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@@ -1799,7 +1831,7 @@ <h2><span class="section-number">37.71.2. </span>Regularization classes<a class=
 </div>
 </section>
 <section id="data-creation">
-<h2><span class="section-number">37.71.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the traning data.</span>
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 </div>
 </section>
 <section id="lasson-regression-from-scratch">
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+<h2><span class="section-number">40.71.4. </span>Lasson Regression from scratch<a class="headerlink" href="#lasson-regression-from-scratch" title="Permalink to this headline">#</a></h2>
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@@ -2059,7 +2091,7 @@ <h2><span class="section-number">37.71.4. </span>Lasson Regression from scratch<
 </div>
 </section>
 <section id="lasso-regression-using-skicit-learn">
-<h2><span class="section-number">37.71.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
@@ -2102,11 +2134,11 @@ <h2><span class="section-number">37.71.5. </span>Lasso Regression using skicit-l
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.71.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Our model (from scratch) also works great as compared to skiti-learn model. Both the models are giving 0.99…% r2_socre which is good.</p>
 </section>
 <section id="ridge-regression-from-scratch">
-<h2><span class="section-number">37.71.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
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 <div class="cell docutils container">
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@@ -2188,7 +2220,7 @@ <h2><span class="section-number">37.71.7. </span>Ridge Regression from scratch<a
 </div>
 </section>
 <section id="ridge-regression-using-scikit-learn">
-<h2><span class="section-number">37.71.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
@@ -2231,7 +2263,7 @@ <h2><span class="section-number">37.71.8. </span>Ridge Regression using scikit-l
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.71.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Pavithra Devi M for creating the Notebook <a class="reference external" href="https://www.kaggle.com/code/ninjaac/lasso-and-ridge-regression-from-scratch">Lasso and Ridge Regression from scratch</a>, lisensed under the <a class="reference external" href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2273,13 +2305,13 @@ <h2><span class="section-number">37.71.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.70. </span>Dropout and Batch Normalization</p>
+            <p class="prev-next-title"><span class="section-number">40.70. </span>Dropout and Batch Normalization</p>
         </div>
     </a>
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-        <p class="prev-next-title"><span class="section-number">37.72. </span>Regularized Linear Models</p>
+        <p class="prev-next-title"><span class="section-number">40.72. </span>Regularized Linear Models</p>
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diff --git a/assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html b/assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html
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-    <title>37.69. Learning Curve To Identify Overfit &amp; Underfit &#8212; Ocademy Open Machine Learning Book</title>
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-    <link rel="next" title="37.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
-    <link rel="prev" title="37.68. Model selection assignment 1" href="model-selection-assignment-1.html" />
+    <link rel="next" title="40.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
+    <link rel="prev" title="40.68. Model selection assignment 1" href="model-selection-assignment-1.html" />
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     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
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+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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-     11.4. Logistic regression
+     12.4. Logistic regression
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <label for="toctree-checkbox-9">
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+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.16. Build an machine learning web application
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     38.18. Kernel method
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-     38.19. Model Selection
+     41.19. Model Selection
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-     38.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
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@@ -1526,105 +1558,105 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.69.1. Imports
+   40.69.1. Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overfitting-and-underfitting-refresher">
-   37.69.2. Overfitting and Underfitting Refresher
+   40.69.2. Overfitting and Underfitting Refresher
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overfitting-aka-variance">
-     37.69.2.1. Overfitting (aka Variance):
+     40.69.2.1. Overfitting (aka Variance):
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasons-for-overfitting">
-     37.69.2.2. Reasons for overfitting
+     40.69.2.2. Reasons for overfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#underfitting-aka-bias">
-     37.69.2.3. Underfitting (aka Bias):
+     40.69.2.3. Underfitting (aka Bias):
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasons-for-underfitting">
-     37.69.2.4. Reasons for underfitting
+     40.69.2.4. Reasons for underfitting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modeling-iris-data-using-logistic-regression">
-   37.69.3. Modeling (Iris data) using Logistic Regression
+   40.69.3. Modeling (Iris data) using Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#identifying-and-handling-multicollinearity-using-vif">
-   37.69.4. Identifying and handling Multicollinearity using VIF
+   40.69.4. Identifying and handling Multicollinearity using VIF
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-learning-curve">
-   37.69.5. Introduction to Learning Curve
+   40.69.5. Introduction to Learning Curve
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   <a class="reference internal nav-link" href="#learning-curve-of-a-good-fit-model">
-   37.69.6. Learning Curve of a Good Fit Model
+   40.69.6. Learning Curve of a Good Fit Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpreting-the-training-loss">
-     37.69.6.1. Interpreting the training loss
+     40.69.6.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpreting-the-validation-loss">
-     37.69.6.2. Interpreting the validation loss
+     40.69.6.2. Interpreting the validation loss
     </a>
    </li>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#learning-curve-of-an-overfit-model">
-   37.69.7. Learning Curve of an Overfit Model
+   40.69.7. Learning Curve of an Overfit Model
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.69.7.1. Interpreting the training loss
+     40.69.7.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.69.7.2. Interpreting the validation loss
+     40.69.7.2. Interpreting the validation loss
     </a>
    </li>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#learning-curve-of-an-underfit-model">
-   37.69.8. Learning Curve of an Underfit Model
+   40.69.8. Learning Curve of an Underfit Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.69.8.1. Interpreting the training loss
+     40.69.8.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.69.8.2. Interpreting the validation loss
+     40.69.8.2. Interpreting the validation loss
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.69.9. Acknowledgments
+   40.69.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1648,105 +1680,105 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.69.1. Imports
+   40.69.1. Imports
   </a>
  </li>
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   <a class="reference internal nav-link" href="#overfitting-and-underfitting-refresher">
-   37.69.2. Overfitting and Underfitting Refresher
+   40.69.2. Overfitting and Underfitting Refresher
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overfitting-aka-variance">
-     37.69.2.1. Overfitting (aka Variance):
+     40.69.2.1. Overfitting (aka Variance):
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasons-for-overfitting">
-     37.69.2.2. Reasons for overfitting
+     40.69.2.2. Reasons for overfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#underfitting-aka-bias">
-     37.69.2.3. Underfitting (aka Bias):
+     40.69.2.3. Underfitting (aka Bias):
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasons-for-underfitting">
-     37.69.2.4. Reasons for underfitting
+     40.69.2.4. Reasons for underfitting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modeling-iris-data-using-logistic-regression">
-   37.69.3. Modeling (Iris data) using Logistic Regression
+   40.69.3. Modeling (Iris data) using Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#identifying-and-handling-multicollinearity-using-vif">
-   37.69.4. Identifying and handling Multicollinearity using VIF
+   40.69.4. Identifying and handling Multicollinearity using VIF
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-learning-curve">
-   37.69.5. Introduction to Learning Curve
+   40.69.5. Introduction to Learning Curve
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#learning-curve-of-a-good-fit-model">
-   37.69.6. Learning Curve of a Good Fit Model
+   40.69.6. Learning Curve of a Good Fit Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpreting-the-training-loss">
-     37.69.6.1. Interpreting the training loss
+     40.69.6.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpreting-the-validation-loss">
-     37.69.6.2. Interpreting the validation loss
+     40.69.6.2. Interpreting the validation loss
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#learning-curve-of-an-overfit-model">
-   37.69.7. Learning Curve of an Overfit Model
+   40.69.7. Learning Curve of an Overfit Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.69.7.1. Interpreting the training loss
+     40.69.7.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.69.7.2. Interpreting the validation loss
+     40.69.7.2. Interpreting the validation loss
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#learning-curve-of-an-underfit-model">
-   37.69.8. Learning Curve of an Underfit Model
+   40.69.8. Learning Curve of an Underfit Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.69.8.1. Interpreting the training loss
+     40.69.8.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.69.8.2. Interpreting the validation loss
+     40.69.8.2. Interpreting the validation loss
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.69.9. Acknowledgments
+   40.69.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1760,9 +1792,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="learning-curve-to-identify-overfit-underfit">
-<h1><span class="section-number">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit<a class="headerlink" href="#learning-curve-to-identify-overfit-underfit" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit<a class="headerlink" href="#learning-curve-to-identify-overfit-underfit" title="Permalink to this headline">#</a></h1>
 <section id="imports">
-<h2><span class="section-number">37.69.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1782,24 +1814,24 @@ <h2><span class="section-number">37.69.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="overfitting-and-underfitting-refresher">
-<h2><span class="section-number">37.69.2. </span>Overfitting and Underfitting Refresher<a class="headerlink" href="#overfitting-and-underfitting-refresher" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.2. </span>Overfitting and Underfitting Refresher<a class="headerlink" href="#overfitting-and-underfitting-refresher" title="Permalink to this headline">#</a></h2>
 <section id="overfitting-aka-variance">
-<h3><span class="section-number">37.69.2.1. </span>Overfitting (aka Variance):<a class="headerlink" href="#overfitting-aka-variance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.1. </span>Overfitting (aka Variance):<a class="headerlink" href="#overfitting-aka-variance" title="Permalink to this headline">#</a></h3>
 <p>A model is said to be overfit if the model is overtrained on the data such that it even learns the noise from the data. In the figure below, the third image shows overfitting where the model has learnt each and every example so perfectly that it misclassifies an unseen/new example. For a model that’s overfit we have a perfect/close to perfect training set score while a poor test/validation score.</p>
 </section>
 <section id="reasons-for-overfitting">
-<h3><span class="section-number">37.69.2.2. </span>Reasons for overfitting<a class="headerlink" href="#reasons-for-overfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.2. </span>Reasons for overfitting<a class="headerlink" href="#reasons-for-overfitting" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Using a complex model for a simple problem which picks up the noise from the data. Example: Using a neural network on Iris dataset</p></li>
 <li><p>Small datasets, as the training set may not be a right representation of the universe</p></li>
 </ol>
 </section>
 <section id="underfitting-aka-bias">
-<h3><span class="section-number">37.69.2.3. </span>Underfitting (aka Bias):<a class="headerlink" href="#underfitting-aka-bias" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.3. </span>Underfitting (aka Bias):<a class="headerlink" href="#underfitting-aka-bias" title="Permalink to this headline">#</a></h3>
 <p>A model is said to be underfit if the model is unable to learn the patterns in the data properly. In the figure below, the first image shows overfitting where the model hasn’t fully learnt each and every example. In such cases we see a low score on both the training set and test/validation set</p>
 </section>
 <section id="reasons-for-underfitting">
-<h3><span class="section-number">37.69.2.4. </span>Reasons for underfitting<a class="headerlink" href="#reasons-for-underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.4. </span>Reasons for underfitting<a class="headerlink" href="#reasons-for-underfitting" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Using a simple model for a complex problem which doesn’t pick up all the patterns from the data. Example: Using a logistic regression for image classification</p></li>
 <li><p>The underlying data has no inherent pattern. Example, trying to predict a student’s marks with his father’s weight.</p></li>
@@ -1807,7 +1839,7 @@ <h3><span class="section-number">37.69.2.4. </span>Reasons for underfitting<a cl
 </section>
 </section>
 <section id="modeling-iris-data-using-logistic-regression">
-<h2><span class="section-number">37.69.3. </span>Modeling (Iris data) using Logistic Regression<a class="headerlink" href="#modeling-iris-data-using-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.3. </span>Modeling (Iris data) using Logistic Regression<a class="headerlink" href="#modeling-iris-data-using-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>In this section we’ll fit a LogisticRegressor to the Iris dataset and identify underfitting, overfitting and good fit.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2420,7 +2452,7 @@ <h2><span class="section-number">37.69.3. </span>Modeling (Iris data) using Logi
 </div>
 </section>
 <section id="identifying-and-handling-multicollinearity-using-vif">
-<h2><span class="section-number">37.69.4. </span>Identifying and handling Multicollinearity using VIF<a class="headerlink" href="#identifying-and-handling-multicollinearity-using-vif" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.4. </span>Identifying and handling Multicollinearity using VIF<a class="headerlink" href="#identifying-and-handling-multicollinearity-using-vif" title="Permalink to this headline">#</a></h2>
 <p>The correlation matrix below shows high multicollinearity among input features. VIF &gt; 5 or 10 implies multicollinearity.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2536,11 +2568,11 @@ <h2><span class="section-number">37.69.4. </span>Identifying and handling Multic
 </div>
 </section>
 <section id="introduction-to-learning-curve">
-<h2><span class="section-number">37.69.5. </span>Introduction to Learning Curve<a class="headerlink" href="#introduction-to-learning-curve" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.5. </span>Introduction to Learning Curve<a class="headerlink" href="#introduction-to-learning-curve" title="Permalink to this headline">#</a></h2>
 <p>Learning curve is a plot that plots the training and validation loss for a sample of training examples by incrementally increasing them. Learning curves helps us in identifying if adding additional training examples could improve the validation score or not. If a model is overfit then adding additional training examples might improve the model performance on unseen data. Similarly, if a model is underfit then adding training examples doesn’t help</p>
 </section>
 <section id="learning-curve-of-a-good-fit-model">
-<h2><span class="section-number">37.69.6. </span>Learning Curve of a Good Fit Model<a class="headerlink" href="#learning-curve-of-a-good-fit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.6. </span>Learning Curve of a Good Fit Model<a class="headerlink" href="#learning-curve-of-a-good-fit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get a good fit model. We’ll set the inverse regularization variable/parameter ‘c’ to 1 (i.e. we are not performing any regularization)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2578,11 +2610,11 @@ <h2><span class="section-number">37.69.6. </span>Learning Curve of a Good Fit Mo
 </div>
 <p>The cross validation accuracy and training accuracy calculated above are close to each other</p>
 <section id="interpreting-the-training-loss">
-<h3><span class="section-number">37.69.6.1. </span>Interpreting the training loss<a class="headerlink" href="#interpreting-the-training-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.6.1. </span>Interpreting the training loss<a class="headerlink" href="#interpreting-the-training-loss" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of a good fit model has a moderately high training loss at the beginning which gradually lowers upon adding training examples and flattens gradually indicating addition of more training examples doesn’t improve the model performance on training data</p>
 </section>
 <section id="interpreting-the-validation-loss">
-<h3><span class="section-number">37.69.6.2. </span>Interpreting the validation loss<a class="headerlink" href="#interpreting-the-validation-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.6.2. </span>Interpreting the validation loss<a class="headerlink" href="#interpreting-the-validation-loss" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of a good fit model has a high validation loss at the beginning which gradually lowers upon adding training examples and flattens gradually indicating addition of more training examples doesn’t improve the model performance on unseen data</p>
 <p>We can also see that upon adding a reasonable number of training examples, both the training and validation loss are close to each other</p>
 <p><strong>Typical Features</strong></p>
@@ -2593,7 +2625,7 @@ <h3><span class="section-number">37.69.6.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="learning-curve-of-an-overfit-model">
-<h2><span class="section-number">37.69.7. </span>Learning Curve of an Overfit Model<a class="headerlink" href="#learning-curve-of-an-overfit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.7. </span>Learning Curve of an Overfit Model<a class="headerlink" href="#learning-curve-of-an-overfit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get an overfit model. We’ll set the inverse regularization variable/parameter ‘c’ to 10000 (high value of ‘c’ causes overfitting)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2631,11 +2663,11 @@ <h2><span class="section-number">37.69.7. </span>Learning Curve of an Overfit Mo
 </div>
 <p>The standard deviation in cross validation accuracy is high compared to underfit and good fit model. Training accuracy is higher than cross validation accuracy, typical to an overfit model, but not too high to detect overfitting. But Overfitting can be detected from the learning curve.</p>
 <section id="id1">
-<h3><span class="section-number">37.69.7.1. </span>Interpreting the training loss<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.7.1. </span>Interpreting the training loss<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an overfit model has a very low training loss at the beginning which gradually increases very slightly upon adding training examples and doesn’t flatten.</p>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.69.7.2. </span>Interpreting the validation loss<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.7.2. </span>Interpreting the validation loss<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an overfit model has a high validation loss at the beginning which gradually lowers upon adding training examples and doesn’t flatten, indicating addition of more training examples can improve the model performance on unseen data</p>
 <p>We can also see that the training and validation losses are far away from each other, which may come close to each other upon adding additional training data</p>
 <p><strong>Typical Features</strong></p>
@@ -2647,7 +2679,7 @@ <h3><span class="section-number">37.69.7.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="learning-curve-of-an-underfit-model">
-<h2><span class="section-number">37.69.8. </span>Learning Curve of an Underfit Model<a class="headerlink" href="#learning-curve-of-an-underfit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.8. </span>Learning Curve of an Underfit Model<a class="headerlink" href="#learning-curve-of-an-underfit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get an underfit model. We’ll set the inverse regularization variable/parameter ‘c’ to 1/10000 (low value of ‘c’ causes underfitting)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2685,11 +2717,11 @@ <h2><span class="section-number">37.69.8. </span>Learning Curve of an Underfit M
 </div>
 <p>The standard deviation in cross validation accuracy is low compared to overfit and good fit model. Underfitting can be detected from the learning curve.</p>
 <section id="id3">
-<h3><span class="section-number">37.69.8.1. </span>Interpreting the training loss<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.8.1. </span>Interpreting the training loss<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an underfit model has a low training loss at the beginning which gradually increases upon adding training examples and suddenly falls to the minimum at the end.</p>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.69.8.2. </span>Interpreting the validation loss<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.8.2. </span>Interpreting the validation loss<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an underfit model has a high validation loss at the beginning which gradually lowers upon adding training examples and suddenly falls to the minimum at the end, indicating addition of more training examples can’t improve the model performance on unseen data</p>
 <p>We can also see that the training and validation losses are very close to each other at the end. Adding additional training examples doesn’t improve the performance on unseen data.</p>
 <p><strong>Typical Features</strong></p>
@@ -2722,7 +2754,7 @@ <h3><span class="section-number">37.69.8.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.69.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ksvmuralidhar">KSV MURALIDHAR</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/ksvmuralidhar/learning-curve-to-identify-overfit-underfit/notebook">Learning Curve To Identify Overfit &amp; Underfit</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2764,13 +2796,13 @@ <h2><span class="section-number">37.69.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.68. </span>Model selection assignment 1</p>
+            <p class="prev-next-title"><span class="section-number">40.68. </span>Model selection assignment 1</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="dropout-and-batch-normalization.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.70. </span>Dropout and Batch Normalization</p>
+        <p class="prev-next-title"><span class="section-number">40.70. </span>Dropout and Batch Normalization</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-advanced/model-selection/model-selection-assignment-1.html b/assignments/ml-advanced/model-selection/model-selection-assignment-1.html
index 46ac057d38..602b57eb04 100644
--- a/assignments/ml-advanced/model-selection/model-selection-assignment-1.html
+++ b/assignments/ml-advanced/model-selection/model-selection-assignment-1.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.68. Model selection assignment 1 &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.68. Model selection assignment 1 &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.69. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
-    <link rel="prev" title="37.67. Decision Trees - Classification" href="../kernel-method/decision_trees_for_classification.html" />
+    <link rel="next" title="40.69. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
+    <link rel="prev" title="40.67. Decision Trees - Classification" href="../kernel-method/decision_trees_for_classification.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
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   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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+   40.68.2. Regularization classes
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  </li>
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   <a class="reference internal nav-link" href="#data-creation">
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   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.68.9. Acknowledgments
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="model-selection-assignment-1">
-<h1><span class="section-number">37.68. </span>Model selection assignment 1<a class="headerlink" href="#model-selection-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.68. </span>Model selection assignment 1<a class="headerlink" href="#model-selection-assignment-1" title="Permalink to this headline">#</a></h1>
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 <section id="common-regression-class">
-<h2><span class="section-number">37.68.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
 <p>The most of things common in Lasson and Ridge Regression. <br/>
 The only different between two regression is, what regularization it is using. <br/></p>
 <ul class="simple">
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-<h2><span class="section-number">37.68.2. </span>Regularization classes<a class="headerlink" href="#regularization-classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.2. </span>Regularization classes<a class="headerlink" href="#regularization-classes" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 </div>
 </section>
 <section id="data-creation">
-<h2><span class="section-number">37.68.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 </div>
 </section>
 <section id="lasson-regression-from-scratch">
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+<h2><span class="section-number">40.68.4. </span>Lasson Regression from scratch<a class="headerlink" href="#lasson-regression-from-scratch" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LassoRegression</span><span class="p">(</span><span class="n">Regression</span><span class="p">):</span>
@@ -2059,7 +2091,7 @@ <h2><span class="section-number">37.68.4. </span>Lasson Regression from scratch<
 </div>
 </section>
 <section id="lasso-regression-using-skicit-learn">
-<h2><span class="section-number">37.68.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
@@ -2102,11 +2134,11 @@ <h2><span class="section-number">37.68.5. </span>Lasso Regression using skicit-l
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.68.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Our model (from scratch) also works great as compared to skiti-learn model. Both the models are giving 0.99…% r2_socre which is good.</p>
 </section>
 <section id="ridge-regression-from-scratch">
-<h2><span class="section-number">37.68.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
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@@ -2188,7 +2220,7 @@ <h2><span class="section-number">37.68.7. </span>Ridge Regression from scratch<a
 </div>
 </section>
 <section id="ridge-regression-using-scikit-learn">
-<h2><span class="section-number">37.68.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
@@ -2231,7 +2263,7 @@ <h2><span class="section-number">37.68.8. </span>Ridge Regression using scikit-l
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.68.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.68.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Pavithra Devi M for creating the Notebook <a class="reference external" href="https://www.kaggle.com/code/ninjaac/lasso-and-ridge-regression-from-scratch">Lasso and Ridge Regression from scratch</a>, lisensed under the <a class="reference external" href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2273,13 +2305,13 @@ <h2><span class="section-number">37.68.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.67. </span>Decision Trees - Classification</p>
+            <p class="prev-next-title"><span class="section-number">40.67. </span>Decision Trees - Classification</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="learning-curve-to-identify-overfit-underfit.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
+        <p class="prev-next-title"><span class="section-number">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
     </div>
     <i class="fas fa-angle-right"></i>
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.73. Random forests intro and regression" href="../ensemble-learning/random-forests-intro-and-regression.html" />
-    <link rel="prev" title="37.71. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
+    <link rel="next" title="40.73. Random forests intro and regression" href="../ensemble-learning/random-forests-intro-and-regression.html" />
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    10. Machine Learning overview
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="regularized-linear-models">
-<h1><span class="section-number">37.72. </span>Regularized Linear Models<a class="headerlink" href="#regularized-linear-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.72. </span>Regularized Linear Models<a class="headerlink" href="#regularized-linear-models" title="Permalink to this headline">#</a></h1>
 <section id="imports">
-<h2><span class="section-number">37.72.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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+<h2><span class="section-number">40.72.2. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>We’re not going to do anything fancy here:</p>
 <ul class="simple">
 <li><p>First I’ll transform the skewed numeric features by taking log(feature + 1) - this will make the features more normal</p></li>
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 <p>Now we are going to use regularized linear regression models from the scikit learn module. I’m going to try both l_1(Lasso) and l_2(Ridge) regularization. I’ll also define a function that returns the cross-validation rmse error so we can evaluate our models and pick the best tuning par</p>
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 <p>The residual plot looks pretty <a class="reference external" href="http://good.To">good.To</a> wrap it up let’s predict on the test set and submit on the leaderboard:</p>
 </section>
 <section id="adding-an-xgboost-model">
-<h2><span class="section-number">37.72.4. </span>Adding an xgboost model:<a class="headerlink" href="#adding-an-xgboost-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.72.4. </span>Adding an xgboost model:<a class="headerlink" href="#adding-an-xgboost-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s add an xgboost model to our linear model to see if we can improve our score:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2186,7 +2218,7 @@ <h2><span class="section-number">37.72.4. </span>Adding an xgboost model:<a clas
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.72.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.72.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Alexandru Papiu for creating the open-source project <a class="reference external" href="https://www.kaggle.com/code/apapiu/regularized-linear-models">Regularized Linear Models</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2228,13 +2260,13 @@ <h2><span class="section-number">37.72.5. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.71. </span>Lasso and Ridge Regression</p>
+            <p class="prev-next-title"><span class="section-number">40.71. </span>Lasso and Ridge Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ensemble-learning/random-forests-intro-and-regression.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.73. </span>Random forests intro and regression</p>
+        <p class="prev-next-title"><span class="section-number">40.73. </span>Random forests intro and regression</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-fundamentals/build-classification-model.html b/assignments/ml-fundamentals/build-classification-model.html
index 47babe0bb6..59ec1edb11 100644
--- a/assignments/ml-fundamentals/build-classification-model.html
+++ b/assignments/ml-fundamentals/build-classification-model.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.88. Build Classification Model &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.88. Build Classification Model &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.89. Parameter play" href="parameter-play.html" />
-    <link rel="prev" title="37.87. Build classification models" href="build-classification-models.html" />
+    <link rel="next" title="40.89. Parameter play" href="parameter-play.html" />
+    <link rel="prev" title="40.87. Build classification models" href="build-classification-models.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
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  </li>
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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    </li>
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
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-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
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-   24. Autoencoder
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-   25. Long-short term memory
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  </li>
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-   27. Deep Q-learning
+   30. Deep Q-learning
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-   29. Image segmentation
+   32. Image segmentation
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-   30. Diffusion Model
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  </li>
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
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-   34. Data engineering
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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-   36. Model deployment
+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
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+   40. Self-paced assignments
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
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    <li class="toctree-l2">
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-     37.27. Analyzing data
+     40.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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-     37.35. Tell a story
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     </a>
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     </a>
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-<h1><span class="section-number">37.88. </span>Build Classification Model<a class="headerlink" href="#build-classification-model" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.88. </span>Build Classification Model<a class="headerlink" href="#build-classification-model" title="Permalink to this headline">#</a></h1>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.88.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.88.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1633,13 +1665,13 @@ <h2><span class="section-number">37.88.1. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.87. </span>Build classification models</p>
+            <p class="prev-next-title"><span class="section-number">40.87. </span>Build classification models</p>
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-        <p class="prev-next-title"><span class="section-number">37.89. </span>Parameter play</p>
+        <p class="prev-next-title"><span class="section-number">40.89. </span>Parameter play</p>
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diff --git a/assignments/ml-fundamentals/build-classification-models.html b/assignments/ml-fundamentals/build-classification-models.html
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+    <title>40.87. Build classification models &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="prev" title="37.86. Study the solvers" href="study-the-solvers.html" />
+    <link rel="next" title="40.88. Build Classification Model" href="build-classification-model.html" />
+    <link rel="prev" title="40.86. Study the solvers" href="study-the-solvers.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
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-   11. Regression models for Machine Learning
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-     11.1. Tools of the trade
+     12.1. Tools of the trade
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+     12.2. Managing data
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-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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+     12.4. Logistic regression
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+   13. Build a web app to use a Machine Learning model
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-     13.2. More classifiers
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-     13.3. Yet other classifiers
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
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+ </li>
+ <li class="toctree-l1 has-children">
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+  <ul>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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-     16.2. Gradient boosting example
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-     16.3. XGBoost
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-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.14. Try it in Excel
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-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="ml-logistic-regression-2.html">
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     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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     <a class="reference internal" href="managing-data.html">
-     37.54. Managing data
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-     37.58. Linear and polynomial regression
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-     37.61. Delicious asian and indian cuisines
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.101. Intro to Autoencoders
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-     37.103. Fun with Variational Autoencoders
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     37.87.3.4. More Data Exploration
+     40.87.3.4. More Data Exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     37.87.3.5. Exploring Categorical Features
+     40.87.3.5. Exploring Categorical Features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   37.87.4. 4. Data Preprocessing
+   40.87.4. 4. Data Preprocessing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.87.4.1. Feature Scaling
+     40.87.4.1. Feature Scaling
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#creating-and-training-a-logistic-regression-model">
-   37.87.5. 5. Creating and Training a Logistic Regression Model
+   40.87.5. 5. Creating and Training a Logistic Regression Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#trying-other-models">
-     37.87.5.1. Trying other models
+     40.87.5.1. Trying other models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   37.87.6. 6.  Model Evaluation
+   40.87.6. 6.  Model Evaluation
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-performance-metrics">
-     37.87.6.1. Classification Performance Metrics
+     40.87.6.1. Classification Performance Metrics
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.87.7. Acknowledgments
+   40.87.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1628,85 +1660,85 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#problem-formulation">
-   37.87.1. 1. Problem Formulation
+   40.87.1. 1. Problem Formulation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#finding-the-data">
-   37.87.2. 2. Finding the Data
+   40.87.2. 2. Finding the Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploring-insights-in-data-or-eda">
-   37.87.3. 3. Exploring Insights in Data or EDA
+   40.87.3. 3. Exploring Insights in Data or EDA
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-summary-statistics">
-     37.87.3.1. Checking summary statistics
+     40.87.3.1. Checking summary statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     37.87.3.2. Checking missing values
+     40.87.3.2. Checking missing values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-correlation-between-features">
-     37.87.3.3. Checking Correlation Between Features
+     40.87.3.3. Checking Correlation Between Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     37.87.3.4. More Data Exploration
+     40.87.3.4. More Data Exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     37.87.3.5. Exploring Categorical Features
+     40.87.3.5. Exploring Categorical Features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   37.87.4. 4. Data Preprocessing
+   40.87.4. 4. Data Preprocessing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.87.4.1. Feature Scaling
+     40.87.4.1. Feature Scaling
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#creating-and-training-a-logistic-regression-model">
-   37.87.5. 5. Creating and Training a Logistic Regression Model
+   40.87.5. 5. Creating and Training a Logistic Regression Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#trying-other-models">
-     37.87.5.1. Trying other models
+     40.87.5.1. Trying other models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   37.87.6. 6.  Model Evaluation
+   40.87.6. 6.  Model Evaluation
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-performance-metrics">
-     37.87.6.1. Classification Performance Metrics
+     40.87.6.1. Classification Performance Metrics
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.87.7. Acknowledgments
+   40.87.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1720,19 +1752,19 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="build-classification-models">
-<h1><span class="section-number">37.87. </span>Build classification models<a class="headerlink" href="#build-classification-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.87. </span>Build classification models<a class="headerlink" href="#build-classification-models" title="Permalink to this headline">#</a></h1>
 <p><a name='0'></a></p>
 <p>In this lab, we will learn about classification where the task is to predict the class or category. Both regression and classification are the main two types of supervised learning.</p>
 <p><a name='1'></a></p>
 <section id="problem-formulation">
-<h2><span class="section-number">37.87.1. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.1. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
 <p>Let’s say you have an idea of a revolutionary mobile phone and you want to establish a start up, but you know little about the price of the mobile phones. You are interested in learning that!</p>
 <p>Fortunately, there is this mobile dataset <a class="reference external" href="https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv">on Kaggle</a> that you can use to learn about the price ranges of mobiles based on their features such as wifi &amp; bluetooth supports etc…</p>
 <p>So, to make it simple, you have a dataset containing the features of mobiles and the problem is to predict the price range, not the exact price.</p>
 <p><a name='2'></a></p>
 </section>
 <section id="finding-the-data">
-<h2><span class="section-number">37.87.2. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.2. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data that we are going to use is found on Kaggle.</p>
 <p>Here are the details of the features. It is 21 features. The target feature is <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">range</span></code> and it has four price ranges: <code class="docutils literal notranslate"><span class="pre">0(low</span> <span class="pre">cost),</span> <span class="pre">1(medium</span> <span class="pre">cost),</span> <span class="pre">2(high</span> <span class="pre">cost)</span> <span class="pre">and</span> <span class="pre">3(very</span> <span class="pre">high</span> <span class="pre">cost).</span></code></p>
 <ul class="simple">
@@ -1849,7 +1881,7 @@ <h2><span class="section-number">37.87.2. </span>2. Finding the Data<a class="he
 <p><a name='3'></a></p>
 </section>
 <section id="exploring-insights-in-data-or-eda">
-<h2><span class="section-number">37.87.3. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.3. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
 <p>In this part, we are going to learn more about the dataset. Let’s start with the summary statistics, but before that, I will copy the training data in order to get it easily when we mess up down the road.</p>
 <div class="cell docutils container">
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@@ -1859,7 +1891,7 @@ <h2><span class="section-number">37.87.3. </span>3. Exploring Insights in Data o
 </div>
 </div>
 <section id="checking-summary-statistics">
-<h3><span class="section-number">37.87.3.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -1869,7 +1901,7 @@ <h3><span class="section-number">37.87.3.1. </span>Checking summary statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">37.87.3.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -1880,7 +1912,7 @@ <h3><span class="section-number">37.87.3.2. </span>Checking missing values<a cla
 <p>We are lucky to not have missing values. If we had some, we would have to fill them with some strategies such as mean, remove them compleletly or leave them as they are. None of those 3 options is always the right choice for imputing missing values in all kinds of problems. it depends on the problem and the size of your dataset. Take an example, by removing a feature, you’re loosing data. And by filling the values, you’re adding noise or so. There is a trade off when it comes to imputing the missing values.</p>
 </section>
 <section id="checking-correlation-between-features">
-<h3><span class="section-number">37.87.3.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">mobile_train</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -1909,7 +1941,7 @@ <h3><span class="section-number">37.87.3.3. </span>Checking Correlation Between
 <p>Let’s take that insights from words to visualization.</p>
 </section>
 <section id="more-data-exploration">
-<h3><span class="section-number">37.87.3.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Again, let’s see what we have in the price ranges.</p>
 <div class="cell docutils container">
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@@ -2027,7 +2059,7 @@ <h3><span class="section-number">37.87.3.4. </span>More Data Exploration<a class
 </div>
 </section>
 <section id="exploring-categorical-features">
-<h3><span class="section-number">37.87.3.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Even if all features have numerical values, but there some of them which are categorical. Let’s inspect them!! It might be that they are already encoded!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2124,7 +2156,7 @@ <h3><span class="section-number">37.87.3.5. </span>Exploring Categorical Feature
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.87.4. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.4. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>In this part, we will prepare the data to be in proper format for ML models. We will do things like feature scaling. We would also had to encode categorical features but they are already encoded. Before we proceed with feature scaling, let’s take training labels from training data.</p>
 <div class="cell docutils container">
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@@ -2135,7 +2167,7 @@ <h2><span class="section-number">37.87.4. </span>4. Data Preprocessing<a class="
 </div>
 </div>
 <section id="feature-scaling">
-<h3><span class="section-number">37.87.4.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.4.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
 <p>This is the only data processing type that we have to take care of here. Let’s make a function that can take handle that.</p>
 <p>We will normalize the data with sklearn <code class="docutils literal notranslate"><span class="pre">MinMaxScaler</span></code> where the numerical values will be scaled to values between 0 and 1.</p>
 <div class="cell docutils container">
@@ -2170,7 +2202,7 @@ <h3><span class="section-number">37.87.4.1. </span>Feature Scaling<a class="head
 </section>
 </section>
 <section id="creating-and-training-a-logistic-regression-model">
-<h2><span class="section-number">37.87.5. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.5. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
 <p>We will start by importing <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> models available in Sklearn linear models and then proceed with model training.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2193,7 +2225,7 @@ <h2><span class="section-number">37.87.5. </span>5. Creating and Training a Logi
 <p>Using <code class="docutils literal notranslate"><span class="pre">log_model.score()</span></code>, we can get an accuracy of the model confidence on the training data and labels. 94.7% is not that bad.</p>
 <p>Let’s train another linear classifier called SGD (Stockastic Gradient Descent) classifier on the same dataset.</p>
 <section id="trying-other-models">
-<h3><span class="section-number">37.87.5.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.5.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
@@ -2234,7 +2266,7 @@ <h3><span class="section-number">37.87.5.1. </span>Trying other models<a class="
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.87.6. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.6. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>Evaluating classification models is not as simple as for regression models. When you have skewed dataset or imbalances, you can get a high accuracy and you can think the model did well when in fact it didn’t.</p>
 <p>In this part, we are going to learn how to evaluate classification models. But first, let’s evaluate the model with cross validation.</p>
 <p>Here is an idea behind cross validation: We want to divide the training set into different training and validation subsets so that we can iteratively train and validate the models on those subsets.</p>
@@ -2341,7 +2373,7 @@ <h2><span class="section-number">37.87.6. </span>6.  Model Evaluation<a class="h
 </div>
 </div>
 <section id="classification-performance-metrics">
-<h3><span class="section-number">37.87.6.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.6.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
 <p>Here are commom classification metrics:</p>
 <ul class="simple">
 <li><p>Accuracy</p></li>
@@ -2414,7 +2446,7 @@ <h3><span class="section-number">37.87.6.1. </span>Classification Performance Me
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.87.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2456,13 +2488,13 @@ <h2><span class="section-number">37.87.7. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.86. </span>Study the solvers</p>
+            <p class="prev-next-title"><span class="section-number">40.86. </span>Study the solvers</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-classification-model.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.88. </span>Build Classification Model</p>
+        <p class="prev-next-title"><span class="section-number">40.88. </span>Build Classification Model</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-fundamentals/create-a-regression-model.html b/assignments/ml-fundamentals/create-a-regression-model.html
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@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.57. Create a regression model &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.57. Create a regression model &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.58. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
-    <link rel="prev" title="37.56. Try a different model" href="try-a-different-model.html" />
+    <link rel="next" title="40.58. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="prev" title="40.56. Try a different model" href="try-a-different-model.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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    <li class="toctree-l2">
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-     11.1. Tools of the trade
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.1. Introduction to clustering
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-     14.2. K-Means clustering
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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-   17. Unsupervised learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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-   27. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
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     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
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     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
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     <a class="reference internal" href="../data-science/data-preparation.html">
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-     37.29. Matplotlib applied
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-     37.37. Exploring for answers
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     <a class="reference internal" href="../data-science/market-research.html">
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="ml-overview-mnist-digits.html">
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="linear-regression/loss-function.html">
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     <a class="reference internal" href="ml-logistic-regression-1.html">
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     <a class="reference internal" href="ml-logistic-regression-2.html">
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     <a class="reference internal" href="ml-neural-network-1.html">
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 <section id="instructions">
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+<h2><span class="section-number">40.57.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section you were shown how to build a model using both Linear and Polynomial Regression. Using this knowledge, find a dataset or use one of Scikit-learn’s built-in sets to build a fresh model. Explain in your notebook why you chose the technique you did, and demonstrate your model’s accuracy. If it is not accurate, explain why.</p>
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+<h2><span class="section-number">40.57.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.62. Explore classification methods" href="explore-classification-methods.html" />
-    <link rel="prev" title="37.60. Pumpkin varieties and color" href="pumpkin-varieties-and-color.html" />
+    <link rel="next" title="40.62. Explore classification methods" href="explore-classification-methods.html" />
+    <link rel="prev" title="40.60. Pumpkin varieties and color" href="pumpkin-varieties-and-color.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
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+<h1><span class="section-number">40.61. </span>Delicious asian and indian cuisines<a class="headerlink" href="#delicious-asian-and-indian-cuisines" title="Permalink to this headline">#</a></h1>
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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    <li class="toctree-l2">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
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+  <ul>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
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     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
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     <a class="reference internal" href="../data-science/data-preparation.html">
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     <a class="reference internal" href="../data-science/analyzing-data.html">
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     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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-     37.35. Tell a story
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-     37.37. Exploring for answers
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     <a class="reference internal" href="../data-science/market-research.html">
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="linear-regression/loss-function.html">
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     <a class="reference internal" href="ml-logistic-regression-1.html">
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     <a class="reference internal" href="ml-logistic-regression-2.html">
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     <a class="reference internal" href="ml-neural-network-1.html">
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   <section class="tex2jax_ignore mathjax_ignore" id="explore-classification-methods">
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+<h1><span class="section-number">40.62. </span>Explore classification methods<a class="headerlink" href="#explore-classification-methods" title="Permalink to this headline">#</a></h1>
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-<h2><span class="section-number">37.62.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.62.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In <a class="reference external" href="https://scikit-learn.org/stable/supervised_learning.html">Scikit-learn documentation</a> you’ll find a large list of ways to classify data. Do a little scavenger hunt in these docs: your goals is to look for classification methods and match a dataset in this curriculum, a question you can ask of it, and a technique of classification. Create a spreadsheet or table in a .doc file and explain how the dataset would work with the classification algorithm.</p>
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+<h2><span class="section-number">40.62.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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+    <link rel="next" title="40.56. Try a different model" href="try-a-different-model.html" />
+    <link rel="prev" title="40.54. Managing data" href="managing-data.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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  </li>
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   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
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  </li>
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
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+   40. Self-paced assignments
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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+     41.11. Machine Learning overview
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+     41.16. Build an machine learning web application
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-     38.20. Convolutional Neural Network
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+<h1><span class="section-number">40.55. </span>Exploring visualizations<a class="headerlink" href="#exploring-visualizations" title="Permalink to this headline">#</a></h1>
 <p>There are several different libraries that are available for data visualization. Create some visualizations using the Pumpkin data in this lesson with matplotlib and seaborn in a sample notebook. Which libraries are easier to work with?</p>
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+<h2><span class="section-number">40.55.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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+            <p class="prev-next-title"><span class="section-number">40.54. </span>Managing data</p>
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.59. Retrying some regression" href="retrying-some-regression.html" />
-    <link rel="prev" title="37.57. Create a regression model" href="create-a-regression-model.html" />
+    <link rel="next" title="40.59. Retrying some regression" href="retrying-some-regression.html" />
+    <link rel="prev" title="40.57. Create a regression model" href="create-a-regression-model.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
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  </li>
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   <a class="reference internal" href="../../deep-learning/time-series.html">
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-classification.html">
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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+   32. Image segmentation
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
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 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
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    </li>
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     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
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     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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-         37.45.2.2.5.3. Final data processing pipeline
+         40.45.2.2.5.3. Final data processing pipeline
         </a>
        </li>
       </ul>
@@ -1644,17 +1676,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#choosing-and-training-a-model">
-     37.45.2.3. Choosing and training a model
+     40.45.2.3. Choosing and training a model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-evaluation">
-     37.45.2.4. Model evaluation
+     40.45.2.4. Model evaluation
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#model-evaluation-with-cross-validation">
-       37.45.2.4.1. Model evaluation with cross validation
+       40.45.2.4.1. Model evaluation with cross validation
       </a>
      </li>
     </ul>
@@ -1663,7 +1695,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.45.3. Acknowledgments
+   40.45.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1687,116 +1719,116 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.45.1. Introduction
+   40.45.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#task">
-   37.45.2. Task
+   40.45.2. Task
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
-     37.45.2.1. Task 1: Exploratory data analysis
+     40.45.2.1. Task 1: Exploratory data analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#split-the-data-set-into-a-training-set-and-a-test-set">
-       37.45.2.1.1. Split the data set into a training set and a test set
+       40.45.2.1.1. Split the data set into a training set and a test set
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-data-statistics">
-       37.45.2.1.2. Checking data statistics
+       40.45.2.1.2. Checking data statistics
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-missing-values">
-       37.45.2.1.3. Checking missing values
+       40.45.2.1.3. Checking missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-values-in-the-categorical-feature-s">
-       37.45.2.1.4. Checking values in the categorical feature(s)
+       40.45.2.1.4. Checking values in the categorical feature(s)
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-correlation-between-features">
-       37.45.2.1.5. Checking Correlation Between Features
+       40.45.2.1.5. Checking Correlation Between Features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plotting-geographical-features">
-       37.45.2.1.6. Plotting geographical features
+       40.45.2.1.6. Plotting geographical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#exploring-relationship-between-individual-features">
-       37.45.2.1.7. Exploring Relationship Between Individual Features
+       40.45.2.1.7. Exploring Relationship Between Individual Features
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-data-preprocessing">
-     37.45.2.2. Task 3: Data preprocessing
+     40.45.2.2. Task 3: Data preprocessing
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-the-input-data-and-output-data-for-training-the-machine-learning-model">
-       37.45.2.2.1. Create the input data and output data for training the machine learning model
+       40.45.2.2.1. Create the input data and output data for training the machine learning model
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#handling-missing-values">
-       37.45.2.2.2. Handling missing values
+       40.45.2.2.2. Handling missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encoding-categorical-features">
-       37.45.2.2.3. Encoding categorical features
+       40.45.2.2.3. Encoding categorical features
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#mapping">
-         37.45.2.2.3.1. Mapping
+         40.45.2.2.3.1. Mapping
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#handling-categorical-features-with-sklearn">
-         37.45.2.2.3.2. Handling categorical features with Sklearn
+         40.45.2.2.3.2. Handling categorical features with Sklearn
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#one-hot-encoding">
-         37.45.2.2.3.3. One hot encoding
+         40.45.2.2.3.3. One hot encoding
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#scaling-numerical">
-       37.45.2.2.4. Scaling numerical
+       40.45.2.2.4. Scaling numerical
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#putting-all-data-preprocessing-steps-into-a-single-pipeline">
-       37.45.2.2.5. Putting all data preprocessing steps into a single pipeline
+       40.45.2.2.5. Putting all data preprocessing steps into a single pipeline
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#creating-a-numerical-features-pipeline">
-         37.45.2.2.5.1. Creating a numerical features pipeline
+         40.45.2.2.5.1. Creating a numerical features pipeline
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#pipeline-for-transforming-categorical-features">
-         37.45.2.2.5.2. Pipeline for transforming categorical features
+         40.45.2.2.5.2. Pipeline for transforming categorical features
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#final-data-processing-pipeline">
-         37.45.2.2.5.3. Final data processing pipeline
+         40.45.2.2.5.3. Final data processing pipeline
         </a>
        </li>
       </ul>
@@ -1805,17 +1837,17 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#choosing-and-training-a-model">
-     37.45.2.3. Choosing and training a model
+     40.45.2.3. Choosing and training a model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-evaluation">
-     37.45.2.4. Model evaluation
+     40.45.2.4. Model evaluation
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#model-evaluation-with-cross-validation">
-       37.45.2.4.1. Model evaluation with cross validation
+       40.45.2.4.1. Model evaluation with cross validation
       </a>
      </li>
     </ul>
@@ -1824,7 +1856,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.45.3. Acknowledgments
+   40.45.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1838,10 +1870,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-california-housing">
-<h1><span class="section-number">37.45. </span>Linear regression - California Housing<a class="headerlink" href="#linear-regression-california-housing" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.45. </span>Linear regression - California Housing<a class="headerlink" href="#linear-regression-california-housing" title="Permalink to this headline">#</a></h1>
 <p>In this assignment, we will predict the price of a house by its region, size, number of bedrooms, etc.</p>
 <section id="introduction">
-<h2><span class="section-number">37.45.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.45.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2176,11 +2208,11 @@ <h2><span class="section-number">37.45.1. </span>Introduction<a class="headerlin
 <p>So, we have 20640 data points and 10 features. In those 10 features, 9 features are input features and the feature <code class="docutils literal notranslate"><span class="pre">median_house_value</span></code> is the target variable/label.</p>
 </section>
 <section id="task">
-<h2><span class="section-number">37.45.2. </span>Task<a class="headerlink" href="#task" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.45.2. </span>Task<a class="headerlink" href="#task" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">37.45.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <section id="split-the-data-set-into-a-training-set-and-a-test-set">
-<h4><span class="section-number">37.45.2.1.1. </span>Split the data set into a training set and a test set<a class="headerlink" href="#split-the-data-set-into-a-training-set-and-a-test-set" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.1. </span>Split the data set into a training set and a test set<a class="headerlink" href="#split-the-data-set-into-a-training-set-and-a-test-set" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
@@ -2190,7 +2222,7 @@ <h4><span class="section-number">37.45.2.1.1. </span>Split the data set into a t
 </div>
 </section>
 <section id="checking-data-statistics">
-<h4><span class="section-number">37.45.2.1.2. </span>Checking data statistics<a class="headerlink" href="#checking-data-statistics" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.2. </span>Checking data statistics<a class="headerlink" href="#checking-data-statistics" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
@@ -2376,7 +2408,7 @@ <h4><span class="section-number">37.45.2.1.2. </span>Checking data statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h4><span class="section-number">37.45.2.1.3. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.3. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -2413,7 +2445,7 @@ <h4><span class="section-number">37.45.2.1.3. </span>Checking missing values<a c
 </div>
 </section>
 <section id="checking-values-in-the-categorical-feature-s">
-<h4><span class="section-number">37.45.2.1.4. </span>Checking values in the categorical feature(s)<a class="headerlink" href="#checking-values-in-the-categorical-feature-s" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.4. </span>Checking values in the categorical feature(s)<a class="headerlink" href="#checking-values-in-the-categorical-feature-s" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="p">[</span><span class="s1">&#39;ocean_proximity&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
@@ -2446,7 +2478,7 @@ <h4><span class="section-number">37.45.2.1.4. </span>Checking values in the cate
 </div>
 </section>
 <section id="checking-correlation-between-features">
-<h4><span class="section-number">37.45.2.1.5. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.5. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -2488,7 +2520,7 @@ <h4><span class="section-number">37.45.2.1.5. </span>Checking Correlation Betwee
 <p>The other interesting insights is that the <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">house</span></code> is closely correlated with the <code class="docutils literal notranslate"><span class="pre">median</span> <span class="pre">income</span></code>, and that makes sense too. For many cases, you will resonably seek house that you will be able to afford based on your income.</p>
 </section>
 <section id="plotting-geographical-features">
-<h4><span class="section-number">37.45.2.1.6. </span>Plotting geographical features<a class="headerlink" href="#plotting-geographical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.6. </span>Plotting geographical features<a class="headerlink" href="#plotting-geographical-features" title="Permalink to this headline">#</a></h4>
 <p>Since we have latitude and longitude, let’s plot it. It can help us to know the location of certain houses on the map and hopefully this will resemble California map.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2540,7 +2572,7 @@ <h4><span class="section-number">37.45.2.1.6. </span>Plotting geographical featu
 <p>Yup, all houses near the ocean are very expensive compared to other areas.</p>
 </section>
 <section id="exploring-relationship-between-individual-features">
-<h4><span class="section-number">37.45.2.1.7. </span>Exploring Relationship Between Individual Features<a class="headerlink" href="#exploring-relationship-between-individual-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.7. </span>Exploring Relationship Between Individual Features<a class="headerlink" href="#exploring-relationship-between-individual-features" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
@@ -2575,9 +2607,9 @@ <h4><span class="section-number">37.45.2.1.7. </span>Exploring Relationship Betw
 </section>
 </section>
 <section id="task-3-data-preprocessing">
-<h3><span class="section-number">37.45.2.2. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.2. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <section id="create-the-input-data-and-output-data-for-training-the-machine-learning-model">
-<h4><span class="section-number">37.45.2.2.1. </span>Create the input data and output data for training the machine learning model<a class="headerlink" href="#create-the-input-data-and-output-data-for-training-the-machine-learning-model" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.1. </span>Create the input data and output data for training the machine learning model<a class="headerlink" href="#create-the-input-data-and-output-data-for-training-the-machine-learning-model" title="Permalink to this headline">#</a></h4>
 <p>Since we are going to prepare the data for the ML model, let’s create an input training data and the training label, label being <code class="docutils literal notranslate"><span class="pre">median_house_value</span></code>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2707,7 +2739,7 @@ <h4><span class="section-number">37.45.2.2.1. </span>Create the input data and o
 </div>
 </section>
 <section id="handling-missing-values">
-<h4><span class="section-number">37.45.2.2.2. </span>Handling missing values<a class="headerlink" href="#handling-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.2. </span>Handling missing values<a class="headerlink" href="#handling-missing-values" title="Permalink to this headline">#</a></h4>
 <p>In this example, we will fill the values with the mean of the concerned features.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2764,7 +2796,7 @@ <h4><span class="section-number">37.45.2.2.2. </span>Handling missing values<a c
 <p>The feature <code class="docutils literal notranslate"><span class="pre">total_bedroom</span></code> was the one having missing values. Looking above, we no longer have the missing values in whole dataframe.</p>
 </section>
 <section id="encoding-categorical-features">
-<h4><span class="section-number">37.45.2.2.3. </span>Encoding categorical features<a class="headerlink" href="#encoding-categorical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.3. </span>Encoding categorical features<a class="headerlink" href="#encoding-categorical-features" title="Permalink to this headline">#</a></h4>
 <p>Categorical features are features which have categorical values. An example in our dataset is <code class="docutils literal notranslate"><span class="pre">ocean_proximity</span></code> that has the following values.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2785,7 +2817,7 @@ <h4><span class="section-number">37.45.2.2.3. </span>Encoding categorical featur
 </div>
 <p>So we have 5 categories: <code class="docutils literal notranslate"><span class="pre">&lt;1H</span> <span class="pre">OCEAN</span></code>, <code class="docutils literal notranslate"><span class="pre">INLAND</span></code>, <code class="docutils literal notranslate"><span class="pre">NEAR</span> <span class="pre">OCEAN</span></code>, <code class="docutils literal notranslate"><span class="pre">NEAR</span> <span class="pre">BAY</span></code>, <code class="docutils literal notranslate"><span class="pre">ISLAND</span></code>.</p>
 <section id="mapping">
-<h5><span class="section-number">37.45.2.2.3.1. </span>Mapping<a class="headerlink" href="#mapping" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.3.1. </span>Mapping<a class="headerlink" href="#mapping" title="Permalink to this headline">#</a></h5>
 <p>Mapping is simple. We create a dictionary of categorical values and their corresponding numerics. And after that, we map it to the categorical feature.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2840,7 +2872,7 @@ <h5><span class="section-number">37.45.2.2.3.1. </span>Mapping<a class="headerli
 <p>Cool, all categories were mapped to their corresponding numerals. That is actually encoding. We are converting the categories (in text) into numbers, typically because ML models expect numeric inputs.</p>
 </section>
 <section id="handling-categorical-features-with-sklearn">
-<h5><span class="section-number">37.45.2.2.3.2. </span>Handling categorical features with Sklearn<a class="headerlink" href="#handling-categorical-features-with-sklearn" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.3.2. </span>Handling categorical features with Sklearn<a class="headerlink" href="#handling-categorical-features-with-sklearn" title="Permalink to this headline">#</a></h5>
 <p>Sklearn has many preprocessing functions to handle categorical features. Ordinary Encoder is one of them. It does the same as what we did before with mapping. The only difference is implementation.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2877,7 +2909,7 @@ <h5><span class="section-number">37.45.2.2.3.2. </span>Handling categorical feat
 </div>
 </section>
 <section id="one-hot-encoding">
-<h5><span class="section-number">37.45.2.2.3.3. </span>One hot encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.3.3. </span>One hot encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h5>
 <p>One hot encoding is most preferred when the categories are not in any order and that is exactly how our categorical feature is. This is what I mean by saying unordered categories: If you have 3 cities and encode them with numbers (1,2,3) respectively, a machine learning model may learn that city 1 is close to city 2 and to city 3. As that is a false assumption to make, the model will likely give incorrect predictions if the city feature plays an important role in the analysis.</p>
 <p>On the flip side, if you have the feature of ordered ranges like low, medium, and high, then numbers can be an effective way because you want to keep the sequence of these ranges.</p>
 <p>In our case, the ocean proximity feature is not in any order. By using one hot, The categories will be converted into binary representation (1s or 0s), and the orginal categorical feature will be splitted into more features, equivalent to the number of categories.</p>
@@ -2924,7 +2956,7 @@ <h5><span class="section-number">37.45.2.2.3.3. </span>One hot encoding<a class=
 </section>
 </section>
 <section id="scaling-numerical">
-<h4><span class="section-number">37.45.2.2.4. </span>Scaling numerical<a class="headerlink" href="#scaling-numerical" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.4. </span>Scaling numerical<a class="headerlink" href="#scaling-numerical" title="Permalink to this headline">#</a></h4>
 <p>Most machine learning models will work well when given small input values, and best if they are in the same range. For that reason, there are two most techniques to scale features:</p>
 <ul class="simple">
 <li><p>Normalization where the features are scaled to values between 0 and 1. And</p></li>
@@ -2994,7 +3026,7 @@ <h4><span class="section-number">37.45.2.2.4. </span>Scaling numerical<a class="
 </div>
 </section>
 <section id="putting-all-data-preprocessing-steps-into-a-single-pipeline">
-<h4><span class="section-number">37.45.2.2.5. </span>Putting all data preprocessing steps into a single pipeline<a class="headerlink" href="#putting-all-data-preprocessing-steps-into-a-single-pipeline" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.5. </span>Putting all data preprocessing steps into a single pipeline<a class="headerlink" href="#putting-all-data-preprocessing-steps-into-a-single-pipeline" title="Permalink to this headline">#</a></h4>
 <p>We are going to do three things:</p>
 <ul class="simple">
 <li><p>Creating a numerical pipeline having all numerical preprocessing steps (handling missing values and standardization)</p></li>
@@ -3002,7 +3034,7 @@ <h4><span class="section-number">37.45.2.2.5. </span>Putting all data preprocess
 <li><p>Combining both pipelines into one pipeline.</p></li>
 </ul>
 <section id="creating-a-numerical-features-pipeline">
-<h5><span class="section-number">37.45.2.2.5.1. </span>Creating a numerical features pipeline<a class="headerlink" href="#creating-a-numerical-features-pipeline" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.5.1. </span>Creating a numerical features pipeline<a class="headerlink" href="#creating-a-numerical-features-pipeline" title="Permalink to this headline">#</a></h5>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
@@ -3062,7 +3094,7 @@ <h5><span class="section-number">37.45.2.2.5.1. </span>Creating a numerical feat
 </div>
 </section>
 <section id="pipeline-for-transforming-categorical-features">
-<h5><span class="section-number">37.45.2.2.5.2. </span>Pipeline for transforming categorical features<a class="headerlink" href="#pipeline-for-transforming-categorical-features" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.5.2. </span>Pipeline for transforming categorical features<a class="headerlink" href="#pipeline-for-transforming-categorical-features" title="Permalink to this headline">#</a></h5>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cat_feats_pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
@@ -3088,7 +3120,7 @@ <h5><span class="section-number">37.45.2.2.5.2. </span>Pipeline for transforming
 </div>
 </section>
 <section id="final-data-processing-pipeline">
-<h5><span class="section-number">37.45.2.2.5.3. </span>Final data processing pipeline<a class="headerlink" href="#final-data-processing-pipeline" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.5.3. </span>Final data processing pipeline<a class="headerlink" href="#final-data-processing-pipeline" title="Permalink to this headline">#</a></h5>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
@@ -3149,7 +3181,7 @@ <h5><span class="section-number">37.45.2.2.5.3. </span>Final data processing pip
 </section>
 </section>
 <section id="choosing-and-training-a-model">
-<h3><span class="section-number">37.45.2.3. </span>Choosing and training a model<a class="headerlink" href="#choosing-and-training-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.3. </span>Choosing and training a model<a class="headerlink" href="#choosing-and-training-a-model" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -3207,7 +3239,7 @@ <h3><span class="section-number">37.45.2.3. </span>Choosing and training a model
 </div>
 </section>
 <section id="model-evaluation">
-<h3><span class="section-number">37.45.2.4. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.4. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
@@ -3245,7 +3277,7 @@ <h3><span class="section-number">37.45.2.4. </span>Model evaluation<a class="hea
 </div>
 </div>
 <section id="model-evaluation-with-cross-validation">
-<h4><span class="section-number">37.45.2.4.1. </span>Model evaluation with cross validation<a class="headerlink" href="#model-evaluation-with-cross-validation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.4.1. </span>Model evaluation with cross validation<a class="headerlink" href="#model-evaluation-with-cross-validation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
@@ -3327,7 +3359,7 @@ <h4><span class="section-number">37.45.2.4.1. </span>Model evaluation with cross
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.45.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.45.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/1_linear_models_for_regression.ipynb">Linear Models for regression</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3369,13 +3401,13 @@ <h2><span class="section-number">37.45.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.44. </span>Regression with Scikit-learn</p>
+            <p class="prev-next-title"><span class="section-number">40.44. </span>Regression with Scikit-learn</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="linear-regression-metrics.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.46. </span>Linear Regression Metrics</p>
+        <p class="prev-next-title"><span class="section-number">40.46. </span>Linear Regression Metrics</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-fundamentals/linear-regression/gradient-descent.html b/assignments/ml-fundamentals/linear-regression/gradient-descent.html
index 879cdded8e..960b161390 100644
--- a/assignments/ml-fundamentals/linear-regression/gradient-descent.html
+++ b/assignments/ml-fundamentals/linear-regression/gradient-descent.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.48. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.48. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.49. Linear Regression Implementation from Scratch" href="linear-regression-from-scratch.html" />
-    <link rel="prev" title="37.47. Loss Function" href="loss-function.html" />
+    <link rel="next" title="40.49. Linear Regression Implementation from Scratch" href="linear-regression-from-scratch.html" />
+    <link rel="prev" title="40.47. Loss Function" href="loss-function.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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+   40.48.4. 1. One-dimensional gradient descent
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     </a>
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+     40.48.2.1. Math (feel free to skip if you find it difficult)
     </a>
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      <span class="math notranslate nohighlight">
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      <span class="math notranslate nohighlight">
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    </li>
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+   40.48.4. 1. One-dimensional gradient descent
   </a>
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     <a class="reference internal nav-link" href="#lets-try">
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+     40.48.4.1. Let’s try!
     </a>
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+   40.48.5. 2. Two-dimensional gradient descent
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
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     </a>
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@@ -1700,9 +1732,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="gradient-descent">
-<h1><span class="section-number">37.48. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.48. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
 <section id="session-objective">
-<h2><span class="section-number">37.48.1. </span>Session Objective<a class="headerlink" href="#session-objective" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.1. </span>Session Objective<a class="headerlink" href="#session-objective" title="Permalink to this headline">#</a></h2>
 <p>In previous sessions, we’ve delved into the application of Linear Regression and Logistic Regression models. You may find the code relatively straightforward and intuitive at this point. However, you might be pondering questions like:</p>
 <ul class="simple">
 <li><p>What exactly occurs when we invoke the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> function?</p></li>
@@ -1711,7 +1743,7 @@ <h2><span class="section-number">37.48.1. </span>Session Objective<a class="head
 <p>This session is designed to provide insight into the functionality of the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> method, which is responsible for training machine learning models and fine-tuning model parameters. The underlying technique at play here is known as “Gradient Descent.”</p>
 </section>
 <section id="lets-explore-and-gain-intuition">
-<h2><span class="section-number">37.48.2. </span>Let’s Explore and Gain Intuition<a class="headerlink" href="#lets-explore-and-gain-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.2. </span>Let’s Explore and Gain Intuition<a class="headerlink" href="#lets-explore-and-gain-intuition" title="Permalink to this headline">#</a></h2>
 <p>To further enhance your understanding and gain a playful insight into Gradient Descent, you can explore the following resources:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/lilipads/gradient_descent_viz">Gradient Descent Visualization</a>: This GitHub repository offers a visualization of the Gradient Descent algorithm, which can be a valuable resource for understanding the optimization process.</p></li>
@@ -1719,7 +1751,7 @@ <h2><span class="section-number">37.48.2. </span>Let’s Explore and Gain Intuit
 </ul>
 <p>These resources will help you build an intuitive grasp of Gradient Descent and its role in training machine learning models. Enjoy your exploration!</p>
 <section id="math-feel-free-to-skip-if-you-find-it-difficult">
-<h3><span class="section-number">37.48.2.1. </span>Math (feel free to skip if you find it difficult)<a class="headerlink" href="#math-feel-free-to-skip-if-you-find-it-difficult" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.1. </span>Math (feel free to skip if you find it difficult)<a class="headerlink" href="#math-feel-free-to-skip-if-you-find-it-difficult" title="Permalink to this headline">#</a></h3>
 <p>The fundamental concept behind gradient descent is rather straightforward: it involves the gradual adjustment of parameters, such as the slope (<span class="math notranslate nohighlight">\(m\)</span>) and the intercept (<span class="math notranslate nohighlight">\(b\)</span>) in our regression equation $y = mx + b, with the aim of minimizing a cost function. This cost function is typically a metric that quantifies the disparity between our model’s predicted results and the actual values. In regression scenarios, the widely employed cost function is the <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">squared</span> <span class="pre">error</span></code> (MSE). When dealing with classification problems, a different set of parameters must be fine-tuned.</p>
 <p>The MSE (Mean Squared Error) is mathematically expressed as:</p>
 <div class="math notranslate nohighlight">
@@ -1730,7 +1762,7 @@ <h3><span class="section-number">37.48.2.1. </span>Math (feel free to skip if yo
 <p>Our primary challenge is to determine the optimal adjustments to parameters <span class="math notranslate nohighlight">\(m\)</span> and $b” to minimize the MSE effectively.</p>
 </section>
 <section id="partial-derivatives">
-<h3><span class="section-number">37.48.2.2. </span>Partial Derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.2. </span>Partial Derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
 <p>In our pursuit of minimizing the Mean Squared Error (MSE), we turn to partial derivatives to understand how each individual parameter influences the MSE. The term “partial” signifies that we are taking derivatives with respect to individual parameters, in this case, <span class="math notranslate nohighlight">\(m\)</span> and $b, separately.</p>
 <p>Consider the following formula, which closely resembles the MSE, but now we’ve introduced the function <span class="math notranslate nohighlight">\(f(m, b)\)</span> into it. The addition of this function doesn’t significantly alter the essence of the calculation, but it allows us to input specific values for <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> to compute the result.</p>
 <div class="math notranslate nohighlight">
@@ -1738,7 +1770,7 @@ <h3><span class="section-number">37.48.2.2. </span>Partial Derivatives<a class="
 <p>For the purposes of calculating partial derivatives, we can temporarily disregard the summation and the terms preceding it, focusing solely on the expression <span class="math notranslate nohighlight">\(y - (mx + b)^2\)</span>. This expression serves as a better starting point for the subsequent partial derivative calculations.</p>
 </section>
 <section id="partial-derivative-with-respect-to-m">
-<h3><span class="section-number">37.48.2.3. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.3. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
 <p>When we calculate the partial derivative with respect to the parameter <span class="math notranslate nohighlight">\(m,&quot; we isolate the parameter \)</span>m” and treat <span class="math notranslate nohighlight">\(b\)</span> as a constant (effectively setting it to 0 for differentiation purposes). To compute this derivative, we utilize the chain rule, which is a fundamental concept in calculus.</p>
 <p>The chain rule is expressed as follows:</p>
 <div class="math notranslate nohighlight">
@@ -1763,7 +1795,7 @@ <h3><span class="section-number">37.48.2.3. </span>Partial Derivative with Respe
 <p>Here, <span class="math notranslate nohighlight">\(\frac{df}{dm}\)</span> signifies the partial derivative of function <span class="math notranslate nohighlight">\(f\)</span> (as previously defined) with respect to the parameter <span class="math notranslate nohighlight">\(m\)</span>. We can now insert this derivative into our summation to complete the calculation.</p>
 </section>
 <section id="partial-derivative-with-respect-to-b">
-<h3><span class="section-number">37.48.2.4. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.4. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
 <p>The process for computing the partial derivative with respect to the parameter <span class="math notranslate nohighlight">\(b&quot; is analogous to our previous derivation with respect to \)</span>m. We still apply the same rules and utilize the chain rule:</p>
 <ol class="simple">
 <li><p>The derivative of <span class="math notranslate nohighlight">\(()^2\)</span> is <span class="math notranslate nohighlight">\(2()\)</span>, which corresponds to how <span class="math notranslate nohighlight">\(x^2\)</span> becomes <span class="math notranslate nohighlight">\(2x\)</span>.</p></li>
@@ -1782,7 +1814,7 @@ <h3><span class="section-number">37.48.2.4. </span>Partial Derivative with Respe
 <p>Similarly to the previous case, <span class="math notranslate nohighlight">\(\frac{df}{db}\)</span> represents the partial derivative of function <span class="math notranslate nohighlight">\(f\)</span> with respect to the parameter $b”. Inserting this derivative into our summation concludes the computation.</p>
 </section>
 <section id="the-final-function">
-<h3><span class="section-number">37.48.2.5. </span>The Final Function<a class="headerlink" href="#the-final-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.5. </span>The Final Function<a class="headerlink" href="#the-final-function" title="Permalink to this headline">#</a></h3>
 <p>Before delving into the code, there are a few essential details to address:</p>
 <ol class="simple">
 <li><p>Gradient descent is an iterative process, and with each iteration (referred to as an “epoch”), we incrementally reduce the Mean Squared Error (MSE). At each iteration, we apply our derived functions to update the values of parameters <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span>.</p></li>
@@ -1793,7 +1825,7 @@ <h3><span class="section-number">37.48.2.5. </span>The Final Function<a class="h
 </section>
 </section>
 <section id="time-to-code">
-<h2><span class="section-number">37.48.3. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.3. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
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 <p>One-dimensional gradient descent is a simple optimization algorithm used to find the minimum (or maximum) of a univariate function.</p>
 <p>Here are the main steps:</p>
 <blockquote>
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+<h2><span class="section-number">40.48.5. </span>2. Two-dimensional gradient descent<a class="headerlink" href="#two-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
 <p>Two-dimensional gradient descent is an optimization algorithm used to find the minimum (or maximum) of a function with two input variables.</p>
 <p>Here are the main steps:</p>
 <blockquote>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.47. </span>Loss Function</p>
+            <p class="prev-next-title"><span class="section-number">40.47. </span>Loss Function</p>
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-        <p class="prev-next-title"><span class="section-number">37.49. </span>Linear Regression Implementation from Scratch</p>
+        <p class="prev-next-title"><span class="section-number">40.49. </span>Linear Regression Implementation from Scratch</p>
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-    <link rel="prev" title="37.48. Gradient descent" href="gradient-descent.html" />
+    <link rel="next" title="40.50. ML logistic regression - assignment 1" href="../ml-logistic-regression-1.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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     <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
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-     37.9. Analyzing text about Data Science
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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    </li>
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.49.1. Introduction
+   40.49.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preparation">
-   37.49.2. Data Preparation
+   40.49.2. Data Preparation
   </a>
  </li>
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     <a class="reference internal nav-link" href="#define-loss-function">
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+     40.49.3.1. Define loss function
     </a>
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     <a class="reference internal nav-link" href="#use-gradient-descent-to-train-the-model">
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+     40.49.3.2. Use gradient descent to train the model
     </a>
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  </li>
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-   37.49.4. Model Evaluation
+   40.49.4. Model Evaluation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.49.5. Conclusion
+   40.49.5. Conclusion
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.49.1. Introduction
+   40.49.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preparation">
-   37.49.2. Data Preparation
+   40.49.2. Data Preparation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-development">
-   37.49.3. Model Development
+   40.49.3. Model Development
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#define-loss-function">
-     37.49.3.1. Define loss function
+     40.49.3.1. Define loss function
     </a>
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     <a class="reference internal nav-link" href="#use-gradient-descent-to-train-the-model">
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+     40.49.3.2. Use gradient descent to train the model
     </a>
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   <a class="reference internal nav-link" href="#model-evaluation">
-   37.49.4. Model Evaluation
+   40.49.4. Model Evaluation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.49.5. Conclusion
+   40.49.5. Conclusion
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  </li>
 </ul>
@@ -1628,9 +1660,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-implementation-from-scratch">
-<h1><span class="section-number">37.49. </span>Linear Regression Implementation from Scratch<a class="headerlink" href="#linear-regression-implementation-from-scratch" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.49. </span>Linear Regression Implementation from Scratch<a class="headerlink" href="#linear-regression-implementation-from-scratch" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.49.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.49.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In previous sections, we have gained some understanding of linear regression, gradient descent, evaluation metrics, and the role of the loss function in this regression technique. In summary, linear regression utilizes gradient descent to optimize the model’s parameters by minimizing the loss function. This enables it to establish a linear relationship between the input features and the target variable, making it a powerful algorithm for predicting continuous values.</p>
 <p>Linear regression is a widely used method in data analysis to describe the relationship between independent variables and a dependent variable using a linear equation. It aims to minimize the error between predicted and actual values by finding the best-fit line or surface. Linear regression can be used for predicting trends, exploring relationships, and identifying patterns in the data.</p>
 <p>This chapter will apply the previously learnt knowledge to implement a linear regression model from scratch. The chapter includes steps for data preparation, model development, and model evaluation, and ultimately summarises the process of developing and evaluating linear regression models.</p>
@@ -1671,7 +1703,7 @@ <h2><span class="section-number">37.49.1. </span>Introduction<a class="headerlin
 </div>
 </section>
 <section id="data-preparation">
-<h2><span class="section-number">37.49.2. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.49.2. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h2>
 <p>Before we can start developing the linear regression model, it is important to prepare the data appropriately. This involves importing the necessary libraries, loading the dataset, performing exploratory data analysis, and cleaning and preprocessing the data.</p>
 <p>Let’s take a look at the code snippet below to understand how we can perform these steps:</p>
 <div class="cell docutils container">
@@ -1726,11 +1758,11 @@ <h2><span class="section-number">37.49.2. </span>Data Preparation<a class="heade
 </div>
 </section>
 <section id="model-development">
-<h2><span class="section-number">37.49.3. </span>Model Development<a class="headerlink" href="#model-development" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.49.3. </span>Model Development<a class="headerlink" href="#model-development" title="Permalink to this headline">#</a></h2>
 <p>Once we have prepared the data, we can proceed with developing the linear regression model. This involves deriving the mathematical formula for linear regression, implementing the formula in code, defining a cost function, and implementing the gradient descent algorithm to train the model.</p>
 <p>Let’s take a look at the code snippet below to understand how we can develop the linear regression model:</p>
 <section id="define-loss-function">
-<h3><span class="section-number">37.49.3.1. </span>Define loss function<a class="headerlink" href="#define-loss-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.49.3.1. </span>Define loss function<a class="headerlink" href="#define-loss-function" title="Permalink to this headline">#</a></h3>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Calculate the loss between the true target variable and the predicted target variable.
 
 Parameters:
@@ -1754,7 +1786,7 @@ <h3><span class="section-number">37.49.3.1. </span>Define loss function<a class=
 </div>
 </section>
 <section id="use-gradient-descent-to-train-the-model">
-<h3><span class="section-number">37.49.3.2. </span>Use gradient descent to train the model<a class="headerlink" href="#use-gradient-descent-to-train-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.49.3.2. </span>Use gradient descent to train the model<a class="headerlink" href="#use-gradient-descent-to-train-the-model" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">gradient_descent</span></code> function is responsible for performing gradient descent to optimize the coefficients of the linear regression model. Here is a breakdown of the steps involved:</p>
 <ol class="simple">
 <li><p>Scale the data: The input features (<code class="docutils literal notranslate"><span class="pre">X_train</span></code> and <code class="docutils literal notranslate"><span class="pre">X_test</span></code>) are scaled using the <code class="docutils literal notranslate"><span class="pre">StandardScaler</span></code> from scikit-learn. This ensures that all features have a similar scale, which can improve the performance of the gradient descent algorithm.</p></li>
@@ -2835,7 +2867,7 @@ <h3><span class="section-number">37.49.3.2. </span>Use gradient descent to train
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.49.4. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.49.4. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>After training the linear regression model, it is important to evaluate its performance to assess how well it is able to make predictions. In this section, we will discuss some commonly used evaluation metrics for regression models.</p>
 <p>When evaluating a machine learning model, we often use certain metrics to measure its performance. Here are some commonly used metrics and plotting methods:</p>
 <div class="cell docutils container">
@@ -2897,7 +2929,7 @@ <h2><span class="section-number">37.49.4. </span>Model Evaluation<a class="heade
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.49.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.49.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we implemented a simple linear regression model and trained and predicted using the from-scratch method. Through this implementation process, we gained a deeper understanding of the linear regression model and learned how to use gradient descent algorithm to minimize the loss function.</p>
 <p>We evaluated the performance of the model and used several common evaluation metrics to measure the accuracy of the model’s predictions. Based on our results, the linear regression model performed well. The values of mean absolute error and root mean squared error were relatively small, indicating that the errors between the model’s predictions and actual results were small. And the value of R-squared was close to 1, indicating that the model was able to explain the variability in the data well.</p>
 <p>Although our model performed well on this task, we also need to be aware of its limitations. Linear regression models assume a linear relationship between input features and target variables. If the data has a nonlinear relationship, the model may not fit the data well. In addition, it may also be affected by problems such as outliers and multicollinearity.</p>
@@ -2949,13 +2981,13 @@ <h2><span class="section-number">37.49.5. </span>Conclusion<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.48. </span>Gradient descent</p>
+            <p class="prev-next-title"><span class="section-number">40.48. </span>Gradient descent</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ml-logistic-regression-1.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.50. </span>ML logistic regression - assignment 1</p>
+        <p class="prev-next-title"><span class="section-number">40.50. </span>ML logistic regression - assignment 1</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
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-    <title>37.46. Linear Regression Metrics &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.46. Linear Regression Metrics &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.47. Loss Function" href="loss-function.html" />
-    <link rel="prev" title="37.45. Linear regression - California Housing" href="california_housing.html" />
+    <link rel="next" title="40.47. Loss Function" href="loss-function.html" />
+    <link rel="prev" title="40.45. Linear regression - California Housing" href="california_housing.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
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+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <label for="toctree-checkbox-8">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
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    </li>
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-     15.3. Feature importance
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-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
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   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
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   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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     <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
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     <a class="reference internal" href="../../data-science/data-science-scenarios.html">
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     <a class="reference internal" href="../../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
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-     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.4. Data Science introduction
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-     38.14. Logistic Regression
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-     38.16. Build an machine learning web application
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-     38.17. Unsupervised learning
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-     38.18. Kernel method
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-     38.19. Model Selection
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-     38.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
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@@ -1528,119 +1560,119 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-squared-error-mse">
-   37.46.1. Mean Squared Error (MSE)
+   40.46.1. Mean Squared Error (MSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-formula">
-     37.46.1.1. The Formula
+     40.46.1.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpretation">
-     37.46.1.2. Interpretation
+     40.46.1.2. Interpretation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-implementation">
-     37.46.1.3. Python Implementation
+     40.46.1.3. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#root-mean-squared-error-rmse">
-   37.46.2. Root Mean Squared Error (RMSE)
+   40.46.2. Root Mean Squared Error (RMSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.46.2.1. The Formula
+     40.46.2.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     37.46.2.2. Python Implementation
+     40.46.2.2. Python Implementation
     </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#r-squared-r2-score">
-   37.46.3. R-squared (R2) Score
+   40.46.3. R-squared (R2) Score
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.46.3.1. The Formula
+     40.46.3.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     37.46.3.2. Python Implementation
+     40.46.3.2. Python Implementation
     </a>
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-   37.46.4. Residual Analysis
+   40.46.4. Residual Analysis
   </a>
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     <a class="reference internal nav-link" href="#id5">
-     37.46.4.1. Python Implementation
+     40.46.4.1. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.46.4.2. Interpretation
+     40.46.4.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#mean-absolute-error-mae">
-   37.46.5. Mean Absolute Error (MAE)
+   40.46.5. Mean Absolute Error (MAE)
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.46.5.1. The Formula
+     40.46.5.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id8">
-     37.46.5.2. Python Implementation
+     40.46.5.2. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     37.46.5.3. Interpretation
+     40.46.5.3. Interpretation
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
-   37.46.6. Mean Absolute Percentage Error (MAPE)
+   40.46.6. Mean Absolute Percentage Error (MAPE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     37.46.6.1. The Formula
+     40.46.6.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     37.46.6.2. Interpretation
+     40.46.6.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion-and-recap">
-   37.46.7. Conclusion and Recap
+   40.46.7. Conclusion and Recap
   </a>
  </li>
 </ul>
@@ -1664,119 +1696,119 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-squared-error-mse">
-   37.46.1. Mean Squared Error (MSE)
+   40.46.1. Mean Squared Error (MSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-formula">
-     37.46.1.1. The Formula
+     40.46.1.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpretation">
-     37.46.1.2. Interpretation
+     40.46.1.2. Interpretation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-implementation">
-     37.46.1.3. Python Implementation
+     40.46.1.3. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#root-mean-squared-error-rmse">
-   37.46.2. Root Mean Squared Error (RMSE)
+   40.46.2. Root Mean Squared Error (RMSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.46.2.1. The Formula
+     40.46.2.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.46.2.2. Python Implementation
+     40.46.2.2. Python Implementation
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#r-squared-r2-score">
-   37.46.3. R-squared (R2) Score
+   40.46.3. R-squared (R2) Score
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.46.3.1. The Formula
+     40.46.3.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.46.3.2. Python Implementation
+     40.46.3.2. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#residual-analysis">
-   37.46.4. Residual Analysis
+   40.46.4. Residual Analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.46.4.1. Python Implementation
+     40.46.4.1. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.46.4.2. Interpretation
+     40.46.4.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-absolute-error-mae">
-   37.46.5. Mean Absolute Error (MAE)
+   40.46.5. Mean Absolute Error (MAE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.46.5.1. The Formula
+     40.46.5.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id8">
-     37.46.5.2. Python Implementation
+     40.46.5.2. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     37.46.5.3. Interpretation
+     40.46.5.3. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
-   37.46.6. Mean Absolute Percentage Error (MAPE)
+   40.46.6. Mean Absolute Percentage Error (MAPE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     37.46.6.1. The Formula
+     40.46.6.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     37.46.6.2. Interpretation
+     40.46.6.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion-and-recap">
-   37.46.7. Conclusion and Recap
+   40.46.7. Conclusion and Recap
   </a>
  </li>
 </ul>
@@ -1790,16 +1822,16 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-metrics">
-<h1><span class="section-number">37.46. </span>Linear Regression Metrics<a class="headerlink" href="#linear-regression-metrics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.46. </span>Linear Regression Metrics<a class="headerlink" href="#linear-regression-metrics" title="Permalink to this headline">#</a></h1>
 <p>Linear regression is a fundamental and widely used technique in machine learning and statistics for predicting continuous values based on input variables. It finds its application in various domains, from finance and economics to healthcare and engineering. When using linear regression, it’s essential to assess the model’s performance accurately. This is where linear regression metrics come into play.</p>
 <p>In this tutorial, we will delve into the world of linear regression metrics, exploring the key evaluation measures that allow us to gauge how well a linear regression model fits the data and makes predictions. These metrics provide valuable insights into the model’s accuracy, precision, and ability to capture the underlying relationships between variables.</p>
 <p>We will cover essential concepts such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2) score, and Mean Absolute Error (MAE). Understanding these metrics is crucial for data scientists, machine learning practitioners, and anyone looking to harness the power of linear regression for predictive modeling.</p>
 <p>Whether you are building models for price predictions, sales forecasts, or any other regression task, mastering these metrics will empower you to make informed decisions and fine-tune your models for optimal performance. Let’s embark on this journey to explore the intricacies of linear regression metrics and enhance our ability to assess and improve regression models.</p>
 <section id="mean-squared-error-mse">
-<h2><span class="section-number">37.46.1. </span>Mean Squared Error (MSE)<a class="headerlink" href="#mean-squared-error-mse" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.1. </span>Mean Squared Error (MSE)<a class="headerlink" href="#mean-squared-error-mse" title="Permalink to this headline">#</a></h2>
 <p>In the realm of linear regression metrics, one fundamental measure of model performance is the <strong>Mean Squared Error (MSE)</strong>. MSE serves as a valuable indicator of how well your linear regression model aligns its predictions with the actual data points. This metric quantifies the average of the squared differences between predicted values and observed values.</p>
 <section id="the-formula">
-<h3><span class="section-number">37.46.1.1. </span>The Formula<a class="headerlink" href="#the-formula" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.1.1. </span>The Formula<a class="headerlink" href="#the-formula" title="Permalink to this headline">#</a></h3>
 <p>Mathematically, the MSE is computed using the following formula:</p>
 <div class="math notranslate nohighlight">
 \[ MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 \]</div>
@@ -1811,11 +1843,11 @@ <h3><span class="section-number">37.46.1.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="interpretation">
-<h3><span class="section-number">37.46.1.2. </span>Interpretation<a class="headerlink" href="#interpretation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.1.2. </span>Interpretation<a class="headerlink" href="#interpretation" title="Permalink to this headline">#</a></h3>
 <p>A lower MSE value indicates that the model’s predictions are closer to the actual values, signifying better model performance. Conversely, a higher MSE suggests that the model’s predictions deviate more from the true values, indicating poorer performance.</p>
 </section>
 <section id="python-implementation">
-<h3><span class="section-number">37.46.1.3. </span>Python Implementation<a class="headerlink" href="#python-implementation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.1.3. </span>Python Implementation<a class="headerlink" href="#python-implementation" title="Permalink to this headline">#</a></h3>
 <p>Let’s take a look at how to calculate MSE in Python. We’ll use a simple example with sample data:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1846,10 +1878,10 @@ <h3><span class="section-number">37.46.1.3. </span>Python Implementation<a class
 </section>
 </section>
 <section id="root-mean-squared-error-rmse">
-<h2><span class="section-number">37.46.2. </span>Root Mean Squared Error (RMSE)<a class="headerlink" href="#root-mean-squared-error-rmse" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.2. </span>Root Mean Squared Error (RMSE)<a class="headerlink" href="#root-mean-squared-error-rmse" title="Permalink to this headline">#</a></h2>
 <p>The Root Mean Squared Error (RMSE) is another important metric for evaluating the performance of a linear regression model. It is the square root of the Mean Squared Error (MSE) and provides a measure of the average magnitude of error between actual and predicted values.</p>
 <section id="id1">
-<h3><span class="section-number">37.46.2.1. </span>The Formula<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.2.1. </span>The Formula<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>The formula for RMSE is:</p>
 <div class="math notranslate nohighlight">
 \[ RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} \]</div>
@@ -1861,7 +1893,7 @@ <h3><span class="section-number">37.46.2.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.46.2.2. </span>Python Implementation<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.2.2. </span>Python Implementation<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate RMSE</span>
@@ -1881,10 +1913,10 @@ <h3><span class="section-number">37.46.2.2. </span>Python Implementation<a class
 </section>
 </section>
 <section id="r-squared-r2-score">
-<h2><span class="section-number">37.46.3. </span>R-squared (R2) Score<a class="headerlink" href="#r-squared-r2-score" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.3. </span>R-squared (R2) Score<a class="headerlink" href="#r-squared-r2-score" title="Permalink to this headline">#</a></h2>
 <p>The R-squared (R2) score, also known as the coefficient of determination, is a measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variables. It provides insight into how well the model is performing compared to a simple mean.</p>
 <section id="id3">
-<h3><span class="section-number">37.46.3.1. </span>The Formula<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.3.1. </span>The Formula<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>The formula for R2 score is:</p>
 <div class="math notranslate nohighlight">
 \[ R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2} \]</div>
@@ -1897,7 +1929,7 @@ <h3><span class="section-number">37.46.3.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.46.3.2. </span>Python Implementation<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.3.2. </span>Python Implementation<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate the mean of actual values</span>
@@ -1926,10 +1958,10 @@ <h3><span class="section-number">37.46.3.2. </span>Python Implementation<a class
 </section>
 </section>
 <section id="residual-analysis">
-<h2><span class="section-number">37.46.4. </span>Residual Analysis<a class="headerlink" href="#residual-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.4. </span>Residual Analysis<a class="headerlink" href="#residual-analysis" title="Permalink to this headline">#</a></h2>
 <p>Residual analysis is a critical step in evaluating the performance of a linear regression model. It involves examining the differences between the observed and predicted values (i.e., the residuals). This helps us understand if there are any patterns or trends that the model might have missed.</p>
 <section id="id5">
-<h3><span class="section-number">37.46.4.1. </span>Python Implementation<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.4.1. </span>Python Implementation<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -1956,17 +1988,17 @@ <h3><span class="section-number">37.46.4.1. </span>Python Implementation<a class
 </div>
 </section>
 <section id="id6">
-<h3><span class="section-number">37.46.4.2. </span>Interpretation<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.4.2. </span>Interpretation<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>If the residuals are randomly scattered around the red line, it suggests that the model is capturing the underlying patterns in the data well.
 If there’s a clear pattern in the residuals (e.g., a curve or a trend), it indicates that the model might be missing some important features.
 Residual analysis is a crucial step in understanding the limitations of the model and can provide insights into potential areas of improvement.</p>
 </section>
 </section>
 <section id="mean-absolute-error-mae">
-<h2><span class="section-number">37.46.5. </span>Mean Absolute Error (MAE)<a class="headerlink" href="#mean-absolute-error-mae" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.5. </span>Mean Absolute Error (MAE)<a class="headerlink" href="#mean-absolute-error-mae" title="Permalink to this headline">#</a></h2>
 <p>The Mean Absolute Error (MAE) is a metric that measures the average absolute differences between actual and predicted values. Unlike MSE, MAE does not square the differences, which makes it less sensitive to outliers.</p>
 <section id="id7">
-<h3><span class="section-number">37.46.5.1. </span>The Formula<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.5.1. </span>The Formula<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <p>The formula for Mean Absolute Errord is:
 $<span class="math notranslate nohighlight">\( MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| \)</span>$
 Where:</p>
@@ -1977,7 +2009,7 @@ <h3><span class="section-number">37.46.5.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id8">
-<h3><span class="section-number">37.46.5.2. </span>Python Implementation<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.5.2. </span>Python Implementation<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate absolute differences between actual and predicted values</span>
@@ -1999,15 +2031,15 @@ <h3><span class="section-number">37.46.5.2. </span>Python Implementation<a class
 </div>
 </section>
 <section id="id9">
-<h3><span class="section-number">37.46.5.3. </span>Interpretation<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.5.3. </span>Interpretation<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h3>
 <p>The MAE gives us an average of how far off our predictions are from the actual values. It provides a more intuitive understanding of error compared to MSE.</p>
 </section>
 </section>
 <section id="mean-absolute-percentage-error-mape">
-<h2><span class="section-number">37.46.6. </span>Mean Absolute Percentage Error (MAPE)<a class="headerlink" href="#mean-absolute-percentage-error-mape" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.6. </span>Mean Absolute Percentage Error (MAPE)<a class="headerlink" href="#mean-absolute-percentage-error-mape" title="Permalink to this headline">#</a></h2>
 <p>The Mean Absolute Percentage Error (MAPE) is a metric used to evaluate the accuracy of a model’s predictions in percentage terms. It measures the average absolute percentage difference between actual and predicted values.</p>
 <section id="id10">
-<h3><span class="section-number">37.46.6.1. </span>The Formula<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.6.1. </span>The Formula<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h3>
 <p>The formula for MAPE is:</p>
 <div class="math notranslate nohighlight">
 \[ MAPE = \frac{1}{n} \sum_{i=1}^{n} \left| \frac{y_i - \hat{y}_i}{y_i} \right| \times 100 \]</div>
@@ -2019,7 +2051,7 @@ <h3><span class="section-number">37.46.6.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id11">
-<h3><span class="section-number">37.46.6.2. </span>Interpretation<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.6.2. </span>Interpretation<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate absolute percentage differences between actual and predicted values</span>
@@ -2042,7 +2074,7 @@ <h3><span class="section-number">37.46.6.2. </span>Interpretation<a class="heade
 </section>
 </section>
 <section id="conclusion-and-recap">
-<h2><span class="section-number">37.46.7. </span>Conclusion and Recap<a class="headerlink" href="#conclusion-and-recap" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.7. </span>Conclusion and Recap<a class="headerlink" href="#conclusion-and-recap" title="Permalink to this headline">#</a></h2>
 <p>In this tutorial, we covered several important metrics used to evaluate the performance of linear regression models. Here’s a quick recap:</p>
 <ul class="simple">
 <li><p>Mean Squared Error (MSE): Measures the average squared difference between actual and predicted values.</p></li>
@@ -2093,13 +2125,13 @@ <h2><span class="section-number">37.46.7. </span>Conclusion and Recap<a class="h
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.45. </span>Linear regression - California Housing</p>
+            <p class="prev-next-title"><span class="section-number">40.45. </span>Linear regression - California Housing</p>
         </div>
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     <a class='right-next' id="next-link" href="loss-function.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.47. </span>Loss Function</p>
+        <p class="prev-next-title"><span class="section-number">40.47. </span>Loss Function</p>
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diff --git a/assignments/ml-fundamentals/linear-regression/loss-function.html b/assignments/ml-fundamentals/linear-regression/loss-function.html
index 3a16865c3a..1245e4d447 100644
--- a/assignments/ml-fundamentals/linear-regression/loss-function.html
+++ b/assignments/ml-fundamentals/linear-regression/loss-function.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.47. Loss Function &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.47. Loss Function &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.48. Gradient descent" href="gradient-descent.html" />
-    <link rel="prev" title="37.46. Linear Regression Metrics" href="linear-regression-metrics.html" />
+    <link rel="next" title="40.48. Gradient descent" href="gradient-descent.html" />
+    <link rel="prev" title="40.46. Linear Regression Metrics" href="linear-regression-metrics.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
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-   25. Long-short term memory
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   <section class="tex2jax_ignore mathjax_ignore" id="loss-function">
-<h1><span class="section-number">37.47. </span>Loss Function<a class="headerlink" href="#loss-function" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.47. </span>Loss Function<a class="headerlink" href="#loss-function" title="Permalink to this headline">#</a></h1>
 <section id="objective-of-this-section">
-<h2><span class="section-number">37.47.1. </span>Objective of this section<a class="headerlink" href="#objective-of-this-section" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.47.1. </span>Objective of this section<a class="headerlink" href="#objective-of-this-section" title="Permalink to this headline">#</a></h2>
 <p>We have already learned math and code for “Gradient Descent”, as well as other optimization techniques.</p>
 <p>In this section, we will learn more about loss functions.</p>
 <p>As a learner, you can focus on learning the L1, L2 loss, and Classification Losses in the regression loss function in this section. And learn about other loss functions.</p>
 </section>
 <section id="concept-of-loss-function">
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+<h2><span class="section-number">40.47.2. </span>Concept of loss function<a class="headerlink" href="#concept-of-loss-function" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A loss function gauges the disparity between the model’s predictions and the actual values. Simply put, it indicates how “off” our model is. By optimizing this function, our objective is to identify parameters that bring the model’s predictions as close as possible to the true values.</p></li>
 <li><p>The function we want to minimize or maximize is called the objective function or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function.
@@ -1690,9 +1722,9 @@ <h2><span class="section-number">37.47.2. </span>Concept of loss function<a clas
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-<h2><span class="section-number">37.47.3. </span>Classification of loss functions<a class="headerlink" href="#classification-of-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.47.3. </span>Classification of loss functions<a class="headerlink" href="#classification-of-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="regression-losses">
-<h3><span class="section-number">37.47.3.1. </span>Regression Losses<a class="headerlink" href="#regression-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.47.3.1. </span>Regression Losses<a class="headerlink" href="#regression-losses" title="Permalink to this headline">#</a></h3>
 <p>These are employed when the objective is to predict a continuous outcome.</p>
 <ul class="simple">
 <li><p>Mean Squared Error (MSE): Measures the average squared discrepancies between predictions and actual values, emphasizing larger errors.</p></li>
@@ -1704,7 +1736,7 @@ <h3><span class="section-number">37.47.3.1. </span>Regression Losses<a class="he
 </ul>
 </section>
 <section id="classification-losses">
-<h3><span class="section-number">37.47.3.2. </span>Classification Losses<a class="headerlink" href="#classification-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.47.3.2. </span>Classification Losses<a class="headerlink" href="#classification-losses" title="Permalink to this headline">#</a></h3>
 <p>Utilized for tasks requiring the prediction of discrete categories.</p>
 <ul class="simple">
 <li><p>Cross Entropy Loss: Quantifies the dissimilarity between the predicted probability distribution and the actual class distribution.</p></li>
@@ -1714,7 +1746,7 @@ <h3><span class="section-number">37.47.3.2. </span>Classification Losses<a class
 </ul>
 </section>
 <section id="structured-losses">
-<h3><span class="section-number">37.47.3.3. </span>Structured Losses<a class="headerlink" href="#structured-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.47.3.3. </span>Structured Losses<a class="headerlink" href="#structured-losses" title="Permalink to this headline">#</a></h3>
 <p>Tailored for intricate tasks involving structured data patterns.</p>
 <ul class="simple">
 <li><p>Sequence Generation Loss: Emblematic examples include the CTC (Connectionist Temporal Classification) designed for undertakings such as speech and text identification.</p></li>
@@ -1722,7 +1754,7 @@ <h3><span class="section-number">37.47.3.3. </span>Structured Losses<a class="he
 </ul>
 </section>
 <section id="regularization-losses">
-<h3><span class="section-number">37.47.3.4. </span>Regularization Losses<a class="headerlink" href="#regularization-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.47.3.4. </span>Regularization Losses<a class="headerlink" href="#regularization-losses" title="Permalink to this headline">#</a></h3>
 <p>Rather than directly influencing the model’s predictions, these losses are integrated into the objective function to counteract excessive model complexity.</p>
 <ul class="simple">
 <li><p>L1 Regularization (Lasso): Enforces sparsity by compelling certain model coefficients to be exactly zero.</p></li>
@@ -1731,9 +1763,9 @@ <h3><span class="section-number">37.47.3.4. </span>Regularization Losses<a class
 </section>
 </section>
 <section id="common-loss-functions">
-<h2><span class="section-number">37.47.4. </span>Common Loss Functions<a class="headerlink" href="#common-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.47.4. </span>Common Loss Functions<a class="headerlink" href="#common-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="regression-loss-functions">
-<h3><span class="section-number">37.47.4.1. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.47.4.1. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>Mean Squared Error, MSE</strong></p></li>
 </ol>
@@ -1809,7 +1841,7 @@ <h3><span class="section-number">37.47.4.1. </span>Regression Loss Functions<a c
 <p>Corresponds to MSE.</p>
 </section>
 <section id="classification-loss-functions">
-<h3><span class="section-number">37.47.4.2. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.47.4.2. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>Hinge Loss</strong></p></li>
 </ol>
@@ -1832,7 +1864,7 @@ <h3><span class="section-number">37.47.4.2. </span>Classification Loss Functions
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 <section id="conclusion">
-<h2><span class="section-number">37.47.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.47.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Loss functions hold a pivotal role in machine learning. By minimizing the loss, we enhance the accuracy of our model’s predictions. A deep understanding of various loss functions aids in selecting the most appropriate optimization technique for specific challenges.</p>
 </section>
 </section>
@@ -1874,13 +1906,13 @@ <h2><span class="section-number">37.47.5. </span>Conclusion<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.46. </span>Linear Regression Metrics</p>
+            <p class="prev-next-title"><span class="section-number">40.46. </span>Linear Regression Metrics</p>
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-        <p class="prev-next-title"><span class="section-number">37.48. </span>Gradient descent</p>
+        <p class="prev-next-title"><span class="section-number">40.48. </span>Gradient descent</p>
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-    <title>37.54. Managing data &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.54. Managing data &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="37.55. Exploring visualizations" href="exploring-visualizations.html" />
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+    <link rel="next" title="40.55. Exploring visualizations" href="exploring-visualizations.html" />
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    10. Machine Learning overview
   </a>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
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   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/time-series.html">
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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+   32. Image segmentation
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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  </li>
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
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     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
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     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     <a class="reference internal" href="../data-science/market-research.html">
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="regression-with-scikit-learn.html">
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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+            <p class="prev-next-title"><span class="section-number">40.53. </span>Regression tools</p>
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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  <li class="toctree-l1 current active has-children">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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     <a class="reference internal" href="../set-up-env/first-assignment.html">
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     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
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-     37.14. Try it in Excel
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     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
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-     37.16. Build your own custom vis
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     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    </li>
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     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="../data-science/data-preparation.html">
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     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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-     37.35. Tell a story
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-     37.37. Exploring for answers
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     <a class="reference internal" href="../data-science/market-research.html">
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="linear-regression/loss-function.html">
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-     37.66. Decision Trees - Intro and Regression
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-     37.116. Basic classification: Classify images of clothing
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-     38.16. Build an machine learning web application
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+     41.19. Model Selection
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-     38.20. Convolutional Neural Network
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+   40.50.1. Build classification models：Predict the price range
   </a>
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  <li class="toc-h2 nav-item toc-entry">
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+   40.50.2. 1. Problem Formulation
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+   40.50.3. 2. Finding the Data
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+   40.50.4. 3. Exploring Insights in Data or EDA
   </a>
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+     40.50.4.1. Checking summary statistics
     </a>
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+     40.50.4.2. Checking missing values
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+     40.50.4.3. Checking Correlation Between Features
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-     37.50.4.4. More Data Exploration
+     40.50.4.4. More Data Exploration
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+     40.50.4.5. Exploring Categorical Features
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+     40.50.7.1. Classification Performance Metrics
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+   40.50.1. Build classification models：Predict the price range
   </a>
  </li>
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   <a class="reference internal nav-link" href="#problem-formulation">
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+   40.50.2. 1. Problem Formulation
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+   40.50.3. 2. Finding the Data
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   <a class="reference internal nav-link" href="#exploring-insights-in-data-or-eda">
-   37.50.4. 3. Exploring Insights in Data or EDA
+   40.50.4. 3. Exploring Insights in Data or EDA
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-summary-statistics">
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+     40.50.4.1. Checking summary statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     37.50.4.2. Checking missing values
+     40.50.4.2. Checking missing values
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-correlation-between-features">
-     37.50.4.3. Checking Correlation Between Features
+     40.50.4.3. Checking Correlation Between Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     37.50.4.4. More Data Exploration
+     40.50.4.4. More Data Exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     37.50.4.5. Exploring Categorical Features
+     40.50.4.5. Exploring Categorical Features
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+   40.50.5. 4. Data Preprocessing
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-     37.50.5.1. Feature Scaling
+     40.50.5.1. Feature Scaling
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#creating-and-training-a-logistic-regression-model">
-   37.50.6. 5. Creating and Training a Logistic Regression Model
+   40.50.6. 5. Creating and Training a Logistic Regression Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#trying-other-models">
-     37.50.6.1. Trying other models
+     40.50.6.1. Trying other models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   37.50.7. 6.  Model Evaluation
+   40.50.7. 6.  Model Evaluation
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-performance-metrics">
-     37.50.7.1. Classification Performance Metrics
+     40.50.7.1. Classification Performance Metrics
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.50.8. Acknowledgments
+   40.50.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1730,21 +1762,21 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="ml-logistic-regression-assignment-1">
-<h1><span class="section-number">37.50. </span>ML logistic regression - assignment 1<a class="headerlink" href="#ml-logistic-regression-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.50. </span>ML logistic regression - assignment 1<a class="headerlink" href="#ml-logistic-regression-assignment-1" title="Permalink to this headline">#</a></h1>
 <section id="build-classification-models-predict-the-price-range">
-<h2><span class="section-number">37.50.1. </span>Build classification models：Predict the price range<a class="headerlink" href="#build-classification-models-predict-the-price-range" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.1. </span>Build classification models：Predict the price range<a class="headerlink" href="#build-classification-models-predict-the-price-range" title="Permalink to this headline">#</a></h2>
 <p>In this lab, we will learn about classification where the task is to predict the class or category. Both regression and classification are the main two types of supervised learning.</p>
 <p><a name='1'></a></p>
 </section>
 <section id="problem-formulation">
-<h2><span class="section-number">37.50.2. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.2. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
 <p>Let’s say you have an idea of a revolutionary mobile phone and you want to establish a start up, but you know little about the price of the mobile phones. You are interested in learning that!</p>
 <p>Fortunately, there is this mobile dataset <a class="reference external" href="https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv">on Kaggle</a> that you can use to learn about the price ranges of mobiles based on their features such as wifi &amp; bluetooth supports etc…</p>
 <p>So, to make it simple, you have a dataset containing the features of mobiles and the problem is to predict the price range, not the exact price.</p>
 <p><a name='2'></a></p>
 </section>
 <section id="finding-the-data">
-<h2><span class="section-number">37.50.3. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.3. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data that we are going to use is found on Kaggle.</p>
 <p>Here are the details of the features. It is 21 features. The target feature is <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">range</span></code> and it has four price ranges: <code class="docutils literal notranslate"><span class="pre">0(low</span> <span class="pre">cost),</span> <span class="pre">1(medium</span> <span class="pre">cost),</span> <span class="pre">2(high</span> <span class="pre">cost)</span> <span class="pre">and</span> <span class="pre">3(very</span> <span class="pre">high</span> <span class="pre">cost).</span></code></p>
 <ul class="simple">
@@ -1866,7 +1898,7 @@ <h2><span class="section-number">37.50.3. </span>2. Finding the Data<a class="he
 <p><a name='3'></a></p>
 </section>
 <section id="exploring-insights-in-data-or-eda">
-<h2><span class="section-number">37.50.4. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.4. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
 <p>In this part, we are going to learn more about the dataset. Let’s start with the summary statistics, but before that, I will copy the training data in order to get it easily when we mess up down the road.</p>
 <div class="cell docutils container">
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@@ -1876,7 +1908,7 @@ <h2><span class="section-number">37.50.4. </span>3. Exploring Insights in Data o
 </div>
 </div>
 <section id="checking-summary-statistics">
-<h3><span class="section-number">37.50.4.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -1886,7 +1918,7 @@ <h3><span class="section-number">37.50.4.1. </span>Checking summary statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">37.50.4.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -1897,7 +1929,7 @@ <h3><span class="section-number">37.50.4.2. </span>Checking missing values<a cla
 <p>We are lucky to not have missing values. If we had some, we would have to fill them with some strategies such as mean, remove them compleletly or leave them as they are. None of those 3 options is always the right choice for imputing missing values in all kinds of problems. it depends on the problem and the size of your dataset. Take an example, by removing a feature, you’re loosing data. And by filling the values, you’re adding noise or so. There is a trade off when it comes to imputing the missing values.</p>
 </section>
 <section id="checking-correlation-between-features">
-<h3><span class="section-number">37.50.4.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">mobile_train</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -1926,7 +1958,7 @@ <h3><span class="section-number">37.50.4.3. </span>Checking Correlation Between
 <p>Let’s take that insights from words to visualization.</p>
 </section>
 <section id="more-data-exploration">
-<h3><span class="section-number">37.50.4.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Again, let’s see what we have in the price ranges.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2044,7 +2076,7 @@ <h3><span class="section-number">37.50.4.4. </span>More Data Exploration<a class
 </div>
 </section>
 <section id="exploring-categorical-features">
-<h3><span class="section-number">37.50.4.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Even if all features have numerical values, but there some of them which are categorical. Let’s inspect them!! It might be that they are already encoded!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2134,7 +2166,7 @@ <h3><span class="section-number">37.50.4.5. </span>Exploring Categorical Feature
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.50.5. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.5. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>In this part, we will prepare the data to be in proper format for ML models. We will do things like feature scaling. We would also had to encode categorical features but they are already encoded. Before we proceed with feature scaling, let’s take training labels from training data.</p>
 <div class="cell docutils container">
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@@ -2145,7 +2177,7 @@ <h2><span class="section-number">37.50.5. </span>4. Data Preprocessing<a class="
 </div>
 </div>
 <section id="feature-scaling">
-<h3><span class="section-number">37.50.5.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.5.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
 <p>This is the only data processing type that we have to take care of here. Let’s make a function that can take handle that.</p>
 <p>We will normalize the data with sklearn <code class="docutils literal notranslate"><span class="pre">MinMaxScaler</span></code> where the numerical values will be scaled to values between 0 and 1.</p>
 <div class="cell docutils container">
@@ -2180,7 +2212,7 @@ <h3><span class="section-number">37.50.5.1. </span>Feature Scaling<a class="head
 </section>
 </section>
 <section id="creating-and-training-a-logistic-regression-model">
-<h2><span class="section-number">37.50.6. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.6. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
 <p>We will start by importing <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> models available in Sklearn linear models and then proceed with model training.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2203,7 +2235,7 @@ <h2><span class="section-number">37.50.6. </span>5. Creating and Training a Logi
 <p>Using <code class="docutils literal notranslate"><span class="pre">log_model.score()</span></code>, we can get an accuracy of the model confidence on the training data and labels. 94.7% is not that bad.</p>
 <p>Let’s train another linear classifier called SGD (Stockastic Gradient Descent) classifier on the same dataset.</p>
 <section id="trying-other-models">
-<h3><span class="section-number">37.50.6.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.6.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
@@ -2244,7 +2276,7 @@ <h3><span class="section-number">37.50.6.1. </span>Trying other models<a class="
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.50.7. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.7. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>Evaluating classification models is not as simple as for regression models. When you have skewed dataset or imbalances, you can get a high accuracy and you can think the model did well when in fact it didn’t.</p>
 <p>In this part, we are going to learn how to evaluate classification models. But first, let’s evaluate the model with cross validation.</p>
 <p>Here is an idea behind cross validation: We want to divide the training set into different training and validation subsets so that we can iteratively train and validate the models on those subsets.</p>
@@ -2351,7 +2383,7 @@ <h2><span class="section-number">37.50.7. </span>6.  Model Evaluation<a class="h
 </div>
 </div>
 <section id="classification-performance-metrics">
-<h3><span class="section-number">37.50.7.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.7.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
 <p>Here are commom classification metrics:</p>
 <ul class="simple">
 <li><p>Accuracy</p></li>
@@ -2424,7 +2456,7 @@ <h3><span class="section-number">37.50.7.1. </span>Classification Performance Me
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.50.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jean de Dieu Nyandwi for creating the open source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">machine_learning_complete</a> . It inspires the majority of the content in this chapter.Original Notebook source from <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/2_linear_models_for_classification.ipynb">Linear Models for Classification</a></p>
 </section>
 </section>
@@ -2466,13 +2498,13 @@ <h2><span class="section-number">37.50.8. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.49. </span>Linear Regression Implementation from Scratch</p>
+            <p class="prev-next-title"><span class="section-number">40.49. </span>Linear Regression Implementation from Scratch</p>
         </div>
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     <a class='right-next' id="next-link" href="ml-logistic-regression-2.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.51. </span>ML logistic regression - assignment 2</p>
+        <p class="prev-next-title"><span class="section-number">40.51. </span>ML logistic regression - assignment 2</p>
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diff --git a/assignments/ml-fundamentals/ml-logistic-regression-2.html b/assignments/ml-fundamentals/ml-logistic-regression-2.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.51. ML logistic regression - assignment 2 &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.51. ML logistic regression - assignment 2 &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.52. ML neural network - Assignment 1" href="ml-neural-network-1.html" />
-    <link rel="prev" title="37.50. ML logistic regression - assignment 1" href="ml-logistic-regression-1.html" />
+    <link rel="next" title="40.52. ML neural network - Assignment 1" href="ml-neural-network-1.html" />
+    <link rel="prev" title="40.50. ML logistic regression - assignment 1" href="ml-logistic-regression-1.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.29. Matplotlib applied
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-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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+<h1><span class="section-number">40.51. </span>ML logistic regression - assignment 2<a class="headerlink" href="#ml-logistic-regression-assignment-2" title="Permalink to this headline">#</a></h1>
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+<h2><span class="section-number">40.51.1. </span>Logistic Regression from scratch<a class="headerlink" href="#logistic-regression-from-scratch" title="Permalink to this headline">#</a></h2>
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-    <link rel="prev" title="37.51. ML logistic regression - assignment 2" href="ml-logistic-regression-2.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.9. Analyzing text about Data Science
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-     37.14. Try it in Excel
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-     37.19. Introduction to probability and statistics
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-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.24. Data processing in Python
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-     37.29. Matplotlib applied
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-     37.35. Tell a story
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-     37.37. Exploring for answers
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     <a class="reference internal" href="../data-science/market-research.html">
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="ml-logistic-regression-1.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.16. Build an machine learning web application
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@@ -1528,47 +1560,47 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   37.52.8. Training loop
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@@ -1646,7 +1678,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="ml-neural-network-assignment-1">
-<h1><span class="section-number">37.52. </span>ML neural network - Assignment 1<a class="headerlink" href="#ml-neural-network-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.52. </span>ML neural network - Assignment 1<a class="headerlink" href="#ml-neural-network-assignment-1" title="Permalink to this headline">#</a></h1>
 <p>In this notebook, we’re going to build a neural network using naught but pure numpy and your willpower. It’s going to be fun, I promise!</p>
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-<h2><span class="section-number">37.52.1. </span>The road ahead<a class="headerlink" href="#the-road-ahead" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.1. </span>The road ahead<a class="headerlink" href="#the-road-ahead" title="Permalink to this headline">#</a></h2>
 <p>We’re going to build a neural network that classifies MNIST digits. To do so, we’ll need a few building blocks:</p>
 <ul class="simple">
 <li><p>Dense layer - a fully-connected layer, <span class="math notranslate nohighlight">\(f(X)=X \cdot W + \vec{b}\)</span></p></li>
@@ -1715,7 +1747,7 @@ <h2><span class="section-number">37.52.1. </span>The road ahead<a class="headerl
 <p>Let’s approach them one at a time.</p>
 </section>
 <section id="nonlinearity-layer">
-<h2><span class="section-number">37.52.2. </span>Nonlinearity layer<a class="headerlink" href="#nonlinearity-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.2. </span>Nonlinearity layer<a class="headerlink" href="#nonlinearity-layer" title="Permalink to this headline">#</a></h2>
 <p>This is the simplest layer you can get: it simply applies a nonlinearity to each element of your network.</p>
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-<h2><span class="section-number">37.52.3. </span>Dense layer<a class="headerlink" href="#dense-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.3. </span>Dense layer<a class="headerlink" href="#dense-layer" title="Permalink to this headline">#</a></h2>
 <p>Now let’s build something more complicated. Unlike nonlinearity, a dense layer actually has something to learn.</p>
 <p>A dense layer applies affine transformation. In a vectorized form, it can be described as:
 $<span class="math notranslate nohighlight">\(f(X)= X \cdot W + \vec b \)</span>$</p>
@@ -1798,7 +1830,7 @@ <h2><span class="section-number">37.52.3. </span>Dense layer<a class="headerlink
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-<h2><span class="section-number">37.52.4. </span>Testing the dense layer<a class="headerlink" href="#testing-the-dense-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.4. </span>Testing the dense layer<a class="headerlink" href="#testing-the-dense-layer" title="Permalink to this headline">#</a></h2>
 <p>Here we have a few tests to make sure your dense layer works properly. You can just run them, get 3 “well done”s and forget they ever existed.</p>
 <p>… or not get 3 “well done”s and go fix stuff. If that is the case, here are some tips for you:</p>
 <ul class="simple">
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-<h2><span class="section-number">37.52.5. </span>The loss function<a class="headerlink" href="#the-loss-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.5. </span>The loss function<a class="headerlink" href="#the-loss-function" title="Permalink to this headline">#</a></h2>
 <p>Since we want to predict probabilities, it would be logical for us to define softmax nonlinearity on top of our network and compute loss given predicted probabilities. However, there is a better way to do so.</p>
 <p>If you write down the expression for crossentropy as a function of softmax logits (a), you’ll see:</p>
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@@ -1876,7 +1908,7 @@ <h2><span class="section-number">37.52.5. </span>The loss function<a class="head
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-<h2><span class="section-number">37.52.6. </span>Full network<a class="headerlink" href="#full-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.6. </span>Full network<a class="headerlink" href="#full-network" title="Permalink to this headline">#</a></h2>
 <p>Now let’s combine what we’ve just built into a working neural network. As we announced, we’re gonna use this monster to classify handwritten digits, so let’s get them loaded.</p>
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-<h2><span class="section-number">37.52.7. </span>Backprop<a class="headerlink" href="#backprop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.7. </span>Backprop<a class="headerlink" href="#backprop" title="Permalink to this headline">#</a></h2>
 <p>You can now define the backpropagation step for the neural network. Please read the docstring.</p>
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 <p>If your implementation of forward and backward are correct, your accuracy should grow from 90~93% to &gt;97% with the default network.</p>
 </section>
 <section id="training-loop">
-<h2><span class="section-number">37.52.8. </span>Training loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.8. </span>Training loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
 <p>As usual, we split data into minibatches, feed each such minibatch into the network and update weights.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2094,7 +2126,7 @@ <h2><span class="section-number">37.52.8. </span>Training loop<a class="headerli
 <p>Good hunting!</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.52.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.52.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/yandexdataschool">yandexdataschool</a> for creating the open-source course <a class="reference external" href="https://github.com/yandexdataschool/Practical_DL"> Practical_DL</a>. It inspires the majority of the content in this assignment.</p>
 </section>
 </section>
@@ -2136,13 +2168,13 @@ <h2><span class="section-number">37.52.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.51. </span>ML logistic regression - assignment 2</p>
+            <p class="prev-next-title"><span class="section-number">40.51. </span>ML logistic regression - assignment 2</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="regression-tools.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.53. </span>Regression tools</p>
+        <p class="prev-next-title"><span class="section-number">40.53. </span>Regression tools</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-fundamentals/ml-overview-iris.html b/assignments/ml-fundamentals/ml-overview-iris.html
index ef5985d16d..8d99f6904c 100644
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+++ b/assignments/ml-fundamentals/ml-overview-iris.html
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-    <title>37.42. Machine Learning overview - assignment 1 &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.42. Machine Learning overview - assignment 1 &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.43. Machine Learning overview - assignment 2" href="ml-overview-mnist-digits.html" />
-    <link rel="prev" title="37.41. Data Science in the cloud: The “Azure ML SDK” way" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html" />
+    <link rel="next" title="40.43. Machine Learning overview - assignment 2" href="ml-overview-mnist-digits.html" />
+    <link rel="prev" title="40.41. Data Science in the cloud: The “Azure ML SDK” way" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <label for="toctree-checkbox-8">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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+  <label for="toctree-checkbox-10">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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  </li>
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
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     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="managing-data.html">
-     37.54. Managing data
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-     37.55. Exploring visualizations
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.67. Decision Trees - Classification
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.103. Fun with Variational Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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-     37.116. Basic classification: Classify images of clothing
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@@ -1526,111 +1558,111 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-the-first-5-rows-of-the-data">
-   37.42.5. Show the first 5 rows of the data
+   40.42.5. Show the first 5 rows of the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#get-a-global-description-of-the-dataset">
-   37.42.6. Get a global description of the dataset
+   40.42.6. Get a global description of the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#group-data">
-   37.42.7. Group data
+   40.42.7. Group data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dividing-data-into-features-and-labels">
-   37.42.8. Dividing data into features and labels
+   40.42.8. Dividing data into features and labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#label-encoding">
-   37.42.9. Label encoding
+   40.42.9. Label encoding
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#spliting-dataset-into-training-set-and-test-set">
-   37.42.10. Spliting dataset into training set and test set
+   40.42.10. Spliting dataset into training set and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   37.42.11. Feature scaling
+   40.42.11. Feature scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-visualization">
-   37.42.12. Data Visualization
+   40.42.12. Data Visualization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pairplot">
-     37.42.12.1. pairplot
+     40.42.12.1. pairplot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boxplots">
-     37.42.12.2. Boxplots
+     40.42.12.2. Boxplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#d-visualization">
-     37.42.12.3. 3D visualization
+     40.42.12.3. 3D visualization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-knn-for-classification">
-   37.42.13. Using KNN for classification
+   40.42.13. Using KNN for classification
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-the-model">
-     37.42.13.1. Train the model
+     40.42.13.1. Train the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#predict-with-test-dataset">
-     37.42.13.2. Predict, with test dataset
+     40.42.13.2. Predict, with test dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-predictions">
-     37.42.13.3. Evaluating predictions
+     40.42.13.3. Evaluating predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-cross-validation-for-parameter-tuning">
-     37.42.13.4. Using cross-validation for parameter tuning
+     40.42.13.4. Using cross-validation for parameter tuning
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.42.14. Acknowledgments
+   40.42.14. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1654,111 +1686,111 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-the-assignment-is-for">
-   37.42.1. What the assignment is for
+   40.42.1. What the assignment is for
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   37.42.2. Import libraries
+   40.42.2. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-dataset">
-   37.42.3. Load Dataset
+   40.42.3. Load Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dimension-shape-of-dataset">
-   37.42.4. Dimension (shape) of Dataset
+   40.42.4. Dimension (shape) of Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-the-first-5-rows-of-the-data">
-   37.42.5. Show the first 5 rows of the data
+   40.42.5. Show the first 5 rows of the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#get-a-global-description-of-the-dataset">
-   37.42.6. Get a global description of the dataset
+   40.42.6. Get a global description of the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#group-data">
-   37.42.7. Group data
+   40.42.7. Group data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dividing-data-into-features-and-labels">
-   37.42.8. Dividing data into features and labels
+   40.42.8. Dividing data into features and labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#label-encoding">
-   37.42.9. Label encoding
+   40.42.9. Label encoding
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#spliting-dataset-into-training-set-and-test-set">
-   37.42.10. Spliting dataset into training set and test set
+   40.42.10. Spliting dataset into training set and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   37.42.11. Feature scaling
+   40.42.11. Feature scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-visualization">
-   37.42.12. Data Visualization
+   40.42.12. Data Visualization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pairplot">
-     37.42.12.1. pairplot
+     40.42.12.1. pairplot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boxplots">
-     37.42.12.2. Boxplots
+     40.42.12.2. Boxplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#d-visualization">
-     37.42.12.3. 3D visualization
+     40.42.12.3. 3D visualization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-knn-for-classification">
-   37.42.13. Using KNN for classification
+   40.42.13. Using KNN for classification
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-the-model">
-     37.42.13.1. Train the model
+     40.42.13.1. Train the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#predict-with-test-dataset">
-     37.42.13.2. Predict, with test dataset
+     40.42.13.2. Predict, with test dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-predictions">
-     37.42.13.3. Evaluating predictions
+     40.42.13.3. Evaluating predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-cross-validation-for-parameter-tuning">
-     37.42.13.4. Using cross-validation for parameter tuning
+     40.42.13.4. Using cross-validation for parameter tuning
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.42.14. Acknowledgments
+   40.42.14. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1772,14 +1804,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="machine-learning-overview-assignment-1">
-<h1><span class="section-number">37.42. </span>Machine Learning overview - assignment 1<a class="headerlink" href="#machine-learning-overview-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.42. </span>Machine Learning overview - assignment 1<a class="headerlink" href="#machine-learning-overview-assignment-1" title="Permalink to this headline">#</a></h1>
 <section id="what-the-assignment-is-for">
-<h2><span class="section-number">37.42.1. </span>What the assignment is for<a class="headerlink" href="#what-the-assignment-is-for" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.1. </span>What the assignment is for<a class="headerlink" href="#what-the-assignment-is-for" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will explore the very classic iris dataset and use the very simple KNN (K-Nearest Neighbors) algorithm for classification.</p>
 <p><strong>You should pay attention to how this assignment follows the Machine Learning Workflow in our lecture session!</strong></p>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">37.42.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1792,7 +1824,7 @@ <h2><span class="section-number">37.42.2. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="load-dataset">
-<h2><span class="section-number">37.42.3. </span>Load Dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.3. </span>Load Dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
 <p>NOTE: Iris dataset includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1803,7 +1835,7 @@ <h2><span class="section-number">37.42.3. </span>Load Dataset<a class="headerlin
 </div>
 </section>
 <section id="dimension-shape-of-dataset">
-<h2><span class="section-number">37.42.4. </span>Dimension (shape) of Dataset<a class="headerlink" href="#dimension-shape-of-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.4. </span>Dimension (shape) of Dataset<a class="headerlink" href="#dimension-shape-of-dataset" title="Permalink to this headline">#</a></h2>
 <p>We can get a quick idea of how many instances (rows) and how many attributes (columns) the data contains with the shape property.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1814,7 +1846,7 @@ <h2><span class="section-number">37.42.4. </span>Dimension (shape) of Dataset<a
 </div>
 </section>
 <section id="show-the-first-5-rows-of-the-data">
-<h2><span class="section-number">37.42.5. </span>Show the first 5 rows of the data<a class="headerlink" href="#show-the-first-5-rows-of-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.5. </span>Show the first 5 rows of the data<a class="headerlink" href="#show-the-first-5-rows-of-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
@@ -1824,7 +1856,7 @@ <h2><span class="section-number">37.42.5. </span>Show the first 5 rows of the da
 </div>
 </section>
 <section id="get-a-global-description-of-the-dataset">
-<h2><span class="section-number">37.42.6. </span>Get a global description of the dataset<a class="headerlink" href="#get-a-global-description-of-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.6. </span>Get a global description of the dataset<a class="headerlink" href="#get-a-global-description-of-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
@@ -1834,7 +1866,7 @@ <h2><span class="section-number">37.42.6. </span>Get a global description of the
 </div>
 </section>
 <section id="group-data">
-<h2><span class="section-number">37.42.7. </span>Group data<a class="headerlink" href="#group-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.7. </span>Group data<a class="headerlink" href="#group-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s now take a look at the number of instances (rows) that belong to each class. We can view this as an absolute count.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1845,7 +1877,7 @@ <h2><span class="section-number">37.42.7. </span>Group data<a class="headerlink"
 </div>
 </section>
 <section id="dividing-data-into-features-and-labels">
-<h2><span class="section-number">37.42.8. </span>Dividing data into features and labels<a class="headerlink" href="#dividing-data-into-features-and-labels" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.8. </span>Dividing data into features and labels<a class="headerlink" href="#dividing-data-into-features-and-labels" title="Permalink to this headline">#</a></h2>
 <p>As we can see dataset contain five columns: SepalLength, SepalWidth, PetalLength, PetalWidth and Name. The actual features are described by columns 0-3. Last column contains labels of samples. Firstly we need to split data into two arrays: X (features) and y (labels).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1872,7 +1904,7 @@ <h2><span class="section-number">37.42.8. </span>Dividing data into features and
 </div>
 </section>
 <section id="label-encoding">
-<h2><span class="section-number">37.42.9. </span>Label encoding<a class="headerlink" href="#label-encoding" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.9. </span>Label encoding<a class="headerlink" href="#label-encoding" title="Permalink to this headline">#</a></h2>
 <p>As we can see labels are categorical. KNeighborsClassifier does not accept string labels. We need to use LabelEncoder to transform them into numbers. Iris-setosa correspond to 0, Iris-versicolor correspond to 1 and Iris-virginica correspond to 2.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1886,7 +1918,7 @@ <h2><span class="section-number">37.42.9. </span>Label encoding<a class="headerl
 </div>
 </section>
 <section id="spliting-dataset-into-training-set-and-test-set">
-<h2><span class="section-number">37.42.10. </span>Spliting dataset into training set and test set<a class="headerlink" href="#spliting-dataset-into-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.10. </span>Spliting dataset into training set and test set<a class="headerlink" href="#spliting-dataset-into-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <p>Let’s split dataset into training set and test set, to check later on whether or not our classifier works correctly.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1899,13 +1931,13 @@ <h2><span class="section-number">37.42.10. </span>Spliting dataset into training
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">37.42.11. </span>Feature scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.11. </span>Feature scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <p>Because features values are in the same order of magnitude, there is no need for feature scaling. Nevertheless in other sercostamses it is extremly important to apply feature scaling before running classification algorythms.</p>
 </section>
 <section id="data-visualization">
-<h2><span class="section-number">37.42.12. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.12. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
 <section id="pairplot">
-<h3><span class="section-number">37.42.12.1. </span>pairplot<a class="headerlink" href="#pairplot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.12.1. </span>pairplot<a class="headerlink" href="#pairplot" title="Permalink to this headline">#</a></h3>
 <p>Pairwise is useful when you want to visualize the distribution of a variable or the relationship between multiple variables separately within subsets of your dataset.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1920,7 +1952,7 @@ <h3><span class="section-number">37.42.12.1. </span>pairplot<a class="headerlink
 </div>
 </section>
 <section id="boxplots">
-<h3><span class="section-number">37.42.12.2. </span>Boxplots<a class="headerlink" href="#boxplots" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.12.2. </span>Boxplots<a class="headerlink" href="#boxplots" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
@@ -1932,7 +1964,7 @@ <h3><span class="section-number">37.42.12.2. </span>Boxplots<a class="headerlink
 </div>
 </section>
 <section id="d-visualization">
-<h3><span class="section-number">37.42.12.3. </span>3D visualization<a class="headerlink" href="#d-visualization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.12.3. </span>3D visualization<a class="headerlink" href="#d-visualization" title="Permalink to this headline">#</a></h3>
 <p>You can also try to visualize high-dimensional datasets in 3D using color, shape, size and other properties of 3D and 2D objects. In this plot we use marks sizes to visualize fourth dimenssion which is Petal Width.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1965,9 +1997,9 @@ <h3><span class="section-number">37.42.12.3. </span>3D visualization<a class="he
 </section>
 </section>
 <section id="using-knn-for-classification">
-<h2><span class="section-number">37.42.13. </span>Using KNN for classification<a class="headerlink" href="#using-knn-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.13. </span>Using KNN for classification<a class="headerlink" href="#using-knn-for-classification" title="Permalink to this headline">#</a></h2>
 <section id="train-the-model">
-<h3><span class="section-number">37.42.13.1. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.13.1. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Fitting clasifier to the Training set</span>
@@ -1985,7 +2017,7 @@ <h3><span class="section-number">37.42.13.1. </span>Train the model<a class="hea
 </div>
 </section>
 <section id="predict-with-test-dataset">
-<h3><span class="section-number">37.42.13.2. </span>Predict, with test dataset<a class="headerlink" href="#predict-with-test-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.13.2. </span>Predict, with test dataset<a class="headerlink" href="#predict-with-test-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -2009,7 +2041,7 @@ <h3><span class="section-number">37.42.13.2. </span>Predict, with test dataset<a
 </div>
 </section>
 <section id="evaluating-predictions">
-<h3><span class="section-number">37.42.13.3. </span>Evaluating predictions<a class="headerlink" href="#evaluating-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.13.3. </span>Evaluating predictions<a class="headerlink" href="#evaluating-predictions" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Building confusion matrix:</span>
@@ -2036,7 +2068,7 @@ <h3><span class="section-number">37.42.13.3. </span>Evaluating predictions<a cla
 </div>
 </section>
 <section id="using-cross-validation-for-parameter-tuning">
-<h3><span class="section-number">37.42.13.4. </span>Using cross-validation for parameter tuning<a class="headerlink" href="#using-cross-validation-for-parameter-tuning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.13.4. </span>Using cross-validation for parameter tuning<a class="headerlink" href="#using-cross-validation-for-parameter-tuning" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
@@ -2085,7 +2117,7 @@ <h3><span class="section-number">37.42.13.4. </span>Using cross-validation for p
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.42.14. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.14. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to SkalskiP for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/skalskip/iris-data-visualization-and-knn-classification">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
 </section>
 </section>
@@ -2127,13 +2159,13 @@ <h2><span class="section-number">37.42.14. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.41. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
+            <p class="prev-next-title"><span class="section-number">40.41. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="ml-overview-mnist-digits.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.43. </span>Machine Learning overview - assignment 2</p>
+        <p class="prev-next-title"><span class="section-number">40.43. </span>Machine Learning overview - assignment 2</p>
     </div>
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     </a>
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.44. Regression with Scikit-learn" href="regression-with-scikit-learn.html" />
-    <link rel="prev" title="37.42. Machine Learning overview - assignment 1" href="ml-overview-iris.html" />
+    <link rel="next" title="40.44. Regression with Scikit-learn" href="regression-with-scikit-learn.html" />
+    <link rel="prev" title="40.42. Machine Learning overview - assignment 1" href="ml-overview-iris.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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-     11.3. Linear and polynomial regression
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-     13.2. More classifiers
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
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+ <li class="toctree-l1 has-children">
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+  </label>
+  <ul>
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+     15.1. Loss function
+    </a>
+   </li>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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    </li>
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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-  <label for="toctree-checkbox-11">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  </li>
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  </li>
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
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-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
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    </li>
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     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="../data-science/matplotlib-applied.html">
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
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     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="regression-with-scikit-learn.html">
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="linear-regression/gradient-descent.html">
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     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="ml-neural-network-1.html">
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     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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@@ -1526,12 +1558,12 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#digit-recognizer">
-   37.43.1. Digit Recognizer
+   40.43.1. Digit Recognizer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.43.2. Acknowledgments
+   40.43.2. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1555,12 +1587,12 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#digit-recognizer">
-   37.43.1. Digit Recognizer
+   40.43.1. Digit Recognizer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.43.2. Acknowledgments
+   40.43.2. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1574,9 +1606,9 @@ <h2> Contents </h2>
               <div>
                 
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-<h1><span class="section-number">37.43. </span>Machine Learning overview - assignment 2<a class="headerlink" href="#machine-learning-overview-assignment-2" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.43. </span>Machine Learning overview - assignment 2<a class="headerlink" href="#machine-learning-overview-assignment-2" title="Permalink to this headline">#</a></h1>
 <section id="digit-recognizer">
-<h2><span class="section-number">37.43.1. </span>Digit Recognizer<a class="headerlink" href="#digit-recognizer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.43.1. </span>Digit Recognizer<a class="headerlink" href="#digit-recognizer" title="Permalink to this headline">#</a></h2>
 <p>Although this is a computer vision problem, we employ here a simple model using <strong>K-Nearest Neighbors</strong> algorithm in this notebook to be a good starting point. We use the <strong>GridSearchCV</strong> to fine tune the hyperparameters such as <em>“n_neighbors”, and “weights”</em>. Furthermore, we use <strong>Data Augmentation</strong> or <strong>Artificial Data Synthesis</strong> technique in this notebook to boost the model’s performance on the test set.</p>
 <div class="cell docutils container">
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@@ -1856,7 +1888,7 @@ <h2><span class="section-number">37.43.1. </span>Digit Recognizer<a class="heade
 <p><strong>Note:</strong> With <strong>Data Augmentation</strong> the accuracy jumped from 91.6% to 95.3% on the test data.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.43.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.43.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to SkalskiP for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/gauthampughazh/digit-recognition-using-knn">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
 </section>
 </section>
@@ -1898,13 +1930,13 @@ <h2><span class="section-number">37.43.2. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.42. </span>Machine Learning overview - assignment 1</p>
+            <p class="prev-next-title"><span class="section-number">40.42. </span>Machine Learning overview - assignment 1</p>
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     </a>
     <a class='right-next' id="next-link" href="regression-with-scikit-learn.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.44. </span>Regression with Scikit-learn</p>
+        <p class="prev-next-title"><span class="section-number">40.44. </span>Regression with Scikit-learn</p>
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diff --git a/assignments/ml-fundamentals/parameter-play.html b/assignments/ml-fundamentals/parameter-play.html
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-    <link rel="prev" title="37.88. Build Classification Model" href="build-classification-model.html" />
+    <link rel="next" title="40.90. How to choose cnn architecture mnist" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html" />
+    <link rel="prev" title="40.88. Build Classification Model" href="build-classification-model.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
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    <li class="toctree-l2">
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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    <li class="toctree-l2">
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-     37.16. Build your own custom vis
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-     37.17. Classifying datasets
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-     37.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="../data-science/matplotlib-applied.html">
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-     37.35. Tell a story
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
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     <a class="reference internal" href="../data-science/market-research.html">
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="regression-with-scikit-learn.html">
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     <a class="reference internal" href="regression-tools.html">
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     <a class="reference internal" href="linear-and-polynomial-regression.html">
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-     37.59. Retrying some regression
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     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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-     37.62. Explore classification methods
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.89.1. Instructions
+   40.89.1. Instructions
   </a>
  </li>
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-   37.89.2. Rubric
+   40.89.2. Rubric
   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.89.3. Acknowledgments
+   40.89.3. Acknowledgments
   </a>
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.89.1. Instructions
+   40.89.1. Instructions
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.89.3. Acknowledgments
+   40.89.3. Acknowledgments
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  </li>
 </ul>
@@ -1601,13 +1633,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="parameter-play">
-<h1><span class="section-number">37.89. </span>Parameter play<a class="headerlink" href="#parameter-play" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.89. </span>Parameter play<a class="headerlink" href="#parameter-play" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.89.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.89.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>There are a lot of parameters that are set by default when working with these classifiers. Intellisense in VS Code can help you dig into them. Adopt one of the ML Classification Techniques in this section and retrain models tweaking various parameter values. Build a notebook explaining why some changes help the model quality while others degrade it. Be detailed in your answer.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.89.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.89.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Criteria</p></th>
@@ -1626,7 +1658,7 @@ <h2><span class="section-number">37.89.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.89.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.89.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1668,13 +1700,13 @@ <h2><span class="section-number">37.89.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.88. </span>Build Classification Model</p>
+            <p class="prev-next-title"><span class="section-number">40.88. </span>Build Classification Model</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.90. </span>How to choose cnn architecture mnist</p>
+        <p class="prev-next-title"><span class="section-number">40.90. </span>How to choose cnn architecture mnist</p>
     </div>
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-    <title>37.60. Pumpkin varieties and color &#8212; Ocademy Open Machine Learning Book</title>
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-    <link rel="next" title="37.61. Delicious asian and indian cuisines" href="delicious-asian-and-indian-cuisines.html" />
-    <link rel="prev" title="37.59. Retrying some regression" href="retrying-some-regression.html" />
+    <link rel="next" title="40.61. Delicious asian and indian cuisines" href="delicious-asian-and-indian-cuisines.html" />
+    <link rel="prev" title="40.59. Retrying some regression" href="retrying-some-regression.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
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+ </li>
+ <li class="toctree-l1 has-children">
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+     15.1. Loss function
+    </a>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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+ </li>
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+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.6. Python programming introduction
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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    <li class="toctree-l2">
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-     37.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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-     37.54. Managing data
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-     37.55. Exploring visualizations
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     <a class="reference internal" href="try-a-different-model.html">
-     37.56. Try a different model
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     <a class="reference internal" href="create-a-regression-model.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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-     37.62. Explore classification methods
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-     37.63. Kernel method assignment 1
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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-     37.71. Lasso and Ridge Regression
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-     37.72. Regularized Linear Models
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-     37.76. Decision trees
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     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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     <a class="reference internal" href="study-the-solvers.html">
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
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-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-<h1><span class="section-number">37.60. </span>Pumpkin varieties and color<a class="headerlink" href="#pumpkin-varieties-and-color" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.60. </span>Pumpkin varieties and color<a class="headerlink" href="#pumpkin-varieties-and-color" title="Permalink to this headline">#</a></h1>
 <p>Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data:</p>
 <p>Let’s look at the relationship between color and variety</p>
 <div class="cell docutils container">
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-<h2><span class="section-number">37.60.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.60.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.59. </span>Retrying some regression</p>
+            <p class="prev-next-title"><span class="section-number">40.59. </span>Retrying some regression</p>
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     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.61. </span>Delicious asian and indian cuisines</p>
+        <p class="prev-next-title"><span class="section-number">40.61. </span>Delicious asian and indian cuisines</p>
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-    <link rel="prev" title="37.52. ML neural network - Assignment 1" href="ml-neural-network-1.html" />
+    <link rel="next" title="40.54. Managing data" href="managing-data.html" />
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    10. Machine Learning overview
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+   11. Summary of machine learning fundamentals
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-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
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-     37.27. Analyzing data
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     <a class="current reference internal" href="#">
-     37.53. Regression tools
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-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="explore-classification-methods.html">
-     37.62. Explore classification methods
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     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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-     37.70. Dropout and Batch Normalization
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-     37.72. Regularized Linear Models
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.84. Case Study: Debugging in Classification
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     <a class="reference internal" href="study-the-solvers.html">
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     <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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     <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-        <p class="prev-next-title"><span class="section-number">37.54. </span>Managing data</p>
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     <a class="reference internal" href="../data-science/analyzing-data.html">
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="../data-science/tell-a-story.html">
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    </li>
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    </li>
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     <a class="current reference internal" href="#">
-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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-     37.54. Managing data
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-     37.55. Exploring visualizations
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-     37.58. Linear and polynomial regression
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.103. Fun with Variational Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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@@ -1543,17 +1575,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   37.44.3. Acknowledgments
+   40.44.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1577,17 +1609,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.44.1. Instructions
+   40.44.1. Instructions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.44.2. Rubric
+   40.44.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.44.3. Acknowledgments
+   40.44.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1601,14 +1633,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="regression-with-scikit-learn">
-<h1><span class="section-number">37.44. </span>Regression with Scikit-learn<a class="headerlink" href="#regression-with-scikit-learn" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.44. </span>Regression with Scikit-learn<a class="headerlink" href="#regression-with-scikit-learn" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.44.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.44.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Take a look at the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud">Linnerud dataset</a> in Scikit-learn. This dataset has multiple <a class="reference external" href="https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset">targets</a>: ‘It consists of three exercise (data) and three physiological (target) variables collected from twenty middle-aged men in a fitness club’.</p>
 <p>In your own words, describe how to create a Regression model that would plot the relationship between the waistline and how many situps are accomplished. Do the same for the other datapoints in this dataset.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.44.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.44.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Criteria</p></th>
@@ -1627,7 +1659,7 @@ <h2><span class="section-number">37.44.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.44.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.44.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1669,13 +1701,13 @@ <h2><span class="section-number">37.44.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.43. </span>Machine Learning overview - assignment 2</p>
+            <p class="prev-next-title"><span class="section-number">40.43. </span>Machine Learning overview - assignment 2</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="linear-regression/california_housing.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.45. </span>Linear regression - California Housing</p>
+        <p class="prev-next-title"><span class="section-number">40.45. </span>Linear regression - California Housing</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-fundamentals/retrying-some-regression.html b/assignments/ml-fundamentals/retrying-some-regression.html
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-    <title>37.59. Retrying some regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.59. Retrying some regression &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.60. Pumpkin varieties and color" href="pumpkin-varieties-and-color.html" />
-    <link rel="prev" title="37.58. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="next" title="40.60. Pumpkin varieties and color" href="pumpkin-varieties-and-color.html" />
+    <link rel="prev" title="40.58. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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+     12.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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+     12.2. Managing data
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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-     13.2. More classifiers
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-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-     37.9. Analyzing text about Data Science
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-     37.11. Write a data ethics case study
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
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    <li class="toctree-l2">
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-     37.27. Analyzing data
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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     <a class="reference internal" href="try-a-different-model.html">
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     <a class="reference internal" href="create-a-regression-model.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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-     37.62. Explore classification methods
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     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="study-the-solvers.html">
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     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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+   40.59.1. Instructions
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+<h1><span class="section-number">40.59. </span>Retrying some regression<a class="headerlink" href="#retrying-some-regression" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.59.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.59.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In the section, you used a subset of the pumpkin data. Now, go back to the original data and try to use all of it, cleaned and standardized, to build a Logistic Regression model.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.59.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.59.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
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 <thead>
 <tr class="row-odd"><th class="head"><p>Criteria</p></th>
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 <section id="acknowledgments">
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+<h2><span class="section-number">40.59.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1668,13 +1700,13 @@ <h2><span class="section-number">37.59.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.58. </span>Linear and polynomial regression</p>
+            <p class="prev-next-title"><span class="section-number">40.58. </span>Linear and polynomial regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="pumpkin-varieties-and-color.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.60. </span>Pumpkin varieties and color</p>
+        <p class="prev-next-title"><span class="section-number">40.60. </span>Pumpkin varieties and color</p>
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     <i class="fas fa-angle-right"></i>
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-    <link rel="prev" title="37.85. Case Study: Debugging in Regression" href="../machine-learning-productionization/debugging-in-regression.html" />
+    <link rel="next" title="40.87. Build classification models" href="build-classification-models.html" />
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    10. Machine Learning overview
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+   11. Summary of machine learning fundamentals
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-     13.4. Applied Machine Learning : build a web app
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
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-     37.27. Analyzing data
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     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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-     37.54. Managing data
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-     37.55. Exploring visualizations
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     <a class="reference internal" href="try-a-different-model.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="create-a-regression-model.html">
-     37.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="retrying-some-regression.html">
-     37.59. Retrying some regression
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     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="explore-classification-methods.html">
-     37.62. Explore classification methods
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
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-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
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-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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               <div>
                 
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-<h1><span class="section-number">37.86. </span>Study the solvers<a class="headerlink" href="#study-the-solvers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.86. </span>Study the solvers<a class="headerlink" href="#study-the-solvers" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.86.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.86.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section you learned about the various solvers that pair algorithms with a machine learning process to create an accurate model. Walk through the solvers listed in the section and pick two. In your own words, compare and contrast these two solvers. What kind of problem do they address? How do they work with various data structures? Why would you pick one over another?</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.86.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.86.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Criteria</p></th>
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 </table>
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 <section id="acknowledgments">
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+<h2><span class="section-number">40.86.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1586,13 +1618,13 @@ <h2><span class="section-number">37.86.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.85. </span>Case Study: Debugging in Regression</p>
+            <p class="prev-next-title"><span class="section-number">40.85. </span>Case Study: Debugging in Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-classification-models.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.87. </span>Build classification models</p>
+        <p class="prev-next-title"><span class="section-number">40.87. </span>Build classification models</p>
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-    <link rel="prev" title="37.55. Exploring visualizations" href="exploring-visualizations.html" />
+    <link rel="next" title="40.57. Create a regression model" href="create-a-regression-model.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
+    </a>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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-     37.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.47. Loss Function
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-tools.html">
-     37.53. Regression tools
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-     37.54. Managing data
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     <a class="reference internal" href="exploring-visualizations.html">
-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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     <a class="reference internal" href="retrying-some-regression.html">
-     37.59. Retrying some regression
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     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="explore-classification-methods.html">
-     37.62. Explore classification methods
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     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.82. Data engineering
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-     37.84. Case Study: Debugging in Classification
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     <a class="reference internal" href="study-the-solvers.html">
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-     37.88. Build Classification Model
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-     37.89. Parameter play
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     <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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-     38.6. NumPy and Pandas
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     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
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+   40.56.1. Instructions
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+<h1><span class="section-number">40.56. </span>Try a different model<a class="headerlink" href="#try-a-different-model" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.56.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.56.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Now that you have built one web app using a trained Regression model, use one of the models from an earlier Regression section to redo this web app. You can keep the style or design it differently to reflect the pumpkin data. Be careful to change the inputs to reflect your model’s training method.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.56.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.56.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Criteria</p></th>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.56.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.56.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1586,13 +1618,13 @@ <h2><span class="section-number">37.56.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.55. </span>Exploring visualizations</p>
+            <p class="prev-next-title"><span class="section-number">40.55. </span>Exploring visualizations</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="create-a-regression-model.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.57. </span>Create a regression model</p>
+        <p class="prev-next-title"><span class="section-number">40.57. </span>Create a regression model</p>
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-    <link rel="prev" title="37.7. Python programming basics" href="python-programming-basics.html" />
+    <link rel="next" title="40.9. Analyzing text about Data Science" href="../data-science/analyzing-text-about-data-science.html" />
+    <link rel="prev" title="40.7. Python programming basics" href="python-programming-basics.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
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+ <li class="toctree-l1 has-children">
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+     15.1. Loss function
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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     <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
+     40.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
+     40.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
+     40.70. Dropout and Batch Normalization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
+     40.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
+     40.72. Regularized Linear Models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
+     40.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
+     40.74. Random forests for classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
+     40.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
+     40.76. Decision trees
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
+     40.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
+     40.78. Gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
+     40.79. Boosting with tuning
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
+     40.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
+     40.82. Data engineering
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    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
+     40.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
+     40.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
+     40.87. Build classification models
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    </li>
    <li class="toctree-l2">
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-     37.88. Build Classification Model
+     40.88. Build Classification Model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
+     40.89. Parameter play
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     <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
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    </li>
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
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+   41. Slides
   </a>
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-     38.3. Python programming advanced
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-     38.4. Data Science introduction
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-     38.6. NumPy and Pandas
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-     38.16. Build an machine learning web application
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+     41.17. Unsupervised learning
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-     38.20. Convolutional Neural Network
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+     40.8.1.1. if-elif-else
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+     40.8.2.1. Fill the missing pieces
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     <a class="reference internal nav-link" href="#range">
-     37.8.2.2. range()
+     40.8.2.2. range()
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     <a class="reference internal nav-link" href="#looping-dictionaries">
-     37.8.2.3. Looping dictionaries
+     40.8.2.3. Looping dictionaries
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     <a class="reference internal nav-link" href="#calculate-the-sum-of-dict-values">
-     37.8.2.4. Calculate the sum of dict values
+     40.8.2.4. Calculate the sum of dict values
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     <a class="reference internal nav-link" href="#create-a-list-of-strings-based-on-a-list-of-numbers">
-     37.8.2.5. Create a list of strings based on a list of numbers
+     40.8.2.5. Create a list of strings based on a list of numbers
     </a>
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+     40.8.6.2. Searching for wanted people
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-     37.8.6.3. Counting average length of words in a sentence
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+     40.8.1.1. if-elif-else
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-     37.8.2.2. range()
+     40.8.2.2. range()
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     <a class="reference internal nav-link" href="#looping-dictionaries">
-     37.8.2.3. Looping dictionaries
+     40.8.2.3. Looping dictionaries
     </a>
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     <a class="reference internal nav-link" href="#calculate-the-sum-of-dict-values">
-     37.8.2.4. Calculate the sum of dict values
+     40.8.2.4. Calculate the sum of dict values
     </a>
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     <a class="reference internal nav-link" href="#create-a-list-of-strings-based-on-a-list-of-numbers">
-     37.8.2.5. Create a list of strings based on a list of numbers
+     40.8.2.5. Create a list of strings based on a list of numbers
     </a>
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  </li>
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+     40.8.6.2. Searching for wanted people
     </a>
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     <a class="reference internal nav-link" href="#counting-average-length-of-words-in-a-sentence">
-     37.8.6.3. Counting average length of words in a sentence
+     40.8.6.3. Counting average length of words in a sentence
     </a>
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   </ul>
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+     40.8.9.1. Dealing with exceptions
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+   40.8.10. Acknowledgments
   </a>
  </li>
 </ul>
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 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="python-programming-advanced">
-<h1><span class="section-number">37.8. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.8. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
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-<h2><span class="section-number">37.8.1. </span>Conditionals<a class="headerlink" href="#conditionals" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.1. </span>Conditionals<a class="headerlink" href="#conditionals" title="Permalink to this headline">#</a></h2>
 <section id="if-elif-else">
-<h3><span class="section-number">37.8.1.1. </span>if-elif-else<a class="headerlink" href="#if-elif-else" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.1.1. </span>if-elif-else<a class="headerlink" href="#if-elif-else" title="Permalink to this headline">#</a></h3>
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+<h2><span class="section-number">40.8.2. </span>For loops<a class="headerlink" href="#for-loops" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces">
-<h3><span class="section-number">37.8.2.1. </span>Fill the missing pieces<a class="headerlink" href="#fill-the-missing-pieces" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.1. </span>Fill the missing pieces<a class="headerlink" href="#fill-the-missing-pieces" title="Permalink to this headline">#</a></h3>
 <p>Fill the <code class="docutils literal notranslate"><span class="pre">____</span></code> parts in the code below.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2097,7 +2129,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="range">
-<h3><span class="section-number">37.8.2.2. </span>range()<a class="headerlink" href="#range" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.2. </span>range()<a class="headerlink" href="#range" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">calculate_sum</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
@@ -2153,7 +2185,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="looping-dictionaries">
-<h3><span class="section-number">37.8.2.3. </span>Looping dictionaries<a class="headerlink" href="#looping-dictionaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.3. </span>Looping dictionaries<a class="headerlink" href="#looping-dictionaries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">my_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;hacker&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;age&#39;</span><span class="p">:</span> <span class="mi">72</span><span class="p">,</span> <span class="s1">&#39;name&#39;</span><span class="p">:</span> <span class="s1">&#39;John Doe&#39;</span><span class="p">}</span>
@@ -2173,7 +2205,7 @@ <h3><span class="section-number">37.8.2.3. </span>Looping dictionaries<a class="
 </div>
 </section>
 <section id="calculate-the-sum-of-dict-values">
-<h3><span class="section-number">37.8.2.4. </span>Calculate the sum of dict values<a class="headerlink" href="#calculate-the-sum-of-dict-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.4. </span>Calculate the sum of dict values<a class="headerlink" href="#calculate-the-sum-of-dict-values" title="Permalink to this headline">#</a></h3>
 <p>Calculate the sum of the values in <code class="docutils literal notranslate"><span class="pre">magic_dict</span></code> by taking only into account numeric values (hint: see <a class="reference external" href="https://docs.python.org/3/library/functions.html#isinstance">isinstance</a>).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2199,7 +2231,7 @@ <h3><span class="section-number">37.8.2.4. </span>Calculate the sum of dict valu
 </div>
 </section>
 <section id="create-a-list-of-strings-based-on-a-list-of-numbers">
-<h3><span class="section-number">37.8.2.5. </span>Create a list of strings based on a list of numbers<a class="headerlink" href="#create-a-list-of-strings-based-on-a-list-of-numbers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.5. </span>Create a list of strings based on a list of numbers<a class="headerlink" href="#create-a-list-of-strings-based-on-a-list-of-numbers" title="Permalink to this headline">#</a></h3>
 <p>The rules:</p>
 <ul class="simple">
 <li><p>If the number is a multiple of five and odd, the string should be <code class="docutils literal notranslate"><span class="pre">'five</span> <span class="pre">odd'</span></code></p></li>
@@ -2232,9 +2264,9 @@ <h3><span class="section-number">37.8.2.5. </span>Create a list of strings based
 </section>
 </section>
 <section id="while-loops">
-<h2><span class="section-number">37.8.3. </span>While loops<a class="headerlink" href="#while-loops" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.3. </span>While loops<a class="headerlink" href="#while-loops" title="Permalink to this headline">#</a></h2>
 <section id="id1">
-<h3><span class="section-number">37.8.3.1. </span>Fill the missing pieces<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.3.1. </span>Fill the missing pieces<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Fill the <code class="docutils literal notranslate"><span class="pre">____</span></code> parts in the code below.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2297,9 +2329,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="break">
-<h2><span class="section-number">37.8.4. </span>Break<a class="headerlink" href="#break" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.4. </span>Break<a class="headerlink" href="#break" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-using-break-statement">
-<h3><span class="section-number">37.8.4.1. </span>Fill the missing pieces using <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-break-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.4.1. </span>Fill the missing pieces using <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-break-statement" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">find_prime_factors</span><span class="p">(</span><span class="n">number</span><span class="p">):</span>
@@ -2369,9 +2401,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="continue">
-<h2><span class="section-number">37.8.5. </span>Continue<a class="headerlink" href="#continue" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.5. </span>Continue<a class="headerlink" href="#continue" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-using-continue-statement">
-<h3><span class="section-number">37.8.5.1. </span>Fill the missing pieces using  <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-continue-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.5.1. </span>Fill the missing pieces using  <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-continue-statement" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">sieve_of_eratosthenes</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
@@ -2459,9 +2491,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="functions">
-<h2><span class="section-number">37.8.6. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.6. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-of-the-gcd-function">
-<h3><span class="section-number">37.8.6.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">gcd</span></code> function<a class="headerlink" href="#fill-the-missing-pieces-of-the-gcd-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.6.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">gcd</span></code> function<a class="headerlink" href="#fill-the-missing-pieces-of-the-gcd-function" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">gcd</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
@@ -2543,7 +2575,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="searching-for-wanted-people">
-<h3><span class="section-number">37.8.6.2. </span>Searching for wanted people<a class="headerlink" href="#searching-for-wanted-people" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.6.2. </span>Searching for wanted people<a class="headerlink" href="#searching-for-wanted-people" title="Permalink to this headline">#</a></h3>
 <p>Implement <code class="docutils literal notranslate"><span class="pre">find_wanted_people</span></code> function which takes a list of names (strings) as argument. The function should return a list of names which are present both in <code class="docutils literal notranslate"><span class="pre">WANTED_PEOPLE</span></code> and in the name list given as argument to the function.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2578,7 +2610,7 @@ <h3><span class="section-number">37.8.6.2. </span>Searching for wanted people<a
 </div>
 </section>
 <section id="counting-average-length-of-words-in-a-sentence">
-<h3><span class="section-number">37.8.6.3. </span>Counting average length of words in a sentence<a class="headerlink" href="#counting-average-length-of-words-in-a-sentence" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.6.3. </span>Counting average length of words in a sentence<a class="headerlink" href="#counting-average-length-of-words-in-a-sentence" title="Permalink to this headline">#</a></h3>
 <p>Create a function <code class="docutils literal notranslate"><span class="pre">average_length_of_words</span></code> which takes a string as an argument and returns the average length of the words in the string. You can assume that there is a single space between each word and that the input does not have punctuation. The result should be rounded to one decimal place (hint: see <a class="reference external" href="https://docs.python.org/3/library/functions.html#round"><code class="docutils literal notranslate"><span class="pre">round</span></code></a>).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2601,9 +2633,9 @@ <h3><span class="section-number">37.8.6.3. </span>Counting average length of wor
 </section>
 </section>
 <section id="lambda">
-<h2><span class="section-number">37.8.7. </span>Lambda<a class="headerlink" href="#lambda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.7. </span>Lambda<a class="headerlink" href="#lambda" title="Permalink to this headline">#</a></h2>
 <section id="id2">
-<h3><span class="section-number">37.8.7.1. </span>Fill the missing pieces<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.7.1. </span>Fill the missing pieces<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">map_function</span><span class="p">(</span><span class="n">nums</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
@@ -2667,9 +2699,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="classes">
-<h2><span class="section-number">37.8.8. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.8. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-of-the-stack-class">
-<h3><span class="section-number">37.8.8.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Stack</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-stack-class" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.8.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Stack</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-stack-class" title="Permalink to this headline">#</a></h3>
 <p>Fill <code class="docutils literal notranslate"><span class="pre">____</span></code> pieces of the <code class="docutils literal notranslate"><span class="pre">Stack</span></code> implemention in order to pass the assertions.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2751,7 +2783,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="fill-the-missing-pieces-of-the-complex-class">
-<h3><span class="section-number">37.8.8.2. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Complex</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-complex-class" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.8.2. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Complex</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-complex-class" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Complex</span><span class="p">:</span>
@@ -2892,9 +2924,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="exceptions">
-<h2><span class="section-number">37.8.9. </span>Exceptions<a class="headerlink" href="#exceptions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.9. </span>Exceptions<a class="headerlink" href="#exceptions" title="Permalink to this headline">#</a></h2>
 <section id="dealing-with-exceptions">
-<h3><span class="section-number">37.8.9.1. </span>Dealing with exceptions<a class="headerlink" href="#dealing-with-exceptions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.9.1. </span>Dealing with exceptions<a class="headerlink" href="#dealing-with-exceptions" title="Permalink to this headline">#</a></h3>
 <p>Fill <code class="docutils literal notranslate"><span class="pre">____</span></code> parts of the implementation below. <code class="docutils literal notranslate"><span class="pre">sum_of_list</span></code> function takes a list as argument and calculates the sum of values in the list. If some element in the list can not be converted to a numeric value, it should be ignored from the sum.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2935,7 +2967,7 @@ <h3><span class="section-number">37.8.9.1. </span>Dealing with exceptions<a clas
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.8.9.2. </span>Dealing with exceptions<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.9.2. </span>Dealing with exceptions<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">square_root</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -3019,7 +3051,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.8.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to below awesome open source projects for Python learning, which inspire this chapter.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/trekhleb/learn-python">learn-python</a> and <a class="reference external" href="https://github.com/trekhleb">Oleksii Trekhleb</a></p></li>
@@ -3067,13 +3099,13 @@ <h2><span class="section-number">37.8.10. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.7. </span>Python programming basics</p>
+            <p class="prev-next-title"><span class="section-number">40.7. </span>Python programming basics</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../data-science/analyzing-text-about-data-science.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.9. </span>Analyzing text about Data Science</p>
+        <p class="prev-next-title"><span class="section-number">40.9. </span>Analyzing text about Data Science</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/prerequisites/python-programming-basics.html b/assignments/prerequisites/python-programming-basics.html
index ff5476e62c..864a513cd2 100644
--- a/assignments/prerequisites/python-programming-basics.html
+++ b/assignments/prerequisites/python-programming-basics.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.7. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.7. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.8. Python programming advanced" href="python-programming-advanced.html" />
-    <link rel="prev" title="37.6. Python programming introduction" href="python-programming-introduction.html" />
+    <link rel="next" title="40.8. Python programming advanced" href="python-programming-advanced.html" />
+    <link rel="prev" title="40.6. Python programming introduction" href="python-programming-introduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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-   35. Model training &amp; evaluation
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.24. Data processing in Python
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-     37.29. Matplotlib applied
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-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
+     40.68. Model selection assignment 1
     </a>
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     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
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     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
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-     37.79. Boosting with tuning
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
+     40.80. Random Forest Classifier with Feature Importance
     </a>
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    <li class="toctree-l2">
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-     37.82. Data engineering
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-     37.83. Counterintuitive Challenges in ML Debugging
+     40.83. Counterintuitive Challenges in ML Debugging
     </a>
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     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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     </a>
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-     37.85. Case Study: Debugging in Regression
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     </a>
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    <li class="toctree-l2">
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-     37.86. Study the solvers
+     40.86. Study the solvers
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-     37.87. Build classification models
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-     37.88. Build Classification Model
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-     37.89. Parameter play
+     40.89. Parameter play
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-     37.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
     </a>
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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     </a>
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
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     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../slides/introduction.html">
-   38. Slides
+   41. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
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    <li class="toctree-l2">
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-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
-     38.2. Python programming basics
+     41.2. Python programming basics
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     <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1528,127 +1560,127 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   37.7.1. Operators
+   40.7.1. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms">
-     37.7.1.1. Implement a function to generate the Fibonacci sequence up to a specified number of terms.
+     40.7.1.1. Implement a function to generate the Fibonacci sequence up to a specified number of terms.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order">
-     37.7.1.2. Implement a function for insertion sort to sort a list in ascending order.
+     40.7.1.2. Implement a function for insertion sort to sort a list in ascending order.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method">
-     37.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
+     40.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#string">
-   37.7.2. String
+   40.7.2. String
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-upper">
-     37.7.2.1. str.upper
+     40.7.2.1. str.upper
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-title">
-     37.7.2.2. str.title
+     40.7.2.2. str.title
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-replace">
-     37.7.2.3. str.replace
+     40.7.2.3. str.replace
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-format-str-join">
-     37.7.2.4. str.format str.join
+     40.7.2.4. str.format str.join
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-split">
-     37.7.2.5. str.split
+     40.7.2.5. str.split
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#numbers">
-   37.7.3. Numbers
+   40.7.3. Numbers
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#creating-formulas">
-     37.7.3.1. Creating formulas
+     40.7.3.1. Creating formulas
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lists">
-   37.7.4. Lists
+   40.7.4. Lists
   </a>
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     <a class="reference internal nav-link" href="#list-append-list-remove-mutable">
-     37.7.4.1. list.append, list.remove, mutable
+     40.7.4.1. list.append, list.remove, mutable
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#slice">
-     37.7.4.2. Slice
+     40.7.4.2. Slice
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#list-extend">
-     37.7.4.3. list.extend
+     40.7.4.3. list.extend
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dictionaries">
-   37.7.5. Dictionaries
+   40.7.5. Dictionaries
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#populating-a-dictionary">
-     37.7.5.1. Populating a dictionary
+     40.7.5.1. Populating a dictionary
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-duplicate-values-from-a-dictionary-using-del">
-     37.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
+     40.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#counting-occurrences-of-elements-using-dict-get">
-     37.7.5.3. Counting Occurrences of Elements using dict.get()
+     40.7.5.3. Counting Occurrences of Elements using dict.get()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#grouping-items-by-category-using-dict-setdefault">
-     37.7.5.4. Grouping Items by Category using dict.setdefault()
+     40.7.5.4. Grouping Items by Category using dict.setdefault()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#accessing-and-merging-dictionaries-into-a-single-dictionary">
-     37.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
+     40.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.7.6. Acknowledgments
+   40.7.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1672,127 +1704,127 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   37.7.1. Operators
+   40.7.1. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms">
-     37.7.1.1. Implement a function to generate the Fibonacci sequence up to a specified number of terms.
+     40.7.1.1. Implement a function to generate the Fibonacci sequence up to a specified number of terms.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order">
-     37.7.1.2. Implement a function for insertion sort to sort a list in ascending order.
+     40.7.1.2. Implement a function for insertion sort to sort a list in ascending order.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method">
-     37.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
+     40.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#string">
-   37.7.2. String
+   40.7.2. String
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-upper">
-     37.7.2.1. str.upper
+     40.7.2.1. str.upper
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-title">
-     37.7.2.2. str.title
+     40.7.2.2. str.title
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-replace">
-     37.7.2.3. str.replace
+     40.7.2.3. str.replace
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-format-str-join">
-     37.7.2.4. str.format str.join
+     40.7.2.4. str.format str.join
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-split">
-     37.7.2.5. str.split
+     40.7.2.5. str.split
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#numbers">
-   37.7.3. Numbers
+   40.7.3. Numbers
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#creating-formulas">
-     37.7.3.1. Creating formulas
+     40.7.3.1. Creating formulas
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lists">
-   37.7.4. Lists
+   40.7.4. Lists
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#list-append-list-remove-mutable">
-     37.7.4.1. list.append, list.remove, mutable
+     40.7.4.1. list.append, list.remove, mutable
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#slice">
-     37.7.4.2. Slice
+     40.7.4.2. Slice
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#list-extend">
-     37.7.4.3. list.extend
+     40.7.4.3. list.extend
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dictionaries">
-   37.7.5. Dictionaries
+   40.7.5. Dictionaries
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#populating-a-dictionary">
-     37.7.5.1. Populating a dictionary
+     40.7.5.1. Populating a dictionary
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-duplicate-values-from-a-dictionary-using-del">
-     37.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
+     40.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#counting-occurrences-of-elements-using-dict-get">
-     37.7.5.3. Counting Occurrences of Elements using dict.get()
+     40.7.5.3. Counting Occurrences of Elements using dict.get()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#grouping-items-by-category-using-dict-setdefault">
-     37.7.5.4. Grouping Items by Category using dict.setdefault()
+     40.7.5.4. Grouping Items by Category using dict.setdefault()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#accessing-and-merging-dictionaries-into-a-single-dictionary">
-     37.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
+     40.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.7.6. Acknowledgments
+   40.7.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1824,7 +1856,7 @@ <h2> Contents </h2>
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="python-programming-basics">
-<h1><span class="section-number">37.7. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.7. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># set up the env</span>
@@ -1839,9 +1871,9 @@ <h1><span class="section-number">37.7. </span>Python programming basics<a class=
 </div>
 </div>
 <section id="operators">
-<h2><span class="section-number">37.7.1. </span>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.1. </span>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
 <section id="implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms">
-<h3><span class="section-number">37.7.1.1. </span>Implement a function to generate the Fibonacci sequence up to a specified number of terms.<a class="headerlink" href="#implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.1.1. </span>Implement a function to generate the Fibonacci sequence up to a specified number of terms.<a class="headerlink" href="#implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fibonacci</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
@@ -1917,7 +1949,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order">
-<h3><span class="section-number">37.7.1.2. </span>Implement a function for insertion sort to sort a list in ascending order.<a class="headerlink" href="#implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.1.2. </span>Implement a function for insertion sort to sort a list in ascending order.<a class="headerlink" href="#implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">insertion_sort</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
@@ -2007,7 +2039,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method">
-<h3><span class="section-number">37.7.1.3. </span>Implement a function to calculate the square root of a number using the Babylonian method.<a class="headerlink" href="#implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.1.3. </span>Implement a function to calculate the square root of a number using the Babylonian method.<a class="headerlink" href="#implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Inspired by SICP http://mitpress.mit.edu/sicp/full-text/book/book-Z-H-10.html#%_sec_1.1.7</span>
@@ -2098,9 +2130,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="string">
-<h2><span class="section-number">37.7.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h2>
 <section id="str-upper">
-<h3><span class="section-number">37.7.2.1. </span>str.upper<a class="headerlink" href="#str-upper" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.1. </span>str.upper<a class="headerlink" href="#str-upper" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">capitalize_first_letter</span><span class="p">(</span><span class="n">string</span><span class="p">):</span>
@@ -2170,7 +2202,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-title">
-<h3><span class="section-number">37.7.2.2. </span>str.title<a class="headerlink" href="#str-title" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.2. </span>str.title<a class="headerlink" href="#str-title" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">capitalize_words</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">exclude_words</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -2248,7 +2280,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-replace">
-<h3><span class="section-number">37.7.2.3. </span>str.replace<a class="headerlink" href="#str-replace" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.3. </span>str.replace<a class="headerlink" href="#str-replace" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">censor_words</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">words</span><span class="p">):</span>
@@ -2327,7 +2359,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-format-str-join">
-<h3><span class="section-number">37.7.2.4. </span>str.format str.join<a class="headerlink" href="#str-format-str-join" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.4. </span>str.format str.join<a class="headerlink" href="#str-format-str-join" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">format_person_info</span><span class="p">(</span><span class="n">people_info</span><span class="p">):</span>
@@ -2412,7 +2444,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-split">
-<h3><span class="section-number">37.7.2.5. </span>str.split<a class="headerlink" href="#str-split" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.5. </span>str.split<a class="headerlink" href="#str-split" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">string</span>
@@ -2523,9 +2555,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="numbers">
-<h2><span class="section-number">37.7.3. </span>Numbers<a class="headerlink" href="#numbers" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.3. </span>Numbers<a class="headerlink" href="#numbers" title="Permalink to this headline">#</a></h2>
 <section id="creating-formulas">
-<h3><span class="section-number">37.7.3.1. </span>Creating formulas<a class="headerlink" href="#creating-formulas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.3.1. </span>Creating formulas<a class="headerlink" href="#creating-formulas" title="Permalink to this headline">#</a></h3>
 <p>Write the following mathematical formula in Python:</p>
 <p><span class="math notranslate nohighlight">\(result = 6a^3 - \frac{8b^2 }{4c} + 11\)</span></p>
 <div class="cell docutils container">
@@ -2608,9 +2640,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </section>
 </section>
 <section id="lists">
-<h2><span class="section-number">37.7.4. </span>Lists<a class="headerlink" href="#lists" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.4. </span>Lists<a class="headerlink" href="#lists" title="Permalink to this headline">#</a></h2>
 <section id="list-append-list-remove-mutable">
-<h3><span class="section-number">37.7.4.1. </span>list.append, list.remove, mutable<a class="headerlink" href="#list-append-list-remove-mutable" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.4.1. </span>list.append, list.remove, mutable<a class="headerlink" href="#list-append-list-remove-mutable" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">permutations</span><span class="p">(</span><span class="n">elements</span><span class="p">):</span>
@@ -2761,7 +2793,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="slice">
-<h3><span class="section-number">37.7.4.2. </span>Slice<a class="headerlink" href="#slice" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.4.2. </span>Slice<a class="headerlink" href="#slice" title="Permalink to this headline">#</a></h3>
 <p>Create a new list without modifiying the original one.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2788,7 +2820,7 @@ <h3><span class="section-number">37.7.4.2. </span>Slice<a class="headerlink" hre
 </div>
 </section>
 <section id="list-extend">
-<h3><span class="section-number">37.7.4.3. </span>list.extend<a class="headerlink" href="#list-extend" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.4.3. </span>list.extend<a class="headerlink" href="#list-extend" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">flatten_nested_lists</span><span class="p">(</span><span class="n">nested_lists</span><span class="p">):</span>
@@ -2863,9 +2895,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="dictionaries">
-<h2><span class="section-number">37.7.5. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.5. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h2>
 <section id="populating-a-dictionary">
-<h3><span class="section-number">37.7.5.1. </span>Populating a dictionary<a class="headerlink" href="#populating-a-dictionary" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.1. </span>Populating a dictionary<a class="headerlink" href="#populating-a-dictionary" title="Permalink to this headline">#</a></h3>
 <p>Create a dictionary by using all the given variables.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2973,7 +3005,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="removing-duplicate-values-from-a-dictionary-using-del">
-<h3><span class="section-number">37.7.5.2. </span>Removing Duplicate Values from a Dictionary using ‘del’<a class="headerlink" href="#removing-duplicate-values-from-a-dictionary-using-del" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.2. </span>Removing Duplicate Values from a Dictionary using ‘del’<a class="headerlink" href="#removing-duplicate-values-from-a-dictionary-using-del" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">remove_duplicates</span><span class="p">(</span><span class="n">dictionary</span><span class="p">):</span>
@@ -3052,7 +3084,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="counting-occurrences-of-elements-using-dict-get">
-<h3><span class="section-number">37.7.5.3. </span>Counting Occurrences of Elements using dict.get()<a class="headerlink" href="#counting-occurrences-of-elements-using-dict-get" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.3. </span>Counting Occurrences of Elements using dict.get()<a class="headerlink" href="#counting-occurrences-of-elements-using-dict-get" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">count_occurrences</span><span class="p">(</span><span class="n">lst</span><span class="p">):</span>
@@ -3125,7 +3157,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="grouping-items-by-category-using-dict-setdefault">
-<h3><span class="section-number">37.7.5.4. </span>Grouping Items by Category using dict.setdefault()<a class="headerlink" href="#grouping-items-by-category-using-dict-setdefault" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.4. </span>Grouping Items by Category using dict.setdefault()<a class="headerlink" href="#grouping-items-by-category-using-dict-setdefault" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">group_by_category</span><span class="p">(</span><span class="n">items</span><span class="p">):</span>
@@ -3266,7 +3298,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="accessing-and-merging-dictionaries-into-a-single-dictionary">
-<h3><span class="section-number">37.7.5.5. </span>Accessing and Merging Dictionaries into a Single Dictionary<a class="headerlink" href="#accessing-and-merging-dictionaries-into-a-single-dictionary" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.5. </span>Accessing and Merging Dictionaries into a Single Dictionary<a class="headerlink" href="#accessing-and-merging-dictionaries-into-a-single-dictionary" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">merge_dicts</span><span class="p">(</span><span class="o">*</span><span class="n">dicts</span><span class="p">):</span>
@@ -3368,7 +3400,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.7.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to below awesome open source projects for Python learning, which inspire this chapter.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/trekhleb/learn-python">learn-python</a> and <a class="reference external" href="https://github.com/trekhleb">Oleksii Trekhleb</a></p></li>
@@ -3416,13 +3448,13 @@ <h2><span class="section-number">37.7.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
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-            <p class="prev-next-title"><span class="section-number">37.6. </span>Python programming introduction</p>
+            <p class="prev-next-title"><span class="section-number">40.6. </span>Python programming introduction</p>
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-        <p class="prev-next-title"><span class="section-number">37.8. </span>Python programming advanced</p>
+        <p class="prev-next-title"><span class="section-number">40.8. </span>Python programming advanced</p>
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diff --git a/assignments/prerequisites/python-programming-introduction.html b/assignments/prerequisites/python-programming-introduction.html
index 2a3c3f1844..bbb4c0b2d1 100644
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-    <title>37.6. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.6. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.7. Python programming basics" href="python-programming-basics.html" />
-    <link rel="prev" title="37.5. Project Plan​ Template" href="../project-plan-template.html" />
+    <link rel="next" title="40.7. Python programming basics" href="python-programming-basics.html" />
+    <link rel="prev" title="40.5. Project Plan​ Template" href="../project-plan-template.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
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  </li>
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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  <li class="toctree-l1 current active has-children">
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-     37.7. Python programming basics
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     <a class="reference internal" href="python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     </a>
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     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.16. Build your own custom vis
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     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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-     37.24. Data processing in Python
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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-     37.49. Linear Regression Implementation from Scratch
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-     37.112. DQN On Foreign Exchange Market
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-     37.116. Basic classification: Classify images of clothing
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-     38.20. Convolutional Neural Network
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+<h2><span class="section-number">40.6.1. </span>Merge two ascending lists into a new ascending list and returns.<a class="headerlink" href="#merge-two-ascending-lists-into-a-new-ascending-list-and-returns" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
@@ -1647,13 +1679,13 @@ <h2><span class="section-number">37.6.1. </span>Merge two ascending lists into a
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.5. </span>Project Plan​ Template</p>
+            <p class="prev-next-title"><span class="section-number">40.5. </span>Project Plan​ Template</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="python-programming-basics.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.7. </span>Python programming basics</p>
+        <p class="prev-next-title"><span class="section-number">40.7. </span>Python programming basics</p>
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diff --git a/assignments/project-plan-template.html b/assignments/project-plan-template.html
index 2c107f29c7..79d83f119b 100644
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+++ b/assignments/project-plan-template.html
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-    <title>37.5. Project Plan​ Template &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.5. Project Plan​ Template &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="prev" title="37.4. Second assignment" href="set-up-env/second-assignment.html" />
+    <link rel="next" title="40.6. Python programming introduction" href="prerequisites/python-programming-introduction.html" />
+    <link rel="prev" title="40.4. Second assignment" href="set-up-env/second-assignment.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
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     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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    </li>
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
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     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
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    </li>
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     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
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     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+  </label>
+  <ul>
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+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
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    </li>
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     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  </li>
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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  </li>
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   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   24. Autoencoder
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  </li>
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   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
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  </li>
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   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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  </li>
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   <a class="reference internal" href="../deep-learning/image-classification.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
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   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  </li>
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   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
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+   39. Model deployment
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 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="README.html">
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+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    </li>
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     <a class="reference internal" href="data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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     <a class="reference internal" href="data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
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     <a class="reference internal" href="ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
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-     37.47. Loss Function
+     40.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.65. Support Vector Machines (SVM) - Classification
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-     37.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
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-     37.113. Art by gan
+     40.113. Art by gan
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-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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+     41.1. Python programming introduction
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-     38.4. Data Science introduction
+     41.4. Data Science introduction
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     </a>
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+     41.6. NumPy and Pandas
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+     41.8. Data Science lifecycle
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-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
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+     41.10. Data Science in real world
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-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
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+     41.12. Linear Regression
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-     38.14. Logistic Regression
+     41.14. Logistic Regression
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+     41.15. Neural Network
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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+     41.17. Unsupervised learning
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+     41.18. Kernel method
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-     38.19. Model Selection
+     41.19. Model Selection
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1527,7 +1559,7 @@ <h1>Project Plan​ Template</h1>
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+<h1><span class="section-number">40.5. </span>Project Plan​ Template<a class="headerlink" href="#project-plan-template" title="Permalink to this headline">#</a></h1>
 <h2>Project background​</h2>
 <blockquote>
 <div><p>Introduce the background of the project.
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         <i class="fas fa-angle-left"></i>
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+            <p class="prev-next-title"><span class="section-number">40.4. </span>Second assignment</p>
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-    <title>37.3. First assignment &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.3. First assignment &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.4. Second assignment" href="second-assignment.html" />
-    <link rel="prev" title="37. Self-paced assignments" href="../README.html" />
+    <link rel="next" title="40.4. Second assignment" href="second-assignment.html" />
+    <link rel="prev" title="40. Self-paced assignments" href="../README.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
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-     14.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-  <label for="toctree-checkbox-11">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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  </li>
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
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  </li>
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   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
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   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
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  </li>
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../README.html">
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+   40. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul class="current">
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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     <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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    10. Machine Learning overview
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   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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-     11.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.2. K-Means clustering
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/dqn.html">
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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     <a class="reference internal" href="first-assignment.html">
-     37.3. First assignment
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
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-     37.16. Build your own custom vis
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     <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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-     37.25. Evaluating data from a form
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     <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     <a class="reference internal" href="../data-science/tell-a-story.html">
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
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-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
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     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.61. Delicious asian and indian cuisines
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-     37.63. Kernel method assignment 1
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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-     37.67. Decision Trees - Classification
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-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.70. Dropout and Batch Normalization
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-     38.15. Neural Network
+     41.15. Neural Network
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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+     41.17. Unsupervised learning
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-     38.18. Kernel method
+     41.18. Kernel method
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-     38.19. Model Selection
+     41.19. Model Selection
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1598,9 +1630,9 @@ <h2> Contents </h2>
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+<h1><span class="section-number">40.4. </span>Second assignment<a class="headerlink" href="#second-assignment" title="Permalink to this headline">#</a></h1>
 <section id="str-upper-str-lower-str-title">
-<h2><span class="section-number">37.4.1. </span><code class="docutils literal notranslate"><span class="pre">str.upper()</span></code>, <code class="docutils literal notranslate"><span class="pre">str.lower()</span></code>, <code class="docutils literal notranslate"><span class="pre">str.title()</span></code><a class="headerlink" href="#str-upper-str-lower-str-title" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.4.1. </span><code class="docutils literal notranslate"><span class="pre">str.upper()</span></code>, <code class="docutils literal notranslate"><span class="pre">str.lower()</span></code>, <code class="docutils literal notranslate"><span class="pre">str.title()</span></code><a class="headerlink" href="#str-upper-str-lower-str-title" title="Permalink to this headline">#</a></h2>
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-            <p class="prev-next-title"><span class="section-number">37.3. </span>First assignment</p>
+            <p class="prev-next-title"><span class="section-number">40.3. </span>First assignment</p>
         </div>
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
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+  <label for="toctree-checkbox-9">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
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   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
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  <li class="toctree-l1 has-children">
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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-     37.24. Data processing in Python
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-     37.45. Linear regression - California Housing
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-     37.70. Dropout and Batch Normalization
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-     37.83. Counterintuitive Challenges in ML Debugging
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-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.82. Data engineering
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-     37.83. Counterintuitive Challenges in ML Debugging
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-     37.84. Case Study: Debugging in Classification
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-     37.85. Case Study: Debugging in Regression
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-     37.89. Parameter play
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-     37.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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-     38.5. Relational vs. non-relational database
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+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
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+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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+     40.3. First assignment
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-     37.4. Second assignment
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+     40.35. Tell a story
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-     37.36. Explore a planetary computer dataset
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-     37.39. Low code/no code Data Science project on Azure ML
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-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     37.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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     </a>
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    <li class="toctree-l2">
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-     37.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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     </a>
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
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     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
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     <a class="reference internal" href="../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
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     <a class="reference internal" href="../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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    <li class="toctree-l2">
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-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+   </i>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
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   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/time-series.html">
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
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  <li class="toctree-l1 has-children">
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
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     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
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     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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-     37.17. Classifying datasets
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     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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-     37.24. Data processing in Python
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-     37.95. Intro to TensorFlow for Deep Learning
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  <li class="toctree-l1 has-children">
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
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  <li class="toctree-l1 has-children">
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+     15.1. Loss function
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.83. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
+     40.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
+     40.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-model.html">
-     37.88. Build Classification Model
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
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     <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
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-     38.2. Python programming basics
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     <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
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     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     38.4. Data Science introduction
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     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
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     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
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     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
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     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
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     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
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     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
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     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
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     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
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     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
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     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
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   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
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-     37.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
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     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
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     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
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     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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-     11.1. Tools of the trade
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-     11.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
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-   21. Convolutional Neural Networks
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-   22. Generative adversarial networks
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-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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-   35. Model training &amp; evaluation
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.17. Classifying datasets
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-     37.19. Introduction to probability and statistics
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-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.8. Python programming advanced
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   30. Deep Q-learning
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+   32. Image segmentation
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+   38. Model training &amp; evaluation
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+   39. Model deployment
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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@@ -1863,10 +1895,10 @@ <h2><span class="section-number">4.4.6. </span>Normal distribution<a class="head
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 7.93829769,  8.05937525, 10.41660607, 10.69403213,  7.61784012,
-       10.25602098,  7.17131072, 10.9703001 ,  9.41131726,  8.61937607,
-       10.43269542, 10.96614082,  9.0656805 ,  7.31195238,  9.15787604,
-       11.29699057, 13.03679162,  8.06700888,  9.18006053,  6.80816743])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 7.80843335,  8.90107311, 12.78867123,  9.54505639, 12.54145584,
+       11.69456647,  8.43707181, 10.14100015,  9.36929727, 10.4144472 ,
+       11.05715281,  7.38528797,  8.65322615, 10.64398114,  7.2764716 ,
+        9.79702792, 10.18401505, 10.62386476, 11.23225109, 10.59479574])
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   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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+     14.4. Applied Machine Learning : build a web app
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
+     40.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
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     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
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     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-model.html">
-     37.88. Build Classification Model
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
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     <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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-     15.3. Feature importance
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-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
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-   21. Convolutional Neural Networks
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-   23. Recurrent Neural Networks
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.17. Classifying datasets
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-     37.19. Introduction to probability and statistics
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-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/market-research.html">
-     37.38. Market research
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/managing-data.html">
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/machine-learning-productionization/data-engineering.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-regression.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/study-the-solvers.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-models.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-model.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/ml-fundamentals/parameter-play.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/autoencoder.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/time-series-forecasting-assignment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/nn-for-classification-assignment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/gan/art-by-gan.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/gan/gan-introduction.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
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     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
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     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
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     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
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     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-     15.1. Bagging
+     17.1. Bagging
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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   <ul>
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
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   <a class="reference internal" href="rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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  </li>
  <li class="toctree-l1 current active">
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-   24. Autoencoder
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   <a class="reference internal" href="lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
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-   26. Time series
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  </li>
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   <a class="reference internal" href="dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
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   <a class="reference internal" href="image-classification.html">
-   28. Image classification
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  </li>
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   <a class="reference internal" href="image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="object-detection.html">
-   31. Object detection
+   34. Object detection
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  </li>
 </ul>
@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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 </ul>
@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="autoencoder">
-<h1><span class="section-number">24. </span>Autoencoder<a class="headerlink" href="#autoencoder" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">27. </span>Autoencoder<a class="headerlink" href="#autoencoder" title="Permalink to this headline">#</a></h1>
 <section id="overview">
-<h2><span class="section-number">24.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction.</p>
 </section>
 <section id="unsupervised-learning">
-<h2><span class="section-number">24.2. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.2. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h2>
 <p>Autoencoder is a kind of unsupervised learning, which means working with datasets without considering a target variable. There are some Applications and Goals for it:</p>
 <ul class="simple">
 <li><p>Finding hidden structures in data.</p></li>
@@ -1684,7 +1716,7 @@ <h2><span class="section-number">24.2. </span>Unsupervised Learning<a class="hea
 <figure class="align-default" id="id1">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/01_PCA1.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/01_PCA1.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 24.1 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.1 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1693,7 +1725,7 @@ <h2><span class="section-number">24.2. </span>Unsupervised Learning<a class="hea
 <figure class="align-default" id="id2">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/02_PCA2.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/02_PCA2.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 24.2 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.2 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1702,17 +1734,17 @@ <h2><span class="section-number">24.2. </span>Unsupervised Learning<a class="hea
 <figure class="align-default" id="id3">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/03_PCA3.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/03_PCA3.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 24.3 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.3 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="fully-connected-autoencoder">
-<h2><span class="section-number">24.3. </span>Fully-connected Autoencoder<a class="headerlink" href="#fully-connected-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.3. </span>Fully-connected Autoencoder<a class="headerlink" href="#fully-connected-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>Here is an example of a basic fully-connected autoencoder</p>
 <figure class="align-default" id="id4">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/04_simple.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/04_simple.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 24.4 </span><span class="caption-text">Illustration of Fully Connected autoencoder</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.4 </span><span class="caption-text">Illustration of Fully Connected autoencoder</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1722,7 +1754,7 @@ <h2><span class="section-number">24.3. </span>Fully-connected Autoencoder<a clas
 <p>The loss function of this simple model is
 $<span class="math notranslate nohighlight">\(L(x, x') = \left\lVert x - x' \right\rVert^2_2 = \sum_i (x_i - x_i')^2\)</span>$</p>
 <section id="potential-autoencoder-applications">
-<h3><span class="section-number">24.3.1. </span>Potential Autoencoder Applications<a class="headerlink" href="#potential-autoencoder-applications" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">27.3.1. </span>Potential Autoencoder Applications<a class="headerlink" href="#potential-autoencoder-applications" title="Permalink to this headline">#</a></h3>
 <p>And there are some potential autoencoder applications, for example:</p>
 <ul class="simple">
 <li><p>After training, disregard the output part, we can use embedding as input to classic machine learning methods (SVM, KNN, Random Forest, …).</p></li>
@@ -1732,13 +1764,13 @@ <h3><span class="section-number">24.3.1. </span>Potential Autoencoder Applicatio
 </section>
 </section>
 <section id="convolutional-autoencoder">
-<h2><span class="section-number">24.4. </span>Convolutional Autoencoder<a class="headerlink" href="#convolutional-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.4. </span>Convolutional Autoencoder<a class="headerlink" href="#convolutional-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>For convolutional autoencoder, we mainly use transposed convolution construct the output, and transposed convolution (sometimes called “deconvolution”) allows us to increase the size of the output feature map compared to the input feature map.</p>
 <p>The difference between regular convolution and transposed convolution can be seen from the following image.</p>
 <figure class="align-default" id="id5">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/05_diff_conv.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/05_diff_conv.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 24.5 </span><span class="caption-text">Difference between regular and transposed convolution</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.5 </span><span class="caption-text">Difference between regular and transposed convolution</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In transposed convolutions, we stride over the output; hence, larger strides will result in larger outputs (opposite to regular convolutions); and we pad the output; hence, larger padding will result in smaller output maps.</p>
@@ -1746,7 +1778,7 @@ <h2><span class="section-number">24.4. </span>Convolutional Autoencoder<a class=
 <figure class="align-default" id="id6">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/06_convmodel.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/06_convmodel.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 24.6 </span><span class="caption-text">Structure of convoluted autoencoder</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.6 </span><span class="caption-text">Structure of convoluted autoencoder</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1760,7 +1792,7 @@ <h2><span class="section-number">24.4. </span>Convolutional Autoencoder<a class=
 </div>
 </section>
 <section id="code">
-<h2><span class="section-number">24.5. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.5. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <p>Let’s build a 2-layers auto-encoder with TensorFlow to compress images to a lower latent space and then reconstruct them. And this project will be done on MNIST dataste.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2015,18 +2047,18 @@ <h2><span class="section-number">24.5. </span>Code<a class="headerlink" href="#c
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">24.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">24.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can refer to this book chapter for further study:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.deeplearningbook.org/contents/autoencoders.html">deeplearningbook</a></p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">24.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-source project <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a> and <a class="reference external" href="https://github.com/aymericdamien">Aymeric Damien</a> for creating the open-source project <a class="reference external" href="https://github.com/aymericdamien/TensorFlow-Examples/">TensorFlow-Examples</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2068,13 +2100,13 @@ <h2><span class="section-number">24.8. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">23. </span>Recurrent Neural Networks</p>
+            <p class="prev-next-title"><span class="section-number">26. </span>Recurrent Neural Networks</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="lstm.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">25. </span>Long-short term memory</p>
+        <p class="prev-next-title"><span class="section-number">28. </span>Long-short term memory</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/deep-learning/cnn.html b/deep-learning/cnn.html
index 8769694f33..a892f3b989 100644
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+++ b/deep-learning/cnn.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>21. Convolutional Neural Networks &#8212; Ocademy Open Machine Learning Book</title>
+    <title>24. Convolutional Neural Networks &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="22. Generative adversarial networks" href="gan.html" />
-    <link rel="prev" title="20. Intro to Deep Learning" href="dl-overview.html" />
+    <link rel="next" title="25. Generative adversarial networks" href="gan.html" />
+    <link rel="prev" title="23. Intro to Deep Learning" href="dl-overview.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
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  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     15.1. Bagging
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    <li class="toctree-l2">
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-     15.2. Random forest
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-     15.3. Feature importance
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-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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-     16.2. Gradient boosting example
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    <li class="toctree-l2">
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-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
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  <li class="toctree-l1 current active">
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-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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+     40.14. Try it in Excel
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
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-     37.45. Linear regression - California Housing
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-     37.50. ML logistic regression - assignment 1
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-     37.52. ML neural network - Assignment 1
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-     37.58. Linear and polynomial regression
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-     37.60. Pumpkin varieties and color
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     </a>
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
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+     41.21. Generative Adversarial Network
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@@ -1526,84 +1558,84 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   24.3. How to fine-tune current CNN architectures?
   </a>
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@@ -1627,84 +1659,84 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+   24.3. How to fine-tune current CNN architectures?
   </a>
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+   24.4. Stylenet / Neural-Style
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+   24.5. Deepdream in TensorFlow
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-   21.6. Your turn! 🚀
+   24.6. Your turn! 🚀
   </a>
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   <a class="reference internal nav-link" href="#self-study">
-   21.7. Self study
+   24.7. Self study
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#research-trend">
-     21.7.1. Research trend
+     24.7.1. Research trend
     </a>
    </li>
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-   21.8. Acknowledgments
+   24.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1730,7 +1762,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="convolutional-neural-networks">
-<h1><span class="section-number">21. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">24. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h1>
 <p>Convolutional Neural Networks (CNNs) are responsible for the latest major breakthroughs in image recognition in the past few years.</p>
 <p>In mathematics, a convolution is a function that is applied over the output of another function. In our case, we will consider applying a matrix multiplication (filter) across an image. See the below diagram for an example of how this may work.</p>
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/01_intro_cnn.png" width="90%" class="bg-white mb-1"><div class="cell docutils container">
@@ -1752,7 +1784,7 @@ <h1><span class="section-number">21. </span>Convolutional Neural Networks<a clas
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/01_intro_cnn2.png" width="90%" class="bg-white mb-1">
 <p>After the max pooling, there is generally an activation layer. One of the more common activation layers is the ReLU (Rectified Linear Unit).</p>
 <section id="mnist-handwritten-digits">
-<h2><span class="section-number">21.1. </span>MNIST handwritten digits<a class="headerlink" href="#mnist-handwritten-digits" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.1. </span>MNIST handwritten digits<a class="headerlink" href="#mnist-handwritten-digits" title="Permalink to this headline">#</a></h2>
 <p>Here we illustrate how to use a simple CNN with three convolutional units to predict the MNIST handwritten digits.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1794,7 +1826,7 @@ <h2><span class="section-number">21.1. </span>MNIST handwritten digits<a class="
 </div>
 </div>
 <section id="code">
-<h3><span class="section-number">21.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -1956,13 +1988,13 @@ <h3><span class="section-number">21.1.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="cifar-10">
-<h2><span class="section-number">21.2. </span>CIFAR-10<a class="headerlink" href="#cifar-10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.2. </span>CIFAR-10<a class="headerlink" href="#cifar-10" title="Permalink to this headline">#</a></h2>
 <p>Here we will build a convolutional neural network to predict the <code class="docutils literal notranslate"><span class="pre">CIFAR-10</span></code> data.</p>
 <p>The script provided will download and unzip the <code class="docutils literal notranslate"><span class="pre">CIFAR-10</span></code> data. Then it will start training a CNN from scratch. You should see similar output at the end of the following two graphs.</p>
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/03_cnn2_loss_acc.png" width="90%" class="bg-white mb-1">
 <p>Here we see the training loss (left) and the test batch accuracy (right).</p>
 <section id="id1">
-<h3><span class="section-number">21.2.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.2.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -2078,7 +2110,7 @@ <h3><span class="section-number">21.2.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="how-to-fine-tune-current-cnn-architectures">
-<h2><span class="section-number">21.3. </span>How to fine-tune current CNN architectures?<a class="headerlink" href="#how-to-fine-tune-current-cnn-architectures" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.3. </span>How to fine-tune current CNN architectures?<a class="headerlink" href="#how-to-fine-tune-current-cnn-architectures" title="Permalink to this headline">#</a></h2>
 <p>The purpose of the script provided in this section is to download the CIFAR-10 data and sort it out in the proper folder structure for running it through the TensorFlow fine-tuning tutorial. The script should create the following folder structure.</p>
 <p>-train_dir
 |–airplane
@@ -2103,7 +2135,7 @@ <h2><span class="section-number">21.3. </span>How to fine-tune current CNN archi
 |–ship
 |–truck</p>
 <section id="id2">
-<h3><span class="section-number">21.3.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.3.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># In this script, we download the CIFAR-10 images and</span>
@@ -2206,7 +2238,7 @@ <h3><span class="section-number">21.3.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="stylenet-neural-style">
-<h2><span class="section-number">21.4. </span>Stylenet / Neural-Style<a class="headerlink" href="#stylenet-neural-style" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.4. </span>Stylenet / Neural-Style<a class="headerlink" href="#stylenet-neural-style" title="Permalink to this headline">#</a></h2>
 <p>The purpose of this script is to illustrate how to do stylenet in TensorFlow. We reference the following <a class="reference external" href="https://arxiv.org/abs/1508.06576">paper</a> for this algorithm.</p>
 <p>But there is some prerequisites,</p>
 <ul class="simple">
@@ -2217,25 +2249,25 @@ <h2><span class="section-number">21.4. </span>Stylenet / Neural-Style<a class="h
 <figure class="align-default" id="id5">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 21.1 </span><span class="caption-text">name: Style image starry night</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 24.1 </span><span class="caption-text">name: Style image starry night</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The context image is below.</p>
 <figure class="align-default" id="id6">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 21.2 </span><span class="caption-text">name: Content image book cover</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 24.2 </span><span class="caption-text">name: Content image book cover</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The final result looks like</p>
 <figure class="align-default" id="id7">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 21.3 </span><span class="caption-text">name: stylenet final result</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 24.3 </span><span class="caption-text">name: stylenet final result</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id3">
-<h3><span class="section-number">21.4.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.4.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We use two images, an original image and a style image</span>
@@ -2466,17 +2498,17 @@ <h3><span class="section-number">21.4.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="deepdream-in-tensorflow">
-<h2><span class="section-number">21.5. </span>Deepdream in TensorFlow<a class="headerlink" href="#deepdream-in-tensorflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.5. </span>Deepdream in TensorFlow<a class="headerlink" href="#deepdream-in-tensorflow" title="Permalink to this headline">#</a></h2>
 <p>Note: There is no new code in this script. It originates from the TensorFlow tutorial located here. However, this code is modified slightly to run on Python 3. The code is also commented very heavily to explain, line-by-line, what occurs in the deepdream demo.</p>
 <p>Here are some potential outputs.</p>
 <figure class="align-default" id="id8">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/06_deepdream_ex.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/06_deepdream_ex.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 21.4 </span><span class="caption-text">name: Deepdream outputs</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 24.4 </span><span class="caption-text">name: Deepdream outputs</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id4">
-<h3><span class="section-number">21.5.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.5.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Using TensorFlow for Deep Dream</span>
@@ -2701,25 +2733,25 @@ <h3><span class="section-number">21.5.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">21.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">21.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can refer to those YouTube videos for further study:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=YRhxdVk_sIs">Convolutional Neural Networks (CNNs) explained, by deeplizard</a></p></li>
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=pj9-rr1wDhM">Convolutional Neural Networks Explained (CNN Visualized), by Futurology</a></p></li>
 </ul>
 <section id="research-trend">
-<h3><span class="section-number">21.7.1. </span>Research trend<a class="headerlink" href="#research-trend" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.7.1. </span>Research trend<a class="headerlink" href="#research-trend" title="Permalink to this headline">#</a></h3>
 <p>State of the Art Convolutional Neural Networks (CNNs) Explained | Deep Learning in 2020:</p>
 <div class="yt-container">
    <iframe src="https://www.youtube.com/embed/YUyec4eCEiY" allowfullscreen></iframe>
 </div></section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">21.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/nfmcclure">Nick</a> for creating the open-source course <a class="reference external" href="https://github.com/nfmcclure/tensorflow_cookbook">tensorflow_cookbook</a>. And thanks to <a class="reference external" href="https://www.tensorflow.org/">TensorFlow</a> for creating the open source project <a class="reference external" href="https://www.tensorflow.org/tutorials/generative/deepdream">DeepDream</a> It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2761,13 +2793,13 @@ <h2><span class="section-number">21.8. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">20. </span>Intro to Deep Learning</p>
+            <p class="prev-next-title"><span class="section-number">23. </span>Intro to Deep Learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gan.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">22. </span>Generative adversarial networks</p>
+        <p class="prev-next-title"><span class="section-number">25. </span>Generative adversarial networks</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/deep-learning/difussion-model.html b/deep-learning/difussion-model.html
index ba8fda6223..4393e3ced1 100644
--- a/deep-learning/difussion-model.html
+++ b/deep-learning/difussion-model.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>30. Diffusion Model &#8212; Ocademy Open Machine Learning Book</title>
+    <title>33. Diffusion Model &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="31. Object detection" href="object-detection.html" />
-    <link rel="prev" title="29. Image segmentation" href="image-segmentation.html" />
+    <link rel="next" title="34. Object detection" href="object-detection.html" />
+    <link rel="prev" title="32. Image segmentation" href="image-segmentation.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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  <li class="toctree-l1">
   <a class="reference internal" href="lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
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   <a class="reference internal" href="time-series.html">
-   26. Time series
+   29. Time series
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-   27. Deep Q-learning
+   30. Deep Q-learning
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  <li class="toctree-l1">
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-   28. Image classification
+   31. Image classification
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-   29. Image segmentation
+   32. Image segmentation
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  <li class="toctree-l1 current active">
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-   30. Diffusion Model
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-   31. Object detection
+   34. Object detection
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@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   </label>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
+     40.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
+     40.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
+     40.70. Dropout and Batch Normalization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
+     40.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
+     40.72. Regularized Linear Models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
+     40.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
+     40.74. Random forests for classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
+     40.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
+     40.76. Decision trees
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
+     40.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
+     40.78. Gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
+     40.79. Boosting with tuning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
+     40.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
+     40.82. Data engineering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
+     40.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
+     40.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
+     40.87. Build classification models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-model.html">
-     37.88. Build Classification Model
+     40.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
+     40.89. Parameter play
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.4. Data Science introduction
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     </a>
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-     38.6. NumPy and Pandas
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     </a>
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-     38.8. Data Science lifecycle
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-     38.9. Data Science in the cloud
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-     38.11. Machine Learning overview
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-     38.14. Logistic Regression
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-     38.15. Neural Network
+     41.15. Neural Network
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     38.18. Kernel method
+     41.18. Kernel method
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     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
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     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
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   </ul>
@@ -1528,117 +1560,117 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#background">
-   30.1. Background
+   33.1. Background
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#expectation">
-     30.1.1. Expectation
+     33.1.1. Expectation
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#definition">
-       30.1.1.1. Definition
+       33.1.1.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#properties">
-       30.1.1.2. Properties
+       33.1.1.2. Properties
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#variance">
-     30.1.2. Variance
+     33.1.2. Variance
     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       30.1.2.1. Definition
+       33.1.2.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       30.1.2.2. Properties
+       33.1.2.2. Properties
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#re-parameterization-trick">
-     30.1.3. Re-parameterization trick
+     33.1.3. Re-parameterization trick
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#mean-expectation">
-       30.1.3.1. Mean (Expectation)
+       33.1.3.1. Mean (Expectation)
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       30.1.3.2. Variance
+       33.1.3.2. Variance
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kl-divergence">
-     30.1.4. KL Divergence
+     33.1.4. KL Divergence
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       30.1.4.1. Definition
+       33.1.4.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#jensens-inequality">
-       30.1.4.2. Jensen’s inequality
+       33.1.4.2. Jensen’s inequality
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#properties-of-kl-divergence">
-       30.1.4.3. Properties of KL Divergence
+       33.1.4.3. Properties of KL Divergence
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evidence-lower-bound-elbo">
-     30.1.5. Evidence lower bound (ELBO)
+     33.1.5. Evidence lower bound (ELBO)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id5">
-       30.1.5.1. Definition
+       33.1.5.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id6">
-       30.1.5.2. Properties
+       33.1.5.2. Properties
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#forward-and-reverse-process">
-     30.1.6. Forward and Reverse process
+     33.1.6. Forward and Reverse process
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#noise-schedule">
-     30.1.7. Noise schedule
+     33.1.7. Noise schedule
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#linear-schedule">
-       30.1.7.1. Linear schedule
+       33.1.7.1. Linear schedule
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#cosine-schedule">
-       30.1.7.2. Cosine schedule
+       33.1.7.2. Cosine schedule
       </a>
      </li>
     </ul>
@@ -1647,49 +1679,49 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#code">
-   30.2. Code
+   33.2. Code
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     30.2.1. Import Libraries
+     33.2.1. Import Libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-loading">
-     30.2.2. Data Loading
+     33.2.2. Data Loading
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     30.2.3. Data Preprocessing
+     33.2.3. Data Preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-augmentation">
-     30.2.4. Data Augmentation
+     33.2.4. Data Augmentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data">
-     30.2.5. Data
+     33.2.5. Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-schedule-beta">
-     30.2.6. Linear schedule-beta
+     33.2.6. Linear schedule-beta
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   30.3. Your turn! 🚀
+   33.3. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   30.4. Acknowledgments
+   33.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1713,117 +1745,117 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#background">
-   30.1. Background
+   33.1. Background
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#expectation">
-     30.1.1. Expectation
+     33.1.1. Expectation
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#definition">
-       30.1.1.1. Definition
+       33.1.1.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#properties">
-       30.1.1.2. Properties
+       33.1.1.2. Properties
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#variance">
-     30.1.2. Variance
+     33.1.2. Variance
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       30.1.2.1. Definition
+       33.1.2.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       30.1.2.2. Properties
+       33.1.2.2. Properties
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#re-parameterization-trick">
-     30.1.3. Re-parameterization trick
+     33.1.3. Re-parameterization trick
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#mean-expectation">
-       30.1.3.1. Mean (Expectation)
+       33.1.3.1. Mean (Expectation)
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       30.1.3.2. Variance
+       33.1.3.2. Variance
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kl-divergence">
-     30.1.4. KL Divergence
+     33.1.4. KL Divergence
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       30.1.4.1. Definition
+       33.1.4.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#jensens-inequality">
-       30.1.4.2. Jensen’s inequality
+       33.1.4.2. Jensen’s inequality
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#properties-of-kl-divergence">
-       30.1.4.3. Properties of KL Divergence
+       33.1.4.3. Properties of KL Divergence
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evidence-lower-bound-elbo">
-     30.1.5. Evidence lower bound (ELBO)
+     33.1.5. Evidence lower bound (ELBO)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id5">
-       30.1.5.1. Definition
+       33.1.5.1. Definition
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id6">
-       30.1.5.2. Properties
+       33.1.5.2. Properties
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#forward-and-reverse-process">
-     30.1.6. Forward and Reverse process
+     33.1.6. Forward and Reverse process
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#noise-schedule">
-     30.1.7. Noise schedule
+     33.1.7. Noise schedule
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#linear-schedule">
-       30.1.7.1. Linear schedule
+       33.1.7.1. Linear schedule
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#cosine-schedule">
-       30.1.7.2. Cosine schedule
+       33.1.7.2. Cosine schedule
       </a>
      </li>
     </ul>
@@ -1832,49 +1864,49 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#code">
-   30.2. Code
+   33.2. Code
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     30.2.1. Import Libraries
+     33.2.1. Import Libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-loading">
-     30.2.2. Data Loading
+     33.2.2. Data Loading
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     30.2.3. Data Preprocessing
+     33.2.3. Data Preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-augmentation">
-     30.2.4. Data Augmentation
+     33.2.4. Data Augmentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data">
-     30.2.5. Data
+     33.2.5. Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-schedule-beta">
-     30.2.6. Linear schedule-beta
+     33.2.6. Linear schedule-beta
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   30.3. Your turn! 🚀
+   33.3. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   30.4. Acknowledgments
+   33.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1899,20 +1931,20 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="diffusion-model">
-<h1><span class="section-number">30. </span>Diffusion Model<a class="headerlink" href="#diffusion-model" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">33. </span>Diffusion Model<a class="headerlink" href="#diffusion-model" title="Permalink to this headline">#</a></h1>
 <section id="background">
-<h2><span class="section-number">30.1. </span>Background<a class="headerlink" href="#background" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">33.1. </span>Background<a class="headerlink" href="#background" title="Permalink to this headline">#</a></h2>
 <p>Before we learn the diffusion model, we have to know some background knowledge in statistics. Perhaps you already have a good mastery of them, let’s review them together.</p>
 <section id="expectation">
-<h3><span class="section-number">30.1.1. </span>Expectation<a class="headerlink" href="#expectation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.1. </span>Expectation<a class="headerlink" href="#expectation" title="Permalink to this headline">#</a></h3>
 <section id="definition">
-<h4><span class="section-number">30.1.1.1. </span>Definition<a class="headerlink" href="#definition" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.1.1. </span>Definition<a class="headerlink" href="#definition" title="Permalink to this headline">#</a></h4>
 <p>In probability theory, expectation (also called expected value, mean, average) is a generalization of the weighted average.</p>
 <p><span class="math notranslate nohighlight">\(E[X] = x_1 p_1 + x_2 p2 + ... +x_n p_n = \sum_{i=1}^n x_i p_1\)</span></p>
 <p>where <span class="math notranslate nohighlight">\(x_i\)</span> and <span class="math notranslate nohighlight">\(p_i\)</span> are i-th a possible outcome and its probability, respectively.</p>
 </section>
 <section id="properties">
-<h4><span class="section-number">30.1.1.2. </span>Properties<a class="headerlink" href="#properties" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.1.2. </span>Properties<a class="headerlink" href="#properties" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p><span class="math notranslate nohighlight">\(E[aX]=aE[X]\)</span> where a is a constant value.</p></li>
 <li><p><span class="math notranslate nohighlight">\(E[X+b]=E[X]+b\)</span> where b is a constant value.</p></li>
@@ -1921,14 +1953,14 @@ <h4><span class="section-number">30.1.1.2. </span>Properties<a class="headerlink
 </section>
 </section>
 <section id="variance">
-<h3><span class="section-number">30.1.2. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.2. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h3>
 <p>Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value.</p>
 <section id="id1">
-<h4><span class="section-number">30.1.2.1. </span>Definition<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.2.1. </span>Definition<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>The variance of a random variable <span class="math notranslate nohighlight">\(X\)</span> is the expected value of the squared deviation from the expectation of <span class="math notranslate nohighlight">\(X\)</span>.</p>
 </section>
 <section id="id2">
-<h4><span class="section-number">30.1.2.2. </span>Properties<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.2.2. </span>Properties<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p><span class="math notranslate nohighlight">\(Var[X]=E[X^2]−E[X]^2\)</span>.</p></li>
 <li><p><span class="math notranslate nohighlight">\(Var[aX]=a^2 Var[X]\)</span> where a is a constant value.</p></li>
@@ -1938,28 +1970,28 @@ <h4><span class="section-number">30.1.2.2. </span>Properties<a class="headerlink
 </section>
 </section>
 <section id="re-parameterization-trick">
-<h3><span class="section-number">30.1.3. </span>Re-parameterization trick<a class="headerlink" href="#re-parameterization-trick" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.3. </span>Re-parameterization trick<a class="headerlink" href="#re-parameterization-trick" title="Permalink to this headline">#</a></h3>
 <p>When we sample data from a probability distribution, backpropagation the gradient is not possible because it is a stochastic process. To make it trainable, the re-parameterization trick is useful.</p>
 <p>Let us assume that <span class="math notranslate nohighlight">\(z\)</span> is sampled data from a gaussian distribution which the mean is <span class="math notranslate nohighlight">\(\mu\)</span> and the variance is <span class="math notranslate nohighlight">\(\sigma^2\)</span>. Then, the mean and variance of <span class="math notranslate nohighlight">\(z\)</span> would be <span class="math notranslate nohighlight">\(\mu\)</span> and <span class="math notranslate nohighlight">\(\sigma^2\)</span>. Therefore, <span class="math notranslate nohighlight">\(z\)</span> can be written as follows.</p>
 <p><span class="math notranslate nohighlight">\(z = N(\mu, \sigma^2 I) = \mu + \sigma \odot \epsilon\)</span>, where <span class="math notranslate nohighlight">\(\epsilon \thicksim N(0, I)\)</span></p>
 <p>where <span class="math notranslate nohighlight">\(\odot\)</span> refers to element-wise product.</p>
 <p>The mean and variance of <span class="math notranslate nohighlight">\(z\)</span> correspond to <span class="math notranslate nohighlight">\(\mu\)</span> and <span class="math notranslate nohighlight">\(\sigma^2\)</span>, respectively.</p>
 <section id="mean-expectation">
-<h4><span class="section-number">30.1.3.1. </span>Mean (Expectation)<a class="headerlink" href="#mean-expectation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.3.1. </span>Mean (Expectation)<a class="headerlink" href="#mean-expectation" title="Permalink to this headline">#</a></h4>
 <p><span class="math notranslate nohighlight">\(E[z] = E[\mu+\sigma \odot \epsilon] = E[\mu] + \sigma E[\epsilon] = \mu\)</span></p>
 <p>The expectation of <span class="math notranslate nohighlight">\(\epsilon\)</span> is 0 by definition.</p>
 </section>
 <section id="id3">
-<h4><span class="section-number">30.1.3.2. </span>Variance<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.3.2. </span>Variance<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p><span class="math notranslate nohighlight">\(Var[z] = Var[\mu + \sigma \odot \epsilon] = Var[\sigma \odot \epsilon] = \sigma^2 Var[\epsilon] = \sigma^2\)</span></p>
 <p>The variance of \epsilon is 1 by definition.</p>
 </section>
 </section>
 <section id="kl-divergence">
-<h3><span class="section-number">30.1.4. </span>KL Divergence<a class="headerlink" href="#kl-divergence" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.4. </span>KL Divergence<a class="headerlink" href="#kl-divergence" title="Permalink to this headline">#</a></h3>
 <p>In mathematical statistics, the Kullback–Leibler divergence (relative entropy), is a type of statistical distance.</p>
 <section id="id4">
-<h4><span class="section-number">30.1.4.1. </span>Definition<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.4.1. </span>Definition<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
 <ol class="simple">
 <li><p>Discrete probability distribution</p></li>
 </ol>
@@ -1970,7 +2002,7 @@ <h4><span class="section-number">30.1.4.1. </span>Definition<a class="headerlink
 <p><span class="math notranslate nohighlight">\(D_{KL} (P||Q) = \int _{− \infty}^{\infty} p(x) log \frac{p(x)}{q(x)}dx\)</span>, where <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(q\)</span> denote the probability densities of <span class="math notranslate nohighlight">\(P\)</span> and <span class="math notranslate nohighlight">\(Q\)</span>.</p>
 </section>
 <section id="jensens-inequality">
-<h4><span class="section-number">30.1.4.2. </span>Jensen’s inequality<a class="headerlink" href="#jensens-inequality" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.4.2. </span>Jensen’s inequality<a class="headerlink" href="#jensens-inequality" title="Permalink to this headline">#</a></h4>
 <p>In mathematics, Jensen’s inequality relates the value of a convex (or concave) function of an integral to the integral of the function.</p>
 <ul class="simple">
 <li><p>Convex function: <span class="math notranslate nohighlight">\(f(E[X]) \leqq E[f(X)]\)</span>;</p></li>
@@ -1978,7 +2010,7 @@ <h4><span class="section-number">30.1.4.2. </span>Jensen’s inequality<a class=
 </ul>
 </section>
 <section id="properties-of-kl-divergence">
-<h4><span class="section-number">30.1.4.3. </span>Properties of KL Divergence<a class="headerlink" href="#properties-of-kl-divergence" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.4.3. </span>Properties of KL Divergence<a class="headerlink" href="#properties-of-kl-divergence" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>KL Divergence is always non-negative;</p></li>
 <li><p>The cross-entropy is always larger than the entropy;</p></li>
@@ -1987,17 +2019,17 @@ <h4><span class="section-number">30.1.4.3. </span>Properties of KL Divergence<a
 </section>
 </section>
 <section id="evidence-lower-bound-elbo">
-<h3><span class="section-number">30.1.5. </span>Evidence lower bound (ELBO)<a class="headerlink" href="#evidence-lower-bound-elbo" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.5. </span>Evidence lower bound (ELBO)<a class="headerlink" href="#evidence-lower-bound-elbo" title="Permalink to this headline">#</a></h3>
 <p>In variational Bayesian methods, the evidence lower bound (often abbreviated ELBO) is a useful lower bound on the log-likelihood of some observed data.</p>
 <section id="id5">
-<h4><span class="section-number">30.1.5.1. </span>Definition<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.5.1. </span>Definition<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h4>
 <p><span class="math notranslate nohighlight">\(ELBO := E_{z∼q_{\phi}}[log \frac{pθ(x,z)}{q_{\phi}(z)}]\)</span></p>
 <p>where <span class="math notranslate nohighlight">\(p_{\theta}(x, z)\)</span> is joint distribution of <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(z\)</span>. <span class="math notranslate nohighlight">\(\theta\)</span> and <span class="math notranslate nohighlight">\(\phi\)</span> are parameters.</p>
 <p>ELBO is used to obtain the lower bound of the evidence (or log evidence). The evidence is the likelihood function evaluated at a fixed <span class="math notranslate nohighlight">\(\theta\)</span>.</p>
 <p><span class="math notranslate nohighlight">\(evidence := log p_{\theta}(x)\)</span></p>
 </section>
 <section id="id6">
-<h4><span class="section-number">30.1.5.2. </span>Properties<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.5.2. </span>Properties<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>The evidence is always larger than ELBO;</p></li>
 <li><p>KL Divergence between <span class="math notranslate nohighlight">\(p_{\theta}(z|x)\)</span> and <span class="math notranslate nohighlight">\(q_{\phi}(z)\)</span> equals <span class="math notranslate nohighlight">\(evidence−ELBO\)</span>.</p></li>
@@ -2005,23 +2037,23 @@ <h4><span class="section-number">30.1.5.2. </span>Properties<a class="headerlink
 </section>
 </section>
 <section id="forward-and-reverse-process">
-<h3><span class="section-number">30.1.6. </span>Forward and Reverse process<a class="headerlink" href="#forward-and-reverse-process" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.6. </span>Forward and Reverse process<a class="headerlink" href="#forward-and-reverse-process" title="Permalink to this headline">#</a></h3>
 <p>The diffusion models make data into a gaussian noise (latent vector) and restore it again. The former is called the forward process, and the latter is called the reverse process.</p>
 <p>In the forward process, we add a gaussian noise to the data step by step (usually hundreds of steps). The transform of an individual step is defined as follows.</p>
 <p><span class="math notranslate nohighlight">\(x_t = q(x_t|x_{t−1}) = N(x_t, \sqrt{1−\beta_t}x_{t−1}, \beta_t I)\)</span></p>
 <p>In the reverse process, we restore image from a gaussian noise (a latent vector). If <span class="math notranslate nohighlight">\(\beta_t\)</span> is small enough, the reverse <span class="math notranslate nohighlight">\(q(x_{t-1}|x_t)\)</span> will also be gaussian. It is noteworthy that the reverse process is tractable when conditioned on <span class="math notranslate nohighlight">\(x_0\)</span>.</p>
 <img src="../../images/deep-learning/diffusion-model/01_structure.png" width="90%" class="bg-white mb-1"></section>
 <section id="noise-schedule">
-<h3><span class="section-number">30.1.7. </span>Noise schedule<a class="headerlink" href="#noise-schedule" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.7. </span>Noise schedule<a class="headerlink" href="#noise-schedule" title="Permalink to this headline">#</a></h3>
 <p>In diffusion models, the noise schedule define the methodology for iteratively adding noise to an image or for updating a sample based on model outputs. I’ll introduce two type of schedules which are linear schedule and cosine schedule. The linear and cosine schedule were introduced by <a class="reference external" href="https://arxiv.org/abs/2006.11239">Denoising Diffusion Probabilistic Models</a> and <a class="reference external" href="https://arxiv.org/abs/2006.11239">Improved Denoising Diffusion Probabilistic Models</a>, respectively.</p>
 <section id="linear-schedule">
-<h4><span class="section-number">30.1.7.1. </span>Linear schedule<a class="headerlink" href="#linear-schedule" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.7.1. </span>Linear schedule<a class="headerlink" href="#linear-schedule" title="Permalink to this headline">#</a></h4>
 <p>In the linear schedule, <span class="math notranslate nohighlight">\(\beta_t\)</span> changes linearly.</p>
 <img src="../../images/deep-learning/diffusion-model/02_linear.png" width="90%" class="bg-white mb-1">
 <p>Illustration of linear schedule</p>
 </section>
 <section id="cosine-schedule">
-<h4><span class="section-number">30.1.7.2. </span>Cosine schedule<a class="headerlink" href="#cosine-schedule" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.7.2. </span>Cosine schedule<a class="headerlink" href="#cosine-schedule" title="Permalink to this headline">#</a></h4>
 <p>Alex Nichol and Prafulla Dhariwal proposed the cosine schedule to prevent an image from turning into noise too quickly. They construct a different noise schedule in terms of <span class="math notranslate nohighlight">\(\overline{\alpha_t}\)</span>.</p>
 <p><span class="math notranslate nohighlight">\(\overline{\alpha_t} = \frac{f(t)}{f(0)}\)</span>, <span class="math notranslate nohighlight">\(f(t) = cos(\frac{\frac{t}{T} + s}{1+s} \frac{\pi}{2})^2\)</span>. By definition, the <span class="math notranslate nohighlight">\(\beta_t\)</span> equals <span class="math notranslate nohighlight">\(1 - \frac{\overline{\alpha_t}}{\overline{\alpha_{t-1}}}\)</span>.</p>
 <img src="../../images/deep-learning/diffusion-model/03_cosine.png" width="90%" class="bg-white mb-1">
@@ -2030,9 +2062,9 @@ <h4><span class="section-number">30.1.7.2. </span>Cosine schedule<a class="heade
 </section>
 </section>
 <section id="code">
-<h2><span class="section-number">30.2. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">33.2. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <section id="import-libraries">
-<h3><span class="section-number">30.2.1. </span>Import Libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.1. </span>Import Libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2069,7 +2101,7 @@ <h3><span class="section-number">30.2.1. </span>Import Libraries<a class="header
 </div>
 </section>
 <section id="data-loading">
-<h3><span class="section-number">30.2.2. </span>Data Loading<a class="headerlink" href="#data-loading" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.2. </span>Data Loading<a class="headerlink" href="#data-loading" title="Permalink to this headline">#</a></h3>
 <p>The dataset can be downloaded from <a class="reference external" href="https://www.kaggle.com/datasets/jessicali9530/celeba-dataset">here</a>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2081,7 +2113,7 @@ <h3><span class="section-number">30.2.2. </span>Data Loading<a class="headerlink
 </div>
 </section>
 <section id="data-preprocessing">
-<h3><span class="section-number">30.2.3. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.3. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
@@ -2092,7 +2124,7 @@ <h3><span class="section-number">30.2.3. </span>Data Preprocessing<a class="head
 </div>
 </section>
 <section id="data-augmentation">
-<h3><span class="section-number">30.2.4. </span>Data Augmentation<a class="headerlink" href="#data-augmentation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.4. </span>Data Augmentation<a class="headerlink" href="#data-augmentation" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">augmentation</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
@@ -2103,7 +2135,7 @@ <h3><span class="section-number">30.2.4. </span>Data Augmentation<a class="heade
 </div>
 </section>
 <section id="data">
-<h3><span class="section-number">30.2.5. </span>Data<a class="headerlink" href="#data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.5. </span>Data<a class="headerlink" href="#data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_dataset</span> <span class="o">=</span> <span class="p">(</span>
@@ -2120,7 +2152,7 @@ <h3><span class="section-number">30.2.5. </span>Data<a class="headerlink" href="
 </div>
 </section>
 <section id="linear-schedule-beta">
-<h3><span class="section-number">30.2.6. </span>Linear schedule-beta<a class="headerlink" href="#linear-schedule-beta" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.6. </span>Linear schedule-beta<a class="headerlink" href="#linear-schedule-beta" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">linear_beta_schedule</span><span class="p">(</span><span class="n">timesteps</span><span class="p">):</span>
@@ -2389,11 +2421,11 @@ <h3><span class="section-number">30.2.6. </span>Linear schedule-beta<a class="he
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">30.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">33.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">30.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">33.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/phykn">Kwangnam Yu</a> for creating the open-source project <a class="reference external" href="https://github.com/phykn/diffusion_models_tutorial">diffusion_models_tutorial</a> and <a class="reference external" href="https://www.kaggle.com/">kaggle</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/folefac/denoising-diffusion-models-with-tensorflow#3.-The-Celeb-A-Dataset-%F0%9F%92%BE">Denoising Diffusion Models with TensorFlow</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2435,13 +2467,13 @@ <h2><span class="section-number">30.4. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">29. </span>Image segmentation</p>
+            <p class="prev-next-title"><span class="section-number">32. </span>Image segmentation</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="object-detection.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">31. </span>Object detection</p>
+        <p class="prev-next-title"><span class="section-number">34. </span>Object detection</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/deep-learning/dl-overview.html b/deep-learning/dl-overview.html
index c90543df0b..6d4496b4d3 100644
--- a/deep-learning/dl-overview.html
+++ b/deep-learning/dl-overview.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>20. Intro to Deep Learning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>23. Intro to Deep Learning &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="21. Convolutional Neural Networks" href="cnn.html" />
-    <link rel="prev" title="19. Model selection" href="../ml-advanced/model-selection.html" />
+    <link rel="next" title="24. Convolutional Neural Networks" href="cnn.html" />
+    <link rel="prev" title="22. Model selection" href="../ml-advanced/model-selection.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
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-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
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-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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  </li>
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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-   24. Autoencoder
+   27. Autoencoder
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-   25. Long-short term memory
+   28. Long-short term memory
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-   26. Time series
+   29. Time series
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-   27. Deep Q-learning
+   30. Deep Q-learning
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-   28. Image classification
+   31. Image classification
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-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
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   <a class="reference internal" href="difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
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   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
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   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
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-     37.27. Analyzing data
+     40.27. Analyzing data
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    </li>
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     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
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-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
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-     37.53. Regression tools
+     40.53. Regression tools
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    <li class="toctree-l2">
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-     37.54. Managing data
+     40.54. Managing data
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-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
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-     37.56. Try a different model
+     40.56. Try a different model
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+     23.4.1. Densely Connected Networks
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+     23.4.2. Convolutional Neural Networks
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 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="intro-to-deep-learning">
-<h1><span class="section-number">20. </span>Intro to Deep Learning<a class="headerlink" href="#intro-to-deep-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">23. </span>Intro to Deep Learning<a class="headerlink" href="#intro-to-deep-learning" title="Permalink to this headline">#</a></h1>
 <p>Deep Learning is the study of Artificial Neural Networks(ANNs). It is a subset of Machine Learning (ML), ML being a subset of Artificial Intelligence.</p>
 <p>Inspired by the human brain’s biological neuron, ANNs has become the building block of modern intelligent systems, from our smartphones that can accurately identify our faces and voices, streaming services (like YouTube and Netflix) that know exactly what we want to watch, cars that can see the visual world and drive themselves, and not to mention AlphaGo that won world chess competition.</p>
 <p>This notebook will introduce a high level overview of Deep Learning.</p>
 <section id="why-deep-learning">
-<h2><span class="section-number">20.1. </span>Why Deep Learning<a class="headerlink" href="#why-deep-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.1. </span>Why Deep Learning<a class="headerlink" href="#why-deep-learning" title="Permalink to this headline">#</a></h2>
 <p>Deep Learning is used to extract the patterns in (a huge amount of) data. Its potential has been seen in complex problems such as vision oriented and natural language.</p>
 <p>Let’s take an example to develop more intuitive understanding of why deep learning has became so popular. Let’s say that as an Engineer, you are asked by your city state to build a program that can recognize car and truck moving in a given road. The program can later be deployed in a less loads-road to warn trucks that pass there that the road was not designed for these types of vehicles.</p>
 <p>With normal programming, you would have to provide and to write all instructions (codes) of that particular program. The chance (close to reality) is that it would be hard, mostly because there are different types of cars and trucks and each can be unique in its ways, which can make it hard to write the rules forming such program.</p>
@@ -1672,7 +1704,7 @@ <h2><span class="section-number">20.1. </span>Why Deep Learning<a class="headerl
 <p>With the advent of the computation power, both paid and free (Google Colab &amp; Kaggle), open source datasets, and intelligent frameworks such as TensorFlow and PyTorch, it has become very much possible to do deep learning, even at personal level. This was not possible a decade ago.</p>
 </section>
 <section id="a-single-hidden-layer-neural-network">
-<h2><span class="section-number">20.2. </span>A Single Hidden Layer Neural Network<a class="headerlink" href="#a-single-hidden-layer-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.2. </span>A Single Hidden Layer Neural Network<a class="headerlink" href="#a-single-hidden-layer-neural-network" title="Permalink to this headline">#</a></h2>
 <p><img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/dl-overview/Neuralnetwork.png" alt="Neuralnetwork.png"><br> Image: A Single hidden layer neural network, Wikimedia</p>
 <p>The image above represent a single hidden layer neural network. Usually, any typical neural network has 3 main layers:</p>
 <ul class="simple">
@@ -1684,7 +1716,7 @@ <h2><span class="section-number">20.2. </span>A Single Hidden Layer Neural Netwo
 <p>With the exception of the input layer, you will find something called activation function being applied to hidden and output layers. What is an activation function?</p>
 </section>
 <section id="common-activation-functions">
-<h2><span class="section-number">20.3. </span>Common Activation Functions<a class="headerlink" href="#common-activation-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.3. </span>Common Activation Functions<a class="headerlink" href="#common-activation-functions" title="Permalink to this headline">#</a></h2>
 <p>Activation functions are used to introduce nonlinearities in the network. I like to say that they dictate the output format of a given layer.</p>
 <p>Or simply put, these type of mathematical functions are used to make a decision of the layer’s output. In later notebooks, this will certainly make sense.</p>
 <p>Here are the 3 most used activation functions:</p>
@@ -1695,10 +1727,10 @@ <h2><span class="section-number">20.3. </span>Common Activation Functions<a clas
 </ul>
 </section>
 <section id="types-of-deep-learning-architectures">
-<h2><span class="section-number">20.4. </span>Types of Deep Learning Architectures<a class="headerlink" href="#types-of-deep-learning-architectures" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.4. </span>Types of Deep Learning Architectures<a class="headerlink" href="#types-of-deep-learning-architectures" title="Permalink to this headline">#</a></h2>
 <p>There are 4 main types of deep learning architectures. This is going to be a high level since they will be covered in the next notebooks.</p>
 <section id="densely-connected-networks">
-<h3><span class="section-number">20.4.1. </span>Densely Connected Networks<a class="headerlink" href="#densely-connected-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.1. </span>Densely Connected Networks<a class="headerlink" href="#densely-connected-networks" title="Permalink to this headline">#</a></h3>
 <p>Densely connected networks are made of stacks of layers from the input to the output.</p>
 <p>The units (or neurons) of any layer in that type of network are connected to all other units of the next layer. This is why they are also called fully connected layers.</p>
 <p>Densely connected layers are generally used for tabular data. Tabular data are these kinds of data that are in a tabular fashion. An example of tabular data is customer records: you have a column of names, roles, data joined, etc…</p>
@@ -1708,7 +1740,7 @@ <h3><span class="section-number">20.4.1. </span>Densely Connected Networks<a cla
 <p>Dense networks are mostly used in simple tasks. When it comes to complex things like image recognition or text processing, they fail because they can’t hold spatial features (in image) or can’t learn a sequence in text for instance. But above all, the more deep they become, they tend to have many parameters which can results in having a big network/complex computations.</p>
 </section>
 <section id="convolutional-neural-networks">
-<h3><span class="section-number">20.4.2. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.2. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
 <p>CNN’s, a.k.a. Convents are widely known as the go-to neural network architectures for computer vision tasks like image recognition and object detection, but they can also be used in other tasks such as texts and time series processing.</p>
 <p>Convents are typically made of convolution, pooling layers, and fully connected layers at the end. Convolutional layers are used to extract the spatial features in images, and pooling layers are used to compress the resulted feature maps from convolution layers. And fully connected layers for classification purposes.</p>
 <p><img alt="on-IOL-iG.png-2.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/dl-overview/KF4gj3zPD.webp" /></p>
@@ -1716,7 +1748,7 @@ <h3><span class="section-number">20.4.2. </span>Convolutional Neural Networks<a
 <p>Conv3D is used in videos and volumetric images such as CT scans.</p>
 </section>
 <section id="recurrent-neural-networks-rnns">
-<h3><span class="section-number">20.4.3. </span>Recurrent Neural Networks (RNNs)<a class="headerlink" href="#recurrent-neural-networks-rnns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.3. </span>Recurrent Neural Networks (RNNs)<a class="headerlink" href="#recurrent-neural-networks-rnns" title="Permalink to this headline">#</a></h3>
 <p>The standard feedforward network (or call them densely connected network) maps the input to output. RNNs go beyond that. They can maintain the recurrence of data at each time step.</p>
 <p><img alt="TF RNN.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/dl-overview/7kzTaiwxp.png" /></p>
 <p>Due to their ability to preserve the recurrence of information, RNNs are commonly used in sequential data such as texts and time series.</p>
@@ -1734,7 +1766,7 @@ <h3><span class="section-number">20.4.3. </span>Recurrent Neural Networks (RNNs)
 <p>The ability of LSTMs to handle long-term sequences makes it a suitable neural network architecture for various sequential tasks such as text classification, sentiment analysis, speech recognition, image caption generation, machine translation.</p>
 </section>
 <section id="transformers">
-<h3><span class="section-number">20.4.4. </span>Transformers<a class="headerlink" href="#transformers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.4. </span>Transformers<a class="headerlink" href="#transformers" title="Permalink to this headline">#</a></h3>
 <p>Although recurrent neural networks are still used for sequential modeling, they have short-term memory problems when used for long sequences, and they are computationally expensive. The RNN’s inability to handle long sequences and expensiveness are the two most motivations of transformers.</p>
 <p>Transformers are one of the latest groundbreaking researches in the natural language community. They are sorely based on the attention mechanisms that learn the relationships between words of the sentence and pays attention to the relevant words.</p>
 <p>One of the most notable things about transformers is that they don’t use any recurrent or convolutional layers. It’s just only attention mechanisms and other standard layers like embedding layer, dense layer, and normalization layers.</p>
@@ -1747,7 +1779,7 @@ <h3><span class="section-number">20.4.4. </span>Transformers<a class="headerlink
 </section>
 </section>
 <section id="challenges-in-training-neural-networks">
-<h2><span class="section-number">20.5. </span>Challenges in Training Neural Networks<a class="headerlink" href="#challenges-in-training-neural-networks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.5. </span>Challenges in Training Neural Networks<a class="headerlink" href="#challenges-in-training-neural-networks" title="Permalink to this headline">#</a></h2>
 <p>Training neural networks is a big challenge and this still an area of research. For example, how do you choose a right architecture? Although the type of data can tell the most architecture that should come into picture at first, it is not a trivial question. Take an example, we know CNN as image first algorithm, bit it has been used in some natural language processing and time series tasks, and it can perform well.</p>
 <p>Also, deep learning models have many hyperparameters. Hyperparameters include things like number of layers, number of neurons, learning rate, activation functions, and optimizers (optimizers are used to reduce loss during training).</p>
 <p>All these choices to be make it hard to train neural networks. Although there has been some techniques that can reduce number of chocs to be made, such as transfer learning which motivates reusing the pretrained models, training this type of models is not an easy thing.</p>
@@ -1791,13 +1823,13 @@ <h2><span class="section-number">20.5. </span>Challenges in Training Neural Netw
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">19. </span>Model selection</p>
+            <p class="prev-next-title"><span class="section-number">22. </span>Model selection</p>
         </div>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">21. </span>Convolutional Neural Networks</p>
+        <p class="prev-next-title"><span class="section-number">24. </span>Convolutional Neural Networks</p>
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diff --git a/deep-learning/dqn.html b/deep-learning/dqn.html
index 538d16bece..7f2f44a199 100644
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@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>27. Deep Q-learning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>30. Deep Q-learning &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="28. Image classification" href="image-classification.html" />
-    <link rel="prev" title="26. Time series" href="time-series.html" />
+    <link rel="next" title="31. Image classification" href="image-classification.html" />
+    <link rel="prev" title="29. Time series" href="time-series.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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   <ul>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
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-     14.2. K-Means clustering
+     16.2. K-Means clustering
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-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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-     15.2. Random forest
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-     15.3. Feature importance
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     16.2. Gradient boosting example
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-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
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@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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-   24. Autoencoder
+   27. Autoencoder
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-   25. Long-short term memory
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-   26. Time series
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+   32. Image segmentation
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-   30. Diffusion Model
+   33. Diffusion Model
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-   31. Object detection
+   34. Object detection
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@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+     40.3. First assignment
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-     37.4. Second assignment
+     40.4. Second assignment
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-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     37.14. Try it in Excel
+     40.14. Try it in Excel
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-     37.24. Data processing in Python
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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     </a>
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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+     40.103. Fun with Variational Autoencoders
     </a>
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-     38.20. Convolutional Neural Network
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@@ -1528,132 +1560,132 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toc-h2 nav-item toc-entry">
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-     27.2.1. Reward
+     30.2.1. Reward
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-     27.2.2. Task-dependent return definitions
+     30.2.2. Task-dependent return definitions
     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#episodic-tasks">
-       27.2.2.1. Episodic tasks
+       30.2.2.1. Episodic tasks
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       <a class="reference internal nav-link" href="#continuing-tasks">
-       27.2.2.2. Continuing tasks
+       30.2.2.2. Continuing tasks
       </a>
      </li>
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-     27.2.3. State
+     30.2.3. State
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-       27.2.3.2. Agent state
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-       27.2.5.1. Example
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       </a>
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-     27.2.8. Exploration and exploitation
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     </a>
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+     30.3.1. RL-solution by policy
     </a>
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-     27.3.2. RL-solution by value function
+     30.3.2. RL-solution by value function
     </a>
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     <a class="reference internal nav-link" href="#rl-solution-by-model-evaluation">
-     27.3.3. RL-solution by model evaluation
+     30.3.3. RL-solution by model evaluation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#rl-agent-taxonomy">
-     27.3.4. RL agent taxonomy
+     30.3.4. RL agent taxonomy
     </a>
    </li>
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-   27.4. Code
+   30.4. Code
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   27.5. Your turn! 🚀
+   30.5. Your turn! 🚀
   </a>
  </li>
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-   27.6. Self study
+   30.6. Self study
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   27.7. Acknowledgments
+   30.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1677,132 +1709,132 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   27.1. Overview
+   30.1. Overview
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-   27.2. Basic terminology
+   30.2. Basic terminology
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reward">
-     27.2.1. Reward
+     30.2.1. Reward
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-dependent-return-definitions">
-     27.2.2. Task-dependent return definitions
+     30.2.2. Task-dependent return definitions
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#episodic-tasks">
-       27.2.2.1. Episodic tasks
+       30.2.2.1. Episodic tasks
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#continuing-tasks">
-       27.2.2.2. Continuing tasks
+       30.2.2.2. Continuing tasks
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#state">
-     27.2.3. State
+     30.2.3. State
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#environment-state">
-       27.2.3.1. Environment state
+       30.2.3.1. Environment state
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#agent-state">
-       27.2.3.2. Agent state
+       30.2.3.2. Agent state
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     27.2.4. Action
+     30.2.4. Action
     </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     27.2.5. Policy
+     30.2.5. Policy
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#example">
-       27.2.5.1. Example
+       30.2.5.1. Example
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#value-functions">
-     27.2.6. Value functions
+     30.2.6. Value functions
     </a>
    </li>
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-     27.2.7. Model
+     30.2.7. Model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploration-and-exploitation">
-     27.2.8. Exploration and exploitation
+     30.2.8. Exploration and exploitation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#main-algorithms">
-   27.3. Main algorithms
+   30.3. Main algorithms
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#rl-solution-by-policy">
-     27.3.1. RL-solution by policy
+     30.3.1. RL-solution by policy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#rl-solution-by-value-function">
-     27.3.2. RL-solution by value function
+     30.3.2. RL-solution by value function
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#rl-solution-by-model-evaluation">
-     27.3.3. RL-solution by model evaluation
+     30.3.3. RL-solution by model evaluation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#rl-agent-taxonomy">
-     27.3.4. RL agent taxonomy
+     30.3.4. RL agent taxonomy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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+   30.4. Code
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   27.5. Your turn! 🚀
+   30.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   27.6. Self study
+   30.6. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   27.7. Acknowledgments
+   30.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1827,15 +1859,15 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="deep-q-learning">
-<h1><span class="section-number">27. </span>Deep Q-learning<a class="headerlink" href="#deep-q-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">30. </span>Deep Q-learning<a class="headerlink" href="#deep-q-learning" title="Permalink to this headline">#</a></h1>
 <section id="overview">
-<h2><span class="section-number">27.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>Usually, we regard Deep Q Network as DQN, and it can also be called Reinforcement Learning (RL) whose aim is to achieve the desired behavior of an agent that learns from its mistakes and improves its performance.</p>
 <p>RL is a type of machine learning that allows us to create AI agents that learn from the environment by interacting with it to maximize its cumulative reward. Here is an image showing the basic RL operation principle:</p>
 <figure class="align-default" id="pipline-of-dqn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/02_pipline.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/02_pipline.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/02_pipline.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.1 </span><span class="caption-text">Pipline of DQN</span><a class="headerlink" href="#pipline-of-dqn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.1 </span><span class="caption-text">Pipline of DQN</span><a class="headerlink" href="#pipline-of-dqn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>From this image, we can see that at each step <span class="math notranslate nohighlight">\(k\)</span>, the agent picks an action <span class="math notranslate nohighlight">\(u_k\)</span>, receives an observation <span class="math notranslate nohighlight">\(y_k\)</span> and receives a reward <span class="math notranslate nohighlight">\(r_k\)</span>, and the environment receives an action <span class="math notranslate nohighlight">\(u_k\)</span>, emits an observation <span class="math notranslate nohighlight">\(y_{k+1}\)</span> and emits a reward <span class="math notranslate nohighlight">\(r_{k+1}\)</span>. Later, the time increments k ← k + 1. A one step time delay is assumed between executing the action and receiving the observation as well as reward. We assume that the resulting time interval <span class="math notranslate nohighlight">\(∆t = t_k − t_{k+1}\)</span> is constant.</p>
@@ -1852,9 +1884,9 @@ <h2><span class="section-number">27.1. </span>Overview<a class="headerlink" href
 </div>
 </section>
 <section id="basic-terminology">
-<h2><span class="section-number">27.2. </span>Basic terminology<a class="headerlink" href="#basic-terminology" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.2. </span>Basic terminology<a class="headerlink" href="#basic-terminology" title="Permalink to this headline">#</a></h2>
 <section id="reward">
-<h3><span class="section-number">27.2.1. </span>Reward<a class="headerlink" href="#reward" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.1. </span>Reward<a class="headerlink" href="#reward" title="Permalink to this headline">#</a></h3>
 <p>A reward is a scalar random variable <span class="math notranslate nohighlight">\(R_k\)</span> with realizations <span class="math notranslate nohighlight">\(r_k\)</span>:</p>
 <ul class="simple">
 <li><p>Often it is considered a real-number <span class="math notranslate nohighlight">\(r_k \in \mathbb{R}\)</span> or an integer <span class="math notranslate nohighlight">\(r_k \in \mathbb{Z}\)</span>.</p></li>
@@ -1883,13 +1915,13 @@ <h3><span class="section-number">27.2.1. </span>Reward<a class="headerlink" href
 <p>Besides, the RL agent’s learning process is heavily linked with the reward distribution over time. Designing expedient rewards functions is therefore crucially important for successfully applying RL. And often there is no predefined way on how to design the “best reward function”.</p>
 </section>
 <section id="task-dependent-return-definitions">
-<h3><span class="section-number">27.2.2. </span>Task-dependent return definitions<a class="headerlink" href="#task-dependent-return-definitions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.2. </span>Task-dependent return definitions<a class="headerlink" href="#task-dependent-return-definitions" title="Permalink to this headline">#</a></h3>
 <section id="episodic-tasks">
-<h4><span class="section-number">27.2.2.1. </span>Episodic tasks<a class="headerlink" href="#episodic-tasks" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.2.1. </span>Episodic tasks<a class="headerlink" href="#episodic-tasks" title="Permalink to this headline">#</a></h4>
 <p>Episodic tasks can naturally break into subsequences (finite horizon), for examples: most games, maze,… And the return becomes a finite sum: <span class="math notranslate nohighlight">\(g_k = r_{k+1} + r_{k+2} + ... + r_{N}\)</span>. Episodes end at their terminal step <span class="math notranslate nohighlight">\(k = N\)</span>.</p>
 </section>
 <section id="continuing-tasks">
-<h4><span class="section-number">27.2.2.2. </span>Continuing tasks<a class="headerlink" href="#continuing-tasks" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.2.2. </span>Continuing tasks<a class="headerlink" href="#continuing-tasks" title="Permalink to this headline">#</a></h4>
 <p>Continuing tasks lack a natural end (infinite horizon), for example: process control task, and the return should be discounted to prevent infinite numbers: <span class="math notranslate nohighlight">\(g_k = r_{k+1} + \gamma r_{k+2} + \gamma^2 r_{k+3} + ... = \sum_{i=1}^{\infty} \gamma^{i} r_{k+i+1}\)</span>. Here, <span class="math notranslate nohighlight">\(\gamma ∈ {\mathbb{R}|0 ≤ \gamma ≤ 1}\)</span> is the discount rate.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1903,9 +1935,9 @@ <h4><span class="section-number">27.2.2.2. </span>Continuing tasks<a class="head
 </section>
 </section>
 <section id="state">
-<h3><span class="section-number">27.2.3. </span>State<a class="headerlink" href="#state" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.3. </span>State<a class="headerlink" href="#state" title="Permalink to this headline">#</a></h3>
 <section id="environment-state">
-<h4><span class="section-number">27.2.3.1. </span>Environment state<a class="headerlink" href="#environment-state" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.3.1. </span>Environment state<a class="headerlink" href="#environment-state" title="Permalink to this headline">#</a></h4>
 <p>Random variable <span class="math notranslate nohighlight">\(X_k^{e}\)</span> with realizations <span class="math notranslate nohighlight">\(x_k^{e}\)</span>:</p>
 <ul class="simple">
 <li><p>Internal status representation of the environment, e.g.physical states (car velocity or motor current), game states (current chess board situation). financial states (stock market status).</p></li>
@@ -1914,7 +1946,7 @@ <h4><span class="section-number">27.2.3.1. </span>Environment state<a class="hea
 </ul>
 </section>
 <section id="agent-state">
-<h4><span class="section-number">27.2.3.2. </span>Agent state<a class="headerlink" href="#agent-state" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.3.2. </span>Agent state<a class="headerlink" href="#agent-state" title="Permalink to this headline">#</a></h4>
 <p>Random variable <span class="math notranslate nohighlight">\(X_k^{a}\)</span> with realizations <span class="math notranslate nohighlight">\(x_k^{a}\)</span>:</p>
 <ul class="simple">
 <li><p>Internal status representation of the agent.</p></li>
@@ -1926,7 +1958,7 @@ <h4><span class="section-number">27.2.3.2. </span>Agent state<a class="headerlin
 </section>
 </section>
 <section id="action">
-<h3><span class="section-number">27.2.4. </span>Action<a class="headerlink" href="#action" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.4. </span>Action<a class="headerlink" href="#action" title="Permalink to this headline">#</a></h3>
 <p>An action is the agent’s degree of freedom in order to maximize its reward. The major distinctions are:</p>
 <ul class="simple">
 <li><p>Finite action set (FAS): <span class="math notranslate nohighlight">\(u_k ∈ {u_{k,1},u_{k,2}, ...} ∈ \mathbb{R}_m\)</span>.</p></li>
@@ -1940,7 +1972,7 @@ <h3><span class="section-number">27.2.4. </span>Action<a class="headerlink" href
 </div>
 </section>
 <section id="policy">
-<h3><span class="section-number">27.2.5. </span>Policy<a class="headerlink" href="#policy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.5. </span>Policy<a class="headerlink" href="#policy" title="Permalink to this headline">#</a></h3>
 <p>A policy <span class="math notranslate nohighlight">\(\pi\)</span> is the agent’s internal strategy on picking actions.</p>
 <ul class="simple">
 <li><p>Deterministic policies: maps state and action directly: <span class="math notranslate nohighlight">\(u_k = \pi (x_k)\)</span>.</p></li>
@@ -1948,7 +1980,7 @@ <h3><span class="section-number">27.2.5. </span>Policy<a class="headerlink" href
 <li><p>RL is all about changing <span class="math notranslate nohighlight">\(\pi\)</span> over time in order to maximize the expected return.</p></li>
 </ul>
 <section id="example">
-<h4><span class="section-number">27.2.5.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.5.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h4>
 <p>Here is a deterministic policy example: find optimal gains <span class="math notranslate nohighlight">\({K_p, K_i, K_d}\)</span> given the reward <span class="math notranslate nohighlight">\(r_k = −e^2_k\)</span></p>
 <ul class="simple">
 <li><p>Agent’s behavior is explicitly determined by <span class="math notranslate nohighlight">\({K_p, K_i, K_d}\)</span>.</p></li>
@@ -1958,13 +1990,13 @@ <h4><span class="section-number">27.2.5.1. </span>Example<a class="headerlink" h
 <figure class="align-default" id="classical-pid-control-loop-with-scalar-quantities">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/03_policy_example.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/03_policy_example.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/03_policy_example.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.2 </span><span class="caption-text">Classical PID control loop with scalar quantities</span><a class="headerlink" href="#classical-pid-control-loop-with-scalar-quantities" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.2 </span><span class="caption-text">Classical PID control loop with scalar quantities</span><a class="headerlink" href="#classical-pid-control-loop-with-scalar-quantities" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="value-functions">
-<h3><span class="section-number">27.2.6. </span>Value functions<a class="headerlink" href="#value-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.6. </span>Value functions<a class="headerlink" href="#value-functions" title="Permalink to this headline">#</a></h3>
 <p>The state-value function is the expected return being in state <span class="math notranslate nohighlight">\(x_k\)</span> following a policy <span class="math notranslate nohighlight">\(\pi:v_{\pi}(x_k)\)</span>.</p>
 <p>Assuming an MDP problem structure the state-value function is <span class="math notranslate nohighlight">\(v_{\pi}(x_k) = \mathbb{E}_{\pi} [G_k | X_k = x_k] = \mathbb{E}_{\pi}[\sum_{i=0}^{\infty} \gamma^i R_{k+i+1} | x_k]\)</span>.</p>
 <p>The action-value function is the expected return being in state <span class="math notranslate nohighlight">\(x_k\)</span> taken an action <span class="math notranslate nohighlight">\(u_k\)</span> and, thereafter, following a policy <span class="math notranslate nohighlight">\(\pi: q_{\pi}(x_k,u_k)\)</span>.</p>
@@ -1972,12 +2004,12 @@ <h3><span class="section-number">27.2.6. </span>Value functions<a class="headerl
 <p>A key task in RL is to estimate <span class="math notranslate nohighlight">\(v_{\pi}(x_k)\)</span> and <span class="math notranslate nohighlight">\(q_{\pi}(x_k,u_k)\)</span> based on sampled data.</p>
 </section>
 <section id="model">
-<h3><span class="section-number">27.2.7. </span>Model<a class="headerlink" href="#model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.7. </span>Model<a class="headerlink" href="#model" title="Permalink to this headline">#</a></h3>
 <p>A model predicts what will happen inside an environment.</p>
 <p>That could be a state model <span class="math notranslate nohighlight">\(\mathcal{P}\)</span>: <span class="math notranslate nohighlight">\(\mathcal{P} = \mathbb{P}[X_{k+1}=x_{k+1}|X_k=x_k, U_k=u_k]\)</span>. Or a reward model <span class="math notranslate nohighlight">\(\mathcal{R}\)</span>: <span class="math notranslate nohighlight">\(\mathcal{R} = \mathbb{P}[R_{k+1}=r_{k+1}|X_k=x_k, U_k=u_k]\)</span>. In general, those models could be stochastic (as denoted above) but in some problems relax to a deterministic form. Using data in order to fit a model is a learning problem of its own and often called system identification.</p>
 </section>
 <section id="exploration-and-exploitation">
-<h3><span class="section-number">27.2.8. </span>Exploration and exploitation<a class="headerlink" href="#exploration-and-exploitation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.8. </span>Exploration and exploitation<a class="headerlink" href="#exploration-and-exploitation" title="Permalink to this headline">#</a></h3>
 <p>In RL the environment is initially unknown. How to act optimal?</p>
 <ul class="simple">
 <li><p>Exploration: find out more about the environment.</p></li>
@@ -1990,7 +2022,7 @@ <h3><span class="section-number">27.2.8. </span>Exploration and exploitation<a c
 </section>
 </section>
 <section id="main-algorithms">
-<h2><span class="section-number">27.3. </span>Main algorithms<a class="headerlink" href="#main-algorithms" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.3. </span>Main algorithms<a class="headerlink" href="#main-algorithms" title="Permalink to this headline">#</a></h2>
 <p>In this section, we will take maze as an example. The problem statement is:</p>
 <ul class="simple">
 <li><p>Reward: <span class="math notranslate nohighlight">\(r_k = −1\)</span>.</p></li>
@@ -2002,15 +2034,15 @@ <h2><span class="section-number">27.3. </span>Main algorithms<a class="headerlin
 <figure class="align-default" id="maze-setup-statement">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/04_maze.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/04_maze.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/04_maze.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.3 </span><span class="caption-text">Maze setup statement</span><a class="headerlink" href="#maze-setup-statement" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.3 </span><span class="caption-text">Maze setup statement</span><a class="headerlink" href="#maze-setup-statement" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="rl-solution-by-policy">
-<h3><span class="section-number">27.3.1. </span>RL-solution by policy<a class="headerlink" href="#rl-solution-by-policy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.1. </span>RL-solution by policy<a class="headerlink" href="#rl-solution-by-policy" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="maze-solved-by-policy">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/05_maze_policy.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/05_maze_policy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/05_maze_policy.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.4 </span><span class="caption-text">Maze solved by policy</span><a class="headerlink" href="#maze-solved-by-policy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.4 </span><span class="caption-text">Maze solved by policy</span><a class="headerlink" href="#maze-solved-by-policy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Key characteristics:</p>
@@ -2020,11 +2052,11 @@ <h3><span class="section-number">27.3.1. </span>RL-solution by policy<a class="h
 </ul>
 </section>
 <section id="rl-solution-by-value-function">
-<h3><span class="section-number">27.3.2. </span>RL-solution by value function<a class="headerlink" href="#rl-solution-by-value-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.2. </span>RL-solution by value function<a class="headerlink" href="#rl-solution-by-value-function" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="maze-solved-by-value-functiony">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/06_maze_valuefunc.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/06_maze_valuefunc.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/06_maze_valuefunc.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.5 </span><span class="caption-text">Maze solved by value function</span><a class="headerlink" href="#maze-solved-by-value-functiony" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.5 </span><span class="caption-text">Maze solved by value function</span><a class="headerlink" href="#maze-solved-by-value-functiony" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Key characteristics:</p>
@@ -2034,11 +2066,11 @@ <h3><span class="section-number">27.3.2. </span>RL-solution by value function<a
 </ul>
 </section>
 <section id="rl-solution-by-model-evaluation">
-<h3><span class="section-number">27.3.3. </span>RL-solution by model evaluation<a class="headerlink" href="#rl-solution-by-model-evaluation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.3. </span>RL-solution by model evaluation<a class="headerlink" href="#rl-solution-by-model-evaluation" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="maze-solved-by-model-evaluation">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_maze_modeleval.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_maze_modeleval.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_maze_modeleval.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.6 </span><span class="caption-text">Maze solved by model evaluation</span><a class="headerlink" href="#maze-solved-by-model-evaluation" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.6 </span><span class="caption-text">Maze solved by model evaluation</span><a class="headerlink" href="#maze-solved-by-model-evaluation" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Key characteristics:</p>
@@ -2049,17 +2081,17 @@ <h3><span class="section-number">27.3.3. </span>RL-solution by model evaluation<
 </ul>
 </section>
 <section id="rl-agent-taxonomy">
-<h3><span class="section-number">27.3.4. </span>RL agent taxonomy<a class="headerlink" href="#rl-agent-taxonomy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.4. </span>RL agent taxonomy<a class="headerlink" href="#rl-agent-taxonomy" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="main-categories-of-reinforcement-learning-algorithms">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_taxonomy.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_taxonomy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_taxonomy.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.7 </span><span class="caption-text">Main categories of reinforcement learning algorithms</span><a class="headerlink" href="#main-categories-of-reinforcement-learning-algorithms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.7 </span><span class="caption-text">Main categories of reinforcement learning algorithms</span><a class="headerlink" href="#main-categories-of-reinforcement-learning-algorithms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="code">
-<h2><span class="section-number">27.4. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.4. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <p>This is an example of DQN discrete model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2531,18 +2563,18 @@ <h2><span class="section-number">27.4. </span>Code<a class="headerlink" href="#c
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">27.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">27.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can refer to this website for further study:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://pylessons.com/CartPole-reinforcement-learning">Introduction to Reinforcement Learning</a></p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">27.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/upb-lea">Paderborn University - LEA</a> for creating the open-source course <a class="reference external" href="https://github.com/upb-lea/reinforcement_learning_course_materials">reinforcement_learning_course_materials</a> and <a class="reference external" href="https://github.com/pacsuta">Gergely Pacsuta</a> for creating the open-source project <a class="reference external" href="https://github.com/pacsuta/tf-nn-maze-solver">tf-nn-maze-solver</a>. They inspire the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <span class="target" id="id1"></span></section>
@@ -2585,13 +2617,13 @@ <h2><span class="section-number">27.7. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">26. </span>Time series</p>
+            <p class="prev-next-title"><span class="section-number">29. </span>Time series</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="image-classification.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">28. </span>Image classification</p>
+        <p class="prev-next-title"><span class="section-number">31. </span>Image classification</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/deep-learning/gan.html b/deep-learning/gan.html
index bd693140ec..41601f8ce0 100644
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>22. Generative adversarial networks &#8212; Ocademy Open Machine Learning Book</title>
+    <title>25. Generative adversarial networks &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="23. Recurrent Neural Networks" href="rnn.html" />
-    <link rel="prev" title="21. Convolutional Neural Networks" href="cnn.html" />
+    <link rel="next" title="26. Recurrent Neural Networks" href="rnn.html" />
+    <link rel="prev" title="24. Convolutional Neural Networks" href="cnn.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
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-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
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  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
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-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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    <li class="toctree-l2">
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
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-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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    </li>
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-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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   </ul>
  </li>
  <li class="toctree-l1">
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
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 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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-   24. Autoencoder
+   27. Autoencoder
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-   25. Long-short term memory
+   28. Long-short term memory
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-   26. Time series
+   29. Time series
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-   27. Deep Q-learning
+   30. Deep Q-learning
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-   28. Image classification
+   31. Image classification
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   <a class="reference internal" href="image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
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   <a class="reference internal" href="difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="object-detection.html">
-   31. Object detection
+   34. Object detection
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  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
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   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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 </ul>
@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
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-<h1><span class="section-number">22. </span>Generative adversarial networks<a class="headerlink" href="#generative-adversarial-networks" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">25. </span>Generative adversarial networks<a class="headerlink" href="#generative-adversarial-networks" title="Permalink to this headline">#</a></h1>
 <p>The original purpose of Generative adversarial networks(GAN) is to generate new data. It classically generates new images, but is applicable to wide range of domains. It learns the training set distribution and can generate new images that have never been seen before. In contrast to e.g., autoregressive models or RNNs (generating one word at a time), GANs generate the whole output all at once.</p>
 <p>GAN is proposed in 2005, the paper is Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. “Generative Adversarial Networks”, arxiv:1406.2661.</p>
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+<h2><span class="section-number">25.1. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">22.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">25.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-source project <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a> and <a class="reference external" href="https://github.com/diegoalejogm">Diego Gomez</a> for creating the open-source project <a class="reference external" href="https://github.com/diegoalejogm/gans">gans</a>. They inspire the majority of the content in this chapter.</p>
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     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="29. Image segmentation" href="image-segmentation.html" />
-    <link rel="prev" title="27. Deep Q-learning" href="dqn.html" />
+    <link rel="next" title="32. Image segmentation" href="image-segmentation.html" />
+    <link rel="prev" title="30. Deep Q-learning" href="dqn.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="time-series.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="dqn.html">
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   <a class="reference internal" href="image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
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+   31.1. What is image classification?
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+     31.4.5. ViT
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     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       28.4.5.1. Code
+       31.4.5.1. Code
       </a>
      </li>
     </ul>
@@ -1608,46 +1640,46 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classic-datasets">
-   28.5. Classic datasets
+   31.5. Classic datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cifar-10-100">
-     28.5.1. CIFAR-10/100
+     31.5.1. CIFAR-10/100
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#imagenet-1000">
-     28.5.2. ImageNet-1000
+     31.5.2. ImageNet-1000
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#standards">
-   28.6. Standards
+   31.6. Standards
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#top-1-accuracy">
-     28.6.1. Top-1 accuracy
+     31.6.1. Top-1 accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#top-5-accuracy">
-     28.6.2. Top-5 accuracy
+     31.6.2. Top-5 accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   28.7. Your turn! 🚀
+   31.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   28.8. Acknowledgments
+   31.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1671,80 +1703,80 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-image-classification">
-   28.1. What is image classification?
+   31.1. What is image classification?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#challenges">
-   28.2. Challenges
+   31.2. Challenges
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pipeline-of-image-classification">
-   28.3. Pipeline of image classification
+   31.3. Pipeline of image classification
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#history-classic-models">
-   28.4. History &amp; classic models
+   31.4. History &amp; classic models
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#vggnet">
-     28.4.1. VGGNet
+     31.4.1. VGGNet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#code">
-       28.4.1.1. Code
+       31.4.1.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#resnet">
-     28.4.2. Resnet
+     31.4.2. Resnet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       28.4.2.1. Code
+       31.4.2.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#densenet">
-     28.4.3. DenseNet
+     31.4.3. DenseNet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       28.4.3.1. Code
+       31.4.3.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#mobilenet">
-     28.4.4. MobileNet
+     31.4.4. MobileNet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       28.4.4.1. Code
+       31.4.4.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#vit">
-     28.4.5. ViT
+     31.4.5. ViT
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       28.4.5.1. Code
+       31.4.5.1. Code
       </a>
      </li>
     </ul>
@@ -1753,46 +1785,46 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classic-datasets">
-   28.5. Classic datasets
+   31.5. Classic datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cifar-10-100">
-     28.5.1. CIFAR-10/100
+     31.5.1. CIFAR-10/100
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#imagenet-1000">
-     28.5.2. ImageNet-1000
+     31.5.2. ImageNet-1000
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#standards">
-   28.6. Standards
+   31.6. Standards
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#top-1-accuracy">
-     28.6.1. Top-1 accuracy
+     31.6.1. Top-1 accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#top-5-accuracy">
-     28.6.2. Top-5 accuracy
+     31.6.2. Top-5 accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   28.7. Your turn! 🚀
+   31.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   28.8. Acknowledgments
+   31.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1817,9 +1849,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="image-classification">
-<h1><span class="section-number">28. </span>Image classification<a class="headerlink" href="#image-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">31. </span>Image classification<a class="headerlink" href="#image-classification" title="Permalink to this headline">#</a></h1>
 <section id="what-is-image-classification">
-<h2><span class="section-number">28.1. </span>What is image classification?<a class="headerlink" href="#what-is-image-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.1. </span>What is image classification?<a class="headerlink" href="#what-is-image-classification" title="Permalink to this headline">#</a></h2>
 <p>In this chapter we will introduce the image classification problem, which is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications.</p>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
@@ -1845,13 +1877,13 @@ <h2><span class="section-number">28.1. </span>What is image classification?<a cl
 <figure class="align-default" id="example-of-the-image-classification-task">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/01_classify_eg.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/01_classify_eg.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/01_classify_eg.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.1 </span><span class="caption-text">Example of the image classification task</span><a class="headerlink" href="#example-of-the-image-classification-task" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.1 </span><span class="caption-text">Example of the image classification task</span><a class="headerlink" href="#example-of-the-image-classification-task" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The task in image classification is to predict a single label (or a distribution over labels as shown here to indicate our confidence) for a given image. Images are 3-dimensional arrays of integers from 0 to 255, of size Width x Height x 3. The 3 represents the three color channels Red, Green, Blue.</p>
 </section>
 <section id="challenges">
-<h2><span class="section-number">28.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
 <p>Since this task of recognizing a visual concept (e.g. cat) is relatively trivial for a human to perform, it is worth considering the challenges involved from the perspective of a Computer Vision algorithm. As we present (an inexhaustive) list of challenges below, keep in mind the raw representation of images as a 3-D array of brightness values:</p>
 <ul class="simple">
 <li><p>Viewpoint variation. A single instance of an object can be oriented in many ways with respect to the camera.</p></li>
@@ -1865,7 +1897,7 @@ <h2><span class="section-number">28.2. </span>Challenges<a class="headerlink" hr
 <p>A good image classification model must be invariant to the cross product of all these variations, while simultaneously retaining sensitivity to the inter-class variations.</p>
 </section>
 <section id="pipeline-of-image-classification">
-<h2><span class="section-number">28.3. </span>Pipeline of image classification<a class="headerlink" href="#pipeline-of-image-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.3. </span>Pipeline of image classification<a class="headerlink" href="#pipeline-of-image-classification" title="Permalink to this headline">#</a></h2>
 <p>We’ve seen that the task in image classification is to take an array of pixels that represents a single image and assign a label to it. Our complete pipeline can be formalized as follows:</p>
 <ul class="simple">
 <li><p>Input: Our input consists of a set of N images, each labeled with one of K different classes. We refer to this data as the training set,</p></li>
@@ -1874,10 +1906,10 @@ <h2><span class="section-number">28.3. </span>Pipeline of image classification<a
 </ul>
 </section>
 <section id="history-classic-models">
-<h2><span class="section-number">28.4. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.4. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
 <p>Since image classification is a classic task for computer vision, there are several models that are well-performed in the past. We can list them as follows: LeNet, AlexNet, VGGNet, GoogleNet, ResNet, DenseNet, SENet, MobileNet, ShuffleNet and ViT. In this part, we will introduce some of them.</p>
 <section id="vggnet">
-<h3><span class="section-number">28.4.1. </span>VGGNet<a class="headerlink" href="#vggnet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.1. </span>VGGNet<a class="headerlink" href="#vggnet" title="Permalink to this headline">#</a></h3>
 <p>The VGG (Visual Geometry Group) multilayer network model has 19 more layers than AlexNet, verifying that increasing the depth in the network structure can directly affect the model performance. The design idea of VGG is to increase the depth of the network and use a small size convolutional kernel instead. As shown in the figure below, three 3×3 convolutional kernels are used to replace the 7×7 convolutional kernels in AlexNet, and two 3×3 convolutional kernels are used to replace the 5×5 convolutional kernels, which can increase the depth of the network and improve the model effect while ensuring the same perceptual field. The number of model parameters and operations can be reduced by using smaller 3×3 Filters, and the image feature information can be better retained. The specific advantages of the improvement are summarized as follows.</p>
 <ul class="simple">
 <li><p>Using small 3×3 filters to replace large convolutional kernels.</p></li>
@@ -1888,11 +1920,11 @@ <h3><span class="section-number">28.4.1. </span>VGGNet<a class="headerlink" href
 <figure class="align-default" id="vgg-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/02_VGG.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/02_VGG.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/02_VGG.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.2 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf"> Structure of VGGNet </a></span><a class="headerlink" href="#vgg-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.2 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf"> Structure of VGGNet </a></span><a class="headerlink" href="#vgg-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="code">
-<h4><span class="section-number">28.4.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -2178,23 +2210,23 @@ <h4><span class="section-number">28.4.1.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="resnet">
-<h3><span class="section-number">28.4.2. </span>Resnet<a class="headerlink" href="#resnet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.2. </span>Resnet<a class="headerlink" href="#resnet" title="Permalink to this headline">#</a></h3>
 <p>ResNet (Residual Network) was proposed by Kaiming He and won the 2015 ILSVRC Grand Prix with an error rate of 3.57%. In the previous network, when the model is not deep enough, its network recognition is not strong, but when the network stack (Plain Network) is very deep, the network gradient disappearance and gradient dispersion are obvious, resulting in the model’s computational effectiveness but not up but down. Therefore, in view of the degradation problem of this deep network, ResNet is designed as an ultra-deep network without the gradient vanishing problem.ResNet has various types depending on the number of layers, from 18 to 1202 layers. As an example, Res Net50 consists of 49 convolutional layers and 1 fully connected layer, as shown in the figure below. This simple addition does not add additional parameters and computation to the network, but can greatly increase the training speed and improve the training effect, and this simple structure can well solve the degradation problem when the model deepens the number of layers. In this way, the network will always be in the optimal state and the performance of the network will not decrease with increasing depth.</p>
 <p>The most important part of ResNet should be the residual block, and here is the structure.</p>
 <figure class="align-default" id="residual-block">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/03_resblock.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/03_resblock.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/03_resblock.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual block </a></span><a class="headerlink" href="#residual-block" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual block </a></span><a class="headerlink" href="#residual-block" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="resnet-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/04_ResNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/04_ResNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/04_ResNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.4 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual network </a></span><a class="headerlink" href="#resnet-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.4 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual network </a></span><a class="headerlink" href="#resnet-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id1">
-<h4><span class="section-number">28.4.2.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.2.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2241,22 +2273,22 @@ <h4><span class="section-number">28.4.2.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="densenet">
-<h3><span class="section-number">28.4.3. </span>DenseNet<a class="headerlink" href="#densenet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.3. </span>DenseNet<a class="headerlink" href="#densenet" title="Permalink to this headline">#</a></h3>
 <p>DenseNet proposes a more radical dense connection mechanism than ResNet: i.e., connecting all layers to each other, specifically each layer accepts all the layers before it as its additional input. resNet short-circuits each layer with some previous layer (usually 2-3 layers), and the connection is made by element-level summation. In DenseNet, each layer is connected (concat) with all the preceding layers in the channel dimension and used as input to the next layer, which is a dense connection. Moreover, DenseNet is directly concat feature maps from different layers, which enables feature reuse and improves efficiency, and this feature is the most important difference between DenseNet and ResNet.</p>
 <figure class="align-default" id="dense-block">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/05_denseblock.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/05_denseblock.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/05_denseblock.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.5 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense block </a></span><a class="headerlink" href="#dense-block" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.5 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense block </a></span><a class="headerlink" href="#dense-block" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="densenet-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/06_DenseNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/06_DenseNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/06_DenseNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.6 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense network </a></span><a class="headerlink" href="#densenet-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.6 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense network </a></span><a class="headerlink" href="#densenet-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id2">
-<h4><span class="section-number">28.4.3.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.3.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2313,23 +2345,23 @@ <h4><span class="section-number">28.4.3.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="mobilenet">
-<h3><span class="section-number">28.4.4. </span>MobileNet<a class="headerlink" href="#mobilenet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.4. </span>MobileNet<a class="headerlink" href="#mobilenet" title="Permalink to this headline">#</a></h3>
 <p>MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem.</p>
 <p>Besides, the standard convolutional filters for normal CNNs are replaced by two layers: depthwise convolution and pointwise convolution to build a depthwise separable filter.</p>
 <figure class="align-default" id="mobilenet-convolution-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/07_mobileconv.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/07_mobileconv.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/07_mobileconv.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.7 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Replacement of standard convolution filter </a></span><a class="headerlink" href="#mobilenet-convolution-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.7 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Replacement of standard convolution filter </a></span><a class="headerlink" href="#mobilenet-convolution-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="mobilenet-body-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/08_MobileNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/08_MobileNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/08_MobileNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 28.8 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Body structure of MobileNet </a></span><a class="headerlink" href="#mobilenet-body-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.8 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Body structure of MobileNet </a></span><a class="headerlink" href="#mobilenet-body-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id3">
-<h4><span class="section-number">28.4.4.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.4.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2383,10 +2415,10 @@ <h4><span class="section-number">28.4.4.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="vit">
-<h3><span class="section-number">28.4.5. </span>ViT<a class="headerlink" href="#vit" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.5. </span>ViT<a class="headerlink" href="#vit" title="Permalink to this headline">#</a></h3>
 <p>Different from the previous models, ViT (Vision Transformer) uses the concept of Transformer. Inspired by the Transformer scaling successes in NLP, they experiment with applying a standard Transformer directly to images, with the fewest possible modifications. To do so, they split an image into patches and provide the sequence of linear embeddings of these patches as an input to a Transformer. Image patches are treated the same way as tokens (words) in an NLP application. They train the model on image classification in supervised fashion.</p>
 <section id="id4">
-<h4><span class="section-number">28.4.5.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.5.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2474,10 +2506,10 @@ <h4><span class="section-number">28.4.5.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="classic-datasets">
-<h2><span class="section-number">28.5. </span>Classic datasets<a class="headerlink" href="#classic-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.5. </span>Classic datasets<a class="headerlink" href="#classic-datasets" title="Permalink to this headline">#</a></h2>
 <p>As we said before, image classification task is mainly trained by datasets, so the importance of dataset is obvious. Here, we will introduce some widely-used datasets.</p>
 <section id="cifar-10-100">
-<h3><span class="section-number">28.5.1. </span>CIFAR-10/100<a class="headerlink" href="#cifar-10-100" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.5.1. </span>CIFAR-10/100<a class="headerlink" href="#cifar-10-100" title="Permalink to this headline">#</a></h3>
 <p>The <a class="reference external" href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 dataset</a> consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.</p>
 <p>The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a “fine” label (the class to which it belongs) and a “coarse” label (the superclass to which it belongs).</p>
 <div class="admonition note">
@@ -2490,7 +2522,7 @@ <h3><span class="section-number">28.5.1. </span>CIFAR-10/100<a class="headerlink
 </div>
 </section>
 <section id="imagenet-1000">
-<h3><span class="section-number">28.5.2. </span>ImageNet-1000<a class="headerlink" href="#imagenet-1000" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.5.2. </span>ImageNet-1000<a class="headerlink" href="#imagenet-1000" title="Permalink to this headline">#</a></h3>
 <p><a class="reference external" href="https://image-net.org/">ImageNet</a> is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a “synonym set” or “synset”. There are more than 100,000 synsets in WordNet; the majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly labeled and sorted images for most of the concepts in the WordNet hierarchy.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2499,22 +2531,22 @@ <h3><span class="section-number">28.5.2. </span>ImageNet-1000<a class="headerlin
 </section>
 </section>
 <section id="standards">
-<h2><span class="section-number">28.6. </span>Standards<a class="headerlink" href="#standards" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.6. </span>Standards<a class="headerlink" href="#standards" title="Permalink to this headline">#</a></h2>
 <section id="top-1-accuracy">
-<h3><span class="section-number">28.6.1. </span>Top-1 accuracy<a class="headerlink" href="#top-1-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.6.1. </span>Top-1 accuracy<a class="headerlink" href="#top-1-accuracy" title="Permalink to this headline">#</a></h3>
 <p>If your predicted label takes the largest one inside the final probability vector as the prediction result, and if the one with the highest probability in your prediction result is correctly classified, then the prediction is correct. Otherwise, the prediction is wrong.</p>
 </section>
 <section id="top-5-accuracy">
-<h3><span class="section-number">28.6.2. </span>Top-5 accuracy<a class="headerlink" href="#top-5-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.6.2. </span>Top-5 accuracy<a class="headerlink" href="#top-5-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Among the 50 classification probabilities of the test image, take the first 5 maximum classification probabilities, whether the correct label (classification) is in it or not, that is, whether it is one of these first 5, if it is, it is a successful classification.</p>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">28.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">28.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.stanford.edu">Stanford</a> for creating the open-source course <a class="reference external" href="https://cs231n.github.io/classification/">CS231n: Deep Learning for Computer Vision</a>, <a class="reference external" href="https://github.com/hoangthang1607">Duc Thang HOANG</a> for creating the open-source project <a class="reference external" href="https://github.com/hoangthang1607/RepVGG-Tensorflow-2">RepVGG-Tensorflow-2</a>, <a class="reference external" href="https://github.com/taki0112">Junho Kim</a> for creating the open-source project <a class="reference external" href="https://github.com/taki0112/ResNet-Tensorflow">ResNet-Tensorflow</a>, <a class="reference external" href="https://github.com/taki0112/Densenet-Tensorflow">Densenet-Tensorflow</a> and <a class="reference external" href="https://github.com/taki0112/vit-tensorflow">vit-tensorflow</a> and <a class="reference external" href="https://github.com/ohadlights">ohadlights</a> for creating the open-source project <a class="reference external" href="https://github.com/ohadlights/mobilenetv2">mobilenetv2</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2556,13 +2588,13 @@ <h2><span class="section-number">28.8. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">27. </span>Deep Q-learning</p>
+            <p class="prev-next-title"><span class="section-number">30. </span>Deep Q-learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="image-segmentation.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">29. </span>Image segmentation</p>
+        <p class="prev-next-title"><span class="section-number">32. </span>Image segmentation</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/deep-learning/image-segmentation.html b/deep-learning/image-segmentation.html
index 4250a6d3ba..8e9a09dfd6 100644
--- a/deep-learning/image-segmentation.html
+++ b/deep-learning/image-segmentation.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>29. Image segmentation &#8212; Ocademy Open Machine Learning Book</title>
+    <title>32. Image segmentation &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="30. Diffusion Model" href="difussion-model.html" />
-    <link rel="prev" title="28. Image classification" href="image-classification.html" />
+    <link rel="next" title="33. Diffusion Model" href="difussion-model.html" />
+    <link rel="prev" title="31. Image classification" href="image-classification.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-     15.1. Bagging
+     17.1. Bagging
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    <li class="toctree-l2">
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-     15.2. Random forest
+     17.2. Random forest
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    </li>
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-     15.3. Feature importance
+     17.3. Feature importance
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-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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    </li>
    <li class="toctree-l2">
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-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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   </ul>
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  <li class="toctree-l1">
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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+     40.14. Try it in Excel
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     </a>
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-     37.45. Linear regression - California Housing
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-     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
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     </a>
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@@ -1746,9 +1778,9 @@ <h2> Contents </h2>
 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="image-segmentation">
-<h1><span class="section-number">29. </span>Image segmentation<a class="headerlink" href="#image-segmentation" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">32. </span>Image segmentation<a class="headerlink" href="#image-segmentation" title="Permalink to this headline">#</a></h1>
 <section id="what-is-image-segmentation">
-<h2><span class="section-number">29.1. </span>What is Image segmentation?<a class="headerlink" href="#what-is-image-segmentation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.1. </span>What is Image segmentation?<a class="headerlink" href="#what-is-image-segmentation" title="Permalink to this headline">#</a></h2>
 <p>In an image segmentation task (specifically semantic segmentation), the network assigns a label (or class) to each input image. However, suppose you want to know the shape of that object, which pixel belongs to which object, etc. In this case, you need to assign a class to each pixel of the image—this task is known as segmentation. A segmentation model returns much more detailed information about the image. Image segmentation has many applications in medical imaging, self-driving cars and satellite imaging, just to name a few.</p>
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@@ -1792,7 +1824,7 @@ <h2><span class="section-number">29.1. </span>What is Image segmentation?<a clas
 </div>
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 <section id="how-to-train-a-model-for-image-segmentation">
-<h2><span class="section-number">29.2. </span>How to train a model for image segmentation?<a class="headerlink" href="#how-to-train-a-model-for-image-segmentation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.2. </span>How to train a model for image segmentation?<a class="headerlink" href="#how-to-train-a-model-for-image-segmentation" title="Permalink to this headline">#</a></h2>
 <p>This tutorial uses the Oxford-IIIT Pet Dataset. The dataset consists of images of 37 pet breeds, with 200 images per breed (~100 each in the training and test splits). Each image includes the corresponding labels, and pixel-wise masks. The masks are class-labels for each pixel. Each pixel is given one of three categories:</p>
 <ul class="simple">
 <li><p>Class 1: Pixel belonging to the pet.</p></li>
@@ -1815,7 +1847,7 @@ <h2><span class="section-number">29.2. </span>How to train a model for image seg
 </div>
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 <section id="prepare-the-oxford-iiit-pets-dataset">
-<h3><span class="section-number">29.2.1. </span>Prepare the Oxford-IIIT Pets dataset<a class="headerlink" href="#prepare-the-oxford-iiit-pets-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.1. </span>Prepare the Oxford-IIIT Pets dataset<a class="headerlink" href="#prepare-the-oxford-iiit-pets-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="n">tfds</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;oxford_iiit_pet:3.*.*&#39;</span><span class="p">,</span> <span class="n">with_info</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -1924,12 +1956,12 @@ <h3><span class="section-number">29.2.1. </span>Prepare the Oxford-IIIT Pets dat
 <figure class="align-default" id="dataset-example-mask">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/01_display_dataset.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/01_display_dataset.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/01_display_dataset.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 29.1 </span><span class="caption-text">An example from the dataset</span><a class="headerlink" href="#dataset-example-mask" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.1 </span><span class="caption-text">An example from the dataset</span><a class="headerlink" href="#dataset-example-mask" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="define-the-model">
-<h3><span class="section-number">29.2.2. </span>Define the model<a class="headerlink" href="#define-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.2. </span>Define the model<a class="headerlink" href="#define-the-model" title="Permalink to this headline">#</a></h3>
 <p>The model being used here is a modified U-Net. A U-Net consists of an encoder (downsampler) and decoder (upsampler). To learn robust features and reduce the number of trainable parameters, use a pretrained model—MobileNetV2—as the encoder. For the decoder, you will use the upsample block, which is already implemented in the pix2pix example in the TensorFlow Examples repo.</p>
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@@ -2016,7 +2048,7 @@ <h3><span class="section-number">29.2.2. </span>Define the model<a class="header
 <p>Note that the number of filters on the last layer is set to the number of it. This will be one output channel per class.output_channels.</p>
 </section>
 <section id="train-the-model">
-<h3><span class="section-number">29.2.3. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.3. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h3>
 <p>Now, all that is left to do is to compile and train the model. Since this is a multiclass classification problem, use the ‘tf.keras.losses.CategoricalCrossentropy’ loss function with the argument set to , since the labels are scalar integers instead of vectors of scores for each pixel of every class.from_logitsTrue. When running inference, the label assigned to the pixel is the channel with the highest value. This is what the function is doing.create_mask.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2422,22 +2454,22 @@ <h3><span class="section-number">29.2.3. </span>Train the model<a class="headerl
 </div>
 </section>
 <section id="make-predictions">
-<h3><span class="section-number">29.2.4. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.4. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
 <p>Now, let’s make some predictions. In the interest of saving time, the number of epochs was kept small, but you may set this higher to achieve more accurate results.</p>
 <p>show_predictions(test_batches, 3)</p>
 <figure class="align-default" id="display-prediction">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/02_show_prediction.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/02_show_prediction.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/02_show_prediction.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 29.2 </span><span class="caption-text">An example of the output prediction</span><a class="headerlink" href="#display-prediction" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.2 </span><span class="caption-text">An example of the output prediction</span><a class="headerlink" href="#display-prediction" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="history-classic-models">
-<h2><span class="section-number">29.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
 <p>In the previous part, we learn the U-Net model. Besides, there are still many excellent models, we will introduce them is this part.</p>
 <section id="code-for-assisting">
-<h3><span class="section-number">29.3.1. </span>Code for assisting<a class="headerlink" href="#code-for-assisting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.1. </span>Code for assisting<a class="headerlink" href="#code-for-assisting" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2661,7 +2693,7 @@ <h3><span class="section-number">29.3.1. </span>Code for assisting<a class="head
 </div>
 </section>
 <section id="fcn">
-<h3><span class="section-number">29.3.2. </span>FCN<a class="headerlink" href="#fcn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.2. </span>FCN<a class="headerlink" href="#fcn" title="Permalink to this headline">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>The link for paper is <a class="reference external" href="https://arxiv.org/pdf/1411.4038v2.pdf">https://arxiv.org/pdf/1411.4038v2.pdf</a>.</p>
@@ -2670,11 +2702,11 @@ <h3><span class="section-number">29.3.2. </span>FCN<a class="headerlink" href="#
 <figure class="align-default" id="display-structure-of-fcn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/03_structure_FCN.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/03_structure_FCN.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/03_structure_FCN.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 29.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1411.4038v2.pdf"> The structure of FCN </a></span><a class="headerlink" href="#display-structure-of-fcn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1411.4038v2.pdf"> The structure of FCN </a></span><a class="headerlink" href="#display-structure-of-fcn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="code">
-<h4><span class="section-number">29.3.2.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">32.3.2.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2811,7 +2843,7 @@ <h4><span class="section-number">29.3.2.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="segnet">
-<h3><span class="section-number">29.3.3. </span>SegNet<a class="headerlink" href="#segnet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.3. </span>SegNet<a class="headerlink" href="#segnet" title="Permalink to this headline">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>The link for paper is <a class="reference external" href="https://arxiv.org/pdf/1511.00561v3.pdf">https://arxiv.org/pdf/1511.00561v3.pdf</a>.</p>
@@ -2820,12 +2852,12 @@ <h3><span class="section-number">29.3.3. </span>SegNet<a class="headerlink" href
 <figure class="align-default" id="display-structure-of-segnet">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/04_structure_SegNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/04_structure_SegNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/04_structure_SegNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 29.4 </span><span class="caption-text">[ The structure of SegNet ]<a class="reference external" href="https://arxiv.org/pdf/1511.00561v3.pdf">https://arxiv.org/pdf/1511.00561v3.pdf</a> )</span><a class="headerlink" href="#display-structure-of-segnet" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.4 </span><span class="caption-text">[ The structure of SegNet ]<a class="reference external" href="https://arxiv.org/pdf/1511.00561v3.pdf">https://arxiv.org/pdf/1511.00561v3.pdf</a> )</span><a class="headerlink" href="#display-structure-of-segnet" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>As we have learnt U-Net, SegNet is similar to it, the main difference is that U-Net does not reuse pooling indices but instead transfers the entire feature map (at the cost of more memory) to the corresponding decoders and concatenates them to upsampled (via deconvolution) decoder feature maps.</p>
 <section id="id1">
-<h4><span class="section-number">29.3.3.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">32.3.3.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2919,7 +2951,7 @@ <h4><span class="section-number">29.3.3.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="deeplab-v3">
-<h3><span class="section-number">29.3.4. </span>DeepLab V3<a class="headerlink" href="#deeplab-v3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.4. </span>DeepLab V3<a class="headerlink" href="#deeplab-v3" title="Permalink to this headline">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>The link for paper is <a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>.</p>
@@ -2928,17 +2960,17 @@ <h3><span class="section-number">29.3.4. </span>DeepLab V3<a class="headerlink"
 <figure class="align-default" id="cascade-dilated-conv-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/05_cascade_DeepLab_structure.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/05_cascade_DeepLab_structure.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/05_cascade_DeepLab_structure.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 29.5 </span><span class="caption-text">[ Cascade dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#cascade-dilated-conv-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.5 </span><span class="caption-text">[ Cascade dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#cascade-dilated-conv-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="parallel-deeplab-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/06_parallel_DeepLab_structure.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/06_parallel_DeepLab_structure.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/06_parallel_DeepLab_structure.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 29.6 </span><span class="caption-text">[ Parallel dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#parallel-deeplab-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.6 </span><span class="caption-text">[ Parallel dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#parallel-deeplab-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id2">
-<h4><span class="section-number">29.3.4.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">32.3.4.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Input</span><span class="p">,</span><span class="n">Dropout</span><span class="p">,</span><span class="n">BatchNormalization</span><span class="p">,</span><span class="n">Activation</span><span class="p">,</span><span class="n">Add</span>
@@ -3139,11 +3171,11 @@ <h4><span class="section-number">29.3.4.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">29.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">29.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/luyanger1799">Yang Lu</a> for creating the open-source project <a class="reference external" href="https://github.com/luyanger1799/Amazing-Semantic-Segmentation">Amazing-Semantic-Segmentation</a>, <a class="reference external" href="https://github.com/tensorflow">tensorflow</a> for creating the open-source course <a class="reference external" href="https://github.com/tensorflow/examples">examples</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3185,13 +3217,13 @@ <h2><span class="section-number">29.5. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">28. </span>Image classification</p>
+            <p class="prev-next-title"><span class="section-number">31. </span>Image classification</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="difussion-model.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">30. </span>Diffusion Model</p>
+        <p class="prev-next-title"><span class="section-number">33. </span>Diffusion Model</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/deep-learning/lstm.html b/deep-learning/lstm.html
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-    <title>25. Long-short term memory &#8212; Ocademy Open Machine Learning Book</title>
+    <title>28. Long-short term memory &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="26. Time series" href="time-series.html" />
-    <link rel="prev" title="24. Autoencoder" href="autoencoder.html" />
+    <link rel="next" title="29. Time series" href="time-series.html" />
+    <link rel="prev" title="27. Autoencoder" href="autoencoder.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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-     11.1. Tools of the trade
+     12.1. Tools of the trade
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-     11.2. Managing data
+     12.2. Managing data
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-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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-     13.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
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-     13.2. More classifiers
+     14.2. More classifiers
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-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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-     14.2. K-Means clustering
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+     40.5. Project Plan​ Template
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-     37.6. Python programming introduction
+     40.6. Python programming introduction
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+     40.14. Try it in Excel
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+     40.16. Build your own custom vis
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+     40.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     37.20. Displaying airport data
+     40.20. Displaying airport data
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-     37.21. Soda profits
+     40.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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-     37.23. Estimation of COVID-19 pandemic
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-     37.24. Data processing in Python
+     40.24. Data processing in Python
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-     37.25. Evaluating data from a form
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-     37.36. Explore a planetary computer dataset
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-     37.37. Exploring for answers
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-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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    </li>
    <li class="toctree-l2">
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-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
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-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     37.45. Linear regression - California Housing
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    </li>
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-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
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-     37.47. Loss Function
+     40.47. Loss Function
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-     37.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
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     </a>
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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    </li>
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-     37.56. Try a different model
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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 <section class="tex2jax_ignore mathjax_ignore" id="long-short-term-memory">
-<h1><span class="section-number">25. </span>Long-short term memory<a class="headerlink" href="#long-short-term-memory" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">28. </span>Long-short term memory<a class="headerlink" href="#long-short-term-memory" title="Permalink to this headline">#</a></h1>
 <p>As we have learned the normal RNNs on the previous lessons, today we are going to use long-short term memory (LSTM) to fix the problems that are induced from the RNN architecture.</p>
 <p>The vanilla LSTM is proposed in 2005, the paper is <a class="reference external" href="https://link.springer.com/chapter/10.1007/11550907_126">Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition</a>, and after that, a lot of paper based on it appeared.</p>
 <section id="overview">
-<h2><span class="section-number">25.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">28.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>The essential problems of RNN are vanishing/exploding gradient problems. The gradient vanishing/exploding problem is a common issue in training deep neural networks. This problem occurs when the gradient of the loss function with respect to the weights of the network becomes very small or very large.</p>
 <p>There are several possibilities will bring on this kind of problem, for example:</p>
 <ul class="simple">
@@ -1641,7 +1673,7 @@ <h2><span class="section-number">25.1. </span>Overview<a class="headerlink" href
 The <span class="math notranslate nohighlight">\(h^{t}\)</span> is still the activation.</p>
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/LSTM/cell_state.png" width="90%" class="bg-white mb-1"></section>
 <section id="special-gates">
-<h2><span class="section-number">25.2. </span>Special Gates<a class="headerlink" href="#special-gates" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">28.2. </span>Special Gates<a class="headerlink" href="#special-gates" title="Permalink to this headline">#</a></h2>
 <p>Then, we take a look at the inside of cell. <span class="math notranslate nohighlight">\(\bigodot\)</span> means element-wise multiplication operator, <span class="math notranslate nohighlight">\(\bigoplus\)</span> means element-wise addition operator and <span class="math notranslate nohighlight">\(\sigma\)</span> means logistic sigmoid activation functions.</p>
 <p>Now, we should pay attention to ‘Gates’.</p>
 <p>The first is ‘Forget Gate’ <span class="math notranslate nohighlight">\(f\)</span>, and it controls which information is remembered, and which is forgotten; can reset the cell state. This function can be written as <span class="math notranslate nohighlight">\(f_t = \sigma (W_{fx}x^{&lt;t&gt;} + W_{fh}h^{&lt;t-1&gt;} + b_f)\)</span></p>
@@ -1649,7 +1681,7 @@ <h2><span class="section-number">25.2. </span>Special Gates<a class="headerlink"
 <p>To brief summarize the previous gates in an expression: <span class="math notranslate nohighlight">\(C^{&lt;t&gt;} = (C^{&lt;t-1&gt;} \bigodot f_t) \bigoplus (i_t \bigodot g_t)\)</span>. Since <span class="math notranslate nohighlight">\(i_t\)</span> is ‘Input Gate’ and <span class="math notranslate nohighlight">\(g_t\)</span> is ‘Input Gate’, <span class="math notranslate nohighlight">\((i_t \bigodot g_t)\)</span> is for updatting the cell state.</p>
 <p>Finally, ‘Output Gate’ is for updating the values of hidden units: <span class="math notranslate nohighlight">\(o_t = \sigma(W_{ox}x^{&lt;t&gt;} + W_{oh}x^{&lt;t-1&gt;} + b_o)\)</span>. So the activateion of the current time is <span class="math notranslate nohighlight">\(h^{&lt;t&gt;} = o_t \bigodot tanh(C^{&lt;t&gt;})\)</span>.</p>
 <section id="code">
-<h3><span class="section-number">25.2.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">28.2.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h3>
 <p>Here we implement an LSTM model on all a data set of Shakespeare works.</p>
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@@ -6173,11 +6205,11 @@ <h3><span class="section-number">25.2.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">25.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">28.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the Long-Short Term Memory Networks by following this TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">25.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">28.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/nfmcclure">Nick</a> for creating the open-source project <a class="reference external" href="https://github.com/nfmcclure/tensorflow_cookbook">tensorflow_cookbook</a> and <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-source project <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -6219,13 +6251,13 @@ <h2><span class="section-number">25.4. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">24. </span>Autoencoder</p>
+            <p class="prev-next-title"><span class="section-number">27. </span>Autoencoder</p>
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-        <p class="prev-next-title"><span class="section-number">26. </span>Time series</p>
+        <p class="prev-next-title"><span class="section-number">29. </span>Time series</p>
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-    <link rel="next" title="32. Overview" href="../machine-learning-productionization/overview.html" />
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    10. Machine Learning overview
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+   11. Summary of machine learning fundamentals
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+ </li>
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   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     15.1. Bagging
+     17.1. Bagging
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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   <ul>
    <li class="toctree-l2">
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
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   <a class="reference internal" href="rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
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   <a class="reference internal" href="autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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  <li class="toctree-l1">
   <a class="reference internal" href="lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
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   <a class="reference internal" href="time-series.html">
-   26. Time series
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  </li>
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   <a class="reference internal" href="dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
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   <a class="reference internal" href="image-classification.html">
-   28. Image classification
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
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 </ul>
@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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+     34.3.1. Faster R-CNN
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#code">
-       31.3.1.1. Code
+       34.3.1.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#mask-r-cnn">
-     31.3.2. Mask R-CNN
+     34.3.2. Mask R-CNN
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       31.3.2.1. Code
+       34.3.2.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fcos">
-     31.3.3. FCOS
+     34.3.3. FCOS
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       31.3.3.1. Code
+       34.3.3.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#detr">
-     31.3.4. DETR
+     34.3.4. DETR
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id6">
-       31.3.4.1. Code
+       34.3.4.1. Code
       </a>
      </li>
     </ul>
@@ -1687,12 +1719,12 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   31.4. Your turn! 🚀
+   34.4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   31.5. Acknowledgments
+   34.5. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1717,27 +1749,27 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="object-detection">
-<h1><span class="section-number">31. </span>Object detection<a class="headerlink" href="#object-detection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">34. </span>Object detection<a class="headerlink" href="#object-detection" title="Permalink to this headline">#</a></h1>
 <section id="what-is-object-detection">
-<h2><span class="section-number">31.1. </span>What is object detection?<a class="headerlink" href="#what-is-object-detection" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.1. </span>What is object detection?<a class="headerlink" href="#what-is-object-detection" title="Permalink to this headline">#</a></h2>
 <p>In this chapter we will introduce the object detection problem which can be described in this way: given an image or a video stream, an object detection model can identify which of a known set of objects might be present and provide information about their positions within the image.</p>
 <p>For example, this screenshot of the example application shows how three objects have been recognized and their positions annotated:</p>
 <figure class="align-default" id="example-of-the-object-detection-task">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/01_example.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/01_example.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/01_example.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.1 </span><span class="caption-text">Example of the object detection task</span><a class="headerlink" href="#example-of-the-object-detection-task" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.1 </span><span class="caption-text">Example of the object detection task</span><a class="headerlink" href="#example-of-the-object-detection-task" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Object detection has now been widely used in many real-world applications, such as autonomous driving, robot vision, video surveillance, etc. The following image shows the growing number of publications that are associated with “object detection” over the past two decades.</p>
 <figure class="align-default" id="number-of-pub">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/02_number_of_pub.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/02_number_of_pub.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/02_number_of_pub.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.2 </span><span class="caption-text">The increasing number of publications in object detection from 1998 to 2021</span><a class="headerlink" href="#number-of-pub" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.2 </span><span class="caption-text">The increasing number of publications in object detection from 1998 to 2021</span><a class="headerlink" href="#number-of-pub" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="challenges">
-<h2><span class="section-number">31.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
 <p>In addition to some common challenges in other computer vision tasks such as objects under different viewpoints, illuminations,and intra-class variations, the challenges in object detection include but are not limited to the following aspects:</p>
 <ul class="simple">
 <li><p>object rotation and scale changes (e.g., small objects);</p></li>
@@ -1747,15 +1779,15 @@ <h2><span class="section-number">31.2. </span>Challenges<a class="headerlink" hr
 </ul>
 </section>
 <section id="history-classic-models">
-<h2><span class="section-number">31.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
 <p>Since image classification is a classic task for computer vision, there are several models that are well-performed in the past. We can list them as follows: Faster R-CNN, Mask R-CNN, YOLO, FCOS, DETR. In this part, we will introduce them in order.</p>
 <section id="faster-r-cnn">
-<h3><span class="section-number">31.3.1. </span>Faster R-CNN<a class="headerlink" href="#faster-r-cnn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.1. </span>Faster R-CNN<a class="headerlink" href="#faster-r-cnn" title="Permalink to this headline">#</a></h3>
 <p>Faster R-CNN is a single, unified network for object detection that utilizes a region proposal network (RPN) with the CNN model2. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals2. It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position.</p>
 <figure class="align-default" id="the-structure-of-faster-rcnn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/03_faster_rcnn.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/03_faster_rcnn.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/03_faster_rcnn.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.3 </span><span class="caption-text">The structure of Faster RCNN <span id="id1">[<a class="reference internal" href="#id85" title="Ross Girshick Shaoqing Ren, Kaiming He and Jian Sun. Faster r-cnn. URL: https://arxiv.org/abs/1506.01497 (visited on 2023).">SRS</a>]</span></span><a class="headerlink" href="#the-structure-of-faster-rcnn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.3 </span><span class="caption-text">The structure of Faster RCNN <span id="id1">[<a class="reference internal" href="#id85" title="Ross Girshick Shaoqing Ren, Kaiming He and Jian Sun. Faster r-cnn. URL: https://arxiv.org/abs/1506.01497 (visited on 2023).">SRS</a>]</span></span><a class="headerlink" href="#the-structure-of-faster-rcnn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1763,7 +1795,7 @@ <h3><span class="section-number">31.3.1. </span>Faster R-CNN<a class="headerlink
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/1506.01497.pdf">here</a>.</p>
 </div>
 <section id="code">
-<h4><span class="section-number">31.3.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
 <p>Due to the complexity, here we just introduce the key parts of the model, Region Proposal Network(RPN).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1803,12 +1835,12 @@ <h4><span class="section-number">31.3.1.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="mask-r-cnn">
-<h3><span class="section-number">31.3.2. </span>Mask R-CNN<a class="headerlink" href="#mask-r-cnn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.2. </span>Mask R-CNN<a class="headerlink" href="#mask-r-cnn" title="Permalink to this headline">#</a></h3>
 <p>Mask R-CNN is a deep neural network aimed to solve both object detection and instance segmentation problems. In other words, it can separate different objects in an image. You give it an image, it gives you the object bounding boxes, classes and masks. Mask R-CNN was developed on top of Faster R-CNN, a Region-Based Convolutional Neural Network. It is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps.</p>
 <figure class="align-default" id="the-structure-of-mask-rcnn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/04_mask_rcnn.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/04_mask_rcnn.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/04_mask_rcnn.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.4 </span><span class="caption-text">The structure of Mask RCNN <span id="id2">[<a class="reference internal" href="#id86" title="Piotr Dollar Kaiming He, Georgia Gkioxari and Ross Girshick. Mask r-cnn. URL: https://arxiv.org/abs/1703.06870 (visited on 2023).">KHG</a>]</span></span><a class="headerlink" href="#the-structure-of-mask-rcnn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.4 </span><span class="caption-text">The structure of Mask RCNN <span id="id2">[<a class="reference internal" href="#id86" title="Piotr Dollar Kaiming He, Georgia Gkioxari and Ross Girshick. Mask r-cnn. URL: https://arxiv.org/abs/1703.06870 (visited on 2023).">KHG</a>]</span></span><a class="headerlink" href="#the-structure-of-mask-rcnn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1816,7 +1848,7 @@ <h3><span class="section-number">31.3.2. </span>Mask R-CNN<a class="headerlink"
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/1703.06870v3.pdf">here</a>.</p>
 </div>
 <section id="id3">
-<h4><span class="section-number">31.3.2.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.2.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p>The key parts in Mask RCNN are:</p>
 <ul class="simple">
 <li><p>RoIAlign</p></li>
@@ -1988,7 +2020,7 @@ <h4><span class="section-number">31.3.2.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="fcos">
-<h3><span class="section-number">31.3.3. </span>FCOS<a class="headerlink" href="#fcos" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.3. </span>FCOS<a class="headerlink" href="#fcos" title="Permalink to this headline">#</a></h3>
 <p>FCOS (Fully Convolutional One-Stage Object Detection) is a one-stage object detection algorithm that uses a fully convolutional architecture to detect objects. It is a simple and effective method for object detection that does not require region proposal networks (RPNs) or anchor boxes.</p>
 <!---
 :::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/05_FCOS.png
@@ -2005,12 +2037,12 @@ <h3><span class="section-number">31.3.3. </span>FCOS<a class="headerlink" href="
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/1904.01355v5.pdf">here</a>.</p>
 </div>
 <section id="id4">
-<h4><span class="section-number">31.3.3.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.3.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
 <p>The key point in FCOS is Center-ness, which is a novel way to calculate target: <span class="math notranslate nohighlight">\(centerness = \sqrt{\frac{min(l,r)}{max(l,r)} \frac{min(t,b)}{max(t,b)}}\)</span></p>
 <figure class="align-default" id="illustration-of-centerness">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/06_centerness.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/06_centerness.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/06_centerness.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.5 </span><span class="caption-text">Illustration of centerness <span id="id5">[<a class="reference internal" href="#id87" title="Hao Chen Zhi Tian, Chunhua Shen and Tong He. Fcos: fully convolutional one-stage object detection. URL: https://arxiv.org/abs/1904.01355 (visited on 2023).">ZTH</a>]</span></span><a class="headerlink" href="#illustration-of-centerness" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.5 </span><span class="caption-text">Illustration of centerness <span id="id5">[<a class="reference internal" href="#id87" title="Hao Chen Zhi Tian, Chunhua Shen and Tong He. Fcos: fully convolutional one-stage object detection. URL: https://arxiv.org/abs/1904.01355 (visited on 2023).">ZTH</a>]</span></span><a class="headerlink" href="#illustration-of-centerness" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="cell docutils container">
@@ -2063,7 +2095,7 @@ <h4><span class="section-number">31.3.3.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="detr">
-<h3><span class="section-number">31.3.4. </span>DETR<a class="headerlink" href="#detr" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.4. </span>DETR<a class="headerlink" href="#detr" title="Permalink to this headline">#</a></h3>
 <p>Similar to image classification, Transformer is also used in object detection task, and the classical one is DEtection TRansformer(DETR).</p>
 <!---
 :::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/07_DETR.png
@@ -2079,7 +2111,7 @@ <h3><span class="section-number">31.3.4. </span>DETR<a class="headerlink" href="
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/2005.12872v3.pdf">here</a>.</p>
 </div>
 <section id="id6">
-<h4><span class="section-number">31.3.4.1. </span>Code<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.4.1. </span>Code<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2145,11 +2177,11 @@ <h4><span class="section-number">31.3.4.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">31.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">31.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/tensorpack">Tensorpack</a> for creating the open-source project <a class="reference external" href="https://github.com/tensorpack/tensorpack">tensorpack</a>, <a class="reference external" href="https://github.com/Burf">Hyungjin Kim</a> for creating the open-source project <a class="reference external" href="https://github.com/Burf/TFDetection">TFDetection</a> and <a class="reference external" href="https://github.com/PaperCodeReview">PaperCodeReview</a> for creating the open-source project <a class="reference external" href="https://github.com/PaperCodeReview/DETR-TF">DETR-TF</a>. They inspire the majority of the content in this chapter.</p>
 <p style="text-align: center;">
 <iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/object-detection/demo.html" width="105%" height="400px;"
@@ -2210,13 +2242,13 @@ <h2><span class="section-number">31.5. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">30. </span>Diffusion Model</p>
+            <p class="prev-next-title"><span class="section-number">33. </span>Diffusion Model</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../machine-learning-productionization/overview.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">32. </span>Overview</p>
+        <p class="prev-next-title"><span class="section-number">35. </span>Overview</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/deep-learning/rnn.html b/deep-learning/rnn.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>23. Recurrent Neural Networks &#8212; Ocademy Open Machine Learning Book</title>
+    <title>26. Recurrent Neural Networks &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="24. Autoencoder" href="autoencoder.html" />
-    <link rel="prev" title="22. Generative adversarial networks" href="gan.html" />
+    <link rel="next" title="27. Autoencoder" href="autoencoder.html" />
+    <link rel="prev" title="25. Generative adversarial networks" href="gan.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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  </li>
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-     15.1. Bagging
+     17.1. Bagging
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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   <ul>
    <li class="toctree-l2">
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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  </li>
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-   24. Autoencoder
+   27. Autoencoder
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
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  </li>
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   <a class="reference internal" href="time-series.html">
-   26. Time series
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  </li>
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-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
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   <a class="reference internal" href="image-classification.html">
-   28. Image classification
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  </li>
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   <a class="reference internal" href="image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="object-detection.html">
-   31. Object detection
+   34. Object detection
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  </li>
 </ul>
@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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 </ul>
@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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+     40.52. ML neural network - Assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
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+     40.54. Managing data
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     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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-     37.56. Try a different model
+     40.56. Try a different model
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-     37.57. Create a regression model
+     40.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
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@@ -1528,22 +1560,22 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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   <a class="reference internal nav-link" href="#your-turn">
-   23.2. Your turn! 🚀
+   26.2. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
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+   26.3. Self study
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   23.4. Acknowledgments
+   26.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1567,22 +1599,22 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+   26.2. Your turn! 🚀
   </a>
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-   23.3. Self study
+   26.3. Self study
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   <a class="reference internal nav-link" href="#acknowledgments">
-   23.4. Acknowledgments
+   26.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1607,7 +1639,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="recurrent-neural-networks">
-<h1><span class="section-number">23. </span>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-networks" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">26. </span>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-networks" title="Permalink to this headline">#</a></h1>
 <blockquote>
 <div><p>The nervous system contains many circular paths, whose activity so regenerates the excitation of any participant neuron that reference to time past becomes indefinite, although it still implies that afferent activity has realized one of a certain class of configurations over time. Precise specification of these implications by means of recursive functions, and determination of those that can be embodied in the activity of nervous nets, completes the theory.</p>
 <p>– Warren McCulloch and Walter Pitts, 1943</p>
@@ -1617,28 +1649,28 @@ <h1><span class="section-number">23. </span>Recurrent Neural Networks<a class="h
 <figure class="align-default" id="sequential-data">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/sequential_data.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/sequential_data.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/sequential_data.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 23.1 </span><span class="caption-text">sequential data</span><a class="headerlink" href="#sequential-data" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.1 </span><span class="caption-text">sequential data</span><a class="headerlink" href="#sequential-data" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>And the RNNs use recurrent edge to update.</p>
 <figure class="align-default" id="rnn1">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn1.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn1.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn1.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 23.2 </span><span class="caption-text">RNN1</span><a class="headerlink" href="#rnn1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.2 </span><span class="caption-text">RNN1</span><a class="headerlink" href="#rnn1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>If unroll over a sequence <span class="math notranslate nohighlight">\((x_0,x_1,x_2)\)</span>.</p>
 <figure class="align-default" id="rnn2">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn2.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn2.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn2.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 23.3 </span><span class="caption-text">RNN2</span><a class="headerlink" href="#rnn2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.3 </span><span class="caption-text">RNN2</span><a class="headerlink" href="#rnn2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Then, the input (w0,w1,…,wt) sequence of words ( 1-hot encoded ) and the output (w1,w2,…,wt+1) shifted sequence of words ( 1-hot encoded ) have the following relation.</p>
 <figure class="align-default" id="rnn3">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn3.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn3.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn3.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 23.4 </span><span class="caption-text">RNN3</span><a class="headerlink" href="#rnn3" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.4 </span><span class="caption-text">RNN3</span><a class="headerlink" href="#rnn3" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The input projection is <span class="math notranslate nohighlight">\(x_t = Emb(\omega_t) = E\omega_t\)</span>, the recurrent connection is <span class="math notranslate nohighlight">\(h_t = g(W^h h_t + x_t + b^h)\)</span>, and the output projection should be <span class="math notranslate nohighlight">\(y = softmax(W^o h_t + b^o)\)</span>.
@@ -1646,7 +1678,7 @@ <h1><span class="section-number">23. </span>Recurrent Neural Networks<a class="h
 <figure class="align-default" id="rnn4">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn4.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn4.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn4.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 23.5 </span><span class="caption-text">RNN4</span><a class="headerlink" href="#rnn4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.5 </span><span class="caption-text">RNN4</span><a class="headerlink" href="#rnn4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s make the backpropagation process more clearly.</p>
@@ -1654,7 +1686,7 @@ <h1><span class="section-number">23. </span>Recurrent Neural Networks<a class="h
 <figure class="align-default" id="backpropagation-for-rnn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/bp_rnn1.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/bp_rnn1.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/bp_rnn1.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 23.6 </span><span class="caption-text">backpropagation for RNN</span><a class="headerlink" href="#backpropagation-for-rnn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.6 </span><span class="caption-text">backpropagation for RNN</span><a class="headerlink" href="#backpropagation-for-rnn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Through the image, we can get the output:</p>
@@ -1675,7 +1707,7 @@ <h1><span class="section-number">23. </span>Recurrent Neural Networks<a class="h
 </ul>
 </div>
 <section id="code">
-<h2><span class="section-number">23.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># A text classifier implemented in TensorFlow to classify SMS spam messages.</span>
@@ -1921,15 +1953,15 @@ <h2><span class="section-number">23.1. </span>Code<a class="headerlink" href="#c
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">23.2. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.2. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the Recurrent Neural Networks by following this TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">23.3. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.3. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">23.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/nfmcclure">Nick</a> for creating the open-source course <a class="reference external" href="https://github.com/nfmcclure/tensorflow_cookbook">tensorflow_cookbook</a> and <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-sourse <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <span class="target" id="id1"></span></section>
@@ -1972,13 +2004,13 @@ <h2><span class="section-number">23.4. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">22. </span>Generative adversarial networks</p>
+            <p class="prev-next-title"><span class="section-number">25. </span>Generative adversarial networks</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="autoencoder.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">24. </span>Autoencoder</p>
+        <p class="prev-next-title"><span class="section-number">27. </span>Autoencoder</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/deep-learning/time-series.html b/deep-learning/time-series.html
index 6581125603..252c4e77f2 100644
--- a/deep-learning/time-series.html
+++ b/deep-learning/time-series.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>26. Time series &#8212; Ocademy Open Machine Learning Book</title>
+    <title>29. Time series &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="27. Deep Q-learning" href="dqn.html" />
-    <link rel="prev" title="25. Long-short term memory" href="lstm.html" />
+    <link rel="next" title="30. Deep Q-learning" href="dqn.html" />
+    <link rel="prev" title="28. Long-short term memory" href="lstm.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="image-classification.html">
-   28. Image classification
+   31. Image classification
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
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     </a>
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    <li class="toctree-l2">
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-     37.88. Build Classification Model
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
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     <a class="reference internal" href="../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.90. How to choose cnn architecture mnist
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
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     <a class="reference internal" href="../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-     38.1. Python programming introduction
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-     38.2. Python programming basics
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     </a>
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     </a>
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-     38.10. Data Science in real world
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     </a>
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-     38.11. Machine Learning overview
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-     38.13. Logistic Regression
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     <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
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-     38.16. Build an machine learning web application
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-     38.17. Unsupervised learning
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     <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
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     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
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     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
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@@ -1528,103 +1560,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overiew">
-   26.1. Overiew
+   29.1. Overiew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-time-series">
-   26.2. What is a Time Series?
+   29.2. What is a Time Series?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-with-time-series">
-   26.3. Linear Regression with Time Series
+   29.3. Linear Regression with Time Series
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#time-step-features">
-     26.3.1. Time-step features
+     29.3.1. Time-step features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lag-features">
-     26.3.2. Lag features
+     29.3.2. Lag features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-tunnel-traffic">
-   26.4. Example - Tunnel Traffic
+   29.4. Example - Tunnel Traffic
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#time-step-feature">
-     26.4.1. Time-step feature
+     29.4.1. Time-step feature
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lag-feature">
-     26.4.2. Lag feature
+     29.4.2. Lag feature
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#forecasting-with-deep-learning">
-   26.5. Forecasting With Deep Learning
+   29.5. Forecasting With Deep Learning
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-forecasting-task">
-     26.5.1. Defining the Forecasting Task
+     29.5.1. Defining the Forecasting Task
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparing-data-for-forecasting">
-     26.5.2. Preparing Data for Forecasting
+     29.5.2. Preparing Data for Forecasting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multistep-forecasting-strategies">
-     26.5.3. Multistep Forecasting Strategies
+     29.5.3. Multistep Forecasting Strategies
     </a>
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      <li class="toc-h4 nav-item toc-entry">
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-       26.5.3.1. Multioutput model
+       29.5.3.1. Multioutput model
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#direct-strategy">
-       26.5.3.2. Direct strategy
+       29.5.3.2. Direct strategy
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#recursive-strategy">
-       26.5.3.3. Recursive strategy
+       29.5.3.3. Recursive strategy
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dirrec-strategy">
-       26.5.3.4. DirRec strategy
+       29.5.3.4. DirRec strategy
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#example-flu-trends">
-     26.5.4. Example - Flu Trends
+     29.5.4. Example - Flu Trends
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       26.5.4.1. Multioutput model
+       29.5.4.1. Multioutput model
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       26.5.4.2. Direct strategy
+       29.5.4.2. Direct strategy
       </a>
      </li>
     </ul>
@@ -1633,12 +1665,12 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   26.6. Your turn! 🚀
+   29.6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   26.7. Acknowledgments
+   29.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1662,103 +1694,103 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overiew">
-   26.1. Overiew
+   29.1. Overiew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-time-series">
-   26.2. What is a Time Series?
+   29.2. What is a Time Series?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-with-time-series">
-   26.3. Linear Regression with Time Series
+   29.3. Linear Regression with Time Series
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#time-step-features">
-     26.3.1. Time-step features
+     29.3.1. Time-step features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lag-features">
-     26.3.2. Lag features
+     29.3.2. Lag features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-tunnel-traffic">
-   26.4. Example - Tunnel Traffic
+   29.4. Example - Tunnel Traffic
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#time-step-feature">
-     26.4.1. Time-step feature
+     29.4.1. Time-step feature
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lag-feature">
-     26.4.2. Lag feature
+     29.4.2. Lag feature
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#forecasting-with-deep-learning">
-   26.5. Forecasting With Deep Learning
+   29.5. Forecasting With Deep Learning
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-forecasting-task">
-     26.5.1. Defining the Forecasting Task
+     29.5.1. Defining the Forecasting Task
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparing-data-for-forecasting">
-     26.5.2. Preparing Data for Forecasting
+     29.5.2. Preparing Data for Forecasting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multistep-forecasting-strategies">
-     26.5.3. Multistep Forecasting Strategies
+     29.5.3. Multistep Forecasting Strategies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#multioutput-model">
-       26.5.3.1. Multioutput model
+       29.5.3.1. Multioutput model
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#direct-strategy">
-       26.5.3.2. Direct strategy
+       29.5.3.2. Direct strategy
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#recursive-strategy">
-       26.5.3.3. Recursive strategy
+       29.5.3.3. Recursive strategy
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dirrec-strategy">
-       26.5.3.4. DirRec strategy
+       29.5.3.4. DirRec strategy
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#example-flu-trends">
-     26.5.4. Example - Flu Trends
+     29.5.4. Example - Flu Trends
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       26.5.4.1. Multioutput model
+       29.5.4.1. Multioutput model
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       26.5.4.2. Direct strategy
+       29.5.4.2. Direct strategy
       </a>
      </li>
     </ul>
@@ -1767,12 +1799,12 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   26.6. Your turn! 🚀
+   29.6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   26.7. Acknowledgments
+   29.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1803,9 +1835,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="time-series">
-<h1><span class="section-number">26. </span>Time series<a class="headerlink" href="#time-series" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">29. </span>Time series<a class="headerlink" href="#time-series" title="Permalink to this headline">#</a></h1>
 <section id="overiew">
-<h2><span class="section-number">26.1. </span>Overiew<a class="headerlink" href="#overiew" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.1. </span>Overiew<a class="headerlink" href="#overiew" title="Permalink to this headline">#</a></h2>
 <p>Time series forecasting is a broad field with a long history and forcasting is perhaps the most common application in the real world. Businesses forecast product demand, governments forecast economic and population growth, meteorologists forecast the weather. The understanding of things to come is a pressing need across science, government, and industry (not to mention our personal lives!), and practitioners in these fields are increasingly applying machine learning to address this need.</p>
 <p>After finishing this course, you’ll know how to:</p>
 <ul class="simple">
@@ -1816,7 +1848,7 @@ <h2><span class="section-number">26.1. </span>Overiew<a class="headerlink" href=
 </ul>
 </section>
 <section id="what-is-a-time-series">
-<h2><span class="section-number">26.2. </span>What is a Time Series?<a class="headerlink" href="#what-is-a-time-series" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.2. </span>What is a Time Series?<a class="headerlink" href="#what-is-a-time-series" title="Permalink to this headline">#</a></h2>
 <p>The basic object of forecasting is the time series, which is a set of observations recorded over time. In forecasting applications, the observations are typically recorded with a regular frequency, like daily or monthly.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -1889,13 +1921,13 @@ <h2><span class="section-number">26.2. </span>What is a Time Series?<a class="he
 <p>This series records the number of hardcover book sales at a retail store over 30 days. Notice that we have a single column of observations <span class="math notranslate nohighlight">\(Hardcover\)</span> with a time index <span class="math notranslate nohighlight">\(Date\)</span>.</p>
 </section>
 <section id="linear-regression-with-time-series">
-<h2><span class="section-number">26.3. </span>Linear Regression with Time Series<a class="headerlink" href="#linear-regression-with-time-series" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.3. </span>Linear Regression with Time Series<a class="headerlink" href="#linear-regression-with-time-series" title="Permalink to this headline">#</a></h2>
 <p>For the first part of this course, we’ll use the linear regression algorithm to construct forecasting models. Linear regression is widely used in practice and adapts naturally to even complex forecasting tasks.</p>
 <p>The linear regression algorithm learns how to make a weighted sum from its input features. For two features, we would have:</p>
 <p><span class="math notranslate nohighlight">\(target = weight_1 * feature_1 + weight_2 * feature_2 + bias\)</span></p>
 <p>During training, the regression algorithm learns values for the parameters <span class="math notranslate nohighlight">\(weight_1\)</span>, <span class="math notranslate nohighlight">\(weight_2\)</span>, and <span class="math notranslate nohighlight">\(bias\)</span> that best fit the <span class="math notranslate nohighlight">\(target\)</span>. (This algorithm is often called ordinary least squares since it chooses values that minimize the squared error between the target and the predictions.) The weights are also called regression coefficients and the bias is also called the intercept because it tells you where the graph of this function crosses the y-axis.</p>
 <section id="time-step-features">
-<h3><span class="section-number">26.3.1. </span>Time-step features<a class="headerlink" href="#time-step-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.3.1. </span>Time-step features<a class="headerlink" href="#time-step-features" title="Permalink to this headline">#</a></h3>
 <p>There are two kinds of features unique to time series: time-step features and lag features.</p>
 <p>Time-step features are features we can derive directly from the time index. The most basic time-step feature is the time dummy, which counts off time steps in the series from beginning to end.</p>
 <div class="cell docutils container">
@@ -2003,7 +2035,7 @@ <h3><span class="section-number">26.3.1. </span>Time-step features<a class="head
 <p>Time-step features let you model time dependence. A series is time dependent if its values can be predicted from the time they occured. In the Hardcover Sales series, we can predict that sales later in the month are generally higher than sales earlier in the month.</p>
 </section>
 <section id="lag-features">
-<h3><span class="section-number">26.3.2. </span>Lag features<a class="headerlink" href="#lag-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.3.2. </span>Lag features<a class="headerlink" href="#lag-features" title="Permalink to this headline">#</a></h3>
 <p>To make a lag feature we shift the observations of the target series so that they appear to have occured later in time. Here we’ve created a 1-step lag feature, though shifting by multiple steps is possible too.</p>
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@@ -2091,7 +2123,7 @@ <h3><span class="section-number">26.3.2. </span>Lag features<a class="headerlink
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 <section id="example-tunnel-traffic">
-<h2><span class="section-number">26.4. </span>Example - Tunnel Traffic<a class="headerlink" href="#example-tunnel-traffic" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.4. </span>Example - Tunnel Traffic<a class="headerlink" href="#example-tunnel-traffic" title="Permalink to this headline">#</a></h2>
 <p>Tunnel Traffic is a time series describing the number of vehicles traveling through the Baregg Tunnel in Switzerland each day from November 2003 to November 2005. In this example, we’ll get some practice applying linear regression to time-step features and lag features.</p>
 <div class="cell docutils container">
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 </div></div></div>
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 <section id="time-step-feature">
-<h3><span class="section-number">26.4.1. </span>Time-step feature<a class="headerlink" href="#time-step-feature" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.4.1. </span>Time-step feature<a class="headerlink" href="#time-step-feature" title="Permalink to this headline">#</a></h3>
 <p>Provided the time series doesn’t have any missing dates, we can create a time dummy by counting out the length of the series.</p>
 <div class="cell docutils container">
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@@ -2308,7 +2340,7 @@ <h3><span class="section-number">26.4.1. </span>Time-step feature<a class="heade
 </div>
 </section>
 <section id="lag-feature">
-<h3><span class="section-number">26.4.2. </span>Lag feature<a class="headerlink" href="#lag-feature" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.4.2. </span>Lag feature<a class="headerlink" href="#lag-feature" title="Permalink to this headline">#</a></h3>
 <p>Pandas provides us a simple method to lag a series, the <span class="math notranslate nohighlight">\(shift\)</span> method.</p>
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@@ -2433,9 +2465,9 @@ <h3><span class="section-number">26.4.2. </span>Lag feature<a class="headerlink"
 </section>
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 <section id="forecasting-with-deep-learning">
-<h2><span class="section-number">26.5. </span>Forecasting With Deep Learning<a class="headerlink" href="#forecasting-with-deep-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.5. </span>Forecasting With Deep Learning<a class="headerlink" href="#forecasting-with-deep-learning" title="Permalink to this headline">#</a></h2>
 <section id="defining-the-forecasting-task">
-<h3><span class="section-number">26.5.1. </span>Defining the Forecasting Task<a class="headerlink" href="#defining-the-forecasting-task" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.1. </span>Defining the Forecasting Task<a class="headerlink" href="#defining-the-forecasting-task" title="Permalink to this headline">#</a></h3>
 <p>There are two things to establish before designing a forecasting model:</p>
 <ul class="simple">
 <li><p>what information is available at the time a forecast is made (features),</p></li>
@@ -2446,13 +2478,13 @@ <h3><span class="section-number">26.5.1. </span>Defining the Forecasting Task<a
 <figure class="align-default" id="def">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 26.1 </span><span class="caption-text">A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data – data for a single prediction, in other words.</span><a class="headerlink" href="#def" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.1 </span><span class="caption-text">A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data – data for a single prediction, in other words.</span><a class="headerlink" href="#def" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The time between the origin and the horizon is the lead time (or sometimes latency) of the forecast. A forecast’s lead time is described by the number of steps from origin to horizon: a “1-step ahead” or “3-step ahead” forecast, say. In practice, it may be necessary for a forecast to begin multiple steps ahead of the origin because of delays in data acquisition or processing.</p>
 </section>
 <section id="preparing-data-for-forecasting">
-<h3><span class="section-number">26.5.2. </span>Preparing Data for Forecasting<a class="headerlink" href="#preparing-data-for-forecasting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.2. </span>Preparing Data for Forecasting<a class="headerlink" href="#preparing-data-for-forecasting" title="Permalink to this headline">#</a></h3>
 <p>In order to forecast time series with ML algorithms, we need to transform the series into a dataframe we can use with those algorithms. (Unless, of course, you are only using deterministic features like trend and seasonality.)</p>
 <p>We saw the first half of this process in Lesson 4 when we created a feature set out of lags. The second half is preparing the target. How we do this depends on the forecasting task.</p>
 <p>Each row in a dataframe represents a single forecast. The time index of the row is the first time in the forecast horizon, but we arrange values for the entire horizon in the same row. For multistep forecasts, this means we are requiring a model to produce multiple outputs, one for each step.</p>
@@ -2648,51 +2680,51 @@ <h3><span class="section-number">26.5.2. </span>Preparing Data for Forecasting<a
 <p>The above illustrates how a dataset would be prepared similar to the Defining a Forecast figure: a three-step forecasting task with a two-step lead time using five lag features. The original time series is y_step_1. The missing values we could either fill-in or drop.</p>
 </section>
 <section id="multistep-forecasting-strategies">
-<h3><span class="section-number">26.5.3. </span>Multistep Forecasting Strategies<a class="headerlink" href="#multistep-forecasting-strategies" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.3. </span>Multistep Forecasting Strategies<a class="headerlink" href="#multistep-forecasting-strategies" title="Permalink to this headline">#</a></h3>
 <p>There are a number of strategies for producing the multiple target steps required for a forecast. We’ll outline four common strategies, each with strengths and weaknesses.</p>
 <section id="multioutput-model">
-<h4><span class="section-number">26.5.3.1. </span>Multioutput model<a class="headerlink" href="#multioutput-model" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.1. </span>Multioutput model<a class="headerlink" href="#multioutput-model" title="Permalink to this headline">#</a></h4>
 <p>Use a model that produces multiple outputs naturally. Linear regression and neural networks can both produce multiple outputs. This strategy is simple and efficient, but not possible for every algorithm you might want to use. XGBoost can’t do this, for instance.</p>
 <figure class="align-default" id="multioutput">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 26.2 </span><span class="caption-text">Multioutput Model</span><a class="headerlink" href="#multioutput" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.2 </span><span class="caption-text">Multioutput Model</span><a class="headerlink" href="#multioutput" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="direct-strategy">
-<h4><span class="section-number">26.5.3.2. </span>Direct strategy<a class="headerlink" href="#direct-strategy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.2. </span>Direct strategy<a class="headerlink" href="#direct-strategy" title="Permalink to this headline">#</a></h4>
 <p>Train a separate model for each step in the horizon: one model forecasts 1-step ahead, another 2-steps ahead, and so on. Forecasting 1-step ahead is a different problem than 2-steps ahead (and so on), so it can help to have a different model make forecasts for each step. The downside is that training lots of models can be computationally expensive.</p>
 <figure class="align-default" id="direct">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 26.3 </span><span class="caption-text">Direct Strategy</span><a class="headerlink" href="#direct" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.3 </span><span class="caption-text">Direct Strategy</span><a class="headerlink" href="#direct" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="recursive-strategy">
-<h4><span class="section-number">26.5.3.3. </span>Recursive strategy<a class="headerlink" href="#recursive-strategy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.3. </span>Recursive strategy<a class="headerlink" href="#recursive-strategy" title="Permalink to this headline">#</a></h4>
 <p>Train a single one-step model and use its forecasts to update the lag features for the next step. With the recursive method, we feed a model’s 1-step forecast back in to that same model to use as a lag feature for the next forecasting step. We only need to train one model, but since errors will propagate from step to step, forecasts can be inaccurate for long horizons.</p>
 <figure class="align-default" id="recursive">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 26.4 </span><span class="caption-text">Recursive Strategy</span><a class="headerlink" href="#recursive" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.4 </span><span class="caption-text">Recursive Strategy</span><a class="headerlink" href="#recursive" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="dirrec-strategy">
-<h4><span class="section-number">26.5.3.4. </span>DirRec strategy<a class="headerlink" href="#dirrec-strategy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.4. </span>DirRec strategy<a class="headerlink" href="#dirrec-strategy" title="Permalink to this headline">#</a></h4>
 <p>A combination of the direct and recursive strategies: train a model for each step and use forecasts from previous steps as new lag features. Step by step, each model gets an additional lag input. Since each model always has an up-to-date set of lag features, the DirRec strategy can capture serial dependence better than Direct, but it can also suffer from error propagation like Recursive.</p>
 <figure class="align-default" id="dirrec">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 26.5 </span><span class="caption-text">DirRec Strategy</span><a class="headerlink" href="#dirrec" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.5 </span><span class="caption-text">DirRec Strategy</span><a class="headerlink" href="#dirrec" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="example-flu-trends">
-<h3><span class="section-number">26.5.4. </span>Example - Flu Trends<a class="headerlink" href="#example-flu-trends" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.4. </span>Example - Flu Trends<a class="headerlink" href="#example-flu-trends" title="Permalink to this headline">#</a></h3>
 <p>In this example we’ll apply the MultiOutput and Direct strategies to the Flu Trends data from Lesson 4, this time making true forecasts for multiple weeks beyond the training period.</p>
 <p>We’ll define our forecasting task to have an 8-week horizon with a 1-week lead time. In other words, we’ll be forecasting eight weeks of flu cases starting with the following week.</p>
 <p>The hidden cell sets up the example and defines a helper function <span class="math notranslate nohighlight">\(plot_multistep\)</span>.</p>
@@ -2791,7 +2823,7 @@ <h3><span class="section-number">26.5.4. </span>Example - Flu Trends<a class="he
 </div>
 </div>
 <section id="id1">
-<h4><span class="section-number">26.5.4.1. </span>Multioutput model<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.4.1. </span>Multioutput model<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>We’ll use linear regression as a MultiOutput strategy. Once we have our data prepared for multiple outputs, training and prediction is the same as always.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2835,7 +2867,7 @@ <h4><span class="section-number">26.5.4.1. </span>Multioutput model<a class="hea
 </div>
 </section>
 <section id="id2">
-<h4><span class="section-number">26.5.4.2. </span>Direct strategy<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.4.2. </span>Direct strategy<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <p>XGBoost can’t produce multiple outputs for regression tasks. But by applying the Direct reduction strategy, we can still use it to produce multi-step forecasts. This is as easy as wrapping it with scikit-learn’s <span class="math notranslate nohighlight">\(MultiOutputRegressor\)</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2881,11 +2913,11 @@ <h4><span class="section-number">26.5.4.2. </span>Direct strategy<a class="heade
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">26.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">26.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/">kaggle</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/learn/time-series">Time Series</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2927,13 +2959,13 @@ <h2><span class="section-number">26.7. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">25. </span>Long-short term memory</p>
+            <p class="prev-next-title"><span class="section-number">28. </span>Long-short term memory</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">27. </span>Deep Q-learning</p>
+        <p class="prev-next-title"><span class="section-number">30. </span>Deep Q-learning</p>
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@@ -370,9 +370,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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-     13.3. Yet other classifiers
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
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-     14.1. Introduction to clustering
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+     40.14. Try it in Excel
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    </li>
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
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-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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@@ -372,9 +372,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
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+  <a class="reference internal" href="ml-fundamentals/ml-summary.html">
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+   40. Self-paced assignments
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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     <a class="reference internal" href="assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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     <a class="reference internal" href="assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     37.6. Python programming introduction
+     40.6. Python programming introduction
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-     37.7. Python programming basics
+     40.7. Python programming basics
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-     37.8. Python programming advanced
+     40.8. Python programming advanced
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     <a class="reference internal" href="assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
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-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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     <a class="reference internal" href="assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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-     37.12. Lines, scatters and bars
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-     37.13. Apply your skills
+     40.13. Apply your skills
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-     37.14. Try it in Excel
+     40.14. Try it in Excel
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-     37.15. Dive into the beehive
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-     37.16. Build your own custom vis
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-     37.17. Classifying datasets
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     <a class="reference internal" href="assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
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    <li class="toctree-l2">
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-     37.26. Data preparation
+     40.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
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-     37.59. Retrying some regression
+     40.59. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
+     40.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
+     40.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
+     40.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
+     40.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
+     40.74. Random forests for classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
+     40.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
+     40.76. Decision trees
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
+     40.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
+     40.78. Gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
+     40.79. Boosting with tuning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
+     40.82. Data engineering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
+     40.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
+     40.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
+     40.87. Build classification models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/build-classification-model.html">
-     37.88. Build Classification Model
+     40.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.90. How to choose cnn architecture mnist
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
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-    <link rel="prev" title="33. Problem framing" href="problem-framing.html" />
+    <link rel="next" title="38. Model training &amp; evaluation" href="model-training-and-evaluation.html" />
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    10. Machine Learning overview
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     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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     </a>
    </li>
    <li class="toctree-l2">
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-     37.87. Build classification models
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     </a>
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-     37.88. Build Classification Model
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/parameter-play.html">
-     37.89. Parameter play
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-     37.90. How to choose cnn architecture mnist
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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    </li>
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-     37.94. Object Recognition in Images using CNN
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     <a class="reference internal" href="../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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     <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-     38.1. Python programming introduction
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-     38.2. Python programming basics
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-     38.19. Model Selection
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@@ -1526,47 +1558,47 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   </a>
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@@ -1655,7 +1687,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-engineering">
-<h1><span class="section-number">34. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
 <p><strong><a class="reference external" href="https://en.wikipedia.org/wiki/Information_engineering">Data engineering</a></strong>, also known as <strong>Information engineering</strong>, is a software engineering approach to designing and developing information systems. A data engineer is someone who creates big data ETL pipelines and makes it possible to take huge amounts of data and translate it into insights.</p>
 <blockquote>
 <div><p>“Data is food for AI.”
@@ -1668,26 +1700,26 @@ <h1><span class="section-number">34. </span>Data engineering<a class="headerlink
 <figure class="align-default" id="big-data-pipeline-on-aws-microsoft-azure-and-google-cloud">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/big-data-pipelines-on-aws-azure-gcp.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/big-data-pipelines-on-aws-azure-gcp.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 34.1 </span><span class="caption-text"><a class="reference external" href="https://www.reddit.com/r/bigdata/comments/mkfsfi/big_data_pipeline_on_aws_microsoft_azure_and/">Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud</a></span><a class="headerlink" href="#big-data-pipeline-on-aws-microsoft-azure-and-google-cloud" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.1 </span><span class="caption-text"><a class="reference external" href="https://www.reddit.com/r/bigdata/comments/mkfsfi/big_data_pipeline_on_aws_microsoft_azure_and/">Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud</a></span><a class="headerlink" href="#big-data-pipeline-on-aws-microsoft-azure-and-google-cloud" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p><strong>Architecture: Capture, process, store, analyze, use</strong></p>
 <figure class="align-default" id="how-to-build-a-data-analytics-pipeline-on-google-cloud">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/how-to-build-a-scalable-data-analytics-pipeline.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/how-to-build-a-scalable-data-analytics-pipeline.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 34.2 </span><span class="caption-text"><a class="reference external" href="https://www.freecodecamp.org/news/scalable-data-analytics-pipeline/">How to build a Data Analytics Pipeline on Google Cloud?</a></span><a class="headerlink" href="#how-to-build-a-data-analytics-pipeline-on-google-cloud" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.2 </span><span class="caption-text"><a class="reference external" href="https://www.freecodecamp.org/news/scalable-data-analytics-pipeline/">How to build a Data Analytics Pipeline on Google Cloud?</a></span><a class="headerlink" href="#how-to-build-a-data-analytics-pipeline-on-google-cloud" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p><strong>Ingestion, storage, cataloging &amp; search, processing, consumption, security &amp; governance</strong></p>
 <figure class="align-default" id="aws-serverless-data-analytics-pipeline">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-analytics-pipeline.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-analytics-pipeline.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 34.3 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">AWS serverless data analytics pipeline</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.3 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">AWS serverless data analytics pipeline</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In this section, the <strong>AWS serverless data analytics pipeline</strong> architecture above is used as a reference to breaking down the data pipeline into 6 layers. Before moving into each of them, we will first have a glance at the concept of data quality. And at the last, we will go through an AWS cloud-based data pipeline architecture showcase.</p>
 <section id="data-quality">
-<h2><span class="section-number">34.1. </span>Data quality<a class="headerlink" href="#data-quality" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.1. </span>Data quality<a class="headerlink" href="#data-quality" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Data_quality">Data quality</a> refers to the state of qualitative or quantitative pieces of information. Generally speaking, high-quality data could “fit for [its] intended uses in operations, decision making and planning”. Data quality is a widely used concept and is split into multiple dimensions traditionally. This section will discuss the most common ones to Machine Learning data engineering, including completeness, duplicates, outlier, and consistency.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -1708,7 +1740,7 @@ <h2><span class="section-number">34.1. </span>Data quality<a class="headerlink"
 <p><strong>Data consistency</strong>. A dataset could be constructed from different data sources. For example, a retailer usually doesn’t have a centralized way to manage its supply chain. Instead, it achieves this by combining different ERP systems across demand, supply, shipment, inventory, and so on. Those ERP systems are vend from different software companies which have their solutions to structure and organize the data, e.g. to present timestamp as Unix epoch vs. the ISO format. Consistency data is necessary to build Machine Learning models. It is also important to help data scientists and business owners to have meaningful business analytics.</p>
 </section>
 <section id="ingestion-layer">
-<h2><span class="section-number">34.2. </span>Ingestion layer<a class="headerlink" href="#ingestion-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.2. </span>Ingestion layer<a class="headerlink" href="#ingestion-layer" title="Permalink to this headline">#</a></h2>
 <p>Data ingestion is used to load data records from one or more sources, e.g. IoT devices, data lakes, databases, SaaS applications, etc., into a target data warehouse. It is the layer between the data source and data processing. Once ingested, the data becomes available in the data pipeline. There are three major ways to ingest data.</p>
 <ul class="simple">
 <li><p><strong>Batch ingestion</strong>, where the ingestion layer collects data from sources incrementally and sends it to the data storage in batch. Data can be grouped based on a schedule or certain rules. This approach is generally used for use cases that don’t require real-time data. And it is typically cost-effective and less expensive.</p></li>
@@ -1717,7 +1749,7 @@ <h2><span class="section-number">34.2. </span>Ingestion layer<a class="headerlin
 </ul>
 </section>
 <section id="storage-layer">
-<h2><span class="section-number">34.3. </span>Storage layer<a class="headerlink" href="#storage-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.3. </span>Storage layer<a class="headerlink" href="#storage-layer" title="Permalink to this headline">#</a></h2>
 <p>The storage layer is responsible to provide durability, scalability, security, and cost-effectivity for big data storage. It supports storing both unstructured data and structured datasets in different formats. The data is usually loaded as-is without any processing to conform to a target format so that the ingestion layer could quickly land the source data into the storage. During processing, the data will be transformed from the original format to the one defined by the catalog layer, which is ready to be consumed downstream. To achieve the above, the storage layer could be organized into 3 different zones.</p>
 <ul class="simple">
 <li><p><strong>Raw data zone</strong>, which works as a transient area to store the ingested data from sources as-is.</p></li>
@@ -1726,11 +1758,11 @@ <h2><span class="section-number">34.3. </span>Storage layer<a class="headerlink"
 </ul>
 </section>
 <section id="cataloging-and-search-layer">
-<h2><span class="section-number">34.4. </span>Cataloging and search layer<a class="headerlink" href="#cataloging-and-search-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.4. </span>Cataloging and search layer<a class="headerlink" href="#cataloging-and-search-layer" title="Permalink to this headline">#</a></h2>
 <p>Once collected into the storage layer, the data needs to be cataloged and accessible to be actually usable in the remaining workflow, especially if the data volume is big. The cataloging and search layer is responsible to store metadata about datasets hosted in the storage layer, which is applied to business in the consumption layer and technical in the processing layer. It provides the ability to manage schema and makes datasets discoverable by providing search capabilities.</p>
 </section>
 <section id="processing-layer">
-<h2><span class="section-number">34.5. </span>Processing layer<a class="headerlink" href="#processing-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.5. </span>Processing layer<a class="headerlink" href="#processing-layer" title="Permalink to this headline">#</a></h2>
 <p>The processing layer is responsible for transforming data to be consumable across the data storage zones, which includes multiple technologies such as data validation, cleanup, normalization, transformation, and enrichment. The processing layer needs to handle large data volumes with diverse data formats. The processing layer also provides the ability to build the processing workflow across the data storage zones, so that more complex processing logic could be better handled. Therefore, the processing layer could be composed of three types of components:</p>
 <ul class="simple">
 <li><p><strong>initializing component</strong>, which is used to create a multi-step data processing workflow on schedule or in response to event triggers,</p></li>
@@ -1739,7 +1771,7 @@ <h2><span class="section-number">34.5. </span>Processing layer<a class="headerli
 </ul>
 </section>
 <section id="consumption-layer">
-<h2><span class="section-number">34.6. </span>Consumption layer<a class="headerlink" href="#consumption-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.6. </span>Consumption layer<a class="headerlink" href="#consumption-layer" title="Permalink to this headline">#</a></h2>
 <p>The consumption layer is responsible to provide scalable and performant tools to gain insights from the vast amount of data in the data storage. To achieve the best flexibility, this layer needs to support different types of data structures and formats, and use data partitioning to optimize cost and performance. The consumption layer is composed using analytics services, tools, and methods that enable:</p>
 <ul class="simple">
 <li><p><strong>interactive SQL</strong> to allow sending a SQL query to search and getting documents streamed back in response,</p></li>
@@ -1749,7 +1781,7 @@ <h2><span class="section-number">34.6. </span>Consumption layer<a class="headerl
 </ul>
 </section>
 <section id="security-and-governance-layer">
-<h2><span class="section-number">34.7. </span>Security and governance layer<a class="headerlink" href="#security-and-governance-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.7. </span>Security and governance layer<a class="headerlink" href="#security-and-governance-layer" title="Permalink to this headline">#</a></h2>
 <p>The security and governance layer is responsible for protecting the data, identities, and processing resources across all other layers. It provides capabilities to manage the below aspects, including but not limited to below:</p>
 <ul class="simple">
 <li><p><strong>authentication and authorization</strong> to configure fine-grained access control for resources in all layers of the architecture,</p></li>
@@ -1759,12 +1791,12 @@ <h2><span class="section-number">34.7. </span>Security and governance layer<a cl
 </ul>
 </section>
 <section id="showcase">
-<h2><span class="section-number">34.8. </span>Showcase<a class="headerlink" href="#showcase" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.8. </span>Showcase<a class="headerlink" href="#showcase" title="Permalink to this headline">#</a></h2>
 <p>Today, most of the main cloud providers offer their own serverless data pipeline solutions. They all target to enable agile and self-service data onboarding and analytics for the whole dataset, although are built on top of different cloud service stacks. By leveraging the data pipeline, engineers and scientists could focus more time on rapidly building data and analytics pipelines to drive insights for the business.</p>
 <figure class="align-default" id="aws-serverless-data-analytics-pipeline-reference-architecture">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-lake-centric-analytics-architecture.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-lake-centric-analytics-architecture.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 34.4 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">Serverless data lake centric analytics architecture</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline-reference-architecture" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.4 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">Serverless data lake centric analytics architecture</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline-reference-architecture" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>This diagram describes a reference architecture that uses AWS-managed services which compose the 6 layers mentioned in the above logical architecture. More specifically to this showcase:</p>
@@ -1779,7 +1811,7 @@ <h2><span class="section-number">34.8. </span>Showcase<a class="headerlink" href
 <p>If the pipeline is built on other cloud computing platforms, equivalent services could be easily found across all the layers. The architecture could also be simplified or customized according to the business needs.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">34.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Data cleaning is a key part of data engineering to improve the <a class="reference external" href="#data-quality">data quality</a>, but it can be deeply frustrating as the situation could be highly varied in different datasets. Sometimes you will see the text fields garbled. Sometimes your dates are formatted incorrectly. In this <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html"><span class="doc std std-doc">assignment</span></a>, you’ll work through three hands-on exercises to deal with messy data.</p>
 </section>
 </section>
@@ -1821,13 +1853,13 @@ <h2><span class="section-number">34.9. </span>Your turn! 🚀<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">33. </span>Problem framing</p>
+            <p class="prev-next-title"><span class="section-number">36. </span>Problem framing</p>
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-        <p class="prev-next-title"><span class="section-number">35. </span>Model training &amp; evaluation</p>
+        <p class="prev-next-title"><span class="section-number">38. </span>Model training &amp; evaluation</p>
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@@ -98,8 +98,8 @@
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     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="37. Self-paced assignments" href="../assignments/README.html" />
-    <link rel="prev" title="35. Model training &amp; evaluation" href="model-training-and-evaluation.html" />
+    <link rel="next" title="40. Self-paced assignments" href="../assignments/README.html" />
+    <link rel="prev" title="38. Model training &amp; evaluation" href="model-training-and-evaluation.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
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   <label for="toctree-checkbox-8">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
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+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-  <label for="toctree-checkbox-11">
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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  </li>
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   <a class="reference internal" href="../deep-learning/autoencoder.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/time-series.html">
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   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
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  </li>
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   <a class="reference internal" href="../deep-learning/image-classification.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-segmentation.html">
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+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="overview.html">
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+   35. Overview
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  </li>
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   <a class="reference internal" href="problem-framing.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="data-engineering.html">
-   34. Data engineering
+   37. Data engineering
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  <li class="toctree-l1">
   <a class="reference internal" href="model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  </li>
  <li class="toctree-l1 current active">
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+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
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-     37.52. ML neural network - Assignment 1
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-<h1><span class="section-number">36. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">39. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h1>
 <p>Jez Humble and David Farley define continuous delivery as the capacity to safely and efficiently introduce a wide range of changes, such as new features, configuration adjustments, bug fixes, and experiments, into production or the hands of users. <a class="reference external" href="https://en.wikipedia.org/wiki/Continuous_delivery">Continuous delivery</a> is a critical component for the automated production of high-quality software throughout the development pipeline. By emphasizing sustainable practices, Humble and Farley highlight the importance of ensuring that changes are made in a consistent and reliable manner.</p>
 <p>Once the Machine Learning model is trained, it needs to be deployed as part of a business application such as a mobile or desktop application. The Machine Learning model requires various data points to produce predictions. The final stage of the Machine Learning workflow is the delivery of the previously engineered Machine Learning model into existing software.</p>
 <p>The model deployment is a very challenging stage, as it needs to handle both traditional software challenges as well as Machine Learning specific challenges.</p>
@@ -1624,39 +1656,39 @@ <h1><span class="section-number">36. </span>Model deployment<a class="headerlink
 <figure class="align-default" id="ml-axis-of-change">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-axis-of-change.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-axis-of-change.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.1 </span><span class="caption-text"><a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">The 3 axis of change in a Machine Learning application — data, model, and code — and a few reasons for them to change</a></span><a class="headerlink" href="#ml-axis-of-change" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.1 </span><span class="caption-text"><a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">The 3 axis of change in a Machine Learning application — data, model, and code — and a few reasons for them to change</a></span><a class="headerlink" href="#ml-axis-of-change" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>At last, Machine Learning development needs the coordination of scientists, software engineers, data engineers, and business professionals. This introduces extra challenges to ensure the model works reliably and delivers the desired result.</p>
 <p>In this section, we will describe the important technical components for implementing Machine Learning model deployment, explain the concepts and demonstrate how different patterns can be used to implement the full end-to-end process.</p>
 <section id="deployment-strategies">
-<h2><span class="section-number">36.1. </span>Deployment strategies<a class="headerlink" href="#deployment-strategies" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.1. </span>Deployment strategies<a class="headerlink" href="#deployment-strategies" title="Permalink to this headline">#</a></h2>
 <p>Deployment strategies are practices used to change or upgrade a running instance of an application.</p>
 <p><strong>Basic deployment</strong> implies that all nodes within a target environment (v1.0) are updated with a new version (v2.0) at the same time. This is the simplest but riskiest strategy because all the nodes will go down if the deployed artifacts are broken. This strategy also results in slow deployment rollback.</p>
 <figure class="align-default" id="basic-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/basic-deployment-strategy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/basic-deployment-strategy.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.2 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Basic deployment strategy</a></span><a class="headerlink" href="#basic-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.2 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Basic deployment strategy</a></span><a class="headerlink" href="#basic-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In practice, the real-world system is usually composed of many inter-depended modules or services. In this case, the <strong>multi-service deployment</strong> could be considered an option. To this strategy, all nodes within a target environment are updated with multiple new services simultaneously. This reduces the risk of system level failure but introduces the difficulty of managing the service dependencies in both deployment and rollback.</p>
 <figure class="align-default" id="multi-service-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/multi-service-deployment-strategy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/multi-service-deployment-strategy.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.3 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Multi-service deployment strategy</a></span><a class="headerlink" href="#multi-service-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.3 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Multi-service deployment strategy</a></span><a class="headerlink" href="#multi-service-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>There are several options to help avoid the above issues. <strong>Rolling deployment</strong> incrementally updates all nodes in a target environment with the service or artifact version in batches. <strong>Canary development</strong> is similar but targets to roll out the new version to a certain subset of users instead of services. Both strategies could reduce the risk of failure by introducing finer granularity control to the deployment process. But they require services to support both new and old versions of an artifact, which make it more complex to manage the rollout and rollback.</p>
 <figure class="align-default" id="rolling-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/rolling-deployment-strategy.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/rolling-deployment-strategy.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.4 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Rolling deployment strategy</a></span><a class="headerlink" href="#rolling-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.4 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Rolling deployment strategy</a></span><a class="headerlink" href="#rolling-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="canary-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/canary-deployment-strategy.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/canary-deployment-strategy.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.5 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Canary deployment strategy</a></span><a class="headerlink" href="#canary-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.5 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Canary deployment strategy</a></span><a class="headerlink" href="#canary-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1671,12 +1703,12 @@ <h2><span class="section-number">36.1. </span>Deployment strategies<a class="hea
 <figure class="align-default" id="blue-green-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/blue-green-deployment-strategy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/blue-green-deployment-strategy.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.6 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Blue/Green deployment strategy</a></span><a class="headerlink" href="#blue-green-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.6 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Blue/Green deployment strategy</a></span><a class="headerlink" href="#blue-green-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="deployment-evolution">
-<h2><span class="section-number">36.2. </span>Deployment evolution<a class="headerlink" href="#deployment-evolution" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.2. </span>Deployment evolution<a class="headerlink" href="#deployment-evolution" title="Permalink to this headline">#</a></h2>
 <p>When computers were extremely large, expensive, and bulky, the software was often bundled together with the hardware by manufacturers. In the 1980s, new forms of software distribution, such as floppy disks and optical media, came together with the popularity of microcomputers, which meant the software deployment must be faster and more user-friendly. And since the internet age, agile software development has become possible. The advent of cloud computing and software as a service make software able to be deployed to a large number of customers in minutes over the internet.</p>
 <p>As software development evolved, the determining factors of software deployment are changing as well. This requires the art-of-the-state deployment strategies and modes to provide better flexibility for modern computer applications.</p>
 <p><strong>Deployment 1.0</strong></p>
@@ -1698,7 +1730,7 @@ <h2><span class="section-number">36.2. </span>Deployment evolution<a class="head
 </div>
 </section>
 <section id="serving">
-<h2><span class="section-number">36.3. </span>Serving<a class="headerlink" href="#serving" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.3. </span>Serving<a class="headerlink" href="#serving" title="Permalink to this headline">#</a></h2>
 <p>It is crucial to clarify the intended experiments before deploying the trained models to users. This ensures that the models adhere to the constraints specified in the problem setup. What kind of feedback should be obtained from users? Should the model be updated online with every new data point? How can the model be personalized for individual users? How often should the Machine Learning algorithm be updated? Is there a possibility of biases and misuses in the model?</p>
 <p>Regardless of which serving pattern to use, there is always an implicit contract between the model and users. The model will usually expect input data in a certain shape, and the changed model will require the contract to be updated for new input or add new features, which may cause integration issues and break the applications using it. Which leads the necessity of continuously monitoring and testing he model. However, to serve and use the model in production, there are a few patterns to achieve.</p>
 <p><strong>Model as module</strong></p>
@@ -1711,7 +1743,7 @@ <h2><span class="section-number">36.3. </span>Serving<a class="headerlink" href=
 <p>This is another approach to have the Machine Learning model treated and published independently, but the application will ingest it as data at runtime instead. This is more commonly used in streaming or real-time scenarios where the application can subscribe to events that are published whenever a new model version is released. The model data will be ingested into memory for predicting, which avoid introducing the extra latency. Deployment strategies such as Blue/Green deployment or Canary deployment can be applied in this scenario. Some of the above serialization options are also applicable for implementing the “model as data” pattern.</p>
 </section>
 <section id="infrastructure-as-code">
-<h2><span class="section-number">36.4. </span>Infrastructure as code<a class="headerlink" href="#infrastructure-as-code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.4. </span>Infrastructure as code<a class="headerlink" href="#infrastructure-as-code" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Infrastructure_as_code">Infrastructure as code</a> (IaC) is the process of managing and provisioning computer data centers through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. The IT infrastructure managed by this process comprises both physical equipment, such as bare-metal servers, as well as virtual machines, and associated configuration resources.</p>
 <p><a class="reference external" href="https://docs.github.com/en/actions">GitHub Actions</a> is one of the most well-known IaC implementations, although people may not treat it as IoC. Github Actions makes it easy to automate all the Github software workflows with CI/CD support. By leveraging Github Actions, the developer could manage any infrastructure tasks, including build, test, deploy as well as code reviews, branch management, and issue triaging. Below is some sample infrastructure code from <a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions quickstart</a>.</p>
 <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">GitHub Actions Demo</span>
@@ -1737,20 +1769,20 @@ <h2><span class="section-number">36.4. </span>Infrastructure as code<a class="he
 <figure class="align-default" id="actions-quickstart-logs">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-logs.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-logs.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.7 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Logs</a></span><a class="headerlink" href="#actions-quickstart-logs" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.7 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Logs</a></span><a class="headerlink" href="#actions-quickstart-logs" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The log shows how each of the steps is processed. And, here is the list of files in the repository.</p>
 <figure class="align-default" id="actions-quickstart-log-detail">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-log-detail.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-log-detail.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.8 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Log Detail</a></span><a class="headerlink" href="#actions-quickstart-log-detail" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.8 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Log Detail</a></span><a class="headerlink" href="#actions-quickstart-log-detail" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The RedHat-sponsored <a class="reference external" href="https://github.com/ansible/ansible">Ansible</a> is an open-source IaC framework handling configuration management, application deployment, cloud provisioning, ad-hoc task execution, network automation, and multi-node orchestration. As a public cloud hosted solution, <a class="reference external" href="https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/Welcome.html">AWS CloudFormation</a> is an IoC service that helps the development teams model and set up AWS resources so that they can spend less time managing those resources and more time focusing on the applications that run in AWS. Both the open-source and hosted frameworks altogeher provide the flexibility for development teams to choose the right solution to adopt based on their requirements.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">36.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the model serving by following this <a class="reference external" href="https://madewithml.com/courses/mlops/api/">turorial</a>.</p>
 </section>
 </section>
@@ -1792,13 +1824,13 @@ <h2><span class="section-number">36.5. </span>Your turn! 🚀<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">35. </span>Model training &amp; evaluation</p>
+            <p class="prev-next-title"><span class="section-number">38. </span>Model training &amp; evaluation</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../assignments/README.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37. </span>Self-paced assignments</p>
+        <p class="prev-next-title"><span class="section-number">40. </span>Self-paced assignments</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/machine-learning-productionization/model-training-and-evaluation.html b/machine-learning-productionization/model-training-and-evaluation.html
index 103d56ab41..52a42d12a9 100644
--- a/machine-learning-productionization/model-training-and-evaluation.html
+++ b/machine-learning-productionization/model-training-and-evaluation.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>35. Model training &amp; evaluation &#8212; Ocademy Open Machine Learning Book</title>
+    <title>38. Model training &amp; evaluation &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="36. Model deployment" href="model-deployment.html" />
-    <link rel="prev" title="34. Data engineering" href="data-engineering.html" />
+    <link rel="next" title="39. Model deployment" href="model-deployment.html" />
+    <link rel="prev" title="37. Data engineering" href="data-engineering.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
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+   20. Kernel method
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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+     40.5. Project Plan​ Template
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-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     37.29. Matplotlib applied
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-     37.35. Tell a story
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-     37.36. Explore a planetary computer dataset
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-     37.37. Exploring for answers
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-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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-     37.47. Loss Function
+     40.47. Loss Function
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-     37.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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-     37.62. Explore classification methods
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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-     37.72. Regularized Linear Models
+     40.72. Regularized Linear Models
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+     38.1.1. Start simple
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     <a class="reference internal nav-link" href="#transfer-learning">
-     35.1.2. Transfer learning
+     38.1.2. Transfer learning
     </a>
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+     38.2.2. Check metrics for important data slices
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+   38.5. Your turn! 🚀
   </a>
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+     38.1.1. Start simple
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#transfer-learning">
-     35.1.2. Transfer learning
+     38.1.2. Transfer learning
     </a>
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     <a class="reference internal nav-link" href="#automl">
-     35.1.3. AutoML
+     38.1.3. AutoML
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-     35.2.1. Evaluate quality using model metrics
+     38.2.1. Evaluate quality using model metrics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-metrics-for-important-data-slices">
-     35.2.2. Check metrics for important data slices
+     38.2.2. Check metrics for important data slices
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-real-world-metrics">
-     35.2.3. Use real-world metrics
+     38.2.3. Use real-world metrics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#optimizing-and-satisfying-metrics">
-     35.2.4. Optimizing and satisfying metrics
+     38.2.4. Optimizing and satisfying metrics
     </a>
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+     38.3.1. Data and feature debugging
     </a>
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-     35.3.2. Model debugging
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+     38.4.1. Add useful features
     </a>
    </li>
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     <a class="reference internal nav-link" href="#tune-hyperparameters">
-     35.4.2. Tune hyperparameters
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     </a>
    </li>
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     <a class="reference internal nav-link" href="#tune-model-depth-and-width">
-     35.4.3. Tune model depth and width
+     38.4.3. Tune model depth and width
     </a>
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-   35.5. Your turn! 🚀
+   38.5. Your turn! 🚀
   </a>
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 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="model-training-evaluation">
-<h1><span class="section-number">35. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">38. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h1>
 <p>Machine Learning modeling, including model selection, training, evaluation and debugging, is very important, but only a small component of the entire Machine Learning pipeline. Some might even argue that it’s the easiest component. Details of specific algorithms won’t be discussed in this section. Instead, we will focus on giving an overview picture of how to choose the right model for the problem.</p>
 <section id="model-selection">
-<h2><span class="section-number">35.1. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.1. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h2>
 <p>Once problems are framed as one of the common Machine Learning tasks, the typical approaches to solve them can usually be located correspondingly. You should first figure out the category of the problem. Is it supervised or unsupervised? Or regression vs. classification? Does it require generation or only prediction? If it is the former, the models will have to be much harder to learn the latent space of the data.</p>
 <p>Note that the model selection also highly depends on how the business problem is defined. For the same problem area, such as a house price prediction task, different targets may result in choosing different Machine Learning models. It can be regression if the required output is raw numbers. But if the goal is to quantize the income into different brackets and predict the bracket, it becomes a classification problem. Similarly, unsupervised learning could be used to learn labels for the data, which could be then used for supervised learning.</p>
 <p>Keep in mind that there could be many ways to frame a problem, and the better one could only be known after the chosen models are trained and evaluated. Even though there are hundreds of ways to select and train a Machine Learning model, it is always a good practice to start with simple data, simple feature engineering, and simple model. Besides, transfer learning could be leveraged to reduce the training time in the context of the neural network. Furthermore, AutoML helps to further save time spent from feature engineering to HPO tuning. We will discuss these three useful tricks for selecting models in detail.</p>
 <section id="start-simple">
-<h3><span class="section-number">35.1.1. </span>Start simple<a class="headerlink" href="#start-simple" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.1.1. </span>Start simple<a class="headerlink" href="#start-simple" title="Permalink to this headline">#</a></h3>
 <p>When searching for a solution, the first goal is to find an effective simple approach for the task. This serves three major purposes.</p>
 <ul class="simple">
 <li><p>Focus on resolving the confidence about if the model could solve the problem, as additional complexity and potential bugs are avoided.</p></li>
@@ -1815,21 +1847,21 @@ <h3><span class="section-number">35.1.1. </span>Start simple<a class="headerlink
 <p>Overall, there is no need to pursue the state-of-the-art approach or the most optimized performance at the very beginning. But it is necessary to keep the eyes on the trap of increasingly complex heuristics.</p>
 </section>
 <section id="transfer-learning">
-<h3><span class="section-number">35.1.2. </span>Transfer learning<a class="headerlink" href="#transfer-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.1.2. </span>Transfer learning<a class="headerlink" href="#transfer-learning" title="Permalink to this headline">#</a></h3>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Transfer_learning">Transfer learning</a> is a research problem in Machine Learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.</p>
 <p>Using a large amount of data and tackling a completely new Machine Learning problem can be very challenging sometimes. Transfer learning could be the starting point if the simplified solution does not work or perform well. It allows utilizing knowledge(model) acquired (trained) for one task to solve other similar tasks.</p>
 <p>What’s more, transfer learning is very commonly adopted in hot areas such as computer vision and natural language processing. It usually gives significantly better performance than training a simple model. And it is even a rare case to train a model from scratch in such areas. Instead, researchers and data scientists prefer starting from a pre-trained model that already learned general features and how to classify objects.</p>
 <figure class="align-default" id="id1">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 35.1 </span><span class="caption-text"><a class="reference external" href="https://www.geeksforgeeks.org/ml-introduction-to-transfer-learning">Transfer learning</a></span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 38.1 </span><span class="caption-text"><a class="reference external" href="https://www.geeksforgeeks.org/ml-introduction-to-transfer-learning">Transfer learning</a></span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Traditionally, there are three major categories of transfer learning strategies and techniques based on the characteristics of the problem and the data. <strong>Inductive transfer learning</strong> is used if the domains are the same between the source and target, but the exact tasks are not. If the problems’ domains are even different, <strong>transductive transfer learning</strong> could be the choice. <strong>Unsupervised transfer learning</strong> is similar to inductive transfer learning, but for unsupervised tasks with unlabeled datasets both in the source and target.</p>
 <figure class="align-default" id="transfer-learning-strategies">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning-strategies.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning-strategies.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 35.2 </span><span class="caption-text"><a class="reference external" href="https://www.v7labs.com/blog/transfer-learning-guide,%20https://www.v7labs.com/blog/transfer-learning-guide">Transfer learning strategies</a></span><a class="headerlink" href="#transfer-learning-strategies" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 38.2 </span><span class="caption-text"><a class="reference external" href="https://www.v7labs.com/blog/transfer-learning-guide,%20https://www.v7labs.com/blog/transfer-learning-guide">Transfer learning strategies</a></span><a class="headerlink" href="#transfer-learning-strategies" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Under the deep learning context, there are multiple levels of complexity in using a pre-trained model. The most straightforward way is to use the pre-trained model directly, but may not be applicable mostly. An idea here is to leverage the pre-trained model’s weighted layers to extract features while retraining the last layer. One step further, a more engaging technique is to fine-tune and train all layers after starting with only the feature layers. If it still does not work, fully training all the layers will be the fallback.</p>
@@ -1839,7 +1871,7 @@ <h3><span class="section-number">35.1.2. </span>Transfer learning<a class="heade
 </div>
 </section>
 <section id="automl">
-<h3><span class="section-number">35.1.3. </span>AutoML<a class="headerlink" href="#automl" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.1.3. </span>AutoML<a class="headerlink" href="#automl" title="Permalink to this headline">#</a></h3>
 <p>Automated Machine Learning(AutoML) provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve the efficiency of Machine Learning and accelerate research on Machine Learning. Designing and tuning Machine Learning systems is a labor and time-intensive task, and also requires extensive expertise. AutoML is focused on automating the model selection and training process.</p>
 <p>As it is named, AutoML helps automate many aspects of Machine Learning model developments and training. It consists of a broader group of methodologies listed here:</p>
 <ul class="simple">
@@ -1857,13 +1889,13 @@ <h3><span class="section-number">35.1.3. </span>AutoML<a class="headerlink" href
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">35.2. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.2. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>In practice, it is challenging to detect if the model is properly learning without under-fitting or overfitting. Ideally, before moving to production, it is always necessary to evaluate the trained model to make sure that everything is working properly. A common approach is to divide the dataset into three parts - training set, validation set and test set.</p>
 <p>The model is trained by using only the train set, and the validation set is used to track the progress and conclude to optimize the model. Then the test set is used to evaluate the performance of the model. Using completely new data allows us to get an unbiased opinion on how well the algorithm works.</p>
 <figure class="align-default" id="recommended-method-of-deviding-the-dataset">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/recommended-method-of-deviding-the-dataset.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/recommended-method-of-deviding-the-dataset.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 35.3 </span><span class="caption-text"><a class="reference external" href="https://towardsdatascience.com/preventing-deep-neural-network-from-overfitting-953458db800a">Recommended method of dividing the data set</a></span><a class="headerlink" href="#recommended-method-of-deviding-the-dataset" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 38.3 </span><span class="caption-text"><a class="reference external" href="https://towardsdatascience.com/preventing-deep-neural-network-from-overfitting-953458db800a">Recommended method of dividing the data set</a></span><a class="headerlink" href="#recommended-method-of-deviding-the-dataset" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>There is no strict heuristic about how to split the dataset, especially when working with big data. The split ratios depend greatly on the specific problem and data volume. Generally speaking, the train vs. validation vs. test split should allow for:</p>
@@ -1873,7 +1905,7 @@ <h2><span class="section-number">35.2. </span>Model evaluation<a class="headerli
 </ul>
 <p>However, while evaluating a Machine Learning model can seem daunting, model metrics show where to start. The following sections discuss how to evaluate performance using metrics.</p>
 <section id="evaluate-quality-using-model-metrics">
-<h3><span class="section-number">35.2.1. </span>Evaluate quality using model metrics<a class="headerlink" href="#evaluate-quality-using-model-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.1. </span>Evaluate quality using model metrics<a class="headerlink" href="#evaluate-quality-using-model-metrics" title="Permalink to this headline">#</a></h3>
 <p>To evaluate your model’s quality, commonly-used metrics are:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://developers.google.com/machine-learning/crash-course/descending-into-ml/training-and-loss">loss</a></p></li>
@@ -1910,16 +1942,16 @@ <h3><span class="section-number">35.2.1. </span>Evaluate quality using model met
 <p>However, since the real world does not always go as what is imagined, it may be the case to try many metrics before finding one that could be satisfied with, and the metrics may change alongside the development, or even after getting the model into production.</p>
 </section>
 <section id="check-metrics-for-important-data-slices">
-<h3><span class="section-number">35.2.2. </span>Check metrics for important data slices<a class="headerlink" href="#check-metrics-for-important-data-slices" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.2. </span>Check metrics for important data slices<a class="headerlink" href="#check-metrics-for-important-data-slices" title="Permalink to this headline">#</a></h3>
 <p>After having a high-quality model, the model might still perform poorly on subsets of the data. For example, the unicorn predictor must predict well both in the Sahara desert and in New York City, and at all times of the day. However, there is less training data for the Sahara desert. Therefore, it is necessary to track model quality specifically for the Sahara desert. Such subsets of data, like the subset corresponding to the Sahara desert, are called <strong>data slices</strong>. Data slices should be separately monitored where performance is especially important or where the model might perform poorly.</p>
 <p>Use the understanding of the data to identify data slices of interest. Then compare model metrics for data slices against the metrics for the entire data set. Checking that the model performs across all data slices helps remove bias. For more, see <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/fairness/evaluating-for-bias">Fairness: Evaluating for Bias</a>.</p>
 </section>
 <section id="use-real-world-metrics">
-<h3><span class="section-number">35.2.3. </span>Use real-world metrics<a class="headerlink" href="#use-real-world-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.3. </span>Use real-world metrics<a class="headerlink" href="#use-real-world-metrics" title="Permalink to this headline">#</a></h3>
 <p>Model metrics do not necessarily measure the real-world impact of the model. For example, the AUC could be increased by changing a hyperparameter, but how did the change affect the user experience? To measure real-world impact, separate metrics need to be defined. Measuring real-world impact helps compare the quality of different iterations of the model.</p>
 </section>
 <section id="optimizing-and-satisfying-metrics">
-<h3><span class="section-number">35.2.4. </span>Optimizing and satisfying metrics<a class="headerlink" href="#optimizing-and-satisfying-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.4. </span>Optimizing and satisfying metrics<a class="headerlink" href="#optimizing-and-satisfying-metrics" title="Permalink to this headline">#</a></h3>
 <p>As models are getting bigger and more resource-intensive, how to scale the mode training becomes more and more important. Thinking beyond the above model metrics, the model utilitarian performance should also be considered. It includes the training speed, inference speed, model size, model stability, etc.</p>
 <p>To the given example <a class="reference external" href="https://github.com/ajaymache/machine-learning-yearning">ng2017mlyearning</a> below, both model accuracy and running time are important to decide which is the best classifier. It may not be natural to derive a single metric from them. Instead, a more real-world thinking-based strategy could be applied. The running time is important, but mostly it could be acceptable once under a certain value, such as 100ms. Whenever this condition is satisfied and the running time is good enough for production, then accuracy needs to be optimized with the best effort. Here, the running time is the satisfying metric and the accuracy is the optimizing metric.</p>
 <figure class="align-default">
@@ -1930,7 +1962,7 @@ <h3><span class="section-number">35.2.4. </span>Optimizing and satisfying metric
 </section>
 </section>
 <section id="model-debugging-improvement">
-<h2><span class="section-number">35.3. </span>Model debugging &amp; improvement<a class="headerlink" href="#model-debugging-improvement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.3. </span>Model debugging &amp; improvement<a class="headerlink" href="#model-debugging-improvement" title="Permalink to this headline">#</a></h2>
 <p>Once the model is working, the next step is to optimize the model’s quality for production readiness. Both debugging and optimizing are critical in the Machine Learning pipeline.</p>
 <p><strong>How is Machine Learning debugging different?</strong> Before diving into any particular Machine Learning debugging method, it is important to understand what differentiates debugging Machine Learning models from traditional software programs. Unlike the latter, an Machine Learning model with poor quality usually does not imply the presence of a bug. Instead, There could be many reasons to cause a model not to perform well. So that to debug poor performance in a model, a broader range of potential causes need to be investigated compared to traditional programming.</p>
 <p>For example, here are a few causes for poor model performance:</p>
@@ -1944,7 +1976,7 @@ <h2><span class="section-number">35.3. </span>Model debugging &amp; improvement<
 </ul>
 <p>Debugging Machine Learning models is complicated by the time it takes to run your experiments. Given the longer iteration cycles, and the larger error space, debugging Machine Learning models is uniquely challenging.</p>
 <section id="data-and-feature-debugging">
-<h3><span class="section-number">35.3.1. </span>Data and feature debugging<a class="headerlink" href="#data-and-feature-debugging" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.3.1. </span>Data and feature debugging<a class="headerlink" href="#data-and-feature-debugging" title="Permalink to this headline">#</a></h3>
 <p>Low-quality data will significantly affect your model’s performance. It’s much easier to detect low-quality data at input instead of guessing at its existence after the model predicts badly. Monitor the data by following the advice in this section.</p>
 <p><strong>Validate input data using rules.</strong></p>
 <p>To monitor the data, one approach is to write rules that the data must satisfy, and continuously check the data against the expected <a class="reference external" href="#data-quality">data quality</a>. This collection of rules is defined by following these steps:</p>
@@ -1979,7 +2011,7 @@ <h3><span class="section-number">35.3.1. </span>Data and feature debugging<a cla
 </ol>
 </section>
 <section id="model-debugging">
-<h3><span class="section-number">35.3.2. </span>Model debugging<a class="headerlink" href="#model-debugging" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.3.2. </span>Model debugging<a class="headerlink" href="#model-debugging" title="Permalink to this headline">#</a></h3>
 <p>After debugging the data, follow these steps to continue debugging the model.</p>
 <p><strong>Check that the model can predict labels.</strong></p>
 <p>Before debugging the model, try to determine whether the features encode predictive signals. Linear correlations could be found between individual features and labels by using correlation matrices.</p>
@@ -2029,7 +2061,7 @@ <h3><span class="section-number">35.3.2. </span>Model debugging<a class="headerl
 </table>
 </section>
 <section id="interpreting-loss-curves">
-<h3><span class="section-number">35.3.3. </span>Interpreting loss curves<a class="headerlink" href="#interpreting-loss-curves" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.3.3. </span>Interpreting loss curves<a class="headerlink" href="#interpreting-loss-curves" title="Permalink to this headline">#</a></h3>
 <p>Machine learning would be a breeze if all our <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/descending-into-ml/training-and-loss">loss curves</a> looked like this the first time we trained our model:</p>
 <figure class="align-default">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/metric-curve-ideal.svg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/metric-curve-ideal.svg" /></figure>
@@ -2096,25 +2128,25 @@ <h3><span class="section-number">35.3.3. </span>Interpreting loss curves<a class
 </section>
 </section>
 <section id="model-optimization">
-<h2><span class="section-number">35.4. </span>Model optimization<a class="headerlink" href="#model-optimization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.4. </span>Model optimization<a class="headerlink" href="#model-optimization" title="Permalink to this headline">#</a></h2>
 <p>Once the model is working, it’s time to optimize the model’s quality. Follow the steps below.</p>
 <section id="add-useful-features">
-<h3><span class="section-number">35.4.1. </span>Add useful features<a class="headerlink" href="#add-useful-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.4.1. </span>Add useful features<a class="headerlink" href="#add-useful-features" title="Permalink to this headline">#</a></h3>
 <p>The model performance could be improved by adding features that encode information not yet encoded by the existing features. Correlation matrices could be used to find linear correlations between individual features and labels. To detect nonlinear correlations between features and labels, the model must be trained with and without the feature, or combination of features, and check for an increase in model quality. The feature’s inclusion must be justified by an increase in model quality.</p>
 </section>
 <section id="tune-hyperparameters">
-<h3><span class="section-number">35.4.2. </span>Tune hyperparameters<a class="headerlink" href="#tune-hyperparameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.4.2. </span>Tune hyperparameters<a class="headerlink" href="#tune-hyperparameters" title="Permalink to this headline">#</a></h3>
 <p>Values of hyperparameters make your model work. However, these hyperparameter values can still be tuned. The values could be tuned manually by trial and error, but manual tuning is time-consuming. Instead, consider using an AutoML hyperparameter tuning service, such as <a class="reference external" href="https://cloud.google.com/ml-engine/docs/tensorflow/hyperparameter-tuning-overview">Google Cloud Machine Learning hyperparameter tuning</a>, <a class="reference external" href="https://github.com/awslabs/autogluon">Auto Gluon</a>, etc. With different sets of hyperparameters, the same model may perform drastically differently on the same dataset. Keep in mind that not all hyperparameters are created equal. A model could be more sensitive to one hyperparameter.</p>
 </section>
 <section id="tune-model-depth-and-width">
-<h3><span class="section-number">35.4.3. </span>Tune model depth and width<a class="headerlink" href="#tune-model-depth-and-width" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.4.3. </span>Tune model depth and width<a class="headerlink" href="#tune-model-depth-and-width" title="Permalink to this headline">#</a></h3>
 <p>While debugging the model, its depth and width are increased to improve the model performance In contrast, during model optimization, the mode depth and width could be either increased or decreased depending on the goals. If the model quality is adequate, then try reducing overfitting and training time by decreasing depth and width. Specifically, try halving the width at each successive layer. Since the model quality will also decrease, it is always a trade-off to balance quality with overfitting and training time.</p>
 <p>Conversely, if the goal is to have higher model quality, then try increasing depth and width. Remember that increases in depth and width are practically limited by accompanying increases in training time and overfitting. To understand overfitting.</p>
 <p>Since depth and width are hyperparameters, hyperparameter tuning could be used to optimize depth and width.</p>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">35.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Understanding the challenges in Machine Learning debugging by completing the <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html"><span class="doc std std-doc">Counterintuitive Challenges in ML Debugging</span></a>.</p>
 <p>Apply the debugging concepts learned by completing the following:</p>
 <ul class="simple">
@@ -2123,13 +2155,13 @@ <h2><span class="section-number">35.5. </span>Your turn! 🚀<a class="headerlin
 </ul>
 </section>
 <section id="self-study">
-<h2><span class="section-number">35.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://blog.statsbot.co/machine-learning-algorithms-183cc73197c">Machine Learning Algorithms: Which One to Choose for Your Problem</a> by Daniil Korbut, Stats and Bots, 2017.</p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">35.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Google for creating the open-source course <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging">Testing and Debugging in Machine Learning</a> which is licensed under <a class="reference external" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a>. It contributes to the majority of <a class="reference external" href="#model-evaluation">Model evaluation</a>, <a class="reference external" href="#model-deugging-improvement">Model debugging &amp; improvement</a>, <a class="reference external" href="#model-optimization">Model optimization</a>, and assignments.</p>
 <p>Thanks to <a class="reference external" href="https://github.com/chiphuyen">chiphuyen</a> for creating the <a class="reference external" href="https://huyenchip.com/machine-learning-systems-design/toc.html">Machine Learning Systems Design</a> which inspires some of the contents in this section.</p>
 </section>
@@ -2172,13 +2204,13 @@ <h2><span class="section-number">35.7. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">34. </span>Data engineering</p>
+            <p class="prev-next-title"><span class="section-number">37. </span>Data engineering</p>
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-        <p class="prev-next-title"><span class="section-number">36. </span>Model deployment</p>
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>32. Overview &#8212; Ocademy Open Machine Learning Book</title>
+    <title>35. Overview &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="33. Problem framing" href="problem-framing.html" />
-    <link rel="prev" title="31. Object detection" href="../deep-learning/object-detection.html" />
+    <link rel="next" title="36. Problem framing" href="problem-framing.html" />
+    <link rel="prev" title="34. Object detection" href="../deep-learning/object-detection.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
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  </li>
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
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     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
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     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
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+  <label for="toctree-checkbox-9">
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+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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    </li>
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     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-     16.2. Gradient boosting example
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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    </li>
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
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   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
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@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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   </a>
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   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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  </li>
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   <a class="reference internal" href="../deep-learning/autoencoder.html">
-   24. Autoencoder
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  </li>
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   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
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   </a>
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  </li>
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   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
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  </li>
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  </li>
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   <a class="reference internal" href="../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
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   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
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   <a class="reference internal" href="problem-framing.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
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-  <label for="toctree-checkbox-12">
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    </li>
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     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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    </li>
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     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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    </li>
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-     37.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
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     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
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     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
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     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
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-     37.21. Soda profits
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    <li class="toctree-l2">
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-     37.24. Data processing in Python
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    <li class="toctree-l2">
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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-     37.47. Loss Function
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-     37.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.116. Basic classification: Classify images of clothing
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+     41.11. Machine Learning overview
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-     38.16. Build an machine learning web application
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+     41.17. Unsupervised learning
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+   35.1. Problem framing
   </a>
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+     35.2.1. Data ingestion
     </a>
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     <a class="reference internal nav-link" href="#data-processing">
-     32.2.2. Data processing
+     35.2.2. Data processing
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   <a class="reference internal nav-link" href="#model-training-evaluation">
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+   35.3. Model training &amp; evaluation
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+     35.3.1. Model algorithm
     </a>
    </li>
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     <a class="reference internal nav-link" href="#model-testing">
-     32.3.2. Model testing
+     35.3.2. Model testing
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   </ul>
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+   35.5. Maintenance
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   <a class="reference internal nav-link" href="#your-turn">
-   32.6. Your turn! 🚀
+   35.6. Your turn! 🚀
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@@ -1606,61 +1638,61 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#problem-framing">
-   32.1. Problem framing
+   35.1. Problem framing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-engineering">
-   32.2. Data engineering
+   35.2. Data engineering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-ingestion">
-     32.2.1. Data ingestion
+     35.2.1. Data ingestion
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-processing">
-     32.2.2. Data processing
+     35.2.2. Data processing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-training-evaluation">
-   32.3. Model training &amp; evaluation
+   35.3. Model training &amp; evaluation
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-algorithm">
-     32.3.1. Model algorithm
+     35.3.1. Model algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-testing">
-     32.3.2. Model testing
+     35.3.2. Model testing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#deployment">
-   32.4. Deployment
+   35.4. Deployment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#maintenance">
-   32.5. Maintenance
+   35.5. Maintenance
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   32.6. Your turn! 🚀
+   35.6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   32.7. Self study
+   35.7. Self study
   </a>
  </li>
 </ul>
@@ -1685,7 +1717,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="overview">
-<h1><span class="section-number">32. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">35. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h1>
 <p>Moving Machine Learning models into production is as important as building them, sometimes even harder. Maintaining data quality and model accuracy over time are just a few of the challenges. To achieve end-to-end system productionization as a whole, the various components and designs need to be identified, from defining a problem to serving the model as a service.</p>
 <p>This chapter combines the foundational concepts of Machine Learning with the functional expertise of modern software development and engineering to help you develop production-ready Machine Learning knowledge.</p>
 <p>Productionization of a Machine Learning solution is not a one-time thing. It is always under improving one-time through the iterative process continuously.</p>
@@ -1704,12 +1736,12 @@ <h1><span class="section-number">32. </span>Overview<a class="headerlink" href="
 </ul>
 <figure class="align-default" id="machine-learning-lifecycle">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-lifecycle.svg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-lifecycle.svg" /><figcaption>
-<p><span class="caption-number">Fig. 32.1 </span><span class="caption-text">Machine Learning Lifecycle</span><a class="headerlink" href="#machine-learning-lifecycle" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.1 </span><span class="caption-text">Machine Learning Lifecycle</span><a class="headerlink" href="#machine-learning-lifecycle" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In the below sections, we will walk through the Machine Learning lifecycle components with a real-world example.</p>
 <section id="problem-framing">
-<h2><span class="section-number">32.1. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.1. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h2>
 <p>To bring a Machine Learning solution to production successfully, the first step is to define a valuable business objective and translate the objective into a Machine Learning solvable problem.</p>
 <p><strong><a class="reference external" href="https://en.wikipedia.org/wiki/COVID-19">COVID-19</a> Projections</strong> is an artificial intelligence solution to accurately forecast infections, deaths, and recovery timelines of the COVID-19/coronavirus pandemic in the US and globally. By the end of April 2020, it was cited by the Centers for Disease Control &amp; Prevention (CDC) as one of the first models to “help inform public health decision-making”.</p>
 <blockquote>
@@ -1755,9 +1787,9 @@ <h2><span class="section-number">32.1. </span>Problem framing<a class="headerlin
 </ol>
 </section>
 <section id="data-engineering">
-<h2><span class="section-number">32.2. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.2. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h2>
 <section id="data-ingestion">
-<h3><span class="section-number">32.2.1. </span>Data ingestion<a class="headerlink" href="#data-ingestion" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.2.1. </span>Data ingestion<a class="headerlink" href="#data-ingestion" title="Permalink to this headline">#</a></h3>
 <p>COVID-19 Projections Death Forecasting model uses the daily death total provided by <a class="reference external" href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series">Johns Hopkins CSSE</a>, which is considered by experts to be the “gold standard” reference data. It sometimes uses US testing data from the <a class="reference external" href="https://covidtracking.com/">COVID Tracking Project</a> in our research and graphs. Below is a piece of sample data from CSSE.</p>
 <div class="cell tag_output_scroll docutils container">
 <div class="cell_input docutils container">
@@ -1939,7 +1971,7 @@ <h3><span class="section-number">32.2.1. </span>Data ingestion<a class="headerli
 <p>The above data is publicly available, and able to be automatically and easily sourced by a web crawler. There are also other ingestion approaches, including synthetic data simulation and manual collecting.</p>
 </section>
 <section id="data-processing">
-<h3><span class="section-number">32.2.2. </span>Data processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.2.2. </span>Data processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h3>
 <p>Typical data processing includes data cleaning, labeling, feature engineering, and augmentation.</p>
 <p>For example, because the CSSE raw data may be noisy, a smoothing algorithm is first to smooth the data. For example, if a state reports 0 death on one day and 300 deaths the next day, the data is smoothed to show 150 deaths on each day. <a class="reference external" href="http://matlab.cheme.cmu.edu/2011/10/30/smooth-transitions-between-discontinuous-functions/#:~:text=Sigmoid%20functions,-A%20sigmoid%20function&amp;text=There%20is%20no%20formal%20justification,or%20from%201%20to%20zero.">Sigmoid Function</a> is used for this process as shown in below <a class="reference external" href="https://github.com/youyanggu/yyg-seir-simulator/blob/b511187a2d4273c92235fdb79017e7a6367e2f4c/region_model.py#L9">code snippet</a>.</p>
 <div class="cell docutils container">
@@ -1972,22 +2004,22 @@ <h3><span class="section-number">32.2.2. </span>Data processing<a class="headerl
 </section>
 </section>
 <section id="model-training-evaluation">
-<h2><span class="section-number">32.3. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.3. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h2>
 <section id="model-algorithm">
-<h3><span class="section-number">32.3.1. </span>Model algorithm<a class="headerlink" href="#model-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.3.1. </span>Model algorithm<a class="headerlink" href="#model-algorithm" title="Permalink to this headline">#</a></h3>
 <p>A <a class="reference external" href="https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search">Grid Search</a> based approach is used as the model algorithm, which is similar to the traditional way of performing hyperparameter optimization. Grid Search builds a model on each parameter combination possible. It iterates through every different combination and stores a model for each of them.</p>
 <p>The algorithm is built on top of an SEIR(susceptible-exposed-infectious-recovered) simulator. SEIR is a modified <a class="reference external" href="https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology">SIR</a> epidemiology model which is applied to the mathematical modeling of infectious diseases.</p>
 <figure class="align-default" id="seir-model">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/SEIR.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/SEIR.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 32.2 </span><span class="caption-text">SEIR Model</span><a class="headerlink" href="#seir-model" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.2 </span><span class="caption-text">SEIR Model</span><a class="headerlink" href="#seir-model" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The simulator generates infection and death data according to the pre-defined static parameters. By comparing the simulated data with real-world data, it gets the simulator performance under a certain parameter setting.</p>
 <figure class="align-default" id="death-forecasting-model-overview">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/death-forecasting-model-overview.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/death-forecasting-model-overview.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 32.3 </span><span class="caption-text"><a class="reference external" href="https://covid19-projections.com/model-details/">Death Forecasting Model Overview</a></span><a class="headerlink" href="#death-forecasting-model-overview" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.3 </span><span class="caption-text"><a class="reference external" href="https://covid19-projections.com/model-details/">Death Forecasting Model Overview</a></span><a class="headerlink" href="#death-forecasting-model-overview" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The training of the Death Forecasting model is basically a Grid Search process. It is found that the brute-force Grid Search method that iterates through the entire parameter space is the most effective in finding an optimal set of parameters. So if there are <span class="math notranslate nohighlight">\(10\)</span> values for one parameter and <span class="math notranslate nohighlight">\(10\)</span> values for another parameter. Then there are <span class="math notranslate nohighlight">\(100\)</span> different parameter combinations for those two parameters. For parameters not able to be estimated as lacking data, the values are considered all equally, resulting in a wider confidence interval.</p>
@@ -1998,41 +2030,41 @@ <h3><span class="section-number">32.3.1. </span>Model algorithm<a class="headerl
 <p>Based on such a strategy, the Grid Search algorithm searches the optimized SEIR simulator parameters from thousands of different combinations. Then the simulator with the optimized parameters could be used to predict the future death data.</p>
 </section>
 <section id="model-testing">
-<h3><span class="section-number">32.3.2. </span>Model testing<a class="headerlink" href="#model-testing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.3.2. </span>Model testing<a class="headerlink" href="#model-testing" title="Permalink to this headline">#</a></h3>
 <p>To evaluate the result, the trained Death Forecasting model has been compared with existing Machine Learning approaches, such as the popular <a class="reference external" href="https://covid19.healthdata.org/">model</a> developed by the <a class="reference external" href="https://en.wikipedia.org/wiki/Institute_for_Health_Metrics_and_Evaluation">Institute for Health Metrics and Evaluation (IHME)</a>. The latter is commonly referred to by the White House and the media. The detailed result could be found on the <a class="reference external" href="https://covid19-projections.com/about/#historical-performance">covid19-projections website</a>.</p>
 <figure class="align-default" id="evaluation-between-different-machine-learning-model-s-covid-19-death-projections">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-model-performance-evaluation.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-model-performance-evaluation.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 32.4 </span><span class="caption-text">Evaluation Between Different Machine Learning Model’s COVID-19 Death Projections</span><a class="headerlink" href="#evaluation-between-different-machine-learning-model-s-covid-19-death-projections" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.4 </span><span class="caption-text">Evaluation Between Different Machine Learning Model’s COVID-19 Death Projections</span><a class="headerlink" href="#evaluation-between-different-machine-learning-model-s-covid-19-death-projections" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The evaluation is more focused on accuracy. There are other <a class="reference external" href="https://mlinproduction.com/testing-machine-learning-models-deployment-series-07/">Test-driven Machine Learning development</a> methods, including unit testing, integration testing, etc.</p>
 <figure class="align-default" id="the-machine-learning-test-pyramid">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/test-pyramid.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/test-pyramid.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 32.5 </span><span class="caption-text">The <a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">Machine Learning Test Pyramid</a> from Martin Fowler</span><a class="headerlink" href="#the-machine-learning-test-pyramid" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.5 </span><span class="caption-text">The <a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">Machine Learning Test Pyramid</a> from Martin Fowler</span><a class="headerlink" href="#the-machine-learning-test-pyramid" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="deployment">
-<h2><span class="section-number">32.4. </span>Deployment<a class="headerlink" href="#deployment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.4. </span>Deployment<a class="headerlink" href="#deployment" title="Permalink to this headline">#</a></h2>
 <p>The model training itself is handled manually. Every day, raw daily projections for all 50 US states and select international countries will be uploaded onto the COVID-19 Projections <a class="reference external" href="https://pages.github.com/">GitHub Pages</a>. This will trigger the website building process, which is managed by Github Action and fully CI/CD.</p>
 <figure class="align-default" id="covid-19-projection-github-action">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-projection-github-action.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-projection-github-action.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 32.6 </span><span class="caption-text">COVID-19 Projection Github Action</span><a class="headerlink" href="#covid-19-projection-github-action" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.6 </span><span class="caption-text">COVID-19 Projection Github Action</span><a class="headerlink" href="#covid-19-projection-github-action" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The Machine Learning model could be also deployed on an embedded device or as a service. You could refer to <a class="reference external" href="https://aws.amazon.com/greengrass/ml/">AWS Greengrass</a> and <a class="reference external" href="https://aws.amazon.com/pm/sagemaker/">AWS SageMaker</a> to learn more.</p>
 </section>
 <section id="maintenance">
-<h2><span class="section-number">32.5. </span>Maintenance<a class="headerlink" href="#maintenance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.5. </span>Maintenance<a class="headerlink" href="#maintenance" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://www.forbes.com/sites/forbestechcouncil/2021/09/23/model-drift-in-data-analytics-what-is-it-so-what-now-what/?sh=54ce17194862">Model drift</a> refers to the degradation of model performance due to changes in data and relationships between input and output variables. In order to deal with model drift, continuous model monitoring is the key.</p>
 <p>The covid19-projection is a part-time project. During the author actively worked on it, the past performance is evaluated weekly. The <a class="reference external" href="https://covid19-projections.com/historical-performance/">historical performance</a> along with other models are presented in the <a class="reference external" href="https://github.com/reichlab/covid19-forecast-hub">COVID-19 Forecast Hub</a>, which is a website that maintains the authoritative, up-to-date record for forecasts of COVID-19 cases, deaths, and hospitalizations in the US. The latest forecasts from these models were sent to the CDC weekly and presented on the CDC COVID-19 Forecasting page. Also, the evaluation results were used to guide the model iteration for better consistency and accuracy.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">32.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>It is time to start your own Machine Learning project!</p>
 <p>In this session/assignment, you should come up with a Machine Learning Project idea. Everyone should, in the next session, present their idea with:</p>
 <ul class="simple">
@@ -2064,7 +2096,7 @@ <h2><span class="section-number">32.6. </span>Your turn! 🚀<a class="headerlin
 </div>
 </section>
 <section id="self-study">
-<h2><span class="section-number">32.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://ml-ops.org/">Machine Learning Operations</a></p></li>
 <li><p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment">MLOps: Model management, deployment, lineage, and monitoring with Azure Machine Learning</a></p></li>
@@ -2109,13 +2141,13 @@ <h2><span class="section-number">32.7. </span>Self study<a class="headerlink" hr
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">31. </span>Object detection</p>
+            <p class="prev-next-title"><span class="section-number">34. </span>Object detection</p>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">33. </span>Problem framing</p>
+        <p class="prev-next-title"><span class="section-number">36. </span>Problem framing</p>
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diff --git a/machine-learning-productionization/problem-framing.html b/machine-learning-productionization/problem-framing.html
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-    <title>33. Problem framing &#8212; Ocademy Open Machine Learning Book</title>
+    <title>36. Problem framing &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="34. Data engineering" href="data-engineering.html" />
-    <link rel="prev" title="32. Overview" href="overview.html" />
+    <link rel="next" title="37. Data engineering" href="data-engineering.html" />
+    <link rel="prev" title="35. Overview" href="overview.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
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  <li class="toctree-l1 has-children">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
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@@ -1526,32 +1558,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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+   36.1. Is Machine Learning the best choice?
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+   36.2. How easy to obtain the data?
   </a>
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   <a class="reference internal nav-link" href="#what-models-to-use">
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+   36.3. What models to use?
   </a>
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   <a class="reference internal nav-link" href="#how-to-put-a-model-into-production">
-   33.4. How to put a model into production?
+   36.4. How to put a model into production?
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   33.5. Your turn! 🚀
+   36.5. Your turn! 🚀
   </a>
  </li>
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+   36.6. Self study
   </a>
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@@ -1575,32 +1607,32 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#is-machine-learning-the-best-choice">
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+   36.1. Is Machine Learning the best choice?
   </a>
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   </a>
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+   36.3. What models to use?
   </a>
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   <a class="reference internal nav-link" href="#how-to-put-a-model-into-production">
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+   36.4. How to put a model into production?
   </a>
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+   36.5. Your turn! 🚀
   </a>
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   </a>
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@@ -1625,7 +1657,7 @@ <h2> Contents </h2>
 </div>
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-<h1><span class="section-number">33. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">36. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h1>
 <p><strong><a class="reference external" href="https://developers.google.com/machine-learning/problem-framing/problem-framing">Problem framing</a></strong> is the process of analyzing a problem to isolate the individual elements that need to be addressed to solve it. It helps you to determine the feasibility of your project, and clearly defines the project goals and success criteria. Under our context, problem framing is to dive deep into the business requirements to figure out a potential Machine Learning based technical solution.</p>
 <blockquote>
 <div><p>“Formal problem framing is the critical beginning for solving an ML problem, as it forces us to better understand both the problem and the data in order to design and build a bridge between them. “
@@ -1638,7 +1670,7 @@ <h1><span class="section-number">33. </span>Problem framing<a class="headerlink"
 </ul>
 <p>Before rushing into the Machine Learning implementation, let’s frame the problem by answering the below questions first.</p>
 <section id="is-machine-learning-the-best-choice">
-<h2><span class="section-number">33.1. </span>Is Machine Learning the best choice?<a class="headerlink" href="#is-machine-learning-the-best-choice" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.1. </span>Is Machine Learning the best choice?<a class="headerlink" href="#is-machine-learning-the-best-choice" title="Permalink to this headline">#</a></h2>
 <p>Machine Learning is not universally applicable. Similar to any other technology, it could only be used to solve certain problems.</p>
 <p><strong>What are the existing solutions?</strong> Machine Learning may be considered either when a problem is new, or existing non-Machine Learning solutions are not optimized. If it is the former, try solving the problem manually using a <a class="reference external" href="https://en.wikipedia.org/wiki/Heuristic">heuristic</a> approach. Otherwise, the existing non-Machine Learning solution could be leveraged as a benchmark to help you make the decision.</p>
 <p><strong>Is the error tolerable?</strong> Achieving 100% accuracy in Machine Learning predictions is challenging. Occasionally, the slight possibility of false negatives may be unacceptable. Consider Deep Learning’s success in expediting MRI reconstruction compared to conventional methods. Despite the significant enhancement in image quality, the issue of false-negative reconstructions persists and warrants improvement.</p>
@@ -1652,13 +1684,13 @@ <h2><span class="section-number">33.1. </span>Is Machine Learning the best choic
 </div>
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 <section id="how-easy-to-obtain-the-data">
-<h2><span class="section-number">33.2. </span>How easy to obtain the data?<a class="headerlink" href="#how-easy-to-obtain-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.2. </span>How easy to obtain the data?<a class="headerlink" href="#how-easy-to-obtain-the-data" title="Permalink to this headline">#</a></h2>
 <p>It is no doubt that data is the driving force of Machine Learning. Make sure you have the data required to train a model, before initiating the Machine Learning execution. Regardless of the challenge to gather data from your own company or real customers, the options below could be helpful.</p>
 <p><strong><a class="reference external" href="https://en.wikipedia.org/wiki/Synthetic_data">Synthetic data</a></strong>, referring to any production data relevant to a given situation that is not obtained through direct measurement, is typically generated by computer simulation. In today’s AI landscape, synthetic data is increasingly gaining importance and is even projected to surpass real data by 2030 (Gartner Inc.). <a class="reference external" href="https://en.wikipedia.org/wiki/Generative_adversarial_network">Generative Adversarial Networks</a>(GANs) stand out as one of the most popular frameworks for generating synthetic data. For instance, <a class="reference external" href="https://blogs.nvidia.com/blog/2020/04/23/ford-ai-data/">Ford has combined gaming engines and GANs to create synthetic data for AI training for self-driving</a>. Other approaches to generating synthetic data include statistical distribution fitting, rule-based procedural generation, benchmarks, simulations, and more.</p>
 <figure class="align-default" id="synthetic-data-will-completely-overshadow-real-data-in-ai-models">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/gartner-chart.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/gartner-chart.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 33.1 </span><span class="caption-text">Synthetic Data Will Completely Overshadow Real Data in AI Models(<a class="reference external" href="https://www.gartner.com/en/documents/4002912">https://www.gartner.com/en/documents/4002912</a>)</span><a class="headerlink" href="#synthetic-data-will-completely-overshadow-real-data-in-ai-models" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 36.1 </span><span class="caption-text">Synthetic Data Will Completely Overshadow Real Data in AI Models(<a class="reference external" href="https://www.gartner.com/en/documents/4002912">https://www.gartner.com/en/documents/4002912</a>)</span><a class="headerlink" href="#synthetic-data-will-completely-overshadow-real-data-in-ai-models" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1677,12 +1709,12 @@ <h2><span class="section-number">33.2. </span>How easy to obtain the data?<a cla
 </div>
 </section>
 <section id="what-models-to-use">
-<h2><span class="section-number">33.3. </span>What models to use?<a class="headerlink" href="#what-models-to-use" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.3. </span>What models to use?<a class="headerlink" href="#what-models-to-use" title="Permalink to this headline">#</a></h2>
 <p>This is one of the most widely discussed topics when people approach a Machine Learning solution. There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. The cheat sheet from Microsoft could be a helpful quick reference.</p>
 <figure class="align-default" id="machine-learning-algorithm-cheat-sheet">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-algorithm-cheat-sheet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-algorithm-cheat-sheet.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 33.2 </span><span class="caption-text"><a class="reference external" href="https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet">Machine Learning Algorithm Cheat Sheet</a></span><a class="headerlink" href="#machine-learning-algorithm-cheat-sheet" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 36.2 </span><span class="caption-text"><a class="reference external" href="https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet">Machine Learning Algorithm Cheat Sheet</a></span><a class="headerlink" href="#machine-learning-algorithm-cheat-sheet" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1704,17 +1736,17 @@ <h2><span class="section-number">33.3. </span>What models to use?<a class="heade
 </div>
 </section>
 <section id="how-to-put-a-model-into-production">
-<h2><span class="section-number">33.4. </span>How to put a model into production?<a class="headerlink" href="#how-to-put-a-model-into-production" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.4. </span>How to put a model into production?<a class="headerlink" href="#how-to-put-a-model-into-production" title="Permalink to this headline">#</a></h2>
 <p>Machine Learning inference is the process of inputting data into a model to calculate an output, which is also referred to as “putting a Machine Learning model into production”. There are two major types of inferences that may impact the feasibility of productionize the trained model.</p>
 <p><strong>Batch inference</strong> means that multiple predictions are requested periodically. The <a class="reference external" href="https://covid19-projections.com/">COVID-19 Projections</a> project is typically a batch inference example, which is mentioned in the previous section. Another good example is Facebook News feed generation, which is a complex and time-consuming artificial intelligent process. The News feeds are pre-generated regularly and stored in the cache. Then the web application could request and present the cached feeds in runtime.</p>
 <p><strong>Online inference</strong>, on the contrary, is the process to handle the prediction in real-time, synchronized, or from continuous data streaming. The online inference is suitable for more latency-sensitive user scenarios such as search engines and autonomous driving. And it requires speeding up both the inferring and input data sampling. It should be noted that the inference is usually just part of the end-to-end workflow of serving a request from the client side. It will be more challenging if the reference is on the critical path.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">33.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Apply the knowledge of this section to refine <a class="reference external" href="overview.html#your-turn">your own Machine Learning project proposal</a>.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">33.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://developers.google.com/machine-learning/problem-framing">Introduction to Machine Learning Problem Framing</a></p></li>
 </ul>
@@ -1758,13 +1790,13 @@ <h2><span class="section-number">33.6. </span>Self study<a class="headerlink" hr
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">32. </span>Overview</p>
+            <p class="prev-next-title"><span class="section-number">35. </span>Overview</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-engineering.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">34. </span>Data engineering</p>
+        <p class="prev-next-title"><span class="section-number">37. </span>Data engineering</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/clustering/clustering-models-for-machine-learning.html b/ml-advanced/clustering/clustering-models-for-machine-learning.html
index 78a78de4b6..1e399307d5 100644
--- a/ml-advanced/clustering/clustering-models-for-machine-learning.html
+++ b/ml-advanced/clustering/clustering-models-for-machine-learning.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
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-    <title>14. Clustering models for Machine Learning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>16. Clustering models for Machine Learning &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="14.1. Introduction to clustering" href="introduction-to-clustering.html" />
-    <link rel="prev" title="13.4. Applied Machine Learning : build a web app" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html" />
+    <link rel="next" title="16.1. Introduction to clustering" href="introduction-to-clustering.html" />
+    <link rel="prev" title="15.2. Gradient descent" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 current active has-children">
   <a class="current reference internal" href="#">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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    <li class="toctree-l2">
     <a class="reference internal" href="../ensemble-learning/bagging.html">
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+     17.1. Bagging
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    <li class="toctree-l2">
     <a class="reference internal" href="../ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
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-     15.3. Feature importance
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-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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    <li class="toctree-l2">
     <a class="reference internal" href="../gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
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-     16.2. Gradient boosting example
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     </a>
    </li>
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     <a class="reference internal" href="../gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
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     <a class="reference internal" href="../gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
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  <li class="toctree-l1">
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-     38.3. Python programming advanced
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     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
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     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
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+     41.5. Relational vs. non-relational database
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     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
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     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
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+     41.8. Data Science lifecycle
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     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
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     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
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-     38.19. Model Selection
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-     38.20. Convolutional Neural Network
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@@ -1538,19 +1570,19 @@ <h1>Clustering models for Machine Learning</h1>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="clustering-models-for-machine-learning">
-<h1><span class="section-number">14. </span>Clustering models for Machine Learning<a class="headerlink" href="#clustering-models-for-machine-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">16. </span>Clustering models for Machine Learning<a class="headerlink" href="#clustering-models-for-machine-learning" title="Permalink to this headline">#</a></h1>
 <p>Clustering is a machine learning task where it looks to find objects that resemble one another and group these into groups called clusters.  What differs clustering from other approaches in machine learning, is that things happen automatically, in fact, it’s fair to say it’s the opposite of supervised learning.</p>
 <p>Nigeria’s diverse audience has diverse musical tastes, let’s look at some music popular in Nigeria. This dataset includes data about various songs’ ‘danceability’ score, ‘acousticness’, loudness, ‘speechiness’, popularity and energy. It will be interesting to discover patterns in this data!</p>
 <figure class="align-default" id="a-turntable">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/turntable.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/turntable.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 14.1 </span><span class="caption-text">A turntable photographed by Marcela Laskoski on Unsplash</span><a class="headerlink" href="#a-turntable" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.1 </span><span class="caption-text">A turntable photographed by Marcela Laskoski on Unsplash</span><a class="headerlink" href="#a-turntable" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="introduction-to-clustering.html">14.1. Introduction to clustering</a></li>
-<li class="toctree-l1"><a class="reference internal" href="k-means-clustering.html">14.2. K-Means clustering</a></li>
+<li class="toctree-l1"><a class="reference internal" href="introduction-to-clustering.html">16.1. Introduction to clustering</a></li>
+<li class="toctree-l1"><a class="reference internal" href="k-means-clustering.html">16.2. K-Means clustering</a></li>
 </ul>
 </div>
 </section>
@@ -1588,17 +1620,17 @@ <h1><span class="section-number">14. </span>Clustering models for Machine Learni
                 
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 <div class='prev-next-area'>
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-            <p class="prev-next-title"><span class="section-number">13.4. </span>Applied Machine Learning : build a web app</p>
+            <p class="prev-next-title"><span class="section-number">15.2. </span>Gradient descent</p>
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-        <p class="prev-next-title"><span class="section-number">14.1. </span>Introduction to clustering</p>
+        <p class="prev-next-title"><span class="section-number">16.1. </span>Introduction to clustering</p>
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diff --git a/ml-advanced/clustering/introduction-to-clustering.html b/ml-advanced/clustering/introduction-to-clustering.html
index 3c40cae319..bcf2d775b5 100644
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+    <title>16.1. Introduction to clustering &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="prev" title="14. Clustering models for Machine Learning" href="clustering-models-for-machine-learning.html" />
+    <link rel="next" title="16.2. K-Means clustering" href="k-means-clustering.html" />
+    <link rel="prev" title="16. Clustering models for Machine Learning" href="clustering-models-for-machine-learning.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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-   12. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
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+ </li>
+ <li class="toctree-l1 has-children">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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   <ul>
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
+     40.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.71. Lasso and Ridge Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.73. Random forests intro and regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.74. Random forests for classification
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.77. Hyperparameter tuning gradient boosting
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.78. Gradient boosting
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.79. Boosting with tuning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
-     37.82. Data engineering
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.85. Case Study: Debugging in Regression
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     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
-     37.86. Study the solvers
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     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
-     37.87. Build classification models
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     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-model.html">
-     37.88. Build Classification Model
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
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    <li class="toctree-l2">
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-     38.16. Build an machine learning web application
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@@ -1526,44 +1558,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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+   16.1.2. Getting started with clustering
   </a>
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-   14.1.3. Clustering algorithms
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   </a>
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   <a class="reference internal nav-link" href="#exercise-cluster-your-data">
-   14.1.4. Exercise - cluster your data
+   16.1.4. Exercise - cluster your data
   </a>
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-     14.1.4.1. Exercise - data distribution
+     16.1.4.1. Exercise - data distribution
     </a>
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+   16.1.5. Your turn! 🚀
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+   16.1.6. Self study
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   </a>
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 </ul>
@@ -1587,44 +1619,44 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+   16.1.2. Getting started with clustering
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+   16.1.3. Clustering algorithms
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+   16.1.4. Exercise - cluster your data
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+     16.1.4.1. Exercise - data distribution
     </a>
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+   16.1.5. Your turn! 🚀
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+   16.1.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1638,10 +1670,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-clustering">
-<h1><span class="section-number">14.1. </span>Introduction to clustering<a class="headerlink" href="#introduction-to-clustering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">16.1. </span>Introduction to clustering<a class="headerlink" href="#introduction-to-clustering" title="Permalink to this headline">#</a></h1>
 <p>Clustering is a type of Unsupervised Learning that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.</p>
 <section id="introduction">
-<h2><span class="section-number">14.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In a professional setting, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans.</p>
 <p>Think a minute about how you might have encountered clustering ‘in the wild’, in a banking, e-commerce, or business setting.</p>
 <div class="admonition note">
@@ -1651,7 +1683,7 @@ <h2><span class="section-number">14.1.1. </span>Introduction<a class="headerlink
 <p>Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.</p>
 </section>
 <section id="getting-started-with-clustering">
-<h2><span class="section-number">14.1.2. </span>Getting started with clustering<a class="headerlink" href="#getting-started-with-clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.2. </span>Getting started with clustering<a class="headerlink" href="#getting-started-with-clustering" title="Permalink to this headline">#</a></h2>
 <p>Scikit-learn offers a large array of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-learn and their appropriate use cases:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1702,7 +1734,7 @@ <h2><span class="section-number">14.1.2. </span>Getting started with clustering<
 <figure class="align-default" id="flat-vs-nonflat-geometry-infographic">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/flat-nonflat.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/flat-nonflat.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 14.2 </span><span class="caption-text">Flat vs Nonflat Geometry Infographic by Dasani Madipalli</span><a class="headerlink" href="#flat-vs-nonflat-geometry-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.2 </span><span class="caption-text">Flat vs Nonflat Geometry Infographic by Dasani Madipalli</span><a class="headerlink" href="#flat-vs-nonflat-geometry-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p><strong>Distances</strong></p>
@@ -1714,7 +1746,7 @@ <h2><span class="section-number">14.1.2. </span>Getting started with clustering<
 <p>Data that is ‘noisy’ is considered to be ‘dense’. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or ‘crowded’ and thus this data needs to be analyzed with the appropriate clustering method. This article demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.</p>
 </section>
 <section id="clustering-algorithms">
-<h2><span class="section-number">14.1.3. </span>Clustering algorithms<a class="headerlink" href="#clustering-algorithms" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.3. </span>Clustering algorithms<a class="headerlink" href="#clustering-algorithms" title="Permalink to this headline">#</a></h2>
 <p>There are over 100 clustering algorithms, and their use depends on the nature of the data at hand. Let’s discuss some of the major ones:</p>
 <ul class="simple">
 <li><p><strong>Hierarchical clustering</strong>. If an object is classified by its proximity to a nearby object, rather than to one farther away, clusters are formed based on their members’ distance to and from other objects. Scikit-learn’s agglomerative clustering is hierarchical.</p></li>
@@ -1722,7 +1754,7 @@ <h2><span class="section-number">14.1.3. </span>Clustering algorithms<a class="h
 <figure class="align-default" id="hierarchical-clustering-infographic">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/hierarchical.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/hierarchical.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 14.3 </span><span class="caption-text">Hierarchical clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#hierarchical-clustering-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.3 </span><span class="caption-text">Hierarchical clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#hierarchical-clustering-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1731,7 +1763,7 @@ <h2><span class="section-number">14.1.3. </span>Clustering algorithms<a class="h
 <figure class="align-default" id="centroid-clustering-infographic">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/centroid.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/centroid.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 14.4 </span><span class="caption-text">Centroid clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#centroid-clustering-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.4 </span><span class="caption-text">Centroid clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#centroid-clustering-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1741,7 +1773,7 @@ <h2><span class="section-number">14.1.3. </span>Clustering algorithms<a class="h
 </ul>
 </section>
 <section id="exercise-cluster-your-data">
-<h2><span class="section-number">14.1.4. </span>Exercise - cluster your data<a class="headerlink" href="#exercise-cluster-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.4. </span>Exercise - cluster your data<a class="headerlink" href="#exercise-cluster-your-data" title="Permalink to this headline">#</a></h2>
 <p>Clustering as a technique is greatly aided by proper visualization, so let’s get started by visualizing our music data. This exercise will help us decide which of the methods of clustering we should most effectively use for the nature of this data.</p>
 <ol class="simple">
 <li><p>Open the <em>notebook.ipynb</em> file in this folder.</p></li>
@@ -2245,7 +2277,7 @@ <h2><span class="section-number">14.1.4. </span>Exercise - cluster your data<a c
 <p>Is there any convergence in this dataset around a song’s perceived popularity and danceability? A FacetGrid shows that there are concentric circles that line up, regardless of genre. Could it be that Nigerian tastes converge at a certain level of danceability for this genre?</p>
 <p>Try different datapoints (energy, loudness, speechiness) and more or different musical genres. What can you discover? Take a look at the <code class="docutils literal notranslate"><span class="pre">df.describe()</span></code> table to see the general spread of the data points.</p>
 <section id="exercise-data-distribution">
-<h3><span class="section-number">14.1.4.1. </span>Exercise - data distribution<a class="headerlink" href="#exercise-data-distribution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">16.1.4.1. </span>Exercise - data distribution<a class="headerlink" href="#exercise-data-distribution" title="Permalink to this headline">#</a></h3>
 <p>Are these three genres significantly different in the perception of their danceability, based on their popularity?</p>
 <ol class="simple">
 <li><p>Examine our top three genres data distribution for popularity and danceability along a given x and y axis.</p></li>
@@ -2295,11 +2327,11 @@ <h3><span class="section-number">14.1.4.1. </span>Exercise - data distribution<a
 </section>
 <hr class="docutils" />
 <section id="your-turn">
-<h2><span class="section-number">14.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">14.1.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Before you apply clustering algorithms, as we have learned, it’s a good idea to understand the nature of your dataset.There are several resources available.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html">right clustering algorithm</a></p></li>
@@ -2307,7 +2339,7 @@ <h2><span class="section-number">14.1.6. </span>Self study<a class="headerlink"
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">14.1.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">Machine Learning for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2349,13 +2381,13 @@ <h2><span class="section-number">14.1.7. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">14. </span>Clustering models for Machine Learning</p>
+            <p class="prev-next-title"><span class="section-number">16. </span>Clustering models for Machine Learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="k-means-clustering.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">14.2. </span>K-Means clustering</p>
+        <p class="prev-next-title"><span class="section-number">16.2. </span>K-Means clustering</p>
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diff --git a/ml-advanced/clustering/k-means-clustering.html b/ml-advanced/clustering/k-means-clustering.html
index 6e883f6f40..38da2ef5d4 100644
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>14.2. K-Means clustering &#8212; Ocademy Open Machine Learning Book</title>
+    <title>16.2. K-Means clustering &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="15. Getting started with ensemble learning" href="../ensemble-learning/getting-started-with-ensemble-learning.html" />
-    <link rel="prev" title="14.1. Introduction to clustering" href="introduction-to-clustering.html" />
+    <link rel="next" title="17. Getting started with ensemble learning" href="../ensemble-learning/getting-started-with-ensemble-learning.html" />
+    <link rel="prev" title="16.1. Introduction to clustering" href="introduction-to-clustering.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
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    <li class="toctree-l2">
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
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+     15.1. Loss function
+    </a>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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-     16.3. XGBoost
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  <li class="toctree-l1">
   <a class="reference internal" href="../kernel-method.html">
-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-     37.13. Apply your skills
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-     37.18. Small diabetes study
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-     37.21. Soda profits
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   14.2.9. Acknowledgments
+   16.2.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1668,7 +1700,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="k-means-clustering">
-<h1><span class="section-number">14.2. </span>K-Means clustering<a class="headerlink" href="#k-means-clustering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">16.2. </span>K-Means clustering<a class="headerlink" href="#k-means-clustering" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier section, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it’s the most common clustering technique. Let’s get started!</p>
 <p>Terms you will learn about:</p>
 <ul class="simple">
@@ -1678,13 +1710,13 @@ <h1><span class="section-number">14.2. </span>K-Means clustering<a class="header
 <li><p>Variance</p></li>
 </ul>
 <section id="introduction">
-<h2><span class="section-number">14.2.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>K-Means Clustering is a method derived from the domain of signal processing. It is used to divide and partition groups of data into ‘k’ clusters using a series of observations. Each observation works to group a given datapoint closest to its nearest ‘mean’, or the center point of a cluster.</p>
 <p>The clusters can be visualized as Voronoi diagrams, which include a point (or ‘seed’) and its corresponding region.</p>
 <figure class="align-default" id="voronoi-diagram">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/voronoi.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/voronoi.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 14.5 </span><span class="caption-text">voronoi diagram infographic by Jen Looper</span><a class="headerlink" href="#voronoi-diagram" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.5 </span><span class="caption-text">voronoi diagram infographic by Jen Looper</span><a class="headerlink" href="#voronoi-diagram" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The K-Means clustering process executes in a three-step process):</p>
@@ -1699,11 +1731,11 @@ <h2><span class="section-number">14.2.1. </span>Introduction<a class="headerlink
 <p>One drawback of using K-Means includes the fact that you will need to establish ‘k’, that is the number of centroids. Fortunately the  ‘elbow method’ helps to estimate a good starting value for ‘k’. You’ll try it in a minute.</p>
 </section>
 <section id="prerequisite">
-<h2><span class="section-number">14.2.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
 <p>You will work in this section’s <em>notebook.ipynb</em> file that includes the data import and preliminary cleaning you did in the last section.</p>
 </section>
 <section id="exercise-preparation">
-<h2><span class="section-number">14.2.3. </span>Exercise - preparation<a class="headerlink" href="#exercise-preparation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.3. </span>Exercise - preparation<a class="headerlink" href="#exercise-preparation" title="Permalink to this headline">#</a></h2>
 <p>Start by taking another look at the songs data.</p>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
@@ -1894,11 +1926,11 @@ <h2><span class="section-number">14.2.3. </span>Exercise - preparation<a class="
 </div>
 </section>
 <section id="silhouette-score">
-<h2><span class="section-number">14.2.4. </span>Silhouette score<a class="headerlink" href="#silhouette-score" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.4. </span>Silhouette score<a class="headerlink" href="#silhouette-score" title="Permalink to this headline">#</a></h2>
 <p>Look for a silhouette score closer to 1. This score varies from -1 to 1, and if the score is 1, the cluster is dense and well-separated from other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters.</p>
 <p>Our score is <strong>.53</strong>, so right in the middle. This indicates that our data is not particularly well-suited to this type of clustering, but let’s continue.</p>
 <section id="exercise-build-a-model">
-<h3><span class="section-number">14.2.4.1. </span>Exercise - build a model<a class="headerlink" href="#exercise-build-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">16.2.4.1. </span>Exercise - build a model<a class="headerlink" href="#exercise-build-a-model" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Import <code class="docutils literal notranslate"><span class="pre">KMeans</span></code> and start the clustering process.</p></li>
 </ol>
@@ -1925,7 +1957,7 @@ <h3><span class="section-number">14.2.4.1. </span>Exercise - build a model<a cla
 </div>
 </section>
 <section id="elbow-method">
-<h3><span class="section-number">14.2.4.2. </span>Elbow method<a class="headerlink" href="#elbow-method" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">16.2.4.2. </span>Elbow method<a class="headerlink" href="#elbow-method" title="Permalink to this headline">#</a></h3>
 <p>Previously, you surmised that, because you have targeted 3 song genres, you should choose 3 clusters. But is that the case?</p>
 <ol class="simple">
 <li><p>Use the ‘elbow method’ to make sure.</p></li>
@@ -1952,7 +1984,7 @@ <h3><span class="section-number">14.2.4.2. </span>Elbow method<a class="headerli
 </section>
 </section>
 <section id="exercise-display-the-clusters">
-<h2><span class="section-number">14.2.5. </span>Exercise - display the clusters<a class="headerlink" href="#exercise-display-the-clusters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.5. </span>Exercise - display the clusters<a class="headerlink" href="#exercise-display-the-clusters" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p>Try the process again, this time setting three clusters, and display the clusters as a scatterplot:</p></li>
 </ol>
@@ -1997,12 +2029,12 @@ <h2><span class="section-number">14.2.5. </span>Exercise - display the clusters<
 <figure class="align-default" id="problem-models">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/problems.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/problems.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 14.6 </span><span class="caption-text">roblem models by Scikit-learn</span><a class="headerlink" href="#problem-models" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.6 </span><span class="caption-text">roblem models by Scikit-learn</span><a class="headerlink" href="#problem-models" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="variance">
-<h2><span class="section-number">14.2.6. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.6. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h2>
 <p>Variance is defined as “the average of the squared differences from the Mean” . In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2010,11 +2042,11 @@ <h2><span class="section-number">14.2.6. </span>Variance<a class="headerlink" hr
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">14.2.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">14.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can use this tool to visualize sample data points and determine their centroids.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/">such as this one</a></p></li>
@@ -2022,7 +2054,7 @@ <h2><span class="section-number">14.2.8. </span>Self study<a class="headerlink"
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">14.2.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">Machine Learning for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2064,13 +2096,13 @@ <h2><span class="section-number">14.2.9. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">14.1. </span>Introduction to clustering</p>
+            <p class="prev-next-title"><span class="section-number">16.1. </span>Introduction to clustering</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ensemble-learning/getting-started-with-ensemble-learning.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">15. </span>Getting started with ensemble learning</p>
+        <p class="prev-next-title"><span class="section-number">17. </span>Getting started with ensemble learning</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/ensemble-learning/bagging.html b/ml-advanced/ensemble-learning/bagging.html
index 449a6ec139..a125231444 100644
--- a/ml-advanced/ensemble-learning/bagging.html
+++ b/ml-advanced/ensemble-learning/bagging.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>15.1. Bagging &#8212; Ocademy Open Machine Learning Book</title>
+    <title>17.1. Bagging &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="15.2. Random forest" href="random-forest.html" />
-    <link rel="prev" title="15. Getting started with ensemble learning" href="getting-started-with-ensemble-learning.html" />
+    <link rel="next" title="17.2. Random forest" href="random-forest.html" />
+    <link rel="prev" title="17. Getting started with ensemble learning" href="getting-started-with-ensemble-learning.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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    </i>
   </label>
   <ul class="current">
    <li class="toctree-l2 current active">
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-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
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     <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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@@ -1528,32 +1560,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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+   17.1.4. Out of bag error
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   <a class="reference internal nav-link" href="#your-turn">
-   15.1.5. Your turn! 🚀
+   17.1.5. Your turn! 🚀
   </a>
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-   15.1.6. Acknowledgments
+   17.1.6. Acknowledgments
   </a>
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 </ul>
@@ -1577,32 +1609,32 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+   17.1.4. Out of bag error
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+   17.1.5. Your turn! 🚀
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  </li>
 </ul>
@@ -1627,12 +1659,12 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="bagging">
-<h1><span class="section-number">15.1. </span>Bagging<a class="headerlink" href="#bagging" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">17.1. </span>Bagging<a class="headerlink" href="#bagging" title="Permalink to this headline">#</a></h1>
 <p>In previous sections, we explored different classification algorithms as well as techniques that can be used to properly validate and evaluate the quality of your models.</p>
 <p>Now, suppose that we have chosen the best possible model for a particular problem and are struggling to further improve its accuracy. In this case, we would need to apply some more advanced machine learning techniques that are collectively referred to as <em>ensembles</em>.</p>
 <p>An <em>ensemble</em> is a set of elements that collectively contribute to a whole. A familiar example is a musical ensemble, which blends the sounds of several musical instruments to create harmony, or architectural ensembles, which are a set of buildings designed as a unit. In ensembles, the (whole) harmonious outcome is more important than the performance of any individual part.</p>
 <section id="ensembles">
-<h2><span class="section-number">15.1.1. </span>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.1. </span>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Condorcet%27s_jury_theorem">Condorcet’s jury theorem</a> (1784) is about an ensemble in some sense. It states that, if each member of the jury makes an independent judgment and the probability of the correct decision by each juror is more than 0.5, then the probability of the correct decision by the whole jury increases with the total number of jurors and tends to one. On the other hand, if the probability of being right is less than 0.5 for each juror, then the probability of the correct decision by the whole jury decreases with the number of jurors and tends to zero.</p>
 <p>Let’s write an analytic expression for this theorem:</p>
 <ul class="simple">
@@ -1650,7 +1682,7 @@ <h2><span class="section-number">15.1.1. </span>Ensembles<a class="headerlink" h
 <p>A similar idea for error reduction was adopted in the field of Machine Learning.</p>
 </section>
 <section id="bootstrapping">
-<h2><span class="section-number">15.1.2. </span>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.2. </span>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Permalink to this headline">#</a></h2>
 <p><em>Bagging</em> (also known as <a class="reference external" href="https://en.wikipedia.org/wiki/Bootstrap_aggregating">Bootstrap aggregation</a>) is one of the first and most basic ensemble techniques. It was proposed by <a class="reference external" href="https://en.wikipedia.org/wiki/Leo_Breiman">Leo Breiman</a> in 1994. Bagging is based on the statistical method of <a class="reference external" href="https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29">bootstrapping</a>, which makes the evaluation of many statistics of complex models feasible.</p>
 <p>The bootstrap method goes as follows. Let there be a sample <span class="math notranslate nohighlight">\(\large X\)</span> of size <span class="math notranslate nohighlight">\(\large N\)</span>. We can make a new sample from the original sample by drawing <span class="math notranslate nohighlight">\(\large N\)</span> elements from the latter randomly and uniformly, with replacement. In other words, we select a random element from the original sample of size <span class="math notranslate nohighlight">\(\large N\)</span> and do this <span class="math notranslate nohighlight">\(\large N\)</span> times. All elements are equally likely to be selected, thus each element is drawn with the equal probability <span class="math notranslate nohighlight">\(\large \frac{1}{N}\)</span>.</p>
 <p>Let’s say we are drawing balls from a bag one at a time. At each step, the selected ball is put back into the bag so that the next selection is made equiprobably i.e. from the same number of balls <span class="math notranslate nohighlight">\(\large N\)</span>. Note that, because we put the balls back, there may be duplicates in the new sample. Let’s call this new sample <span class="math notranslate nohighlight">\(\large X_1\)</span>.</p>
@@ -1658,7 +1690,7 @@ <h2><span class="section-number">15.1.2. </span>Bootstrapping<a class="headerlin
 <figure class="align-default" id="process-of-bootstrap">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Process_of_bootstrap.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Process_of_bootstrap.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Process_of_bootstrap.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.1 </span><span class="caption-text">Process of bootstrap</span><a class="headerlink" href="#process-of-bootstrap" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.1 </span><span class="caption-text">Process of bootstrap</span><a class="headerlink" href="#process-of-bootstrap" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>For our example, we’ll use the familiar <code class="docutils literal notranslate"><span class="pre">telecom_churn</span></code> dataset. Previously, when we discussed feature importance, we saw that one of the most important features in this dataset is the number of calls to customer service. Let’s visualize the data and look at the distribution of this feature.</p>
@@ -1864,7 +1896,7 @@ <h2><span class="section-number">15.1.2. </span>Bootstrapping<a class="headerlin
 </div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7fdc5c6d6340&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7fcc886b2940&gt;
 </pre></div>
 </div>
 <img alt="../../_images/bagging_10_1.png" src="../../_images/bagging_10_1.png" />
@@ -1929,7 +1961,7 @@ <h2><span class="section-number">15.1.2. </span>Bootstrapping<a class="headerlin
 <p>For the interpretation of confidence intervals, you can address this <a class="reference external" href="https://www.graphpad.com/guides/prism/7/statistics/stat_more_about_confidence_interval.htm?toc=0&amp;printWindow">concise note</a> or any course on statistics. It’s not correct to say that a confidence interval contains 95% of values. Note that the interval for the loyal customers is narrower, which is reasonable since they make fewer calls (0, 1 or 2) in comparison with the churned clients who call until they are fed up and decide to switch providers.</p>
 </section>
 <section id="id1">
-<h2><span class="section-number">15.1.3. </span>Bagging<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.3. </span>Bagging<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <p>Now that you’ve grasped the idea of bootstrapping, we can move on to <em>bagging</em>.</p>
 <p>Suppose that we have a training set <span class="math notranslate nohighlight">\(\large X\)</span>. Using bootstrapping, we generate samples <span class="math notranslate nohighlight">\(\large X_1, \dots, X_M\)</span>. Now, for each bootstrap sample, we train its own classifier <span class="math notranslate nohighlight">\(\large a_i(x)\)</span>. The final classifier will average the outputs from all these individual classifiers. In the case of classification, this technique corresponds to voting:
 $<span class="math notranslate nohighlight">\(\large a(x) = \frac{1}{M}\sum_{i = 1}^M a_i(x).\)</span>$</p>
@@ -1937,7 +1969,7 @@ <h2><span class="section-number">15.1.3. </span>Bagging<a class="headerlink" hre
 <figure class="align-default" id="illustration-of-bagging">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Illustration_of_Bagging.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Illustration_of_Bagging.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Illustration_of_Bagging.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.2 </span><span class="caption-text">Illustration of Bagging</span><a class="headerlink" href="#illustration-of-bagging" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.2 </span><span class="caption-text">Illustration of Bagging</span><a class="headerlink" href="#illustration-of-bagging" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s consider a regression problem with base algorithms <span class="math notranslate nohighlight">\(\large b_1(x), \dots , b_n(x)\)</span>. Assume that there exists an ideal target function of true answers <span class="math notranslate nohighlight">\(\large y(x)\)</span> defined for all inputs and that the distribution <span class="math notranslate nohighlight">\(\large p(x)\)</span> is defined. We can then express the error for each regression function as follows:</p>
@@ -1977,7 +2009,7 @@ <h2><span class="section-number">15.1.3. </span>Bagging<a class="headerlink" hre
 <figure class="align-default" id="comparison-of-bagging-and-tree">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Comparison_of_Bagging_and_Tree.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Comparison_of_Bagging_and_Tree.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Comparison_of_Bagging_and_Tree.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.3 </span><span class="caption-text">Comparison of Bagging and Tree</span><a class="headerlink" href="#comparison-of-bagging-and-tree" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.3 </span><span class="caption-text">Comparison of Bagging and Tree</span><a class="headerlink" href="#comparison-of-bagging-and-tree" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The error for the decision tree:</p>
@@ -1991,7 +2023,7 @@ <h2><span class="section-number">15.1.3. </span>Bagging<a class="headerlink" hre
 <p>The example above is unlikely to be applicable to any real work. This is because we made a strong assumption that our individual errors are uncorrelated. More often than not, this is way too optimistic for real-world applications. When this assumption is false, the reduction in error will not be as significant. In the following sections, we will discuss some more sophisticated ensemble methods, which enable more accurate predictions in real-world problems.</p>
 </section>
 <section id="out-of-bag-error">
-<h2><span class="section-number">15.1.4. </span>Out of bag error<a class="headerlink" href="#out-of-bag-error" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.4. </span>Out of bag error<a class="headerlink" href="#out-of-bag-error" title="Permalink to this headline">#</a></h2>
 <p>In case of Random Forest, there is no need to use cross-validation or hold-out samples in order to get an unbiased error estimation. Why? Because, in ensemble techniques, the error estimation takes place internally.</p>
 <p>Random trees are constructed using different bootstrap samples of the original dataset. Approximately 37% of inputs are left out of a particular bootstrap sample and are not used in the construction of the <span class="math notranslate nohighlight">\(\large k\)</span>-th tree.</p>
 <p>This is easy to prove. Suppose there are <span class="math notranslate nohighlight">\(\large \ell\)</span> examples in our dataset. At each step, each data point has equal probability of ending up in a bootstrap sample with replacement, probability <span class="math notranslate nohighlight">\(\large\frac{1}{\ell}.\)</span> The probability that there is no such bootstrap sample that contains a particular dataset element (i.e. it has been omitted <span class="math notranslate nohighlight">\(\large \ell\)</span> times) equals <span class="math notranslate nohighlight">\(\large (1 - \frac{1}{\ell})^\ell\)</span>. When <span class="math notranslate nohighlight">\(\large \ell \rightarrow +\infty\)</span>, it becomes equal to the <a class="reference external" href="https://en.wikipedia.org/wiki/List_of_limits">Second Remarkable Limit</a> <span class="math notranslate nohighlight">\(\large \frac{1}{e}\)</span>. Then, the probability of selecting a specific example is <span class="math notranslate nohighlight">\(\large \approx  1 - \frac{1}{e} \approx 63\%\)</span>.</p>
@@ -1999,18 +2031,18 @@ <h2><span class="section-number">15.1.4. </span>Out of bag error<a class="header
 <figure class="align-default" id="principle-of-oobe">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Principle_of_OOBE.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Principle_of_OOBE.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Principle_of_OOBE.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.4 </span><span class="caption-text">Principle of OOBE</span><a class="headerlink" href="#principle-of-oobe" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.4 </span><span class="caption-text">Principle of OOBE</span><a class="headerlink" href="#principle-of-oobe" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The top part of the figure above represents our original dataset. We split it into the training (left) and test (right) sets. In the left image, we draw a grid that perfectly divides our dataset according to classes. Now, we use the same grid to estimate the share of the correct answers on our test set. We can see that our classifier gave incorrect answers in those 4 cases that have not been used during training (on the left). Hence, the accuracy of our classifier is <span class="math notranslate nohighlight">\(\large \frac{11}{15}*100\% = 73.33\%\)</span>.</p>
 <p>To sum up, each base algorithm is trained on <span class="math notranslate nohighlight">\(\large \approx 63\%\)</span> of the original examples. It can be validated on the remaining <span class="math notranslate nohighlight">\(\large \approx 37\%\)</span>. The Out-of-Bag estimate is nothing more than the mean estimate of the base algorithms on those <span class="math notranslate nohighlight">\(\large \approx 37\%\)</span> of inputs that were left out of training.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">15.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">15.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source content <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-5-ensembles-part-1-bagging/notebook">Ensembles and random forest</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2052,13 +2084,13 @@ <h2><span class="section-number">15.1.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">15. </span>Getting started with ensemble learning</p>
+            <p class="prev-next-title"><span class="section-number">17. </span>Getting started with ensemble learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="random-forest.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">15.2. </span>Random forest</p>
+        <p class="prev-next-title"><span class="section-number">17.2. </span>Random forest</p>
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     </a>
diff --git a/ml-advanced/ensemble-learning/feature-importance.html b/ml-advanced/ensemble-learning/feature-importance.html
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+    <title>17.3. Feature importance &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="16. Introduction to Gradient Boosting" href="../gradient-boosting/introduction-to-gradient-boosting.html" />
-    <link rel="prev" title="15.2. Random forest" href="random-forest.html" />
+    <link rel="next" title="18. Introduction to Gradient Boosting" href="../gradient-boosting/introduction-to-gradient-boosting.html" />
+    <link rel="prev" title="17.2. Random forest" href="random-forest.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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-     13.2. More classifiers
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-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
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+  <label for="toctree-checkbox-9">
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+  </label>
+  <ul>
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
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+   22. Model selection
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-   21. Convolutional Neural Networks
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-   22. Generative adversarial networks
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  <li class="toctree-l1 has-children">
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.7. Python programming basics
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-     37.14. Try it in Excel
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-     37.15. Dive into the beehive
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-     37.17. Classifying datasets
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-     37.18. Small diabetes study
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-     37.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
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     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
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     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
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     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
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-     37.112. DQN On Foreign Exchange Market
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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    </li>
    <li class="toctree-l2">
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
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    <li class="toctree-l2">
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-     38.19. Model Selection
+     41.19. Model Selection
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1528,39 +1560,39 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#intuition">
-   15.3.1. Intuition
+   17.3.1. Intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#permutation-importance">
-     15.3.1.1. Permutation importance
+     17.3.1.1. Permutation importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#illustrating-permutation-importance">
-   15.3.2. Illustrating permutation importance
+   17.3.2. Illustrating permutation importance
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sklearn-random-forest-feature-importance">
-   15.3.3. Sklearn Random Forest Feature Importance
+   17.3.3. Sklearn Random Forest Feature Importance
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#practical-example">
-   15.3.4. Practical example
+   17.3.4. Practical example
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   15.3.5. Your turn! 🚀
+   17.3.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   15.3.6. Acknowledgments
+   17.3.6. Acknowledgments
   </a>
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#intuition">
-   15.3.1. Intuition
+   17.3.1. Intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#permutation-importance">
-     15.3.1.1. Permutation importance
+     17.3.1.1. Permutation importance
     </a>
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-   15.3.2. Illustrating permutation importance
+   17.3.2. Illustrating permutation importance
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sklearn-random-forest-feature-importance">
-   15.3.3. Sklearn Random Forest Feature Importance
+   17.3.3. Sklearn Random Forest Feature Importance
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#practical-example">
-   15.3.4. Practical example
+   17.3.4. Practical example
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   15.3.5. Your turn! 🚀
+   17.3.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   15.3.6. Acknowledgments
+   17.3.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1641,22 +1673,22 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="feature-importance">
-<h1><span class="section-number">15.3. </span>Feature importance<a class="headerlink" href="#feature-importance" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">17.3. </span>Feature importance<a class="headerlink" href="#feature-importance" title="Permalink to this headline">#</a></h1>
 <p>It’s quite often that you want to make out the exact reasons of the algorithm outputting a particular answer. Or at the very least to find out which input features contributed most to the result. With Random Forest, you can obtain such information quite easily.</p>
 <section id="intuition">
-<h2><span class="section-number">15.3.1. </span>Intuition<a class="headerlink" href="#intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.1. </span>Intuition<a class="headerlink" href="#intuition" title="Permalink to this headline">#</a></h2>
 <p>From the picture below, it is intuitively clear that, in our credit scoring problem, <em>Age</em> is much more important than <em>Income</em>. This can be formally explained using the concept of <em>information gain</em>.</p>
 <figure class="align-default" id="example-of-credit-scoring-problem">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/feature-importance/Example_of_credit_scoring_problem.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/feature-importance/Example_of_credit_scoring_problem.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/feature-importance/Example_of_credit_scoring_problem.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.7 </span><span class="caption-text">Example of credit scoring problem</span><a class="headerlink" href="#example-of-credit-scoring-problem" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.7 </span><span class="caption-text">Example of credit scoring problem</span><a class="headerlink" href="#example-of-credit-scoring-problem" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In the case of many decision trees or a random forest, the closer the mean position of a feature over all the trees to the root, the more significant it is for a given classification or regression problem. Gains in the splitting criterion, such as the <em>Gini impurity</em>, obtained at each optimal split in every tree is a measure of importance that is directly associated with the splitting feature. The value of this score is distinct for each feature and accumulates over all the trees.</p>
 <p>Let’s go a little deeper into the details.</p>
 <p>There exist a lot of methods to assess feature importances. Leo Breinman in his works suggested to evaluate the importance of a variable by measuring decrease of accuracy of the forest when the variable is randomly permuted or decrease of impurity of a nodes where the given variable is used for splitting. The former method is often called <strong>permutation importance</strong>. The latter method is used in <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>.</p>
 <section id="permutation-importance">
-<h3><span class="section-number">15.3.1.1. </span>Permutation importance<a class="headerlink" href="#permutation-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.3.1.1. </span>Permutation importance<a class="headerlink" href="#permutation-importance" title="Permalink to this headline">#</a></h3>
 <p>Inspired by this <a class="reference external" href="https://www.researchgate.net/publication/5231126_Conditional_Variable_Importance_for_Random_Forests">article</a>.
 The average reduction in accuracy caused by a variable is determined during the calculation of the out-of-bag error. The greater the reduction in accuracy due to an exclusion or permutation of the variable, the higher its <em>importance score</em>. For this reason, variables with a greater average reduction in accuracy are generally more significant for classification.</p>
 <p>The rationale for calculating permutation importance is the following: By randomly permuting the predictor variable <span class="math notranslate nohighlight">\(X_j\)</span>, its original association with the response <span class="math notranslate nohighlight">\(Y\)</span> is broken. When the permuted variable <span class="math notranslate nohighlight">\(X_j\)</span>, together with all the others non-permuted variables, is used the response for the out-of-bag observations, the prediction <em>accuracy</em> decreases substantially if the original <span class="math notranslate nohighlight">\(X_j\)</span> was associated with response. Thus, as a measure of variable importance, the difference in prediction accuracy before and after permuting is used.</p>
@@ -1679,7 +1711,7 @@ <h3><span class="section-number">15.3.1.1. </span>Permutation importance<a class
 </section>
 </section>
 <section id="illustrating-permutation-importance">
-<h2><span class="section-number">15.3.2. </span>Illustrating permutation importance<a class="headerlink" href="#illustrating-permutation-importance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.2. </span>Illustrating permutation importance<a class="headerlink" href="#illustrating-permutation-importance" title="Permalink to this headline">#</a></h2>
 <p>Let’s assume that we have a toy dataset with 10 instances. Target variable can be either <strong>‘N’</strong> or <strong>‘P’</strong>.</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}\begin{array}{c|c|c|c|c|c|c|c|c|c}
@@ -1749,7 +1781,7 @@ <h2><span class="section-number">15.3.2. </span>Illustrating permutation importa
 <p>The above mentioned steps are to be done for each out-of-bag sample <span class="math notranslate nohighlight">\(\overline{\mathfrak{B}}^{(t)}\)</span>. To get not normalized <em>permutation importance</em> we sum all computed differences and divide by the number of trees. Normalization is done by dividing <em>not normalized permutation importance</em> by standard error.</p>
 </section>
 <section id="sklearn-random-forest-feature-importance">
-<h2><span class="section-number">15.3.3. </span>Sklearn Random Forest Feature Importance<a class="headerlink" href="#sklearn-random-forest-feature-importance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.3. </span>Sklearn Random Forest Feature Importance<a class="headerlink" href="#sklearn-random-forest-feature-importance" title="Permalink to this headline">#</a></h2>
 <p>Inspired by <a class="reference external" href="https://medium.com/&#64;srnghn/the-mathematics-of-decision-trees-random-forest-and-feature-importance-in-scikit-learn-and-spark-f2861df67e3">this</a> article.
 Sklearn library uses another approach to determine feature importance. The rationale for that method is that the more gain in information the node (with splitting feature <span class="math notranslate nohighlight">\(X_j\)</span>) provides, the higher its importance.</p>
 <p>The average reduction in the Gini impurity – or MSE for regression – represents the contribution of each feature to the homogeneity of nodes and leaves in the resulting Random Forest model. Each time a selected feature is used for splitting, the Gini impurity of the child nodes is calculated and compared with that of the original node.</p>
@@ -2011,7 +2043,7 @@ <h2><span class="section-number">15.3.3. </span>Sklearn Random Forest Feature Im
 <p>Voila!</p>
 </section>
 <section id="practical-example">
-<h2><span class="section-number">15.3.4. </span>Practical example<a class="headerlink" href="#practical-example" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.4. </span>Practical example<a class="headerlink" href="#practical-example" title="Permalink to this headline">#</a></h2>
 <p>Let’s consider the results of a survey given to visitors of hostels listed on <a class="reference external" href="http://Booking.com">Booking.com</a> and <a class="reference external" href="http://TripAdvisor.com">TripAdvisor.com</a>. Our features here are the average ratings for different categories including service quality, room condition, value for money, etc. Our target variable is the hostel’s overall rating on the website.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2083,11 +2115,11 @@ <h2><span class="section-number">15.3.4. </span>Practical example<a class="heade
 <p>The picture above shows that, more often than not, customers pay great attention to staff and the price-quality ratio. This couple of factors affects the resulting overall rating the most. The difference between these two features and other features is not very large, so we can conclude that exclusion of any of these features will lead to a reduction of model’s accuracy. However, based on our analysis, we can recommend hostel owners to focus primarily on staff training and price-to-quality ratio.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">15.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">15.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source content <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-5-ensembles-part-3-feature-importance/notebook">Ensembles and random forest</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2129,13 +2161,13 @@ <h2><span class="section-number">15.3.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">15.2. </span>Random forest</p>
+            <p class="prev-next-title"><span class="section-number">17.2. </span>Random forest</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../gradient-boosting/introduction-to-gradient-boosting.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">16. </span>Introduction to Gradient Boosting</p>
+        <p class="prev-next-title"><span class="section-number">18. </span>Introduction to Gradient Boosting</p>
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--- a/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html
+++ b/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>15. Getting started with ensemble learning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>17. Getting started with ensemble learning &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="15.1. Bagging" href="bagging.html" />
-    <link rel="prev" title="14.2. K-Means clustering" href="../clustering/k-means-clustering.html" />
+    <link rel="next" title="17.1. Bagging" href="bagging.html" />
+    <link rel="prev" title="16.2. K-Means clustering" href="../clustering/k-means-clustering.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
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-   15. Getting started with ensemble learning
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   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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-     15.2. Random forest
+     17.2. Random forest
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-     15.3. Feature importance
+     17.3. Feature importance
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-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
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+ <li class="toctree-l1">
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+ <li class="toctree-l1">
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   </a>
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   </a>
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     <a class="reference internal nav-link" href="#practice-with-random-forests-in-a-real-problem">
-     15.2.3.1. Practice with random forests in a real problem
+     17.2.3.1. Practice with random forests in a real problem
     </a>
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-   15.2.7. Similarities between Random Forest and k-Nearest Neighbors
+   17.2.7. Similarities between Random Forest and k-Nearest Neighbors
   </a>
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   <a class="reference internal nav-link" href="#transformation-of-a-dataset-into-a-high-dimensional-representation">
-   15.2.8. Transformation of a dataset into a high-dimensional representation
+   17.2.8. Transformation of a dataset into a high-dimensional representation
   </a>
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+   17.2.9. Pros and cons of random forests
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pros">
-     15.2.9.1. Pros:
+     17.2.9.1. Pros:
     </a>
    </li>
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     <a class="reference internal nav-link" href="#cons">
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     </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   15.2.10. Your turn! 🚀
+   17.2.10. Your turn! 🚀
   </a>
  </li>
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  </li>
 </ul>
@@ -1715,7 +1747,7 @@ <h2> Contents </h2>
 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="random-forest">
-<h1><span class="section-number">15.2. </span>Random forest<a class="headerlink" href="#random-forest" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">17.2. </span>Random forest<a class="headerlink" href="#random-forest" title="Permalink to this headline">#</a></h1>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Leo_Breiman">Leo Breiman</a> managed to apply bootstrapping not only in statistics but also in machine learning. He, along with Adel Cutler, extended and improved the random forest algorithm <a class="reference external" href="http://ect.bell-labs.com/who/tkh/publications/papers/odt.pdf">proposed by Tin Kam Ho</a>. They combined the construction of uncorrelated trees using <a class="reference external" href="https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29">CART</a>, bagging, and the <a class="reference external" href="https://en.wikipedia.org/wiki/Random_subspace_method">random subspace method</a>.</p>
 <p>Decision trees are a good choice for the base classifier in bagging because they are quite sophisticated and can achieve zero classification error on any sample. The random subspace method reduces the correlation between the trees and thus prevents overfitting. With bagging, the base algorithms are trained on different random subsets of the original feature set.</p>
 <p>The following algorithm constructs an ensemble of models using the random subspace method:</p>
@@ -1788,7 +1820,7 @@ <h1><span class="section-number">15.2. </span>Random forest<a class="headerlink"
 </div></div>
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 <section id="algorithm">
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+<h2><span class="section-number">17.2.1. </span>Algorithm<a class="headerlink" href="#algorithm" title="Permalink to this headline">#</a></h2>
 <p>The algorithm for constructing a random forest of <span class="math notranslate nohighlight">\(\large N\)</span> trees goes as follows:</p>
 <ul class="simple">
 <li><p>For each <span class="math notranslate nohighlight">\(\large k = 1, \dots, N\)</span>:</p>
@@ -1811,7 +1843,7 @@ <h2><span class="section-number">15.2.1. </span>Algorithm<a class="headerlink" h
 <p>You can see random forest as bagging of decision trees with the modification of selecting a random subset of features at each split.</p>
 </section>
 <section id="comparison-with-decision-trees-and-bagging">
-<h2><span class="section-number">15.2.2. </span>Comparison with Decision Trees and Bagging<a class="headerlink" href="#comparison-with-decision-trees-and-bagging" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.2. </span>Comparison with Decision Trees and Bagging<a class="headerlink" href="#comparison-with-decision-trees-and-bagging" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Disable warnings in Anaconda</span>
@@ -1996,7 +2028,7 @@ <h2><span class="section-number">15.2.2. </span>Comparison with Decision Trees a
 <p>Now, let’s investigate some parameters which can help us increase the model accuracy.</p>
 </section>
 <section id="parameters">
-<h2><span class="section-number">15.2.3. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.3. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h2>
 <p>The <a class="reference external" href="http://scikit-learn.org/stable/">scikit-learn library</a> implements random forests by providing two estimators: <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomForestRegressor</span></code>.</p>
 <p>The full list of random forest parameters for regression is shown below:</p>
 <ul class="simple">
@@ -2030,7 +2062,7 @@ <h2><span class="section-number">15.2.3. </span>Parameters<a class="headerlink"
 <li><p><code class="docutils literal notranslate"><span class="pre">max_depth</span></code> — the maximum depth of the tree.</p></li>
 </ul>
 <section id="practice-with-random-forests-in-a-real-problem">
-<h3><span class="section-number">15.2.3.1. </span>Practice with random forests in a real problem<a class="headerlink" href="#practice-with-random-forests-in-a-real-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.2.3.1. </span>Practice with random forests in a real problem<a class="headerlink" href="#practice-with-random-forests-in-a-real-problem" title="Permalink to this headline">#</a></h3>
 <p>In this example we will look at predicting customer churn. This is a classification problem, so we will use accuracy for model evaluation.</p>
 <p>First, let’s build a simple classifier which we will use as a baseline. For the sake of simplicity, we will use only numeric features.</p>
 <div class="cell docutils container">
@@ -2338,7 +2370,7 @@ <h3><span class="section-number">15.2.3.1. </span>Practice with random forests i
 </section>
 </section>
 <section id="variance-and-decorrelation">
-<h2><span class="section-number">15.2.4. </span>Variance and Decorrelation<a class="headerlink" href="#variance-and-decorrelation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.4. </span>Variance and Decorrelation<a class="headerlink" href="#variance-and-decorrelation" title="Permalink to this headline">#</a></h2>
 <p>Let’s write the variance of a random forest as</p>
 <div class="math notranslate nohighlight">
 \[\large Var f(x) = \rho(x)\sigma^2(x)\]</div>
@@ -2360,12 +2392,12 @@ <h2><span class="section-number">15.2.4. </span>Variance and Decorrelation<a cla
 <figure class="align-default" id="comparison-of-tree-and-random-forest">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.5 </span><span class="caption-text">Comparison of Tree and Random Forest</span><a class="headerlink" href="#comparison-of-tree-and-random-forest" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.5 </span><span class="caption-text">Comparison of Tree and Random Forest</span><a class="headerlink" href="#comparison-of-tree-and-random-forest" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="bias">
-<h2><span class="section-number">15.2.5. </span>Bias<a class="headerlink" href="#bias" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.5. </span>Bias<a class="headerlink" href="#bias" title="Permalink to this headline">#</a></h2>
 <p>Just as in bagging, the bias of a random forest is the same as the bias of a single tree <span class="math notranslate nohighlight">\(\large T(x,\Theta(Z))\)</span>:</p>
 <div class="math notranslate nohighlight">
 \[\begin{split} \large \begin{array}{rcl} Bias &amp;=&amp; \mu(x) - E_Z \, f_{rf}(x) \\
@@ -2373,13 +2405,13 @@ <h2><span class="section-number">15.2.5. </span>Bias<a class="headerlink" href="
 <p>In absolute value, the bias is usually higher than that of an unprunned tree because randomization and sample space reduction impose their own restrictions on the model. Therefore, the improvements in prediction accuracy obtained by bagging and random forests are solely the result of variance reduction.</p>
 </section>
 <section id="extremely-randomized-trees">
-<h2><span class="section-number">15.2.6. </span>Extremely Randomized Trees<a class="headerlink" href="#extremely-randomized-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.6. </span>Extremely Randomized Trees<a class="headerlink" href="#extremely-randomized-trees" title="Permalink to this headline">#</a></h2>
 <p>Extremely Randomized Trees employ a greater degree of randomization at the cut-point choice when splitting a tree node. As in random forests, a random subset of features is used. But, instead of the search for the optimal thresholds, their values are selected at random for each possible feature, and the best one among these randomly generated thresholds is used as the best rule to split the node. This usually trades off a slight reduction in the model variance with a small increase of the bias.</p>
 <p>In the <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> library, there are 2 implementations of Extremely Randomized Trees: <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier">ExtraTreesClassifier</a> and <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor">ExtraTreesRegressor</a>.</p>
 <p>This method should be used if you have greatly overfit with random forests or gradient boosting.</p>
 </section>
 <section id="similarities-between-random-forest-and-k-nearest-neighbors">
-<h2><span class="section-number">15.2.7. </span>Similarities between Random Forest and k-Nearest Neighbors<a class="headerlink" href="#similarities-between-random-forest-and-k-nearest-neighbors" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.7. </span>Similarities between Random Forest and k-Nearest Neighbors<a class="headerlink" href="#similarities-between-random-forest-and-k-nearest-neighbors" title="Permalink to this headline">#</a></h2>
 <p>The random forest method is similar to the nearest neighbors technique. Random forests predictions are based on labels of alike examples from the training set. The more often these examples appear in the same leaf of a tree, the higher their similarity. Let’s prove this formally.</p>
 <p>Let’s consider a regression problem with the quadratic loss function. Let <span class="math notranslate nohighlight">\(\large T_n(x)\)</span> be the number of the leaf of the <span class="math notranslate nohighlight">\(\large n\)</span>-th tree in a random forest with input <span class="math notranslate nohighlight">\(\large x\)</span>.
 The algorithm response for the input vector <span class="math notranslate nohighlight">\(\large x\)</span> equals the averaged response over all the examples of the training sample that fall into the leaf <span class="math notranslate nohighlight">\(\large T_n(x)\)</span>. This can be written as</p>
@@ -2398,20 +2430,20 @@ <h2><span class="section-number">15.2.7. </span>Similarities between Random Fore
 <figure class="align-default" id="similarity-between-random-forests-and-knn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.6 </span><span class="caption-text">Similarity between random forests and knn</span><a class="headerlink" href="#similarity-between-random-forests-and-knn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.6 </span><span class="caption-text">Similarity between random forests and knn</span><a class="headerlink" href="#similarity-between-random-forests-and-knn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="transformation-of-a-dataset-into-a-high-dimensional-representation">
-<h2><span class="section-number">15.2.8. </span>Transformation of a dataset into a high-dimensional representation<a class="headerlink" href="#transformation-of-a-dataset-into-a-high-dimensional-representation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.8. </span>Transformation of a dataset into a high-dimensional representation<a class="headerlink" href="#transformation-of-a-dataset-into-a-high-dimensional-representation" title="Permalink to this headline">#</a></h2>
 <p>Random forests are mostly used in supervised learning, but there is a way to apply them in the unsupervised setting.</p>
 <p>Using the <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> method <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding">RandomTreesEmbedding</a>, we can transform our dataset into a high-dimensional, <a class="reference external" href="https://en.wikipedia.org/wiki/Sparse_matrix">sparse</a> representation. We first build extremely randomized trees and then use the index of the leaf containing the example as a new feature.</p>
 <p>For example, if the input appears in the first leaf, we assign <span class="math notranslate nohighlight">\(1\)</span> as the feature value; if not, we assign <span class="math notranslate nohighlight">\(0\)</span>. This is a so-called <em>binary coding</em>. We can control the number of features and the sparseness of data by increasing or decreasing the number of trees and their depth. Because nearby data points are likely to fall into the same leaf, this transformation provides an implicit nonparametric estimate of their density.</p>
 </section>
 <section id="pros-and-cons-of-random-forests">
-<h2><span class="section-number">15.2.9. </span>Pros and cons of random forests<a class="headerlink" href="#pros-and-cons-of-random-forests" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.9. </span>Pros and cons of random forests<a class="headerlink" href="#pros-and-cons-of-random-forests" title="Permalink to this headline">#</a></h2>
 <section id="pros">
-<h3><span class="section-number">15.2.9.1. </span>Pros:<a class="headerlink" href="#pros" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.2.9.1. </span>Pros:<a class="headerlink" href="#pros" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>High prediction accuracy; will perform better than linear algorithms in most problems; the accuracy is comparable with that of boosting.</p></li>
 <li><p>Robust to outliers, thanks to random sampling.</p></li>
@@ -2429,7 +2461,7 @@ <h3><span class="section-number">15.2.9.1. </span>Pros:<a class="headerlink" hre
 </ul>
 </section>
 <section id="cons">
-<h3><span class="section-number">15.2.9.2. </span>Cons:<a class="headerlink" href="#cons" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.2.9.2. </span>Cons:<a class="headerlink" href="#cons" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>In comparison with a single decision tree, Random Forest’s output is more difficult to interpret.</p></li>
 <li><p>There are no formal p-values for feature significance estimation.</p></li>
@@ -2443,11 +2475,11 @@ <h3><span class="section-number">15.2.9.2. </span>Cons:<a class="headerlink" hre
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">15.2.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">15.2.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source content <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-5-ensembles-part-2-random-forest/notebook">Ensembles and random forest</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2489,13 +2521,13 @@ <h2><span class="section-number">15.2.11. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">15.1. </span>Bagging</p>
+            <p class="prev-next-title"><span class="section-number">17.1. </span>Bagging</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="feature-importance.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">15.3. </span>Feature importance</p>
+        <p class="prev-next-title"><span class="section-number">17.3. </span>Feature importance</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/gradient-boosting/gradient-boosting-example.html b/ml-advanced/gradient-boosting/gradient-boosting-example.html
index 118f3fa870..fc03b42b93 100644
--- a/ml-advanced/gradient-boosting/gradient-boosting-example.html
+++ b/ml-advanced/gradient-boosting/gradient-boosting-example.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>16.2. Gradient boosting example &#8212; Ocademy Open Machine Learning Book</title>
+    <title>18.2. Gradient boosting example &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="16.3. XGBoost" href="xgboost.html" />
-    <link rel="prev" title="16.1. Gradient Boosting" href="gradient-boosting.html" />
+    <link rel="next" title="18.3. XGBoost" href="xgboost.html" />
+    <link rel="prev" title="18.1. Gradient Boosting" href="gradient-boosting.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   14. Clustering models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    <li class="toctree-l2">
     <a class="reference internal" href="../clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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-   15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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-     15.1. Bagging
+     17.1. Bagging
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-     15.2. Random forest
+     17.2. Random forest
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-     15.3. Feature importance
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-   16. Introduction to Gradient Boosting
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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-     16.3. XGBoost
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     </a>
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-     16.4. XGBoost + k-fold CV + Feature Importance
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     </a>
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+   19. Unsupervised learning
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   22. Generative adversarial networks
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+   26. Recurrent Neural Networks
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+   27. Autoencoder
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-   25. Long-short term memory
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-   27. Deep Q-learning
+   30. Deep Q-learning
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-   29. Image segmentation
+   32. Image segmentation
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-   30. Diffusion Model
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+   34. Object detection
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@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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-   36. Model deployment
+   39. Model deployment
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@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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-     37.115. Generative Adversarial Networks (GANs)
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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+     41.21. Generative Adversarial Network
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-gradient-boosting">
-   16.2.1. What is gradient boosting?
+   18.2.1. What is gradient boosting?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementing-gradient-boosting">
-   16.2.2. Implementing Gradient boosting
+   18.2.2. Implementing Gradient boosting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   16.2.3. Your turn! 🚀
+   18.2.3. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   16.2.4. Acknowledgments
+   18.2.4. Acknowledgments
   </a>
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 </ul>
@@ -1567,22 +1599,22 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-gradient-boosting">
-   16.2.1. What is gradient boosting?
+   18.2.1. What is gradient boosting?
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementing-gradient-boosting">
-   16.2.2. Implementing Gradient boosting
+   18.2.2. Implementing Gradient boosting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   16.2.3. Your turn! 🚀
+   18.2.3. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   16.2.4. Acknowledgments
+   18.2.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1607,9 +1639,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="gradient-boosting-example">
-<h1><span class="section-number">16.2. </span>Gradient boosting example<a class="headerlink" href="#gradient-boosting-example" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.2. </span>Gradient boosting example<a class="headerlink" href="#gradient-boosting-example" title="Permalink to this headline">#</a></h1>
 <section id="what-is-gradient-boosting">
-<h2><span class="section-number">16.2.1. </span>What is gradient boosting?<a class="headerlink" href="#what-is-gradient-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.2.1. </span>What is gradient boosting?<a class="headerlink" href="#what-is-gradient-boosting" title="Permalink to this headline">#</a></h2>
 <p>If you’re inside the world of machine learning, it’s for sure you have heard about gradient boosting algorithms such as xgboost or lightgbm. Indeed, gradient boosting represents the state-of-start for a lot of machine learning task, but how does it work? We’ll try to answer this question specifically for the case of gradient boosting for trees which is the most popular case up today</p>
 <p>For me, understanding gradient boosting is all about undestarding its link to gradient descent. Remember gradient descent is the algorithm to minimize a loss function <span class="math notranslate nohighlight">\(L(\theta)\)</span> by sustracting the gradient to the parameters</p>
 <div class="math notranslate nohighlight">
@@ -1626,7 +1658,7 @@ <h2><span class="section-number">16.2.1. </span>What is gradient boosting?<a cla
 <p>This equation would be the <em>gradient descent</em> version of trees. Maybe this is nothing new for you, but I hope it gives you a better or new way of seeing gradient boosting.</p>
 </section>
 <section id="implementing-gradient-boosting">
-<h2><span class="section-number">16.2.2. </span>Implementing Gradient boosting<a class="headerlink" href="#implementing-gradient-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.2.2. </span>Implementing Gradient boosting<a class="headerlink" href="#implementing-gradient-boosting" title="Permalink to this headline">#</a></h2>
 <p>Now let’s get our hands on some data and see a woking example</p>
 <div class="cell docutils container">
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@@ -1971,11 +2003,11 @@ <h2><span class="section-number">16.2.2. </span>Implementing Gradient boosting<a
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">16.2.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.2.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">16.2.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.2.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Alexis Moraga for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/senoratiramisu/understanding-gradient-boosting">Understanding Gradient Boosting</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2017,13 +2049,13 @@ <h2><span class="section-number">16.2.4. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">16.1. </span>Gradient Boosting</p>
+            <p class="prev-next-title"><span class="section-number">18.1. </span>Gradient Boosting</p>
         </div>
     </a>
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-        <p class="prev-next-title"><span class="section-number">16.3. </span>XGBoost</p>
+        <p class="prev-next-title"><span class="section-number">18.3. </span>XGBoost</p>
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     </a>
diff --git a/ml-advanced/gradient-boosting/gradient-boosting.html b/ml-advanced/gradient-boosting/gradient-boosting.html
index 65f7c5a844..c94689b2c8 100644
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-    <title>16.1. Gradient Boosting &#8212; Ocademy Open Machine Learning Book</title>
+    <title>18.1. Gradient Boosting &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="16.2. Gradient boosting example" href="gradient-boosting-example.html" />
-    <link rel="prev" title="16. Introduction to Gradient Boosting" href="introduction-to-gradient-boosting.html" />
+    <link rel="next" title="18.2. Gradient boosting example" href="gradient-boosting-example.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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  </li>
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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    </li>
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@@ -1528,78 +1560,78 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   18.1.1. Introduction and history of boosting
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+     18.1.1.1. History of GBM
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+     18.1.2.1. ML problem statement
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     <a class="reference internal nav-link" href="#functional-gradient-descent">
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+     18.1.2.2. Functional gradient descent
     </a>
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-     16.1.2.3. Friedman’s classic GBM algorithm
+     18.1.2.3. Friedman’s classic GBM algorithm
     </a>
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     <a class="reference internal nav-link" href="#step-by-step-example-how-gbm-works">
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+     18.1.2.4. Step-By-Step example: How GBM Works
     </a>
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                             <ul class="visible nav section-nav flex-column">
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+   18.1.1. Introduction and history of boosting
   </a>
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+     18.1.1.1. History of GBM
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   </ul>
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-   16.1.2. GBM algorithm
+   18.1.2. GBM algorithm
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ml-problem-statement">
-     16.1.2.1. ML problem statement
+     18.1.2.1. ML problem statement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#functional-gradient-descent">
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+     18.1.2.2. Functional gradient descent
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#friedmans-classic-gbm-algorithm">
-     16.1.2.3. Friedman’s classic GBM algorithm
+     18.1.2.3. Friedman’s classic GBM algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#step-by-step-example-how-gbm-works">
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+     18.1.2.4. Step-By-Step example: How GBM Works
     </a>
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+     18.1.3.1. Regression loss functions
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+     18.1.3.2. Classification loss functions
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    </li>
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+   18.1.5. Your turn! 🚀
   </a>
  </li>
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+   18.1.6. Acknowledgments
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 </ul>
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 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="gradient-boosting">
-<h1><span class="section-number">16.1. </span>Gradient Boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.1. </span>Gradient Boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
 <figure class="align-default" id="boosting">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Boosting.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Boosting.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Boosting.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.2 </span><span class="caption-text">Boosting</span><a class="headerlink" href="#boosting" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.2 </span><span class="caption-text">Boosting</span><a class="headerlink" href="#boosting" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Today we are going to have a look at one of the most popular and practical machine learning algorithms: gradient boosting.
@@ -1732,7 +1764,7 @@ <h1><span class="section-number">16.1. </span>Gradient Boosting<a class="headerl
 <iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/Gradient-boosting/index.html" width="105%" height="700px;" style="border:none;"  scrolling="yes"></iframe>
 A demo of Gradient Boosting. <a href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/Gradient-boosting/index.html">[source]</a>
 </p><section id="introduction-and-history-of-boosting">
-<h2><span class="section-number">16.1.1. </span>Introduction and history of boosting<a class="headerlink" href="#introduction-and-history-of-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.1. </span>Introduction and history of boosting<a class="headerlink" href="#introduction-and-history-of-boosting" title="Permalink to this headline">#</a></h2>
 <p>Almost everyone in machine learning has heard about gradient boosting. Many data scientists include this algorithm in their data scientist’s toolbox because of the good results it yields on any given (unknown) problem.</p>
 <p>Furthermore, XGBoost is often the standard recipe for <a class="reference external" href="https://github.com/dmlc/xgboost/blob/master/demo/README.md#usecases">winning</a> <a class="reference external" href="http://blog.kaggle.com/tag/xgboost/">ML competitions</a>. It is so popular that the idea of stacking XGBoosts has become a meme. Moreover, boosting is an important component in <a class="reference external" href="https://en.wikipedia.org/wiki/Learning_to_rank#Practical_usage_by_search_engines">many recommender systems</a>; sometimes, it is even considered a <a class="reference external" href="https://yandex.com/company/technologies/matrixnet/">brand</a>.
 Let’s look at the history and development of boosting.</p>
@@ -1745,14 +1777,14 @@ <h2><span class="section-number">16.1.1. </span>Introduction and history of boos
 <figure class="align-default" id="initial-iterative-process">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initial_iterative_process.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initial_iterative_process.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initial_iterative_process.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.3 </span><span class="caption-text">Initial iterative process</span><a class="headerlink" href="#initial-iterative-process" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.3 </span><span class="caption-text">Initial iterative process</span><a class="headerlink" href="#initial-iterative-process" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The size of point corresponds to its weight, which was assigned for an incorrect prediction.  On each iteration, we can see that these weights are growing – the stumps cannot cope with this problem. Although, if we take a weighted vote for the stumps, we will get the correct classifications:</p>
 <figure class="align-default" id="weighting-the-stump">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Weighting_the_stump"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Weighting_the_stump" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Weighting_the_stump" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.4 </span><span class="caption-text">Weighting the stump</span><a class="headerlink" href="#weighting-the-stump" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.4 </span><span class="caption-text">Weighting the stump</span><a class="headerlink" href="#weighting-the-stump" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Pseudocode:</p>
@@ -1778,11 +1810,11 @@ <h2><span class="section-number">16.1.1. </span>Introduction and history of boos
 <figure class="align-default" id="comparison-between-various-boost-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Comparison_between_various_boost_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Comparison_between_various_boost_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Comparison_between_various_boost_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.5 </span><span class="caption-text">Comparison between various boost function</span><a class="headerlink" href="#comparison-between-various-boost-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.5 </span><span class="caption-text">Comparison between various boost function</span><a class="headerlink" href="#comparison-between-various-boost-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="history-of-gbm">
-<h3><span class="section-number">16.1.1.1. </span>History of GBM<a class="headerlink" href="#history-of-gbm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.1.1. </span>History of GBM<a class="headerlink" href="#history-of-gbm" title="Permalink to this headline">#</a></h3>
 <p>It took more than 10 years after the introduction of GBM for it to become an essential part of the data science toolbox.<br />
 GBM was extended to apply to different statistics problems: GLMboost and GAMboost for strengthening already existing GAM models, CoxBoost for survival curves, and RankBoost and LambdaMART for ranking.<br />
 Many realizations of GBM also appeared under different names and on different platforms: Stochastic GBM, GBDT (Gradient Boosted Decision Trees), GBRT (Gradient Boosted Regression Trees), MART (Multiple Additive Regression Trees), and more. In addition, the ML community was very segmented and dissociated, which made it hard to track just how widespread boosting had become.</p>
@@ -1792,14 +1824,14 @@ <h3><span class="section-number">16.1.1.1. </span>History of GBM<a class="header
 </section>
 </section>
 <section id="gbm-algorithm">
-<h2><span class="section-number">16.1.2. </span>GBM algorithm<a class="headerlink" href="#gbm-algorithm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.2. </span>GBM algorithm<a class="headerlink" href="#gbm-algorithm" title="Permalink to this headline">#</a></h2>
 <section id="ml-problem-statement">
-<h3><span class="section-number">16.1.2.1. </span>ML problem statement<a class="headerlink" href="#ml-problem-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.1. </span>ML problem statement<a class="headerlink" href="#ml-problem-statement" title="Permalink to this headline">#</a></h3>
 <p>We are going to solve the problem of function approximation in a general supervised learning setting. We have a set of features <span class="math notranslate nohighlight">\( \large x \)</span> and target variables <span class="math notranslate nohighlight">\(\large y, \large \left\{ (x_i, y_i) \right\}_{i=1, \ldots,n}\)</span> which we use to restore the dependence <span class="math notranslate nohighlight">\(\large y = f(x) \)</span>. We restore the dependence by approximating <span class="math notranslate nohighlight">\( \large \hat{f}(x) \)</span> and by understanding which approximation is better when we use the loss function <span class="math notranslate nohighlight">\( \large L(y,f) \)</span>, which we want to minimize: <span class="math notranslate nohighlight">\( \large y \approx \hat{f}(x), \large \hat{f}(x) = \underset{f(x)}{\arg\min} \ L(y,f(x)) \)</span></p>
 <figure class="align-default" id="find-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Find_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Find_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Find_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.6 </span><span class="caption-text">Find function</span><a class="headerlink" href="#find-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.6 </span><span class="caption-text">Find function</span><a class="headerlink" href="#find-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>At this moment, we do not make any assumptions regarding the type of dependence <span class="math notranslate nohighlight">\( \large f(x) \)</span>, the model of our approximation <span class="math notranslate nohighlight">\( \large \hat{f}(x) \)</span>, or the distribution of the target variable (<span class="math notranslate nohighlight">\( \large y \)</span>). We only expect that the function <span class="math notranslate nohighlight">\( \large L(y,f) \)</span> is differentiable. Our formula is very general; let’s define it for a particular data set with a population mean <span class="math notranslate nohighlight">\( \large \hat {f}(x) \)</span>. Our expression for minimizing the loss of the data is the following:</p>
@@ -1826,12 +1858,12 @@ <h3><span class="section-number">16.1.2.1. </span>ML problem statement<a class="
 <figure class="align-default" id="iteration-steps">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Iteration_steps.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Iteration_steps.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Iteration_steps.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.7 </span><span class="caption-text">Iteration steps</span><a class="headerlink" href="#iteration-steps" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.7 </span><span class="caption-text">Iteration steps</span><a class="headerlink" href="#iteration-steps" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="functional-gradient-descent">
-<h3><span class="section-number">16.1.2.2. </span>Functional gradient descent<a class="headerlink" href="#functional-gradient-descent" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.2. </span>Functional gradient descent<a class="headerlink" href="#functional-gradient-descent" title="Permalink to this headline">#</a></h3>
 <p>Let’s imagine for a second that we can perform optimization in the function space and iteratively search for the approximations <span class="math notranslate nohighlight">\(\large \hat{f}(x)\)</span> as functions themselves. We will express our approximation as a sum of incremental improvements, each being a function. For convenience, we will immediately start with the sum from the initial approximation <span class="math notranslate nohighlight">\(\large \hat{f_0}(x)\)</span>:<span class="math notranslate nohighlight">\(\large \hat{f}(x) = \sum_{i = 0}^M \hat{f_i}(x)\)</span></p>
 <p>Nothing has happened yet; we have only decided that we will search for our approximation <span class="math notranslate nohighlight">\(\large \hat{f}(x)\)</span> not as a big model with plenty of parameters (as an example, neural network), but as a sum of functions, pretending we move in functional space.</p>
 <p>In order to accomplish this task, we need to limit our search by some function family <span class="math notranslate nohighlight">\(\large \hat{f}(x) = h(x, \theta)\)</span>. There are a few issues here – first of all, the sum of models can be more complicated than any model from this family; secondly, the general objective is still in functional space. Let’s note that, on every step, we will need to select an optimal coefficient <span class="math notranslate nohighlight">\(\large \rho \in \mathbb{R}\)</span>. For step <span class="math notranslate nohighlight">\(\large t\)</span>, the problem is the following:</p>
@@ -1849,12 +1881,12 @@ <h3><span class="section-number">16.1.2.2. </span>Functional gradient descent<a
 <figure class="align-default" id="regression-for-you">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_for_you.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_for_you.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_for_you.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.8 </span><span class="caption-text">Regression for you</span><a class="headerlink" href="#regression-for-you" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.8 </span><span class="caption-text">Regression for you</span><a class="headerlink" href="#regression-for-you" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="friedmans-classic-gbm-algorithm">
-<h3><span class="section-number">16.1.2.3. </span>Friedman’s classic GBM algorithm<a class="headerlink" href="#friedmans-classic-gbm-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.3. </span>Friedman’s classic GBM algorithm<a class="headerlink" href="#friedmans-classic-gbm-algorithm" title="Permalink to this headline">#</a></h3>
 <p>We can now define the classic GBM algorithm suggested by Jerome Friedman in 1999. It is a supervised algorithm that has the following components:</p>
 <ul class="simple">
 <li><p>dataset <span class="math notranslate nohighlight">\(\large \left\{ (x_i, y_i) \right\}_{i=1, \ldots,n}\)</span>;</p></li>
@@ -1882,12 +1914,12 @@ <h3><span class="section-number">16.1.2.3. </span>Friedman’s classic GBM algor
 </ol>
 </section>
 <section id="step-by-step-example-how-gbm-works">
-<h3><span class="section-number">16.1.2.4. </span>Step-By-Step example: How GBM Works<a class="headerlink" href="#step-by-step-example-how-gbm-works" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.4. </span>Step-By-Step example: How GBM Works<a class="headerlink" href="#step-by-step-example-how-gbm-works" title="Permalink to this headline">#</a></h3>
 <p>Let’s see an example of how GBM works. In this toy example, we will restore a noisy function <span class="math notranslate nohighlight">\(\large y = cos(x) + \epsilon, \epsilon \sim \mathcal{N}(0, \frac{1}{5}), x \in [-5,5]\)</span>.</p>
 <figure class="align-default" id="regression-problem">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_problem.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_problem.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_problem.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.9 </span><span class="caption-text">Regression problem</span><a class="headerlink" href="#regression-problem" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.9 </span><span class="caption-text">Regression problem</span><a class="headerlink" href="#regression-problem" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>This is a regression problem with a real-valued target, so we will choose to use the mean squared error loss function. We will generate 300 pairs of observations and approximate them with decision trees of depth 2. Let’s put together everything we need to use GBM:</p>
@@ -1904,7 +1936,7 @@ <h3><span class="section-number">16.1.2.4. </span>Step-By-Step example: How GBM
 <figure class="align-default" id="current-approximation-and-every-tree">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Current_approximation_and_every_tree.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Current_approximation_and_every_tree.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Current_approximation_and_every_tree.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.10 </span><span class="caption-text">Current approximation and every tree</span><a class="headerlink" href="#current-approximation-and-every-tree" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.10 </span><span class="caption-text">Current approximation and every tree</span><a class="headerlink" href="#current-approximation-and-every-tree" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>By the second iteration, our trees have recovered the basic form of the function. However, at the first iteration, we see that the algorithm has built only the “left branch” of the function (<span class="math notranslate nohighlight">\(\large x \in [-5, -4]\)</span>). This was due to the fact that our trees simply did not have enough depth to build a symmetrical branch at once, and it focused on the left branch with the larger error. Therefore, the right branch appeared only after the second iteration.</p>
@@ -1912,17 +1944,17 @@ <h3><span class="section-number">16.1.2.4. </span>Step-By-Step example: How GBM
 <figure class="align-default" id="how-gbm-works">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/How_GBM_works.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/How_GBM_works.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/How_GBM_works.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.11 </span><span class="caption-text">How GBM Works</span><a class="headerlink" href="#how-gbm-works" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.11 </span><span class="caption-text">How GBM Works</span><a class="headerlink" href="#how-gbm-works" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="loss-functions">
-<h2><span class="section-number">16.1.3. </span>Loss functions<a class="headerlink" href="#loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.3. </span>Loss functions<a class="headerlink" href="#loss-functions" title="Permalink to this headline">#</a></h2>
 <p>If we want to solve a classification problem instead of regression, what would change? We only need to choose a suitable loss function <span class="math notranslate nohighlight">\(\large L(y, f)\)</span>. This is the most important, high-level moment that determines exactly how we will optimize and what characteristics we can expect in the final model.</p>
 <p>As a rule, we do not need to invent this ourselves – researchers have already done it for us. Today, we will explore loss functions for the two most common objectives: regression <span class="math notranslate nohighlight">\(\large y \in \mathbb{R}\)</span> and binary classification <span class="math notranslate nohighlight">\(\large y \in \left\{-1, 1\right\}\)</span>.</p>
 <section id="regression-loss-functions">
-<h3><span class="section-number">16.1.3.1. </span>Regression loss functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.3.1. </span>Regression loss functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
 <p>Let’s start with a regression problem for <span class="math notranslate nohighlight">\(\large y \in \mathbb{R}\)</span>. In order to choose the appropriate loss function, we need to consider which of the properties of the conditional distribution <span class="math notranslate nohighlight">\(\large (y|x)\)</span> we want to restore. The most common options are:</p>
 <ul class="simple">
 <li><p><span class="math notranslate nohighlight">\(\large L(y, f) = (y - f)^2\)</span> a.k.a. <span class="math notranslate nohighlight">\(\large L_2\)</span> loss or Gaussian loss. It is the classical conditional mean, which is the simplest and most common case. If we do not have any additional information or requirements for a model to be robust, we can use the Gaussian loss.</p></li>
@@ -1933,7 +1965,7 @@ <h3><span class="section-number">16.1.3.1. </span>Regression loss functions<a cl
 <figure class="align-default" id="asymmetric-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.12 </span><span class="caption-text">Asymmetric function</span><a class="headerlink" href="#asymmetric-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.12 </span><span class="caption-text">Asymmetric function</span><a class="headerlink" href="#asymmetric-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s use loss function <span class="math notranslate nohighlight">\(\large L_q\)</span> on our data. The goal is to restore the conditional 75%-quantile of cosine. Let us put everyting together for GBM:</p>
@@ -1951,7 +1983,7 @@ <h3><span class="section-number">16.1.3.1. </span>Regression loss functions<a cl
 <figure class="align-default" id="standard-line-search">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Standard_line_search.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Standard_line_search.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Standard_line_search.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.13 </span><span class="caption-text">Standard line search</span><a class="headerlink" href="#standard-line-search" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.13 </span><span class="caption-text">Standard line search</span><a class="headerlink" href="#standard-line-search" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We can observe that, on each iteration, <span class="math notranslate nohighlight">\(\large r_{i} \)</span> take only 2 possible values, but GBM is still able to restore our initial function.</p>
@@ -1961,7 +1993,7 @@ <h3><span class="section-number">16.1.3.1. </span>Regression loss functions<a cl
 <figure class="align-default" id="functions-and-relevant-gbm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Functions_and_relevant_GBM.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Functions_and_relevant_GBM.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Functions_and_relevant_GBM.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.14 </span><span class="caption-text">Functions and relevant GBM</span><a class="headerlink" href="#functions-and-relevant-gbm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.14 </span><span class="caption-text">Functions and relevant GBM</span><a class="headerlink" href="#functions-and-relevant-gbm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In this example, we used splines as the base algorithm. See, it does not always have to be trees for boosting?</p>
@@ -1970,7 +2002,7 @@ <h3><span class="section-number">16.1.3.1. </span>Regression loss functions<a cl
 </spoiler></p>
 </section>
 <section id="classification-loss-functions">
-<h3><span class="section-number">16.1.3.2. </span>Classification loss functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.3.2. </span>Classification loss functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
 <p>Now, let’s look at the binary classification problem <span class="math notranslate nohighlight">\(\large y \in \left\{-1, 1\right\}\)</span>. We saw that GBM can even optimize non-differentiable loss functions. Technically, it is possible to solve this problem with a regression <span class="math notranslate nohighlight">\(\large L_2\)</span> loss, but it wouldn’t be correct.</p>
 <p>The distribution of the target variable requires us to use log-likehood, so we need to have different loss functions for targets multiplied by their predictions:  <span class="math notranslate nohighlight">\(\large y \cdot f\)</span>. The most common choices would be the following:</p>
 <ul class="simple">
@@ -1980,14 +2012,14 @@ <h3><span class="section-number">16.1.3.2. </span>Classification loss functions<
 <figure class="align-default" id="adaboost-and-logistic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/AdaBoost_and_logistic.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/AdaBoost_and_logistic.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/AdaBoost_and_logistic.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.15 </span><span class="caption-text">AdaBoost and logistic</span><a class="headerlink" href="#adaboost-and-logistic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.15 </span><span class="caption-text">AdaBoost and logistic</span><a class="headerlink" href="#adaboost-and-logistic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s generate some new toy data for our classification problem. As a basis, we will take our noisy cosine, and we will use the sign function for classes of the target variable. Our toy data looks like the following (jitter-noise is added for clarity):</p>
 <figure class="align-default" id="toy-data">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Toy_data.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Toy_data.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Toy_data.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.16 </span><span class="caption-text">Toy data</span><a class="headerlink" href="#toy-data" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.16 </span><span class="caption-text">Toy data</span><a class="headerlink" href="#toy-data" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We will use logistic loss to look for what we actually boost. So, again, we put together what we will use for GBM:</p>
@@ -2003,25 +2035,25 @@ <h3><span class="section-number">16.1.3.2. </span>Classification loss functions<
 <figure class="align-default" id="initialization-of-algorithm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initialization_of_algorithm.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initialization_of_algorithm.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initialization_of_algorithm.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.17 </span><span class="caption-text">Initialization of algorithm</span><a class="headerlink" href="#initialization-of-algorithm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.17 </span><span class="caption-text">Initialization of algorithm</span><a class="headerlink" href="#initialization-of-algorithm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Our optimal initial approximation is around -0.273. You could have guessed that it was negative because it is more profitable to predict everything as the most popular class, but there is no formula for the exact value. Now let’s finally start GBM, and look what actually happens under the hood:</p>
 <figure class="align-default" id="result-of-gbm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Result_of_GBM.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Result_of_GBM.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Result_of_GBM.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.18 </span><span class="caption-text">Result of GBM</span><a class="headerlink" href="#result-of-gbm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.18 </span><span class="caption-text">Result of GBM</span><a class="headerlink" href="#result-of-gbm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The algorithm successfully restored the separation between our classes. You can see how the “lower” areas are separating because the trees are more confident in the correct prediction of the negative class and how the two steps of mixed classes are forming. It is clear that we have a lot of correctly classified observations and some amount of observations with large errors that appeared due to the noise in the data.</p>
 </section>
 <section id="weights">
-<h3><span class="section-number">16.1.3.3. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.3.3. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h3>
 <p>Sometimes, there is a situation where we want a more specific loss function for our problem. For example, in financial time series, we may want to give bigger weight to large movements in the time series; for churn prediction, it is more useful to predict the churn of clients with high LTV (or lifetime value: how much money a client will bring in the future).</p>
 <figure class="align-default" id="statistical-learning">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Statistical_learning.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Statistical_learning.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Statistical_learning.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.19 </span><span class="caption-text">Statistical learning</span><a class="headerlink" href="#statistical-learning" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.19 </span><span class="caption-text">Statistical learning</span><a class="headerlink" href="#statistical-learning" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The statistical warrior would invent their own loss function, write out the gradient for it (for more effective training, include the Hessian), and carefully check whether this function satisfies the required properties. However, there is a high probability of making a mistake somewhere, running up against computational difficulties, and spending an inordinate amount of time on research.</p>
@@ -2041,14 +2073,14 @@ <h3><span class="section-number">16.1.3.3. </span>Weights<a class="headerlink" h
 <figure class="align-default" id="asymmetric-weight-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_weight_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_weight_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_weight_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.20 </span><span class="caption-text">Asymmetric weight function</span><a class="headerlink" href="#asymmetric-weight-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.20 </span><span class="caption-text">Asymmetric weight function</span><a class="headerlink" href="#asymmetric-weight-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>With these weights, we expect to get two properties: less detailing for negative values of <span class="math notranslate nohighlight">\(\large x\)</span> and the form of the function, similar to the initial cosine. We take the other GBM’s tunings from our previous example with classification including the line search for optimal coefficients. Let’s look what we’ve got:</p>
 <figure class="align-default" id="adjusted-result-of-gbm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Adjusted_result_of_GBM.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Adjusted_result_of_GBM.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Adjusted_result_of_GBM.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.21 </span><span class="caption-text">Adjusted result of GBM</span><a class="headerlink" href="#adjusted-result-of-gbm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.21 </span><span class="caption-text">Adjusted result of GBM</span><a class="headerlink" href="#adjusted-result-of-gbm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We achieved the result that we expected. First, we can see how strongly the pseudo-residuals differ; on the initial iteration, they look almost like the original cosine. Second, the left part of the function’s graph was often ignored in favor of the right one, which had larger weights. Third, the function that we got on the third iteration received enough attention and started looking similar to the original cosine (also started to slightly overfit).</p>
@@ -2056,36 +2088,36 @@ <h3><span class="section-number">16.1.3.3. </span>Weights<a class="headerlink" h
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">16.1.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Today, we learned the theory behind gradient boosting. GBM is not just some specific algorithm but a common methodology for building ensembles of models. In addition, this methodology is sufficiently flexible and expandable – it is possible to train a large number of models, taking into consideration different loss-functions with a variety of weighting functions.</p>
 <p>Practice and ML competitions show that, in standard problems (except for image, audio, and very sparse data), GBM is often the most effective algorithm (not to mention stacking and high-level ensembles, where GBM is almost always a part of them).  Also, there are many adaptations of GBM <a class="reference external" href="https://arxiv.org/abs/1603.04119">for Reinforcement Learning</a> (Minecraft, ICML 2016). By the way, the Viola-Jones algorithm, which is still used in computer vision, <a class="reference external" href="https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework#Learning_algorithm">is based on AdaBoost</a>.</p>
 <p>In this article, we intentionally omitted questions concerning GBM’s regularization, stochasticity, and hyper-parameters. It was not accidental that we used a small number of iterations <span class="math notranslate nohighlight">\(\large M = 3\)</span> throughout. If we used 30 trees instead of 3 and trained the GBM as described, the result would not be that predictable:</p>
 <figure class="align-default" id="good-fit">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Good_fit.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Good_fit.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Good_fit.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.22 </span><span class="caption-text">Good fit</span><a class="headerlink" href="#good-fit" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.22 </span><span class="caption-text">Good fit</span><a class="headerlink" href="#good-fit" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="overfitting">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Overfitting.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Overfitting.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Overfitting.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.23 </span><span class="caption-text">Overfitting</span><a class="headerlink" href="#overfitting" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.23 </span><span class="caption-text">Overfitting</span><a class="headerlink" href="#overfitting" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="gradient-boosting-interactive-playground">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Gradient_boosting_interactive_playground.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Gradient_boosting_interactive_playground.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Gradient_boosting_interactive_playground.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.24 </span><span class="caption-text">Gradient Boosting Interactive Playground</span><a class="headerlink" href="#gradient-boosting-interactive-playground" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.24 </span><span class="caption-text">Gradient Boosting Interactive Playground</span><a class="headerlink" href="#gradient-boosting-interactive-playground" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>You can go to the <a class="reference external" href="http://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html">Gradient Boosting Interactive Playground</a> to learn more.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">16.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">16.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-10-gradient-boosting/notebook">Gradient Boosting</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2127,13 +2159,13 @@ <h2><span class="section-number">16.1.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">16. </span>Introduction to Gradient Boosting</p>
+            <p class="prev-next-title"><span class="section-number">18. </span>Introduction to Gradient Boosting</p>
         </div>
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     <a class='right-next' id="next-link" href="gradient-boosting-example.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">16.2. </span>Gradient boosting example</p>
+        <p class="prev-next-title"><span class="section-number">18.2. </span>Gradient boosting example</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html b/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html
index 843d625d07..1ede11ddc7 100644
--- a/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html
+++ b/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>16. Introduction to Gradient Boosting &#8212; Ocademy Open Machine Learning Book</title>
+    <title>18. Introduction to Gradient Boosting &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="16.1. Gradient Boosting" href="gradient-boosting.html" />
-    <link rel="prev" title="15.3. Feature importance" href="../ensemble-learning/feature-importance.html" />
+    <link rel="next" title="18.1. Gradient Boosting" href="gradient-boosting.html" />
+    <link rel="prev" title="17.3. Feature importance" href="../ensemble-learning/feature-importance.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
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+ <li class="toctree-l1">
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-     38.8. Data Science lifecycle
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-     38.9. Data Science in the cloud
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-     38.11. Machine Learning overview
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-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-gradient-boosting">
-<h1><span class="section-number">16. </span>Introduction to Gradient Boosting<a class="headerlink" href="#introduction-to-gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18. </span>Introduction to Gradient Boosting<a class="headerlink" href="#introduction-to-gradient-boosting" title="Permalink to this headline">#</a></h1>
 <p>Gradient Boosting is a popular machine learning algorithm that is used for both regression and classification tasks. It is a type of ensemble method that combines multiple weak learners into a single strong learner by sequentially fitting new models to the errors made by previous models.</p>
 <p>The idea behind gradient boosting is to use a loss function to measure the difference between the predicted and actual values, and then fit a new model to the residual errors. In each iteration, the model is trained on the errors made by the previous models, and the final prediction is obtained by summing the predictions of all models.</p>
 <p>The main advantage of gradient boosting is that it can handle complex data patterns and can achieve high accuracy on a wide range of tasks. However, it is computationally expensive and can be sensitive to overfitting if not properly tuned.</p>
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 <figcaption>
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+<p><span class="caption-number">Fig. 18.1 </span><span class="caption-text">Gradient Boosting Process Diagram</span><a class="headerlink" href="#gradient-boosting" title="Permalink to this image">#</a></p>
 </figcaption>
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-<li class="toctree-l1"><a class="reference internal" href="xgboost.html">16.3. XGBoost</a></li>
-<li class="toctree-l1"><a class="reference internal" href="xgboost-k-fold-cv-feature-importance.html">16.4. XGBoost + k-fold CV + Feature Importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="gradient-boosting.html">18.1. Gradient Boosting</a></li>
+<li class="toctree-l1"><a class="reference internal" href="gradient-boosting-example.html">18.2. Gradient boosting example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="xgboost.html">18.3. XGBoost</a></li>
+<li class="toctree-l1"><a class="reference internal" href="xgboost-k-fold-cv-feature-importance.html">18.4. XGBoost + k-fold CV + Feature Importance</a></li>
 </ul>
 </div>
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@@ -1596,13 +1628,13 @@ <h1><span class="section-number">16. </span>Introduction to Gradient Boosting<a
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">15.3. </span>Feature importance</p>
+            <p class="prev-next-title"><span class="section-number">17.3. </span>Feature importance</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gradient-boosting.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">16.1. </span>Gradient Boosting</p>
+        <p class="prev-next-title"><span class="section-number">18.1. </span>Gradient Boosting</p>
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     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html b/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html
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-    <title>16.4. XGBoost + k-fold CV + Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
+    <title>18.4. XGBoost + k-fold CV + Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="17. Unsupervised learning" href="../unsupervised-learning.html" />
-    <link rel="prev" title="16.3. XGBoost" href="xgboost.html" />
+    <link rel="next" title="19. Unsupervised learning" href="../unsupervised-learning.html" />
+    <link rel="prev" title="18.3. XGBoost" href="xgboost.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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-     38.4. Data Science introduction
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-     38.16. Build an machine learning web application
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   <a class="reference internal nav-link" href="#introduction-to-xgboost-algorithm">
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+   18.4.1. Introduction to XGBoost Algorithm
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     <a class="reference internal nav-link" href="#evolution-of-tree-based-algorithms">
-     16.4.1.1. Evolution of tree-based algorithms
+     18.4.1.1. Evolution of tree-based algorithms
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#main-features-of-xgboost">
-     16.4.1.2. Main features of XGBoost
+     18.4.1.2. Main features of XGBoost
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+   18.4.2. Bagging Vs Boosting
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+     18.4.2.1. Bagging
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    <li class="toc-h3 nav-item toc-entry">
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-     16.4.2.2. Boosting
+     18.4.2.2. Boosting
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+     18.4.3.1. Gradient boosting
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+     18.4.3.2. Gradient Boosted Trees
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+     18.4.3.3. Extreme gradient boosting (XGBoost)
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-       16.4.4.3.2. Preview dataset
+       18.4.4.3.2. Preview dataset
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-       16.4.4.3.3. Summary of dataset
+       18.4.4.3.3. Summary of dataset
       </a>
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-       16.4.4.3.4. Summary statistics of dataset
+       18.4.4.3.4. Summary statistics of dataset
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-       16.4.4.3.5. Check for missing values
+       18.4.4.3.5. Check for missing values
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-     16.4.4.4. Declare feature vector and target variable
+     18.4.4.4. Declare feature vector and target variable
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-     16.4.4.5. Split data into separate training and test set
+     18.4.4.5. Split data into separate training and test set
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-     16.4.4.6. Train the XGBoost Classifier
+     18.4.4.6. Train the XGBoost Classifier
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     <a class="reference internal nav-link" href="#make-predictions-with-xgboost-classifier">
-     16.4.4.7. Make predictions with XGBoost classifier
+     18.4.4.7. Make predictions with XGBoost classifier
     </a>
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     <a class="reference internal nav-link" href="#check-accuracy-score">
-     16.4.4.8. Check accuracy score
+     18.4.4.8. Check accuracy score
     </a>
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   <a class="reference internal nav-link" href="#k-fold-cross-validation-using-xgboost">
-   16.4.5. k-fold Cross Validation using XGBoost
+   18.4.5. k-fold Cross Validation using XGBoost
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+   18.4.6. Feature importance with XGBoost
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+   18.4.7. Results and conclusion
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-   16.4.8. Your turn! 🚀
+   18.4.8. Your turn! 🚀
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+   18.4.9. Acknowledgments
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@@ -1702,157 +1734,157 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-xgboost-algorithm">
-   16.4.1. Introduction to XGBoost Algorithm
+   18.4.1. Introduction to XGBoost Algorithm
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evolution-of-tree-based-algorithms">
-     16.4.1.1. Evolution of tree-based algorithms
+     18.4.1.1. Evolution of tree-based algorithms
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#main-features-of-xgboost">
-     16.4.1.2. Main features of XGBoost
+     18.4.1.2. Main features of XGBoost
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bagging-vs-boosting">
-   16.4.2. Bagging Vs Boosting
+   18.4.2. Bagging Vs Boosting
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#bagging">
-     16.4.2.1. Bagging
+     18.4.2.1. Bagging
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting">
-     16.4.2.2. Boosting
+     18.4.2.2. Boosting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#xgboost-algorithm-intuition">
-   16.4.3. XGBoost algorithm intuition
+   18.4.3. XGBoost algorithm intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#gradient-boosting">
-     16.4.3.1. Gradient boosting
+     18.4.3.1. Gradient boosting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#gradient-boosted-trees">
-     16.4.3.2. Gradient Boosted Trees
+     18.4.3.2. Gradient Boosted Trees
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#extreme-gradient-boosting-xgboost">
-     16.4.3.3. Extreme gradient boosting (XGBoost)
+     18.4.3.3. Extreme gradient boosting (XGBoost)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementing-xgboost-in-python">
-   16.4.4. Implementing XGBoost in Python
+   18.4.4. Implementing XGBoost in Python
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     16.4.4.1. Load libraries
+     18.4.4.1. Load libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#read-dataset">
-     16.4.4.2. Read dataset
+     18.4.4.2. Read dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#eda">
-     16.4.4.3. EDA
+     18.4.4.3. EDA
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#shape-of-dataset">
-       16.4.4.3.1. Shape of dataset
+       18.4.4.3.1. Shape of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-dataset">
-       16.4.4.3.2. Preview dataset
+       18.4.4.3.2. Preview dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-dataset">
-       16.4.4.3.3. Summary of dataset
+       18.4.4.3.3. Summary of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-statistics-of-dataset">
-       16.4.4.3.4. Summary statistics of dataset
+       18.4.4.3.4. Summary statistics of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-for-missing-values">
-       16.4.4.3.5. Check for missing values
+       18.4.4.3.5. Check for missing values
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     16.4.4.4. Declare feature vector and target variable
+     18.4.4.4. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     16.4.4.5. Split data into separate training and test set
+     18.4.4.5. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-the-xgboost-classifier">
-     16.4.4.6. Train the XGBoost Classifier
+     18.4.4.6. Train the XGBoost Classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-predictions-with-xgboost-classifier">
-     16.4.4.7. Make predictions with XGBoost classifier
+     18.4.4.7. Make predictions with XGBoost classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-accuracy-score">
-     16.4.4.8. Check accuracy score
+     18.4.4.8. Check accuracy score
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-fold-cross-validation-using-xgboost">
-   16.4.5. k-fold Cross Validation using XGBoost
+   18.4.5. k-fold Cross Validation using XGBoost
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-importance-with-xgboost">
-   16.4.6. Feature importance with XGBoost
+   18.4.6. Feature importance with XGBoost
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   16.4.7. Results and conclusion
+   18.4.7. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   16.4.8. Your turn! 🚀
+   18.4.8. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   16.4.9. Acknowledgments
+   18.4.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1877,11 +1909,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="xgboost-k-fold-cv-feature-importance">
-<h1><span class="section-number">16.4. </span>XGBoost + k-fold CV + Feature Importance<a class="headerlink" href="#xgboost-k-fold-cv-feature-importance" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.4. </span>XGBoost + k-fold CV + Feature Importance<a class="headerlink" href="#xgboost-k-fold-cv-feature-importance" title="Permalink to this headline">#</a></h1>
 <p>As we all know that a lot of competitions were won using only one algorithm - <strong>XGBoost</strong>. It is one of the most popular machine learning algorithm these days. It works well for both types of tasks - regression and classification.</p>
 <p>So, in this kernel, we will discuss XGBoost and develop a simple baseline XGBoost model with Python.</p>
 <section id="introduction-to-xgboost-algorithm">
-<h2><span class="section-number">16.4.1. </span>Introduction to XGBoost Algorithm<a class="headerlink" href="#introduction-to-xgboost-algorithm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.1. </span>Introduction to XGBoost Algorithm<a class="headerlink" href="#introduction-to-xgboost-algorithm" title="Permalink to this headline">#</a></h2>
 <ul>
 <li><p><strong>XGBoost</strong> stands for <strong>Extreme Gradient Boosting.</strong></p></li>
 <li><p>It is a performant machine learning library based on the paper <a class="reference external" href="https://statweb.stanford.edu/~jhf/ftp/trebst.pdf">Greedy Function Approximation: A Gradient Boosting Machine, by Friedman</a></p></li>
@@ -1896,7 +1928,7 @@ <h2><span class="section-number">16.4.1. </span>Introduction to XGBoost Algorith
 </li>
 </ul>
 <section id="evolution-of-tree-based-algorithms">
-<h3><span class="section-number">16.4.1.1. </span>Evolution of tree-based algorithms<a class="headerlink" href="#evolution-of-tree-based-algorithms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.1.1. </span>Evolution of tree-based algorithms<a class="headerlink" href="#evolution-of-tree-based-algorithms" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>The tree based algorithms have evolved over the years.</p></li>
 <li><p>XGBoost belongs to a family of tree-based algorithms.</p></li>
@@ -1905,12 +1937,12 @@ <h3><span class="section-number">16.4.1.1. </span>Evolution of tree-based algori
 <figure class="align-default" id="id1">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Evolution_of_tree_based_algorithms.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Evolution_of_tree_based_algorithms.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Evolution_of_tree_based_algorithms.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.26 </span><span class="caption-text">Evolution of tree-based algorithms</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.26 </span><span class="caption-text">Evolution of tree-based algorithms</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="main-features-of-xgboost">
-<h3><span class="section-number">16.4.1.2. </span>Main features of XGBoost<a class="headerlink" href="#main-features-of-xgboost" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.1.2. </span>Main features of XGBoost<a class="headerlink" href="#main-features-of-xgboost" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>The primary reasons we should use this algorithm are its <strong>accuracy</strong>, <strong>efficiency</strong> and <strong>feasibility</strong>.</p></li>
 <li><p>It is a linear model and a <a class="reference external" href="https://en.wikipedia.org/wiki/Decision_tree_learning">tree learning</a> algorithm that does parallel computations on a single machine.</p></li>
@@ -1926,7 +1958,7 @@ <h3><span class="section-number">16.4.1.2. </span>Main features of XGBoost<a cla
 </section>
 </section>
 <section id="bagging-vs-boosting">
-<h2><span class="section-number">16.4.2. </span>Bagging Vs Boosting<a class="headerlink" href="#bagging-vs-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.2. </span>Bagging Vs Boosting<a class="headerlink" href="#bagging-vs-boosting" title="Permalink to this headline">#</a></h2>
 <ul>
 <li><p>To know bagging and boosting, we need to know ensemble methods.</p></li>
 <li><p>Ensemble methods combine several decision trees to produce better predictive performance than utilizing a single decision tree.</p></li>
@@ -1942,7 +1974,7 @@ <h2><span class="section-number">16.4.2. </span>Bagging Vs Boosting<a class="hea
 </li>
 </ul>
 <section id="bagging">
-<h3><span class="section-number">16.4.2.1. </span>Bagging<a class="headerlink" href="#bagging" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.2.1. </span>Bagging<a class="headerlink" href="#bagging" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Bagging (or Bootstrap Aggregation), is a simple and very powerful ensemble method.</p></li>
 <li><p>Bagging is the application of the Bootstrap procedure to a high-variance machine learning algorithm, typically decision trees.</p></li>
@@ -1954,12 +1986,12 @@ <h3><span class="section-number">16.4.2.1. </span>Bagging<a class="headerlink" h
 <figure class="align-default" id="process-of-bagging">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_bagging.webp"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_bagging.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_bagging.webp" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.27 </span><span class="caption-text">Process of Bagging</span><a class="headerlink" href="#process-of-bagging" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.27 </span><span class="caption-text">Process of Bagging</span><a class="headerlink" href="#process-of-bagging" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="boosting">
-<h3><span class="section-number">16.4.2.2. </span>Boosting<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.2.2. </span>Boosting<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Boosting is a sequential process, where each subsequent model attempts to correct the errors of the previous model. The succeeding models are dependent on the previous model.</p></li>
 <li><p>In this technique, learners are learned sequentially with early learners fitting simple models to the data and then analyzing data for errors. In other words, we fit consecutive trees (random sample) and at every step, the goal is to solve for net error from the prior tree.</p></li>
@@ -1970,13 +2002,13 @@ <h3><span class="section-number">16.4.2.2. </span>Boosting<a class="headerlink"
 <figure class="align-default" id="process-of-boosting">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_boosting.webp"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_boosting.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_boosting.webp" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.28 </span><span class="caption-text">Process of Boosting</span><a class="headerlink" href="#process-of-boosting" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.28 </span><span class="caption-text">Process of Boosting</span><a class="headerlink" href="#process-of-boosting" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="xgboost-algorithm-intuition">
-<h2><span class="section-number">16.4.3. </span>XGBoost algorithm intuition<a class="headerlink" href="#xgboost-algorithm-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.3. </span>XGBoost algorithm intuition<a class="headerlink" href="#xgboost-algorithm-intuition" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>XGBoost is a powerful and lightning fast machine learning library. It’s commonly used to win some professional level competitions.</p></li>
 <li><p>It’s an intimidating algorithm, especially because of the number of large parameters that XGBoost provides.</p></li>
@@ -1984,7 +2016,7 @@ <h2><span class="section-number">16.4.3. </span>XGBoost algorithm intuition<a cl
 <li><p>So, in this section, we will discuss - <strong>gradient boosting</strong>, <strong>gradient boosted trees</strong> and <strong>XGBoost</strong>. The purpose of this section is to clarify these concepts.</p></li>
 </ul>
 <section id="gradient-boosting">
-<h3><span class="section-number">16.4.3.1. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.3.1. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.</p></li>
 <li><p>It builds the model in a stage-wise fashion like other boosting methods do and it generalizes them by allowing optimization of an arbitrary differentiable loss function.</p></li>
@@ -2010,7 +2042,7 @@ <h3><span class="section-number">16.4.3.1. </span>Gradient boosting<a class="hea
 </ul>
 </section>
 <section id="gradient-boosted-trees">
-<h3><span class="section-number">16.4.3.2. </span>Gradient Boosted Trees<a class="headerlink" href="#gradient-boosted-trees" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.3.2. </span>Gradient Boosted Trees<a class="headerlink" href="#gradient-boosted-trees" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Gradient boosted trees consider the special case where the simple model h is a decision tree.</p></li>
 <li><p>It can be depicted with the following diagram which is taken from XGBoost’s documentation.</p></li>
@@ -2018,7 +2050,7 @@ <h3><span class="section-number">16.4.3.2. </span>Gradient Boosted Trees<a class
 <figure class="align-default" id="id2">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Gradient_boosted_trees.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Gradient_boosted_trees.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Gradient_boosted_trees.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.29 </span><span class="caption-text">Gradient Boosted Trees</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.29 </span><span class="caption-text">Gradient Boosted Trees</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -2029,7 +2061,7 @@ <h3><span class="section-number">16.4.3.2. </span>Gradient Boosted Trees<a class
 <figure class="align-default" id="new-tree-minimizing-loss">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/New_tree_minimizing_loss.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/New_tree_minimizing_loss.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/New_tree_minimizing_loss.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.30 </span><span class="caption-text">New Tree minimizing loss</span><a class="headerlink" href="#new-tree-minimizing-loss" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.30 </span><span class="caption-text">New Tree minimizing loss</span><a class="headerlink" href="#new-tree-minimizing-loss" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -2049,7 +2081,7 @@ <h3><span class="section-number">16.4.3.2. </span>Gradient Boosted Trees<a class
 </ul>
 </section>
 <section id="extreme-gradient-boosting-xgboost">
-<h3><span class="section-number">16.4.3.3. </span>Extreme gradient boosting (XGBoost)<a class="headerlink" href="#extreme-gradient-boosting-xgboost" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.3.3. </span>Extreme gradient boosting (XGBoost)<a class="headerlink" href="#extreme-gradient-boosting-xgboost" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>XGBoost is one of the fastest implementations of gradient boosted trees. It does this by tackling one of the major inefficiencies of gradient boosted trees.</p></li>
 <li><p>Consider the case where there are thousands of features, and therefore thousands of possible splits. Now, if we consider the potential loss for all possible splits to create a new branch we have thousands of potential splits and losses.</p></li>
@@ -2060,9 +2092,9 @@ <h3><span class="section-number">16.4.3.3. </span>Extreme gradient boosting (XGB
 </section>
 </section>
 <section id="implementing-xgboost-in-python">
-<h2><span class="section-number">16.4.4. </span>Implementing XGBoost in Python<a class="headerlink" href="#implementing-xgboost-in-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.4. </span>Implementing XGBoost in Python<a class="headerlink" href="#implementing-xgboost-in-python" title="Permalink to this headline">#</a></h2>
 <section id="load-libraries">
-<h3><span class="section-number">16.4.4.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -2074,7 +2106,7 @@ <h3><span class="section-number">16.4.4.1. </span>Load libraries<a class="header
 </div>
 </section>
 <section id="read-dataset">
-<h3><span class="section-number">16.4.4.2. </span>Read dataset<a class="headerlink" href="#read-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.2. </span>Read dataset<a class="headerlink" href="#read-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/wholesale_customers_data.csv&#39;</span>
@@ -2086,9 +2118,9 @@ <h3><span class="section-number">16.4.4.2. </span>Read dataset<a class="headerli
 </div>
 </section>
 <section id="eda">
-<h3><span class="section-number">16.4.4.3. </span>EDA<a class="headerlink" href="#eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.3. </span>EDA<a class="headerlink" href="#eda" title="Permalink to this headline">#</a></h3>
 <section id="shape-of-dataset">
-<h4><span class="section-number">16.4.4.3.1. </span>Shape of dataset<a class="headerlink" href="#shape-of-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.1. </span>Shape of dataset<a class="headerlink" href="#shape-of-dataset" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>I will start off by checking the shape of the dataset.</p></li>
 </ul>
@@ -2107,7 +2139,7 @@ <h4><span class="section-number">16.4.4.3.1. </span>Shape of dataset<a class="he
 <p>We can see that there are 440 instances and 8 attributes in the dataset.</p>
 </section>
 <section id="preview-dataset">
-<h4><span class="section-number">16.4.4.3.2. </span>Preview dataset<a class="headerlink" href="#preview-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.2. </span>Preview dataset<a class="headerlink" href="#preview-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -2214,7 +2246,7 @@ <h4><span class="section-number">16.4.4.3.2. </span>Preview dataset<a class="hea
 </ul>
 </section>
 <section id="summary-of-dataset">
-<h4><span class="section-number">16.4.4.3.3. </span>Summary of dataset<a class="headerlink" href="#summary-of-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.3. </span>Summary of dataset<a class="headerlink" href="#summary-of-dataset" title="Permalink to this headline">#</a></h4>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
@@ -2243,7 +2275,7 @@ <h4><span class="section-number">16.4.4.3.3. </span>Summary of dataset<a class="
 </div>
 </section>
 <section id="summary-statistics-of-dataset">
-<h4><span class="section-number">16.4.4.3.4. </span>Summary statistics of dataset<a class="headerlink" href="#summary-statistics-of-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.4. </span>Summary statistics of dataset<a class="headerlink" href="#summary-statistics-of-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -2374,7 +2406,7 @@ <h4><span class="section-number">16.4.4.3.4. </span>Summary statistics of datase
 </div>
 </section>
 <section id="check-for-missing-values">
-<h4><span class="section-number">16.4.4.3.5. </span>Check for missing values<a class="headerlink" href="#check-for-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.5. </span>Check for missing values<a class="headerlink" href="#check-for-missing-values" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -2399,7 +2431,7 @@ <h4><span class="section-number">16.4.4.3.5. </span>Check for missing values<a c
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h3><span class="section-number">16.4.4.4. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.4. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Channel&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -2570,7 +2602,7 @@ <h3><span class="section-number">16.4.4.4. </span>Declare feature vector and tar
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h3><span class="section-number">16.4.4.5. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.5. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># split X and y into training and testing sets</span>
@@ -2583,7 +2615,7 @@ <h3><span class="section-number">16.4.4.5. </span>Split data into separate train
 </div>
 </section>
 <section id="train-the-xgboost-classifier">
-<h3><span class="section-number">16.4.4.6. </span>Train the XGBoost Classifier<a class="headerlink" href="#train-the-xgboost-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.6. </span>Train the XGBoost Classifier<a class="headerlink" href="#train-the-xgboost-classifier" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>In order to train the XGBoost classifier, we need to know different parameters that XGBoost provides.</p></li>
 <li><p>We will discuss these parameters in later sections.</p></li>
@@ -2660,7 +2692,7 @@ <h3><span class="section-number">16.4.4.6. </span>Train the XGBoost Classifier<a
 </div>
 </section>
 <section id="make-predictions-with-xgboost-classifier">
-<h3><span class="section-number">16.4.4.7. </span>Make predictions with XGBoost classifier<a class="headerlink" href="#make-predictions-with-xgboost-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.7. </span>Make predictions with XGBoost classifier<a class="headerlink" href="#make-predictions-with-xgboost-classifier" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># make predictions on test data</span>
@@ -2672,7 +2704,7 @@ <h3><span class="section-number">16.4.4.7. </span>Make predictions with XGBoost
 </div>
 </section>
 <section id="check-accuracy-score">
-<h3><span class="section-number">16.4.4.8. </span>Check accuracy score<a class="headerlink" href="#check-accuracy-score" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.8. </span>Check accuracy score<a class="headerlink" href="#check-accuracy-score" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># compute and print accuracy score</span>
@@ -2693,7 +2725,7 @@ <h3><span class="section-number">16.4.4.8. </span>Check accuracy score<a class="
 </section>
 </section>
 <section id="k-fold-cross-validation-using-xgboost">
-<h2><span class="section-number">16.4.5. </span>k-fold Cross Validation using XGBoost<a class="headerlink" href="#k-fold-cross-validation-using-xgboost" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.5. </span>k-fold Cross Validation using XGBoost<a class="headerlink" href="#k-fold-cross-validation-using-xgboost" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>To build more robust models with XGBoost, we should always perform k-fold cross validation.</p></li>
 <li><p>In this way, we ensure that the original training dataset is used for both training and validation.</p></li>
@@ -2801,7 +2833,7 @@ <h2><span class="section-number">16.4.5. </span>k-fold Cross Validation using XG
 </div>
 </section>
 <section id="feature-importance-with-xgboost">
-<h2><span class="section-number">16.4.6. </span>Feature importance with XGBoost<a class="headerlink" href="#feature-importance-with-xgboost" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.6. </span>Feature importance with XGBoost<a class="headerlink" href="#feature-importance-with-xgboost" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>XGBoost provides a way to examine the importance of each feature in the original dataset within the model.</p></li>
 <li><p>It involves counting the number of times each feature is split on across all boosting trees in the model.</p></li>
@@ -2830,7 +2862,7 @@ <h2><span class="section-number">16.4.6. </span>Feature importance with XGBoost<
 </ul>
 </section>
 <section id="results-and-conclusion">
-<h2><span class="section-number">16.4.7. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.7. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In this kernel, we implement XGBoost with Python and Scikit-Learn to classify the customers from two different channels as Horeca (Hotel/Retail/Café) customers or Retail channel (nominal) customers.</p></li>
 <li><p>The y labels contain values as 1 and 2. We have converted them into 0 and 1 for further analysis.</p></li>
@@ -2840,11 +2872,11 @@ <h2><span class="section-number">16.4.7. </span>Results and conclusion<a class="
 </ul>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">16.4.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">16.4.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/prashant111">Prashant Banerjee</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/prashant111/xgboost-k-fold-cv-feature-importance">XGBoost + k-fold CV + Feature Importance</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2886,13 +2918,13 @@ <h2><span class="section-number">16.4.9. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">16.3. </span>XGBoost</p>
+            <p class="prev-next-title"><span class="section-number">18.3. </span>XGBoost</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../unsupervised-learning.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">17. </span>Unsupervised learning</p>
+        <p class="prev-next-title"><span class="section-number">19. </span>Unsupervised learning</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/gradient-boosting/xgboost.html b/ml-advanced/gradient-boosting/xgboost.html
index 69cc3667e0..77f95df036 100644
--- a/ml-advanced/gradient-boosting/xgboost.html
+++ b/ml-advanced/gradient-boosting/xgboost.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>16.3. XGBoost &#8212; Ocademy Open Machine Learning Book</title>
+    <title>18.3. XGBoost &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="16.4. XGBoost + k-fold CV + Feature Importance" href="xgboost-k-fold-cv-feature-importance.html" />
-    <link rel="prev" title="16.2. Gradient boosting example" href="gradient-boosting-example.html" />
+    <link rel="next" title="18.4. XGBoost + k-fold CV + Feature Importance" href="xgboost-k-fold-cv-feature-importance.html" />
+    <link rel="prev" title="18.2. Gradient boosting example" href="gradient-boosting-example.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
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-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../unsupervised-learning-pca-and-clustering.html">
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 <section class="tex2jax_ignore mathjax_ignore" id="xgboost">
-<h1><span class="section-number">16.3. </span>XGBoost<a class="headerlink" href="#xgboost" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.3. </span>XGBoost<a class="headerlink" href="#xgboost" title="Permalink to this headline">#</a></h1>
 <section id="what-is-xgboost">
-<h2><span class="section-number">16.3.1. </span>What is XGBoost<a class="headerlink" href="#what-is-xgboost" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.1. </span>What is XGBoost<a class="headerlink" href="#what-is-xgboost" title="Permalink to this headline">#</a></h2>
 <p><strong>XGBoost</strong> is the leading model for working with standard tabular data (the type of data you store in Pandas DataFrames, as opposed to more exotic types of data like images and videos). XGBoost models dominate many famous competitions.</p>
 <p>To reach peak accuracy, XGBoost models require more knowledge and  <em>model tuning</em> than techniques like Random Forest. After this section, you’ill be able to</p>
 <ul class="simple">
@@ -1704,7 +1736,7 @@ <h2><span class="section-number">16.3.1. </span>What is XGBoost<a class="headerl
 <figure class="align-default" id="gradient-boosted-decision-trees">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost/Gradient_boosted_decision_trees.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost/Gradient_boosted_decision_trees.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost/Gradient_boosted_decision_trees.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 16.25 </span><span class="caption-text">Gradient Boosted Decision Trees</span><a class="headerlink" href="#gradient-boosted-decision-trees" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.25 </span><span class="caption-text">Gradient Boosted Decision Trees</span><a class="headerlink" href="#gradient-boosted-decision-trees" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We go through cycles that repeatedly builds new models and combines them into an <strong>ensemble</strong> model.  We start the cycle by calculating the errors for each observation in the dataset.  We then build a new model to predict those.  We add predictions from this error-predicting model to the “ensemble of models.”</p>
@@ -1713,7 +1745,7 @@ <h2><span class="section-number">16.3.1. </span>What is XGBoost<a class="headerl
 <p>This process may sound complicated, but the code to use it is straightforward. We’ll fill in some additional explanatory details in the <strong>model tuning</strong> section below.</p>
 </section>
 <section id="example">
-<h2><span class="section-number">16.3.2. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.2. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h2>
 <p>We will start with the data pre-loaded into <strong>train_X</strong>, <strong>test_X</strong>, <strong>train_y</strong>, <strong>test_y</strong>.</p>
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 <section id="model-tuning">
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 <section id="n-estimators-and-early-stopping-rounds">
-<h3><span class="section-number">16.3.3.1. </span><code class="docutils literal notranslate"><span class="pre">n_estimators</span> <span class="pre">and</span> <span class="pre">early_stopping_rounds</span></code><a class="headerlink" href="#n-estimators-and-early-stopping-rounds" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.3.3.1. </span><code class="docutils literal notranslate"><span class="pre">n_estimators</span> <span class="pre">and</span> <span class="pre">early_stopping_rounds</span></code><a class="headerlink" href="#n-estimators-and-early-stopping-rounds" title="Permalink to this headline">#</a></h3>
 <p>XGBoost has a few parameters that can dramatically affect your model’s accuracy and training speed.  The first parameters you should understand are: <code class="docutils literal notranslate"><span class="pre">n_estimators</span> <span class="pre">and</span> <span class="pre">early_stopping_rounds</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> specifies how many times to go through the modeling cycle described above.</p>
 <p>In the <a class="reference external" href="http://i.imgur.com/2q85n9s.png">underfitting vs overfitting graph</a>, n_estimators moves you further to the right.  Too low a value causes underfitting, which is inaccurate predictions on both training data and new data. Too large a value causes overfitting, which is accurate predictions on training data, but inaccurate predictions on new data (which is what we care about). You can experiment with your dataset to find the ideal.  Typical values range from 100-1000, though this depends a lot on the <code class="docutils literal notranslate"><span class="pre">learning</span> <span class="pre">rate</span></code> discussed below.</p>
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 <p>When using <code class="docutils literal notranslate"><span class="pre">early_stopping_rounds</span></code>, you need to set aside some of your data for checking the number of rounds to use.  If you later want to fit a model with all of your data, set <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> to whatever value you found to be optimal when run with early stopping.</p>
 </section>
 <section id="learning-rate">
-<h3><span class="section-number">16.3.3.2. </span>learning_rate<a class="headerlink" href="#learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.3.3.2. </span>learning_rate<a class="headerlink" href="#learning-rate" title="Permalink to this headline">#</a></h3>
 <p>Here’s a subtle but important trick for better XGBoost models:</p>
 <p>Instead of getting predictions by simply adding up the predictions from each component model, we will multiply the predictions from each model by a small number before adding them in.  This means each tree we add to the ensemble helps us less.  In practice, this reduces the model’s propensity to overfit.</p>
 <p>So, you can use a higher value of <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> without overfitting.  If you use early stopping, the appropriate number of trees will be set automatically.</p>
@@ -1865,22 +1897,22 @@ <h3><span class="section-number">16.3.3.2. </span>learning_rate<a class="headerl
 </div>
 </section>
 <section id="n-jobs">
-<h3><span class="section-number">16.3.3.3. </span><code class="docutils literal notranslate"><span class="pre">n_jobs</span></code><a class="headerlink" href="#n-jobs" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.3.3.3. </span><code class="docutils literal notranslate"><span class="pre">n_jobs</span></code><a class="headerlink" href="#n-jobs" title="Permalink to this headline">#</a></h3>
 <p>On larger datasets where runtime is a consideration, you can use parallelism to build your models faster.  It’s common to set the parameter <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> equal to the number of cores on your machine.  On smaller datasets, this won’t help.</p>
 <p>The resulting model won’t be any better, so micro-optimizing for fitting time is typically nothing but a distraction. But, it’s useful in large datasets where you would otherwise spend a long time waiting during the <code class="docutils literal notranslate"><span class="pre">fit</span></code> command.</p>
 <p>XGBoost has a multitude of other parameters, but these will go a very long way in helping you fine-tune your XGBoost model for optimal performance.</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">16.3.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>XGBoost is currently the dominant algorithm for building accurate models on conventional data (also called tabular or strutured data).  Go apply it to improve your models!</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">16.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">16.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/dansbecker">DanB</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/dansbecker/xgboost/input?select=train.csv">XGBoost</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1922,13 +1954,13 @@ <h2><span class="section-number">16.3.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">16.2. </span>Gradient boosting example</p>
+            <p class="prev-next-title"><span class="section-number">18.2. </span>Gradient boosting example</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="xgboost-k-fold-cv-feature-importance.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">16.4. </span>XGBoost + k-fold CV + Feature Importance</p>
+        <p class="prev-next-title"><span class="section-number">18.4. </span>XGBoost + k-fold CV + Feature Importance</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/ml-advanced/kernel-method.html b/ml-advanced/kernel-method.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>18. Kernel method &#8212; Ocademy Open Machine Learning Book</title>
+    <title>20. Kernel method &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="19. Model selection" href="model-selection.html" />
-    <link rel="prev" title="17. Unsupervised learning" href="unsupervised-learning.html" />
+    <link rel="next" title="21. Unsupervised learning: PCA and clustering" href="unsupervised-learning-pca-and-clustering.html" />
+    <link rel="prev" title="19. Unsupervised learning" href="unsupervised-learning.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
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-     11.4. Logistic regression
+     12.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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-     13.2. More classifiers
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-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+  </label>
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+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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+     16.1. Introduction to clustering
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-     14.2. K-Means clustering
+     16.2. K-Means clustering
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-   15. Getting started with ensemble learning
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-     15.2. Random forest
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-     16.2. Gradient boosting example
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-     16.3. XGBoost
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     <a class="reference internal" href="gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
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  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="model-selection.html">
-   19. Model selection
+   22. Model selection
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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-   24. Autoencoder
+   27. Autoencoder
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-   25. Long-short term memory
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+   30. Deep Q-learning
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-   29. Image segmentation
+   32. Image segmentation
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+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   35. Overview
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-   33. Problem framing
+   36. Problem framing
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-   34. Data engineering
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-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
+     40.7. Python programming basics
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-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    <li class="toctree-l2">
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-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    <li class="toctree-l2">
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-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     37.14. Try it in Excel
+     40.14. Try it in Excel
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-     37.21. Soda profits
+     40.21. Soda profits
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     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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-     37.35. Tell a story
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     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     </a>
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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-     37.54. Managing data
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    <li class="toctree-l2">
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-     37.55. Exploring visualizations
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-     37.56. Try a different model
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     </a>
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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     </a>
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-     37.59. Retrying some regression
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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     </a>
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-     37.61. Delicious asian and indian cuisines
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     </a>
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-     37.62. Explore classification methods
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-     37.63. Kernel method assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
    </li>
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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     </a>
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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    <li class="toctree-l2">
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-     37.71. Lasso and Ridge Regression
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-     37.72. Regularized Linear Models
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-     37.73. Random forests intro and regression
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-     37.74. Random forests for classification
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-     37.75. Beyond random forests: more ensemble models
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     </a>
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
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-     37.79. Boosting with tuning
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.82. Data engineering
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     <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.83. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.84. Case Study: Debugging in Classification
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-     37.85. Case Study: Debugging in Regression
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-     37.86. Study the solvers
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     </a>
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-     37.87. Build classification models
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-     37.88. Build Classification Model
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-     37.89. Parameter play
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-     37.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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    </li>
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     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
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-     37.115. Generative Adversarial Networks (GANs)
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     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1324,7 +1356,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <div class="menu-dropdown__content">
     <ul>
       <li>
-        <a href="https://mybinder.org/v2/gh/ocademy-ai/machine-learning/release?urlpath=lab/tree/open-machine-learning-jupyter-book/ml-advanced/kernel-method.md"
+        <a href="https://mybinder.org/v2/gh/ocademy-ai/machine-learning/release?urlpath=lab/tree/open-machine-learning-jupyter-book/ml-advanced/kernel-method.ipynb"
    class="headerbtn"
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 data-placement="left"
@@ -1342,7 +1374,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
       </li>
       
       <li>
-        <a href="https://colab.research.google.com/github/ocademy-ai/machine-learning/blob/release/open-machine-learning-jupyter-book/ml-advanced/kernel-method.md"
+        <a href="https://colab.research.google.com/github/ocademy-ai/machine-learning/blob/release/open-machine-learning-jupyter-book/ml-advanced/kernel-method.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1437,7 +1469,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
       </li>
       
       <li>
-        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/ml-advanced/kernel-method.md"
+        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/ml-advanced/kernel-method.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1469,23 +1501,6 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
-title="Download notebook file"
->
-  
-
-<span class="headerbtn__icon-container">
-  <i class="fas fa-code"></i>
-  </span>
-<span class="headerbtn__text-container">.ipynb</span>
-</a>
-
-      </li>
-      
-      <li>
-        <a href="../_sources/ml-advanced/kernel-method.md"
-   class="headerbtn"
-   data-toggle="tooltip"
-data-placement="left"
 title="Download source file"
 >
   
@@ -1493,7 +1508,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <span class="headerbtn__icon-container">
   <i class="fas fa-file"></i>
   </span>
-<span class="headerbtn__text-container">.md</span>
+<span class="headerbtn__text-container">.ipynb</span>
 </a>
 
       </li>
@@ -1543,12 +1558,12 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#motivating-support-vector-machines">
-   18.1. Motivating Support Vector Machines
+   20.1. Motivating Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-maximizing-the-margin">
-   18.2. Support Vector Machines: Maximizing the
+   20.2. Support Vector Machines: Maximizing the
    <em>
     Margin
    </em>
@@ -1556,57 +1571,68 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fitting-a-support-vector-machine">
-   18.3. Fitting a support vector machine
+   20.3. Fitting a support vector machine
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#beyond-linear-boundaries-kernel-svm">
-   18.4. Beyond linear boundaries: Kernel SVM
+   20.4. Beyond linear boundaries: Kernel SVM
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hinge-loss">
-   18.5. Hinge loss
+   20.5. Hinge loss
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rbf-kernel">
-   18.6. RBF Kernel
+   20.6. RBF Kernel
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-regression-svr">
-   18.7. Support Vector Regression (SVR)
+   20.7. Support Vector Regression (SVR)
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#figure-https-static-1300131294-cos-ap-shanghai-myqcloud-com-images-svm-svr1-jpeg">
+   20.8. {figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#name-support-vector-regression-width-90">
+   20.9. name: support vector regression
+width: 90%
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-v-s-logistic-regression">
-   18.8. SVM v.s. logistic regression
+   20.10. SVM v.s. logistic regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svr-v-s-linear-regression">
-   18.9. SVR v.s. linear regression
+   20.11. SVR v.s. linear regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machine-summary">
-   18.10. Support Vector Machine Summary
+   20.12. Support Vector Machine Summary
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-lets-dive-into-the-math-of-svm">
-   18.11. [Optional] Let’s dive into the math of SVM …
+   20.13. [Optional] Let’s dive into the math of SVM …
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#acknowledgement">
-   18.12. Acknowledgement
+  <a class="reference internal nav-link" href="#your-turn">
+   20.14. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#your-turn">
-   18.13. Your turn! 🚀
+  <a class="reference internal nav-link" href="#acknowledgement">
+   20.15. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1630,12 +1656,12 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#motivating-support-vector-machines">
-   18.1. Motivating Support Vector Machines
+   20.1. Motivating Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-maximizing-the-margin">
-   18.2. Support Vector Machines: Maximizing the
+   20.2. Support Vector Machines: Maximizing the
    <em>
     Margin
    </em>
@@ -1643,57 +1669,68 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fitting-a-support-vector-machine">
-   18.3. Fitting a support vector machine
+   20.3. Fitting a support vector machine
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#beyond-linear-boundaries-kernel-svm">
-   18.4. Beyond linear boundaries: Kernel SVM
+   20.4. Beyond linear boundaries: Kernel SVM
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hinge-loss">
-   18.5. Hinge loss
+   20.5. Hinge loss
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rbf-kernel">
-   18.6. RBF Kernel
+   20.6. RBF Kernel
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-regression-svr">
-   18.7. Support Vector Regression (SVR)
+   20.7. Support Vector Regression (SVR)
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#figure-https-static-1300131294-cos-ap-shanghai-myqcloud-com-images-svm-svr1-jpeg">
+   20.8. {figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#name-support-vector-regression-width-90">
+   20.9. name: support vector regression
+width: 90%
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-v-s-logistic-regression">
-   18.8. SVM v.s. logistic regression
+   20.10. SVM v.s. logistic regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svr-v-s-linear-regression">
-   18.9. SVR v.s. linear regression
+   20.11. SVR v.s. linear regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machine-summary">
-   18.10. Support Vector Machine Summary
+   20.12. Support Vector Machine Summary
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-lets-dive-into-the-math-of-svm">
-   18.11. [Optional] Let’s dive into the math of SVM …
+   20.13. [Optional] Let’s dive into the math of SVM …
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#acknowledgement">
-   18.12. Acknowledgement
+  <a class="reference internal nav-link" href="#your-turn">
+   20.14. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#your-turn">
-   18.13. Your turn! 🚀
+  <a class="reference internal nav-link" href="#acknowledgement">
+   20.15. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1706,41 +1743,107 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <section class="tex2jax_ignore mathjax_ignore" id="kernel-method">
-<h1><span class="section-number">18. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
+  <div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>numpy<span class="w"> </span>pandas<span class="w"> </span>matplotlib<span class="w"> </span>scikit-learn<span class="w"> </span>jupyterlab-myst<span class="w"> </span>ipython<span class="w"> </span>seaborn<span class="w"> </span>ipywidgets
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_circles</span>
+<span class="kn">from</span> <span class="nn">mpl_toolkits</span> <span class="kn">import</span> <span class="n">mplot3d</span>
+<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span><span class="p">,</span> <span class="n">fixed</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section class="tex2jax_ignore mathjax_ignore" id="kernel-method">
+<h1><span class="section-number">20. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
 <p>SVMs are a powerful and flexible class of algorithms used for classification and regression. In this section, we will explore the intuition behind SVMs and their use in classification problems.
 To start with, let’s understand the basic concept of SVMs.
 Support Vector Machines (SVMs) are supervised learning algorithms that can be used for classification and regression tasks. SVMs try to find the best decision boundary that separates data points of different classes. The decision boundary is chosen such that it maximizes the margin between the data points of different classes.
 The margin is defined as the minimum distance between the decision boundary and the closest data points of each class. This makes SVMs very robust to outliers as the decision boundary is chosen based on the data that is closest to it.
 In classification problems, the goal is to find a decision boundary that separates the data into two or more classes. SVMs can be used for binary classification, where the classes are only two, or for multiclass classification, where there are more than two classes.</p>
-<section id="motivating-support-vector-machines">
-<h2><span class="section-number">18.1. </span>Motivating Support Vector Machines<a class="headerlink" href="#motivating-support-vector-machines" title="Permalink to this headline">#</a></h2>
-<p>In this section we will consider differentiated classification: rather than modelling each category, we simply find a line or curve or flowform that separates these categories from each other.</p>
-<div class="cell docutils container">
+<div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
-
-<span class="c1"># use seaborn plotting defaults</span>
-<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo1/demo.html&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">A demo of SVM. &lt;a href=&quot;https://cs.stanford.edu/~karpathy/svmjs/demo/&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo1/demo.html" width="105%" height="700px;" style="border:none;"></iframe>
+A demo of SVM. <a href="https://cs.stanford.edu/~karpathy/svmjs/demo/">[source]</a>
+</p>
+</div></div>
 </div>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo2/index.html&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">A demo of SVM. &lt;a href=&quot;http://macheads101.com/demos/svm-playground/&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/svm-vis/demo2/index.html" width="105%" height="700px;" style="border:none;"></iframe>
+A demo of SVM. <a href="http://macheads101.com/demos/svm-playground/">[source]</a>
+</p>
+</div></div>
+</div>
+<section id="motivating-support-vector-machines">
+<h2><span class="section-number">20.1. </span>Motivating Support Vector Machines<a class="headerlink" href="#motivating-support-vector-machines" title="Permalink to this headline">#</a></h2>
+<p>In this section we will consider differentiated classification: rather than modelling each category, we simply find a line or curve or flowform that separates these categories from each other.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
-<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                  <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.60</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">);</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># use seaborn plotting defaults</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.60</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/kernel-method_2_0.png" src="../_images/kernel-method_2_0.png" />
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f392e551430&gt;
+</pre></div>
+</div>
+<img alt="../_images/kernel-method_9_1.png" src="../_images/kernel-method_9_1.png" />
 </div>
 </div>
 <p>A linear discriminant classifier may draw straight lines to separate two sets of data, but there may be multiple split lines that perfectly distinguish between the two categories.</p>
@@ -1748,70 +1851,78 @@ <h2><span class="section-number">18.1. </span>Motivating Support Vector Machines
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">xfit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.1</span><span class="p">],</span> <span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.1</span><span class="p">],</span> <span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
 
 <span class="k">for</span> <span class="n">m</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.65</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">),</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">2.9</span><span class="p">)]:</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xfit</span><span class="p">,</span> <span class="n">m</span> <span class="o">*</span> <span class="n">xfit</span> <span class="o">+</span> <span class="n">b</span><span class="p">,</span> <span class="s1">&#39;-k&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xfit</span><span class="p">,</span> <span class="n">m</span> <span class="o">*</span> <span class="n">xfit</span> <span class="o">+</span> <span class="n">b</span><span class="p">,</span> <span class="s2">&quot;-k&quot;</span><span class="p">)</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/kernel-method_4_0.png" src="../_images/kernel-method_4_0.png" />
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(-1.0, 3.5)
+</pre></div>
+</div>
+<img alt="../_images/kernel-method_11_1.png" src="../_images/kernel-method_11_1.png" />
 </div>
 </div>
 <p>All three separators are good if we want to distinguish between these samples. But they have some significant differences. In the following, I will explain these separators with an example to show the difference between them. Now, suppose you want a new data point, which is marked with an “X” in this graph. So, which separator would you choose? This is a very interesting question! Because you can choose to give this new data point a different label. It’s up to you!
 Obviously we can’t simply “draw a line between classes”, but we do need to think a bit deeper.</p>
 </section>
 <section id="support-vector-machines-maximizing-the-margin">
-<h2><span class="section-number">18.2. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.2. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
 <p>SVM proposes a new solution algorithm which finds the nearest straight line by applying an appropriate boundary fill to each line. The following is an example of an improvement to the SVM algorithm.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">xfit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
 
 <span class="k">for</span> <span class="n">m</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.65</span><span class="p">,</span> <span class="mf">0.33</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">,</span> <span class="mf">0.55</span><span class="p">),</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">2.9</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)]:</span>
     <span class="n">yfit</span> <span class="o">=</span> <span class="n">m</span> <span class="o">*</span> <span class="n">xfit</span> <span class="o">+</span> <span class="n">b</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xfit</span><span class="p">,</span> <span class="n">yfit</span><span class="p">,</span> <span class="s1">&#39;-k&#39;</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">xfit</span><span class="p">,</span> <span class="n">yfit</span> <span class="o">-</span> <span class="n">d</span><span class="p">,</span> <span class="n">yfit</span> <span class="o">+</span> <span class="n">d</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span>
-                     <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#AAAAAA&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xfit</span><span class="p">,</span> <span class="n">yfit</span><span class="p">,</span> <span class="s2">&quot;-k&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span>
+        <span class="n">xfit</span><span class="p">,</span> <span class="n">yfit</span> <span class="o">-</span> <span class="n">d</span><span class="p">,</span> <span class="n">yfit</span> <span class="o">+</span> <span class="n">d</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#AAAAAA&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span>
+    <span class="p">)</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/kernel-method_6_0.png" src="../_images/kernel-method_6_0.png" />
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(-1.0, 3.5)
+</pre></div>
+</div>
+<img alt="../_images/kernel-method_15_1.png" src="../_images/kernel-method_15_1.png" />
 </div>
 </div>
 <p>In SVM, the best models are those that maximise this margin, and SVM is an optimal model that achieves the maximum margin and can effectively use the boundary information in the dataset to predict the distribution of new data points, thereby improving prediction accuracy. Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data.</p>
 </section>
 <section id="fitting-a-support-vector-machine">
-<h2><span class="section-number">18.3. </span>Fitting a support vector machine<a class="headerlink" href="#fitting-a-support-vector-machine" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.3. </span>Fitting a support vector machine<a class="headerlink" href="#fitting-a-support-vector-machine" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="svm-for-linear-classification-2d-case">
-<a class="reference internal image-reference" href="../_images/svm2d.png"><img alt="../_images/svm2d.png" src="../_images/svm2d.png" style="width: 90%;" /></a>
+<a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm2d.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm2d.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm2d.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.1 </span><span class="caption-text">SVM for Linear classification : 2D case. <a class="reference external" href="https://online.stat.psu.edu/stat555/node/102/">source</a></span><a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 20.1 </span><span class="caption-text">SVM for Linear classification : 2D case. <a class="reference external" href="https://online.stat.psu.edu/stat555/node/102/">source</a></span><a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="svm-for-linear-classification-3d-case">
-<a class="reference internal image-reference" href="../_images/svm3d.gif"><img alt="../_images/svm3d.gif" src="../_images/svm3d.gif" style="width: 90%;" /></a>
+<a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm3d.gif"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm3d.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm3d.gif" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.2 </span><span class="caption-text">SVM for Linear classification : 3D case. <a class="reference external" href="https://www.youtube.com/watch?v=3liCbRZPrZA">source</a></span><a class="headerlink" href="#svm-for-linear-classification-3d-case" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 20.2 </span><span class="caption-text">SVM for Linear classification : 3D case. <a class="reference external" href="https://www.youtube.com/watch?v=3liCbRZPrZA">source</a></span><a class="headerlink" href="#svm-for-linear-classification-3d-case" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Now we will use a linear kernel and set the C parameter to a large size:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span> <span class="c1"># &quot;Support vector classifier&quot;</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1E10</span><span class="p">)</span>
-<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">);</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1e10</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">SVC</label><div class="sk-toggleable__content"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div></div></div>
 </div>
 <p>Let’s create a quick and easy function to draw the SVM decision boundary:</p>
 <div class="cell docutils container">
@@ -1822,24 +1933,28 @@ <h2><span class="section-number">18.3. </span>Fitting a support vector machine<a
         <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
     <span class="n">xlim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()</span>
     <span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
-    
+
     <span class="c1"># create grid to evaluate model</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">xlim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xlim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
     <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">ylim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ylim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
     <span class="n">Y</span><span class="p">,</span> <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
     <span class="n">xy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">X</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">Y</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span><span class="o">.</span><span class="n">T</span>
     <span class="n">P</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">xy</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-    
+
     <span class="c1"># plot decision boundary and margins</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
-               <span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-               <span class="n">linestyles</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;--&#39;</span><span class="p">])</span>
-    
+    <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">]</span>
+    <span class="p">)</span>
+
     <span class="c1"># plot support vectors</span>
     <span class="k">if</span> <span class="n">plot_support</span><span class="p">:</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
-                   <span class="n">model</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
-                   <span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">facecolors</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">);</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+            <span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
+            <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+            <span class="n">facecolors</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xlim</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
 </pre></div>
@@ -1848,13 +1963,13 @@ <h2><span class="section-number">18.3. </span>Fitting a support vector machine<a
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">)</span>
-<span class="n">plot_svc_decision_function</span><span class="p">(</span><span class="n">model</span><span class="p">);</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
+<span class="n">plot_svc_decision_function</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/kernel-method_11_0.png" src="../_images/kernel-method_11_0.png" />
+<img alt="../_images/kernel-method_22_0.png" src="../_images/kernel-method_22_0.png" />
 </div>
 </div>
 <p>This is a maximum expansion of the space between the two points. Note that some of the practice points just reach the boundary and are marked with black circles. Those points are important in the matching process and are called, support vectors, which is the name of this algorithm. In Skeeter-learning, the identification of these points is stored in the support_vectors_ property of the classifier at:</p>
@@ -1877,76 +1992,77 @@ <h2><span class="section-number">18.3. </span>Fitting a support vector machine<a
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_svm</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                      <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.60</span><span class="p">)</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.60</span><span class="p">)</span>
     <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="n">N</span><span class="p">]</span>
     <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="n">N</span><span class="p">]</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1E10</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1e10</span><span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-    
+
     <span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span> <span class="ow">or</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
     <span class="n">plot_svc_decision_function</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
 
+
 <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
 <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mf">0.0625</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">axi</span><span class="p">,</span> <span class="n">N</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">[</span><span class="mi">60</span><span class="p">,</span> <span class="mi">120</span><span class="p">]):</span>
     <span class="n">plot_svm</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">axi</span><span class="p">)</span>
-    <span class="n">axi</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;N = </span><span class="si">{0}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
+    <span class="n">axi</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;N = </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/kernel-method_15_0.png" src="../_images/kernel-method_15_0.png" />
+<img alt="../_images/kernel-method_26_0.png" src="../_images/kernel-method_26_0.png" />
 </div>
 </div>
 <p>One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, as can be seen from the comparison between the two figures, and increases the number of training points without affecting the classification results of the model.</p>
 <p>With the interactive component in IPython, you can interactively see this feature of the SVM model</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span><span class="p">,</span> <span class="n">fixed</span>
-<span class="n">interact</span><span class="p">(</span><span class="n">plot_svm</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">200</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">fixed</span><span class="p">(</span><span class="kc">None</span><span class="p">));</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">interact</span><span class="p">(</span><span class="n">plot_svm</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">200</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">fixed</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
 <script type="application/vnd.jupyter.widget-view+json">
-{"version_major": 2, "version_minor": 0, "model_id": "a3bd290cd925471c8811c407f72e06be"}
-</script></div>
+{"version_major": 2, "version_minor": 0, "model_id": "36073c34201f4ebc92035242737f4d2f"}
+</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;function __main__.plot_svm(N=10, ax=None)&gt;
+</pre></div>
+</div>
+</div>
 </div>
 </section>
 <section id="beyond-linear-boundaries-kernel-svm">
-<h2><span class="section-number">18.4. </span>Beyond linear boundaries: Kernel SVM<a class="headerlink" href="#beyond-linear-boundaries-kernel-svm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.4. </span>Beyond linear boundaries: Kernel SVM<a class="headerlink" href="#beyond-linear-boundaries-kernel-svm" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="kernel-svm-visualization">
-<a class="reference internal image-reference" href="../_images/svm1.gif"><img alt="../_images/svm1.gif" src="../_images/svm1.gif" style="width: 90%;" /></a>
+<a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm1.gif"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm1.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm1.gif" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.3 </span><span class="caption-text">Kernel SVM visualization (with a polynomial kernel). <a class="reference external" href="https://www.youtube.com/embed/3liCbRZPrZA">source</a></span><a class="headerlink" href="#kernel-svm-visualization" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 20.3 </span><span class="caption-text">Kernel SVM visualization (with a polynomial kernel). <a class="reference external" href="https://www.youtube.com/embed/3liCbRZPrZA">source</a></span><a class="headerlink" href="#kernel-svm-visualization" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Kernels is an “easy algorithm” that saves us from the tedious calculations in a high-dimensional space. If support vector machines and kernels are combined to project data into a high-dimensional space, making it possible to handle non-linear relationships with linear classifiers, the power of SVMs can be enormous.</p>
 <p>The following is the case for non-linearly separable data:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_circles</span>
-<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_circles</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">.1</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">.1</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_circles</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
 
-<span class="n">clf</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">)</span>
-<span class="n">plot_svc_decision_function</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">plot_support</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
+<span class="n">plot_svc_decision_function</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">plot_support</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/kernel-method_19_0.png" src="../_images/kernel-method_19_0.png" />
+<img alt="../_images/kernel-method_32_0.png" src="../_images/kernel-method_32_0.png" />
 </div>
 </div>
 <p>It is possible to project the data into higher dimensions, so that a linear partition line is sufficient. As an example, we can use a simple projection to compute a radial basis function with the centre on the middle block as follows:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">X</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -1954,76 +2070,214 @@ <h2><span class="section-number">18.4. </span>Beyond linear boundaries: Kernel S
 <p>This additional dimension of data can be visualised in a 3D diagram.:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mpl_toolkits</span> <span class="kn">import</span> <span class="n">mplot3d</span>
-
-<span class="k">def</span> <span class="nf">plot_3D</span><span class="p">(</span><span class="n">elev</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">):</span>
-    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s1">&#39;3d&#39;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_3D</span><span class="p">(</span><span class="n">elev</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">):</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">view_init</span><span class="p">(</span><span class="n">elev</span><span class="o">=</span><span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="n">azim</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;x&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
+
 
-<span class="n">interact</span><span class="p">(</span><span class="n">plot_3D</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="mi">90</span><span class="p">],</span> <span class="n">azip</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="mi">180</span><span class="p">),</span>
-         <span class="n">X</span><span class="o">=</span><span class="n">fixed</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">y</span><span class="o">=</span><span class="n">fixed</span><span class="p">(</span><span class="n">y</span><span class="p">));</span>
+<span class="n">interact</span><span class="p">(</span><span class="n">plot_3D</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="mi">90</span><span class="p">],</span> <span class="n">azip</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="mi">180</span><span class="p">),</span> <span class="n">X</span><span class="o">=</span><span class="n">fixed</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">y</span><span class="o">=</span><span class="n">fixed</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
 <script type="application/vnd.jupyter.widget-view+json">
-{"version_major": 2, "version_minor": 0, "model_id": "6513f023a7404b9489170027a494f6aa"}
-</script></div>
+{"version_major": 2, "version_minor": 0, "model_id": "8e413f422b514ce9b6702041eb3f84d1"}
+</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;function __main__.plot_3D(elev=30, azim=30, X=array([[ 0.15278667,  0.08754135],
+       [ 0.88776962,  0.55169207],
+       [ 0.0175161 ,  0.00581797],
+       [-0.03293004,  0.08032821],
+       [-0.08149743, -0.01213793],
+       [ 0.53659766, -0.83491858],
+       [ 0.01172199,  0.19987014],
+       [-0.1378877 , -1.11147378],
+       [-0.30522876, -1.0236209 ],
+       [-0.00967435, -0.04958209],
+       [-0.69488029, -0.82765447],
+       [-0.83290807,  0.73422577],
+       [ 0.38340851, -0.93498342],
+       [-0.07976452,  1.07321413],
+       [ 0.11763265,  0.02313519],
+       [-0.82550868, -0.59334504],
+       [-0.71482822,  0.46504383],
+       [-0.86077117, -0.36267205],
+       [ 0.19471909, -0.12152657],
+       [ 0.65092388,  0.84343447],
+       [ 0.00218701,  0.06979334],
+       [ 0.2694147 , -0.08437284],
+       [-0.66882299,  0.79744786],
+       [-0.1227612 ,  0.0091283 ],
+       [ 0.86953002, -0.42673771],
+       [ 0.15587695, -0.11650909],
+       [ 0.11406084,  0.09823773],
+       [-0.88358554,  0.30650771],
+       [ 0.02171009,  0.02866704],
+       [ 0.08350555,  0.01970158],
+       [-0.00444559,  0.1125669 ],
+       [ 0.90066217, -0.00201512],
+       [ 0.8500079 ,  0.39855311],
+       [ 0.18344853,  0.84739884],
+       [-0.1659346 ,  0.79934654],
+       [-0.4536259 ,  0.85477917],
+       [ 0.2608994 ,  0.11308053],
+       [-0.22566311,  0.05513599],
+       [-0.0702449 , -0.02943798],
+       [ 0.07207316, -0.14122285],
+       [-0.10889015, -0.94868588],
+       [ 0.10877787, -0.01349189],
+       [ 0.3218363 , -0.89171456],
+       [ 0.77189331, -0.62225296],
+       [-0.71049159, -0.80547056],
+       [ 0.10246497,  0.03551549],
+       [ 0.7962894 , -0.67868216],
+       [-0.13439286, -0.0425603 ],
+       [-0.86709372, -0.04658835],
+       [ 0.86692223, -0.32990488],
+       [-0.04708409, -0.98467294],
+       [-0.01985221,  0.21339964],
+       [ 0.57508758, -0.86051746],
+       [-0.98458523,  0.1754371 ],
+       [ 0.97546499, -0.12454195],
+       [ 0.83418533,  0.55109972],
+       [-0.00715439, -0.06195971],
+       [ 0.63729169, -0.73276037],
+       [ 0.00787188, -0.19414517],
+       [ 0.99228882, -0.36517149],
+       [ 0.97855486,  0.46671096],
+       [ 0.16163413, -0.14833525],
+       [ 0.80784441,  0.20039064],
+       [-0.38842777,  0.81108501],
+       [ 0.27599245, -0.04757169],
+       [-0.10623676, -0.09212593],
+       [-0.03217266, -0.19218528],
+       [ 0.19661439, -0.07878261],
+       [ 0.6081741 ,  0.74909025],
+       [-0.17918718,  0.13732297],
+       [ 0.38388112,  0.97864687],
+       [-0.01464867,  0.98895967],
+       [-0.81027914,  0.52387965],
+       [ 0.07666073,  0.02963504],
+       [-0.22138944,  0.10367395],
+       [-0.14352846, -0.23872363],
+       [-0.09533841,  0.11673468],
+       [ 0.10664529, -0.01724643],
+       [ 0.19780732,  0.03979512],
+       [-0.8246408 , -0.294974  ],
+       [ 0.01868388, -0.19378998],
+       [-0.31512877,  0.20140169],
+       [-0.07894496,  0.01099375],
+       [-0.11623599, -0.08897925],
+       [-1.13022044, -0.14071768],
+       [-0.09475275, -0.14934714],
+       [-0.09872077,  0.05952164],
+       [-0.10733517, -0.0017876 ],
+       [-0.20963348, -0.08731546],
+       [ 1.16134512,  0.29343543],
+       [-0.06191778,  0.16440757],
+       [-0.06662133,  1.04519054],
+       [-0.64779594, -0.87717018],
+       [-0.01723783, -0.02969478],
+       [-0.29973701, -1.04487339],
+       [-0.99398617, -0.13452968],
+       [ 0.0054101 ,  0.00787013],
+       [-0.64982804, -0.60720857],
+       [ 0.0094717 ,  0.78652367],
+       [ 0.04320483,  0.00926372]]), y=array([1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1,
+       0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
+       0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,
+       1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
+       1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1]))&gt;
+</pre></div>
+</div>
+</div>
 </div>
 <p>Next we describe how to select and adjust the radial basis function centres.
 This type of basis function transformation is known as a kernel transformation, and this form of transformation solves the problem of shifting the centre of the basis function to a certain extent.
 However there is a disadvantage: there is the problem of computational overload as a new basis function needs to be computed each time. We can solve this problem with the help of the “kernel trick” applet, where we change the original linear kernel to a radial basis function kernel and then use the kernel model hyperparameters.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1E6</span><span class="p">)</span>
-<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">);</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;rbf&quot;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1e6</span><span class="p">)</span>
+<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_html"><style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(C=1000000.0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">SVC</label><div class="sk-toggleable__content"><pre>SVC(C=1000000.0)</pre></div></div></div></div></div></div></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;autumn&#39;</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;autumn&quot;</span><span class="p">)</span>
 <span class="n">plot_svc_decision_function</span><span class="p">(</span><span class="n">clf</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
-            <span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">facecolors</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+    <span class="n">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="n">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+    <span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
+    <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">facecolors</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/kernel-method_26_0.png" src="../_images/kernel-method_26_0.png" />
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f392c0bffa0&gt;
+</pre></div>
+</div>
+<img alt="../_images/kernel-method_39_1.png" src="../_images/kernel-method_39_1.png" />
 </div>
 </div>
 <p>Currently, for linearly divisible data, we usually use a linear support vector machine. However, if the data is non-linear, such as a classification problem, we can transform it into a linearly divisible problem, thus improving the generalisation of the model. In this case, kernel tricks are used to transform linearly divisible problems into non-linearly divisible problems.</p>
 </section>
 <section id="hinge-loss">
-<h2><span class="section-number">18.5. </span>Hinge loss<a class="headerlink" href="#hinge-loss" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.5. </span>Hinge loss<a class="headerlink" href="#hinge-loss" title="Permalink to this headline">#</a></h2>
 <p>An alternative to cross-entropy for binary classification problems is the hinge loss function. It is mainly studied for the Support Vector Machine (SVM) model. It is designed to perform binary classification of target values which in the set {-1, 1}</p>
 </section>
 <section id="rbf-kernel">
-<h2><span class="section-number">18.6. </span>RBF Kernel<a class="headerlink" href="#rbf-kernel" title="Permalink to this headline">#</a></h2>
-<div class="yt-container">
-   <iframe src="https://www.youtube.com/embed/NYwVM6_EuxQ" allowfullscreen></iframe>
+<h2><span class="section-number">20.6. </span>RBF Kernel<a class="headerlink" href="#rbf-kernel" title="Permalink to this headline">#</a></h2>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://www.youtube.com/embed/NYwVM6_EuxQ&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">video of RBF kernel. &lt;a href=&quot;https://www.youtube.com/embed/NYwVM6_EuxQ&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://www.youtube.com/embed/NYwVM6_EuxQ" width="105%" height="700px;" style="border:none;"></iframe>
+video of RBF kernel. <a href="https://www.youtube.com/embed/NYwVM6_EuxQ">[source]</a>
+</p>
+</div></div>
 </div>
 </section>
 <section id="support-vector-regression-svr">
-<h2><span class="section-number">18.7. </span>Support Vector Regression (SVR)<a class="headerlink" href="#support-vector-regression-svr" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.7. </span>Support Vector Regression (SVR)<a class="headerlink" href="#support-vector-regression-svr" title="Permalink to this headline">#</a></h2>
 <p>SVR regression (Squeeze-and-VectorRegression), also known as Squeeze Regression, is an optimisation problem. The basic idea is to find a regression plane such that the variables in all data sets are closest to a point on this plane, so that all points on the plane are correlated.
 Objective function:
 In SVR regression, the objective function is to minimise the two-parametric number of weights while keeping the points in each training set as far away as possible from the support vector on one side of their own category.</p>
-<figure class="align-default" id="support-vector-regression">
-<a class="reference internal image-reference" href="../_images/svr1.jpeg"><img alt="../_images/svr1.jpeg" src="../_images/svr1.jpeg" style="width: 90%;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 18.4 </span><span class="caption-text">An illustration of support vector regression</span><a class="headerlink" href="#support-vector-regression" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
+</section>
+<section id="figure-https-static-1300131294-cos-ap-shanghai-myqcloud-com-images-svm-svr1-jpeg">
+<h2><span class="section-number">20.8. </span>{figure} <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg">https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg</a><a class="headerlink" href="#figure-https-static-1300131294-cos-ap-shanghai-myqcloud-com-images-svm-svr1-jpeg" title="Permalink to this headline">#</a></h2>
+</section>
+<section id="name-support-vector-regression-width-90">
+<h2><span class="section-number">20.9. </span>name: support vector regression
+width: 90%<a class="headerlink" href="#name-support-vector-regression-width-90" title="Permalink to this headline">#</a></h2>
+<p>An illustration of support vector regression</p>
 </section>
 <section id="svm-v-s-logistic-regression">
-<h2><span class="section-number">18.8. </span>SVM v.s. logistic regression<a class="headerlink" href="#svm-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.10. </span>SVM v.s. logistic regression<a class="headerlink" href="#svm-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>Similarities: Both are linear classification algorithms
 Differences:</p>
 <ol class="simple">
@@ -2035,18 +2289,15 @@ <h2><span class="section-number">18.8. </span>SVM v.s. logistic regression<a cla
 <li><p>Optimization methods: LR is generally based on gradient descent, SVM on SMO</p></li>
 <li><p>For non-linear separable problems, SVM is more scalable than LR</p></li>
 </ol>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
 <p><a class="reference external" href="https://www.geeksforgeeks.org/differentiate-between-support-vector-machine-and-logistic-regression/">https://www.geeksforgeeks.org/differentiate-between-support-vector-machine-and-logistic-regression/</a></p>
-</div>
 </section>
 <section id="svr-v-s-linear-regression">
-<h2><span class="section-number">18.9. </span>SVR v.s. linear regression<a class="headerlink" href="#svr-v-s-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.11. </span>SVR v.s. linear regression<a class="headerlink" href="#svr-v-s-linear-regression" title="Permalink to this headline">#</a></h2>
 <p>The problem of overfitting is a very tricky one in the field of statistics. In this case, many machine learning methods, such as least squares (OLSE), will perform poorly. Support vector machines (SVR), on the other hand, can minimise the overfitting problem. SVR allows for non-linear fitting when there is enough data for training.
 A final issue to consider is OLSE linear regression. While linear regression is effective for most problems, it is not always effective for some special cases. For example, in OLSE linear regression, there is some bias in fitting the variables linearly because the model is not hyperplane. In contrast, SVR allows for non-linear fitting problems.</p>
 </section>
 <section id="support-vector-machine-summary">
-<h2><span class="section-number">18.10. </span>Support Vector Machine Summary<a class="headerlink" href="#support-vector-machine-summary" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.12. </span>Support Vector Machine Summary<a class="headerlink" href="#support-vector-machine-summary" title="Permalink to this headline">#</a></h2>
 <p>The basic model of SVM: is defined as a linear classifier with maximum interval on the feature space. Specifically, when linearly separable, it finds the optimal classification hyperplane for both classes of samples in the original space. When linearly indistinguishable, relaxation variables are added and samples from a low-dimensional input space are mapped to a higher-dimensional space via a non-linear mapping to make them linearly distinguishable, so that the optimal classification hyperplane can be found in that feature space.</p>
 <p>Advantages of SVM:</p>
 <ol class="simple">
@@ -2059,34 +2310,46 @@ <h2><span class="section-number">18.10. </span>Support Vector Machine Summary<a
 </ol>
 </section>
 <section id="optional-lets-dive-into-the-math-of-svm">
-<h2><span class="section-number">18.11. </span>[Optional] Let’s dive into the math of SVM …<a class="headerlink" href="#optional-lets-dive-into-the-math-of-svm" title="Permalink to this headline">#</a></h2>
-<div class="yt-container">
-   <iframe src="https://www.youtube.com/embed/05VABNfa1ds" allowfullscreen></iframe>
+<h2><span class="section-number">20.13. </span>[Optional] Let’s dive into the math of SVM …<a class="headerlink" href="#optional-lets-dive-into-the-math-of-svm" title="Permalink to this headline">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://www.youtube.com/embed/05VABNfa1ds&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">math of SVM. &lt;a href=&quot;https://www.youtube.com/embed/05VABNfa1ds&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
 </div>
-</section>
-<section id="acknowledgement">
-<h2><span class="section-number">18.12. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
-<p>Some code comes from <a class="reference external" href="https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html">jakevdp</a>, for which the code part is licenced under MIT licence.</p>
-<p style="text-align: center;">
-<iframe src="../html/svm-vis/demo1/demo.html" width="105%" height="650px;"
-style="border:none;"></iframe>
-A demo of SVM. <a href="https://cs.stanford.edu/~karpathy/svmjs/demo/">[source]</a>
-</p>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
 <p style="text-align: center;">
-<iframe src="../html/svm-vis/demo2/index.html" width="105%" height="600px;"
-style="border:none;"></iframe>
-A demo of SVM. <a href="http://macheads101.com/demos/svm-playground/">[source]</a>
+<iframe src="https://www.youtube.com/embed/05VABNfa1ds" width="105%" height="700px;" style="border:none;"></iframe>
+math of SVM. <a href="https://www.youtube.com/embed/05VABNfa1ds">[source]</a>
 </p>
+</div></div>
+</div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">18.13. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.14. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>You can follow this <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html"><span class="doc std std-doc">assignment</span></a> to practise Support Vector Machines with examples.</p>
 </section>
+<section id="acknowledgement">
+<h2><span class="section-number">20.15. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
+<p>Thanks for <a class="reference external" href="https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html">jakevdp</a>, for which the code part is licenced under MIT licence.</p>
+</section>
 </section>
 
 
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"}}]}}, 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"}}]}}, 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"}}]}}}, 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@@ -2127,13 +2390,13 @@ <h2><span class="section-number">18.13. </span>Your turn! 🚀<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">17. </span>Unsupervised learning</p>
+            <p class="prev-next-title"><span class="section-number">19. </span>Unsupervised learning</p>
         </div>
     </a>
-    <a class='right-next' id="next-link" href="model-selection.html" title="next page">
+    <a class='right-next' id="next-link" href="unsupervised-learning-pca-and-clustering.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">19. </span>Model selection</p>
+        <p class="prev-next-title"><span class="section-number">21. </span>Unsupervised learning: PCA and clustering</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/model-selection.html b/ml-advanced/model-selection.html
index 95dc8977ff..9f76bb2e30 100644
--- a/ml-advanced/model-selection.html
+++ b/ml-advanced/model-selection.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>19. Model selection &#8212; Ocademy Open Machine Learning Book</title>
+    <title>22. Model selection &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="20. Intro to Deep Learning" href="../deep-learning/dl-overview.html" />
-    <link rel="prev" title="18. Kernel method" href="kernel-method.html" />
+    <link rel="next" title="23. Intro to Deep Learning" href="../deep-learning/dl-overview.html" />
+    <link rel="prev" title="21. Unsupervised learning: PCA and clustering" href="unsupervised-learning-pca-and-clustering.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+     40.42. Machine Learning overview - assignment 1
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-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     37.46. Linear Regression Metrics
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-     37.47. Loss Function
+     40.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.66. Decision Trees - Intro and Regression
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+     22.1.1. Overview
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+     22.1.2. Underfitting
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       <a class="reference internal nav-link" href="#how-to-avoid-underfitting">
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+       22.1.2.1. How To Avoid Underfitting?
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     </ul>
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+     22.1.3. Overfitting
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+       22.1.3.1. How To Avoid Overfitting?
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+     22.1.4. Good Fitting
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+     22.1.5. A simple example of linear regression
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       \(\lambda\)
      </span>
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+   22.10. Your turn! 🚀
   </a>
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+   22.1. Over-fitting and under-fitting
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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+     22.1.1. Overview
     </a>
    </li>
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     <a class="reference internal nav-link" href="#underfitting">
-     19.1.2. Underfitting
+     22.1.2. Underfitting
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-to-avoid-underfitting">
-       19.1.2.1. How To Avoid Underfitting?
+       22.1.2.1. How To Avoid Underfitting?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overfitting">
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+     22.1.3. Overfitting
     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-to-avoid-overfitting">
-       19.1.3.1. How To Avoid Overfitting?
+       22.1.3.1. How To Avoid Overfitting?
       </a>
      </li>
     </ul>
    </li>
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     <a class="reference internal nav-link" href="#good-fitting">
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+     22.1.4. Good Fitting
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     <a class="reference internal nav-link" href="#a-simple-example-of-linear-regression">
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+   22.7. L1 and L2 Regularization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-impact-of-the-value-of-lambda">
-     19.7.1. The impact of the value of
+     22.7.1. The impact of the value of
      <span class="math notranslate nohighlight">
       \(\lambda\)
      </span>
@@ -1741,24 +1773,24 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   19.8. Dropout
+   22.8. Dropout
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prediction-after-dropout">
-     19.8.1. Prediction after dropout
+     22.8.1. Prediction after dropout
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusions">
-   19.9. Conclusions
+   22.9. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   19.10. Your turn! 🚀
+   22.10. Your turn! 🚀
   </a>
  </li>
 </ul>
@@ -1783,22 +1815,22 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="model-selection">
-<h1><span class="section-number">19. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">22. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
 <section id="over-fitting-and-under-fitting">
-<h2><span class="section-number">19.1. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.1. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
 <section id="overview">
-<h3><span class="section-number">19.1.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h3>
 <p>Remember that the main objective of any machine learning model is to generalize the learning based on training data, so that it will be able to do predictions accurately on unknown data. As you can notice the words ‘Overfitting’ and ‘Underfitting’ are kind of opposite of the term ‘Generalization’. Overfitting and underfitting models don’t generalize well and results in poor performance.</p>
 <p>These are the samples of over-fitting and under-fitting in regression:</p>
 <figure class="align-default" id="over-fitting-regression-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/under_over_justalright.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/under_over_justalright.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.1 </span><span class="caption-text">Over-fitting and under-fitting in regression</span><a class="headerlink" href="#over-fitting-regression-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.1 </span><span class="caption-text">Over-fitting and under-fitting in regression</span><a class="headerlink" href="#over-fitting-regression-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="underfitting">
-<h3><span class="section-number">19.1.2. </span>Underfitting<a class="headerlink" href="#underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.2. </span>Underfitting<a class="headerlink" href="#underfitting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Underfitting occurs when machine learning model don’t fit the training data well enough. It is usually caused by simple function that cannot capture the underlying trend in the data.</p></li>
 <li><p>Underfitting models have high error in training as well as test set. This behavior is called as ‘Low Bias’</p></li>
@@ -1806,7 +1838,7 @@ <h3><span class="section-number">19.1.2. </span>Underfitting<a class="headerlink
 <li><p>Since underfitting models don’t perform well on training set, it’s very easy to detect underfitting</p></li>
 </ul>
 <section id="how-to-avoid-underfitting">
-<h4><span class="section-number">19.1.2.1. </span>How To Avoid Underfitting?<a class="headerlink" href="#how-to-avoid-underfitting" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">22.1.2.1. </span>How To Avoid Underfitting?<a class="headerlink" href="#how-to-avoid-underfitting" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>Increasing the model complexity. e.g. If linear function under fit then try using polynomial features</p></li>
 <li><p>Increase the number of features by performing the feature engineering</p></li>
@@ -1814,13 +1846,13 @@ <h4><span class="section-number">19.1.2.1. </span>How To Avoid Underfitting?<a c
 </section>
 </section>
 <section id="overfitting">
-<h3><span class="section-number">19.1.3. </span>Overfitting<a class="headerlink" href="#overfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.3. </span>Overfitting<a class="headerlink" href="#overfitting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Overfitting occurs when machine learning model tries to fit the training data too well. It is usually caused by complicated function that creates lots of unnecessary curves and angles that are not related with data and end up capturing the noise in data.</p></li>
 <li><p>Overfitting models have low error in training set but high error in test set. This behavior is called as ‘High Variance’</p></li>
 </ul>
 <section id="how-to-avoid-overfitting">
-<h4><span class="section-number">19.1.3.1. </span>How To Avoid Overfitting?<a class="headerlink" href="#how-to-avoid-overfitting" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">22.1.3.1. </span>How To Avoid Overfitting?<a class="headerlink" href="#how-to-avoid-overfitting" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>Since overfitting algorithm captures the noise in data, reducing the number of features will help. We can manually select only important features or can use model selection algorithm for same</p></li>
 <li><p>We can also use the ‘Regularization’ technique. It works well when we have lots of slightly useful features. Sklearn linear model(Ridge and LASSO) uses regularization parameter ‘alpha’ to control the size of the coefficients by imposing a penalty. Please refer below tutorials for more details.</p></li>
@@ -1828,7 +1860,7 @@ <h4><span class="section-number">19.1.3.1. </span>How To Avoid Overfitting?<a cl
 </section>
 </section>
 <section id="good-fitting">
-<h3><span class="section-number">19.1.4. </span>Good Fitting<a class="headerlink" href="#good-fitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.4. </span>Good Fitting<a class="headerlink" href="#good-fitting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>It is a sweet spot between Underfitting and Overfitting model</p></li>
 <li><p>A good fitting model generalizes the learnings from training data and provide accurate predictions on new data</p></li>
@@ -1838,74 +1870,74 @@ <h3><span class="section-number">19.1.4. </span>Good Fitting<a class="headerlink
 <figure class="align-default" id="over-fitting-classification-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/classification.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/classification.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.2 </span><span class="caption-text">Over-fitting and under-fitting in classification</span><a class="headerlink" href="#over-fitting-classification-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.2 </span><span class="caption-text">Over-fitting and under-fitting in classification</span><a class="headerlink" href="#over-fitting-classification-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="a-simple-example-of-linear-regression">
-<h3><span class="section-number">19.1.5. </span>A simple example of linear regression<a class="headerlink" href="#a-simple-example-of-linear-regression" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.5. </span>A simple example of linear regression<a class="headerlink" href="#a-simple-example-of-linear-regression" title="Permalink to this headline">#</a></h3>
 <p>This is a simple graphical representation of linear regression training.</p>
 <figure class="align-default" id="datapoints-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-datapoints.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-datapoints.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.3 </span><span class="caption-text">Training data points</span><a class="headerlink" href="#datapoints-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.3 </span><span class="caption-text">Training data points</span><a class="headerlink" href="#datapoints-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="over-fitting-train-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.4 </span><span class="caption-text">Over-fitting model fits very well on training data</span><a class="headerlink" href="#over-fitting-train-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.4 </span><span class="caption-text">Over-fitting model fits very well on training data</span><a class="headerlink" href="#over-fitting-train-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="over-fitting-test-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting-testdata.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting-testdata.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.5 </span><span class="caption-text">Over-fitting model fits poorly on test data</span><a class="headerlink" href="#over-fitting-test-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.5 </span><span class="caption-text">Over-fitting model fits poorly on test data</span><a class="headerlink" href="#over-fitting-test-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="under-fitting-train-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.6 </span><span class="caption-text">Under-fitting model fits poorly on training data</span><a class="headerlink" href="#under-fitting-train-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.6 </span><span class="caption-text">Under-fitting model fits poorly on training data</span><a class="headerlink" href="#under-fitting-train-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="under-fitting-test-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting-test-data.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting-test-data.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.7 </span><span class="caption-text">Under-fitting model fits poorly on test data</span><a class="headerlink" href="#under-fitting-test-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.7 </span><span class="caption-text">Under-fitting model fits poorly on test data</span><a class="headerlink" href="#under-fitting-test-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="perfect-fitting-train-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.8 </span><span class="caption-text">Perfect-fitting model fits well on training data</span><a class="headerlink" href="#perfect-fitting-train-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.8 </span><span class="caption-text">Perfect-fitting model fits well on training data</span><a class="headerlink" href="#perfect-fitting-train-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="perfect-fitting-test-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit-test-data.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit-test-data.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.9 </span><span class="caption-text">Perfect-fitting model fits well on test data</span><a class="headerlink" href="#perfect-fitting-test-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.9 </span><span class="caption-text">Perfect-fitting model fits well on test data</span><a class="headerlink" href="#perfect-fitting-test-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="bias-variance-tradeoff">
-<h2><span class="section-number">19.2. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.2. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="graphicalillustration-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/graphicalillustration.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/graphicalillustration.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.10 </span><span class="caption-text">Graphical illustration of variance and bias</span><a class="headerlink" href="#graphicalillustration-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.10 </span><span class="caption-text">Graphical illustration of variance and bias</span><a class="headerlink" href="#graphicalillustration-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="model-complexity-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/total_error.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/total_error.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.11 </span><span class="caption-text">Model complexity v.s. error</span><a class="headerlink" href="#model-complexity-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.11 </span><span class="caption-text">Model complexity v.s. error</span><a class="headerlink" href="#model-complexity-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="interpreting-the-learning-curves">
-<h2><span class="section-number">19.3. </span>Interpreting the Learning Curves<a class="headerlink" href="#interpreting-the-learning-curves" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.3. </span>Interpreting the Learning Curves<a class="headerlink" href="#interpreting-the-learning-curves" title="Permalink to this headline">#</a></h2>
 <p>You might think about the information in the training data as being of two kinds: <em>signal</em> and <em>noise</em>. The signal is the part that generalizes, the part that can help our model make predictions from new data. The noise is that part that is <em>only</em> true of the training data; the noise is all of the random fluctuation that comes from data in the real-world or all of the incidental, non-informative patterns that can’t actually help the model make predictions. The noise is the part might look useful but really isn’t.</p>
 <p>We train a model by choosing weights or parameters that minimize the loss on a training set. You might know, however, that to accurately assess a model’s performance, we need to evaluate it on a new set of data, the <em>validation</em> data.</p>
 <p>When we train a model we’ve been plotting the loss on the training set epoch by epoch. To this we’ll add a plot the validation data too. These plots we call the <strong>learning curves</strong>. To train deep learning models effectively, we need to be able to interpret them.</p>
@@ -1915,29 +1947,29 @@ <h2><span class="section-number">19.3. </span>Interpreting the Learning Curves<a
 <p>We’ll look at a couple ways of getting more signal out of the training data while reducing the amount of noise.</p>
 </section>
 <section id="capacity">
-<h2><span class="section-number">19.4. </span>Capacity<a class="headerlink" href="#capacity" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.4. </span>Capacity<a class="headerlink" href="#capacity" title="Permalink to this headline">#</a></h2>
 <p>A model’s <strong>capacity</strong> refers to the size and complexity of the patterns it is able to learn. For neural networks, this will largely be determined by how many neurons it has and how they are connected together. If it appears that your network is underfitting the data, you should try increasing its capacity.</p>
 <p>You can increase the capacity of a network either by making it <em>wider</em> (more units to existing layers) or by making it <em>deeper</em> (adding more layers). Wider networks have an easier time learning more linear relationships, while deeper networks prefer more nonlinear ones. Which is better just depends on the dataset.</p>
 <p>You’ll explore how the capacity of a network can affect its performance in the exercise.</p>
 </section>
 <section id="early-stopping">
-<h2><span class="section-number">19.5. </span>Early Stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.5. </span>Early Stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
 <p>We mentioned that when a model is too eagerly learning noise, the validation loss may start to increase during training. To prevent this, we can simply stop the training whenever it seems the validation loss isn’t decreasing anymore. Interrupting the training this way is called <strong>early stopping</strong>.</p>
 <p>Once we detect that the validation loss is starting to rise again, we can reset the weights back to where the minimum occured. This ensures that the model won’t continue to learn noise and overfit the data.</p>
 <p>Training with early stopping also means we’re in less danger of stopping the training too early, before the network has finished learning signal. So besides preventing overfitting from training too long, early stopping can also prevent <em>underfitting</em> from not training long enough. Just set your training epochs to some large number (more than you’ll need), and early stopping will take care of the rest.</p>
 </section>
 <section id="adding-early-stopping">
-<h2><span class="section-number">19.6. </span>Adding Early Stopping<a class="headerlink" href="#adding-early-stopping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.6. </span>Adding Early Stopping<a class="headerlink" href="#adding-early-stopping" title="Permalink to this headline">#</a></h2>
 <p>In Keras, we include early stopping in our training through a callback. A <strong>callback</strong> is just a function you want run every so often while the network trains. The early stopping callback will run after every epoch. (Keras has <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/callbacks">a variety of useful callbacks</a> pre-defined, but you can <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LambdaCallback">define your own</a>, too.)</p>
 <figure class="align-default" id="earlystopping-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/traintestoverfitting.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/traintestoverfitting.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.12 </span><span class="caption-text">Early stopping</span><a class="headerlink" href="#earlystopping-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.12 </span><span class="caption-text">Early stopping</span><a class="headerlink" href="#earlystopping-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="l1-and-l2-regularization">
-<h2><span class="section-number">19.7. </span>L1 and L2 Regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.7. </span>L1 and L2 Regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
 <p>You may be familiar with Occam’s Razor principle: given two explanations for something, the explanation most likely to be correct is the ‘simplest’ one, the one that makes the least amount of assumptions. This also applies to the models learned by neural networks: given some training data and a network architecture, there are multiple sets of weights values (multiple models) that could explain the data, and simple models are less likely to overfit than complex ones.</p>
 <p>A ‘simple model’ in this context is a model where the distribution of parameter values has less entropy (or a model with fewer parmeters altogether, as we saw in the section above). Thus a common way to mitigate overfitting is to put constraints on the complexity of a network by forcing its weeights only to  take small values, which makes the distribution of weight values more ‘regular’. This is called ‘weight regularization’, and it is done by adding to the loss function of the network a cost associated with having large weights. This cost comes in two flavors:</p>
 <ul class="simple">
@@ -1952,59 +1984,59 @@ <h2><span class="section-number">19.7. </span>L1 and L2 Regularization<a class="
 <figure class="align-default" id="circlesquare-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/circlesquare.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/circlesquare.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.13 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#circlesquare-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.13 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#circlesquare-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="explainedairegularization-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/L1L2contour.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/L1L2contour.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.14 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#explainedairegularization-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.14 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#explainedairegularization-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="berkeley189s21-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ridgelassoItayEvron.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ridgelassoItayEvron.gif" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.15 </span><span class="caption-text">Different <span class="math notranslate nohighlight">\(\beta\)</span> and ellipses</span><a class="headerlink" href="#berkeley189s21-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.15 </span><span class="caption-text">Different <span class="math notranslate nohighlight">\(\beta\)</span> and ellipses</span><a class="headerlink" href="#berkeley189s21-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="p-norm-balls">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/p-norm_balls.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/p-norm_balls.webp" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.16 </span><span class="caption-text">Different p norm</span><a class="headerlink" href="#p-norm-balls" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.16 </span><span class="caption-text">Different p norm</span><a class="headerlink" href="#p-norm-balls" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="elasticnet-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/elastic_net_balls.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/elastic_net_balls.webp" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.17 </span><span class="caption-text">ElasticNet</span><a class="headerlink" href="#elasticnet-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.17 </span><span class="caption-text">ElasticNet</span><a class="headerlink" href="#elasticnet-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="the-impact-of-the-value-of-lambda">
-<h3><span class="section-number">19.7.1. </span>The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span><a class="headerlink" href="#the-impact-of-the-value-of-lambda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.7.1. </span>The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span><a class="headerlink" href="#the-impact-of-the-value-of-lambda" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="impact-of-lambda-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/lagrange-animation.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/lagrange-animation.gif" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.18 </span><span class="caption-text">The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span></span><a class="headerlink" href="#impact-of-lambda-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.18 </span><span class="caption-text">The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span></span><a class="headerlink" href="#impact-of-lambda-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="dropout">
-<h2><span class="section-number">19.8. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.8. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
 <p>Dropout is one of the most effective and most commonly used regularization techniques for neural network, developed by Hinton and his students at the University of Toronto. Dropout, applied to a layer, consists of randomly “dropping out” (i.e. set to zero) a number of output features of the layer during training. Let’s say a given layer would normally have returned a vector [0.2, 0.5, 1.3, 0.8, 1.1] for a given input sample during training; aafter applying dropout, this vector will have a few zero entries distributed at random, e.g. [0, 0.5, 1.3, 0, 1.1]. The ‘dropout rate’ is the fraction of the features that are being zeroed-out; it is usually set between 0.2 and 0.5. At test time, no units are dropped out, and instead the layer’s output values are scaled down by a factor equal to the dropout rate, so as to balance for the fact that more units are active than at training time.</p>
 <p>In tf.keras you can introduce a dropout in a network via the Dropout layer, which gets applied to the output of layer right before.</p>
 <figure class="align-default" id="dropout-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/dropoutgif.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/dropoutgif.gif" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.19 </span><span class="caption-text">Dropout</span><a class="headerlink" href="#dropout-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.19 </span><span class="caption-text">Dropout</span><a class="headerlink" href="#dropout-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="prediction-after-dropout">
-<h3><span class="section-number">19.8.1. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.8.1. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="prediction-after-dropout-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/kUc8r.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/kUc8r.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.20 </span><span class="caption-text">Prediction after dropout</span><a class="headerlink" href="#prediction-after-dropout-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.20 </span><span class="caption-text">Prediction after dropout</span><a class="headerlink" href="#prediction-after-dropout-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>During training, p neuron activations (usually, p=0.5, so 50%) are dropped. Doing this at the testing stage is not our goal (the goal is to achieve a better generalization). From the other hand, keeping all activations will lead to an input that is unexpected to the network, more precisely, too high (50% higher) input activations for the following layer</p>
@@ -2013,28 +2045,28 @@ <h3><span class="section-number">19.8.1. </span>Prediction after dropout<a class
 </section>
 </section>
 <section id="conclusions">
-<h2><span class="section-number">19.9. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.9. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="training-size-matters-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ZahidHasan.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ZahidHasan.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.21 </span><span class="caption-text">Training size matters</span><a class="headerlink" href="#training-size-matters-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.21 </span><span class="caption-text">Training size matters</span><a class="headerlink" href="#training-size-matters-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="steps-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/steps.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/steps.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.22 </span><span class="caption-text">How to choose a good model</span><a class="headerlink" href="#steps-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.22 </span><span class="caption-text">How to choose a good model</span><a class="headerlink" href="#steps-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="conclusion-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/Bias-vs.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/Bias-vs.webp" />
 <figcaption>
-<p><span class="caption-number">Fig. 19.23 </span><span class="caption-text">Conclusion</span><a class="headerlink" href="#conclusion-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.23 </span><span class="caption-text">Conclusion</span><a class="headerlink" href="#conclusion-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">19.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Machine learning model selection and dealing with overfitting and underfitting are crucial aspects of the machine learning pipeline. In this assignment, you’ll have the opportunity to apply your understanding of these concepts and techniques. Please complete the following tasks:
 <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html"><span class="doc std std-doc">assignment</span></a></p>
 </section>
@@ -2073,17 +2105,17 @@ <h2><span class="section-number">19.10. </span>Your turn! 🚀<a class="headerli
                 
     <!-- Previous / next buttons -->
 <div class='prev-next-area'>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">18. </span>Kernel method</p>
+            <p class="prev-next-title"><span class="section-number">21. </span>Unsupervised learning: PCA and clustering</p>
         </div>
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     <a class='right-next' id="next-link" href="../deep-learning/dl-overview.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">20. </span>Intro to Deep Learning</p>
+        <p class="prev-next-title"><span class="section-number">23. </span>Intro to Deep Learning</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/ml-advanced/unsupervised-learning-pca-and-clustering.html b/ml-advanced/unsupervised-learning-pca-and-clustering.html
new file mode 100644
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+  PREREQUISITES
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
+   1. Python programming introduction
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../prerequisites/python-programming-basics.html">
+   2. Python programming basics
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
+   3. Python programming advanced
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/introduction/introduction.html">
+   4. Introduction
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+  <label for="toctree-checkbox-1">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/working-with-data/working-with-data.html">
+   5. Working with data
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+  <label for="toctree-checkbox-2">
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+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/working-with-data/relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/working-with-data/non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/working-with-data/numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 has-children">
+    <a class="reference internal" href="../data-science/working-with-data/pandas/pandas.html">
+     5.4. Pandas
+    </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../data-science/data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
+   12. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
+     12.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
+     12.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
+     12.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
+     12.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
+   13. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
+   14. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
+     14.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
+     14.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
+     14.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="clustering/clustering-models-for-machine-learning.html">
+   16. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="clustering/introduction-to-clustering.html">
+     16.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="clustering/k-means-clustering.html">
+     16.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="ensemble-learning/getting-started-with-ensemble-learning.html">
+   17. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="ensemble-learning/bagging.html">
+     17.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="ensemble-learning/random-forest.html">
+     17.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="ensemble-learning/feature-importance.html">
+     17.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="gradient-boosting/introduction-to-gradient-boosting.html">
+   18. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="gradient-boosting/gradient-boosting.html">
+     18.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="gradient-boosting/gradient-boosting-example.html">
+     18.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="gradient-boosting/xgboost.html">
+     18.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     18.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="unsupervised-learning.html">
+   19. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="kernel-method.html">
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   21. Unsupervised learning: PCA and clustering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="model-selection.html">
+   22. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/dl-overview.html">
+   23. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/cnn.html">
+   24. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/gan.html">
+   25. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/rnn.html">
+   26. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/autoencoder.html">
+   27. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/lstm.html">
+   28. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/time-series.html">
+   29. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/dqn.html">
+   30. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/image-classification.html">
+   31. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/image-segmentation.html">
+   32. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/difussion-model.html">
+   33. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/object-detection.html">
+   34. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/overview.html">
+   35. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
+   36. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
+   37. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
+   38. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
+   39. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../assignments/README.html">
+   40. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
+     40.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
+     40.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/project-plan-template.html">
+     40.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
+     40.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
+     40.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
+     40.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
+     40.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
+     40.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
+     40.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
+     40.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
+     40.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
+     40.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
+     40.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
+     40.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
+     40.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
+     40.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
+     40.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
+     40.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/soda-profits.html">
+     40.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
+     40.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     40.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
+     40.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
+     40.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-preparation.html">
+     40.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
+     40.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     40.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
+     40.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
+     40.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     40.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
+     40.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/market-research.html">
+     40.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     40.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     40.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
+     40.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     40.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     40.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     40.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     40.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     40.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     40.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     40.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     40.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     40.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
+     40.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
+     40.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
+     40.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
+     40.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
+     40.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
+     40.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     40.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
+     40.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     40.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     40.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/explore-classification-methods.html">
+     40.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     40.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     40.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     40.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     40.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     40.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     40.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     40.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     40.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     40.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     40.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     40.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     40.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     40.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     40.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     40.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     40.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
+     40.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     40.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
+     40.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
+     40.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/study-the-solvers.html">
+     40.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-models.html">
+     40.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-model.html">
+     40.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/parameter-play.html">
+     40.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     40.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     40.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     40.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     40.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     40.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
+     40.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     40.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
+     40.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
+     40.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     40.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     40.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
+     40.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
+     40.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     40.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../slides/introduction.html">
+   41. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/python-programming/python-programming-introduction.html">
+     41.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/python-programming/python-programming-basics.html">
+     41.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/python-programming/python-programming-advanced.html">
+     41.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-introduction.html">
+     41.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/relational-vs-non-relational-database.html">
+     41.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/numpy-and-pandas.html">
+     41.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-visualization.html">
+     41.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-lifecycle.html">
+     41.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-in-the-cloud.html">
+     41.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-in-real-world.html">
+     41.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/ml-overview.html">
+     41.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/linear-regression.html">
+     41.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression.html">
+     41.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression-condensed.html">
+     41.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/neural-network.html">
+     41.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/build-an-ml-web-app.html">
+     41.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-advanced/unsupervised-learning.html">
+     41.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
+     41.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
+     41.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/deep-learning/cnn.html">
+     41.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/deep-learning/gan.html">
+     41.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+
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+</nav></div>
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+</div>
+<label for="__page-toc"
+  class="headerbtn headerbtn-page-toc"
+  
+>
+  
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+    </div>
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+
+<!-- Table of contents -->
+<div class="col-md-3 bd-toc show noprint">
+    <div class="tocsection onthispage pt-5 pb-3">
+        <i class="fas fa-list"></i> Contents
+    </div>
+    <nav id="bd-toc-nav" aria-label="Page">
+        <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#introduction">
+   21.1. 1. Introduction
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#principal-component-analysis-pca">
+   21.2. 2. Principal Component Analysis (PCA)
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#intuition-theories-and-application-issues">
+     21.2.1. Intuition, theories, and application issues
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#examples">
+   21.3. Examples
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#fishers-iris-dataset">
+     21.3.1. Fisher’s iris dataset
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#clustering">
+   21.4. 2. Clustering
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#k-means">
+     21.4.1. K-means
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#choosing-the-number-of-clusters-for-k-means">
+   21.5. Choosing the number of clusters for K-means
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#issues">
+   21.6. Issues
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#spectral-clustering">
+   21.7. Spectral clustering
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#agglomerative-clustering">
+   21.8. Agglomerative clustering
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#accuracy-metrics">
+   21.9. Accuracy metrics
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#demo-assignment">
+   21.10. 4. Demo assignment
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#useful-resources">
+   21.11. 5. Useful resources
+  </a>
+ </li>
+</ul>
+
+    </nav>
+</div>
+    </div>
+    <div class="article row">
+        <div class="col pl-md-3 pl-lg-5 content-container">
+            <!-- Table of contents that is only displayed when printing the page -->
+            <div id="jb-print-docs-body" class="onlyprint">
+                <h1>Unsupervised learning: PCA and clustering</h1>
+                <!-- Table of contents -->
+                <div id="print-main-content">
+                    <div id="jb-print-toc">
+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
+                        <nav aria-label="Page">
+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#introduction">
+   21.1. 1. Introduction
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#principal-component-analysis-pca">
+   21.2. 2. Principal Component Analysis (PCA)
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#intuition-theories-and-application-issues">
+     21.2.1. Intuition, theories, and application issues
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#examples">
+   21.3. Examples
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#fishers-iris-dataset">
+     21.3.1. Fisher’s iris dataset
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#clustering">
+   21.4. 2. Clustering
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#k-means">
+     21.4.1. K-means
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#choosing-the-number-of-clusters-for-k-means">
+   21.5. Choosing the number of clusters for K-means
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#issues">
+   21.6. Issues
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#spectral-clustering">
+   21.7. Spectral clustering
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#agglomerative-clustering">
+   21.8. Agglomerative clustering
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#accuracy-metrics">
+   21.9. Accuracy metrics
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#demo-assignment">
+   21.10. 4. Demo assignment
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#useful-resources">
+   21.11. 5. Useful resources
+  </a>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
+            </div>
+            <main id="main-content" role="main">
+                
+              <div>
+                
+  <div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies </span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>pandas<span class="w"> </span>scikit-learn<span class="w"> </span>numpy<span class="w"> </span>matplotlib<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython<span class="w"> </span>seaborn<span class="w"> </span>scikit-learn
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="o">%</span><span class="n">config</span> <span class="n">InlineBackend</span><span class="o">.</span><span class="n">figure_format</span> <span class="o">=</span> <span class="s1">&#39;retina&#39;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">sklearn.decomposition</span>
+<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">KMeans</span><span class="p">,</span>
+    <span class="n">AgglomerativeClustering</span><span class="p">,</span>
+    <span class="n">AffinityPropagation</span><span class="p">,</span>
+    <span class="n">SpectralClustering</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
+<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
+<span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">TSNE</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">scipy.cluster</span> <span class="kn">import</span> <span class="n">hierarchy</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">pdist</span>
+<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span> 
+
+<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Wall time: 16.5 s
+</pre></div>
+</div>
+</div>
+</div>
+<section class="tex2jax_ignore mathjax_ignore" id="unsupervised-learning-pca-and-clustering">
+<h1><span class="section-number">21. </span>Unsupervised learning: PCA and clustering<a class="headerlink" href="#unsupervised-learning-pca-and-clustering" title="Permalink to this headline">#</a></h1>
+<p>In this lesson, we will work with unsupervised learning methods such as Principal Component Analysis (PCA) and clustering. You will learn why and how we can reduce the dimensionality of the original data and what the main approaches are for grouping similar data points.</p>
+<section id="introduction">
+<h2><span class="section-number">21.1. </span>1. Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<p>The main feature of unsupervised learning algorithms, when compared to classification and regression methods, is that input data are unlabeled (i.e. no labels or classes given) and that the algorithm learns the structure of the data without any assistance. This creates two main differences. First, it allows us to process large amounts of data because the data does not need to be manually labeled. Second, it is difficult to evaluate the quality of an unsupervised algorithm due to the absence of an explicit goodness metric as used in supervised learning.</p>
+<p>One of the most common tasks in unsupervised learning is dimensionality reduction. On one hand, dimensionality reduction may help with data visualization (e.g. t-SNA method) while, on the other hand, it may help deal with the multicollinearity of your data and prepare the data for a supervised learning method (e.g. decision trees).</p>
+</section>
+<section id="principal-component-analysis-pca">
+<h2><span class="section-number">21.2. </span>2. Principal Component Analysis (PCA)<a class="headerlink" href="#principal-component-analysis-pca" title="Permalink to this headline">#</a></h2>
+<section id="intuition-theories-and-application-issues">
+<h3><span class="section-number">21.2.1. </span>Intuition, theories, and application issues<a class="headerlink" href="#intuition-theories-and-application-issues" title="Permalink to this headline">#</a></h3>
+<p>Principal Component Analysis is one of the easiest, most intuitive, and most frequently used methods for dimensionality reduction, projecting data onto its orthogonal feature subspace.</p>
+<img align="right" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp" width="400"><div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span><span class="p">,</span> <span class="n">display</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">image. &lt;a href=&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp" width="105%" height="700px;" style="border:none;"></iframe>
+image. <a href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp">[source]</a>
+</p>
+</div></div>
+</div>
+<p>More generally speaking, all observations can be considered as an ellipsoid in a subspace of an initial feature space, and the new basis set in this subspace is aligned with the ellipsoid axes. This assumption lets us remove highly correlated features since basis set vectors are orthogonal.
+In the general case, the resulting ellipsoid dimensionality matches the initial space dimensionality, but the assumption that our data lies in a subspace with a smaller dimension allows us to cut off the “excessive” space with the new projection (subspace). We accomplish this in a ‘greedy’ fashion, sequentially selecting each of the ellipsoid axes by identifying where the dispersion is maximal.</p>
+<blockquote>
+<div><p>“To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say ‘fourteen’ very loudly. Everyone does it.” - Geoffrey Hinton</p>
+</div></blockquote>
+<p>Let’s take a look at the mathematical formulation of this process:</p>
+<p>In order to decrease the dimensionality of our data from <span class="math notranslate nohighlight">\(n\)</span> to <span class="math notranslate nohighlight">\(k\)</span> with <span class="math notranslate nohighlight">\(k \leq n\)</span>, we sort our list of axes in order of decreasing dispersion and take the top-<span class="math notranslate nohighlight">\(k\)</span> of them.</p>
+<p>We begin by computing the dispersion and the covariance of the initial features. This is usually done with the covariance matrix. According to the covariance definition, the covariance of two features is computed as follows: $<span class="math notranslate nohighlight">\(cov(X_i, X_j) = E[(X_i - \mu_i) (X_j - \mu_j)] = E[X_i X_j] - \mu_i \mu_j,\)</span><span class="math notranslate nohighlight">\( where \)</span>\mu_i<span class="math notranslate nohighlight">\( is the expected value of the \)</span>i$th feature. It is worth noting that the covariance is symmetric, and the covariance of a vector with itself is equal to its dispersion.</p>
+<p>Therefore the covariance matrix is symmetric with the dispersion of the corresponding features on the diagonal. Non-diagonal values are the covariances of the corresponding pair of features. In terms of matrices where <span class="math notranslate nohighlight">\(\mathbf{X}\)</span> is the matrix of observations, the covariance matrix is as follows:</p>
+<div class="math notranslate nohighlight">
+\[\Sigma = E[(\mathbf{X} - E[\mathbf{X}]) (\mathbf{X} - E[\mathbf{X}])^{T}]\]</div>
+<p>Quick recap: matrices, as linear operators, have eigenvalues and eigenvectors. They are very convenient because they describe parts of our space that do not rotate and only stretch when we apply linear operators on them; eigenvectors remain in the same direction but are stretched by a corresponding eigenvalue. Formally, a matrix <span class="math notranslate nohighlight">\(M\)</span> with eigenvector <span class="math notranslate nohighlight">\(w_i\)</span> and eigenvalue <span class="math notranslate nohighlight">\(\lambda_i\)</span> satisfy this equation: <span class="math notranslate nohighlight">\(M w_i = \lambda_i w_i\)</span>.</p>
+<p>The covariance matrix for a sample <span class="math notranslate nohighlight">\(\mathbf{X}\)</span> can be written as a product of <span class="math notranslate nohighlight">\(\mathbf{X}^{T} \mathbf{X}\)</span>. According to the <a class="reference external" href="https://en.wikipedia.org/wiki/Rayleigh_quotient">Rayleigh quotient</a>, the maximum variation of our sample lies along the eigenvector of this matrix and is consistent with the maximum eigenvalue. Therefore, the principal components we aim to retain from the data are just the eigenvectors corresponding to the top-<span class="math notranslate nohighlight">\(k\)</span> largest eigenvalues of the matrix.</p>
+<p>The next steps are easier to digest. We multiply the matrix of our data <span class="math notranslate nohighlight">\(X\)</span> by these components to get the projection of our data onto the orthogonal basis of the chosen components. If the number of components was smaller than the initial space dimensionality, remember that we will lose some information upon applying this transformation.</p>
+</section>
+</section>
+<section id="examples">
+<h2><span class="section-number">21.3. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h2>
+<section id="fishers-iris-dataset">
+<h3><span class="section-number">21.3.1. </span>Fisher’s iris dataset<a class="headerlink" href="#fishers-iris-dataset" title="Permalink to this headline">#</a></h3>
+<p>Let’s start by uploading all of the essential modules and try out the iris example from the <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> documentation.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Loading the dataset</span>
+<span class="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">()</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
+
+<span class="c1"># Let&#39;s create a beautiful 3d-plot</span>
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">Axes3D</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rect</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">.95</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">elev</span><span class="o">=</span><span class="mi">48</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">134</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">cla</span><span class="p">()</span>
+
+<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="p">[(</span><span class="s1">&#39;Setosa&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;Versicolour&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;Virginica&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]:</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">text3D</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
+              <span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">+</span> <span class="mf">1.5</span><span class="p">,</span>
+              <span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">name</span><span class="p">,</span>
+              <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
+              <span class="n">bbox</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">))</span>
+<span class="c1"># Change the order of labels, so that they match</span>
+<span class="n">y_clr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">choose</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_clr</span><span class="p">,</span> 
+           <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">nipy_spectral</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">w_xaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">w_yaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">w_zaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>d:\anaconda3\envs\myconda1\lib\site-packages\ipykernel_launcher.py:9: MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.
+  if __name__ == &#39;__main__&#39;:
+d:\anaconda3\envs\myconda1\lib\site-packages\ipykernel_launcher.py:20: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
+Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[Text(0.0, 0, &#39;&#39;),
+ Text(1.0, 0, &#39;&#39;),
+ Text(2.0, 0, &#39;&#39;),
+ Text(3.0, 0, &#39;&#39;),
+ Text(4.0, 0, &#39;&#39;),
+ Text(5.0, 0, &#39;&#39;),
+ Text(6.0, 0, &#39;&#39;),
+ Text(7.0, 0, &#39;&#39;),
+ Text(8.0, 0, &#39;&#39;)]
+</pre></div>
+</div>
+<img alt="../_images/unsupervised-learning-pca-and-clustering_11_2.png" src="../_images/unsupervised-learning-pca-and-clustering_11_2.png" />
+</div>
+</div>
+<p>Now let’s see how PCA will improve the results of a simple model that is not able to correctly fit all of the training data:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Train, test splits</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+<span class="p">)</span>
+
+<span class="c1"># Decision trees with depth = 2</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">preds</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">{:.5f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy: 0.88889
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let’s try this again, but, this time, let’s reduce the dimensionality to 2 dimensions:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Using PCA from sklearn PCA</span>
+<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">X_centered</span> <span class="o">=</span> <span class="n">X</span> <span class="o">-</span> <span class="n">X</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_centered</span><span class="p">)</span>
+<span class="n">X_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_centered</span><span class="p">)</span>
+
+<span class="c1"># Plotting the results of PCA</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;bo&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Setosa&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;go&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Versicolour&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;ro&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Virginica&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x15efcfd6708&gt;
+</pre></div>
+</div>
+<img alt="../_images/unsupervised-learning-pca-and-clustering_15_1.png" src="../_images/unsupervised-learning-pca-and-clustering_15_1.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Test-train split and apply PCA</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X_pca</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+<span class="p">)</span>
+
+<span class="n">clf</span> <span class="o">=</span> <span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">preds</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">{:.5f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy: 0.91111
+</pre></div>
+</div>
+</div>
+</div>
+<p>The accuracy did not increase significantly in this case, but, with other datasets with a high number of dimensions, PCA can drastically improve the accuracy of decision trees and other ensemble methods.</p>
+<p>Now let’s check out the percent of variance that can be explained by each of the selected components.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">component</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> component: </span><span class="si">{}% o</span><span class="s2">f initial variance&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+            <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="p">)</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="s2">&quot; + &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
+            <span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2"> x </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">value</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">component</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)</span>
+        <span class="p">)</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1 component: 92.46% of initial variance
+0.361 x sepal length (cm) + -0.085 x sepal width (cm) + 0.857 x petal length (cm) + 0.358 x petal width (cm)
+2 component: 5.31% of initial variance
+0.657 x sepal length (cm) + 0.730 x sepal width (cm) + -0.173 x petal length (cm) + -0.075 x petal width (cm)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let’s look at the handwritten numbers dataset that we used before in the <a class="reference external" href="https://habrahabr.ru/company/ods/blog/322534/#derevya-resheniy-i-metod-blizhayshih-sosedey-v-zadache-raspoznavaniya-rukopisnyh-cifr-mnist">3rd lesson</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let’s start by visualizing our data. Fetch the first 10 numbers. The numbers are represented by 8 x 8 matrixes with the color intensity for each pixel. Every matrix is flattened into a vector of 64 numbers, so we get the feature version of the data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># f, axes = plt.subplots(5, 2, sharey=True, figsize=(16,6))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">([</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]),</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/unsupervised-learning-pca-and-clustering_22_0.png" src="../_images/unsupervised-learning-pca-and-clustering_22_0.png" />
+</div>
+</div>
+<p>Our data has 64 dimensions, but we are going to reduce it to only 2 and see that, even with just 2 dimensions, we can clearly see that digits separate into clusters.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">X_reduced</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Projecting </span><span class="si">%d</span><span class="s2">-dimensional data to 2D&quot;</span> <span class="o">%</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+    <span class="n">X_reduced</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="n">X_reduced</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+    <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
+    <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
+    <span class="n">s</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
+    <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s2">&quot;nipy_spectral&quot;</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;MNIST. PCA projection&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Projecting 64-dimensional data to 2D
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;MNIST. PCA projection&#39;)
+</pre></div>
+</div>
+<img alt="../_images/unsupervised-learning-pca-and-clustering_24_2.png" src="../_images/unsupervised-learning-pca-and-clustering_24_2.png" />
+</div>
+</div>
+<p>Indeed, with t-SNE, the picture looks better since PCA has a linear constraint while t-SNE does not. However, even with such a small dataset, the t-SNE algorithm takes significantly more time to complete than PCA.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">17</span><span class="p">)</span>
+
+<span class="n">X_tsne</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_tsne</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_tsne</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> 
+            <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;nipy_spectral&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;MNIST. t-SNE projection&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>d:\anaconda3\envs\myconda1\lib\site-packages\sklearn\manifold\_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from &#39;random&#39; to &#39;pca&#39; in 1.2.
+  FutureWarning,
+d:\anaconda3\envs\myconda1\lib\site-packages\sklearn\manifold\_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to &#39;auto&#39; in 1.2.
+  FutureWarning,
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;MNIST. t-SNE projection&#39;)
+</pre></div>
+</div>
+<img alt="../_images/unsupervised-learning-pca-and-clustering_26_2.png" src="../_images/unsupervised-learning-pca-and-clustering_26_2.png" />
+</div>
+</div>
+<p>In practice, we would choose the number of principal components such that we can explain 90% of the initial data dispersion (via the <code class="docutils literal notranslate"><span class="pre">explained_variance_ratio</span></code>). Here, that means retaining 21 principal components; therefore, we reduce the dimensionality from 64 features to 21.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Number of components&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Total explained variance&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">63</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">21</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/unsupervised-learning-pca-and-clustering_28_0.png" src="../_images/unsupervised-learning-pca-and-clustering_28_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="clustering">
+<h2><span class="section-number">21.4. </span>2. Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline">#</a></h2>
+<p>The main idea behind clustering is pretty straightforward. Basically, we say to ourselves, “I have these points here, and I can see that they organize into groups. It would be nice to describe these things more concretely, and, when a new point comes in, assign it to the correct group.” This general idea encourages exploration and opens up a variety of algorithms for clustering.</p>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span><span class="p">,</span> <span class="n">display</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/unsupervised-learning-pca-and-clustering/The%20examples%20of%20the%20outcomes%20from%20different%20algorithms%20from%20scikit-learn.png&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">image. &lt;a href=&quot;https://habrastorage.org/getpro/habr/post_images/8b9/ae5/586/8b9ae55861f22a2809e8b3a00ef815ad.png&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/unsupervised-learning-pca-and-clustering/The%20examples%20of%20the%20outcomes%20from%20different%20algorithms%20from%20scikit-learn.png" width="105%" height="700px;" style="border:none;"></iframe>
+image. <a href="https://habrastorage.org/getpro/habr/post_images/8b9/ae5/586/8b9ae55861f22a2809e8b3a00ef815ad.png">[source]</a>
+</p>
+</div></div>
+</div>
+<p>The algorithms listed below do not cover all the clustering methods out there, but they are the most commonly used ones.</p>
+<section id="k-means">
+<h3><span class="section-number">21.4.1. </span>K-means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h3>
+<p>K-means algorithm is the most popular and yet simplest of all the clustering algorithms. Here is how it works:</p>
+<ol class="simple">
+<li><p>Select the number of clusters <span class="math notranslate nohighlight">\(k\)</span> that you think is the optimal number.</p></li>
+<li><p>Initialize <span class="math notranslate nohighlight">\(k\)</span> points as “centroids” randomly within the space of our data.</p></li>
+<li><p>Attribute each observation to its closest centroid.</p></li>
+<li><p>Update the centroids to the center of all the attributed set of observations.</p></li>
+<li><p>Repeat steps 3 and 4 a fixed number of times or until all of the centroids are stable (i.e. no longer change in step 4).</p></li>
+</ol>
+<p>This algorithm is easy to describe and visualize. Let’s take a look.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Let&#39;s begin by allocation 3 cluster&#39;s points</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">150</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+
+<span class="n">X</span><span class="p">[</span><span class="mi">50</span><span class="p">:</span><span class="mi">100</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[</span><span class="mi">50</span><span class="p">:</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+
+<span class="n">X</span><span class="p">[</span><span class="mi">100</span><span class="p">:</span><span class="mi">150</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[</span><span class="mi">100</span><span class="p">:</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;bo&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x15efe146708&gt;]
+</pre></div>
+</div>
+<img alt="../_images/unsupervised-learning-pca-and-clustering_33_1.png" src="../_images/unsupervised-learning-pca-and-clustering_33_1.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Scipy has function that takes 2 tuples and return</span>
+<span class="c1"># calculated distance between them</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">cdist</span>
+
+<span class="c1"># Randomly allocate the 3 centroids</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">centroids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
+<span class="n">centroids</span> <span class="o">=</span> <span class="n">centroids</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+
+<span class="n">cent_history</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">cent_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">centroids</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+    <span class="c1"># Calculating the distance from a point to a centroid</span>
+    <span class="n">distances</span> <span class="o">=</span> <span class="n">cdist</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">centroids</span><span class="p">)</span>
+    <span class="c1"># Checking what&#39;s the closest centroid for the point</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">distances</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Labeling the point according the point&#39;s distance</span>
+    <span class="n">centroids</span> <span class="o">=</span> <span class="n">centroids</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">centroids</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">centroids</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">centroids</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="p">:],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="n">cent_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">centroids</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Let&#39;s plot K-means</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+    <span class="n">distances</span> <span class="o">=</span> <span class="n">cdist</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">cent_history</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">distances</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;bo&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;cluster #1&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;co&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;cluster #2&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;mo&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;cluster #3&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cent_history</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cent_history</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;rX&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Step </span><span class="si">{:}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/unsupervised-learning-pca-and-clustering_35_0.png" src="../_images/unsupervised-learning-pca-and-clustering_35_0.png" />
+</div>
+</div>
+<p>Here, we used Euclidean distance, but the algorithm will converge with any other metric. You can not only vary the number of steps or the convergence criteria but also the distance measure between the points and cluster centroids.</p>
+<p>Another “feature” of this algorithm is its sensitivity to the initial positions of the cluster centroids. You can run the algorithm several times and then average all the centroid results.</p>
+</section>
+</section>
+<section id="choosing-the-number-of-clusters-for-k-means">
+<h2><span class="section-number">21.5. </span>Choosing the number of clusters for K-means<a class="headerlink" href="#choosing-the-number-of-clusters-for-k-means" title="Permalink to this headline">#</a></h2>
+<p>In contrast to the supervised learning tasks such as classification and regression, clustering requires more effort to choose the optimization criterion. Usually, when working with k-means, we optimize the sum of squared distances between the observations and their centroids.</p>
+<div class="math notranslate nohighlight">
+\[\Large J(C) = \sum_{k=1}^K\sum_{i~\in~C_k} ||x_i - \mu_k|| \rightarrow \min\limits_C,\]</div>
+<p>where <span class="math notranslate nohighlight">\(C\)</span> – is a set of clusters with power <span class="math notranslate nohighlight">\(K\)</span>, <span class="math notranslate nohighlight">\(\mu_k\)</span> is a centroid of a cluster <span class="math notranslate nohighlight">\(C_k\)</span>.</p>
+<p>This definition seems reasonable – we want our observations to be as close to their centroids as possible. But, there is a problem – the optimum is reached when the number of centroids is equal to the number of observations, so you would end up with every single observation as its own separate cluster.</p>
+<p>In order to avoid that case, we should choose a number of clusters after which a function <span class="math notranslate nohighlight">\(J(C_k)\)</span> is decreasing less rapidly. More formally,
+$<span class="math notranslate nohighlight">\(\Large D(k) = \frac{|J(C_k) - J(C_{k+1})|}{|J(C_{k-1}) - J(C_k)|}  \rightarrow \min\limits_k \)</span>$</p>
+<p>Let’s look at an example.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inertia</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">):</span>
+    <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">inertia</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">inertia_</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">inertia</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;s&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;$k$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;$J(C_k)$&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>d:\anaconda3\envs\myconda1\lib\site-packages\sklearn\cluster\_kmeans.py:1037: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
+  &quot;KMeans is known to have a memory leak on Windows &quot;
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;$J(C_k)$&#39;)
+</pre></div>
+</div>
+<img alt="../_images/unsupervised-learning-pca-and-clustering_38_2.png" src="../_images/unsupervised-learning-pca-and-clustering_38_2.png" />
+</div>
+</div>
+<p>We see that <span class="math notranslate nohighlight">\(J(C_k)\)</span> decreases significantly until the number of clusters is 3 and then does not change as much anymore. This means that the optimal number of clusters is 3.</p>
+</section>
+<section id="issues">
+<h2><span class="section-number">21.6. </span>Issues<a class="headerlink" href="#issues" title="Permalink to this headline">#</a></h2>
+<p>Inherently, K-means is NP-hard. For <span class="math notranslate nohighlight">\(d\)</span> dimensions, <span class="math notranslate nohighlight">\(k\)</span> clusters, and <span class="math notranslate nohighlight">\(n\)</span> observations, we will find a solution in <span class="math notranslate nohighlight">\(O(n^{d k+1})\)</span> in time. There are some heuristics to deal with this; an example is MiniBatch K-means, which takes portions (batches) of data instead of fitting the whole dataset and then moves centroids by taking the average of the previous steps. Compare the implementation of K-means and MiniBatch K-means in the <a class="reference external" href="http://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html">sckit-learn documentation</a>.</p>
+<p>The <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html">implemetation</a> of the algorithm using <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> has its benefits such as the possibility to state the number of initializations with the <code class="docutils literal notranslate"><span class="pre">n_init</span></code> function parameter, which enables us to identify more robust centroids. Moreover, these runs can be done in parallel to decrease the computation time.</p>
+</section>
+<section id="spectral-clustering">
+<h2><span class="section-number">21.7. </span>Spectral clustering<a class="headerlink" href="#spectral-clustering" title="Permalink to this headline">#</a></h2>
+<p>Spectral clustering combines some of the approaches described above to create a stronger clustering method.</p>
+<p>First of all, this algorithm requires us to define the similarity matrix for observations called the adjacency matrix. This can be done in a similar fashion as in the Affinity Propagation algorithm: <span class="math notranslate nohighlight">\(A_{i, j} = - ||x_i - x_j||^{2}\)</span>. This matrix describes a full graph with the observations as vertices and the estimated similarity value between a pair of observations as edge weights for that pair of vertices. For the metric defined above and two-dimensional observations, this is pretty intuitive - two observations are similar if the edge between them is shorter.
+We’d like to split up the graph into two subgraphs in such a way that each observation in each subgraph would be similar to another observation in that subgraph. Formally, this is a Normalized cuts problem; for more details, we recommend reading <a class="reference external" href="http://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf">this paper</a>.</p>
+</section>
+<section id="agglomerative-clustering">
+<h2><span class="section-number">21.8. </span>Agglomerative clustering<a class="headerlink" href="#agglomerative-clustering" title="Permalink to this headline">#</a></h2>
+<p>The following algorithm is the simplest and easiest to understand among all the the clustering algorithms without a fixed number of clusters.</p>
+<p>The algorithm is fairly simple:</p>
+<ol class="simple">
+<li><p>We start by assigning each observation to its own cluster</p></li>
+<li><p>Then sort the pairwise distances between the centers of clusters in descending order</p></li>
+<li><p>Take the nearest two neigbor clusters and merge them together, and recompute the centers</p></li>
+<li><p>Repeat steps 2 and 3 until all the data is merged into one cluster</p></li>
+</ol>
+<p>The process of searching for the nearest cluster can be conducted with different methods of bounding the observations:</p>
+<ol class="simple">
+<li><p>Single linkage
+<span class="math notranslate nohighlight">\(d(C_i, C_j) = min_{x_i \in C_i, x_j \in C_j} ||x_i - x_j||\)</span></p></li>
+<li><p>Complete linkage
+<span class="math notranslate nohighlight">\(d(C_i, C_j) = max_{x_i \in C_i, x_j \in C_j} ||x_i - x_j||\)</span></p></li>
+<li><p>Average linkage
+<span class="math notranslate nohighlight">\(d(C_i, C_j) = \frac{1}{n_i n_j} \sum_{x_i \in C_i} \sum_{x_j \in C_j} ||x_i - x_j||\)</span></p></li>
+<li><p>Centroid linkage
+<span class="math notranslate nohighlight">\(d(C_i, C_j) = ||\mu_i - \mu_j||\)</span></p></li>
+</ol>
+<p>The 3rd one is the most effective in computation time since it does not require recomputing the distances every time the clusters are merged.</p>
+<p>The results can be visualized as a beautiful cluster tree (dendogram) to help recognize the moment the algorithm should be stopped to get optimal results. There are plenty of Python tools to build these dendograms for agglomerative clustering.</p>
+<p>Let’s consider an example with the clusters we got from K-means:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">150</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+
+<span class="n">X</span><span class="p">[</span><span class="mi">50</span><span class="p">:</span><span class="mi">100</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[</span><span class="mi">50</span><span class="p">:</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+
+<span class="n">X</span><span class="p">[</span><span class="mi">100</span><span class="p">:</span><span class="mi">150</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">X</span><span class="p">[</span><span class="mi">100</span><span class="p">:</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+
+<span class="c1"># pdist will calculate the upper triangle of the pairwise distance matrix</span>
+<span class="n">distance_mat</span> <span class="o">=</span> <span class="n">pdist</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="c1"># linkage — is an implementation if agglomerative algorithm</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">hierarchy</span><span class="o">.</span><span class="n">linkage</span><span class="p">(</span><span class="n">distance_mat</span><span class="p">,</span> <span class="s2">&quot;single&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">dn</span> <span class="o">=</span> <span class="n">hierarchy</span><span class="o">.</span><span class="n">dendrogram</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">color_threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/unsupervised-learning-pca-and-clustering_43_0.png" src="../_images/unsupervised-learning-pca-and-clustering_43_0.png" />
+</div>
+</div>
+</section>
+<section id="accuracy-metrics">
+<h2><span class="section-number">21.9. </span>Accuracy metrics<a class="headerlink" href="#accuracy-metrics" title="Permalink to this headline">#</a></h2>
+<p>As opposed to classfication, it is difficult to assess the quality of results from clustering. Here, a metric cannot depend on the labels but only on the goodness of split. Secondly, we do not usually have true labels of the observations when we use clustering.</p>
+<p>There are <em>internal</em> and <em>external</em> goodness metrics. External metrics use the information about the known true split while internal metrics do not use any external information and assess the goodness of clusters based only on the initial data. The optimal number of clusters is usually defined with respect to some internal metrics.</p>
+<p>All the metrics described below are implemented in <code class="docutils literal notranslate"><span class="pre">sklearn.metrics</span></code>.</p>
+<p><strong>Adjusted Rand Index (ARI)</strong></p>
+<p>Here, we assume that the true labels of objects are known. This metric does not depend on the labels’ values but on the data cluster split. Let <span class="math notranslate nohighlight">\(N\)</span> be the number of observations in a sample. Let <span class="math notranslate nohighlight">\(a\)</span> to be the number of observation pairs with the same labels and located in the same cluster, and let <span class="math notranslate nohighlight">\(b\)</span> to be the number of observations with different labels and located in different clusters. The Rand Index can be calculated using the following formula:</p>
+<div class="math notranslate nohighlight">
+\[\Large \text{RI} = \frac{2(a + b)}{n(n-1)}.\]</div>
+<p>In other words, it evaluates a share of observations for which these splits (initial and clustering result) are consistent. The Rand Index (RI) evaluates the similarity of the two splits of the same sample. In order for this index to be close to zero for any clustering outcomes with any <span class="math notranslate nohighlight">\(n\)</span> and number of clusters, it is essential to scale it, hence the Adjusted Rand Index:</p>
+<div class="math notranslate nohighlight">
+\[\Large \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI}) - E[\text{RI}]}.\]</div>
+<p>This metric is symmetric and does not depend in the label permutation. Therefore, this index is a measure of distances between different sample splits. <span class="math notranslate nohighlight">\(\text{ARI}\)</span> takes on values in the <span class="math notranslate nohighlight">\([-1, 1]\)</span> range. Negative values indicate the independence of splits, and positive values indicate that these splits are consistent (they match <span class="math notranslate nohighlight">\(\text{ARI} = 1\)</span>).</p>
+<p><strong>Adjusted Mutual Information (AMI)</strong></p>
+<p>This metric is similar to <span class="math notranslate nohighlight">\(\text{ARI}\)</span>. It is also symmetric and does not depend on the labels’ values and permutation. It is defined by the <a class="reference external" href="https://en.wikipedia.org/wiki/Entropy_(information_theory)">entropy</a> function and interprets a sample split as a discrete distribution (likelihood of assigning to a cluster is equal to the percent of objects in it). The <span class="math notranslate nohighlight">\(MI\)</span> index is defined as the <a class="reference external" href="https://en.wikipedia.org/wiki/Mutual_information">mutual information</a> for two distributions, corresponding to the sample split into clusters. Intuitively, the mutual information measures the share of information common for both clustering splits i.e. how information about one of them decreases the uncertainty of the other one.</p>
+<p>Similarly to the <span class="math notranslate nohighlight">\(\text{ARI}\)</span>, the <span class="math notranslate nohighlight">\(\text{AMI}\)</span> is defined. This allows us to get rid of the <span class="math notranslate nohighlight">\(MI\)</span> index’s increase with the number of clusters. The <span class="math notranslate nohighlight">\(\text{AMI}\)</span> lies in the <span class="math notranslate nohighlight">\([0, 1]\)</span> range. Values close to zero mean the splits are independent, and those close to 1 mean they are similar (with complete match at <span class="math notranslate nohighlight">\(\text{AMI} = 1\)</span>).</p>
+<p><strong>Homogeneity, completeness, V-measure</strong></p>
+<p>Formally, these metrics are also defined based on the entropy function and the conditional entropy function, interpreting the sample splits as discrete distributions:</p>
+<div class="math notranslate nohighlight">
+\[\Large h = 1 - \frac{H(C\mid K)}{H(C)}, c = 1 - \frac{H(K\mid C)}{H(K)},\]</div>
+<p>where <span class="math notranslate nohighlight">\(K\)</span> is a clustering result and <span class="math notranslate nohighlight">\(C\)</span> is the initial split. Therefore, <span class="math notranslate nohighlight">\(h\)</span> evaluates whether each cluster is composed of same class objects, and <span class="math notranslate nohighlight">\(c\)</span> measures how well the same class objects fit the clusters. These metrics are not symmetric. Both lie in the <span class="math notranslate nohighlight">\([0, 1]\)</span> range, and values closer to 1 indicate more accurate clustering results. These metrics’ values are not scaled as the <span class="math notranslate nohighlight">\(\text{ARI}\)</span> or <span class="math notranslate nohighlight">\(\text{AMI}\)</span> metrics are and thus depend on the number of clusters. A random clustering result will not have metrics’ values closer to zero when the number of clusters is big enough and the number of objects is small. In such a case, it would be more reasonable to use <span class="math notranslate nohighlight">\(\text{ARI}\)</span>. However, with a large number of observations (more than 100) and the number of clusters less than 10, this issue is less critical and can be ignored.</p>
+<p><span class="math notranslate nohighlight">\(V\)</span>-measure is a combination of <span class="math notranslate nohighlight">\(h\)</span>, and <span class="math notranslate nohighlight">\(c\)</span> and is their harmonic mean:
+$<span class="math notranslate nohighlight">\(\Large v = 2\frac{hc}{h+c}.\)</span>$
+It is symmetric and measures how consistent two clustering results are.</p>
+<p><strong>Silhouette</strong></p>
+<p>In contrast to the metrics described above, this coefficient does not imply the knowledge about the true labels of the objects. It lets us estimate the quality of the clustering using only the initial, unlabeled sample and the clustering result. To start with, for each observation, the silhouette coefficient is computed. Let <span class="math notranslate nohighlight">\(a\)</span> be the mean of the distance between an object and other objects within one cluster and <span class="math notranslate nohighlight">\(b\)</span> be the mean distance from an object to an object from the nearest cluster (different from the one the object belongs to). Then the silhouette measure for this object is</p>
+<div class="math notranslate nohighlight">
+\[\Large s = \frac{b - a}{\max(a, b)}.\]</div>
+<p>The silhouette of a sample is a mean value of silhouette values from this sample. Therefore, the silhouette distance shows to which extent the distance between the objects of the same class differ from the mean distance between the objects from different clusters. This coefficient takes values in the <span class="math notranslate nohighlight">\([-1, 1]\)</span> range. Values close to -1 correspond to bad clustering results while values closer to 1 correspond to dense, well-defined clusters. Therefore, the higher the silhouette value is, the better the results from clustering.</p>
+<p>With the help of silhouette, we can identify the optimal number of clusters <span class="math notranslate nohighlight">\(k\)</span> (if we don’t know it already from the data) by taking the number of clusters that maximizes the silhouette coefficient.</p>
+<p>To conclude, let’s take a look at how these metrics perform with the MNIST handwritten numbers dataset:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">target</span>
+
+<span class="n">algorithms</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">algorithms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+<span class="n">algorithms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">AffinityPropagation</span><span class="p">())</span>
+<span class="n">algorithms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+    <span class="n">SpectralClustering</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">affinity</span><span class="o">=</span><span class="s2">&quot;nearest_neighbors&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">algorithms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">AgglomerativeClustering</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">algo</span> <span class="ow">in</span> <span class="n">algorithms</span><span class="p">:</span>
+    <span class="n">algo</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+        <span class="p">(</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;ARI&quot;</span><span class="p">:</span> <span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">labels_</span><span class="p">),</span>
+                <span class="s2">&quot;AMI&quot;</span><span class="p">:</span> <span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_mutual_info_score</span><span class="p">(</span>
+                    <span class="n">y</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">labels_</span><span class="p">,</span> <span class="n">average_method</span><span class="o">=</span><span class="s2">&quot;arithmetic&quot;</span>
+                <span class="p">),</span>
+                <span class="s2">&quot;Homogenity&quot;</span><span class="p">:</span> <span class="n">metrics</span><span class="o">.</span><span class="n">homogeneity_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">labels_</span><span class="p">),</span>
+                <span class="s2">&quot;Completeness&quot;</span><span class="p">:</span> <span class="n">metrics</span><span class="o">.</span><span class="n">completeness_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">labels_</span><span class="p">),</span>
+                <span class="s2">&quot;V-measure&quot;</span><span class="p">:</span> <span class="n">metrics</span><span class="o">.</span><span class="n">v_measure_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">labels_</span><span class="p">),</span>
+                <span class="s2">&quot;Silhouette&quot;</span><span class="p">:</span> <span class="n">metrics</span><span class="o">.</span><span class="n">silhouette_score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">labels_</span><span class="p">),</span>
+            <span class="p">}</span>
+        <span class="p">)</span>
+    <span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ARI&quot;</span><span class="p">,</span> <span class="s2">&quot;AMI&quot;</span><span class="p">,</span> <span class="s2">&quot;Homogenity&quot;</span><span class="p">,</span> <span class="s2">&quot;Completeness&quot;</span><span class="p">,</span> <span class="s2">&quot;V-measure&quot;</span><span class="p">,</span> <span class="s2">&quot;Silhouette&quot;</span><span class="p">],</span>
+    <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;K-means&quot;</span><span class="p">,</span> <span class="s2">&quot;Affinity&quot;</span><span class="p">,</span> <span class="s2">&quot;Spectral&quot;</span><span class="p">,</span> <span class="s2">&quot;Agglomerative&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+
+<span class="n">results</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>ARI</th>
+      <th>AMI</th>
+      <th>Homogenity</th>
+      <th>Completeness</th>
+      <th>V-measure</th>
+      <th>Silhouette</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>K-means</th>
+      <td>0.662295</td>
+      <td>0.736567</td>
+      <td>0.735448</td>
+      <td>0.742972</td>
+      <td>0.739191</td>
+      <td>0.182097</td>
+    </tr>
+    <tr>
+      <th>Affinity</th>
+      <td>0.174871</td>
+      <td>0.612364</td>
+      <td>0.958899</td>
+      <td>0.486801</td>
+      <td>0.645767</td>
+      <td>0.115161</td>
+    </tr>
+    <tr>
+      <th>Spectral</th>
+      <td>0.756461</td>
+      <td>0.852040</td>
+      <td>0.831691</td>
+      <td>0.876614</td>
+      <td>0.853562</td>
+      <td>0.182729</td>
+    </tr>
+    <tr>
+      <th>Agglomerative</th>
+      <td>0.794003</td>
+      <td>0.866832</td>
+      <td>0.857513</td>
+      <td>0.879096</td>
+      <td>0.868170</td>
+      <td>0.178497</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="demo-assignment">
+<h2><span class="section-number">21.10. </span>4. Demo assignment<a class="headerlink" href="#demo-assignment" title="Permalink to this headline">#</a></h2>
+<p>To practice with PCA and clustering, you can complete <a class="reference external" href="https://www.kaggle.com/kashnitsky/a7-demo-unsupervised-learning">this assignment</a> where you’ll be analyzing data from accelerometers and gyros of Samsung Galaxy S3 mobile phones. The assignment is just for you to practice, and goes with <a class="reference external" href="https://www.kaggle.com/kashnitsky/a7-demo-unsupervised-learning-solution">solution</a>.</p>
+</section>
+<section id="useful-resources">
+<h2><span class="section-number">21.11. </span>5. Useful resources<a class="headerlink" href="#useful-resources" title="Permalink to this headline">#</a></h2>
+<ul class="simple">
+<li><p>Medium <a class="reference external" href="https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-7-unsupervised-learning-pca-and-clustering-db7879568417">“story”</a> based on this notebook</p></li>
+<li><p>Main course <a class="reference external" href="https://mlcourse.ai">site</a>, <a class="reference external" href="https://github.com/Yorko/mlcourse.ai">course repo</a>, and YouTube <a class="reference external" href="https://www.youtube.com/watch?v=QKTuw4PNOsU&amp;list=PLVlY_7IJCMJeRfZ68eVfEcu-UcN9BbwiX">channel</a></p></li>
+<li><p>Course materials as a <a class="reference external" href="https://www.kaggle.com/kashnitsky/mlcourse">Kaggle Dataset</a></p></li>
+<li><p>Overview of clustering methods in the <a class="reference external" href="http://scikit-learn.org/stable/modules/clustering.html">scikit-learn doc</a>.</p></li>
+<li><p><a class="reference external" href="http://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues">Q&amp;A</a> for PCA with examples</p></li>
+<li><p><a class="reference external" href="https://github.com/diefimov/MTH594_MachineLearning/blob/master/ipython/Lecture10.ipynb">Notebook</a> on k-means and the EM-algorithm</p></li>
+</ul>
+</section>
+</section>
+
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   <ul>
    <li class="toctree-l2">
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-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     15.1. Bagging
+     17.1. Bagging
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     <a class="reference internal" href="ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
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    <li class="toctree-l2">
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-     15.3. Feature importance
+     17.3. Feature importance
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-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
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-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
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-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
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+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
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-   19. Model selection
+   22. Model selection
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 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
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  <li class="toctree-l1">
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-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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  </li>
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   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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  </li>
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   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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  </li>
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   <a class="reference internal" href="../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
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   <a class="reference internal" href="../deep-learning/time-series.html">
-   26. Time series
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  </li>
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   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
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   <a class="reference internal" href="../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
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  </li>
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   <a class="reference internal" href="../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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  </li>
 </ul>
@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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 </ul>
@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
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     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
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     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
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     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
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-     37.116. Basic classification: Classify images of clothing
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@@ -1528,101 +1560,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal nav-link" href="#k-means">
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+   19.1. K-Means
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#decision-boundaries">
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+     19.1.1. Decision Boundaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-k-means-algorithm">
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+     19.1.2. The K-Means Algorithm
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#k-means-variability">
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+     19.1.3. K-Means Variability
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#inertia">
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+     19.1.4. Inertia
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     <a class="reference internal nav-link" href="#multiple-initializations">
-     17.1.5. Multiple Initializations
+     19.1.5. Multiple Initializations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#centroid-initialization-methods">
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+     19.1.6. Centroid initialization methods
     </a>
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     <a class="reference internal nav-link" href="#accelerated-k-means">
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+     19.1.7. Accelerated K-Means
     </a>
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     <a class="reference internal nav-link" href="#mini-batch-k-means">
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+     19.5.2. Agglomerative Clustering
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@@ -1648,101 +1680,101 @@ <h2> Contents </h2>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-means">
-   17.1. K-Means
+   19.1. K-Means
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#decision-boundaries">
-     17.1.1. Decision Boundaries
+     19.1.1. Decision Boundaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-k-means-algorithm">
-     17.1.2. The K-Means Algorithm
+     19.1.2. The K-Means Algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#k-means-variability">
-     17.1.3. K-Means Variability
+     19.1.3. K-Means Variability
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#inertia">
-     17.1.4. Inertia
+     19.1.4. Inertia
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multiple-initializations">
-     17.1.5. Multiple Initializations
+     19.1.5. Multiple Initializations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#centroid-initialization-methods">
-     17.1.6. Centroid initialization methods
+     19.1.6. Centroid initialization methods
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#accelerated-k-means">
-     17.1.7. Accelerated K-Means
+     19.1.7. Accelerated K-Means
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#mini-batch-k-means">
-     17.1.8. Mini-Batch K-Means
+     19.1.8. Mini-Batch K-Means
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pca">
-   17.2. PCA
+   19.2. PCA
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#finding-the-optimal-number-of-clusters">
-     17.2.1. Finding the optimal number of clusters
+     19.2.1. Finding the optimal number of clusters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#limits-of-k-means">
-     17.2.2. Limits of K-Means
+     19.2.2. Limits of K-Means
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-clustering-for-image-segmentation">
-     17.2.3. Using Clustering for Image Segmentation
+     19.2.3. Using Clustering for Image Segmentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-clustering-for-preprocessing">
-     17.2.4. Using Clustering for Preprocessing
+     19.2.4. Using Clustering for Preprocessing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lda">
-   17.3. LDA
+   19.3. LDA
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dbscan">
-   17.4. DBSCAN
+   19.4. DBSCAN
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hierarchical-clustering">
-   17.5. Hierarchical Clustering
+   19.5. Hierarchical Clustering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#spectral-clustering">
-     17.5.1. Spectral Clustering
+     19.5.1. Spectral Clustering
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#agglomerative-clustering">
-     17.5.2. Agglomerative Clustering
+     19.5.2. Agglomerative Clustering
     </a>
    </li>
   </ul>
@@ -1758,7 +1790,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="unsupervised-learning">
-<h1><span class="section-number">17. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">19. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h1>
 <p>First, let’s import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20.</p>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
@@ -1817,7 +1849,7 @@ <h1><span class="section-number">17. </span>Unsupervised learning<a class="heade
 </div>
 </div>
 <section id="k-means">
-<h2><span class="section-number">17.1. </span>K-Means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.1. </span>K-Means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -1985,7 +2017,7 @@ <h2><span class="section-number">17.1. </span>K-Means<a class="headerlink" href=
 </div>
 </div>
 <section id="decision-boundaries">
-<h3><span class="section-number">17.1.1. </span>Decision Boundaries<a class="headerlink" href="#decision-boundaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.1. </span>Decision Boundaries<a class="headerlink" href="#decision-boundaries" title="Permalink to this headline">#</a></h3>
 <p>Let’s plot the model’s decision boundaries. This gives us a <em>Voronoi diagram</em>:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2081,7 +2113,7 @@ <h3><span class="section-number">17.1.1. </span>Decision Boundaries<a class="hea
 </div>
 </section>
 <section id="the-k-means-algorithm">
-<h3><span class="section-number">17.1.2. </span>The K-Means Algorithm<a class="headerlink" href="#the-k-means-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.2. </span>The K-Means Algorithm<a class="headerlink" href="#the-k-means-algorithm" title="Permalink to this headline">#</a></h3>
 <p>The KMeans class applies an optimized algorithm by default. To get the original K-Means algorithm (for educational purposes only), you must set init=“random”, n_init=1 and algorithm=“full”. These hyperparameters will be explained below.</p>
 <p>Let’s run the K-Means algorithm for 1, 2 and 3 iterations, to see how the centroids move around:</p>
 <div class="cell docutils container">
@@ -2147,7 +2179,7 @@ <h3><span class="section-number">17.1.2. </span>The K-Means Algorithm<a class="h
 </div>
 </section>
 <section id="k-means-variability">
-<h3><span class="section-number">17.1.3. </span>K-Means Variability<a class="headerlink" href="#k-means-variability" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.3. </span>K-Means Variability<a class="headerlink" href="#k-means-variability" title="Permalink to this headline">#</a></h3>
 <p>In the original K-Means algorithm, the centroids are just initialized randomly, and the algorithm simply runs a single iteration to gradually improve the centroids, as we saw above.</p>
 <p>However, one major problem with this approach is that if you run K-Means multiple times (or with different random seeds), it can converge to very different solutions, as you can see below:</p>
 <div class="cell docutils container">
@@ -2195,7 +2227,7 @@ <h3><span class="section-number">17.1.3. </span>K-Means Variability<a class="hea
 </div>
 </section>
 <section id="inertia">
-<h3><span class="section-number">17.1.4. </span>Inertia<a class="headerlink" href="#inertia" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.4. </span>Inertia<a class="headerlink" href="#inertia" title="Permalink to this headline">#</a></h3>
 <p>To select the best model, we will need a way to evaluate a K-Mean model’s performance. Unfortunately, clustering is an unsupervised task, so we do not have the targets. But at least we can measure the distance between each instance and its centroid. This is the idea behind the <em>inertia</em> metric:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2238,7 +2270,7 @@ <h3><span class="section-number">17.1.4. </span>Inertia<a class="headerlink" hre
 </div>
 </section>
 <section id="multiple-initializations">
-<h3><span class="section-number">17.1.5. </span>Multiple Initializations<a class="headerlink" href="#multiple-initializations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.5. </span>Multiple Initializations<a class="headerlink" href="#multiple-initializations" title="Permalink to this headline">#</a></h3>
 <p>So one approach to solve the variability issue is to simply run the K-Means algorithm multiple times with different random initializations, and select the solution that minimizes the inertia. For example, here are the inertias of the two “bad” models shown in the previous figure:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2292,7 +2324,7 @@ <h3><span class="section-number">17.1.5. </span>Multiple Initializations<a class
 </div>
 </section>
 <section id="centroid-initialization-methods">
-<h3><span class="section-number">17.1.6. </span>Centroid initialization methods<a class="headerlink" href="#centroid-initialization-methods" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.6. </span>Centroid initialization methods<a class="headerlink" href="#centroid-initialization-methods" title="Permalink to this headline">#</a></h3>
 <p>Instead of initializing the centroids entirely randomly, it is preferable to initialize them using the following algorithm, proposed in a <a class="reference external" href="https://goo.gl/eNUPw6">2006 paper</a> by David Arthur and Sergei Vassilvitskii:</p>
 <ul class="simple">
 <li><p>Take one centroid <span class="math notranslate nohighlight">\(c_1\)</span>, chosen uniformly at random from the dataset.</p></li>
@@ -2327,7 +2359,7 @@ <h3><span class="section-number">17.1.6. </span>Centroid initialization methods<
 </div>
 </section>
 <section id="accelerated-k-means">
-<h3><span class="section-number">17.1.7. </span>Accelerated K-Means<a class="headerlink" href="#accelerated-k-means" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.7. </span>Accelerated K-Means<a class="headerlink" href="#accelerated-k-means" title="Permalink to this headline">#</a></h3>
 <p>The K-Means algorithm can be significantly accelerated by avoiding many unnecessary distance calculations: this is achieved by exploiting the triangle inequality (given three points A, B and C, the distance AC is always such that AC ≤ AB + BC) and by keeping track of lower and upper bounds for distances between instances and centroids.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2357,7 +2389,7 @@ <h3><span class="section-number">17.1.7. </span>Accelerated K-Means<a class="hea
 <p>There’s no big difference in this case, as the dataset is fairly small.</p>
 </section>
 <section id="mini-batch-k-means">
-<h3><span class="section-number">17.1.8. </span>Mini-Batch K-Means<a class="headerlink" href="#mini-batch-k-means" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.8. </span>Mini-Batch K-Means<a class="headerlink" href="#mini-batch-k-means" title="Permalink to this headline">#</a></h3>
 <p>Scikit-Learn also implements a variant of the K-Means algorithm that supports mini-batches.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2391,7 +2423,7 @@ <h3><span class="section-number">17.1.8. </span>Mini-Batch K-Means<a class="head
 </section>
 </section>
 <section id="pca">
-<h2><span class="section-number">17.2. </span>PCA<a class="headerlink" href="#pca" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.2. </span>PCA<a class="headerlink" href="#pca" title="Permalink to this headline">#</a></h2>
 <p>If the dataset does not fit in memory, the simplest option is to use the memmap class, just like we did for incremental PCA in the previous chapter. First let’s load MNIST:</p>
 <p>Warning: since Scikit-Learn 0.24, fetch_openml() returns a Pandas DataFrame by default. To avoid this and keep the same code as in the book, we use as_frame=False.</p>
 <div class="cell docutils container">
@@ -2579,7 +2611,7 @@ <h2><span class="section-number">17.2. </span>PCA<a class="headerlink" href="#pc
 </div>
 </div>
 <section id="finding-the-optimal-number-of-clusters">
-<h3><span class="section-number">17.2.1. </span>Finding the optimal number of clusters<a class="headerlink" href="#finding-the-optimal-number-of-clusters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.1. </span>Finding the optimal number of clusters<a class="headerlink" href="#finding-the-optimal-number-of-clusters" title="Permalink to this headline">#</a></h3>
 <p>What if the number of clusters was set to a lower or greater value than 5?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2777,7 +2809,7 @@ <h3><span class="section-number">17.2.1. </span>Finding the optimal number of cl
 <p>As you can see, <span class="math notranslate nohighlight">\(k=5\)</span> looks like the best option here, as all clusters are roughly the same size, and they all cross the dashed line, which represents the mean silhouette score.</p>
 </section>
 <section id="limits-of-k-means">
-<h3><span class="section-number">17.2.2. </span>Limits of K-Means<a class="headerlink" href="#limits-of-k-means" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.2. </span>Limits of K-Means<a class="headerlink" href="#limits-of-k-means" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
@@ -2838,7 +2870,7 @@ <h3><span class="section-number">17.2.2. </span>Limits of K-Means<a class="heade
 </div>
 </section>
 <section id="using-clustering-for-image-segmentation">
-<h3><span class="section-number">17.2.3. </span>Using Clustering for Image Segmentation<a class="headerlink" href="#using-clustering-for-image-segmentation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.3. </span>Using Clustering for Image Segmentation<a class="headerlink" href="#using-clustering-for-image-segmentation" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Download the ladybug image</span>
@@ -2928,7 +2960,7 @@ <h3><span class="section-number">17.2.3. </span>Using Clustering for Image Segme
 </div>
 </section>
 <section id="using-clustering-for-preprocessing">
-<h3><span class="section-number">17.2.4. </span>Using Clustering for Preprocessing<a class="headerlink" href="#using-clustering-for-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.4. </span>Using Clustering for Preprocessing<a class="headerlink" href="#using-clustering-for-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>Let’s tackle the <em>digits dataset</em> which is a simple MNIST-like dataset containing 1,797 grayscale 8×8 images representing digits 0 to 9.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2962,7 +2994,7 @@ <h3><span class="section-number">17.2.4. </span>Using Clustering for Preprocessi
 </section>
 </section>
 <section id="lda">
-<h2><span class="section-number">17.3. </span>LDA<a class="headerlink" href="#lda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.3. </span>LDA<a class="headerlink" href="#lda" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -3633,7 +3665,7 @@ <h2><span class="section-number">17.3. </span>LDA<a class="headerlink" href="#ld
 </ol>
 </section>
 <section id="dbscan">
-<h2><span class="section-number">17.4. </span>DBSCAN<a class="headerlink" href="#dbscan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.4. </span>DBSCAN<a class="headerlink" href="#dbscan" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -3900,9 +3932,9 @@ <h2><span class="section-number">17.4. </span>DBSCAN<a class="headerlink" href="
 </div>
 </section>
 <section id="hierarchical-clustering">
-<h2><span class="section-number">17.5. </span>Hierarchical Clustering<a class="headerlink" href="#hierarchical-clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.5. </span>Hierarchical Clustering<a class="headerlink" href="#hierarchical-clustering" title="Permalink to this headline">#</a></h2>
 <section id="spectral-clustering">
-<h3><span class="section-number">17.5.1. </span>Spectral Clustering<a class="headerlink" href="#spectral-clustering" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.5.1. </span>Spectral Clustering<a class="headerlink" href="#spectral-clustering" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">SpectralClustering</span>
@@ -3986,7 +4018,7 @@ <h3><span class="section-number">17.5.1. </span>Spectral Clustering<a class="hea
 </div>
 </section>
 <section id="agglomerative-clustering">
-<h3><span class="section-number">17.5.2. </span>Agglomerative Clustering<a class="headerlink" href="#agglomerative-clustering" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.5.2. </span>Agglomerative Clustering<a class="headerlink" href="#agglomerative-clustering" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">AgglomerativeClustering</span>
@@ -4083,13 +4115,13 @@ <h3><span class="section-number">17.5.2. </span>Agglomerative Clustering<a class
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">16.4. </span>XGBoost + k-fold CV + Feature Importance</p>
+            <p class="prev-next-title"><span class="section-number">18.4. </span>XGBoost + k-fold CV + Feature Importance</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="kernel-method.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">18. </span>Kernel method</p>
+        <p class="prev-next-title"><span class="section-number">20. </span>Kernel method</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
index 456dcce3e8..2f05fbff96 100644
--- a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
+++ b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>12. Build a web app to use a Machine Learning model &#8212; Ocademy Open Machine Learning Book</title>
+    <title>13. Build a web app to use a Machine Learning model &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="13. Getting started with classification" href="classification/getting-started-with-classification.html" />
-    <link rel="prev" title="11.4. Logistic regression" href="regression/logistic-regression.html" />
+    <link rel="next" title="14. Getting started with classification" href="classification/getting-started-with-classification.html" />
+    <link rel="prev" title="12.4. Logistic regression" href="regression/logistic-regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 current active">
   <a class="current reference internal" href="#">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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   <a class="reference internal nav-link" href="#exercise-clean-your-data">
-   12.3. Exercise - clean your data
+   13.3. Exercise - clean your data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-build-your-model">
-   12.4. Exercise - build your model
+   13.4. Exercise - build your model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-pickle-your-model">
-   12.5. Exercise - ‘pickle’ your model
+   13.5. Exercise - ‘pickle’ your model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-build-a-flask-app">
-   12.6. Exercise - build a Flask app
+   13.6. Exercise - build a Flask app
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   12.7. Your turn! 🚀
+   13.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   12.8. Self study
+   13.8. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgements">
-   12.9. Acknowledgements
+   13.9. Acknowledgements
   </a>
  </li>
 </ul>
@@ -1614,54 +1629,54 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#building-an-app">
-   12.1. Building an app
+   13.1. Building an app
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#considerations">
-     12.1.1. Considerations
+     13.1.1. Considerations
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#tool">
-   12.2. Tool
+   13.2. Tool
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-clean-your-data">
-   12.3. Exercise - clean your data
+   13.3. Exercise - clean your data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-build-your-model">
-   12.4. Exercise - build your model
+   13.4. Exercise - build your model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-pickle-your-model">
-   12.5. Exercise - ‘pickle’ your model
+   13.5. Exercise - ‘pickle’ your model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-build-a-flask-app">
-   12.6. Exercise - build a Flask app
+   13.6. Exercise - build a Flask app
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   12.7. Your turn! 🚀
+   13.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   12.8. Self study
+   13.8. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgements">
-   12.9. Acknowledgements
+   13.9. Acknowledgements
   </a>
  </li>
 </ul>
@@ -1674,8 +1689,35 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <section class="tex2jax_ignore mathjax_ignore" id="build-a-web-app-to-use-a-machine-learning-model">
-<h1><span class="section-number">12. </span>Build a web app to use a Machine Learning model<a class="headerlink" href="#build-a-web-app-to-use-a-machine-learning-model" title="Permalink to this headline">#</a></h1>
+  <div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>pandas<span class="w"> </span>scikit-learn<span class="w"> </span>numpy<span class="w"> </span>matplotlib<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython<span class="w"> </span>requests<span class="w"> </span>flask
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">request</span><span class="p">,</span> <span class="n">render_template</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">classification_report</span>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section class="tex2jax_ignore mathjax_ignore" id="build-a-web-app-to-use-a-machine-learning-model">
+<h1><span class="section-number">13. </span>Build a web app to use a Machine Learning model<a class="headerlink" href="#build-a-web-app-to-use-a-machine-learning-model" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will train an ML model on a data set that’s out of this world: <em>UFO sightings over the past century</em>, sourced from NUFORC’s database.</p>
 <p>You will learn:</p>
 <ul class="simple">
@@ -1685,10 +1727,10 @@ <h1><span class="section-number">12. </span>Build a web app to use a Machine Lea
 <p>We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model ‘in the wild’, so to speak: in a web app.</p>
 <p>To do this, you need to build a web app using Flask.</p>
 <section id="building-an-app">
-<h2><span class="section-number">12.1. </span>Building an app<a class="headerlink" href="#building-an-app" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.1. </span>Building an app<a class="headerlink" href="#building-an-app" title="Permalink to this headline">#</a></h2>
 <p>There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app.</p>
 <section id="considerations">
-<h3><span class="section-number">12.1.1. </span>Considerations<a class="headerlink" href="#considerations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">13.1.1. </span>Considerations<a class="headerlink" href="#considerations" title="Permalink to this headline">#</a></h3>
 <p>There are many questions you need to ask:</p>
 <ul class="simple">
 <li><p><strong>Is it a web app or a mobile app?</strong> If you are building a mobile app or need to use the model in an IoT context, you could use <a class="reference external" href="https://www.tensorflow.org/lite/">TensorFlow Lite</a> and use the model in an Android or iOS app.</p></li>
@@ -1707,13 +1749,13 @@ <h3><span class="section-number">12.1.1. </span>Considerations<a class="headerli
 </section>
 </section>
 <section id="tool">
-<h2><span class="section-number">12.2. </span>Tool<a class="headerlink" href="#tool" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.2. </span>Tool<a class="headerlink" href="#tool" title="Permalink to this headline">#</a></h2>
 <p>For this task, you need two tools: Flask and Pickle, both of which run on Python.</p>
 <p>✅ What’s <a class="reference external" href="https://palletsprojects.com/p/flask/">Flask</a>? Defined as a ‘micro-framework’ by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at <a class="reference external" href="https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott">this Learn module</a> to practice building with Flask.</p>
 <p>✅ What’s <a class="reference external" href="https://docs.python.org/3/library/pickle.html">Pickle</a>? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you ‘pickle’ a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to ‘un-pickle’ a file. A pickled file has the suffix <code class="docutils literal notranslate"><span class="pre">.pkl</span></code>.</p>
 </section>
 <section id="exercise-clean-your-data">
-<h2><span class="section-number">12.3. </span>Exercise - clean your data<a class="headerlink" href="#exercise-clean-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.3. </span>Exercise - clean your data<a class="headerlink" href="#exercise-clean-your-data" title="Permalink to this headline">#</a></h2>
 <p>In this section, you’ll use data from 80,000 UFO sightings, gathered by <a class="reference external" href="https://nuforc.org">NUFORC</a> (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:</p>
 <ul class="simple">
 <li><p><strong>Long example description.</strong> “A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot”.</p></li>
@@ -1724,10 +1766,9 @@ <h2><span class="section-number">12.3. </span>Exercise - clean your data<a class
 <p>Import <code class="docutils literal notranslate"><span class="pre">pandas</span></code>, <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code>, and <code class="docutils literal notranslate"><span class="pre">numpy</span></code> as you did in the previous section and import the ufos spreadsheet. You can take a look at a sample data set:</p>
 <div class="cell tag_output_scroll docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-    
-<span class="n">ufos</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../assets/data/ufos.csv&#39;</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ufos</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+    <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ufos.csv&quot;</span>
+<span class="p">)</span>
 <span class="n">ufos</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
@@ -1736,59 +1777,137 @@ <h2><span class="section-number">12.3. </span>Exercise - clean your data<a class
 <p>Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the <code class="docutils literal notranslate"><span class="pre">Country</span></code> field.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ufos</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-    <span class="s1">&#39;Seconds&#39;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;duration (seconds)&#39;</span><span class="p">],</span> 
-    <span class="s1">&#39;Country&#39;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;country&#39;</span><span class="p">],</span>
-    <span class="s1">&#39;Latitude&#39;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">],</span>
-    <span class="s1">&#39;Longitude&#39;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span>
-<span class="p">})</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ufos</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;Seconds&quot;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;duration (seconds)&quot;</span><span class="p">],</span>
+        <span class="s2">&quot;Country&quot;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;country&quot;</span><span class="p">],</span>
+        <span class="s2">&quot;Latitude&quot;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">],</span>
+        <span class="s2">&quot;Longitude&quot;</span><span class="p">:</span> <span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;longitude&quot;</span><span class="p">],</span>
+    <span class="p">}</span>
+<span class="p">)</span>
 
 <span class="n">ufos</span><span class="o">.</span><span class="n">Country</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([&#39;us&#39;, nan, &#39;gb&#39;, &#39;ca&#39;, &#39;au&#39;, &#39;de&#39;], dtype=object)
+</pre></div>
+</div>
+</div>
 </div>
 <p>Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ufos</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ufos</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-<span class="n">ufos</span> <span class="o">=</span> <span class="n">ufos</span><span class="p">[(</span><span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;Seconds&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;Seconds&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">60</span><span class="p">)]</span>
+<span class="n">ufos</span> <span class="o">=</span> <span class="n">ufos</span><span class="p">[(</span><span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;Seconds&quot;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;Seconds&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">60</span><span class="p">)]</span>
 
 <span class="n">ufos</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
+Int64Index: 25863 entries, 2 to 80330
+Data columns (total 4 columns):
+ #   Column     Non-Null Count  Dtype  
+---  ------     --------------  -----  
+ 0   Seconds    25863 non-null  float64
+ 1   Country    25863 non-null  object 
+ 2   Latitude   25863 non-null  float64
+ 3   Longitude  25863 non-null  float64
+dtypes: float64(3), object(1)
+memory usage: 1010.3+ KB
+</pre></div>
+</div>
+</div>
 </div>
-<p>Import Scikit-learn’s <code class="docutils literal notranslate"><span class="pre">LabelEncoder</span></code> library to convert the text values for countries to a number:</p>
+<p>Use Scikit-learn’s <code class="docutils literal notranslate"><span class="pre">LabelEncoder</span></code> library to convert the text values for countries to a number:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
-
-<span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;Country&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;Country&#39;</span><span class="p">])</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;Country&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;Country&quot;</span><span class="p">])</span>
 
 <span class="n">ufos</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>LabelEncoder encodes data alphabetically.</p>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Seconds</th>
+      <th>Country</th>
+      <th>Latitude</th>
+      <th>Longitude</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2</th>
+      <td>20.0</td>
+      <td>3</td>
+      <td>53.200000</td>
+      <td>-2.916667</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>20.0</td>
+      <td>4</td>
+      <td>28.978333</td>
+      <td>-96.645833</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>30.0</td>
+      <td>4</td>
+      <td>35.823889</td>
+      <td>-80.253611</td>
+    </tr>
+    <tr>
+      <th>23</th>
+      <td>60.0</td>
+      <td>4</td>
+      <td>45.582778</td>
+      <td>-122.352222</td>
+    </tr>
+    <tr>
+      <th>24</th>
+      <td>3.0</td>
+      <td>3</td>
+      <td>51.783333</td>
+      <td>-0.783333</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 </section>
 <section id="exercise-build-your-model">
-<h2><span class="section-number">12.4. </span>Exercise - build your model<a class="headerlink" href="#exercise-build-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.4. </span>Exercise - build your model<a class="headerlink" href="#exercise-build-your-model" title="Permalink to this headline">#</a></h2>
 <p>Now you can get ready to train a model by dividing the data into the training and testing group.</p>
 <p>Select the three features you want to train on as your X vector, and the y vector will be the <code class="docutils literal notranslate"><span class="pre">Country</span></code>. You want to be able to input <code class="docutils literal notranslate"><span class="pre">Seconds</span></code>, <code class="docutils literal notranslate"><span class="pre">Latitude</span></code> and <code class="docutils literal notranslate"><span class="pre">Longitude</span></code> and get a country id to return.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-
-<span class="n">Selected_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Seconds&#39;</span><span class="p">,</span> <span class="s1">&#39;Latitude&#39;</span><span class="p">,</span> <span class="s1">&#39;Longitude&#39;</span><span class="p">]</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Selected_features</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Seconds&quot;</span><span class="p">,</span> <span class="s2">&quot;Latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;Longitude&quot;</span><span class="p">]</span>
 
 <span class="n">X</span> <span class="o">=</span> <span class="n">ufos</span><span class="p">[</span><span class="n">Selected_features</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">ufos</span><span class="p">[</span><span class="s1">&#39;Country&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">ufos</span><span class="p">[</span><span class="s2">&quot;Country&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
 
 <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 </pre></div>
@@ -1798,16 +1917,31 @@ <h2><span class="section-number">12.4. </span>Exercise - build your model<a clas
 <p>Train your model using logistic regression:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">classification_report</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-
-<span class="n">model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;sag&quot;</span><span class="p">,</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;sag&quot;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
 <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Predicted labels: &#39;</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: &#39;</span><span class="p">,</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicted labels: &quot;</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: &quot;</span><span class="p">,</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+           0       1.00      1.00      1.00        41
+           1       0.86      0.31      0.45       250
+           2       1.00      1.00      1.00         8
+           3       1.00      1.00      1.00       131
+           4       0.96      1.00      0.98      4743
+
+    accuracy                           0.96      5173
+   macro avg       0.96      0.86      0.89      5173
+weighted avg       0.96      0.96      0.96      5173
+
+Predicted labels:  [4 4 4 ... 3 4 4]
+Accuracy:  0.9640440750048328
 </pre></div>
 </div>
 </div>
@@ -1816,17 +1950,26 @@ <h2><span class="section-number">12.4. </span>Exercise - build your model<a clas
 <p>The model you created isn’t very revolutionary as you should be able to infer a <code class="docutils literal notranslate"><span class="pre">Country</span></code> from its <code class="docutils literal notranslate"><span class="pre">Latitude</span></code> and <code class="docutils literal notranslate"><span class="pre">Longitude</span></code>, but it’s a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.</p>
 </section>
 <section id="exercise-pickle-your-model">
-<h2><span class="section-number">12.5. </span>Exercise - ‘pickle’ your model<a class="headerlink" href="#exercise-pickle-your-model" title="Permalink to this headline">#</a></h2>
-<p>Now, it’s time to <em>pickle</em> your model! You can do that in a few lines of code. Once it’s <em>pickled</em>, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,</p>
+<h2><span class="section-number">13.5. </span>Exercise - ‘pickle’ your model<a class="headerlink" href="#exercise-pickle-your-model" title="Permalink to this headline">#</a></h2>
+<p>Now, it’s time to <em>pickle</em> your model! You can do that in a few lines of code. Once it’s <em>pickled</em>, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># download .pkl file from the cloud</span>
+<span class="n">cloud_url</span> <span class="o">=</span> <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ufo-model.pkl&quot;</span>
+<span class="n">model_filename</span> <span class="o">=</span> <span class="s2">&quot;ufo-model.pkl&quot;</span>
+<span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">cloud_url</span><span class="p">)</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_filename</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">local_file</span><span class="p">:</span>
+    <span class="n">local_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
 
-<span class="n">model_filename</span> <span class="o">=</span> <span class="s1">&#39;../assets/pickle/ufo-model.pkl&#39;</span>
-<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_filename</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">))</span>
+<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_filename</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">))</span>
 
-<span class="n">model</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;../assets/pickle/ufo-model.pkl&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="mi">50</span><span class="p">,</span> <span class="mi">44</span><span class="p">,</span> <span class="o">-</span><span class="mi">12</span><span class="p">]])))</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">&quot;ufo-model.pkl&quot;</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">50</span><span class="p">,</span> <span class="mi">44</span><span class="p">,</span> <span class="o">-</span><span class="mi">12</span><span class="p">]]))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[3]
 </pre></div>
 </div>
 </div>
@@ -1834,7 +1977,7 @@ <h2><span class="section-number">12.5. </span>Exercise - ‘pickle’ your model
 <p>The model returns <strong>‘3’</strong>, which is the country code for the UK. Wild! 👽</p>
 </section>
 <section id="exercise-build-a-flask-app">
-<h2><span class="section-number">12.6. </span>Exercise - build a Flask app<a class="headerlink" href="#exercise-build-a-flask-app" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.6. </span>Exercise - build a Flask app<a class="headerlink" href="#exercise-build-a-flask-app" title="Permalink to this headline">#</a></h2>
 <p>Now you can build a Flask app to call your model and return similar results but in a more visually pleasing way.</p>
 <p>Start by creating a folder called <code class="docutils literal notranslate"><span class="pre">/web-app</span></code> next to the <code class="docutils literal notranslate"><span class="pre">notebook.ipynb</span></code> file where your <code class="docutils literal notranslate"><span class="pre">ufo-model.pkl</span></code> file resides.</p>
 <p>In that folder create three more folders: <code class="docutils literal notranslate"><span class="pre">/static</span></code>, with a folder <code class="docutils literal notranslate"><span class="pre">/css</span></code> inside it, and <code class="docutils literal notranslate"><span class="pre">/templates</span></code>. You should now have the following files and directories:</p>
@@ -1936,19 +2079,17 @@ <h2><span class="section-number">12.6. </span>Exercise - build a Flask app<a cla
 <span class="p">&lt;/</span><span class="nt">html</span><span class="p">&gt;</span>
 </pre></div>
 </div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
 <p>Take a look at the templating in this file. Notice the ‘mustache’ syntax around variables that will be provided by the app, like the prediction text: <code class="docutils literal notranslate"><span class="pre">{{}}</span></code>. There’s also a form that posts a prediction to the <code class="docutils literal notranslate"><span class="pre">/predict</span></code> route.</p>
 <p>Finally, you’re ready to build the python file that drives the consumption of the model and the display of predictions:</p>
-</div>
 <p>In <code class="docutils literal notranslate"><span class="pre">app.py</span></code> add:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">request</span><span class="p">,</span> <span class="n">render_template</span>
-<span class="kn">import</span> <span class="nn">pickle</span>
-
-<span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
 
-<span class="n">model</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s2">&quot;../assets/pickle/ufo-model.pkl&quot;</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">))</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="nb">open</span><span class="p">(</span>
+        <span class="s2">&quot;ufo-model.pkl&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;rb&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+<span class="p">)</span>
 
 
 <span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)</span>
@@ -1958,7 +2099,6 @@ <h2><span class="section-number">12.6. </span>Exercise - build a Flask app<a cla
 
 <span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s2">&quot;/predict&quot;</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;POST&quot;</span><span class="p">])</span>
 <span class="k">def</span> <span class="nf">predict</span><span class="p">():</span>
-
     <span class="n">int_features</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">request</span><span class="o">.</span><span class="n">form</span><span class="o">.</span><span class="n">values</span><span class="p">()]</span>
     <span class="n">final_features</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">int_features</span><span class="p">)]</span>
     <span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">final_features</span><span class="p">)</span>
@@ -1973,13 +2113,10 @@ <h2><span class="section-number">12.6. </span>Exercise - build a Flask app<a cla
 
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="n">app</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">app</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
 <p>when you add <a class="reference external" href="https://www.askpython.com/python-modules/flask/flask-debug-mode"><code class="docutils literal notranslate"><span class="pre">debug=True</span></code></a> while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don’t enable this mode in a production app.</p>
-</div>
 <p>If you run <code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">app.py</span></code> or <code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">app.py</span></code> - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!</p>
 <p>Before doing that, take a look at the parts of <code class="docutils literal notranslate"><span class="pre">app.py</span></code>:</p>
 <ol class="simple">
@@ -1995,15 +2132,15 @@ <h2><span class="section-number">12.6. </span>Exercise - build a Flask app<a cla
 <p>Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">12.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called <code class="docutils literal notranslate"><span class="pre">/train</span></code>. What are the pros and cons of pursuing this method? Practice it by following this <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html"><span class="doc std std-doc">assignment</span></a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">12.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.</p>
 </section>
 <section id="acknowledgements">
-<h2><span class="section-number">12.9. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.9. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2017,16 +2154,16 @@ <h2><span class="section-number">12.9. </span>Acknowledgements<a class="headerli
         },
         codeMirrorConfig: {
             theme: "abcdef",
-            mode: "Python3"
+            mode: "python"
         },
         kernelOptions: {
-            kernelName: "Python3",
+            kernelName: "python3",
             path: "./ml-fundamentals"
         },
         predefinedOutput: true
     }
     </script>
-    <script>kernelName = 'Python3'</script>
+    <script>kernelName = 'python3'</script>
     <script type="text/javascript">
         function init() {
             WaveDrom.ProcessAll();
@@ -2045,13 +2182,13 @@ <h2><span class="section-number">12.9. </span>Acknowledgements<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">11.4. </span>Logistic regression</p>
+            <p class="prev-next-title"><span class="section-number">12.4. </span>Logistic regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="classification/getting-started-with-classification.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">13. </span>Getting started with classification</p>
+        <p class="prev-next-title"><span class="section-number">14. </span>Getting started with classification</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-fundamentals/classification/applied-ml-build-a-web-app.html b/ml-fundamentals/classification/applied-ml-build-a-web-app.html
index 777358bcf4..d1aaf45201 100644
--- a/ml-fundamentals/classification/applied-ml-build-a-web-app.html
+++ b/ml-fundamentals/classification/applied-ml-build-a-web-app.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>13.4. Applied Machine Learning : build a web app &#8212; Ocademy Open Machine Learning Book</title>
+    <title>14.4. Applied Machine Learning : build a web app &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
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-    <link rel="prev" title="13.3. Yet other classifiers" href="yet-other-classifiers.html" />
+    <link rel="next" title="15. parameter-optimization" href="../parameter-optimization/parameter-optimization.html" />
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    10. Machine Learning overview
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  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
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-     13.4. Applied Machine Learning : build a web app
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+    <link rel="next" title="14.1. Introduction to classification" href="introduction-to-classification.html" />
+    <link rel="prev" title="13. Build a web app to use a Machine Learning model" href="../build-a-web-app-to-use-a-machine-learning-model.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="introduction-to-classification.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
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   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/dqn.html">
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+   30. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-classification.html">
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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+   32. Image segmentation
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../assignments/README.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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-     37.24. Data processing in Python
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 <p>In Asia and India, food traditions are extremely diverse, and very delicious! Let’s look at data about regional cuisines to try to understand their ingredients.</p>
 <p><img alt="Thai food seller" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/thai-food.jpg" /></p>
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-<li class="toctree-l1"><a class="reference internal" href="applied-ml-build-a-web-app.html">13.4. Applied Machine Learning : build a web app</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="more-classifiers.html">14.2. More classifiers</a></li>
+<li class="toctree-l1"><a class="reference internal" href="yet-other-classifiers.html">14.3. Yet other classifiers</a></li>
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-            <p class="prev-next-title"><span class="section-number">12. </span>Build a web app to use a Machine Learning model</p>
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="13.2. More classifiers" href="more-classifiers.html" />
-    <link rel="prev" title="13. Getting started with classification" href="getting-started-with-classification.html" />
+    <link rel="next" title="14.2. More classifiers" href="more-classifiers.html" />
+    <link rel="prev" title="14. Getting started with classification" href="getting-started-with-classification.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="getting-started-with-classification.html">
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   <ul class="current">
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
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  </li>
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   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
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  </li>
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../assignments/README.html">
-   37. Self-paced assignments
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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-     37.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.103. Fun with Variational Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
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  </li>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
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   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hello-classifier">
-   13.1.2. Hello ‘classifier’
+   14.1.2. Hello ‘classifier’
   </a>
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+   14.1.3. Exercise - clean and balance your data
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-learning-about-cuisines">
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+   14.1.4. Exercise - learning about cuisines
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-   13.1.5. Discovering ingredients
+   14.1.5. Discovering ingredients
   </a>
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   <a class="reference internal nav-link" href="#balance-the-dataset">
-   13.1.6. Balance the dataset
+   14.1.6. Balance the dataset
   </a>
  </li>
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-   13.1.7. Self study
+   14.1.7. Self study
   </a>
  </li>
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   <a class="reference internal nav-link" href="#your-turn">
-   13.1.8. Your turn! 🚀
+   14.1.8. Your turn! 🚀
   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   13.1.9. Acknowledgments
+   14.1.9. Acknowledgments
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 </ul>
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+   14.1.2. Hello ‘classifier’
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   <a class="reference internal nav-link" href="#discovering-ingredients">
-   13.1.5. Discovering ingredients
+   14.1.5. Discovering ingredients
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#balance-the-dataset">
-   13.1.6. Balance the dataset
+   14.1.6. Balance the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.1.7. Self study
+   14.1.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.1.8. Your turn! 🚀
+   14.1.8. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.1.9. Acknowledgments
+   14.1.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1655,12 +1687,12 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-classification">
-<h1><span class="section-number">13.1. </span>Introduction to classification<a class="headerlink" href="#introduction-to-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">14.1. </span>Introduction to classification<a class="headerlink" href="#introduction-to-classification" title="Permalink to this headline">#</a></h1>
 <p>In these four sections, you will explore a fundamental focus of classic machine learning <em>classification</em>. We will walk through using various classification algorithms with a dataset about all the brilliant cuisines of Asia and India. Hope you’re hungry!</p>
 <figure class="align-default" id="celebrate-pan-asian-cuisines-in-these-lessons">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/pinch.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/pinch.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/pinch.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.1 </span><span class="caption-text">Image by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#celebrate-pan-asian-cuisines-in-these-lessons" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.1 </span><span class="caption-text">Image by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#celebrate-pan-asian-cuisines-in-these-lessons" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Classification is a form of <a class="reference external" href="https://wikipedia.org/wiki/Supervised_learning">supervised learning</a> that bears a lot in common with regression techniques. If machine learning is all about predicting values or names to things by using datasets, then classification generally falls into two groups: <em>binary classification</em> and <em>multiclass classification</em>.</p>
@@ -1722,13 +1754,13 @@ <h1><span class="section-number">13.1. </span>Introduction to classification<a c
 </div></div>
 </div>
 <section id="introduction">
-<h2><span class="section-number">13.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value (“is this email spam or not?”), to complex image classification and segmentation using computer vision, it’s always useful to be able to sort data into classes and ask questions of it.</p>
 <p>To state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables.</p>
 <figure class="align-default" id="binary-vs-multiclass-classification">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/binary-multiclass.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/binary-multiclass.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/binary-multiclass.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.2 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#binary-vs-multiclass-classification" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.2 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#binary-vs-multiclass-classification" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let’s learn a bit about the various ways machine learning can be leveraged to classify data.</p>
@@ -1765,12 +1797,12 @@ <h2><span class="section-number">13.1.1. </span>Introduction<a class="headerlink
 <p>Click the video above. The whole premise of the show ‘Chopped’ is the ‘mystery basket’ where chefs have to make some dish out of a random choice of ingredients. Surely a ML model would have helped!</p>
 </section>
 <section id="hello-classifier">
-<h2><span class="section-number">13.1.2. </span>Hello ‘classifier’<a class="headerlink" href="#hello-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.2. </span>Hello ‘classifier’<a class="headerlink" href="#hello-classifier" title="Permalink to this headline">#</a></h2>
 <p>The question we want to ask of this cuisine dataset is actually a <strong>multiclass question</strong>, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?</p>
 <p>Scikit-learn offers several different algorithms to use to classify data, depending on the kind of problem you want to solve. In the next two sections, you’ll learn about several of these algorithms.</p>
 </section>
 <section id="exercise-clean-and-balance-your-data">
-<h2><span class="section-number">13.1.3. </span>Exercise - clean and balance your data<a class="headerlink" href="#exercise-clean-and-balance-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.3. </span>Exercise - clean and balance your data<a class="headerlink" href="#exercise-clean-and-balance-your-data" title="Permalink to this headline">#</a></h2>
 <p>The first task at hand, before starting this project, is to clean and <strong>balance</strong> your data to get better results. Start with the blank <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html"><span class="doc std std-doc">delicious-asian-and-indian-cuisines.ipynb</span></a> file.</p>
 <p>The first thing to install is <a class="reference external" href="https://imbalanced-learn.org/stable/">imblearn</a>. This is a Scikit-learn package that will allow you to better balance the data (you will learn more about this task in a minute).</p>
 <p>1. Import the packages you need to import your data and visualize it, also import <code class="docutils literal notranslate"><span class="pre">SMOTE</span></code> from <code class="docutils literal notranslate"><span class="pre">imblearn</span></code>.</p>
@@ -1990,7 +2022,7 @@ <h2><span class="section-number">13.1.3. </span>Exercise - clean and balance you
 </div>
 </section>
 <section id="exercise-learning-about-cuisines">
-<h2><span class="section-number">13.1.4. </span>Exercise - learning about cuisines<a class="headerlink" href="#exercise-learning-about-cuisines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.4. </span>Exercise - learning about cuisines<a class="headerlink" href="#exercise-learning-about-cuisines" title="Permalink to this headline">#</a></h2>
 <p>Now the work starts to become more interesting. Let’s discover the distribution of data, per cuisine</p>
 <p>1. Plot the data as bars by calling <code class="docutils literal notranslate"><span class="pre">barh()</span></code>:</p>
 <div class="cell docutils container">
@@ -2036,7 +2068,7 @@ <h2><span class="section-number">13.1.4. </span>Exercise - learning about cuisin
 </div>
 </section>
 <section id="discovering-ingredients">
-<h2><span class="section-number">13.1.5. </span>Discovering ingredients<a class="headerlink" href="#discovering-ingredients" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.5. </span>Discovering ingredients<a class="headerlink" href="#discovering-ingredients" title="Permalink to this headline">#</a></h2>
 <p>Now you can dig deeper into the data and learn what are the typical ingredients per cuisine. You should clean out recurrent data that creates confusion between cuisines, so let’s learn about this problem.</p>
 <p>1. Create a function <code class="docutils literal notranslate"><span class="pre">create_ingredient()</span></code> in Python to create an ingredient dataframe. This function will start by dropping an unhelpful column and sort through ingredients by their count:</p>
 <div class="cell docutils container">
@@ -2307,7 +2339,7 @@ <h2><span class="section-number">13.1.5. </span>Discovering ingredients<a class=
 </div>
 </section>
 <section id="balance-the-dataset">
-<h2><span class="section-number">13.1.6. </span>Balance the dataset<a class="headerlink" href="#balance-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.6. </span>Balance the dataset<a class="headerlink" href="#balance-the-dataset" title="Permalink to this headline">#</a></h2>
 <p>Now that you have cleaned the data, use <a class="reference external" href="https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html">SMOTE</a> - “Synthetic Minority Over-sampling Technique” - to balance it.</p>
 <p>1. Call <code class="docutils literal notranslate"><span class="pre">fit_resample()</span></code>, this strategy generates new samples by interpolation.</p>
 <div class="cell docutils container">
@@ -2375,16 +2407,16 @@ <h2><span class="section-number">13.1.6. </span>Balance the dataset<a class="hea
 </section>
 <hr class="docutils" />
 <section id="self-study">
-<h2><span class="section-number">13.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>This curriculum contains several interesting datasets. Dig through the <a class="reference external" href="https://github.com/YinYi000/machine-learning/tree/main/open-machine-learning-jupyter-book/assets/data">/data/classification</a> folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">13.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Explore SMOTE’s API. What use cases is it best used for? What problems does it solve?</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html"><span class="doc std std-doc">Explore classification methods</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">13.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2426,13 +2458,13 @@ <h2><span class="section-number">13.1.9. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">13. </span>Getting started with classification</p>
+            <p class="prev-next-title"><span class="section-number">14. </span>Getting started with classification</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">13.2. </span>More classifiers</p>
+        <p class="prev-next-title"><span class="section-number">14.2. </span>More classifiers</p>
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diff --git a/ml-fundamentals/classification/more-classifiers.html b/ml-fundamentals/classification/more-classifiers.html
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>13.2. More classifiers &#8212; Ocademy Open Machine Learning Book</title>
+    <title>14.2. More classifiers &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="13.3. Yet other classifiers" href="yet-other-classifiers.html" />
-    <link rel="prev" title="13.1. Introduction to classification" href="introduction-to-classification.html" />
+    <link rel="next" title="14.3. Yet other classifiers" href="yet-other-classifiers.html" />
+    <link rel="prev" title="14.1. Introduction to classification" href="introduction-to-classification.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 current active has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
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-     13.2. More classifiers
+     14.2. More classifiers
     </a>
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    <li class="toctree-l2">
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-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     16.2. Gradient boosting example
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-     16.3. XGBoost
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-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
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-   19. Model selection
+   22. Model selection
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-     38.20. Convolutional Neural Network
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-     38.21. Generative Adversarial Network
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@@ -1526,54 +1558,54 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-predict-a-national-cuisine">
-   13.2.1. Exercise - predict a national cuisine
+   14.2.1. Exercise - predict a national cuisine
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#choosing-your-classifier">
-   13.2.2. Choosing your classifier
+   14.2.2. Choosing your classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-classifier-to-go-with">
-     13.2.2.1. What classifier to go with?
+     14.2.2.1. What classifier to go with?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#a-better-approach">
-     13.2.2.2. A better approach
+     14.2.2.2. A better approach
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasoning">
-     13.2.2.3. Reasoning
+     14.2.2.3. Reasoning
     </a>
    </li>
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     <a class="reference internal nav-link" href="#using-scikit-learn">
-     13.2.2.4. Using Scikit-learn
+     14.2.2.4. Using Scikit-learn
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#exercise-split-the-data">
-   13.2.3. Exercise - split the data
+   14.2.3. Exercise - split the data
   </a>
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-   13.2.4. Exercise - apply logistic regression
+   14.2.4. Exercise - apply logistic regression
   </a>
  </li>
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   <a class="reference internal nav-link" href="#your-turn">
-   13.2.5. Your turn! 🚀
+   14.2.5. Your turn! 🚀
   </a>
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-   13.2.6. Acknowledgments
+   14.2.6. Acknowledgments
   </a>
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 </ul>
@@ -1597,54 +1629,54 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-predict-a-national-cuisine">
-   13.2.1. Exercise - predict a national cuisine
+   14.2.1. Exercise - predict a national cuisine
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#choosing-your-classifier">
-   13.2.2. Choosing your classifier
+   14.2.2. Choosing your classifier
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-classifier-to-go-with">
-     13.2.2.1. What classifier to go with?
+     14.2.2.1. What classifier to go with?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#a-better-approach">
-     13.2.2.2. A better approach
+     14.2.2.2. A better approach
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasoning">
-     13.2.2.3. Reasoning
+     14.2.2.3. Reasoning
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-scikit-learn">
-     13.2.2.4. Using Scikit-learn
+     14.2.2.4. Using Scikit-learn
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-split-the-data">
-   13.2.3. Exercise - split the data
+   14.2.3. Exercise - split the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-apply-logistic-regression">
-   13.2.4. Exercise - apply logistic regression
+   14.2.4. Exercise - apply logistic regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.2.5. Your turn! 🚀
+   14.2.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.2.6. Acknowledgments
+   14.2.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1669,11 +1701,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="more-classifiers">
-<h1><span class="section-number">13.2. </span>More classifiers<a class="headerlink" href="#more-classifiers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">14.2. </span>More classifiers<a class="headerlink" href="#more-classifiers" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will use the dataset you saved from the last section full of balanced, clean data all about cuisines.</p>
 <p>You will use this dataset with a variety of classifiers to <em>predict a given national cuisine based on a group of ingredients</em>. While doing so, you’ll learn more about some of the ways that algorithms can be leveraged for classification tasks.</p>
 <section id="exercise-predict-a-national-cuisine">
-<h2><span class="section-number">13.2.1. </span>Exercise - predict a national cuisine<a class="headerlink" href="#exercise-predict-a-national-cuisine" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.1. </span>Exercise - predict a national cuisine<a class="headerlink" href="#exercise-predict-a-national-cuisine" title="Permalink to this headline">#</a></h2>
 <p>1. Working in this section’s <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/ml-fundamentals/build-classification-models.ipynb">build-classification-models</a> file, import that file along with the Pandas library:</p>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
@@ -2061,7 +2093,7 @@ <h2><span class="section-number">13.2.1. </span>Exercise - predict a national cu
 <p>Now you are ready to train your model!</p>
 </section>
 <section id="choosing-your-classifier">
-<h2><span class="section-number">13.2.2. </span>Choosing your classifier<a class="headerlink" href="#choosing-your-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.2. </span>Choosing your classifier<a class="headerlink" href="#choosing-your-classifier" title="Permalink to this headline">#</a></h2>
 <p>Now that your data is clean and ready for training, you have to decide which algorithm to use for the job.</p>
 <p>Scikit-learn groups classification under Supervised Learning, and in that category you will find many ways to classify. <a class="reference external" href="https://scikit-learn.org/stable/supervised_learning.html">The variety</a> is quite bewildering at first sight. The following methods all include classification techniques:</p>
 <ul class="simple">
@@ -2079,12 +2111,12 @@ <h2><span class="section-number">13.2.2. </span>Choosing your classifier<a class
 <p>You can also use <a class="reference external" href="https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification">neural networks to classify data</a>, but that is outside the scope of this section.</p>
 </div>
 <section id="what-classifier-to-go-with">
-<h3><span class="section-number">13.2.2.1. </span>What classifier to go with?<a class="headerlink" href="#what-classifier-to-go-with" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.1. </span>What classifier to go with?<a class="headerlink" href="#what-classifier-to-go-with" title="Permalink to this headline">#</a></h3>
 <p>So, which classifier should you choose? Often, running through several and looking for a good result is a way to test. Scikit-learn offers a <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html">side-by-side comparison</a> on a created dataset, comparing KNeighbors, SVC two ways, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB and QuadraticDiscrinationAnalysis, showing the results visualized:</p>
 <figure class="align-default" id="comparison-of-classifiers">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/comparison.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/comparison.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/comparison.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.3 </span><span class="caption-text">Comparison of classifiers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/comparison.png">🔗source</a></span><a class="headerlink" href="#comparison-of-classifiers" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.3 </span><span class="caption-text">Comparison of classifiers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/comparison.png">🔗source</a></span><a class="headerlink" href="#comparison-of-classifiers" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -2094,12 +2126,12 @@ <h3><span class="section-number">13.2.2.1. </span>What classifier to go with?<a
 </div>
 </section>
 <section id="a-better-approach">
-<h3><span class="section-number">13.2.2.2. </span>A better approach<a class="headerlink" href="#a-better-approach" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.2. </span>A better approach<a class="headerlink" href="#a-better-approach" title="Permalink to this headline">#</a></h3>
 <p>A better way than wildly guessing, however, is to follow the ideas on this downloadable <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott">ML Cheat sheet</a>. Here, we discover that, for our multiclass problem, we have some choices:</p>
 <figure class="align-default" id="cheatsheet-for-multiclass-problems">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/cheatsheet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/cheatsheet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/cheatsheet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.4 </span><span class="caption-text">Cheatsheet for multiclass problems <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/cheatsheet.png">🔗source</a></span><a class="headerlink" href="#cheatsheet-for-multiclass-problems" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.4 </span><span class="caption-text">Cheatsheet for multiclass problems <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/cheatsheet.png">🔗source</a></span><a class="headerlink" href="#cheatsheet-for-multiclass-problems" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -2112,7 +2144,7 @@ <h3><span class="section-number">13.2.2.2. </span>A better approach<a class="hea
 </div>
 </section>
 <section id="reasoning">
-<h3><span class="section-number">13.2.2.3. </span>Reasoning<a class="headerlink" href="#reasoning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.3. </span>Reasoning<a class="headerlink" href="#reasoning" title="Permalink to this headline">#</a></h3>
 <p>Let’s see if we can reason our way through different approaches given the constraints we have:</p>
 <ul class="simple">
 <li><p><strong>Neural networks are too heavy</strong>. Given our clean, but minimal dataset, and the fact that we are running training locally via notebooks, neural networks are too heavyweight for this task.</p></li>
@@ -2122,7 +2154,7 @@ <h3><span class="section-number">13.2.2.3. </span>Reasoning<a class="headerlink"
 </ul>
 </section>
 <section id="using-scikit-learn">
-<h3><span class="section-number">13.2.2.4. </span>Using Scikit-learn<a class="headerlink" href="#using-scikit-learn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.4. </span>Using Scikit-learn<a class="headerlink" href="#using-scikit-learn" title="Permalink to this headline">#</a></h3>
 <p>We will be using Scikit-learn to analyze our data. However, there are many ways to use logistic regression in Scikit-learn. Take a look at the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression">parameters to pass</a>.</p>
 <p>Essentially there are two important parameters - <code class="docutils literal notranslate"><span class="pre">multi_class</span></code> and <code class="docutils literal notranslate"><span class="pre">solver</span></code> - that we need to specify, when we ask Scikit-learn to perform a logistic regression. The <code class="docutils literal notranslate"><span class="pre">multi_class</span></code> value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all <code class="docutils literal notranslate"><span class="pre">multi_class</span></code> values.</p>
 <p>According to the docs, in the multiclass case, the training algorithm:</p>
@@ -2139,13 +2171,13 @@ <h3><span class="section-number">13.2.2.4. </span>Using Scikit-learn<a class="he
 <figure class="align-default" id="solvers">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/solvers.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/solvers.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/solvers.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.5 </span><span class="caption-text">Solvers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/solvers.png">🔗source</a></span><a class="headerlink" href="#solvers" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.5 </span><span class="caption-text">Solvers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/solvers.png">🔗source</a></span><a class="headerlink" href="#solvers" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="exercise-split-the-data">
-<h2><span class="section-number">13.2.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
 <p>We can focus on logistic regression for our first training trial since you recently learned about the latter in a previous section.
 Split your data into training and testing groups by calling <code class="docutils literal notranslate"><span class="pre">train_test_split()</span></code>:</p>
 <div class="cell docutils container">
@@ -2157,7 +2189,7 @@ <h2><span class="section-number">13.2.3. </span>Exercise - split the data<a clas
 </div>
 </section>
 <section id="exercise-apply-logistic-regression">
-<h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regression<a class="headerlink" href="#exercise-apply-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.4. </span>Exercise - apply logistic regression<a class="headerlink" href="#exercise-apply-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>Since you are using the multiclass case, you need to choose what <em>scheme</em> to use and what <em>solver</em> to set. Use LogisticRegression with a multiclass setting and the <strong>liblinear</strong> solver to train.</p>
 <p>1. Create a logistic regression with multi_class set to <code class="docutils literal notranslate"><span class="pre">ovr</span></code> and the solver set to <code class="docutils literal notranslate"><span class="pre">liblinear</span></code>:</p>
 <div class="cell docutils container">
@@ -2171,7 +2203,7 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy is 0.8056713928273561
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy is 0.7889908256880734
 </pre></div>
 </div>
 </div>
@@ -2194,10 +2226,10 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;basil&#39;, &#39;cayenne&#39;, &#39;cilantro&#39;, &#39;cucumber&#39;, &#39;fish&#39;, &#39;lime_juice&#39;,
-       &#39;mint&#39;, &#39;vegetable_oil&#39;],
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;asparagus&#39;, &#39;black_bean&#39;, &#39;pepper&#39;, &#39;sesame_oil&#39;, &#39;soy_sauce&#39;,
+       &#39;starch&#39;, &#39;vegetable_oil&#39;, &#39;wheat&#39;, &#39;wine&#39;],
       dtype=&#39;object&#39;)
-cuisine: thai
+cuisine: chinese
 </pre></div>
 </div>
 </div>
@@ -2247,24 +2279,24 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
   </thead>
   <tbody>
     <tr>
-      <th>thai</th>
-      <td>0.964721</td>
+      <th>chinese</th>
+      <td>0.890961</td>
     </tr>
     <tr>
       <th>japanese</th>
-      <td>0.015793</td>
+      <td>0.100333</td>
     </tr>
     <tr>
       <th>korean</th>
-      <td>0.009957</td>
+      <td>0.007937</td>
     </tr>
     <tr>
-      <th>indian</th>
-      <td>0.008700</td>
+      <th>thai</th>
+      <td>0.000573</td>
     </tr>
     <tr>
-      <th>chinese</th>
-      <td>0.000828</td>
+      <th>indian</th>
+      <td>0.000197</td>
     </tr>
   </tbody>
 </table>
@@ -2285,27 +2317,27 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.72      0.71      0.71       233
-      indian       0.91      0.88      0.90       222
-    japanese       0.78      0.78      0.78       254
-      korean       0.86      0.81      0.83       252
-        thai       0.77      0.85      0.81       238
+     chinese       0.75      0.63      0.68       241
+      indian       0.90      0.91      0.91       233
+    japanese       0.73      0.74      0.74       247
+      korean       0.79      0.79      0.79       228
+        thai       0.77      0.88      0.82       250
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.79      1199
+   macro avg       0.79      0.79      0.79      1199
+weighted avg       0.79      0.79      0.79      1199
 </pre></div>
 </div>
 </div>
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">13.2.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>In this section, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of ‘solver’ to understand what goes on behind the scenes.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html"><span class="doc std std-doc">Study the solvers</span></a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">13.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2347,13 +2379,13 @@ <h2><span class="section-number">13.2.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">13.1. </span>Introduction to classification</p>
+            <p class="prev-next-title"><span class="section-number">14.1. </span>Introduction to classification</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="yet-other-classifiers.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">13.3. </span>Yet other classifiers</p>
+        <p class="prev-next-title"><span class="section-number">14.3. </span>Yet other classifiers</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html
index 6b4e57a6a8..8497e6d935 100644
--- a/ml-fundamentals/classification/yet-other-classifiers.html
+++ b/ml-fundamentals/classification/yet-other-classifiers.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>13.3. Yet other classifiers &#8212; Ocademy Open Machine Learning Book</title>
+    <title>14.3. Yet other classifiers &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="13.4. Applied Machine Learning : build a web app" href="applied-ml-build-a-web-app.html" />
-    <link rel="prev" title="13.2. More classifiers" href="more-classifiers.html" />
+    <link rel="next" title="14.4. Applied Machine Learning : build a web app" href="applied-ml-build-a-web-app.html" />
+    <link rel="prev" title="14.2. More classifiers" href="more-classifiers.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input checked="" class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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@@ -621,27 +653,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../slides/introduction.html">
-   38. Slides
+   41. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
-     38.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,80 +1558,80 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preparation">
-   13.3.1. Preparation
+   14.3.1. Preparation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#a-classification-map">
-   13.3.2. A classification map
+   14.3.2. A classification map
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-plan">
-     13.3.2.1. The plan
+     14.3.2.1. The plan
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-split-the-data">
-   13.3.3. Exercise - split the data
+   14.3.3. Exercise - split the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-svc-classifier">
-   13.3.4. Linear SVC classifier
+   14.3.4. Linear SVC classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-linear-svc">
-     13.3.4.1. Exercise - apply a linear SVC
+     14.3.4.1. Exercise - apply a linear SVC
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-neighbors-classifier">
-   13.3.5. K-Neighbors classifier
+   14.3.5. K-Neighbors classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-the-k-neighbors-classifier">
-     13.3.5.1. Exercise - apply the K-Neighbors classifier
+     14.3.5.1. Exercise - apply the K-Neighbors classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-classifier">
-   13.3.6. Support Vector Classifier
+   14.3.6. Support Vector Classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-support-vector-classifier">
-     13.3.6.1. Exercise - apply a Support Vector Classifier
+     14.3.6.1. Exercise - apply a Support Vector Classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ensemble-classifiers">
-   13.3.7. Ensemble Classifiers
+   14.3.7. Ensemble Classifiers
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.3.8. Self Study
+   14.3.8. Self Study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.3.9. Your turn! 🚀
+   14.3.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.3.10. Acknowledgments
+   14.3.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1623,80 +1655,80 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preparation">
-   13.3.1. Preparation
+   14.3.1. Preparation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#a-classification-map">
-   13.3.2. A classification map
+   14.3.2. A classification map
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-plan">
-     13.3.2.1. The plan
+     14.3.2.1. The plan
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-split-the-data">
-   13.3.3. Exercise - split the data
+   14.3.3. Exercise - split the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-svc-classifier">
-   13.3.4. Linear SVC classifier
+   14.3.4. Linear SVC classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-linear-svc">
-     13.3.4.1. Exercise - apply a linear SVC
+     14.3.4.1. Exercise - apply a linear SVC
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-neighbors-classifier">
-   13.3.5. K-Neighbors classifier
+   14.3.5. K-Neighbors classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-the-k-neighbors-classifier">
-     13.3.5.1. Exercise - apply the K-Neighbors classifier
+     14.3.5.1. Exercise - apply the K-Neighbors classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-classifier">
-   13.3.6. Support Vector Classifier
+   14.3.6. Support Vector Classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-support-vector-classifier">
-     13.3.6.1. Exercise - apply a Support Vector Classifier
+     14.3.6.1. Exercise - apply a Support Vector Classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ensemble-classifiers">
-   13.3.7. Ensemble Classifiers
+   14.3.7. Ensemble Classifiers
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.3.8. Self Study
+   14.3.8. Self Study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.3.9. Your turn! 🚀
+   14.3.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.3.10. Acknowledgments
+   14.3.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1721,23 +1753,23 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="yet-other-classifiers">
-<h1><span class="section-number">13.3. </span>Yet other classifiers<a class="headerlink" href="#yet-other-classifiers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">14.3. </span>Yet other classifiers<a class="headerlink" href="#yet-other-classifiers" title="Permalink to this headline">#</a></h1>
 <p>In this second classification section, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.</p>
 <section id="preparation">
-<h2><span class="section-number">13.3.1. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.1. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h2>
 <p>We have loaded your <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/ml-fundamentals/build-classification-model.ipynb">build-classification-model.ipynb</a> file with the cleaned dataset and have divided it into x and y dataframes, ready for the model building process.</p>
 </section>
 <section id="a-classification-map">
-<h2><span class="section-number">13.3.2. </span>A classification map<a class="headerlink" href="#a-classification-map" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.2. </span>A classification map<a class="headerlink" href="#a-classification-map" title="Permalink to this headline">#</a></h2>
 <p>Previously, you learned about the various options you have when classifying data using Microsoft’s cheat sheet. Scikit-learn offers a similar, but more granular cheat sheet that can further help narrow down your estimators (another term for classifiers):</p>
 <figure class="align-default" id="ml-map-from-scikit-learn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/map.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/map.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/map.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.6 </span><span class="caption-text">ML Map from Scikit-learn. <a class="reference external" href="https://scikit-learn.org/stable/tutorial/machine_learning_map/">Ref</a></span><a class="headerlink" href="#ml-map-from-scikit-learn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.6 </span><span class="caption-text">ML Map from Scikit-learn. <a class="reference external" href="https://scikit-learn.org/stable/tutorial/machine_learning_map/">Ref</a></span><a class="headerlink" href="#ml-map-from-scikit-learn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="the-plan">
-<h3><span class="section-number">13.3.2.1. </span>The plan<a class="headerlink" href="#the-plan" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.2.1. </span>The plan<a class="headerlink" href="#the-plan" title="Permalink to this headline">#</a></h3>
 <p>This map is very helpful once you have a clear grasp of your data, as you can ‘walk’ along its paths to a decision:</p>
 <ul class="simple">
 <li><p>We have &gt;50 samples</p></li>
@@ -1759,7 +1791,7 @@ <h3><span class="section-number">13.3.2.1. </span>The plan<a class="headerlink"
 </section>
 </section>
 <section id="exercise-split-the-data">
-<h2><span class="section-number">13.3.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
 <p>Following this path, we should start by importing some libraries to use.</p>
 <p>1. Import the needed libraries:</p>
 <div class="cell docutils container">
@@ -1790,10 +1822,10 @@ <h2><span class="section-number">13.3.3. </span>Exercise - split the data<a clas
 </div>
 </section>
 <section id="linear-svc-classifier">
-<h2><span class="section-number">13.3.4. </span>Linear SVC classifier<a class="headerlink" href="#linear-svc-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.4. </span>Linear SVC classifier<a class="headerlink" href="#linear-svc-classifier" title="Permalink to this headline">#</a></h2>
 <p>Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques (learn more about these below). In this method, you can choose a ‘kernel’ to decide how to cluster the labels. The ‘C’ parameter refers to ‘regularization’ which regulates the influence of parameters. The kernel can be one of <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC">several</a>; here we set it to ‘linear’ to ensure that we leverage linear SVC. Probability defaults to ‘false’; here we set it to ‘true’ to gather probability estimates. We set the random state to ‘0’ to shuffle the data to get probabilities.</p>
 <section id="exercise-apply-a-linear-svc">
-<h3><span class="section-number">13.3.4.1. </span>Exercise - apply a linear SVC<a class="headerlink" href="#exercise-apply-a-linear-svc" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.4.1. </span>Exercise - apply a linear SVC<a class="headerlink" href="#exercise-apply-a-linear-svc" title="Permalink to this headline">#</a></h3>
 <p>Start by creating an array of classifiers. You will add progressively to this array as we test.</p>
 <p>1. Start with a Linear SVC:</p>
 <div class="cell docutils container">
@@ -1826,18 +1858,18 @@ <h3><span class="section-number">13.3.4.1. </span>Exercise - apply a linear SVC<
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.4% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.73      0.73       245
-      indian       0.92      0.88      0.90       250
-    japanese       0.78      0.79      0.79       237
-      korean       0.83      0.75      0.79       226
-        thai       0.77      0.85      0.81       241
+     chinese       0.70      0.76      0.73       247
+      indian       0.88      0.91      0.90       229
+    japanese       0.79      0.72      0.76       243
+      korean       0.84      0.76      0.80       241
+        thai       0.80      0.85      0.83       239
 
     accuracy                           0.80      1199
-   macro avg       0.81      0.80      0.80      1199
-weighted avg       0.81      0.80      0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 </pre></div>
 </div>
 </div>
@@ -1846,10 +1878,10 @@ <h3><span class="section-number">13.3.4.1. </span>Exercise - apply a linear SVC<
 </section>
 </section>
 <section id="k-neighbors-classifier">
-<h2><span class="section-number">13.3.5. </span>K-Neighbors classifier<a class="headerlink" href="#k-neighbors-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.5. </span>K-Neighbors classifier<a class="headerlink" href="#k-neighbors-classifier" title="Permalink to this headline">#</a></h2>
 <p>K-Neighbors is part of the “neighbors” family of ML methods, which can be used for both supervised and unsupervised learning. In this method, a predefined number of points is created and data are gathered around these points such that generalized labels can be predicted for the data.</p>
 <section id="exercise-apply-the-k-neighbors-classifier">
-<h3><span class="section-number">13.3.5.1. </span>Exercise - apply the K-Neighbors classifier<a class="headerlink" href="#exercise-apply-the-k-neighbors-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.5.1. </span>Exercise - apply the K-Neighbors classifier<a class="headerlink" href="#exercise-apply-the-k-neighbors-classifier" title="Permalink to this headline">#</a></h3>
 <p>The previous classifier was good, and worked well with the data, but maybe we can get better accuracy. Try a K-Neighbors classifier.</p>
 <p>1. Add a line to your classifier array (add a comma after the Linear SVC item):</p>
 <div class="cell docutils container">
@@ -1861,31 +1893,31 @@ <h3><span class="section-number">13.3.5.1. </span>Exercise - apply the K-Neighbo
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.4% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.73      0.73       245
-      indian       0.92      0.88      0.90       250
-    japanese       0.78      0.79      0.79       237
-      korean       0.83      0.75      0.79       226
-        thai       0.77      0.85      0.81       241
+     chinese       0.70      0.76      0.73       247
+      indian       0.88      0.91      0.90       229
+    japanese       0.79      0.72      0.76       243
+      korean       0.84      0.76      0.80       241
+        thai       0.80      0.85      0.83       239
 
     accuracy                           0.80      1199
-   macro avg       0.81      0.80      0.80      1199
-weighted avg       0.81      0.80      0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 75.1% 
+Accuracy (train) for KNN classifier: 72.8% 
               precision    recall  f1-score   support
 
-     chinese       0.68      0.67      0.68       245
-      indian       0.91      0.81      0.86       250
-    japanese       0.66      0.86      0.75       237
-      korean       0.88      0.59      0.71       226
-        thai       0.72      0.81      0.76       241
+     chinese       0.66      0.66      0.66       247
+      indian       0.84      0.84      0.84       229
+    japanese       0.64      0.86      0.73       243
+      korean       0.89      0.49      0.64       241
+        thai       0.72      0.79      0.76       239
 
-    accuracy                           0.75      1199
-   macro avg       0.77      0.75      0.75      1199
-weighted avg       0.77      0.75      0.75      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.73      1199
+weighted avg       0.75      0.73      0.72      1199
 </pre></div>
 </div>
 </div>
@@ -1898,10 +1930,10 @@ <h3><span class="section-number">13.3.5.1. </span>Exercise - apply the K-Neighbo
 </section>
 </section>
 <section id="support-vector-classifier">
-<h2><span class="section-number">13.3.6. </span>Support Vector Classifier<a class="headerlink" href="#support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.6. </span>Support Vector Classifier<a class="headerlink" href="#support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <p>Support-Vector classifiers are part of the <a class="reference external" href="https://wikipedia.org/wiki/Support-vector_machine">Support-Vector Machine</a> family of ML methods that are used for classification and regression tasks. SVMs “map training examples to points in space” to maximize the distance between two categories. Subsequent data is mapped into this space so their category can be predicted.</p>
 <section id="exercise-apply-a-support-vector-classifier">
-<h3><span class="section-number">13.3.6.1. </span>Exercise - apply a Support Vector Classifier<a class="headerlink" href="#exercise-apply-a-support-vector-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.6.1. </span>Exercise - apply a Support Vector Classifier<a class="headerlink" href="#exercise-apply-a-support-vector-classifier" title="Permalink to this headline">#</a></h3>
 <p>Let’s try for a little better accuracy with a Support Vector Classifier.</p>
 <p>1. Add a comma after the K-Neighbors item, and then add this line:</p>
 <div class="cell docutils container">
@@ -1913,45 +1945,45 @@ <h3><span class="section-number">13.3.6.1. </span>Exercise - apply a Support Vec
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.4% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.73      0.73       245
-      indian       0.92      0.88      0.90       250
-    japanese       0.78      0.79      0.79       237
-      korean       0.83      0.75      0.79       226
-        thai       0.77      0.85      0.81       241
+     chinese       0.70      0.76      0.73       247
+      indian       0.88      0.91      0.90       229
+    japanese       0.79      0.72      0.76       243
+      korean       0.84      0.76      0.80       241
+        thai       0.80      0.85      0.83       239
 
     accuracy                           0.80      1199
-   macro avg       0.81      0.80      0.80      1199
-weighted avg       0.81      0.80      0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 75.1% 
+Accuracy (train) for KNN classifier: 72.8% 
               precision    recall  f1-score   support
 
-     chinese       0.68      0.67      0.68       245
-      indian       0.91      0.81      0.86       250
-    japanese       0.66      0.86      0.75       237
-      korean       0.88      0.59      0.71       226
-        thai       0.72      0.81      0.76       241
+     chinese       0.66      0.66      0.66       247
+      indian       0.84      0.84      0.84       229
+    japanese       0.64      0.86      0.73       243
+      korean       0.89      0.49      0.64       241
+        thai       0.72      0.79      0.76       239
 
-    accuracy                           0.75      1199
-   macro avg       0.77      0.75      0.75      1199
-weighted avg       0.77      0.75      0.75      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.73      1199
+weighted avg       0.75      0.73      0.72      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 84.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.2% 
               precision    recall  f1-score   support
 
-     chinese       0.80      0.76      0.78       245
-      indian       0.93      0.93      0.93       250
-    japanese       0.85      0.84      0.84       237
-      korean       0.89      0.82      0.86       226
-        thai       0.79      0.90      0.84       241
+     chinese       0.74      0.77      0.75       247
+      indian       0.92      0.93      0.92       229
+    japanese       0.82      0.80      0.81       243
+      korean       0.87      0.79      0.83       241
+        thai       0.81      0.88      0.85       239
 
-    accuracy                           0.85      1199
-   macro avg       0.85      0.85      0.85      1199
-weighted avg       0.85      0.85      0.85      1199
+    accuracy                           0.83      1199
+   macro avg       0.83      0.83      0.83      1199
+weighted avg       0.83      0.83      0.83      1199
 </pre></div>
 </div>
 </div>
@@ -1964,7 +1996,7 @@ <h3><span class="section-number">13.3.6.1. </span>Exercise - apply a Support Vec
 </section>
 </section>
 <section id="ensemble-classifiers">
-<h2><span class="section-number">13.3.7. </span>Ensemble Classifiers<a class="headerlink" href="#ensemble-classifiers" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.7. </span>Ensemble Classifiers<a class="headerlink" href="#ensemble-classifiers" title="Permalink to this headline">#</a></h2>
 <p>Let’s follow the path to the very end, even though the previous test was quite good. Let’s try some ‘Ensemble Classifiers, specifically Random Forest and AdaBoost:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1976,73 +2008,73 @@ <h2><span class="section-number">13.3.7. </span>Ensemble Classifiers<a class="he
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.4% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 80.1% 
               precision    recall  f1-score   support
 
-     chinese       0.72      0.73      0.73       245
-      indian       0.92      0.88      0.90       250
-    japanese       0.78      0.79      0.79       237
-      korean       0.83      0.75      0.79       226
-        thai       0.77      0.85      0.81       241
+     chinese       0.70      0.76      0.73       247
+      indian       0.88      0.91      0.90       229
+    japanese       0.79      0.72      0.76       243
+      korean       0.84      0.76      0.80       241
+        thai       0.80      0.85      0.83       239
 
     accuracy                           0.80      1199
-   macro avg       0.81      0.80      0.80      1199
-weighted avg       0.81      0.80      0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 75.1% 
+Accuracy (train) for KNN classifier: 72.8% 
               precision    recall  f1-score   support
 
-     chinese       0.68      0.67      0.68       245
-      indian       0.91      0.81      0.86       250
-    japanese       0.66      0.86      0.75       237
-      korean       0.88      0.59      0.71       226
-        thai       0.72      0.81      0.76       241
+     chinese       0.66      0.66      0.66       247
+      indian       0.84      0.84      0.84       229
+    japanese       0.64      0.86      0.73       243
+      korean       0.89      0.49      0.64       241
+        thai       0.72      0.79      0.76       239
 
-    accuracy                           0.75      1199
-   macro avg       0.77      0.75      0.75      1199
-weighted avg       0.77      0.75      0.75      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.73      1199
+weighted avg       0.75      0.73      0.72      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 84.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.2% 
               precision    recall  f1-score   support
 
-     chinese       0.80      0.76      0.78       245
-      indian       0.93      0.93      0.93       250
-    japanese       0.85      0.84      0.84       237
-      korean       0.89      0.82      0.86       226
-        thai       0.79      0.90      0.84       241
+     chinese       0.74      0.77      0.75       247
+      indian       0.92      0.93      0.92       229
+    japanese       0.82      0.80      0.81       243
+      korean       0.87      0.79      0.83       241
+        thai       0.81      0.88      0.85       239
 
-    accuracy                           0.85      1199
-   macro avg       0.85      0.85      0.85      1199
-weighted avg       0.85      0.85      0.85      1199
+    accuracy                           0.83      1199
+   macro avg       0.83      0.83      0.83      1199
+weighted avg       0.83      0.83      0.83      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 85.7% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 85.3% 
               precision    recall  f1-score   support
 
-     chinese       0.83      0.79      0.81       245
-      indian       0.93      0.90      0.91       250
-    japanese       0.89      0.84      0.86       237
-      korean       0.86      0.85      0.85       226
-        thai       0.79      0.90      0.84       241
+     chinese       0.82      0.78      0.80       247
+      indian       0.92      0.93      0.93       229
+    japanese       0.83      0.80      0.82       243
+      korean       0.86      0.84      0.85       241
+        thai       0.83      0.92      0.87       239
 
-    accuracy                           0.86      1199
-   macro avg       0.86      0.86      0.86      1199
-weighted avg       0.86      0.86      0.86      1199
+    accuracy                           0.85      1199
+   macro avg       0.85      0.85      0.85      1199
+weighted avg       0.85      0.85      0.85      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 69.9% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 72.0% 
               precision    recall  f1-score   support
 
-     chinese       0.59      0.51      0.55       245
-      indian       0.86      0.84      0.85       250
-    japanese       0.72      0.66      0.69       237
-      korean       0.65      0.78      0.71       226
-        thai       0.67      0.71      0.69       241
+     chinese       0.64      0.44      0.52       247
+      indian       0.90      0.87      0.88       229
+    japanese       0.66      0.70      0.68       243
+      korean       0.66      0.81      0.73       241
+        thai       0.75      0.80      0.77       239
 
-    accuracy                           0.70      1199
-   macro avg       0.70      0.70      0.70      1199
-weighted avg       0.70      0.70      0.70      1199
+    accuracy                           0.72      1199
+   macro avg       0.72      0.72      0.72      1199
+weighted avg       0.72      0.72      0.71      1199
 </pre></div>
 </div>
 </div>
@@ -2060,16 +2092,16 @@ <h2><span class="section-number">13.3.7. </span>Ensemble Classifiers<a class="he
 </section>
 <hr class="docutils" />
 <section id="self-study">
-<h2><span class="section-number">13.3.8. </span>Self Study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.8. </span>Self Study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>There’s a lot of jargon in these sections, so take a minute to review <a class="reference external" href="https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott">this list</a> of useful terminology!</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">13.3.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Each of these techniques has a large number of parameters that you can tweak. Research each one’s default parameters and think about what tweaking these parameters would mean for the model’s quality.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html"><span class="doc std std-doc">Parameter play</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">13.3.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2111,13 +2143,13 @@ <h2><span class="section-number">13.3.10. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">13.2. </span>More classifiers</p>
+            <p class="prev-next-title"><span class="section-number">14.2. </span>More classifiers</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="applied-ml-build-a-web-app.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">13.4. </span>Applied Machine Learning : build a web app</p>
+        <p class="prev-next-title"><span class="section-number">14.4. </span>Applied Machine Learning : build a web app</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-fundamentals/ml-overview.html b/ml-fundamentals/ml-overview.html
index e9e3a692b4..9ddc69d757 100644
--- a/ml-fundamentals/ml-overview.html
+++ b/ml-fundamentals/ml-overview.html
@@ -100,7 +100,7 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="11. Regression models for Machine Learning" href="regression/regression-models-for-machine-learning.html" />
+    <link rel="next" title="11. Summary of machine learning fundamentals" href="ml-summary.html" />
     <link rel="prev" title="9. Data Science in the real world" href="../data-science/data-science-in-the-wild.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
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+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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+   32. Image segmentation
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+     40.5. Project Plan​ Template
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-     37.6. Python programming introduction
+     40.6. Python programming introduction
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+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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-     37.13. Apply your skills
+     40.13. Apply your skills
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-     37.14. Try it in Excel
+     40.14. Try it in Excel
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-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
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+     40.17. Classifying datasets
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-     37.18. Small diabetes study
+     40.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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-     37.24. Data processing in Python
+     40.24. Data processing in Python
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-     37.25. Evaluating data from a form
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-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
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-     37.29. Matplotlib applied
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-     37.35. Tell a story
+     40.35. Tell a story
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-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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    </li>
    <li class="toctree-l2">
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-     37.37. Exploring for answers
+     40.37. Exploring for answers
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    </li>
    <li class="toctree-l2">
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-     37.38. Market research
+     40.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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    </li>
    <li class="toctree-l2">
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-     37.40. Data Science project using Azure ML SDK
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
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-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     37.43. Machine Learning overview - assignment 2
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    </li>
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-     37.44. Regression with Scikit-learn
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-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
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-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
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-     37.47. Loss Function
+     40.47. Loss Function
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-     37.48. Gradient descent
+     40.48. Gradient descent
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-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
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-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
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-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
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+     40.53. Regression tools
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+     40.54. Managing data
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-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
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-     37.56. Try a different model
+     40.56. Try a different model
     </a>
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-     37.57. Create a regression model
+     40.57. Create a regression model
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-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
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-     37.59. Retrying some regression
+     40.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
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+     40.61. Delicious asian and indian cuisines
     </a>
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-     37.62. Explore classification methods
+     40.62. Explore classification methods
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-     37.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
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-        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/ml-fundamentals/ml-overview.md"
+        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/ml-fundamentals/ml-overview.ipynb"
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    10.1. What is Machine Learning?
   </a>
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning">
-   10.2. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
-  </a>
- </li>
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   <a class="reference internal nav-link" href="#ordinary-programming-vs-machine-learning">
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- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#applications-of-machine-learning">
-   10.4. Applications of Machine Learning
+   10.2. Ordinary Programming vs Machine Learning
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+   10.4. When And When Not to Use Machine Learning
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+   10.5. Types of Machine Learning Systems
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-     10.7.1. Supervised Learning
-    </a>
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-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#unsupervised-learning">
-     10.7.2. Unsupervised Learning
+     10.5.1. Supervised Learning
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     <a class="reference internal nav-link" href="#reinforcement-learning">
-     10.7.3. Reinforcement Learning
+     10.5.2. Reinforcement Learning
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+   10.6. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#defining-a-problem">
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+     10.6.1. Defining a Problem
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     <a class="reference internal nav-link" href="#collecting-data">
-     10.8.2. Collecting Data
+     10.6.2. Collecting Data
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     <a class="reference internal nav-link" href="#establishing-a-baseline">
-     10.8.3. Establishing a Baseline
+     10.6.3. Establishing a Baseline
     </a>
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     <a class="reference internal nav-link" href="#exploratory-data-analysis-eda">
-     10.8.4. Exploratory data analysis(EDA)
+     10.6.4. Exploratory data analysis(EDA)
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     <a class="reference internal nav-link" href="#data-preprocessing">
-     10.8.5. Data Preprocessing
+     10.6.5. Data Preprocessing
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    </li>
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     <a class="reference internal nav-link" href="#selecting-and-training-a-model">
-     10.8.6. Selecting and Training a Model
+     10.6.6. Selecting and Training a Model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#performing-error-analysis">
-     10.8.7. Performing Error Analysis
+     10.6.7. Performing Error Analysis
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#deploying-a-model">
-     10.8.8. Deploying a Model
+     10.6.8. Deploying a Model
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    </li>
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   <a class="reference internal nav-link" href="#your-turn">
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    10.1. What is Machine Learning?
   </a>
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning">
-   10.2. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
-  </a>
- </li>
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   <a class="reference internal nav-link" href="#ordinary-programming-vs-machine-learning">
-   10.3. Ordinary Programming vs Machine Learning
-  </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#applications-of-machine-learning">
-   10.4. Applications of Machine Learning
+   10.2. Ordinary Programming vs Machine Learning
   </a>
  </li>
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   <a class="reference internal nav-link" href="#key-terminology">
-   10.5. Key terminology
+   10.3. Key terminology
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#labels">
-     10.5.1. Labels
+     10.3.1. Labels
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#features">
-     10.5.2. Features
+     10.3.2. Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#examples">
-     10.5.3. Examples
+     10.3.3. Examples
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#models">
-     10.5.4. Models
+     10.3.4. Models
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-vs-classification">
-     10.5.5. Regression vs classification
+     10.3.5. Regression vs classification
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#when-and-when-not-to-use-machine-learning">
-   10.6. When And When Not to Use Machine Learning
+   10.4. When And When Not to Use Machine Learning
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#types-of-machine-learning-systems">
-   10.7. Types of Machine Learning Systems
+   10.5. Types of Machine Learning Systems
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#supervised-learning">
-     10.7.1. Supervised Learning
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#unsupervised-learning">
-     10.7.2. Unsupervised Learning
+     10.5.1. Supervised Learning
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reinforcement-learning">
-     10.7.3. Reinforcement Learning
+     10.5.2. Reinforcement Learning
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#typical-machine-learning-workflow">
-   10.8. Typical Machine Learning Workflow
+   10.6. Typical Machine Learning Workflow
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-a-problem">
-     10.8.1. Defining a Problem
+     10.6.1. Defining a Problem
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#collecting-data">
-     10.8.2. Collecting Data
+     10.6.2. Collecting Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#establishing-a-baseline">
-     10.8.3. Establishing a Baseline
+     10.6.3. Establishing a Baseline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis-eda">
-     10.8.4. Exploratory data analysis(EDA)
+     10.6.4. Exploratory data analysis(EDA)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     10.8.5. Data Preprocessing
+     10.6.5. Data Preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#selecting-and-training-a-model">
-     10.8.6. Selecting and Training a Model
+     10.6.6. Selecting and Training a Model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#performing-error-analysis">
-     10.8.7. Performing Error Analysis
+     10.6.7. Performing Error Analysis
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#deploying-a-model">
-     10.8.8. Deploying a Model
+     10.6.8. Deploying a Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   10.9. Your turn! 🚀
+   10.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   10.10. Acknowledgments
+   10.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1764,10 +1766,6 @@ <h2> Contents </h2>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="machine-learning-overview">
 <h1><span class="section-number">10. </span>Machine Learning overview<a class="headerlink" href="#machine-learning-overview" title="Permalink to this headline">#</a></h1>
-<blockquote class="epigraph">
-<div><p>Machine intelligence is the last invention that humanity will ever need to make.</p>
-<p class="attribution">—Nick Bostrom</p>
-</div></blockquote>
 <p>We are living in the age of the intelligent machines. In the last decade, Machine Learning powered systems went from beating the world-class chess players to now influencing most of our daily decisions like what we watch on streaming services like Netflix and YouTube. What is Machine Learning? And why is it important to be studying it now?</p>
 <p>We hear that Machine Learning is transforming many industries, but what exactly can we use it for? And what are the set of problems that this technology is or not suited for?</p>
 <p>This introduction will give you the basics of Machine Learning or these things that you will always need to know. More concretely, it will cover the high level foundational knowledge such as:</p>
@@ -1797,63 +1795,40 @@ <h2><span class="section-number">10.1. </span>What is Machine Learning?<a class=
 </div></blockquote>
 <p>What does that mean?</p>
 <p>In simple words, Machine Learning algorithms are trained on data rather than being programmed explicitly.</p>
-</section>
-<section id="artificial-intelligence-data-science-machine-learning-and-deep-learning">
-<h2><span class="section-number">10.2. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning" title="Permalink to this headline">#</a></h2>
-<p>AI or Artificial intelligence, data science, Machine Learning, and deep learning are used interchangeably, but they are quite different.</p>
-<p>AI is a branch of computer science concerned with building intelligent machines capable of performing tasks at the level of human. AI seeks to mimic human. AI is an interdisciplinary field that involves Machine Learning, programming, robotics, data science, etc…</p>
-<p>Machine Learning on the other hand is the branch of AI and as we saw, it is concerned with giving the machine the ability to learn from data. Machine Learning algorithms consists of shallow or classical algorithms such as decision trees and deep learning algorithms such as convolutional neural networks. We will learn more about these algorithms in the next parts.</p>
-<p>Deep learning is a branch of Machine Learning that deals with the study of artificial neural networks and it was inspired by the human brain. Classical Machine Learning algorithms needs a lot of feature engineering, but deep learning algorithms can extract features in huge amount of data such as images themselves.</p>
 <figure class="align-default" id="ai-ml-dl">
-<a class="reference internal image-reference" href="../_images/ai-ml-dl.png"><img alt="../_images/ai-ml-dl.png" src="../_images/ai-ml-dl.png" style="width: 90%;" /></a>
+<a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/ai-ml-dl.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/ai-ml-dl.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/ai-ml-dl.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 10.1 </span><span class="caption-text">The difference between AI, Machine Learning and deep learning</span><a class="headerlink" href="#ai-ml-dl" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="ordinary-programming-vs-machine-learning">
-<h2><span class="section-number">10.3. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.2. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
 <p>In ordinary programming, the job of the programmer is to clearly write every single rule that makes up the task he/she is trying to accomplish. In order to get the results, she/he must write all rules that acts up on the data.</p>
 <p>Machine Learning flips that. Instead of having to write the rules that makes up a particular application, we can feed data and results(or labels) to the Machine Learning model, and its job can be to determine the set of rules that map the data and labels.</p>
 <p>Let’s take a real world example. If you wanted to build an application that given a picture of person can determine if he/she is wearing or not wearing a facemask, you can just feed a bunch of images of people with and without facemasks to the Machine Learning model, and the model can learn the rules or patterns that map the images to whether they have a facemask or not.</p>
 <p>You can even extend that further and use those learned rules to recognize facemasks in the images that were never seen by the that model.</p>
 <figure class="align-default" id="traditional-ml-png">
-<a class="reference internal image-reference" href="../_images/traditional-ml.png"><img alt="../_images/traditional-ml.png" src="../_images/traditional-ml.png" style="width: 90%;" /></a>
+<a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/traditional-ml.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/traditional-ml.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/traditional-ml.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 10.2 </span><span class="caption-text">Traditional Programming vs Deep Learning</span><a class="headerlink" href="#traditional-ml-png" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Approaching a facemask recognition problem with rule based programming would really be a hard problem. You would have to write lots of code that would later turn out to not work typically because your program will be tested on different kinds of facemasks and people in various scenarios and it’s almost impossible to express that in rules. Whereas with Machine Learning, all you need is a bunch of images of people with and without facemasks, and there you are few steps away from getting an effective facemask recognizer.</p>
 </section>
-<section id="applications-of-machine-learning">
-<h2><span class="section-number">10.4. </span>Applications of Machine Learning<a class="headerlink" href="#applications-of-machine-learning" title="Permalink to this headline">#</a></h2>
-<p>Before we step into further Machine Learning foundations, let’s look at some of its most exciting applications.</p>
-<p>Machine Learning has transformed many industries, from banking, manufacturing, streaming, autonomous vehicles, agriculture, etc…In fact, most of the tech products and services we use daily possess some sorts of Machine Learning algorithms running in their backgrounds.</p>
-<p>Here are the most commonly Machine Learning use cases:</p>
-<ul class="simple">
-<li><p><strong>Fraud detection</strong>: Banks and other financial organizations can use Machine Learning to detect frauds in real time.</p></li>
-<li><p><strong>Loan repayment prediction</strong>: Banks can also use the historical data of their clients to predict if they will be able to repay back the loans before granting it to them.</p></li>
-<li><p><strong>diagnosing diseases and predicting the survival rate</strong>: Machine Learning is increasingly finding its value in medicine. It can assist medical professionals in diagnosing diseases in handful of minutes. Medical professionals can also use Machine Learning to predict the likelihood or a course of disease or survival rate(prognosis).</p></li>
-<li><p><strong>Detecting defects in industry</strong>: Some manufacturing companies use machine vision to inspect defects in the products which ultimately result in speeding up the production process, automating the inspection task, reducing the cost and human workload. You can learn more about visual inspection in <a class="reference external" href="https://landing.ai/wp-content/uploads/2020/11/MachineVisionSurvey.pdf">2020 State of AI-Based Machine Vision by Landing AI</a>.</p></li>
-<li><p><strong>Churn prediction</strong>:  Organizations that provides some kinds of services can use Machine Learning to predict if a given customer is likely to opt out from the service or cancel subscription. This can help the organization to improve the customer experiences in order to retain the customers.</p></li>
-<li><p><strong>Spam detection</strong>: Almost all email providers such as Gmail or Outlook possess the ability to detect spams from all incoming emails to protect the users from fake promotions and scams.</p></li>
-<li><p><strong>Autonomous vehicles</strong>: Today’s autonomous vehicles such as self-driving cars use Machine Learning and deep learning systems to navigate in the roads. Using computer vision, they can be able to detect the pedestrian and traffic lights and signs and other surrounding objects.</p></li>
-</ul>
-<p>There are many more applications of Machine Learning and the list could go on. For example, a given ads agency can use Machine Learning to learn the kinds of things that their visitors are interested in and they can use the results to place the relevant ads on the website. Same for streaming services like YouTube and Netflix. These services uses Machine Learning to suggest the best media to their clients based on their interests.</p>
-</section>
 <section id="key-terminology">
-<h2><span class="section-number">10.5. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.3. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
 <p>What is Machine Learning? Concisely put, it is the following:</p>
 <ul class="simple">
 <li><p>ML systems learn how to combine input to produce useful predictions on never-before-seen data.</p></li>
 </ul>
 <p>Let’s explore fundamental Machine Learning terminology.</p>
 <section id="labels">
-<h3><span class="section-number">10.5.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
 <p>A <code class="docutils literal notranslate"><span class="pre">label</span></code> is the thing we’re predicting—the <code class="docutils literal notranslate"><span class="pre">y</span></code> variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.</p>
 </section>
 <section id="features">
-<h3><span class="section-number">10.5.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
 <p>A <code class="docutils literal notranslate"><span class="pre">feature</span></code> is an input variable—the <code class="docutils literal notranslate"><span class="pre">x</span></code> variable in simple linear regression. A simple Machine Learning project might use a single feature, while a more sophisticated Machine Learning project could use millions of features, specified as:</p>
 <div class="math notranslate nohighlight">
 \[x_1, x_2, ..., x_N\]</div>
@@ -1866,7 +1841,7 @@ <h3><span class="section-number">10.5.2. </span>Features<a class="headerlink" hr
 </ul>
 </section>
 <section id="examples">
-<h3><span class="section-number">10.5.3. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.3. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
 <p>An <code class="docutils literal notranslate"><span class="pre">example</span></code> is a particular instance of data, <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong>. (We put <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong> in boldface to indicate that it is a vector.) We break examples into two categories:</p>
 <ul class="simple">
 <li><p>labeled examples</p></li>
@@ -1884,7 +1859,7 @@ <h3><span class="section-number">10.5.3. </span>Examples<a class="headerlink" hr
 <p>Once we’ve trained our model with labeled examples, we use that model to predict the label on unlabeled examples. In the spam detector, unlabeled examples are new emails that humans haven’t yet labeled.</p>
 </section>
 <section id="models">
-<h3><span class="section-number">10.5.4. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.4. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
 <p>A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with “spam”. Let’s highlight two phases of a model’s life:</p>
 <ul class="simple">
 <li><p><strong>Training</strong> means creating or <strong>learning</strong> the model. That is, you show the model labeled examples and enable the model to gradually learn the relationships between features and label.</p></li>
@@ -1892,7 +1867,7 @@ <h3><span class="section-number">10.5.4. </span>Models<a class="headerlink" href
 </ul>
 </section>
 <section id="regression-vs-classification">
-<h3><span class="section-number">10.5.5. </span>Regression vs classification<a class="headerlink" href="#regression-vs-classification" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.5. </span>Regression vs classification<a class="headerlink" href="#regression-vs-classification" title="Permalink to this headline">#</a></h3>
 <p>A <strong>regression</strong> model predicts continuous values. For example, regression models make predictions that answer questions like the following:</p>
 <ul class="simple">
 <li><p>What is the value of a house in California?</p></li>
@@ -1906,7 +1881,7 @@ <h3><span class="section-number">10.5.5. </span>Regression vs classification<a c
 </section>
 </section>
 <section id="when-and-when-not-to-use-machine-learning">
-<h2><span class="section-number">10.6. </span>When And When Not to Use Machine Learning<a class="headerlink" href="#when-and-when-not-to-use-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.4. </span>When And When Not to Use Machine Learning<a class="headerlink" href="#when-and-when-not-to-use-machine-learning" title="Permalink to this headline">#</a></h2>
 <p>Machine Learning is an incredible technology and it has shown a lot of successes in solving various real world problems. However, like any other technology, Machine Learning is not suitable for solving all kinds of problems. It is thus equally important to know when and when not to use Machine Learning.</p>
 <p>When to use Machine Learning? Machine Learning is preferred when approaching:</p>
 <ul class="simple">
@@ -1926,7 +1901,7 @@ <h2><span class="section-number">10.6. </span>When And When Not to Use Machine L
 <p>We talked about when you should use Machine Learning and when you should not, but also, there are other areas where Machine Learning is being heavily used but with extra care and human in the loop. Example of such critical areas include medicine, self driving cars, etc…In some of those areas like self-driving cars(or driver assistants), Machine Learning is surely a big factor but also because the cost of error made by the model can be very high, human assistance becomes important.</p>
 </section>
 <section id="types-of-machine-learning-systems">
-<h2><span class="section-number">10.7. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.5. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
 <p>In broad, there are 3 main types of Machine Learning systems that are:</p>
 <ul class="simple">
 <li><p>Supervised learning</p></li>
@@ -1935,7 +1910,7 @@ <h2><span class="section-number">10.7. </span>Types of Machine Learning Systems<
 </ul>
 <p>Let’s review all of these types to get a high level understanding of what’s they really mean.</p>
 <section id="supervised-learning">
-<h3><span class="section-number">10.7.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.5.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
 <p>Most Machine Learning tasks fall into supervised learning type. As the name implies, a supervised learning model is trained with input data along with some form of guidance that we can call labels. In other words, a supervised learning model maps the input data (or X in many textbooks) to output labels (y). Labels are also known as targets and they act as a description of the input data.</p>
 <p>The example of facemask recognition that we used in above sections is a good example of supervised learning. In broad, there are 2 main kinds of supervised learning problems that are:</p>
 <ul class="simple">
@@ -1943,7 +1918,7 @@ <h3><span class="section-number">10.7.1. </span>Supervised Learning<a class="hea
 <li><p><strong>Regression problems</strong> where the goal is to predict a continuous value of something. A classical example for this category is to predict the price of the used car given its features such as brand, age, number of doors, number of sits, safety level, maintenance cost, etc…</p></li>
 </ul>
 <figure class="align-default" id="class-reg-png">
-<a class="reference internal image-reference" href="../_images/class-reg.png"><img alt="../_images/class-reg.png" src="../_images/class-reg.png" style="width: 90%;" /></a>
+<a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 10.3 </span><span class="caption-text">Graphical representation of classification and regression problems</span><a class="headerlink" href="#class-reg-png" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -1956,26 +1931,17 @@ <h3><span class="section-number">10.7.1. </span>Supervised Learning<a class="hea
 <li><p>Image segmentation where the goal is to group the pixels that make up particular objects in the image.</p></li>
 </ul>
 <figure class="align-default" id="vision-png">
-<a class="reference internal image-reference" href="../_images/vision.png"><img alt="../_images/vision.png" src="../_images/vision.png" style="width: 90%;" /></a>
+<a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/vision.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/vision.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/vision.png" style="width: 90%;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 10.4 </span><span class="caption-text">Advanced Tasks: Object detection, image segmentation, image captioning. You can test these advanced algorithms on your images using <a class="reference external" href="https://vision-explorer.allenai.org">vision-explorer</a></span><a class="headerlink" href="#vision-png" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Some of those tasks can involve both classification and regression. Take an example for object detection, a task of recognizing and localizing an object in an image: it involves classification (recognizing the object among many other objects) and regression (predicting the coordinates of the objects in an image to make a bounding box).</p>
 <p>If any of the things we are talking about sounds unfamiliar, do not worry. There is no way to explain all things at once, but as we progress, things will get clear.</p>
-</section>
-<section id="unsupervised-learning">
-<h3><span class="section-number">10.7.2. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h3>
-<p>Supervised learning algorithms are trained with data and labels. Conversely, unsupervised learning algorithms are trained on unlabelled data.</p>
-<p>Unsupervised learning algorithms are primarily used for:</p>
-<ul class="simple">
-<li><p>Clustering: K-Means</p></li>
-<li><p>Dimension reduction and data visualization: Principal Component Analysis(PCA), t-Distributed Stochastic Neighbor Embedding(t-SNE).</p></li>
-</ul>
-<p>There are many more algorithms that could also be used for those 2 tasks, but, we will cover K-Means, PCA, and t-SNE because they are commonly used.</p>
+<p><a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png">https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png</a></p>
 </section>
 <section id="reinforcement-learning">
-<h3><span class="section-number">10.7.3. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.5.2. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
 <p>Reinforcement learning is a special type of Machine Learning that is most applicable in robotics and games.</p>
 <p>In reinforcement learning, a learning system called <em>an agent</em> can perceives the environment, performs some actions, and gets <em>rewarded</em> or <em>penalized</em> depending on how it is performing. The main goal of the agent is to accumulate as much as rewards as possible.</p>
 <p>The <em>agent</em> learns the best strategy(<em>policy</em>) necessary for getting the most reward itself.</p>
@@ -1985,7 +1951,7 @@ <h3><span class="section-number">10.7.3. </span>Reinforcement Learning<a class="
 </section>
 </section>
 <section id="typical-machine-learning-workflow">
-<h2><span class="section-number">10.8. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.6. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
 <p>Although every Machine Learning problem is unique, they all follow a similar workflow. In this section, we will learn how to approach Machine Learning problems systematically.</p>
 <p>Overall, a typical Machine Learning project workflow consists of:</p>
 <ul class="simple">
@@ -2000,7 +1966,7 @@ <h2><span class="section-number">10.8. </span>Typical Machine Learning Workflow<
 </ul>
 <p>Let’s talk about each step in brief.</p>
 <section id="defining-a-problem">
-<h3><span class="section-number">10.8.1. </span>Defining a Problem<a class="headerlink" href="#defining-a-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.1. </span>Defining a Problem<a class="headerlink" href="#defining-a-problem" title="Permalink to this headline">#</a></h3>
 <p>Everything starts here. Problem definition is the important and the initial step in any Machine Learning project. This is where you make sure you understand the problem really well. Understanding the problem will give you proper intuitions about the next steps to follow such as right learning algorithms, etc. But wait, what does it mean to understand the problem?</p>
 <p>Understanding the problem is all about diving deep into the details of the problem at hand and asking the right questions. First, it’s important to narrow down the problem until you can have a simple and a well-defined goal. Here are examples of simple goals: To <em>classify</em> products into different categories, to <em>predict</em> the price of a used car given its features (such as brand, age, etc…), to <em>recognize</em> if a person is wearing a facemask, to <em>divide</em> customers into different groups that share similar behaviors, etc…As you can see, the goal can tell whether the problem is classification, regression, or clustering…</p>
 <p>At this stage, avoid vague words. The simpler you can formulate the problem, the better things will be down the hall. It’s also worth questioning if the project should be pursued. Most problems do not require Machine Learning.</p>
@@ -2008,7 +1974,7 @@ <h3><span class="section-number">10.8.1. </span>Defining a Problem<a class="head
 <p>After you understand the problem and have an idea of the data you want, the next step is to collect it.</p>
 </section>
 <section id="collecting-data">
-<h3><span class="section-number">10.8.2. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.2. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
 <p>This is usually the next stage after formulating a problem. Before we talk about collecting data, let’s even understand the meaning of data. According to <a class="reference external" href="https://en.wikipedia.org/wiki/Data">wikipedia</a>, “data are a set of values of qualitative or quantitative variables about one or more persons or objects.”</p>
 <p>There are 2 main types of data that are:</p>
 <ul class="simple">
@@ -2020,12 +1986,12 @@ <h3><span class="section-number">10.8.2. </span>Collecting Data<a class="headerl
 <p>Also, when collecting the data, quality is better than quantity. There are times when small data but good data can outwork big poor data. The amount of data you need is going to depend on the problem you’re solving and its scope, but whatever the problem is, aiming to collect good data is the way to go.</p>
 </section>
 <section id="establishing-a-baseline">
-<h3><span class="section-number">10.8.3. </span>Establishing a Baseline<a class="headerlink" href="#establishing-a-baseline" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.3. </span>Establishing a Baseline<a class="headerlink" href="#establishing-a-baseline" title="Permalink to this headline">#</a></h3>
 <p>Without a benchmark, you won’t know how to evaluate your results properly. A baseline is the simplest model that can solve your problem with minimal requirements. It does not have to be a model. It can be an existing open-source application, a statistical analysis or intuitions you get from data at a quick glance.</p>
 <p>The single most purpose of a baseline is to act as a reference point when comparing the actual model with the baseline. The ultimate goal is to beat a baseline, and sometime, if you can’t beat it, it might mean the project is not worth pursuing, or the baseline can be all you need.</p>
 </section>
 <section id="exploratory-data-analysis-eda">
-<h3><span class="section-number">10.8.4. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.4. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
 <p>Before manipulating the data, it is quite important to learn about the dataset. This can be overlooked, but doing it well will help you know the effective strategies to be applied while cleaning the data.</p>
 <p>Go through some values, plot some features, and try to understand the correlation between them. If the work is vision-related, visualize some images, and spot what’s missing in the images. Are they diverse enough? What types of image scenarios can you add? Or what are images that can mislead the model?</p>
 <p>Here are other things that you would want to inspect in the data:</p>
@@ -2039,7 +2005,7 @@ <h3><span class="section-number">10.8.4. </span>Exploratory data analysis(EDA)<a
 <p>Also, to avoid data leakage, do not touch the test set in EDA or anywhere before the model training.</p>
 </section>
 <section id="data-preprocessing">
-<h3><span class="section-number">10.8.5. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.5. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>Data processing is perhaps the biggest part of any Machine Learning project. There is a notion that Data Scientists and Machine Learning Engineers spend more than 80% of their time preparing the data and this makes sense because the real-world data are messy.</p>
 <p>In this step, it is where you convert the raw data to go in a format that can be accepted by the Machine Learning algorithms.</p>
 <p>Data preprocessing is hard because there are different types of data and the way you process one is different from the other. For example, in structured data, the way you process numerical features is going to be different from categorical features. Also in unstructured data, the way you manipulate images is going to be different from how you manipulate texts or sounds.</p>
@@ -2052,7 +2018,7 @@ <h3><span class="section-number">10.8.5. </span>Data Preprocessing<a class="head
 <p>In many textbooks and courses, data preprocessing is also referred to as data cleaning or data preparation. Feature engineering is also a part of data preprocessing. Feature engineering is a creative task and requires some extra knowledge about the data and the problem as it involves creating new features from existing features.</p>
 </section>
 <section id="selecting-and-training-a-model">
-<h3><span class="section-number">10.8.6. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.6. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
 <p>Selecting, creating, and training a Machine Learning model is the tiniest part of any typical Machine Learning workflow. There are different types of models but in broad, most of them fall into these categories: linear models such as linear and logistic regression, tree-based models such as decision trees, ensemble models such as random forests, and finally neural networks.</p>
 <p>Depending on your problem, you can choose any relevant model from these categories or tries many of them. But, it is worth mentioning that getting a model that can ultimately solve a given problem is no <em>free lunch scenario</em>. You have to experiment with different models to get one that works for your problem and dataset.</p>
 <p>To reduce your modeling curve, here are a few things that you can consider while choosing a Machine Learning model:</p>
@@ -2065,7 +2031,7 @@ <h3><span class="section-number">10.8.6. </span>Selecting and Training a Model<a
 <p>As you can see, there is a trade-off during model selection. You want explainability, choose models that can provide that for you, most models don’t. You have a small dataset or you care about the training time, same thing, a right model for you.</p>
 </section>
 <section id="performing-error-analysis">
-<h3><span class="section-number">10.8.7. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.7. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
 <p>Performing error analysis will guide you throughout the process of improving the results of the model. The improvement can either be from the data or the model.</p>
 <p>One of the best ways to do error analysis is to plot the learning curve and to try noticing where the model is failing and what might be the reason and the right actions that you can take to reduce the errors.</p>
 <p>To improve the model part, you can try different model configuration values or hyperparameters. You can also try different models to see one that works well. But also, a good model comes from good data, so it’s important to spend time examining the results of the model with respect to the input data.</p>
@@ -2079,16 +2045,16 @@ <h3><span class="section-number">10.8.7. </span>Performing Error Analysis<a clas
 <p>When improving the data, you can create artificial data (a.k.a data augmentation). This will work well most of the time. The whole error analysis is an iterative process, keep doing it and always aim to improve the data than the model.</p>
 </section>
 <section id="deploying-a-model">
-<h3><span class="section-number">10.8.8. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.8. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
 <p>Model deployment is the last part of this workflow. When all the previous steps have gone right, and you are happy about the results of the model on the test set, the next step will be to deploy the model so that the users can start to make requests and get predictions or enhanced services.</p>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">10.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">10.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/Nyandwi">Nyandwi</a> for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Complete Machine Learning Package</a>. It inspires the majority of the content in this chapter. Some contents come from <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/framing/ml-terminology">Google Machine Learning Crash Course</a>.</p>
 </section>
 </section>
@@ -2133,10 +2099,10 @@ <h2><span class="section-number">10.10. </span>Acknowledgments<a class="headerli
             <p class="prev-next-title"><span class="section-number">9. </span>Data Science in the real world</p>
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-        <p class="prev-next-title"><span class="section-number">11. </span>Regression models for Machine Learning</p>
+        <p class="prev-next-title"><span class="section-number">11. </span>Summary of machine learning fundamentals</p>
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+  PREREQUISITES
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
+   1. Python programming introduction
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../prerequisites/python-programming-basics.html">
+   2. Python programming basics
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
+   3. Python programming advanced
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/introduction/introduction.html">
+   4. Introduction
+  </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/working-with-data/working-with-data.html">
+   5. Working with data
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+    <a class="reference internal" href="../data-science/working-with-data/relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/working-with-data/non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/working-with-data/numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 has-children">
+    <a class="reference internal" href="../data-science/working-with-data/pandas/pandas.html">
+     5.4. Pandas
+    </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../data-science/data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="regression/regression-models-for-machine-learning.html">
+   12. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="regression/tools-of-the-trade.html">
+     12.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="regression/managing-data.html">
+     12.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="regression/linear-and-polynomial-regression.html">
+     12.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="regression/logistic-regression.html">
+     12.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="build-a-web-app-to-use-a-machine-learning-model.html">
+   13. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="classification/getting-started-with-classification.html">
+   14. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="classification/introduction-to-classification.html">
+     14.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="classification/more-classifiers.html">
+     14.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="classification/yet-other-classifiers.html">
+     14.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="classification/applied-ml-build-a-web-app.html">
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+   16. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
+     16.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
+     16.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+   17. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
+     17.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
+     17.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
+     17.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
+   18. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
+     18.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+     18.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
+     18.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     18.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
+   19. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/kernel-method.html">
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/model-selection.html">
+   22. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/dl-overview.html">
+   23. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/cnn.html">
+   24. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/gan.html">
+   25. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/rnn.html">
+   26. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/autoencoder.html">
+   27. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/lstm.html">
+   28. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/time-series.html">
+   29. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/dqn.html">
+   30. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/image-classification.html">
+   31. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/image-segmentation.html">
+   32. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/difussion-model.html">
+   33. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../deep-learning/object-detection.html">
+   34. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/overview.html">
+   35. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
+   36. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
+   37. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
+   38. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
+   39. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../assignments/README.html">
+   40. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
+     40.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
+     40.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/project-plan-template.html">
+     40.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
+     40.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
+     40.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
+     40.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
+     40.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
+     40.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
+     40.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
+     40.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
+     40.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
+     40.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
+     40.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
+     40.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
+     40.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
+     40.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
+     40.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
+     40.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/soda-profits.html">
+     40.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
+     40.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     40.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
+     40.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
+     40.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-preparation.html">
+     40.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
+     40.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     40.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
+     40.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
+     40.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     40.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
+     40.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/market-research.html">
+     40.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     40.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     40.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
+     40.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     40.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     40.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     40.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     40.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     40.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     40.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     40.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     40.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     40.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
+     40.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
+     40.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
+     40.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
+     40.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
+     40.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
+     40.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     40.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
+     40.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     40.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     40.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/explore-classification-methods.html">
+     40.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     40.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     40.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     40.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     40.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     40.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     40.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     40.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     40.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     40.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     40.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     40.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     40.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     40.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     40.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     40.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     40.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
+     40.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     40.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
+     40.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
+     40.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/study-the-solvers.html">
+     40.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-models.html">
+     40.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-model.html">
+     40.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/parameter-play.html">
+     40.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     40.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     40.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     40.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     40.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     40.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
+     40.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     40.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
+     40.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
+     40.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     40.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     40.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
+     40.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
+     40.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     40.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../slides/introduction.html">
+   41. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/python-programming/python-programming-introduction.html">
+     41.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/python-programming/python-programming-basics.html">
+     41.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/python-programming/python-programming-advanced.html">
+     41.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-introduction.html">
+     41.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/relational-vs-non-relational-database.html">
+     41.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/numpy-and-pandas.html">
+     41.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-visualization.html">
+     41.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-lifecycle.html">
+     41.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-in-the-cloud.html">
+     41.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/data-science/data-science-in-real-world.html">
+     41.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/ml-overview.html">
+     41.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/linear-regression.html">
+     41.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression.html">
+     41.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression-condensed.html">
+     41.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/neural-network.html">
+     41.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-fundamentals/build-an-ml-web-app.html">
+     41.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-advanced/unsupervised-learning.html">
+     41.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
+     41.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
+     41.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/deep-learning/cnn.html">
+     41.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../slides/deep-learning/gan.html">
+     41.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+
+    </div>
+</nav></div>
+        <div class="bd-sidebar__bottom">
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+<div class="col-md-3 bd-toc show noprint">
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+        <i class="fas fa-list"></i> Contents
+    </div>
+    <nav id="bd-toc-nav" aria-label="Page">
+        <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#machine-learning-landscape-discriminative-models">
+   11.1. Machine Learning Landscape : Discriminative Models
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#how-to-conceive-a-new-machine-learning-algorithm-discriminative-models">
+   11.2. How to conceive a “new” Machine Learning algorithm : Discriminative Models
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#machine-learning-landscape-generative-models">
+   11.3. Machine Learning Landscape : Generative Models
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#lda">
+   11.4. LDA
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#unsupervised-learning">
+   11.5. Unsupervised learning
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#semi-supervised-learning">
+   11.6. Semi-supervised learning
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   11.7. Your turn! 🚀
+  </a>
+ </li>
+</ul>
+
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+                <h1>Summary of machine learning fundamentals</h1>
+                <!-- Table of contents -->
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+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
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+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#machine-learning-landscape-discriminative-models">
+   11.1. Machine Learning Landscape : Discriminative Models
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#how-to-conceive-a-new-machine-learning-algorithm-discriminative-models">
+   11.2. How to conceive a “new” Machine Learning algorithm : Discriminative Models
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#machine-learning-landscape-generative-models">
+   11.3. Machine Learning Landscape : Generative Models
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#lda">
+   11.4. LDA
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#unsupervised-learning">
+   11.5. Unsupervised learning
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#semi-supervised-learning">
+   11.6. Semi-supervised learning
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   11.7. Your turn! 🚀
+  </a>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
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+            <main id="main-content" role="main">
+                
+              <div>
+                
+  <section class="tex2jax_ignore mathjax_ignore" id="summary-of-machine-learning-fundamentals">
+<h1><span class="section-number">11. </span>Summary of machine learning fundamentals<a class="headerlink" href="#summary-of-machine-learning-fundamentals" title="Permalink to this headline">#</a></h1>
+<section id="machine-learning-landscape-discriminative-models">
+<h2><span class="section-number">11.1. </span>Machine Learning Landscape : Discriminative Models<a class="headerlink" href="#machine-learning-landscape-discriminative-models" title="Permalink to this headline">#</a></h2>
+<p>Most of supervised machine learning can be looked at using the following framework:
+You have a set of training points <span class="math notranslate nohighlight">\((x_i, y_i)\)</span>, and you want to find a function f that “fits the data well”,
+that is, <span class="math notranslate nohighlight">\(yi \approx f(x_i)\)</span> for most <span class="math notranslate nohighlight">\(i\)</span>.</p>
+<p>You will start by doing the following:</p>
+<ul class="simple">
+<li><p>Define the form of <span class="math notranslate nohighlight">\(f\)</span>. For instance, we can define <span class="math notranslate nohighlight">\(f = wx + b\)</span>, for some constants <span class="math notranslate nohighlight">\(w\)</span> and <span class="math notranslate nohighlight">\(b\)</span>.
+Note that this is a set of functions — for different values of <span class="math notranslate nohighlight">\(w\)</span> and <span class="math notranslate nohighlight">\(b\)</span>,
+you will get different functions <span class="math notranslate nohighlight">\(f\)</span>, and you want to find an <span class="math notranslate nohighlight">\(f\)</span>
+from this set that does the “best”.</p></li>
+<li><p>As you might have noticed, we have been talking about this notion of “best”,
+which is ill-defined up to this point. So, we need to make this more concrete.
+The goal here, as stated above, is to have <span class="math notranslate nohighlight">\(y_i \approx f(x_i)\)</span>
+for most <span class="math notranslate nohighlight">\(i\)</span>.</p></li>
+</ul>
+<p>The above two steps essentially define the <strong>function class</strong> and the <strong>loss function</strong> respectively.</p>
+<p>Depending on how you choose your function class and the loss function,
+you get different supervised learning models or even unsupervised learning models:</p>
+<ul class="simple">
+<li><p>Linear function class with squared-error loss function — Linear regression</p></li>
+<li><p>Linear function class with logistic loss function — Logistic regression</p></li>
+<li><p>Linear function class with hinge loss — SVM</p></li>
+<li><p>Function class containing a network of neurons with cross-entropy loss — Neural networks</p></li>
+</ul>
+<p>and so on.</p>
+</section>
+<section id="how-to-conceive-a-new-machine-learning-algorithm-discriminative-models">
+<h2><span class="section-number">11.2. </span>How to conceive a “new” Machine Learning algorithm : Discriminative Models<a class="headerlink" href="#how-to-conceive-a-new-machine-learning-algorithm-discriminative-models" title="Permalink to this headline">#</a></h2>
+<ul class="simple">
+<li><p><a class="reference external" href="https://math.stackexchange.com/questions/1530298/variant-of-linear-regression-using-perpendicular-distance-instead-of-vertical">How about perpendicular distance instead of vertical distance for Linear Regression?</a></p></li>
+<li><p><a class="reference external" href="https://www.quora.com/How-can-one-use-kernels-utilizing-the-kernel-trick-in-logistic-regression">How about Logistic Regression with Kernel Trick?</a></p></li>
+<li><p>[How about a Neural Network with Kernel Trick]</p></li>
+<li><p>[How about a Neural Network not by Layers but inter-connected]</p></li>
+<li><p>[How about horizontal or vertical lines - then we have Decision Tree]</p></li>
+<li><p>[How about counting numbers - then we have KNN]</p></li>
+<li><p><a class="reference external" href="https://stats.stackexchange.com/questions/44166/kernelised-k-nearest-neighbour">How about counting numbers with Kernel trick</a></p></li>
+</ul>
+</section>
+<section id="machine-learning-landscape-generative-models">
+<h2><span class="section-number">11.3. </span>Machine Learning Landscape : Generative Models<a class="headerlink" href="#machine-learning-landscape-generative-models" title="Permalink to this headline">#</a></h2>
+</section>
+<section id="lda">
+<h2><span class="section-number">11.4. </span>LDA<a class="headerlink" href="#lda" title="Permalink to this headline">#</a></h2>
+</section>
+<section id="unsupervised-learning">
+<h2><span class="section-number">11.5. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h2>
+</section>
+<section id="semi-supervised-learning">
+<h2><span class="section-number">11.6. </span>Semi-supervised learning<a class="headerlink" href="#semi-supervised-learning" title="Permalink to this headline">#</a></h2>
+<p><a class="reference external" href="https://www.baeldung.com/cs/svm-vs-neural-network">Semi-supervised learning</a></p>
+</section>
+<section id="your-turn">
+<h2><span class="section-number">11.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<p>TBD</p>
+<div hidden>
+    https://www.baeldung.com/cs/svm-vs-neural-network
+</div></section>
+</section>
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+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/introduction/introduction.html">
+   4. Introduction
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+  <label for="toctree-checkbox-1">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
+   5. Working with data
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+  <label for="toctree-checkbox-2">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 has-children">
+    <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
+     5.4. Pandas
+    </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../data-science/data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
+   12. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/tools-of-the-trade.html">
+     12.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/managing-data.html">
+     12.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/linear-and-polynomial-regression.html">
+     12.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/logistic-regression.html">
+     12.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
+   13. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../classification/getting-started-with-classification.html">
+   14. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/introduction-to-classification.html">
+     14.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/more-classifiers.html">
+     14.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/yet-other-classifiers.html">
+     14.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/applied-ml-build-a-web-app.html">
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 current active has-children">
+  <a class="reference internal" href="parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul class="current">
+   <li class="toctree-l2">
+    <a class="reference internal" href="loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2 current active">
+    <a class="current reference internal" href="#">
+     15.2. Gradient descent
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+   16. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
+     16.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
+     16.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+   17. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
+     17.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
+     17.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
+     17.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
+   18. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
+     18.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+     18.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
+     18.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     18.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
+   19. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/kernel-method.html">
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/model-selection.html">
+   22. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/dl-overview.html">
+   23. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/cnn.html">
+   24. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/gan.html">
+   25. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/rnn.html">
+   26. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/autoencoder.html">
+   27. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/lstm.html">
+   28. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/time-series.html">
+   29. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/dqn.html">
+   30. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/image-classification.html">
+   31. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/image-segmentation.html">
+   32. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/difussion-model.html">
+   33. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/object-detection.html">
+   34. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/overview.html">
+   35. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
+   36. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
+   37. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
+   38. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
+   39. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../assignments/README.html">
+   40. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
+     40.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
+     40.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/project-plan-template.html">
+     40.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
+     40.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
+     40.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
+     40.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
+     40.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
+     40.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
+     40.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
+     40.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
+     40.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
+     40.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
+     40.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
+     40.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
+     40.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
+     40.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
+     40.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
+     40.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
+     40.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
+     40.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     40.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
+     40.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
+     40.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
+     40.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
+     40.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     40.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
+     40.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
+     40.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     40.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
+     40.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/market-research.html">
+     40.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     40.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     40.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
+     40.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     40.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     40.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     40.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     40.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     40.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     40.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     40.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     40.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     40.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
+     40.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
+     40.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
+     40.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
+     40.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
+     40.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
+     40.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     40.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
+     40.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     40.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     40.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
+     40.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     40.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     40.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     40.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     40.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     40.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     40.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     40.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     40.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     40.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     40.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     40.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     40.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     40.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     40.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     40.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     40.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
+     40.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     40.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
+     40.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
+     40.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
+     40.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
+     40.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-model.html">
+     40.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
+     40.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     40.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     40.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     40.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     40.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     40.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
+     40.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     40.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
+     40.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
+     40.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     40.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     40.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
+     40.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
+     40.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     40.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../slides/introduction.html">
+   41. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
+     41.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
+     41.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
+     41.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
+     41.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
+     41.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
+     41.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-visualization.html">
+     41.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
+     41.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
+     41.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
+     41.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
+     41.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
+     41.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
+     41.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
+     41.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
+     41.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
+     41.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
+     41.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
+     41.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
+     41.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/deep-learning/cnn.html">
+     41.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/deep-learning/gan.html">
+     41.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+
+    </div>
+</nav></div>
+        <div class="bd-sidebar__bottom">
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+<!-- Table of contents -->
+<div class="col-md-3 bd-toc show noprint">
+    <div class="tocsection onthispage pt-5 pb-3">
+        <i class="fas fa-list"></i> Contents
+    </div>
+    <nav id="bd-toc-nav" aria-label="Page">
+        <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#objective-of-this-session">
+   15.2.1. Objective of this session
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#video">
+   15.2.2. Video
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#lets-be-playful-to-gain-some-intuition">
+   15.2.3. Let’s be playful … to gain some intuition
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#some-mathematics-to-gain-more-insight">
+   15.2.4. Some mathematics … to gain more insight
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#abstract">
+     15.2.4.1. Abstract
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivatives">
+     15.2.4.2. Partial derivatives
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-m">
+     15.2.4.3. Partial derivative with respect to
+     <span class="math notranslate nohighlight">
+      \(m\)
+     </span>
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-b">
+     15.2.4.4. Partial derivative with respect to
+     <span class="math notranslate nohighlight">
+      \(b\)
+     </span>
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#final-function">
+     15.2.4.5. Final function
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#time-to-code">
+   15.2.5. Time to code!
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#linear-regression-with-gradient-descent">
+     15.2.5.1. Linear regression With gradient descent
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#logistic-regression-with-gradient-descent">
+     15.2.5.2. Logistic regression with gradient descent
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   15.2.6. Your turn 🚀
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#optional-at-the-frontier-of-machine-learning-research">
+   15.2.7. [optional] At the frontier of Machine Learning Research
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#bibliography">
+   15.2.8. Bibliography
+  </a>
+ </li>
+</ul>
+
+    </nav>
+</div>
+    </div>
+    <div class="article row">
+        <div class="col pl-md-3 pl-lg-5 content-container">
+            <!-- Table of contents that is only displayed when printing the page -->
+            <div id="jb-print-docs-body" class="onlyprint">
+                <h1>Gradient descent</h1>
+                <!-- Table of contents -->
+                <div id="print-main-content">
+                    <div id="jb-print-toc">
+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
+                        <nav aria-label="Page">
+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#objective-of-this-session">
+   15.2.1. Objective of this session
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#video">
+   15.2.2. Video
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#lets-be-playful-to-gain-some-intuition">
+   15.2.3. Let’s be playful … to gain some intuition
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#some-mathematics-to-gain-more-insight">
+   15.2.4. Some mathematics … to gain more insight
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#abstract">
+     15.2.4.1. Abstract
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivatives">
+     15.2.4.2. Partial derivatives
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-m">
+     15.2.4.3. Partial derivative with respect to
+     <span class="math notranslate nohighlight">
+      \(m\)
+     </span>
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-b">
+     15.2.4.4. Partial derivative with respect to
+     <span class="math notranslate nohighlight">
+      \(b\)
+     </span>
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#final-function">
+     15.2.4.5. Final function
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#time-to-code">
+   15.2.5. Time to code!
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#linear-regression-with-gradient-descent">
+     15.2.5.1. Linear regression With gradient descent
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#logistic-regression-with-gradient-descent">
+     15.2.5.2. Logistic regression with gradient descent
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   15.2.6. Your turn 🚀
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#optional-at-the-frontier-of-machine-learning-research">
+   15.2.7. [optional] At the frontier of Machine Learning Research
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#bibliography">
+   15.2.8. Bibliography
+  </a>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
+            </div>
+            <main id="main-content" role="main">
+                
+              <div>
+                
+  <div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>skylearn<span class="w"> </span>matplotlib<span class="w"> </span>numpy<span class="w"> </span>pandas<span class="w"> </span>sklearn<span class="w"> </span>ipython<span class="w"> </span>jupyterlab_myst
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><span class=" -Color -Color-Red">ERROR: Could not find a version that satisfies the requirement skylearn (from versions: none)</span>
+<span class=" -Color -Color-Red">ERROR: No matching distribution found for skylearn</span>
+
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section class="tex2jax_ignore mathjax_ignore" id="gradient-descent">
+<h1><span class="section-number">15.2. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
+<section id="objective-of-this-session">
+<h2><span class="section-number">15.2.1. </span>Objective of this session<a class="headerlink" href="#objective-of-this-session" title="Permalink to this headline">#</a></h2>
+<p>We have already learnt how to use Linear Regression and Logistic Regression models.</p>
+<p>The code might seem quite easy and intuitive for you. And you might naturally ask:</p>
+<ul class="simple">
+<li><p>What’s behind the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> function?</p></li>
+<li><p>Why sometimes it takes quite a bit for this <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> function to finish running?</p></li>
+</ul>
+<p>In this session, you will learn that the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> is the training of ML models,
+i.e. tuning of parameters for ML models. And the technique behind is called “Gradient Descent”.</p>
+</section>
+<section id="video">
+<h2><span class="section-number">15.2.2. </span>Video<a class="headerlink" href="#video" title="Permalink to this headline">#</a></h2>
+<p>The corresponding video (in Chinese) for this notebook is <a class="reference external" href="https://www.bilibili.com/video/BV1SY4y1G7o9/">👉 available here on Bilibili</a>.
+You can (and should) watch the video before diving into the details of gradient descent:</p>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://player.bilibili.com/player.html?aid=642485873&amp;cid=764796592&amp;page=1&amp;high_quality=1&amp;danmaku=0&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">video. &lt;a href=&quot;https://player.bilibili.com/player.html?aid=642485873&amp;cid=764796592&amp;page=1&amp;high_quality=1&amp;danmaku=0&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://player.bilibili.com/player.html?aid=642485873&cid=764796592&page=1&high_quality=1&danmaku=0" width="105%" height="700px;" style="border:none;"></iframe>
+video. <a href="https://player.bilibili.com/player.html?aid=642485873&cid=764796592&page=1&high_quality=1&danmaku=0">[source]</a>
+</p>
+</div></div>
+</div>
+</section>
+<section id="lets-be-playful-to-gain-some-intuition">
+<h2><span class="section-number">15.2.3. </span>Let’s be playful … to gain some intuition<a class="headerlink" href="#lets-be-playful-to-gain-some-intuition" title="Permalink to this headline">#</a></h2>
+<ul class="simple">
+<li><p><a class="reference external" href="https://playground.tensorflow.org/#activation=sigmoid&amp;batchSize=10&amp;dataset=circle%C2%AEDataset=reg-plane&amp;learningRate=0.00001%C2%AEularizationRate=0&amp;noise=0&amp;networkShape=&amp;seed=0.71864&amp;showTestData=false&amp;discretize=false&amp;percTrainData=50&amp;x=true&amp;y=true&amp;xTimesY=true&amp;xSquared=true&amp;ySquared=true&amp;cosX=false&amp;sinX=false&amp;cosY=false&amp;sinY=false&amp;collectStats=false&amp;problem=classification&amp;initZero=false&amp;hideText=false">Tensorflow Playground</a></p></li>
+<li><p><a class="reference external" href="https://github.com/lilipads/gradient_descent_viz">Gradient Descent Visualization</a></p></li>
+<li><p><a class="reference external" href="https://bl.ocks.org/EmilienDupont/aaf429be5705b219aaaf8d691e27ca87">Optimization Algorithms Visualization</a></p></li>
+</ul>
+</section>
+<section id="some-mathematics-to-gain-more-insight">
+<h2><span class="section-number">15.2.4. </span>Some mathematics … to gain more insight<a class="headerlink" href="#some-mathematics-to-gain-more-insight" title="Permalink to this headline">#</a></h2>
+<section id="abstract">
+<h3><span class="section-number">15.2.4.1. </span>Abstract<a class="headerlink" href="#abstract" title="Permalink to this headline">#</a></h3>
+<p>The idea behind gradient descent is simple - by gradually tuning parameters, such as slope (<span class="math notranslate nohighlight">\(m\)</span>) and the intercept (<span class="math notranslate nohighlight">\(b\)</span>) in our regression function <span class="math notranslate nohighlight">\(y = mx + b\)</span>, we minimize cost.
+By cost, we usually mean some kind of a function that tells us how far off our model predicted result. For regression problems we often use <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">squared</span> <span class="pre">error</span></code> (MSE) cost function. If we use gradient descent for the classification problem, we will have a different set of parameters to tune.</p>
+<div class="math notranslate nohighlight">
+\[ MSE = \frac{1}{n}\sum_{i=1}^{n} (y_i - \hat{y_i})^2 \quad \textrm{where} \quad \hat{y_i} = mx_i + b \]</div>
+<p>Now we have to figure out how to tweak parameters <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> to reduce MSE.</p>
+</section>
+<section id="partial-derivatives">
+<h3><span class="section-number">15.2.4.2. </span>Partial derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
+<p>We use partial derivatives to find how each individual parameter affects MSE, so that’s where word <em>partial</em> comes from. In simple words, we take the derivative with respect to <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> <strong>separately</strong>. Take a look at the formula below. It looks almost exactly the same as MSE, but this time we added f(m, b) to it. It essentially changes nothing, except now we can plug <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> numbers into it and calculate the result.</p>
+<div class="math notranslate nohighlight">
+\[𝑓(𝑚,𝑏)= \frac{1}{n}\sum_{i=1}^{n}(y_i - (mx_i+b))^2\]</div>
+<p>This formula (or better say function) is better representation for further calculations of partial derivatives. We can ignore sum for now and what comes before that and focus only on <span class="math notranslate nohighlight">\(y - (mx + b)^2\)</span>.</p>
+</section>
+<section id="partial-derivative-with-respect-to-m">
+<h3><span class="section-number">15.2.4.3. </span>Partial derivative with respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
+<p>With respect to <span class="math notranslate nohighlight">\(m\)</span> means we derive parameter <span class="math notranslate nohighlight">\(m\)</span> and basically ignore what is going on with <span class="math notranslate nohighlight">\(b\)</span>, or we can say its 0. To derive with respect to <span class="math notranslate nohighlight">\(m\)</span> we will use chain rule.</p>
+<div class="math notranslate nohighlight">
+\[ [f(g(x))]' = f'(g(x)) * g(x)' \: - \textrm{chain rule}\]</div>
+<p>Chain rule applies when one function sits inside of another. If you’re new to this, you’d be surprised that <span class="math notranslate nohighlight">\(()^2\)</span> is outside function, and <span class="math notranslate nohighlight">\(y-(\boldsymbol{m}x+b)\)</span> sits inside it. So, the chain rule says that we should take a derivative of outside function, keep inside function unchanged and then multiply by derivative of the inside function. Lets write these steps down:</p>
+<div class="math notranslate nohighlight">
+\[ (y - (mx + b))^2 \]</div>
+<ol class="simple">
+<li><p>Derivative of <span class="math notranslate nohighlight">\(()^2\)</span> is <span class="math notranslate nohighlight">\(2()\)</span>, same as <span class="math notranslate nohighlight">\(x^2\)</span> becomes <span class="math notranslate nohighlight">\(2x\)</span></p></li>
+<li><p>We do nothing with <span class="math notranslate nohighlight">\(y - (mx + b)\)</span>, so it stays the same</p></li>
+<li><p>Derivative of <span class="math notranslate nohighlight">\(y - (mx + b)\)</span> with respect to <strong><em>m</em></strong> is <span class="math notranslate nohighlight">\((0 - (x + 0))\)</span> or <span class="math notranslate nohighlight">\(-x\)</span>, because <strong><em>y</em></strong> and <strong><em>b</em></strong> are constants, they become 0, and derivative of <strong><em>mx</em></strong> is <strong><em>x</em></strong></p></li>
+</ol>
+<p>Multiply all parts we get following: <span class="math notranslate nohighlight">\(2 * (y - (mx+b)) * -x\)</span>.</p>
+<p>Looks nicer if we move -x to the left: <span class="math notranslate nohighlight">\(-2x *(y-(mx+b))\)</span>. There we have it. The final version of our derivative is the following:</p>
+<div class="math notranslate nohighlight">
+\[\frac{\partial f}{\partial m} = \frac{1}{n}\sum_{i=1}^{n}-2x_i(y_i - (mx_i+b))\]</div>
+<p>Here, <span class="math notranslate nohighlight">\(\frac{df}{dm}\)</span> means we find partial derivative of function f (we mentioned it earlier) with respect to m. We plug our derivative to the summation and we’re done.</p>
+</section>
+<section id="partial-derivative-with-respect-to-b">
+<h3><span class="section-number">15.2.4.4. </span>Partial derivative with respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
+<p>Same rules apply to the derivative with respect to b.</p>
+<ol class="simple">
+<li><p><span class="math notranslate nohighlight">\(()^2\)</span> becomes <span class="math notranslate nohighlight">\(2()\)</span>, same as <span class="math notranslate nohighlight">\(x^2\)</span> becomes <span class="math notranslate nohighlight">\(2x\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(y - (mx + b)\)</span> stays the same</p></li>
+<li><p><span class="math notranslate nohighlight">\(y - (mx + b)\)</span> becomes <span class="math notranslate nohighlight">\((0 - (0 + 1))\)</span> or <span class="math notranslate nohighlight">\(-1\)</span>, because <strong><em>y</em></strong> and <strong><em>mx</em></strong> are constants, they become 0, and derivative of <strong><em>b</em></strong> is 1</p></li>
+</ol>
+<p>Multiply all the parts together and we get <span class="math notranslate nohighlight">\(-2(y-(mx+b))\)</span></p>
+<div class="math notranslate nohighlight">
+\[\frac{\partial f}{\partial b} = \frac{1}{n}\sum_{i=1}^{n}-2(y_i - (mx_i+b))\]</div>
+</section>
+<section id="final-function">
+<h3><span class="section-number">15.2.4.5. </span>Final function<a class="headerlink" href="#final-function" title="Permalink to this headline">#</a></h3>
+<p>Few details we should discuss before jumping into code:</p>
+<ol class="simple">
+<li><p>Gradient descent is an iterative process and with each iteration (<span class="math notranslate nohighlight">\(epoch\)</span>) we slightly minimizing MSE, so each time we use our derived functions to update parameters <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span>.</p></li>
+<li><p>Because it’s iterative, we should choose how many iterations we take, or make algorithm stop when we approach minima of MSE. In other words when algorithm is no longer improving MSE, we know it reached minimum.</p></li>
+<li><p>Gradient descent has an additional parameter learning rate (<span class="math notranslate nohighlight">\(lr\)</span>), which helps control how fast or slow algorithm going towards minima of MSE</p></li>
+</ol>
+<p>That’s about it. So you can already understand that Gradient Descent for the most part is just process of taking derivatives and using them over and over to minimize function.</p>
+</section>
+</section>
+<section id="time-to-code">
+<h2><span class="section-number">15.2.5. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
+<section id="linear-regression-with-gradient-descent">
+<h3><span class="section-number">15.2.5.1. </span>Linear regression With gradient descent<a class="headerlink" href="#linear-regression-with-gradient-descent" title="Permalink to this headline">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LinearRegression</span><span class="p">:</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0003</span><span class="p">,</span> <span class="n">n_iters</span><span class="o">=</span><span class="mi">3000</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span> <span class="o">=</span> <span class="n">n_iters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="c1"># init parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_features</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="c1"># gradient descent</span>
+        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span><span class="p">):</span>
+            <span class="c1"># approximate y with linear combination of weights and x, plus bias</span>
+            <span class="n">y_predicted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
+
+            <span class="c1"># compute gradients</span>
+            <span class="n">dw</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="p">(</span><span class="n">y_predicted</span> <span class="o">-</span> <span class="n">y</span><span class="p">))</span>
+            <span class="n">db</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_predicted</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span>
+            <span class="c1"># update parameters</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">dw</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">db</span>
+
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+        <span class="n">y_predicted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
+        <span class="k">return</span> <span class="n">y_predicted</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">prostate</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span>
+    <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-fundamental/parameter-optimization/gradient-descent/prostate.data&quot;</span>
+<span class="p">)</span>
+<span class="n">prostate</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">prostate</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">prostate</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;lpsa&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">prostate</span><span class="p">[</span><span class="s2">&quot;lpsa&quot;</span><span class="p">]</span>
+
+<span class="n">regressor</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
+
+<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;lr&#39;: 0.0003, &#39;n_iters&#39;: 3000, &#39;weights&#39;: array([0.36114314, 0.15172482, 0.01138062, 0.07103796, 0.10143793,
+       0.14812986, 0.09146885, 0.00270041]), &#39;bias&#39;: 0.014542612245156494}
+0    -1.470137
+1    -1.226722
+2    -1.633534
+3    -1.145394
+4    -1.385705
+        ...   
+92    0.985388
+93    1.125408
+94    1.936285
+95    1.776223
+96    1.680470
+Name: lpsa, Length: 97, dtype: float64
+</pre></div>
+</div>
+<img alt="../../_images/gradient-descent_17_1.png" src="../../_images/gradient-descent_17_1.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LinearRegressionWithSGD</span><span class="p">:</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0003</span><span class="p">,</span> <span class="n">n_iters</span><span class="o">=</span><span class="mi">5000</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span> <span class="o">=</span> <span class="n">n_iters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="c1"># init parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_features</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">5</span>
+        <span class="c1"># stochastic gradient descent</span>
+        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span><span class="p">):</span>
+            <span class="c1"># approximate y with linear combination of weights and x, plus bias</span>
+            <span class="n">y_predicted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
+
+            <span class="n">indexes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">batch_size</span><span class="p">)</span>  <span class="c1"># random sample</span>
+
+            <span class="n">Xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">indexes</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">indexes</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">y_predicted_s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">y_predicted</span><span class="p">,</span> <span class="n">indexes</span><span class="p">)</span>
+
+            <span class="c1"># compute gradients</span>
+            <span class="n">dw</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Xs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="p">(</span><span class="n">y_predicted_s</span> <span class="o">-</span> <span class="n">ys</span><span class="p">))</span>
+            <span class="n">db</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_predicted_s</span> <span class="o">-</span> <span class="n">ys</span><span class="p">)</span>
+            <span class="c1"># update parameters</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">dw</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">db</span>
+
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+        <span class="n">y_predicted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
+        <span class="k">return</span> <span class="n">y_predicted</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">prostate</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span>
+    <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-fundamental/parameter-optimization/gradient-descent/prostate.data&quot;</span>
+<span class="p">)</span>
+<span class="n">prostate</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">prostate</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">prostate</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;lpsa&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">prostate</span><span class="p">[</span><span class="s2">&quot;lpsa&quot;</span><span class="p">]</span>
+
+<span class="n">regressor</span> <span class="o">=</span> <span class="n">LinearRegressionWithSGD</span><span class="p">()</span>
+
+<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;lr&#39;: 0.0003, &#39;n_iters&#39;: 5000, &#39;weights&#39;: array([0.45435798, 0.22027281, 0.01499419, 0.09052001, 0.13980599,
+       0.1272024 , 0.11232141, 0.00300401]), &#39;bias&#39;: 0.020622681906119007}
+0    -1.919807
+1    -1.704383
+2    -2.197924
+3    -1.600980
+4    -2.048270
+        ...   
+92    0.056160
+93    0.212952
+94    1.129678
+95    0.790657
+96    0.694381
+Name: lpsa, Length: 97, dtype: float64
+</pre></div>
+</div>
+<img alt="../../_images/gradient-descent_19_1.png" src="../../_images/gradient-descent_19_1.png" />
+</div>
+</div>
+</section>
+<section id="logistic-regression-with-gradient-descent">
+<h3><span class="section-number">15.2.5.2. </span>Logistic regression with gradient descent<a class="headerlink" href="#logistic-regression-with-gradient-descent" title="Permalink to this headline">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LogisticRegression</span><span class="p">:</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">n_iters</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span> <span class="o">=</span> <span class="n">n_iters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="c1"># init parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_features</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="c1"># gradient descent</span>
+        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span><span class="p">):</span>
+            <span class="c1"># approximate y with linear combination of weights and x, plus bias</span>
+            <span class="n">linear_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
+            <span class="c1"># apply sigmoid function</span>
+            <span class="n">y_predicted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sigmoid</span><span class="p">(</span><span class="n">linear_model</span><span class="p">)</span>
+
+            <span class="c1"># compute gradients</span>
+            <span class="n">dw</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="p">(</span><span class="n">y_predicted</span> <span class="o">-</span> <span class="n">y</span><span class="p">))</span>
+            <span class="n">db</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_predicted</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span>
+            <span class="c1"># update parameters</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">dw</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">db</span>
+
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
+        <span class="n">linear_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
+        <span class="n">y_predicted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sigmoid</span><span class="p">(</span><span class="n">linear_model</span><span class="p">)</span>
+        <span class="n">y_predicted_cls</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">y_predicted</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_predicted_cls</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">heart</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+    <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-fundamental/parameter-optimization/gradient-descent/SA_heart.csv&quot;</span>
+<span class="p">)</span>
+<span class="n">heart</span><span class="o">.</span><span class="n">famhist</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">to_replace</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Present&quot;</span><span class="p">,</span> <span class="s2">&quot;Absent&quot;</span><span class="p">],</span> <span class="n">value</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">heart</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;row.names&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">heart</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">heart</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+<span class="p">)</span>
+
+<span class="n">regressor</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">,</span> <span class="n">n_iters</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+
+<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">perf</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;LR classification perf:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">perf</span><span class="p">)</span>
+
+<span class="n">error_rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y_test</span> <span class="o">!=</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;LR classification error rate:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">error_rate</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LR classification perf:
+ [[88  9]
+ [40 16]]
+LR classification error rate:
+ 0.3202614379084967
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="your-turn">
+<h2><span class="section-number">15.2.6. </span>Your turn 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<p>Modify <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> so that the training will use SGD instead of GD.</p>
+</section>
+<section id="optional-at-the-frontier-of-machine-learning-research">
+<h2><span class="section-number">15.2.7. </span>[optional] At the frontier of Machine Learning Research<a class="headerlink" href="#optional-at-the-frontier-of-machine-learning-research" title="Permalink to this headline">#</a></h2>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://www.youtube.com/embed/mdKjMPmcWjY&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">video. &lt;a href=&quot;https://www.youtube.com/embed/mdKjMPmcWjY&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://www.youtube.com/embed/mdKjMPmcWjY" width="105%" height="700px;" style="border:none;"></iframe>
+video. <a href="https://www.youtube.com/embed/mdKjMPmcWjY">[source]</a>
+</p>
+</div></div>
+</div>
+</section>
+<section id="bibliography">
+<h2><span class="section-number">15.2.8. </span>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">#</a></h2>
+<ul class="simple">
+<li><p><a class="reference external" href="https://www.youtube.com/watch?v=sDv4f4s2SB8">Gradient Descent, Step-by-Step - StatQuest</a></p></li>
+<li><p><a class="reference external" href="https://www.youtube.com/watch?v=vMh0zPT0tLI">Stochastic Gradient Descent, Clearly Explained!!! - StatQuest</a></p></li>
+<li><p><a class="reference external" href="http://43.142.12.204:12345/05-ML_04-Under-the-Hood.html">http://43.142.12.204:12345/05-ML_04-Under-the-Hood.html</a></p></li>
+<li><p><a class="reference external" href="http://43.142.12.204:9999/GradientDescentAnimation.html">http://43.142.12.204:9999/GradientDescentAnimation.html</a></p></li>
+</ul>
+</section>
+</section>
+
+    <script type="text/x-thebe-config">
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+            <p class="prev-next-subtitle">previous</p>
+            <p class="prev-next-title"><span class="section-number">15.1. </span>Loss function</p>
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+<div class="header header-item noprint"></div>
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+      <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+</div><form class="bd-search d-flex align-items-center" action="../../search.html" method="get">
+  <i class="icon fas fa-search"></i>
+  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search this book..." aria-label="Search this book..." autocomplete="off" >
+</form><nav class="bd-links" id="bd-docs-nav" aria-label="Main">
+    <div class="bd-toc-item active">
+        <p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  PREREQUISITES
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
+   1. Python programming introduction
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
+   2. Python programming basics
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
+   3. Python programming advanced
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/introduction/introduction.html">
+   4. Introduction
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+  <label for="toctree-checkbox-1">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
+   5. Working with data
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+  <label for="toctree-checkbox-2">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 has-children">
+    <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
+     5.4. Pandas
+    </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../data-science/data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
+   12. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/tools-of-the-trade.html">
+     12.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/managing-data.html">
+     12.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/linear-and-polynomial-regression.html">
+     12.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/logistic-regression.html">
+     12.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
+   13. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../classification/getting-started-with-classification.html">
+   14. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/introduction-to-classification.html">
+     14.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/more-classifiers.html">
+     14.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/yet-other-classifiers.html">
+     14.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/applied-ml-build-a-web-app.html">
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 current active has-children">
+  <a class="reference internal" href="parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul class="current">
+   <li class="toctree-l2 current active">
+    <a class="current reference internal" href="#">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="gradient-descent.html">
+     15.2. Gradient descent
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+   16. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
+     16.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
+     16.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+   17. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
+     17.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
+     17.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
+     17.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
+   18. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
+     18.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+     18.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
+     18.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     18.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
+   19. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/kernel-method.html">
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/model-selection.html">
+   22. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/dl-overview.html">
+   23. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/cnn.html">
+   24. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/gan.html">
+   25. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/rnn.html">
+   26. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/autoencoder.html">
+   27. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/lstm.html">
+   28. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/time-series.html">
+   29. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/dqn.html">
+   30. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/image-classification.html">
+   31. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/image-segmentation.html">
+   32. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/difussion-model.html">
+   33. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/object-detection.html">
+   34. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/overview.html">
+   35. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
+   36. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
+   37. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
+   38. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
+   39. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../assignments/README.html">
+   40. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
+     40.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
+     40.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/project-plan-template.html">
+     40.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
+     40.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
+     40.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
+     40.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
+     40.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
+     40.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
+     40.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
+     40.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
+     40.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
+     40.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
+     40.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
+     40.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
+     40.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
+     40.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
+     40.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
+     40.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
+     40.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
+     40.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     40.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
+     40.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
+     40.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
+     40.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
+     40.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     40.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
+     40.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
+     40.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     40.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
+     40.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/market-research.html">
+     40.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     40.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     40.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
+     40.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     40.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     40.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     40.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     40.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     40.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     40.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     40.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     40.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     40.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
+     40.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
+     40.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
+     40.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
+     40.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
+     40.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
+     40.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     40.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
+     40.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     40.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     40.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
+     40.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     40.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     40.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     40.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     40.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     40.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     40.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     40.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     40.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     40.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     40.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     40.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     40.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     40.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     40.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     40.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     40.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
+     40.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     40.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
+     40.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
+     40.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
+     40.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
+     40.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-model.html">
+     40.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
+     40.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     40.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     40.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     40.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     40.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     40.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
+     40.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     40.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
+     40.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
+     40.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     40.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     40.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
+     40.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
+     40.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     40.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../slides/introduction.html">
+   41. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
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+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
+     41.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
+     41.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
+     41.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
+     41.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
+     41.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
+     41.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-visualization.html">
+     41.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
+     41.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
+     41.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
+     41.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
+     41.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
+     41.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
+     41.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
+     41.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
+     41.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
+     41.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
+     41.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
+     41.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
+     41.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/deep-learning/cnn.html">
+     41.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/deep-learning/gan.html">
+     41.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+
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+<div class="col-md-3 bd-toc show noprint">
+    <div class="tocsection onthispage pt-5 pb-3">
+        <i class="fas fa-list"></i> Contents
+    </div>
+    <nav id="bd-toc-nav" aria-label="Page">
+        <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#objective-of-this-section">
+   15.1.1. Objective of this section
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#whats-the-difference-between-a-loss-function-and-a-cost-function">
+   15.1.2. What’s the Difference between a Loss Function and a Cost Function?
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#regression-loss-functions">
+   15.1.3. Regression Loss Functions
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#squared-error-loss">
+     15.1.3.1. Squared Error Loss
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#absolute-error-loss">
+     15.1.3.2. Absolute Error Loss
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#classification-loss-functions">
+   15.1.4. Classification Loss Functions
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#binary-cross-entropy-loss">
+     15.1.4.1. Binary Cross Entropy Loss
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#multi-class-cross-entropy-loss">
+     15.1.4.2. Multi-Class Cross-Entropy Loss
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#optional-at-the-frontier-of-machine-learning-research">
+   15.1.5. [optional] At the frontier of Machine Learning Research
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#bibliography">
+   15.1.6. Bibliography
+  </a>
+ </li>
+</ul>
+
+    </nav>
+</div>
+    </div>
+    <div class="article row">
+        <div class="col pl-md-3 pl-lg-5 content-container">
+            <!-- Table of contents that is only displayed when printing the page -->
+            <div id="jb-print-docs-body" class="onlyprint">
+                <h1>Loss function</h1>
+                <!-- Table of contents -->
+                <div id="print-main-content">
+                    <div id="jb-print-toc">
+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
+                        <nav aria-label="Page">
+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#objective-of-this-section">
+   15.1.1. Objective of this section
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#whats-the-difference-between-a-loss-function-and-a-cost-function">
+   15.1.2. What’s the Difference between a Loss Function and a Cost Function?
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#regression-loss-functions">
+   15.1.3. Regression Loss Functions
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#squared-error-loss">
+     15.1.3.1. Squared Error Loss
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#absolute-error-loss">
+     15.1.3.2. Absolute Error Loss
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#classification-loss-functions">
+   15.1.4. Classification Loss Functions
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#binary-cross-entropy-loss">
+     15.1.4.1. Binary Cross Entropy Loss
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#multi-class-cross-entropy-loss">
+     15.1.4.2. Multi-Class Cross-Entropy Loss
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#optional-at-the-frontier-of-machine-learning-research">
+   15.1.5. [optional] At the frontier of Machine Learning Research
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#bibliography">
+   15.1.6. Bibliography
+  </a>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
+            </div>
+            <main id="main-content" role="main">
+                
+              <div>
+                
+  <div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>pandas<span class="w"> </span>numpy<span class="w"> </span>matplotlib<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython<span class="w"> </span>
+</pre></div>
+</div>
+</div>
+</div>
+<section class="tex2jax_ignore mathjax_ignore" id="loss-function">
+<h1><span class="section-number">15.1. </span>Loss function<a class="headerlink" href="#loss-function" title="Permalink to this headline">#</a></h1>
+<p>The function we want to minimize or maximize is called the objective function or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function.</p>
+<p>— Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville</p>
+<section id="objective-of-this-section">
+<h2><span class="section-number">15.1.1. </span>Objective of this section<a class="headerlink" href="#objective-of-this-section" title="Permalink to this headline">#</a></h2>
+<p>We have already learned math and code for “Gradient Descent”, as well as other optimization techniques.</p>
+<p>In this section, we will learn more about loss functions for Linear Regression and Logistic Regression.</p>
+</section>
+<section id="whats-the-difference-between-a-loss-function-and-a-cost-function">
+<h2><span class="section-number">15.1.2. </span>What’s the Difference between a Loss Function and a Cost Function?<a class="headerlink" href="#whats-the-difference-between-a-loss-function-and-a-cost-function" title="Permalink to this headline">#</a></h2>
+<p>A loss function is for a single training example. It is also sometimes called an error function. A cost function, on the other hand, is the average loss over the entire training dataset. The optimization strategies aim at minimizing the cost function.</p>
+</section>
+<section id="regression-loss-functions">
+<h2><span class="section-number">15.1.3. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h2>
+<section id="squared-error-loss">
+<h3><span class="section-number">15.1.3.1. </span>Squared Error Loss<a class="headerlink" href="#squared-error-loss" title="Permalink to this headline">#</a></h3>
+<p>Squared Error loss for each training example,
+also known as <strong>L2 Loss</strong>, is the square of the
+difference between the actual and the predicted values:</p>
+<div class="math notranslate nohighlight">
+\[L = (y - f(x))^2\]</div>
+<p>The corresponding cost function is the
+Mean of these Squared Errors (MSE).</p>
+</section>
+<section id="absolute-error-loss">
+<h3><span class="section-number">15.1.3.2. </span>Absolute Error Loss<a class="headerlink" href="#absolute-error-loss" title="Permalink to this headline">#</a></h3>
+<p>Absolute Error for each training example
+is the distance between the predicted and the actual values,
+irrespective of the sign. Absolute Error is also
+known as the <strong>L1 loss</strong>:</p>
+<div class="math notranslate nohighlight">
+\[L = \lvert y - f(x) \rvert\]</div>
+<p>The corresponding cost function is the Mean of these Absolute Errors (MAE).</p>
+</section>
+</section>
+<section id="classification-loss-functions">
+<h2><span class="section-number">15.1.4. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h2>
+<section id="binary-cross-entropy-loss">
+<h3><span class="section-number">15.1.4.1. </span>Binary Cross Entropy Loss<a class="headerlink" href="#binary-cross-entropy-loss" title="Permalink to this headline">#</a></h3>
+<p>Cross-entropy is the default loss function to use for binary classification problems.</p>
+<p>It is intended for use with binary classification where the target values are in the set {0, 1}.</p>
+<p>Mathematically, it is the preferred loss function
+under the inference framework of maximum likelihood.
+It is the loss function to be evaluated first and only
+changed if you have a good reason.</p>
+<p>Cross-entropy will calculate a score that summarizes
+the average difference between the actual and predicted
+probability distributions for predicting class 1.
+The score is minimized and a perfect cross-entropy value is 0.</p>
+<p>This YouTube video by Andrew Ng explains very well Binary Cross Entropy Loss (make sure
+that you have access to YouTube for this web page to render correctly):</p>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://www.youtube.com/embed/SHEPb1JHw5o&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">video. &lt;a href=&quot;https://www.youtube.com/embed/SHEPb1JHw5o&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://www.youtube.com/embed/SHEPb1JHw5o" width="105%" height="700px;" style="border:none;"></iframe>
+video. <a href="https://www.youtube.com/embed/SHEPb1JHw5o">[source]</a>
+</p>
+</div></div>
+</div>
+</section>
+<section id="multi-class-cross-entropy-loss">
+<h3><span class="section-number">15.1.4.2. </span>Multi-Class Cross-Entropy Loss<a class="headerlink" href="#multi-class-cross-entropy-loss" title="Permalink to this headline">#</a></h3>
+<p>Cross-entropy is the default loss function to
+use for multi-class classification problems.</p>
+<p>In this case, it is intended for use with
+multi-class classification where the target values
+are in the set {0, 1, 3, …, n}, where each class is
+assigned a unique integer value.</p>
+<p>Mathematically, it is the preferred loss
+function under the inference framework of
+maximum likelihood. It is the loss function
+to be evaluated first and only changed if you have a good reason.</p>
+<p>Cross-entropy will calculate a score that
+summarizes the average difference between
+the actual and predicted probability distributions
+for all classes in the problem. The score is minimized
+and a perfect cross-entropy value is 0.</p>
+</section>
+</section>
+<section id="optional-at-the-frontier-of-machine-learning-research">
+<h2><span class="section-number">15.1.5. </span>[optional] At the frontier of Machine Learning Research<a class="headerlink" href="#optional-at-the-frontier-of-machine-learning-research" title="Permalink to this headline">#</a></h2>
+<div class="cell tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;p style=&quot;text-align: center;&quot;&gt;</span>
+<span class="sd">&lt;iframe src=&quot;https://www.youtube.com/embed/QBbC3Cjsnjg&quot; width=&quot;105%&quot; height=&quot;700px;&quot; style=&quot;border:none;&quot;&gt;&lt;/iframe&gt;</span>
+<span class="sd">video. &lt;a href=&quot;https://www.youtube.com/embed/QBbC3Cjsnjg&quot;&gt;[source]&lt;/a&gt;</span>
+<span class="sd">&lt;/p&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<p style="text-align: center;">
+<iframe src="https://www.youtube.com/embed/QBbC3Cjsnjg" width="105%" height="700px;" style="border:none;"></iframe>
+video. <a href="https://www.youtube.com/embed/QBbC3Cjsnjg">[source]</a>
+</p>
+</div></div>
+</div>
+<p>With its corresponding paper: <a class="reference external" href="https://arxiv.org/abs/1701.03077">A General and Adaptive Robust Loss Function</a></p>
+</section>
+<section id="bibliography">
+<h2><span class="section-number">15.1.6. </span>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">#</a></h2>
+<ul class="simple">
+<li><p><a class="reference external" href="https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html">ML cheat sheet for loss functions</a></p></li>
+<li><p><a class="reference external" href="https://www.youtube.com/watch?v=ErfnhcEV1O8">A Short Introduction to Entropy, Cross-Entropy and KL-Divergence</a></p></li>
+</ul>
+</section>
+</section>
+
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+  PREREQUISITES
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
+   1. Python programming introduction
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
+   2. Python programming basics
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
+   3. Python programming advanced
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/introduction/introduction.html">
+   4. Introduction
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+  <label for="toctree-checkbox-1">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
+   5. Working with data
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+  <label for="toctree-checkbox-2">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 has-children">
+    <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
+     5.4. Pandas
+    </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../data-science/data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
+   12. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/tools-of-the-trade.html">
+     12.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/managing-data.html">
+     12.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/linear-and-polynomial-regression.html">
+     12.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../regression/logistic-regression.html">
+     12.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
+   13. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../classification/getting-started-with-classification.html">
+   14. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/introduction-to-classification.html">
+     14.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/more-classifiers.html">
+     14.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/yet-other-classifiers.html">
+     14.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../classification/applied-ml-build-a-web-app.html">
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 current active has-children">
+  <a class="current reference internal" href="#">
+   15. parameter-optimization
+  </a>
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="gradient-descent.html">
+     15.2. Gradient descent
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+   16. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
+     16.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
+     16.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+   17. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
+     17.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
+     17.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
+     17.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
+   18. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
+     18.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+     18.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
+     18.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     18.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
+   19. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/kernel-method.html">
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/model-selection.html">
+   22. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/dl-overview.html">
+   23. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/cnn.html">
+   24. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/gan.html">
+   25. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/rnn.html">
+   26. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/autoencoder.html">
+   27. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/lstm.html">
+   28. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/time-series.html">
+   29. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/dqn.html">
+   30. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/image-classification.html">
+   31. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/image-segmentation.html">
+   32. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/difussion-model.html">
+   33. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../deep-learning/object-detection.html">
+   34. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/overview.html">
+   35. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
+   36. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
+   37. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
+   38. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
+   39. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../assignments/README.html">
+   40. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
+     40.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
+     40.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/project-plan-template.html">
+     40.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
+     40.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
+     40.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
+     40.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
+     40.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
+     40.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
+     40.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
+     40.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
+     40.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
+     40.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
+     40.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
+     40.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
+     40.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
+     40.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
+     40.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
+     40.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
+     40.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
+     40.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     40.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
+     40.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
+     40.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
+     40.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
+     40.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     40.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
+     40.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
+     40.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     40.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
+     40.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/market-research.html">
+     40.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     40.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     40.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
+     40.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     40.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     40.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     40.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     40.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     40.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     40.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     40.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     40.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     40.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
+     40.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
+     40.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
+     40.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
+     40.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
+     40.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
+     40.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     40.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
+     40.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     40.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     40.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
+     40.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     40.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     40.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     40.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     40.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     40.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     40.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     40.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     40.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     40.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     40.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     40.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     40.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     40.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     40.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     40.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     40.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
+     40.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     40.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
+     40.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
+     40.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
+     40.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
+     40.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-model.html">
+     40.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
+     40.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     40.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     40.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     40.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     40.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     40.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
+     40.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     40.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
+     40.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
+     40.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     40.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     40.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
+     40.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
+     40.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     40.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../slides/introduction.html">
+   41. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
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+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
+     41.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
+     41.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
+     41.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
+     41.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
+     41.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
+     41.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-visualization.html">
+     41.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
+     41.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
+     41.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
+     41.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
+     41.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
+     41.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
+     41.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
+     41.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
+     41.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
+     41.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
+     41.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
+     41.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
+     41.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/deep-learning/cnn.html">
+     41.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../slides/deep-learning/gan.html">
+     41.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
+ </li>
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-    <link rel="next" title="11.4. Logistic regression" href="logistic-regression.html" />
-    <link rel="prev" title="11.2. Managing data" href="managing-data.html" />
+    <link rel="next" title="12.4. Logistic regression" href="logistic-regression.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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  </li>
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+   11. Summary of machine learning fundamentals
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  <li class="toctree-l1 current active has-children">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
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-     37.17. Classifying datasets
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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-     37.75. Beyond random forests: more ensemble models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.76. Decision trees
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
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     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
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-     38.12. Linear Regression
+     41.12. Linear Regression
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-     38.13. Logistic Regression
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-     38.14. Logistic Regression
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-     38.15. Neural Network
+     41.15. Neural Network
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
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    <li class="toctree-l2">
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     38.18. Kernel method
+     41.18. Kernel method
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    </li>
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     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1528,74 +1560,74 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   11.3.1. Build a regression model using Scikit-learn: regression four ways
+   12.3.1. Build a regression model using Scikit-learn: regression four ways
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#introduction">
-     11.3.1.1. Introduction
+     12.3.1.1. Introduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prerequisite">
-     11.3.1.2. Prerequisite
+     12.3.1.2. Prerequisite
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparation">
-     11.3.1.3. Preparation
+     12.3.1.3. Preparation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#a-linear-regression-line">
-   11.3.2. A linear regression line
+   12.3.2. A linear regression line
   </a>
  </li>
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   <a class="reference internal nav-link" href="#correlation">
-   11.3.3. Correlation
+   12.3.3. Correlation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   11.3.4. Looking for Correlation
+   12.3.4. Looking for Correlation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#simple-linear-regression">
-   11.3.5. Simple Linear Regression
+   12.3.5. Simple Linear Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-regression">
-   11.3.6. Polynomial Regression
+   12.3.6. Polynomial Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#categorical-features">
-   11.3.7. Categorical Features
+   12.3.7. Categorical Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#putting-it-all-together">
-   11.3.8. Putting it all together
+   12.3.8. Putting it all together
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   11.3.9. Self study
+   12.3.9. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.3.10. Your turn! 🚀
+   12.3.10. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.3.11. Acknowledgments
+   12.3.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1619,74 +1651,74 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-a-regression-model-using-scikit-learn-regression-four-ways">
-   11.3.1. Build a regression model using Scikit-learn: regression four ways
+   12.3.1. Build a regression model using Scikit-learn: regression four ways
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#introduction">
-     11.3.1.1. Introduction
+     12.3.1.1. Introduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prerequisite">
-     11.3.1.2. Prerequisite
+     12.3.1.2. Prerequisite
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparation">
-     11.3.1.3. Preparation
+     12.3.1.3. Preparation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#a-linear-regression-line">
-   11.3.2. A linear regression line
+   12.3.2. A linear regression line
   </a>
  </li>
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   <a class="reference internal nav-link" href="#correlation">
-   11.3.3. Correlation
+   12.3.3. Correlation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#looking-for-correlation">
-   11.3.4. Looking for Correlation
+   12.3.4. Looking for Correlation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#simple-linear-regression">
-   11.3.5. Simple Linear Regression
+   12.3.5. Simple Linear Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-regression">
-   11.3.6. Polynomial Regression
+   12.3.6. Polynomial Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#categorical-features">
-   11.3.7. Categorical Features
+   12.3.7. Categorical Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#putting-it-all-together">
-   11.3.8. Putting it all together
+   12.3.8. Putting it all together
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   11.3.9. Self study
+   12.3.9. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.3.10. Your turn! 🚀
+   12.3.10. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.3.11. Acknowledgments
+   12.3.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1711,11 +1743,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="linear-and-polynomial-regression">
-<h1><span class="section-number">11.3. </span>Linear and polynomial regression<a class="headerlink" href="#linear-and-polynomial-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.3. </span>Linear and polynomial regression<a class="headerlink" href="#linear-and-polynomial-regression" title="Permalink to this headline">#</a></h1>
 <figure class="align-default" id="linear-vs-polynomial-regression-infographic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/linear-polynomial.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/linear-polynomial.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/linear-polynomial.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.5 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#linear-vs-polynomial-regression-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.5 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#linear-vs-polynomial-regression-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1723,7 +1755,7 @@ <h1><span class="section-number">11.3. </span>Linear and polynomial regression<a
 <p>Run the notebook accompanying this lesson and look at the Month to Price scatterplot. Does the data associating Month to Price for pumpkin sales seem to have high or low correlation, according to our visual interpretation of the scatterplot? Does that change if we use more fine-grained measure instead of <code class="docutils literal notranslate"><span class="pre">Month</span></code>, eg. <em>day of the year</em> (i.e. number of days since the beginning of the year)?</p>
 </div>
 <section id="build-a-regression-model-using-scikit-learn-regression-four-ways">
-<h2><span class="section-number">11.3.1. </span>Build a regression model using Scikit-learn: regression four ways<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-regression-four-ways" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.1. </span>Build a regression model using Scikit-learn: regression four ways<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-regression-four-ways" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -1814,7 +1846,7 @@ <h2><span class="section-number">11.3.1. </span>Build a regression model using S
 </div></div>
 </div>
 <section id="introduction">
-<h3><span class="section-number">11.3.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.3.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h3>
 <p>So far we have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. We have also visualized it using Matplotlib.</p>
 <p>Now we are ready to dive deeper into regression for Machine Learning. While visualization allows we to make sense of data, the real power of Machine Learning comes from <em>training models</em>. Models are trained on historical data to automatically capture data dependencies, and they allow we to predict outcomes for new data, which the model has not seen before.</p>
 <p>In this lesson, we will learn more about two types of regression: <em>basic linear regression</em> and <em>polynomial regression</em>, along with some of the math underlying these techniques. Those models will allow us to predict pumpkin prices depending on different input data.</p>
@@ -1824,11 +1856,11 @@ <h3><span class="section-number">11.3.1.1. </span>Introduction<a class="headerli
 </div>
 </section>
 <section id="prerequisite">
-<h3><span class="section-number">11.3.1.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.3.1.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h3>
 <p>We should be familiar by now with the structure of the pumpkin data that we are examining. We can find it preloaded and pre-cleaned in this section’s <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html"><span class="doc std std-doc">linear and polynomial regression.ipynb</span></a> file. In the file, the pumpkin price is displayed per bushel in a new data frame.  Make sure we can run these notebooks in kernels in Visual Studio Code.</p>
 </section>
 <section id="preparation">
-<h3><span class="section-number">11.3.1.3. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.3.1.3. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h3>
 <p>As a reminder, we are loading this data so as to ask questions about it.</p>
 <ul class="simple">
 <li><p>When is the best time to buy pumpkins?</p></li>
@@ -1841,7 +1873,7 @@ <h3><span class="section-number">11.3.1.3. </span>Preparation<a class="headerlin
 </section>
 </section>
 <section id="a-linear-regression-line">
-<h2><span class="section-number">11.3.2. </span>A linear regression line<a class="headerlink" href="#a-linear-regression-line" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.2. </span>A linear regression line<a class="headerlink" href="#a-linear-regression-line" title="Permalink to this headline">#</a></h2>
 <p>As we learned in section 1, the goal of a linear regression exercise is to be able to plot a line to:</p>
 <ul class="simple">
 <li><p><strong>Show variable relationships</strong>. Show the relationship between variables</p></li>
@@ -1863,7 +1895,7 @@ <h2><span class="section-number">11.3.2. </span>A linear regression line<a class
 <div><figure class="align-default" id="calculate-the-slope">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/slope.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/slope.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/slope.png" style="width: 60%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.6 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#calculate-the-slope" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.6 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#calculate-the-slope" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </div></blockquote>
@@ -1873,7 +1905,7 @@ <h2><span class="section-number">11.3.2. </span>A linear regression line<a class
 <div><figure class="align-default" id="complete-the-equation">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/calculation.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/calculation.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/calculation.png" style="width: 60%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.7 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#complete-the-equation" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.7 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#complete-the-equation" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </div></blockquote>
@@ -1883,7 +1915,7 @@ <h2><span class="section-number">11.3.2. </span>A linear regression line<a class
 </div>
 </section>
 <section id="correlation">
-<h2><span class="section-number">11.3.3. </span>Correlation<a class="headerlink" href="#correlation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.3. </span>Correlation<a class="headerlink" href="#correlation" title="Permalink to this headline">#</a></h2>
 <p>One more term to understand is the <strong>Correlation Coefficient</strong> between given X and Y variables. Using a scatterplot, we can quickly visualize this coefficient. A plot with data points scattered in a neat line has a high correlation, but a plot with data points scattered everywhere between X and Y has a low correlation.</p>
 <p>A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.</p>
 <div class="admonition seealso">
@@ -2213,7 +2245,7 @@ <h2><span class="section-number">11.3.3. </span>Correlation<a class="headerlink"
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f9521a92d90&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7fceeed11e20&gt;
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_12_1.png" src="../../_images/linear-and-polynomial-regression_12_1.png" />
@@ -2235,19 +2267,19 @@ <h2><span class="section-number">11.3.3. </span>Correlation<a class="headerlink"
 <p>Now that we have an understanding of the math behind linear regression, let’s create a Regression model to see if we can predict which package of pumpkins will have the best pumpkin prices. Someone buying pumpkins for a holiday pumpkin patch might want this information to be able to optimize their purchases of pumpkin packages for the patch.</p>
 </section>
 <section id="looking-for-correlation">
-<h2><span class="section-number">11.3.4. </span>Looking for Correlation<a class="headerlink" href="#looking-for-correlation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.4. </span>Looking for Correlation<a class="headerlink" href="#looking-for-correlation" title="Permalink to this headline">#</a></h2>
 <p>From the previous section, we have probably seen that the average price for different months looks like this:</p>
 <figure class="align-default" id="average-price-by-month">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/barchart.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/barchart.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/barchart.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.8 </span><span class="caption-text">Average price by month<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Average price by month. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/images/barchart.png (visited on 2022).">Looa</a>]</span></span><a class="headerlink" href="#average-price-by-month" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.8 </span><span class="caption-text">Average price by month<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Average price by month. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/images/barchart.png (visited on 2022).">Looa</a>]</span></span><a class="headerlink" href="#average-price-by-month" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>This suggests that there should be some correlation, and we can try training a linear regression model to predict the relationship between <code class="docutils literal notranslate"><span class="pre">Month</span></code> and <code class="docutils literal notranslate"><span class="pre">Price</span></code>, or between <code class="docutils literal notranslate"><span class="pre">DayOfYear</span></code> and <code class="docutils literal notranslate"><span class="pre">Price</span></code>. Here is the scatter plot that shows the latter relationship:</p>
 <figure class="align-default" id="scatter-plot-of-price-vs-day-of-year">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/scatter-dayofyear.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/scatter-dayofyear.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/scatter-dayofyear.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.9 </span><span class="caption-text">Scatter plot of Price vs. Day of Year<span id="id2">[<a class="reference internal" href="#id66" title="Jen Looper. Scatter plot of price vs. day of year. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/scatter-dayofyear.png (visited on 2022).">Loob</a>]</span></span><a class="headerlink" href="#scatter-plot-of-price-vs-day-of-year" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.9 </span><span class="caption-text">Scatter plot of Price vs. Day of Year<span id="id2">[<a class="reference internal" href="#id66" title="Jen Looper. Scatter plot of price vs. day of year. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/scatter-dayofyear.png (visited on 2022).">Loob</a>]</span></span><a class="headerlink" href="#scatter-plot-of-price-vs-day-of-year" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>It looks like there are different clusters of prices corresponding to different pumpkin varieties. To confirm this hypothesis, let’s plot each pumpkin category using a different color. By passing an <code class="docutils literal notranslate"><span class="pre">ax</span></code> parameter to the <code class="docutils literal notranslate"><span class="pre">scatter</span></code> plotting function we can plot all points on the same graph:</p>
@@ -2315,7 +2347,7 @@ <h2><span class="section-number">11.3.4. </span>Looking for Correlation<a class=
 <p>Another approach would be to fill those empty values with mean values from the corresponding column.</p>
 </section>
 <section id="simple-linear-regression">
-<h2><span class="section-number">11.3.5. </span>Simple Linear Regression<a class="headerlink" href="#simple-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.5. </span>Simple Linear Regression<a class="headerlink" href="#simple-linear-regression" title="Permalink to this headline">#</a></h2>
 <p>To train our Linear Regression model, we will use the <strong>Scikit-learn</strong> library.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2399,7 +2431,7 @@ <h2><span class="section-number">11.3.5. </span>Simple Linear Regression<a class
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f9510d8ceb0&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7fcede025eb0&gt;]
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_34_1.png" src="../../_images/linear-and-polynomial-regression_34_1.png" />
@@ -2407,7 +2439,7 @@ <h2><span class="section-number">11.3.5. </span>Simple Linear Regression<a class
 </div>
 </section>
 <section id="polynomial-regression">
-<h2><span class="section-number">11.3.6. </span>Polynomial Regression<a class="headerlink" href="#polynomial-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.6. </span>Polynomial Regression<a class="headerlink" href="#polynomial-regression" title="Permalink to this headline">#</a></h2>
 <p style="text-align: center;">
 <iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/polynomial-regression-demo/simulation.html" width="105%" height="900px;"
 style="border:none;" scrolling="auto"></iframe>
@@ -2445,7 +2477,7 @@ <h2><span class="section-number">11.3.6. </span>Polynomial Regression<a class="h
 <figure class="align-default" id="id4">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/poly-results.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/poly-results.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/poly-results.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.10 </span><span class="caption-text">Polynomial regression<span id="id3">[<a class="reference internal" href="#id67" title="Dmitri Soshnikov. Polynomial regression. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/poly-results.png (visited on 2022).">Sosb</a>]</span></span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.10 </span><span class="caption-text">Polynomial regression<span id="id3">[<a class="reference internal" href="#id67" title="Dmitri Soshnikov. Polynomial regression. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/poly-results.png (visited on 2022).">Sosb</a>]</span></span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Using Polynomial Regression, we can get slightly lower MSE and higher determination, but not significantly. We need to take into account other features!</p>
@@ -2456,13 +2488,13 @@ <h2><span class="section-number">11.3.6. </span>Polynomial Regression<a class="h
 <p>🎃 Congratulations, we just created a model that can help predict the price of pie pumpkins. We can probably repeat the same procedure for all pumpkin types, but that would be tedious. Let’s learn now how to take pumpkin variety into account in our model!</p>
 </section>
 <section id="categorical-features">
-<h2><span class="section-number">11.3.7. </span>Categorical Features<a class="headerlink" href="#categorical-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.7. </span>Categorical Features<a class="headerlink" href="#categorical-features" title="Permalink to this headline">#</a></h2>
 <p>In the ideal world, we want to be able to predict prices for different pumpkin varieties using the same model. However, the <code class="docutils literal notranslate"><span class="pre">Variety</span></code> column is somewhat different from columns like <code class="docutils literal notranslate"><span class="pre">Month</span></code>, because it contains non-numeric values. Such columns are called <strong>categorical</strong>.</p>
 <p>Here we can see how the average price depends on variety:</p>
 <figure class="align-default" id="average-price-by-variety">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/price-by-variety.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/price-by-variety.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/price-by-variety.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.11 </span><span class="caption-text">Average price by variety<span id="id5">[<a class="reference internal" href="#id68" title="Dmitri Soshnikov. Average price by variety. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/price-by-variety.png (visited on 2022).">Sosa</a>]</span></span><a class="headerlink" href="#average-price-by-variety" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.11 </span><span class="caption-text">Average price by variety<span id="id5">[<a class="reference internal" href="#id68" title="Dmitri Soshnikov. Average price by variety. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/price-by-variety.png (visited on 2022).">Sosa</a>]</span></span><a class="headerlink" href="#average-price-by-variety" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>To take variety into account, we first need to convert it to numeric form or <strong>encode</strong>** it. There are several ways we can do it:</p>
@@ -2611,7 +2643,7 @@ <h2><span class="section-number">11.3.7. </span>Categorical Features<a class="he
 <p>Here we also take into account <code class="docutils literal notranslate"><span class="pre">City</span></code> and <code class="docutils literal notranslate"><span class="pre">Package</span></code> type, which gives us MSE 2.84 (10%), and determination 0.94!</p>
 </section>
 <section id="putting-it-all-together">
-<h2><span class="section-number">11.3.8. </span>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.8. </span>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this headline">#</a></h2>
 <p>To make the best model, we can use combined (one-hot encoded categorical + numeric) data from the above example together with Polynomial Regression. Here is the complete code for our convenience:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2684,16 +2716,16 @@ <h2><span class="section-number">11.3.8. </span>Putting it all together<a class=
 <p>🏆 Well done! We created four Regression models in one section and improved the model quality to 97%. In the final section on Regression, we will learn about Logistic Regression to determine categories.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">11.3.9. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.9. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this section, we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the <a class="reference external" href="https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning">Stanford Statistical Learning course</a></p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">11.3.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Test several different variables in this notebook to see how correlation corresponds to model accuracy.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html"><span class="doc std std-doc">Create a regression model</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">11.3.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <div class="docutils container" id="id6">
@@ -2752,13 +2784,13 @@ <h2><span class="section-number">11.3.11. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">11.2. </span>Managing data</p>
+            <p class="prev-next-title"><span class="section-number">12.2. </span>Managing data</p>
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     <a class='right-next' id="next-link" href="logistic-regression.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">11.4. </span>Logistic regression</p>
+        <p class="prev-next-title"><span class="section-number">12.4. </span>Logistic regression</p>
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diff --git a/ml-fundamentals/regression/logistic-regression.html b/ml-fundamentals/regression/logistic-regression.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>11.4. Logistic regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>12.4. Logistic regression &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="12. Build a web app to use a Machine Learning model" href="../build-a-web-app-to-use-a-machine-learning-model.html" />
-    <link rel="prev" title="11.3. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="next" title="13. Build a web app to use a Machine Learning model" href="../build-a-web-app-to-use-a-machine-learning-model.html" />
+    <link rel="prev" title="12.3. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    </li>
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     <a class="reference internal" href="managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
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  </li>
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+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-     15.2. Random forest
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-     15.3. Feature importance
+     17.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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    </li>
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-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
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-     16.3. XGBoost
+     18.3. XGBoost
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-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
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-   22. Generative adversarial networks
+   25. Generative adversarial networks
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-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.13. Logistic Regression
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-     38.14. Logistic Regression
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+     41.15. Neural Network
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-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     38.18. Kernel method
+     41.18. Kernel method
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-     38.19. Model Selection
+     41.19. Model Selection
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     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1528,101 +1560,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   11.4.1. Introduction
+   12.4.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prerequisite">
-   11.4.2. Prerequisite
+   12.4.2. Prerequisite
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#define-the-question">
-   11.4.3. Define the question
+   12.4.3. Define the question
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-logistic-regression">
-   11.4.4. About logistic regression
+   12.4.4. About logistic regression
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#binary-classification">
-     11.4.4.1. Binary classification
+     12.4.4.1. Binary classification
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#other-classifications">
-     11.4.4.2. Other classifications
+     12.4.4.2. Other classifications
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#its-still-linear">
-     11.4.4.3. It’s still linear
+     12.4.4.3. It’s still linear
     </a>
    </li>
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     <a class="reference internal nav-link" href="#variables-do-not-have-to-correlate">
-     11.4.4.4. Variables DO NOT have to correlate
+     12.4.4.4. Variables DO NOT have to correlate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#you-need-a-lot-of-clean-data">
-     11.4.4.5. You need a lot of clean data
+     12.4.4.5. You need a lot of clean data
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#exercise-tidy-the-data">
-   11.4.5. Exercise - tidy the data
+   12.4.5. Exercise - tidy the data
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#visualization-side-by-side-grid">
-     11.4.5.1. Visualization - side-by-side grid
+     12.4.5.1. Visualization - side-by-side grid
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-a-swarm-plot">
-     11.4.5.2. Use a swarm plot
+     12.4.5.2. Use a swarm plot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#violin-plot">
-     11.4.5.3. Violin plot
+     12.4.5.3. Violin plot
     </a>
    </li>
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-   11.4.6. Build your model
+   12.4.6. Build your model
   </a>
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   <a class="reference internal nav-link" href="#better-comprehension-via-a-confusion-matrix">
-   11.4.7. Better comprehension via a confusion matrix
+   12.4.7. Better comprehension via a confusion matrix
   </a>
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   <a class="reference internal nav-link" href="#visualize-the-roc-curve-of-this-model">
-   11.4.8. Visualize the ROC curve of this model
+   12.4.8. Visualize the ROC curve of this model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.4.9. Your turn! 🚀
+   12.4.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   11.4.10. Self study
+   12.4.10. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.4.11. Acknowledgments
+   12.4.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1646,101 +1678,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   11.4.1. Introduction
+   12.4.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prerequisite">
-   11.4.2. Prerequisite
+   12.4.2. Prerequisite
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#define-the-question">
-   11.4.3. Define the question
+   12.4.3. Define the question
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-logistic-regression">
-   11.4.4. About logistic regression
+   12.4.4. About logistic regression
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#binary-classification">
-     11.4.4.1. Binary classification
+     12.4.4.1. Binary classification
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#other-classifications">
-     11.4.4.2. Other classifications
+     12.4.4.2. Other classifications
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#its-still-linear">
-     11.4.4.3. It’s still linear
+     12.4.4.3. It’s still linear
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#variables-do-not-have-to-correlate">
-     11.4.4.4. Variables DO NOT have to correlate
+     12.4.4.4. Variables DO NOT have to correlate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#you-need-a-lot-of-clean-data">
-     11.4.4.5. You need a lot of clean data
+     12.4.4.5. You need a lot of clean data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-tidy-the-data">
-   11.4.5. Exercise - tidy the data
+   12.4.5. Exercise - tidy the data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#visualization-side-by-side-grid">
-     11.4.5.1. Visualization - side-by-side grid
+     12.4.5.1. Visualization - side-by-side grid
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-a-swarm-plot">
-     11.4.5.2. Use a swarm plot
+     12.4.5.2. Use a swarm plot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#violin-plot">
-     11.4.5.3. Violin plot
+     12.4.5.3. Violin plot
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-your-model">
-   11.4.6. Build your model
+   12.4.6. Build your model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#better-comprehension-via-a-confusion-matrix">
-   11.4.7. Better comprehension via a confusion matrix
+   12.4.7. Better comprehension via a confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualize-the-roc-curve-of-this-model">
-   11.4.8. Visualize the ROC curve of this model
+   12.4.8. Visualize the ROC curve of this model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.4.9. Your turn! 🚀
+   12.4.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   11.4.10. Self study
+   12.4.10. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.4.11. Acknowledgments
+   12.4.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1765,11 +1797,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="logistic-regression">
-<h1><span class="section-number">11.4. </span>Logistic regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.4. </span>Logistic regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
 <figure class="align-default" id="logistic-vs-linear-regression-infographic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/logistic-linear.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/logistic-linear.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/logistic-linear.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.12 </span><span class="caption-text">Logistic regression to predict categories. Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#logistic-vs-linear-regression-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.12 </span><span class="caption-text">Logistic regression to predict categories. Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#logistic-vs-linear-regression-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p style="text-align: center;">
@@ -1777,7 +1809,7 @@ <h1><span class="section-number">11.4. </span>Logistic regression<a class="heade
 style="border:none;" scrolling="auto"></iframe>
 A demo of logistic-regression. <a href="https://mlu-explain.github.io/logistic-regression/"> [source]</a >
 </p ><section id="introduction">
-<h2><span class="section-number">11.4.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In this final section on Regression, one of the basic <em>classic</em> Machine Learning techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict binary categories. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not?</p>
 <p>In this section, you will learn:</p>
 <ul class="simple">
@@ -1790,7 +1822,7 @@ <h2><span class="section-number">11.4.1. </span>Introduction<a class="headerlink
 </div>
 </section>
 <section id="prerequisite">
-<h2><span class="section-number">11.4.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
 <p>Having worked with the pumpkin data, we are now familiar enough with it to realize that there’s one binary category that we can work with: <code class="docutils literal notranslate"><span class="pre">Color</span></code>.</p>
 <p>Let’s build a logistic regression model to predict that, given some variables, <em>what color a given pumpkin is likely to be</em> (orange 🎃 or white 👻).</p>
 <div class="admonition note">
@@ -1799,7 +1831,7 @@ <h2><span class="section-number">11.4.2. </span>Prerequisite<a class="headerlink
 </div>
 </section>
 <section id="define-the-question">
-<h2><span class="section-number">11.4.3. </span>Define the question<a class="headerlink" href="#define-the-question" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.3. </span>Define the question<a class="headerlink" href="#define-the-question" title="Permalink to this headline">#</a></h2>
 <p>For our purposes, we will express this as a binary: ‘Orange’ or ‘Not Orange’. There is also a ‘striped’ category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -1807,20 +1839,20 @@ <h2><span class="section-number">11.4.3. </span>Define the question<a class="hea
 </div>
 </section>
 <section id="about-logistic-regression">
-<h2><span class="section-number">11.4.4. </span>About logistic regression<a class="headerlink" href="#about-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.4. </span>About logistic regression<a class="headerlink" href="#about-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>Logistic regression differs from linear regression, which you learned about previously, in a few important ways.</p>
 <section id="binary-classification">
-<h3><span class="section-number">11.4.4.1. </span>Binary classification<a class="headerlink" href="#binary-classification" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.1. </span>Binary classification<a class="headerlink" href="#binary-classification" title="Permalink to this headline">#</a></h3>
 <p>Logistic regression does not offer the same features as linear regression. The former offers a prediction about a binary category (“orange or not orange”) whereas the latter is capable of predicting continual values, for example given the origin of a pumpkin and the time of harvest, <em>how much its price will rise</em>.</p>
 <figure class="align-default" id="pumpkin-classification-model">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/pumpkin-classifier.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/pumpkin-classifier.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/pumpkin-classifier.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.13 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#pumpkin-classification-model" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.13 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#pumpkin-classification-model" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="other-classifications">
-<h3><span class="section-number">11.4.4.2. </span>Other classifications<a class="headerlink" href="#other-classifications" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.2. </span>Other classifications<a class="headerlink" href="#other-classifications" title="Permalink to this headline">#</a></h3>
 <p>There are other types of logistic regression, including multinomial and ordinal:</p>
 <ul class="simple">
 <li><p><strong>Multinomial</strong>, which involves having more than one category - “Orange, White, and Striped”.</p></li>
@@ -1829,20 +1861,20 @@ <h3><span class="section-number">11.4.4.2. </span>Other classifications<a class=
 <figure class="align-default" id="multinomial-vs-ordinal-regression">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/multinomial-ordinal.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/multinomial-ordinal.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/multinomial-ordinal.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.14 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#multinomial-vs-ordinal-regression" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.14 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#multinomial-vs-ordinal-regression" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="its-still-linear">
-<h3><span class="section-number">11.4.4.3. </span>It’s still linear<a class="headerlink" href="#its-still-linear" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.3. </span>It’s still linear<a class="headerlink" href="#its-still-linear" title="Permalink to this headline">#</a></h3>
 <p>Even though this type of Regression is all about ‘category predictions’, it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It’s good to get an idea of whether there is any linearity dividing these variables or not.</p>
 </section>
 <section id="variables-do-not-have-to-correlate">
-<h3><span class="section-number">11.4.4.4. </span>Variables DO NOT have to correlate<a class="headerlink" href="#variables-do-not-have-to-correlate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.4. </span>Variables DO NOT have to correlate<a class="headerlink" href="#variables-do-not-have-to-correlate" title="Permalink to this headline">#</a></h3>
 <p>Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don’t have to align. That works for this data which has somewhat weak correlations.</p>
 </section>
 <section id="you-need-a-lot-of-clean-data">
-<h3><span class="section-number">11.4.4.5. </span>You need a lot of clean data<a class="headerlink" href="#you-need-a-lot-of-clean-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.5. </span>You need a lot of clean data<a class="headerlink" href="#you-need-a-lot-of-clean-data" title="Permalink to this headline">#</a></h3>
 <p>Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1851,7 +1883,7 @@ <h3><span class="section-number">11.4.4.5. </span>You need a lot of clean data<a
 </section>
 </section>
 <section id="exercise-tidy-the-data">
-<h2><span class="section-number">11.4.5. </span>Exercise - tidy the data<a class="headerlink" href="#exercise-tidy-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.5. </span>Exercise - tidy the data<a class="headerlink" href="#exercise-tidy-the-data" title="Permalink to this headline">#</a></h2>
 <p>First, clean the data a bit, dropping null values and selecting only some of the columns:</p>
 <ol class="simple">
 <li><p>Add the following code:</p></li>
@@ -2076,7 +2108,7 @@ <h2><span class="section-number">11.4.5. </span>Exercise - tidy the data<a class
 </div>
 </div>
 <section id="visualization-side-by-side-grid">
-<h3><span class="section-number">11.4.5.1. </span>Visualization - side-by-side grid<a class="headerlink" href="#visualization-side-by-side-grid" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.5.1. </span>Visualization - side-by-side grid<a class="headerlink" href="#visualization-side-by-side-grid" title="Permalink to this headline">#</a></h3>
 <p>By now you have loaded up the <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html"><span class="doc std std-doc">starter notebook</span></a> with pumpkin data once again and cleaned it so as to preserve a dataset containing a few variables, including <code class="docutils literal notranslate"><span class="pre">Color</span></code>. Let’s visualize the dataframe in the notebook using a different library: <a class="reference external" href="https://seaborn.pydata.org/index.html">Seaborn</a>, which is built on Matplotlib which we used earlier.</p>
 <p>Seaborn offers some neat ways to visualize your data. For example, you can compare distributions of the data for each point in a side-by-side grid.</p>
 <ol class="simple">
@@ -2092,7 +2124,7 @@ <h3><span class="section-number">11.4.5.1. </span>Visualization - side-by-side g
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.PairGrid at 0x7fc71c164a60&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.PairGrid at 0x7fe1e42576a0&gt;
 </pre></div>
 </div>
 <img alt="../../_images/logistic-regression_19_1.png" src="../../_images/logistic-regression_19_1.png" />
@@ -2105,7 +2137,7 @@ <h3><span class="section-number">11.4.5.1. </span>Visualization - side-by-side g
 </div>
 </section>
 <section id="use-a-swarm-plot">
-<h3><span class="section-number">11.4.5.2. </span>Use a swarm plot<a class="headerlink" href="#use-a-swarm-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.5.2. </span>Use a swarm plot<a class="headerlink" href="#use-a-swarm-plot" title="Permalink to this headline">#</a></h3>
 <p>Since Color is a binary category (Orange or Not), it’s called ‘categorical data’ and needs ‘a more <a class="reference external" href="https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar">specialized approach</a> to visualization’. There are other ways to visualize the relationship of this category with other variables.</p>
 <p>You can visualize variables side-by-side with Seaborn plots.</p>
 <ol class="simple">
@@ -2138,7 +2170,7 @@ <h3><span class="section-number">11.4.5.2. </span>Use a swarm plot<a class="head
 </div>
 </section>
 <section id="violin-plot">
-<h3><span class="section-number">11.4.5.3. </span>Violin plot<a class="headerlink" href="#violin-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.5.3. </span>Violin plot<a class="headerlink" href="#violin-plot" title="Permalink to this headline">#</a></h3>
 <p>A ‘violin’ type plot is useful as you can easily visualize the way that data in the two categories is distributed. Violin plots don’t work so well with smaller datasets as the distribution is displayed more ‘smoothly’.</p>
 <ol class="simple">
 <li><p>As parameters <code class="docutils literal notranslate"><span class="pre">x=Color</span></code>, <code class="docutils literal notranslate"><span class="pre">kind=&quot;violin&quot;</span></code> and call <code class="docutils literal notranslate"><span class="pre">catplot()</span></code>:</p></li>
@@ -2151,7 +2183,7 @@ <h3><span class="section-number">11.4.5.3. </span>Violin plot<a class="headerlin
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7fc71c1646a0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7fe19dc90070&gt;
 </pre></div>
 </div>
 <img alt="../../_images/logistic-regression_24_1.png" src="../../_images/logistic-regression_24_1.png" />
@@ -2169,7 +2201,7 @@ <h3><span class="section-number">11.4.5.3. </span>Violin plot<a class="headerlin
 <figure class="align-default" id="logistic-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/sigmoid.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/sigmoid.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/sigmoid.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.15 </span><span class="caption-text">Logistic function<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Logistic function. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/sigmoid.png (visited on 2022).">Loo</a>]</span></span><a class="headerlink" href="#logistic-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.15 </span><span class="caption-text">Logistic function<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Logistic function. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/sigmoid.png (visited on 2022).">Loo</a>]</span></span><a class="headerlink" href="#logistic-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </div>
@@ -2180,7 +2212,7 @@ <h3><span class="section-number">11.4.5.3. </span>Violin plot<a class="headerlin
 </section>
 </section>
 <section id="build-your-model">
-<h2><span class="section-number">11.4.6. </span>Build your model<a class="headerlink" href="#build-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.6. </span>Build your model<a class="headerlink" href="#build-your-model" title="Permalink to this headline">#</a></h2>
 <p>Building a model to find these binary classification is surprisingly straightforward in Scikit-learn.</p>
 <p>1 . Select the variables you want to use in your classification model and split the training and test sets calling <code class="docutils literal notranslate"><span class="pre">train_test_split()</span></code>:</p>
 <div class="cell docutils container">
@@ -2241,7 +2273,7 @@ <h2><span class="section-number">11.4.6. </span>Build your model<a class="header
 </div>
 </section>
 <section id="better-comprehension-via-a-confusion-matrix">
-<h2><span class="section-number">11.4.7. </span>Better comprehension via a confusion matrix<a class="headerlink" href="#better-comprehension-via-a-confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.7. </span>Better comprehension via a confusion matrix<a class="headerlink" href="#better-comprehension-via-a-confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>While you can get a scoreboard report <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report">terms</a> by printing out the items above, you might be able to understand your model more easily by using a <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix">confusion matrix</a> to help us understand how the model is performing.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2301,7 +2333,7 @@ <h2><span class="section-number">11.4.7. </span>Better comprehension via a confu
 </div>
 </section>
 <section id="visualize-the-roc-curve-of-this-model">
-<h2><span class="section-number">11.4.8. </span>Visualize the ROC curve of this model<a class="headerlink" href="#visualize-the-roc-curve-of-this-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.8. </span>Visualize the ROC curve of this model<a class="headerlink" href="#visualize-the-roc-curve-of-this-model" title="Permalink to this headline">#</a></h2>
 <p>This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables.</p>
 <p>Let’s do one more visualization to see the so-called ‘ROC’ score:</p>
 <div class="cell docutils container">
@@ -2342,16 +2374,16 @@ <h2><span class="section-number">11.4.8. </span>Visualize the ROC curve of this
 <p>In future lessons on classifications, you will learn how to iterate to improve your model’s scores. But for now, congratulations! You’ve completed these regression sections!</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">11.4.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>There’s a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try <a class="reference external" href="https://www.kaggle.com/search?q=logistic+regression+datasets">Kaggle</a> for interesting datasets.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html"><span class="doc std std-doc">Retrying some regression</span></a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">11.4.10. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.10. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Read the first few pages of <a class="reference external" href="https://web.stanford.edu/~jurafsky/slp3/5.pdf">this paper from Stanford</a> on some practical uses for logistic regression. Think about tasks that are better suited for one or the other type of regression tasks that we have studied up to this point. What would work best?</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">11.4.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <div class="docutils container" id="id2">
@@ -2401,13 +2433,13 @@ <h2><span class="section-number">11.4.11. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">11.3. </span>Linear and polynomial regression</p>
+            <p class="prev-next-title"><span class="section-number">12.3. </span>Linear and polynomial regression</p>
         </div>
     </a>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">12. </span>Build a web app to use a Machine Learning model</p>
+        <p class="prev-next-title"><span class="section-number">13. </span>Build a web app to use a Machine Learning model</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/ml-fundamentals/regression/managing-data.html b/ml-fundamentals/regression/managing-data.html
index 31b6babc13..b3ca44f76d 100644
--- a/ml-fundamentals/regression/managing-data.html
+++ b/ml-fundamentals/regression/managing-data.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>11.2. Managing data &#8212; Ocademy Open Machine Learning Book</title>
+    <title>12.2. Managing data &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="11.3. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
-    <link rel="prev" title="11.1. Tools of the trade" href="tools-of-the-trade.html" />
+    <link rel="next" title="12.3. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="prev" title="12.1. Tools of the trade" href="tools-of-the-trade.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
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-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.65. Support Vector Machines (SVM) - Classification
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-     37.66. Decision Trees - Intro and Regression
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-     37.68. Model selection assignment 1
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-     37.92. Sign Language Digits Classification with CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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-     38.2. Python programming basics
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-     38.3. Python programming advanced
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-     38.4. Data Science introduction
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-     38.16. Build an machine learning web application
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-     38.17. Unsupervised learning
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-     38.18. Kernel method
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-     38.19. Model Selection
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-     38.20. Convolutional Neural Network
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
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@@ -1526,93 +1558,93 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-a-regression-model-using-scikit-learn-prepare-and-visualize-data">
-   11.2.1. Build a regression model using Scikit-learn: prepare and visualize data
+   12.2.1. Build a regression model using Scikit-learn: prepare and visualize data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   11.2.2. Introduction
+   12.2.2. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#asking-the-right-question-of-your-data">
-   11.2.3. Asking the right question of your data
+   12.2.3. Asking the right question of your data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#case-study-the-pumpkin-market">
-   11.2.4. Case study: ‘the pumpkin market’
+   12.2.4. Case study: ‘the pumpkin market’
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparing-data">
-     11.2.4.1. Preparing data
+     12.2.4.1. Preparing data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-pumpkin-data-early-conclusions">
-     11.2.4.2. The pumpkin data - early conclusions
+     12.2.4.2. The pumpkin data - early conclusions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-analyze-the-pumpkin-data">
-   11.2.5. Exercise - analyze the pumpkin data
+   12.2.5. Exercise - analyze the pumpkin data
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#first-check-for-missing-dates">
-     11.2.5.1. First, check for missing dates
+     12.2.5.1. First, check for missing dates
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#second-determine-average-price-of-pumpkin">
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+     12.2.5.2. Second, determine average price of pumpkin
     </a>
    </li>
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     <a class="reference internal nav-link" href="#but-wait-theres-something-odd-here">
-     11.2.5.3. But wait! There’s something odd here
+     12.2.5.3. But wait! There’s something odd here
     </a>
    </li>
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     <a class="reference internal nav-link" href="#but-wait-theres-one-more-thing-to-do">
-     11.2.5.4. But wait! There’s one more thing to do
+     12.2.5.4. But wait! There’s one more thing to do
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualization-strategies">
-   11.2.6. Visualization strategies
+   12.2.6. Visualization strategies
   </a>
  </li>
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   <a class="reference internal nav-link" href="#exercise-experiment-with-matplotlib">
-   11.2.7. Exercise - experiment with Matplotlib
+   12.2.7. Exercise - experiment with Matplotlib
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-it-useful">
-     11.2.7.1. Make it useful
+     12.2.7.1. Make it useful
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#self-study">
-   11.2.8. Self study
+   12.2.8. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.2.9. Your turn! 🚀
+   12.2.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.2.10. Acknowledgments
+   12.2.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1636,93 +1668,93 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-a-regression-model-using-scikit-learn-prepare-and-visualize-data">
-   11.2.1. Build a regression model using Scikit-learn: prepare and visualize data
+   12.2.1. Build a regression model using Scikit-learn: prepare and visualize data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   11.2.2. Introduction
+   12.2.2. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#asking-the-right-question-of-your-data">
-   11.2.3. Asking the right question of your data
+   12.2.3. Asking the right question of your data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#case-study-the-pumpkin-market">
-   11.2.4. Case study: ‘the pumpkin market’
+   12.2.4. Case study: ‘the pumpkin market’
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparing-data">
-     11.2.4.1. Preparing data
+     12.2.4.1. Preparing data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-pumpkin-data-early-conclusions">
-     11.2.4.2. The pumpkin data - early conclusions
+     12.2.4.2. The pumpkin data - early conclusions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-analyze-the-pumpkin-data">
-   11.2.5. Exercise - analyze the pumpkin data
+   12.2.5. Exercise - analyze the pumpkin data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#first-check-for-missing-dates">
-     11.2.5.1. First, check for missing dates
+     12.2.5.1. First, check for missing dates
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#second-determine-average-price-of-pumpkin">
-     11.2.5.2. Second, determine average price of pumpkin
+     12.2.5.2. Second, determine average price of pumpkin
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#but-wait-theres-something-odd-here">
-     11.2.5.3. But wait! There’s something odd here
+     12.2.5.3. But wait! There’s something odd here
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#but-wait-theres-one-more-thing-to-do">
-     11.2.5.4. But wait! There’s one more thing to do
+     12.2.5.4. But wait! There’s one more thing to do
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualization-strategies">
-   11.2.6. Visualization strategies
+   12.2.6. Visualization strategies
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-experiment-with-matplotlib">
-   11.2.7. Exercise - experiment with Matplotlib
+   12.2.7. Exercise - experiment with Matplotlib
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-it-useful">
-     11.2.7.1. Make it useful
+     12.2.7.1. Make it useful
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   11.2.8. Self study
+   12.2.8. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.2.9. Your turn! 🚀
+   12.2.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.2.10. Acknowledgments
+   12.2.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1746,18 +1778,18 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="managing-data">
-<h1><span class="section-number">11.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h1>
 <section id="build-a-regression-model-using-scikit-learn-prepare-and-visualize-data">
-<h2><span class="section-number">11.2.1. </span>Build a regression model using Scikit-learn: prepare and visualize data<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-prepare-and-visualize-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.1. </span>Build a regression model using Scikit-learn: prepare and visualize data<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-prepare-and-visualize-data" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="data-visualization-infographic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/data-visualization.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/data-visualization.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/data-visualization.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.4 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#data-visualization-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.4 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#data-visualization-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="introduction">
-<h2><span class="section-number">11.2.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Now that you are set up with the tools you need to start tackling machine learning model building with Scikit-learn, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it’s very important to understand how to ask the right question to properly unlock the potentials of your dataset.</p>
 <p>In this section, you will learn:</p>
 <ul class="simple">
@@ -1788,29 +1820,29 @@ <h2><span class="section-number">11.2.2. </span>Introduction<a class="headerlink
 </div>
 </section>
 <section id="asking-the-right-question-of-your-data">
-<h2><span class="section-number">11.2.3. </span>Asking the right question of your data<a class="headerlink" href="#asking-the-right-question-of-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.3. </span>Asking the right question of your data<a class="headerlink" href="#asking-the-right-question-of-your-data" title="Permalink to this headline">#</a></h2>
 <p>The question you need answered will determine what type of Machine Learning algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.</p>
 <p>Take a look at the <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/us-pumpkins.csv">data</a> provided for this section. You can open this .csv file in VS Code. A quick skim immediately shows that there are blanks and a mix of strings and numeric data. There’s also a strange column called ‘Package’ where the data is a mix between ‘sacks’, ‘bins’ and other values. The data, in fact, is a bit of a mess.</p>
 <p>In fact, it is not very common to be gifted a dataset that is completely ready to use to create a Machine Learning model out of the box. In this section, you will learn how to prepare a raw dataset using standard Python libraries. You will also learn various techniques to visualize the data.</p>
 </section>
 <section id="case-study-the-pumpkin-market">
-<h2><span class="section-number">11.2.4. </span>Case study: ‘the pumpkin market’<a class="headerlink" href="#case-study-the-pumpkin-market" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.4. </span>Case study: ‘the pumpkin market’<a class="headerlink" href="#case-study-the-pumpkin-market" title="Permalink to this headline">#</a></h2>
 <p>You will find a .csv file in the root <code class="docutils literal notranslate"><span class="pre">/assets/data/</span></code> folder called <a href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/us-pumpkins.csv">us-pumpkins.csv</a> which includes 1757 lines of data about the market for pumpkins, sorted into groupings by city. This is raw data extracted from the <a class="reference external" href="https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice">Specialty Crops Terminal Markets Standard Reports</a> distributed by the United States Department of Agriculture.</p>
 <section id="preparing-data">
-<h3><span class="section-number">11.2.4.1. </span>Preparing data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.4.1. </span>Preparing data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h3>
 <p>This data is in the public domain. It can be downloaded in many separate files, per city, from the USDA website. To avoid too many separate files, we have concatenated all the city data into one spreadsheet, thus we have already <em>prepared</em> the data a bit. Next, let’s take a closer look at the data.</p>
 </section>
 <section id="the-pumpkin-data-early-conclusions">
-<h3><span class="section-number">11.2.4.2. </span>The pumpkin data - early conclusions<a class="headerlink" href="#the-pumpkin-data-early-conclusions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.4.2. </span>The pumpkin data - early conclusions<a class="headerlink" href="#the-pumpkin-data-early-conclusions" title="Permalink to this headline">#</a></h3>
 <p>What do you notice about this data? You already saw that there is a mix of strings, numbers, blanks and strange values that you need to make sense of.</p>
 <p>What question can you ask of this data, using a Regression technique? What about “Predict the price of a pumpkin for sale during a given month”. Looking again at the data, there are some changes you need to make to create the data structure necessary for the task.</p>
 </section>
 </section>
 <section id="exercise-analyze-the-pumpkin-data">
-<h2><span class="section-number">11.2.5. </span>Exercise - analyze the pumpkin data<a class="headerlink" href="#exercise-analyze-the-pumpkin-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.5. </span>Exercise - analyze the pumpkin data<a class="headerlink" href="#exercise-analyze-the-pumpkin-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s use <a class="reference external" href="https://pandas.pydata.org/">Pandas</a>, a very useful tool for shaping data, to analyze and prepare this pumpkin data.</p>
 <section id="first-check-for-missing-dates">
-<h3><span class="section-number">11.2.5.1. </span>First, check for missing dates<a class="headerlink" href="#first-check-for-missing-dates" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.1. </span>First, check for missing dates<a class="headerlink" href="#first-check-for-missing-dates" title="Permalink to this headline">#</a></h3>
 <p>You will first need to take steps to check for missing dates:</p>
 <ol class="simple">
 <li><p>Convert the dates to a month format (these are US dates, so the format is <code class="docutils literal notranslate"><span class="pre">MM/DD/YYYY</span></code>).</p></li>
@@ -2049,7 +2081,7 @@ <h3><span class="section-number">11.2.5.1. </span>First, check for missing dates
 </div>
 </section>
 <section id="second-determine-average-price-of-pumpkin">
-<h3><span class="section-number">11.2.5.2. </span>Second, determine average price of pumpkin<a class="headerlink" href="#second-determine-average-price-of-pumpkin" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.2. </span>Second, determine average price of pumpkin<a class="headerlink" href="#second-determine-average-price-of-pumpkin" title="Permalink to this headline">#</a></h3>
 <p>Think about how to determine the average price of a pumpkin in a given month. What columns would you pick for this task? Hint: you’ll need 3 columns.</p>
 <p>Solution: take the average of the <code class="docutils literal notranslate"><span class="pre">Low</span> <span class="pre">Price</span></code> and <code class="docutils literal notranslate"><span class="pre">High</span> <span class="pre">Price</span></code> columns to populate the new Price column, and convert the Date column to only show the month. Fortunately, according to the check above, there is no missing data for dates or prices.</p>
 <p>1 . To calculate the average, add the following code:</p>
@@ -2076,7 +2108,7 @@ <h3><span class="section-number">11.2.5.2. </span>Second, determine average pric
 <p>Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.</p>
 </section>
 <section id="but-wait-theres-something-odd-here">
-<h3><span class="section-number">11.2.5.3. </span>But wait! There’s something odd here<a class="headerlink" href="#but-wait-theres-something-odd-here" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.3. </span>But wait! There’s something odd here<a class="headerlink" href="#but-wait-theres-something-odd-here" title="Permalink to this headline">#</a></h3>
 <p>If you look at the <code class="docutils literal notranslate"><span class="pre">Package</span></code> column, pumpkins are sold in many different configurations. Some are sold in ‘1 1/9 bushel’ measures, and some in ‘1/2 bushel’ measures, some per pumpkin, some per pound, and some in big boxes with varying widths.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2096,7 +2128,7 @@ <h3><span class="section-number">11.2.5.3. </span>But wait! There’s something
 <p>If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.</p>
 </section>
 <section id="but-wait-theres-one-more-thing-to-do">
-<h3><span class="section-number">11.2.5.4. </span>But wait! There’s one more thing to do<a class="headerlink" href="#but-wait-theres-one-more-thing-to-do" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.4. </span>But wait! There’s one more thing to do<a class="headerlink" href="#but-wait-theres-one-more-thing-to-do" title="Permalink to this headline">#</a></h3>
 <p>Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, so do some math to standardize it.</p>
 <ol class="simple">
 <li><p>Add these lines after the block creating the new_pumpkins dataframe:</p></li>
@@ -2122,7 +2154,7 @@ <h3><span class="section-number">11.2.5.4. </span>But wait! There’s one more t
 </section>
 </section>
 <section id="visualization-strategies">
-<h2><span class="section-number">11.2.6. </span>Visualization strategies<a class="headerlink" href="#visualization-strategies" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.6. </span>Visualization strategies<a class="headerlink" href="#visualization-strategies" title="Permalink to this headline">#</a></h2>
 <p>Part of the data scientist’s role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.</p>
 <p>Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.</p>
 <p>One data visualization library that works well in Jupyter notebooks is <a class="reference external" href="https://matplotlib.org/">Matplotlib</a> (which you also saw in the previous lesson).</p>
@@ -2132,7 +2164,7 @@ <h2><span class="section-number">11.2.6. </span>Visualization strategies<a class
 </div>
 </section>
 <section id="exercise-experiment-with-matplotlib">
-<h2><span class="section-number">11.2.7. </span>Exercise - experiment with Matplotlib<a class="headerlink" href="#exercise-experiment-with-matplotlib" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.7. </span>Exercise - experiment with Matplotlib<a class="headerlink" href="#exercise-experiment-with-matplotlib" title="Permalink to this headline">#</a></h2>
 <p>Try to create some basic plots to display the new dataframe you just created. What would a basic line plot show?</p>
 <p>1 . Import Matplotlib at the top of the file, under the Pandas import:</p>
 <div class="cell tag_hide-input tag_output-scoll docutils container">
@@ -2164,7 +2196,7 @@ <h2><span class="section-number">11.2.7. </span>Exercise - experiment with Matpl
 <p>Is this a useful plot? Does anything about it surprise you?</p>
 <p>It’s not particularly useful as all it does is display in your data as a spread of points in a given month.</p>
 <section id="make-it-useful">
-<h3><span class="section-number">11.2.7.1. </span>Make it useful<a class="headerlink" href="#make-it-useful" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.7.1. </span>Make it useful<a class="headerlink" href="#make-it-useful" title="Permalink to this headline">#</a></h3>
 <p>To get charts to display useful data, you usually need to group the data somehow. Let’s try creating a plot where the y axis shows the months and the data demonstrates the distribution of data.</p>
 <ol class="simple">
 <li><p>Add a cell to create a grouped bar chart:</p></li>
@@ -2191,16 +2223,16 @@ <h3><span class="section-number">11.2.7.1. </span>Make it useful<a class="header
 </section>
 </section>
 <section id="self-study">
-<h2><span class="section-number">11.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Take a look at the many ways to visualize data. Make a list of the various libraries available and note which are best for given types of tasks, for example 2D visualizations vs. 3D visualizations. What do you discover?</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">11.2.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?</p>
 <p>Assignment - <a class="reference external" href="https://github.com/ocademy-ai/machine-learning/open-machine-learning-jupyter-book/assignments/ml-fundamentals/exploring-visualizations.md">Exploring visualization</a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">11.2.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2242,13 +2274,13 @@ <h2><span class="section-number">11.2.10. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">11.1. </span>Tools of the trade</p>
+            <p class="prev-next-title"><span class="section-number">12.1. </span>Tools of the trade</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="linear-and-polynomial-regression.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">11.3. </span>Linear and polynomial regression</p>
+        <p class="prev-next-title"><span class="section-number">12.3. </span>Linear and polynomial regression</p>
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>11. Regression models for Machine Learning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>12. Regression models for Machine Learning &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="11.1. Tools of the trade" href="tools-of-the-trade.html" />
-    <link rel="prev" title="10. Machine Learning overview" href="../ml-overview.html" />
+    <link rel="next" title="12.1. Tools of the trade" href="tools-of-the-trade.html" />
+    <link rel="prev" title="11. Summary of machine learning fundamentals" href="../ml-summary.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 current active has-children">
   <a class="current reference internal" href="#">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
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+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-   35. Model training &amp; evaluation
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-     37.5. Project Plan​ Template
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+<h1><span class="section-number">12. </span>Regression models for Machine Learning<a class="headerlink" href="#regression-models-for-machine-learning" title="Permalink to this headline">#</a></h1>
 <p>In North America, pumpkins are often carved into scary faces for Halloween. Let’s discover more about these fascinating vegetables!</p>
 <figure class="align-default" id="jack-o-lanterns">
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 <figcaption>
-<p><span class="caption-number">Fig. 11.1 </span><span class="caption-text">Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a></span><a class="headerlink" href="#jack-o-lanterns" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.1 </span><span class="caption-text">Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a></span><a class="headerlink" href="#jack-o-lanterns" title="Permalink to this image">#</a></p>
 </figcaption>
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-<li class="toctree-l1"><a class="reference internal" href="logistic-regression.html">11.4. Logistic regression</a></li>
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-            <p class="prev-next-title"><span class="section-number">10. </span>Machine Learning overview</p>
+            <p class="prev-next-title"><span class="section-number">11. </span>Summary of machine learning fundamentals</p>
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+        <p class="prev-next-title"><span class="section-number">12.1. </span>Tools of the trade</p>
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-     11.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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    <li class="toctree-l2">
     <a class="reference internal" href="../classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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     <a class="reference internal" href="../classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+    <a class="reference internal" href="../parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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-     15.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
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-   21. Convolutional Neural Networks
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-   23. Recurrent Neural Networks
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-   27. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../deep-learning/object-detection.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.17. Classifying datasets
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-     37.18. Small diabetes study
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-     37.19. Introduction to probability and statistics
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-     37.21. Soda profits
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-     37.59. Retrying some regression
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-     37.63. Kernel method assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.112. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
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@@ -1526,66 +1558,66 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#get-started-with-python-and-scikit-learn-for-regression-models">
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   <a class="reference internal nav-link" href="#introduction">
-   11.1.2. Introduction
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   </a>
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   <a class="reference internal nav-link" href="#installations-and-configurations">
-   11.1.3. Installations and configurations
+   12.1.3. Installations and configurations
   </a>
  </li>
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   <a class="reference internal nav-link" href="#your-machine-learning-authoring-environment">
-   11.1.4. Your Machine Learning authoring environment
+   12.1.4. Your Machine Learning authoring environment
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-work-with-a-notebook">
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+     12.1.4.1. Exercise - work with a notebook
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   <a class="reference internal nav-link" href="#up-and-running-with-scikit-learn">
-   11.1.5. Up and running with Scikit-learn
+   12.1.5. Up and running with Scikit-learn
   </a>
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   <a class="reference internal nav-link" href="#exercise-your-first-scikit-learn-notebook">
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+   12.1.6. Exercise - your first Scikit-learn notebook
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    <li class="toc-h3 nav-item toc-entry">
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-     11.1.6.1. Import libraries
+     12.1.6.1. Import libraries
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-diabetes-dataset">
-     11.1.6.2. The diabetes dataset
+     12.1.6.2. The diabetes dataset
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   <a class="reference internal nav-link" href="#your-turn">
-   11.1.8. Your turn! 🚀
+   12.1.8. Your turn! 🚀
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#get-started-with-python-and-scikit-learn-for-regression-models">
-   11.1.1. Get started with Python and Scikit-learn for regression models
+   12.1.1. Get started with Python and Scikit-learn for regression models
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   11.1.2. Introduction
+   12.1.2. Introduction
   </a>
  </li>
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   <a class="reference internal nav-link" href="#installations-and-configurations">
-   11.1.3. Installations and configurations
+   12.1.3. Installations and configurations
   </a>
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   <a class="reference internal nav-link" href="#your-machine-learning-authoring-environment">
-   11.1.4. Your Machine Learning authoring environment
+   12.1.4. Your Machine Learning authoring environment
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-work-with-a-notebook">
-     11.1.4.1. Exercise - work with a notebook
+     12.1.4.1. Exercise - work with a notebook
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#up-and-running-with-scikit-learn">
-   11.1.5. Up and running with Scikit-learn
+   12.1.5. Up and running with Scikit-learn
   </a>
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   <a class="reference internal nav-link" href="#exercise-your-first-scikit-learn-notebook">
-   11.1.6. Exercise - your first Scikit-learn notebook
+   12.1.6. Exercise - your first Scikit-learn notebook
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     11.1.6.1. Import libraries
+     12.1.6.1. Import libraries
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-diabetes-dataset">
-     11.1.6.2. The diabetes dataset
+     12.1.6.2. The diabetes dataset
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   11.1.7. Self study
+   12.1.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.1.8. Your turn! 🚀
+   12.1.8. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.1.9. Acknowledgments
+   12.1.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1693,18 +1725,18 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="tools-of-the-trade">
-<h1><span class="section-number">11.1. </span>Tools of the trade<a class="headerlink" href="#tools-of-the-trade" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.1. </span>Tools of the trade<a class="headerlink" href="#tools-of-the-trade" title="Permalink to this headline">#</a></h1>
 <section id="get-started-with-python-and-scikit-learn-for-regression-models">
-<h2><span class="section-number">11.1.1. </span>Get started with Python and Scikit-learn for regression models<a class="headerlink" href="#get-started-with-python-and-scikit-learn-for-regression-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.1. </span>Get started with Python and Scikit-learn for regression models<a class="headerlink" href="#get-started-with-python-and-scikit-learn-for-regression-models" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="summary-of-regressions-in-a-sketchnote">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/ml-regression.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/ml-regression.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/ml-regression.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.2 </span><span class="caption-text">Sketchnote by <a class="reference external" href="https://www.twitter.com/girlie_mac">Tomomi Imura</a></span><a class="headerlink" href="#summary-of-regressions-in-a-sketchnote" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.2 </span><span class="caption-text">Sketchnote by <a class="reference external" href="https://www.twitter.com/girlie_mac">Tomomi Imura</a></span><a class="headerlink" href="#summary-of-regressions-in-a-sketchnote" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="introduction">
-<h2><span class="section-number">11.1.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In these four sections, you will discover how to build regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!</p>
 <p>In this section, you will learn how to:</p>
 <ul class="simple">
@@ -1715,7 +1747,7 @@ <h2><span class="section-number">11.1.2. </span>Introduction<a class="headerlink
 </ul>
 </section>
 <section id="installations-and-configurations">
-<h2><span class="section-number">11.1.3. </span>Installations and configurations<a class="headerlink" href="#installations-and-configurations" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.3. </span>Installations and configurations<a class="headerlink" href="#installations-and-configurations" title="Permalink to this headline">#</a></h2>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p>Click here for a video : <a class="reference external" href="https://youtu.be/yyQM70vi7V8">Setup Python with Visual Studio Code</a>, by author <a class="reference external" href="https://www.youtube.com/&#64;alfredodeza1">Alfredo Deza</a>.</p>
@@ -1731,11 +1763,11 @@ <h2><span class="section-number">11.1.3. </span>Installations and configurations
 <p>4 . <strong>Install Jupyter Notebook</strong>. You will need to <a class="reference external" href="https://pypi.org/project/jupyter/">install the Jupyter package</a>.</p>
 </section>
 <section id="your-machine-learning-authoring-environment">
-<h2><span class="section-number">11.1.4. </span>Your Machine Learning authoring environment<a class="headerlink" href="#your-machine-learning-authoring-environment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.4. </span>Your Machine Learning authoring environment<a class="headerlink" href="#your-machine-learning-authoring-environment" title="Permalink to this headline">#</a></h2>
 <p>You are going to use <strong>notebooks</strong> to develop your Python code and create machine learning models. This type of file is a common tool for data scientists, and they can be identified by their suffix or extension <code class="docutils literal notranslate"><span class="pre">.ipynb</span></code>.</p>
 <p>Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.</p>
 <section id="exercise-work-with-a-notebook">
-<h3><span class="section-number">11.1.4.1. </span>Exercise - work with a notebook<a class="headerlink" href="#exercise-work-with-a-notebook" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.1.4.1. </span>Exercise - work with a notebook<a class="headerlink" href="#exercise-work-with-a-notebook" title="Permalink to this headline">#</a></h3>
 <p>In this section, you will find the file <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html"><span class="doc std std-doc">regression-tools.ipynb</span></a>.</p>
 <ol>
 <li><p>Open <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html"><span class="doc std std-doc">regression-tools.ipynb</span></a> in Visual Studio Code.</p>
@@ -1755,7 +1787,7 @@ <h3><span class="section-number">11.1.4.1. </span>Exercise - work with a noteboo
 <figure class="align-default" id="vs-code-with-a-notebook-open">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/notebook.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/notebook.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/notebook.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.3 </span><span class="caption-text">VS Code with a notebook open{cite}“VS_Code_with_a_notebook_open”</span><a class="headerlink" href="#vs-code-with-a-notebook-open" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.3 </span><span class="caption-text">VS Code with a notebook open{cite}“VS_Code_with_a_notebook_open”</span><a class="headerlink" href="#vs-code-with-a-notebook-open" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>You can interleaf your code with comments to self-document the notebook.</p>
@@ -1766,14 +1798,14 @@ <h3><span class="section-number">11.1.4.1. </span>Exercise - work with a noteboo
 </section>
 </section>
 <section id="up-and-running-with-scikit-learn">
-<h2><span class="section-number">11.1.5. </span>Up and running with Scikit-learn<a class="headerlink" href="#up-and-running-with-scikit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.5. </span>Up and running with Scikit-learn<a class="headerlink" href="#up-and-running-with-scikit-learn" title="Permalink to this headline">#</a></h2>
 <p>Now that Python is set up in your local environment, and you are comfortable with Jupyter notebooks, let’s get equally comfortable with Scikit-learn (pronounce it <code class="docutils literal notranslate"><span class="pre">sci</span></code> as in <code class="docutils literal notranslate"><span class="pre">science</span></code>). Scikit-learn provides an <a class="reference external" href="https://scikit-learn.org/stable/modules/classes.html#api-ref">extensive API</a> to help you perform ML tasks.</p>
 <p>According to their <a class="reference external" href="https://scikit-learn.org/stable/getting_started.html">website</a>, “Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.”</p>
 <p>In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call ‘traditional machine learning’ tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming ‘AI for Beginners’ curriculum.</p>
 <p>Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let’s explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.</p>
 </section>
 <section id="exercise-your-first-scikit-learn-notebook">
-<h2><span class="section-number">11.1.6. </span>Exercise - your first Scikit-learn notebook<a class="headerlink" href="#exercise-your-first-scikit-learn-notebook" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.6. </span>Exercise - your first Scikit-learn notebook<a class="headerlink" href="#exercise-your-first-scikit-learn-notebook" title="Permalink to this headline">#</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>This tutorial was inspired by the <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py">linear regression example</a> on Scikit-learn’s web site.</p>
@@ -1786,7 +1818,7 @@ <h2><span class="section-number">11.1.6. </span>Exercise - your first Scikit-lea
 </div>
 <p>Let’s get started on this task.</p>
 <section id="import-libraries">
-<h3><span class="section-number">11.1.6.1. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.1.6.1. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
 <p>For this task we will import some libraries:</p>
 <ul class="simple">
 <li><p><strong>matplotlib</strong>. It’s a useful <a class="reference external" href="https://matplotlib.org/">graphing tool</a> and we will use it to create a line plot.</p></li>
@@ -1807,7 +1839,7 @@ <h3><span class="section-number">11.1.6.1. </span>Import libraries<a class="head
 <p>Above you are importing <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy</span></code> and you are importing <code class="docutils literal notranslate"><span class="pre">datasets</span></code>, <code class="docutils literal notranslate"><span class="pre">linear_model</span></code> and <code class="docutils literal notranslate"><span class="pre">model_selection</span></code> from <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>. <code class="docutils literal notranslate"><span class="pre">model_selection</span></code> is used for splitting data into training and test sets.</p>
 </section>
 <section id="the-diabetes-dataset">
-<h3><span class="section-number">11.1.6.2. </span>The diabetes dataset<a class="headerlink" href="#the-diabetes-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.1.6.2. </span>The diabetes dataset<a class="headerlink" href="#the-diabetes-dataset" title="Permalink to this headline">#</a></h3>
 <p>The built-in <a class="reference external" href="https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset">diabetes dataset</a> includes 442 samples of data around diabetes, with 10 feature variables, some of which include:</p>
 <ul class="simple">
 <li><p>age: age in years</p></li>
@@ -1915,17 +1947,17 @@ <h3><span class="section-number">11.1.6.2. </span>The diabetes dataset<a class="
 </section>
 </section>
 <section id="self-study">
-<h2><span class="section-number">11.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at <a class="reference external" href="https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef">this video</a></p>
 <p>Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this <a class="reference external" href="https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott">tutorial</a> to deepen your understanding.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">11.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Plot a different variable from this dataset. Hint: edit this line: <code class="docutils literal notranslate"><span class="pre">X</span> <span class="pre">=</span> <span class="pre">X[:,</span> <span class="pre">np.newaxis,</span> <span class="pre">2]</span></code>. Given this dataset’s target, what are you able to discover about the progression of diabetes as a disease?</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html"><span class="doc std std-doc">Regression with scikit-learn</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">11.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <span class="target" id="id1"></span></section>
@@ -1968,13 +2000,13 @@ <h2><span class="section-number">11.1.9. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">11. </span>Regression models for Machine Learning</p>
+            <p class="prev-next-title"><span class="section-number">12. </span>Regression models for Machine Learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="managing-data.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">11.2. </span>Managing data</p>
+        <p class="prev-next-title"><span class="section-number">12.2. </span>Managing data</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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  </li>
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+  </a>
+ </li>
+ <li class="toctree-l1">
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  </li>
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-     37.26. Data preparation
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     </a>
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-     37.27. Analyzing data
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     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
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-     37.29. Matplotlib applied
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-     37.35. Tell a story
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     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
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-     37.39. Low code/no code Data Science project on Azure ML
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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-     37.44. Regression with Scikit-learn
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
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-     37.48. Gradient descent
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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-     37.53. Regression tools
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-     37.54. Managing data
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-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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-     37.61. Delicious asian and indian cuisines
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     </a>
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-     37.62. Explore classification methods
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-     37.63. Kernel method assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
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-     37.65. Support Vector Machines (SVM) - Classification
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     </a>
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-     37.66. Decision Trees - Intro and Regression
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     </a>
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-     37.67. Decision Trees - Classification
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     </a>
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     </a>
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-     37.70. Dropout and Batch Normalization
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-     37.71. Lasso and Ridge Regression
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-     37.72. Regularized Linear Models
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-     37.73. Random forests intro and regression
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-     37.74. Random forests for classification
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-     37.75. Beyond random forests: more ensemble models
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     </a>
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
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-     37.79. Boosting with tuning
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.83. Counterintuitive Challenges in ML Debugging
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-     37.84. Case Study: Debugging in Classification
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-     37.85. Case Study: Debugging in Regression
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-     37.86. Study the solvers
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-     37.87. Build classification models
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-     37.88. Build Classification Model
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-     37.89. Parameter play
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-     37.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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    </li>
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     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
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-     37.104. Time Series Forecasting Assignment
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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     </a>
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     <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
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-     37.115. Generative Adversarial Networks (GANs)
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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-     38.2. Python programming basics
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    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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    </li>
   </ul>
diff --git a/prerequisites/python-programming-basics.html b/prerequisites/python-programming-basics.html
index 8541d90158..62a82a2868 100644
--- a/prerequisites/python-programming-basics.html
+++ b/prerequisites/python-programming-basics.html
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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    </li>
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
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     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
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+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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   </a>
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     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-     16.3. XGBoost
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     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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  </li>
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   <a class="reference internal" href="../deep-learning/autoencoder.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/time-series.html">
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   <a class="reference internal" href="../deep-learning/dqn.html">
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   <a class="reference internal" href="../deep-learning/image-classification.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-segmentation.html">
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+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
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+   35. Overview
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
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     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
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     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
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     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
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     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
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     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.83. Counterintuitive Challenges in ML Debugging
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-     37.85. Case Study: Debugging in Regression
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-     37.90. How to choose cnn architecture mnist
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.99. Google Stock Price Prediction RNN
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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-  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
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-     38.2. Python programming basics
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-     38.4. Data Science introduction
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-     38.5. Relational vs. non-relational database
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-     38.6. NumPy and Pandas
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-     38.12. Linear Regression
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-     38.16. Build an machine learning web application
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    10. Machine Learning overview
   </a>
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+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
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  <li class="toctree-l1 has-children">
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-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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-     13.2. More classifiers
+     14.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -556,62 +588,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
   </a>
  </li>
 </ul>
@@ -623,27 +655,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
+   37. Data engineering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
+   39. Model deployment
   </a>
  </li>
 </ul>
@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../assignments/README.html">
-   37. Self-paced assignments
+   40. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
+     40.3. First assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
+     40.4. Second assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
+     40.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     37.38. Market research
+     40.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
+     40.47. Loss Function
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
+     40.48. Gradient descent
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
+     40.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
+     40.54. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
+     40.55. Exploring visualizations
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
+     40.56. Try a different model
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
+     40.59. Retrying some regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.83. Counterintuitive Challenges in ML Debugging
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-     37.84. Case Study: Debugging in Classification
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-     37.85. Case Study: Debugging in Regression
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-     37.92. Sign Language Digits Classification with CNN
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Intro to Autoencoders
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-     37.106. Neural Networks for Classification with TensorFlow
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-     38.5. Relational vs. non-relational database
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-     38.11. Machine Learning overview
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-     38.16. Build an machine learning web application
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@@ -376,9 +376,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
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  </li>
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+  <a class="reference internal" href="ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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-     13.4. Applied Machine Learning : build a web app
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+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="ml-advanced/unsupervised-learning-pca-and-clustering.html">
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+     40.5. Project Plan​ Template
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+     40.14. Try it in Excel
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+     40.19. Introduction to probability and statistics
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+     40.24. Data processing in Python
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-     37.25. Evaluating data from a form
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-     37.36. Explore a planetary computer dataset
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-     37.39. Low code/no code Data Science project on Azure ML
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-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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-     37.45. Linear regression - California Housing
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+     40.47. Loss Function
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-     37.49. Linear Regression Implementation from Scratch
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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-     37.66. Decision Trees - Intro and Regression
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-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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50,159,171,172,173,179,194,195],"long":[1,8,14,33,35,36,45,47,48,53,55,56,59,64,68,80,82,100,101,104,111,114,119,129,130,136,137,141,154,157,159,165,171,172,173,187,193,194],"micha\u0142":180,"new":[7,9,14,17,22,23,31,33,34,35,41,43,45,47,48,49,50,52,53,54,55,59,60,61,62,64,68,80,84,92,93,99,100,101,102,104,106,108,109,112,113,114,116,118,120,124,126,127,129,130,131,132,137,138,140,141,143,146,147,150,151,152,153,154,155,156,157,158,162,165,166,167,168,170,171,172,174,176,177,179,180,181,182,184,186,191,194],"null":[38,44,46,48,60,74,117,120,122,145,155,159,166,167],"office2016\u7b80\u4f53\u4e2d\u658764\u4f4d":38,"p\u03b8":128,"pikach\u00fa":12,"public":[1,14,50,56,57,99,106,112,114,116,135,140,142,143,165,168,171,175,176,177],"return":[2,3,7,12,14,18,22,24,25,29,30,31,33,34,35,36,37,38,39,40,41,43,44,46,47,48,49,50,52,53,54,55,56,57,58,60,63,64,65,66,68,74,75,76,80,81,82,84,92,93,100,101,117,118,119,120,121,122,124,126,127,128,131,132,133,134,135,136,137,141,142,147,150,151,152,155,157,158,159,162,172,173,179,180,182,186,187,188,189,190,193,194],"short":[26,45,59,100,116,119,129,130,132,143,159,171,172,194],"static":[5,29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,51,66,109,110,111,116,122,127,131,132,133,137,142,145,146,147,148,150,152,154,155,158,159,162,163,164,166,167,168,171,172,178,194],"super":[29,30,31,33,36,37,43,63,65,121,128,132,133,134,135,151,171,193],"switch":[0,7,14,47,50,141,147,171,193],"throw":172,"transient":139,"true":[1,7,9,14,18,22,24,29,30,31,33,34,35,36,37,38,39,40,41,42,44,46,47,48,49,50,51,52,53,54,55,56,57,58,60,62,64,66,68,74,76,77,78,80,82,92,96,97,100,101,108,109,116,117,119,120,121,122,124,127,128,130,131,132,133,134,135,137,142,143,145,147,148,150,154,155,156,157,158,159,164,166,167,168,170,171,172,173,178,179,182,184,188,192,193,194,195],"try":[1,3,4,5,7,9,11,14,16,18,25,29,31,35,36,44,45,47,49,50,51,52,53,54,56,57,58,59,60,61,62,63,64,65,66,76,82,83,89,92,100,101,102,104,107,108,109,110,111,114,117,118,120,121,122,124,127,133,134,135,141,142,143,145,146,150,151,152,154,155,156,157,158,159,161,163,164,165,166,167,168,169,170,172,173,186,191,194],"var":[18,38,51,55,68,80,108,128,135,147,150,166,178],"void":119,"while":[0,1,7,29,31,32,33,36,40,46,47,48,50,53,57,58,59,60,61,64,75,85,93,99,101,102,103,104,106,108,111,112,113,114,116,117,119,120,121,122,124,127,130,132,133,139,141,143,150,156,157,158,159,163,165,166,167,172,174,175,179,180,181,182,186,187,191,192,194],"y\u00fcksek":35,A:[0,1,4,5,6,7,12,13,14,15,18,19,21,23,26,28,29,32,36,40,41,43,45,47,48,49,50,51,52,56,57,58,59,62,63,64,65,66,68,72,74,75,76,77,78,80,82,85,88,89,90,92,93,99,100,101,102,104,106,108,109,110,112,113,114,116,117,118,119,120,121,122,124,126,127,130,131,132,133,135,136,137,139,140,141,142,144,145,146,147,148,150,151,155,156,158,159,162,165,167,168,170,171,172,173,176,177,179,180,182,184,187,188,191,192,193,194,195],AND:[92,93,108,119,121,171,172],AS:[22,25,45,47,48,92,93,171,172],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,127,129,130,133,136,139,140,141,142,143,147,156,158,168,172,176,180,184,187,194],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,122,124,132,133,134,139,140,141,147,150,151,155,156,157,158,162,165,166,167,168,171,172,173,177,179,182,191,193,194],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,130,140,141,143,147,151,152,157,165,168,170,171,172,179,191,192],BE:[92,93,171,172],BUT:[92,93,171,172],BY:[101,141],Be:[85,91,104,108,119,159],Being:[43,62,101,104,122],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,127,129,137,139,140,141,146,150,151,152,154,156,157,158,165,167,170,171,172,173,184,188],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,101,109,114,117,119,122,124,128,137,139,140,142,145,147,148,155,158,162,165,166,167,171,182],FOR:[92,93,171,172],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,120,121,122,126,129,130,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,155,156,157,158,159,162,163,164,165,166,167,168,170,171,172,173,174,184,188,191,192,194],IN:[25,82,92,93,171,172],IS:[22,45,47,48,54,92,93,97,145,171,172],IT:[54,99,140],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,128,129,130,132,134,136,139,140,141,143,145,147,150,151,152,154,156,157,158,159,162,164,165,166,167,168,170,171,172,173,180,186,188,191,193,194,195],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,129,130,131,132,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,157,158,159,161,162,163,164,165,166,167,168,169,170,171,172,173,174,177,179,180,181,186,188,189,190,191,192,194,195],Is:[50,93,97,99,103,106,111,112,113,128,140,141,145,159,165,167,168,195],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,139,140,141,142,143,144,145,146,147,148,150,151,152,153,154,155,156,157,158,159,162,163,164,165,166,168,170,171,172,173,175,176,182,186,187,188,189,191,192,194],Its:[129,150,155,167],NEAR:[61,74],NO:[92,93,171,172],NOT:[82,92,93,119,131,171,172],Near:[109,178],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,130,147,156,158,163,171,172,175,178],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,134,151,163,166,167,172,187,194],OF:[22,45,47,48,92,93,131,171,172],ON:180,ONE:7,OR:[22,45,47,48,92,93,119,171,172],Of:[50,101,102,104,114,158,173,174],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,141,147,150,151,154,155,158,159,165,166,170,171,177,187],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,129,135,141,146,148,150,156,165,166,168,171,172,173,177,178,179,184,186,191,194],Or:[32,40,50,58,74,102,104,119,129,130,141,145,148,165,171,172,187,191,194],Such:[1,7,30,40,43,49,50,54,116,141,142,166,171,193],THAT:82,THE:[92,93,171,172],TO:[54,92,93,136,171,172],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,130,146,148,151,152,158,159,165,167,172,173,191],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,159,162,163,165,166,167,173,174,176,177,178,182,183,185,186,187,188,189,190,191,192,195],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,128,134,136,141,142,143,146,147,148,150,151,155,159,166,170,171,172,173,187],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,129,132,134,136,137,139,140,141,142,143,145,146,148,150,151,154,158,159,161,162,164,165,166,167,169,170,171,172,173,191,192,193,194],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,132,140,146,150,151,155,157,158,165,171,173,175,177,179,180,182,193],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,127,128,129,132,133,134,137,138,139,140,141,142,143,147,148,151,152,154,155,156,158,159,162,165,166,167,168,170,171,172,173,177,178,180,183,186,187,191,193,194,195],WITH:[92,93,171,172],Will:[143,167,173],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,129,140,141,148,150,151,156,158,165,171,180,187,192],_0:151,_1:148,_2:148,_:[18,29,31,33,37,41,51,56,81,126,127,128,130,131,132,133,134,136,137,148,151,158,171,172,182,188,189,194],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__:92,__add__:92,__all__:171,__annotations__:[171,193],__builtins__:171,__cached__:171,__call__:[63,65,127,128,133],__class__:[37,171],__dict__:[189,190],__doc__:[171,193],__eq__:92,__file__:171,__finalize__:121,__future__:37,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,127,128,130,131,132,133,134,135,152,171,188,189,193],__iter__:[33,131],__len__:33,__loader__:171,__main__:[35,130,159],__mul__:92,__name__:[35,37,130,159,171],__operators__:134,__package__:171,__repr__:55,__spec__:171,__str__:92,__sub__:92,__truediv__:92,__version__:[41,130,158,193],_aspp:133,_attach:[118,180],_bin:54,_branch:133,_build_model:35,_bunch:[57,58],_caller:120,_consolidate_inplac:121,_constructor:121,_conv_block:133,_conv_bn_relu:133,_conv_relu:133,_data:121,_deeplabv3:133,_deprecate_mismatched_index:121,_deprecated_arg:121,_etag:[118,180],_fcn_16:133,_fcn_32:133,_fcn_8:133,_fuse_bn_tensor:132,_get_axi:121,_get_block_manager_axi:121,_get_list_axi:121,_get_slice_axi:121,_get_valu:121,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[77,78,148,158],_identity_block:133,_ilocindex:121,_index:56,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_j:[148,158],_k:130,_label:57,_left:120,_locationindex:121,_locindex:121,_m:130,_make_stag:132,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_method:179,_mgr:121,_novalu:179,_other:120,_pad_1x1_to_3x3_tensor:132,_recognized_scalar:121,_rid:[118,180],_right:120,_sec_1:93,_segnet:133,_self:[118,180],_sigmoid:[81,189],_skip:3,_slice:121,_static:[121,122],_subplot:74,_sum:179,_t:[118,180],_t_sne:186,_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_vendor:130,a0958ad901d7:118,a0:[120,179],a10:122,a1:[119,120,179],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:172,a23:[171,193],a2:[119,120,179],a3:120,a3z5kdkfn3tbq:59,a4:120,a5:120,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:82,a_dict:172,a_i:[82,147],a_list:172,a_n:150,aaaaaa:[156,184],aafter:157,aaron:[29,50,78,131],ab:[50,63,65,77,92,93,121,127,131,135,158,171,172,194],abadi:131,abbrevi:[124,128],abc:[93,121,122,172,179,195],abcd:[7,117,121,179],abcdef:121,abcmous:[112,176],abil:[43,52,54,68,76,77,80,108,129,139,150,156,159,165,171,173,184,191],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,122,129,137,140,142,145,151,154,157,159,162,165,166,167,168,170,176,186,189,190,193],abnorm:29,abnorml:66,abo:38,aboslut:157,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,120,122,124,125,129,130,133,135,137,138,139,140,141,142,144,145,146,147,150,151,152,154,155,157,158,159,161,163,164,165,166,168,169,170,171,172,173,174,176,177,180,188,191,193,195],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,120,121,122,124,127,128,129,130,131,132,133,135,137,139,140,141,142,145,146,147,148,150,154,155,157,158,162,165,166,167,168,169,170,171,172,173,178,187,188],above_cutoff:158,abracadabra:172,abraham:195,abs_vector:[172,194],absenc:[54,186],absent:121,absolut:[47,76,78,83,92,116,119,141,150,154,157,171,172,173,194],absolute_error:77,absolute_percentage_error:77,abund:[110,178],ac:158,academ:[112,115,138,176],academi:193,acc:[33,39,47,49,52,57,127,136,192],acc_and_loss:127,acceler:[100,110,111,141,166,178],acceleromet:116,accept:[16,40,57,68,80,83,100,103,106,112,119,121,122,132,141,151,155,165,171,177,191,192],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,121,139,142,143,159,166,171,172,176,177,179,187,193,194],accessor:122,accident:151,acclaim:155,accommod:[7,36,47,117,172],accompani:[116,141,166],accomplish:[88,141,151,165,186,191],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,121,132,139,142,145,147,150,155,159,163,165,166,167,168,170,186],accordingli:[41,55,127,145,171,193],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,143,147,151,166,167,176,179],accumul:[1,50,92,106,137,148,165,172,191],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,129,133,135,139,142,147,154,157,162,165,166,167,177],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,133,136,141,142,143,146,147,148,150,151,153,154,156,158,159,163,164,166,167,174,176,182,186],accuracy_metr:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,150,155,159,163,164,167,186,189,190],achiev:[32,33,40,48,50,54,56,59,103,106,119,130,132,133,139,140,141,142,143,150,151,152,153,155,156,157,158,171],achitectur:131,aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,176],acoust:[144,145,146],acquir:[6,103,106,141,177],acquisit:[3,102,106,114,137,174,176],acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,120,122,124,127,137,139,140,141,151,155,158,171,172,174,176],act:[3,14,22,24,35,53,62,93,104,112,119,122,127,130,135,165,166,179,191],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,140,142,159,163,165,171,172,176,191],action_prob:130,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,131,132,133,134,135,136,142,151,157,158,162,176,181,182,187,192],activateion:134,activespac:180,actor:171,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,121,122,127,128,129,137,139,145,151,153,155,157,158,162,165,166,167,171,173,175,177,181,188,191],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:150,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,127,128,132,138,140,141,150,151,152,154,156,165,166,172,188,194],ada:164,adaboost:[151,164],adaboost_clf:49,adaboostclassifi:[49,56,163,164],adagradoptim:141,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,128,131,133,134,182],adamax:36,adamharlei:[127,181],adamoptim:[127,141],adapt:[47,58,62,99,112,132,137,141,151,176,192],adaptiveaveragepooling2d:132,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,127,128,132,133,134,135,140,150,151,152,154,155,156,157,159,164,165,166,167,168,170,171,172,173,180,182,184,187,188,191,192,194],add_1:134,add_:31,add_artist:[110,178],add_legend:145,add_selectbox:187,add_slid:187,add_subplot:[35,37,47,75,130],add_trick:171,add_weight:132,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,130,132,133,134,135,137,141,143,147,151,152,154,155,156,158,166,172,173,179,180,194,195],addition:[31,117,119,124,140,143,145,147,151,156,180,184],additon:32,address:[75,76,90,103,104,106,112,137,139,140,143,147,154,165,171,176,177],adel:150,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,141],adher:[48,106,140],adjac:171,adject:[172,194],adjunct:165,adjust:[29,33,36,45,55,57,58,75,132,140,141,150,151,156,164],admin2:142,admin:180,administr:[143,173],admonit:[120,121,122],adobe_premier:38,adolesc:171,adopt:[59,85,102,112,140,141,147,151,174,176,195],adult:[171,193],advanc:[35,43,66,102,112,116,122,123,125,138,140,141,143,147,165,172,180,183],advantag:[40,50,54,68,80,122,132,140,143,150,153,154,156,172],advent:[49,129,140],advers:28,adversari:[36,143,183],advertis:104,advic:141,advis:[7,46,53,58,61,117,150],advoc:112,ae:[31,128],aebf:[118,180],aeroplan:7,aerospik:180,aesthet:22,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,130,132,141,148,154,156,157,171,174,176,184],affer:136,affin:[82,145],affinity_matrix_:158,afford:[7,74,117,165],african:[112,143,176],afro:[145,146],afropop:[145,146],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,130,132,133,134,136,137,140,141,142,145,146,148,150,151,154,155,159,164,165,166,168,171,172,173,187,189,190,192,193,194],afterward:[32,119],ag:[9,18,22,50,51,74,88,92,93,100,101,114,116,118,119,120,140,145,147,148,152,155,162,165,169,170,171,172,173,179,180,189,190,191,193,194,195],again:[7,14,17,40,41,47,49,50,51,52,53,57,58,59,68,80,82,117,122,128,146,151,155,157,167,168,171,172,173,186,187],against:[0,18,41,47,50,59,77,78,104,112,114,116,120,121,137,141,143,151,157,159,170,185],agaricu:110,age_distribut:24,age_median_imput:22,age_sal_tre:50,age_tre:50,agefil:22,agenc:[104,165],agenda:[102,174],agent:[112,165,191],ageron:158,agg:[18,38,158],aggfunc:120,agglom:145,agglomerativeclust:158,aggreg:[7,14,49,106,111,127,147,150,155],agil:[139,140],agnost:140,ago:[129,151],agre:[22,45,47,48,112],agricultur:[102,111,165,168,174,191],ahead:[49,52,57,104,137],ahnjovq9nfghs6fj4piqib3brpgnscyflm6riahdtaeyfclwo1cf:59,ai:[12,18,25,100,101,102,108,112,114,120,121,122,130,139,140,142,143,159,165,170,175,176,181,191,193],aid:[54,62,78,145,166],aim:[54,75,76,104,130,132,135,148,165,185],air:113,airbu:29,airflow:140,airlin:7,airplan:127,airport:[102,124,174,180],aka:[36,140],akkio:175,al:[31,68,80,111,112,143,178],alabama:142,alacazam:172,albeit:[45,167],albifron:178,album:145,alcohol:[48,101],alekseynp:158,alert:139,alex:[33,128],alexa:141,alexand:[125,127],alexandru:66,alexei:59,alexi:152,alexnet:132,alfredo:170,alg:56,algebra:[42,51,54,59,84,119,192],algo:151,algorithm:[3,31,41,49,51,52,53,55,56,57,58,59,60,61,71,75,76,82,83,84,90,92,99,100,101,102,112,119,127,128,129,132,135,137,138,139,140,141,143,146,147,148,152,153,154,156,157,162,163,165,168,171,174,176,184,185,186,188,189,190,191],algoritm:151,algorythm:83,alia:[119,186],alic:[172,179],align:[22,77,112,119,127,132,133,135,137,145,146,148,150,158,159,162,166,167,186],alik:[0,143,150],all:[0,1,3,6,7,8,11,12,14,16,18,19,22,25,26,27,29,31,32,33,34,36,37,38,39,40,41,43,46,48,49,50,51,52,54,56,57,58,59,60,62,64,66,68,76,80,82,84,89,92,93,100,101,102,103,104,106,107,108,110,112,114,116,117,118,119,120,122,128,129,130,131,132,133,134,136,139,140,141,142,145,146,147,148,150,151,152,153,154,155,156,157,158,159,162,163,165,167,168,170,171,172,173,174,176,177,179,184,185,186,188,191,192,193,194,195],all_attr:31,all_clfs_acc:49,all_data:66,all_nod:1,all_photo:31,allbeit:82,allclos:82,allegrograph:180,allei:[54,66],allevi:[49,52,54],allianc:106,alloc:[29,40,50,112,182,186],allow:[1,3,14,18,48,50,54,59,75,76,77,99,100,101,103,111,112,113,114,116,118,119,120,121,122,124,126,130,132,133,139,140,141,145,151,154,155,156,162,163,166,170,171,172,173,186,187,193,194,195],allowfullscreen:[116,149,162,168],allpub:66,allud:50,almeida:48,almond:[110,162,163,178],almost:[7,31,36,40,43,50,57,62,102,104,117,124,150,151,165,166,171,172,187,191],alon:106,along:[1,7,33,36,40,48,51,54,59,68,75,76,80,103,104,109,117,118,119,120,121,122,136,140,142,145,150,163,164,165,166,171,178,191],alongsid:[71,109,141],alot:[54,129],alpha:[36,55,66,75,77,83,109,128,131,147,150,151,155,156,157,158,172,178,184,186,189,190,194],alpha_:128,alpha_t:[128,151],alpha_t_bar:128,alpha_tb_t:151,alphabet:[113,118,159],alphago:[129,165],alphas_cumprod:128,alphas_cumprod_prev:128,alphas_t:128,alq:54,alreadi:[40,43,49,50,52,54,60,63,65,68,78,80,82,93,100,106,114,121,122,128,133,136,141,147,151,158,168,170,171,173,177,179],alright:[36,82],also:[0,1,3,7,14,16,18,20,23,28,29,30,31,32,33,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in:[33,36,40,48,49,50,52,53,54,56,59,61,62,68,74,75,80,81,82,84,99,102,106,108,113,127,129,132,133,137,139,140,141,142,143,147,150,151,153,154,155,156,164,165,166,170,171,172,177,188,189,194],combined_imag:36,come:[7,35,43,48,51,57,62,64,66,68,77,80,98,103,104,106,108,109,112,113,114,116,119,120,129,132,137,138,142,145,147,150,152,155,156,157,165,166,171,172,173,179,184,186,187],comedi:173,comfort:[7,46,54,117,170],comma:[93,119,164,172,194],command:[47,51,101,112,113,124,130,154,170,172,173,180,194],comment:[45,48,50,104,112,113,127,170,176,193],commerc:145,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,134,135,137,139,140,141,142,145,146,151,154,156,157,158,162,168,169,170,171,173,175,179,194],common_el:172,common_norm:[109,178],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,129,139,140,141,142,155,157,165,171,192],commun:[28,43,103,105,106,112,114,129,138,140,142,151,165,173,182],comp:136,compact:127,compani:[6,104,113,139,143,151,165,172,173],company_s:56,company_typ:56,companyx:172,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,128,132,141,142,143,147,148,150,155,163,165,167,172,191,194],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,141,147,151,156,163,171],compat:[15,55,101,119,121,127,130,134,136,138],compatible_format:193,compel:78,compens:[155,158],compet:151,competit:[129,137,143,151,154,155],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,133,140,173,179,182],compilaton:40,complaint:[104,112,176],compleletli:[68,80],complementari:137,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,128,132,135,136,137,139,141,143,155,157,158,165,166,167,168,171,172,173,175,186,193,194],complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,129,132,135,137,138,139,140,141,143,147,153,156,157,158,162,165,171,173,181,182,184,185,192,195],complex_numb:171,complex_number_1:[172,194],complex_number_2:[172,194],complex_number__1:194,complexnumb:171,complexnumberwithconstructor:171,compli:112,complianc:[22,45,47,48,112,176],compliant:112,complic:[36,50,82,114,119,139,140,141,151,154,157,158,168,182],compon:[75,76,101,102,108,118,126,130,137,139,140,141,142,147,151,154,156,165,173,174],components_:[158,186],compos:[36,37,61,74,82,126,139,140,151,188],compose_greet_func:171,compose_greet_func_with_closur:171,composit:[119,150],compound:[172,179,194],compound_stmt:171,comprehend:49,comprehens:[93,108,122,141,166],compress:[29,30,31,106,126,129,132],compris:[39,101,140],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,127,129,131,132,135,136,138,139,140,141,143,147,148,150,151,155,156,157,162,165,167,170,172,174,175,176,187,188,189,191,194,195],computation:[33,36,50,56,119,121,129,132,137,153],computationn:33,compute_reciproc:179,compute_target:[9,100],con:[7,47,56,101,112,159],concat:[22,30,36,38,42,54,56,66,128,132,133,134,135,137,162,171,172],concaten:[34,38,55,76,119,120,128,133,135,168,172,182,188,189,190,194],concatenated_str:171,concav:128,conceiv:[142,171],concentr:145,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,132,138,139,140,141,142,143,148,151,155,156,157,163,167,170,172,181,194],conceptu:151,concern:[7,47,54,58,59,74,106,109,112,117,139,140,143,151,165,166,178,191],concis:[119,147,165,171,172,194],conclud:[56,59,75,102,108,116,141,148],conclus:[24,50,103,112,114,166],concret:[143,165,186],concurr:[84,100,101],conda:0,condens:130,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,128,132,141,148,150,151,166,167,168,171,172,173,193,194],condition2:54,condorcet:147,conduct:[56,100,112,176],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,132,141,142,145,147,151],config:[9,38,50,66,134,137,147,150,186,187],configur:[10,41,45,47,99,101,136,139,140,141,165,168,169,171],confirm:[14,30,45,47,48,59,103,106,112,158,166,176,177],conflict:[93,104,112,122],conform:[113,122,139,141],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,148,150,155,157,162,171],confusingli:158,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,163,164,167,189,190],confusion_mtx:34,congratul:[100,101,166,167,170,173],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,130,132,133,136,141,157,171,172,182],connectionist:78,connecttimeouterror:130,conquer:151,consciou:7,consecut:[14,32,40,49,155],consent:[112,176],consequ:[28,102,112,130,176],conserv:[109,178],conservationstatu:[109,178],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,130,132,134,137,140,141,142,143,145,147,148,150,151,152,155,156,157,158,159,165,167,170,171,172,184,186,193,194],consider:[5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class=\"section-number\">37. </span>Self-paced assignments","<span class=\"section-number\">37.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">37.27. </span>Analyzing data","<span class=\"section-number\">37.9. </span>Analyzing text about Data Science","<span class=\"section-number\">37.13. </span>Apply your skills","<span class=\"section-number\">37.16. </span>Build your own custom vis","<span class=\"section-number\">37.17. </span>Classifying datasets","<span class=\"section-number\">37.26. </span>Data preparation","<span class=\"section-number\">37.24. </span>Data processing in Python","<span class=\"section-number\">37.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">37.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">37.10. </span>Data Science scenarios","<span class=\"section-number\">37.20. </span>Displaying airport data","<span class=\"section-number\">37.15. </span>Dive into the beehive","<span class=\"section-number\">37.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">37.25. </span>Evaluating data from a form","<span class=\"section-number\">37.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">37.37. </span>Exploring for answers","<span class=\"section-number\">37.19. </span>Introduction to probability and statistics","<span class=\"section-number\">37.12. </span>Lines, scatters and bars","<span class=\"section-number\">37.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">37.38. </span>Market research","<span class=\"section-number\">37.29. </span>Matplotlib applied","<span class=\"section-number\">37.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">37.18. </span>Small diabetes study","<span class=\"section-number\">37.21. </span>Soda profits","<span class=\"section-number\">37.35. </span>Tell a story","<span class=\"section-number\">37.14. </span>Try it in Excel","<span class=\"section-number\">37.11. </span>Write a data ethics case study","<span class=\"section-number\">37.101. </span>Intro to Autoencoders","<span class=\"section-number\">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">37.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">37.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">37.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">37.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">37.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">37.113. </span>Art by gan","<span class=\"section-number\">37.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">37.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">37.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">37.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">37.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">37.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">37.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">37.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">37.82. </span>Data engineering","<span class=\"section-number\">37.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">37.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">37.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">37.76. </span>Decision trees","<span class=\"section-number\">37.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">37.74. </span>Random forests for classification","<span class=\"section-number\">37.73. </span>Random forests intro and regression","<span class=\"section-number\">37.79. </span>Boosting with tuning","<span class=\"section-number\">37.78. </span>Gradient boosting","<span class=\"section-number\">37.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">37.67. </span>Decision Trees - Classification","<span class=\"section-number\">37.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">37.63. </span>Kernel method assignment 1","<span class=\"section-number\">37.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">37.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">37.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">37.68. </span>Model selection assignment 1","<span class=\"section-number\">37.72. </span>Regularized Linear Models","<span class=\"section-number\">37.88. </span>Build Classification Model","<span class=\"section-number\">37.87. </span>Build classification models","<span class=\"section-number\">37.57. </span>Create a regression model","<span class=\"section-number\">37.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">37.62. </span>Explore classification methods","<span class=\"section-number\">37.55. </span>Exploring visualizations","<span class=\"section-number\">37.58. </span>Linear and polynomial regression","<span class=\"section-number\">37.45. </span>Linear regression - California Housing","<span class=\"section-number\">37.48. </span>Gradient descent","<span class=\"section-number\">37.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">37.46. </span>Linear Regression Metrics","<span class=\"section-number\">37.47. </span>Loss Function","<span class=\"section-number\">37.54. </span>Managing data","<span class=\"section-number\">37.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">37.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">37.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">37.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">37.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">37.89. </span>Parameter play","<span class=\"section-number\">37.60. </span>Pumpkin varieties and color","<span class=\"section-number\">37.53. </span>Regression tools","<span class=\"section-number\">37.44. </span>Regression with Scikit-learn","<span class=\"section-number\">37.59. </span>Retrying some regression","<span class=\"section-number\">37.86. </span>Study the solvers","<span class=\"section-number\">37.56. </span>Try a different model","<span class=\"section-number\">37.8. </span>Python programming advanced","<span class=\"section-number\">37.7. </span>Python programming basics","<span class=\"section-number\">37.6. </span>Python programming introduction","<span class=\"section-number\">37.5. </span>Project Plan\u200b Template","<span class=\"section-number\">37.3. </span>First assignment","<span class=\"section-number\">37.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">5.4.2. </span>Data Selection","<span class=\"section-number\">5.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">24. </span>Autoencoder","<span class=\"section-number\">21. </span>Convolutional Neural Networks","<span class=\"section-number\">30. </span>Diffusion Model","<span class=\"section-number\">20. </span>Intro to Deep Learning","<span class=\"section-number\">27. </span>Deep Q-learning","<span class=\"section-number\">22. </span>Generative adversarial networks","<span class=\"section-number\">28. </span>Image classification","<span class=\"section-number\">29. </span>Image segmentation","<span class=\"section-number\">25. </span>Long-short term memory","<span class=\"section-number\">31. </span>Object detection","<span class=\"section-number\">23. </span>Recurrent Neural Networks","<span class=\"section-number\">26. </span>Time series","Learn AI together, for free","<span class=\"section-number\">34. </span>Data engineering","<span class=\"section-number\">36. </span>Model deployment","<span class=\"section-number\">35. </span>Model training &amp; evaluation","<span class=\"section-number\">32. </span>Overview","<span class=\"section-number\">33. </span>Problem framing","<span class=\"section-number\">14. </span>Clustering models for Machine Learning","<span class=\"section-number\">14.1. </span>Introduction to clustering","<span class=\"section-number\">14.2. </span>K-Means clustering","<span class=\"section-number\">15.1. </span>Bagging","<span class=\"section-number\">15.3. </span>Feature importance","<span class=\"section-number\">15. </span>Getting started with ensemble learning","<span class=\"section-number\">15.2. </span>Random forest","<span class=\"section-number\">16.1. </span>Gradient Boosting","<span class=\"section-number\">16.2. </span>Gradient boosting example","<span class=\"section-number\">16. </span>Introduction to Gradient Boosting","<span class=\"section-number\">16.3. </span>XGBoost","<span class=\"section-number\">16.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">18. </span>Kernel method","<span class=\"section-number\">19. </span>Model selection","<span class=\"section-number\">17. </span>Unsupervised learning","<span class=\"section-number\">12. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">13.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">13. </span>Getting started with classification","<span class=\"section-number\">13.1. </span>Introduction to classification","<span class=\"section-number\">13.2. </span>More classifiers","<span class=\"section-number\">13.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11.3. </span>Linear and polynomial regression","<span class=\"section-number\">11.4. </span>Logistic regression","<span class=\"section-number\">11.2. </span>Managing data","<span class=\"section-number\">11. </span>Regression models for Machine Learning","<span class=\"section-number\">11.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">38.10. </span>Data Science in real world","<span class=\"section-number\">38.9. </span>Data Science in the cloud","<span class=\"section-number\">38.4. </span>Data Science introduction","<span class=\"section-number\">38.8. </span>Data Science lifecycle","<span class=\"section-number\">38.7. </span>Data visualization","<span class=\"section-number\">38.6. </span>NumPy and Pandas","<span class=\"section-number\">38.5. </span>Relational vs. non-relational database","<span class=\"section-number\">38.20. </span>Convolutional Neural Network","<span class=\"section-number\">38.21. </span>Generative Adversarial Network","<span class=\"section-number\">38. </span>Slides","<span class=\"section-number\">38.18. </span>Kernel method","<span class=\"section-number\">38.19. </span>Model Selection","<span class=\"section-number\">38.17. </span>Unsupervised learning","<span class=\"section-number\">38.16. </span>Build an machine learning web application","<span class=\"section-number\">38.12. </span>Linear Regression","<span class=\"section-number\">38.13. </span>Logistic Regression","<span class=\"section-number\">38.14. </span>Logistic Regression","<span class=\"section-number\">38.11. </span>Machine Learning overview","<span class=\"section-number\">38.15. </span>Neural Network","<span class=\"section-number\">38.3. </span>Python programming advanced","<span class=\"section-number\">38.2. </span>Python programming basics","<span class=\"section-number\">38.1. </span>Python programming introduction"],titleterms:{"0":59,"1":[3,24,30,32,43,49,50,52,53,54,56,57,58,59,60,61,65,68,74,75,80,82,83,104,113,119,132,174,175,176,177,178,179,180,181,182,184,188,190,194,195],"10":[40,56,127,132],"100":[51,59,132],"1000":[59,132],"11":56,"12":56,"13":56,"15":39,"19":[1,8,14],"1d":119,"2":[3,24,30,32,43,44,49,50,51,52,53,54,56,57,58,60,61,68,75,80,81,84,104,113,119,174,175,176,177,178,179,180,181,182,184,188,190,194,195],"2d":[30,119,184],"3":[3,24,32,39,43,49,50,52,53,54,56,57,58,60,61,68,74,80,104,119,174,175,176,177,178,179,181,182,188,194,195],"3d":[30,83,108,184],"4":[3,24,32,43,49,50,51,52,53,54,56,57,58,60,61,68,80,104,174,175,176,177,178,179,180,194,195],"5":[24,32,43,49,50,53,54,56,57,58,60,61,68,80,83,104,132,174,175,176,177,178,180,194,195],"50":56,"500":56,"6":[43,50,52,53,54,56,57,58,60,61,68,80,174,175,176,177,178],"7":[43,50,52,53,56,57,58,60,61,174,177,178],"8":[52,56,177],"9":56,"boolean":[119,172,173,194,195],"break":[92,171,193],"case":[28,45,47,48,50,104,112,168,184],"class":[35,39,47,50,51,63,65,92,171,193],"default":[51,59,171],"do":[47,114,167,168,172,176,184],"final":[49,74,75],"float":[7,172,194],"function":[43,51,59,75,76,78,82,92,93,119,129,130,151,171,173,179,189,190,192,193],"import":[9,29,33,37,41,44,49,51,52,53,54,56,57,58,59,60,61,64,66,83,117,128,141,148,155,170,171,188,189,190,192],"long":134,"new":[56,94,119,122,189,190],"null":[7,59,179],"public":37,"return":[94,130,171],"short":134,"true":59,"try":[0,27,48,68,75,80,91,171,193],"while":[92,171,193],A:[31,129,157,163,164,166],And:165,At:46,But:168,By:151,For:92,Is:143,It:[124,167,180],NOT:167,Not:165,On:35,One:[74,75,192],That:189,The:[9,36,43,51,53,75,77,82,100,101,117,124,157,158,164,168,170,171,172,175,179,180,181,184,193,194],There:168,To:[64,157],With:[34,137],about:[3,33,111,162,167,178],absolut:77,acceler:158,access:[93,119,122],accuraci:[32,41,47,59,132,155,192],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,92,93,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,127,128,130,131,132,133,134,135,136,137,138,141,145,146,147,148,150,151,152,154,155,156,159,162,163,164,165,166,167,168,170,171,172,173,181,184,186,187,188,189,192],ackowledg:182,action:130,activ:129,actual:104,ad:[30,31,47,62,66,119,157],adaboost:49,adam:192,add:[48,119,141],addit:122,advanc:[32,92,119,120,171,193],adversari:[37,131,182],after:[30,157,185],ag:24,against:48,agent:[35,130],agglom:158,aggreg:[119,120,176,179],ahead:82,ai:[36,41,138,186],airport:12,aka:64,algorithm:[50,54,130,142,145,150,151,155,158],align:122,all:[24,47,74,111,124,166,178,180],an:[9,31,64,66,100,101,104,118,119,159,181,187],anagram:172,analysi:[8,38,49,51,52,53,54,56,57,58,59,60,61,74,77,99,103,165,186,191],analyz:[1,2,3,18,103,119,168,177],anchor:[39,51],anim:108,ann:44,annot:[171,193],anomal:47,anomali:29,anoth:37,answer:17,api:[34,43],app:[159,160],append:[93,179],appli:[4,22,47,112,160,163,164,176],applic:[50,126,165,187,191],approach:[51,104,124,163,180],ar:172,arbitrari:171,architectur:[32,127,129,181,192],argument:[171,193],arithmet:[122,172,194],arrai:[119,122,179],art:36,artifici:[165,191],artwork:36,ascend:[93,94],asian:70,ask:168,assert:51,assign:[0,12,25,39,44,59,65,80,81,82,83,84,96,97,119,120,122,190],assist:133,attribut:[59,119,122,179],auc:[56,59],audienc:104,augment:[32,34,128],author:170,autoencod:[29,30,31,126],automl:[9,100,101,141],avail:119,averag:[92,168],avoid:[108,157],axi:119,azur:[9,10,20,100,101,118],b:75,babylonian:93,background:128,backprop:82,bag:[49,147,150,155],balanc:162,bar:[19,22,178],base:[30,92,121,155],basebal:18,baselin:[47,48,165,191],basi:59,basic:[29,32,41,43,93,112,119,122,130,172,173,176,179,194,195],batch:[33,62,158],beehiv:13,begin:104,behind:55,best:[9,32,37,100,143],beta:128,better:[163,167],between:[24,54,56,68,74,80,119,150,176,179],beyond:[40,49,156,184],bi:54,bia:[64,150,157,185],bibliographi:25,big:192,binai:54,binari:[40,167],binder:0,bird:[109,178],bit:[31,119],bitcoin:38,blend:54,bmi:24,boost:[49,54,55,56,151,152,153,155],bootstrap:147,bound:128,boundari:[156,158,184],boxplot:[24,83],bp:24,brain:181,broadcast:[119,179],bug:48,build:[5,29,31,32,36,41,50,51,67,68,80,108,146,159,160,166,167,168,178,187],c:59,cach:187,calcul:[47,92,93,172],california:74,call:193,callabl:121,callback:40,can:[50,56,114,176],candid:56,capac:157,captur:[106,177],cardin:51,cast:[172,194],catalog:139,catboost:54,categor:[7,51,54,57,68,74,80,166,188],categori:93,categorical_crossentropi:192,caus:45,central:[18,116],centroid:158,chain:122,challeng:[1,14,22,45,112,119,129,132,135,176],chang:[34,54],changin:34,channel:104,chart:[108,111,178,187],check:[30,47,48,51,53,57,59,68,74,80,141,155,168,172,195],checkbox:187,checklist:112,choic:[122,143],choos:[32,50,74,99,101,108,163,175],churn:50,cifar:[127,132],citi:[56,124,180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1,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,128,129,130,132,134,136,139,140,141,143,145,147,150,151,152,154,156,157,158,159,160,163,165,166,168,171,172,173,175,176,177,178,185,191,193,196,198,199,200],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,129,130,131,132,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,157,158,159,160,162,163,164,165,166,168,169,171,172,173,174,175,176,177,178,179,182,184,185,186,191,193,194,195,196,197,199,200],Is:[50,93,97,99,103,106,111,112,113,128,140,141,145,160,166,172,173,200],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,139,140,141,142,143,144,145,146,147,148,150,151,152,153,154,155,156,157,158,159,160,163,164,165,166,168,169,171,173,175,176,177,178,180,181,187,191,192,193,194,196,197,199],Its:[129,150,155,172],NEAR:[61,74],NO:[92,93,176,177],NOT:[82,92,93,119,131,176,177],Near:[109,183],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,130,147,156,158,164,168,176,177,180,183],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,134,151,164,171,172,177,192,199],OF:[22,45,47,48,92,93,131,176,177],ON:185,ONE:7,OR:[22,45,47,48,92,93,119,176,177],Of:[50,101,102,104,114,158,178,179],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,141,147,150,151,154,155,156,158,159,160,166,171,175,176,182,192],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,129,135,141,146,148,150,156,159,166,171,173,176,177,178,182,183,184,189,191,196,199],Or:[32,40,50,58,74,102,104,119,129,130,141,145,148,166,176,177,192,196,199],Such:[1,7,30,40,43,49,50,54,116,141,142,171,176,198],THAT:82,THE:[92,93,176,177],TO:[54,92,93,136,176,177],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,130,146,148,151,152,158,160,166,168,172,177,178,196],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,159,160,163,164,166,167,168,169,171,172,178,179,181,182,183,187,188,190,191,192,193,194,195,196,197,200],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,128,134,136,141,142,143,146,147,148,150,151,155,159,160,171,175,176,177,178,192],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,129,132,134,136,137,139,140,141,142,143,145,146,148,150,151,154,158,159,160,162,163,165,166,168,171,172,174,175,176,177,178,196,197,198,199],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,132,140,146,150,151,155,157,158,159,176,178,180,182,184,185,187,198],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,127,128,129,132,133,134,137,138,139,140,141,142,143,147,148,151,152,154,155,156,158,159,160,163,166,168,171,172,173,175,176,177,178,182,183,185,188,191,192,196,198,199,200],WITH:[92,93,176,177],Will:[143,172,178],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,129,140,141,148,150,151,156,158,159,166,169,176,185,192,197],_0:151,_1:148,_2:148,_:[18,29,31,33,37,41,51,56,81,126,127,128,130,131,132,133,134,136,137,148,151,158,168,176,177,187,193,194,199],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__:92,__add__:92,__all__:176,__annotations__:[176,198],__builtins__:176,__cached__:176,__call__:[63,65,127,128,133],__class__:[37,176],__dict__:[168,194,195],__doc__:[176,198],__eq__:92,__file__:176,__finalize__:121,__future__:37,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,127,128,130,131,132,133,134,135,152,168,176,193,194,198],__iter__:[33,131],__len__:33,__loader__:176,__main__:[35,130,156,159,160],__mul__:92,__name__:[35,37,130,159,160,176],__operators__:134,__package__:176,__repr__:55,__spec__:176,__str__:92,__sub__:92,__truediv__:92,__version__:[41,130,158,198],_aspp:133,_attach:[118,185],_bin:54,_branch:133,_build_model:3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114,116,117,118,119,121,122,127,128,129,137,139,145,151,153,155,157,158,163,166,169,171,172,176,178,180,182,186,193,196],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:150,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,127,128,132,138,140,141,150,151,152,154,156,159,166,168,171,177,193,199],ada:165,adaboost:[151,165],adaboost_clf:49,adaboostclassifi:[49,56,164,165],adagradoptim:141,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,128,131,133,134,187],adamax:36,adamharlei:[127,186],adamoptim:[127,141],adapt:[47,58,62,99,112,132,137,141,151,169,181,197],adaptiveaveragepooling2d:132,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,127,128,132,133,134,135,140,150,151,152,154,155,156,157,160,165,166,171,172,173,175,176,177,178,185,187,189,192,193,196,197,199],add_1:134,add_:31,add_artist:[110,183],add_ax:159,add_legend:145,add_selectbox:192,add_slid:192,add_subplot:[35,37,47,75,130],add_trick:176,add_weight:132,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,130,132,133,134,135,137,141,143,147,151,152,154,155,156,158,168,171,177,178,184,185,199,200],addition:[31,117,119,124,140,143,145,147,151,156,185,189],additon:32,address:[75,76,90,103,104,106,112,137,139,140,143,147,154,166,176,181,182],adel:150,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,141],adher:[48,106,140],adjac:[159,176],adject:[177,199],adjunct:166,adjust:[29,33,36,45,55,57,58,75,132,140,141,150,151,156,159,165],adjusted_mutual_info_scor:159,adjusted_rand_scor:159,admin2:142,admin:185,administr:[143,178],admonit:[120,121,122],adobe_premier:38,adolesc:176,adopt:[59,85,102,112,140,141,147,151,179,181,200],adult:[176,198],advanc:[35,43,66,102,112,116,122,123,125,138,140,141,143,147,159,166,177,185,188],advantag:[40,50,54,68,80,122,132,140,143,150,153,154,156,177],advent:[49,129,140],advers:28,adversari:[36,143,188],advertis:104,advic:141,advis:[7,46,53,58,61,117,150],advoc:112,ae5:159,ae:[31,128],aebf:[118,185],aeroplan:7,aerospik:185,aesthet:22,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,130,132,141,148,154,156,157,168,176,179,181,189],affer:136,affin:[82,145,159],affinity_matrix_:158,affinitypropag:159,afford:[7,74,117,166],african:[112,143,181],afro:[145,146],afropop:[145,146],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,130,132,133,134,136,137,140,141,142,145,146,148,150,151,154,155,159,160,165,166,171,173,176,177,178,192,194,195,197,198,199],afterward:[32,119],ag:[9,18,22,50,51,74,88,92,93,100,101,114,116,118,119,120,140,145,147,148,152,155,163,166,174,175,176,177,178,184,185,194,195,196,198,199,200],again:[7,14,17,40,41,47,49,50,51,52,53,57,58,59,68,80,82,117,122,128,146,151,155,157,159,172,173,176,177,178,191,192],against:[0,18,41,47,50,59,77,78,104,112,114,116,120,121,137,141,143,151,157,160,175,190],agaricu:110,age_distribut:24,age_median_imput:22,age_sal_tre:50,age_tre:50,agefil:22,agenc:104,agenda:[102,179],agent:[112,166,196],ageron:158,agg:[18,38,158],aggfunc:120,agglom:145,agglomerativeclust:[158,159],aggreg:[7,14,49,106,111,127,147,150,155],agil:[139,140],agnost:140,ago:[129,151],agre:[22,45,47,48,112],agricultur:[102,111,173,179,196],ahead:[49,52,57,104,137],ahnjovq9nfghs6fj4piqib3brpgnscyflm6riahdtaeyfclwo1cf:59,ai:[12,18,25,100,101,102,108,112,114,120,121,122,130,139,140,142,143,160,166,175,180,181,186,196,198],aid:[54,62,78,145,168,171],aim:[54,75,76,104,130,132,135,148,159,166,169,190],air:113,airbu:29,airflow:140,airlin:7,airplan:127,airport:[102,124,179,185],aka:[36,140],akkio:180,al:[31,68,80,111,112,143,183],alabama:142,alacazam:177,albeit:[45,172],albifron:183,album:145,alcohol:[48,101],alekseynp:158,alert:139,alex:[33,128],alexa:141,alexand:[125,127],alexandru:66,alexei:59,alexi:152,alexnet:132,alfredo:175,alg:56,algebra:[42,51,54,59,84,119,197],algo:[151,159],algorithm:[3,31,41,49,51,52,53,55,56,57,58,59,60,61,71,75,76,82,83,84,90,92,99,100,101,102,112,119,127,128,129,132,135,137,138,139,140,141,143,146,147,148,152,153,154,156,157,159,163,164,166,168,173,176,179,181,189,190,191,193,194,195,196],algoritm:151,algorythm:83,alia:[119,159,191],alic:[177,184],align:[22,77,112,119,127,132,133,135,137,145,146,148,150,156,158,159,160,163,168,169,171,172,191],alik:[0,143,150],all:[0,1,3,6,7,8,11,12,14,16,18,19,22,25,26,27,29,31,32,33,34,36,37,38,39,40,41,43,46,48,49,50,51,52,54,56,57,58,59,60,62,64,66,68,76,80,82,84,89,92,93,100,101,102,103,104,106,107,108,110,112,114,116,117,118,119,120,122,128,129,130,131,132,133,134,136,139,140,141,142,145,146,147,148,150,151,152,153,154,155,156,157,158,159,160,163,164,166,168,169,172,173,175,176,177,178,179,181,182,184,189,190,191,193,196,197,198,199,200],all_attr:31,all_clfs_acc:49,all_data:66,all_nod:1,all_photo:31,allbeit:82,allclos:82,allegrograph:185,allei:[54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:35,cognit:[1,99,119,181],coher:[26,146,185],coin:[166,196],coinbas:38,coincid:[108,128],col1:119,col2:119,col:[38,44,45,51,54,56,59,110,111,122,150,183,184,192],col_nam:[51,54,59],col_vector:119,col_wrap:[111,183],cola:25,colab:[40,43,45,47,48,129],cold:[106,182],colder:139,coll:[118,185],collabor:[102,112,140,142],collaps:111,collect:[3,6,11,31,33,35,41,49,50,52,58,76,88,99,100,102,104,106,108,112,113,114,118,119,122,124,134,136,139,141,142,147,155,156,171,175,176,177,178,179,181,182,185,197,198,199,200],collector:39,collinear:66,colnam:122,colon:176,coloni:[13,111,183],color:[1,14,18,22,29,30,33,34,38,39,41,42,49,50,51,52,54,56,68,76,77,80,83,104,109,110,111,112,113,116,119,132,133,137,145,146,148,150,156,158,159,160,171,172,173,175,177,181,183,189,191,193,194,195,199],color_palett:137,color_threshold:159,colorbar:[41,159,191],colorblind:111,colorjitt:37,colormap:158,colour:132,colsample_bylevel:[66,154,155],colsample_bynod:[66,154,155],colsample_bytre:[66,154,155],colum:54,column1:14,column2:14,column:[1,6,14,17,18,22,24,29,30,31,38,39,40,43,44,45,46,47,48,52,53,55,56,57,58,59,60,64,66,68,74,76,80,83,100,101,103,110,111,113,117,118,119,120,121,124,129,137,142,145,146,150,152,155,159,160,163,164,168,171,172,173,177,183,184,185,193,199],column_diff:14,column_filt:14,column_index:122,column_nam:[14,22,24],column_name_to_diff:14,column_or_1d:57,column_to_diff:14,column_to_format:46,column_to_format_uniqu:46,column_valu:[14,22,24],column_value_fil:22,column_value_map:22,columnar:[118,185],columns_to_plot:24,columntransform:[61,74,193],com:[3,12,14,18,25,29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,47,48,51,56,66,68,80,101,104,108,109,110,111,112,114,116,120,121,122,127,131,132,133,135,137,142,143,145,146,147,148,149,150,152,154,155,158,159,160,163,164,165,166,167,168,169,171,172,173,176,177,180,181,183,185,186,187],comapani:56,combin:[33,36,40,48,49,50,52,53,54,56,59,61,62,68,74,75,80,81,82,84,99,102,106,108,113,127,129,132,133,137,139,140,141,142,143,147,150,151,153,154,155,156,159,165,166,168,171,175,176,177,182,193,194,199],combined_imag:36,come:[7,35,43,48,51,57,62,64,66,68,77,80,98,103,104,106,108,109,112,113,114,116,119,120,129,132,137,138,142,145,147,150,152,155,157,159,166,168,171,176,177,178,184,189,191,192],comedi:178,comfort:[7,46,54,117,175],comma:[93,119,165,177,199],command:[47,51,101,112,113,124,130,154,175,177,178,185,199],comment:[45,48,50,104,112,113,127,175,181,198],commerc:145,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,134,135,137,139,140,141,142,145,146,151,154,156,157,158,159,163,173,174,175,176,178,180,184,199],common_el:177,common_norm:[109,183],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,129,139,140,141,142,155,157,159,166,176,197],commun:[28,43,103,105,106,112,114,129,138,140,142,151,166,178,187],comp:136,compact:127,compani:[6,104,113,139,143,151,177,178],company_s:56,company_typ:56,companyx:177,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,128,132,141,142,143,147,148,150,155,159,164,166,172,177,196,199],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,141,147,151,156,164,176],compat:[15,55,101,119,121,127,130,134,136,138],compatible_format:198,compel:78,compens:[155,158],compet:151,competit:[129,137,143,151,154,155],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,133,140,178,184,187],compilaton:40,complaint:[104,112,181],compleletli:[68,80],complementari:137,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,128,132,135,136,137,139,141,143,155,157,158,159,166,171,172,173,176,177,178,180,191,198,199],completeness_scor:159,complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,129,132,135,137,138,139,140,141,143,147,153,156,157,158,163,166,176,178,186,187,189,190,197,200],complex_numb:176,complex_number_1:[177,199],complex_number_2:[177,199],complex_number__1:199,complexnumb:176,complexnumberwithconstructor:176,compli:112,complianc:[22,45,47,48,112,181],compliant:112,complic:[36,50,82,114,119,139,140,141,151,154,157,158,173,187],compon:[75,76,101,102,108,118,126,130,137,139,140,141,142,147,151,154,156,178,179],components_:[158,159,191],compos:[36,37,61,74,82,126,139,140,151,159,193],compose_greet_func:176,compose_greet_func_with_closur:176,composit:[119,150],compound:[177,184,199],compound_stmt:176,comprehend:49,comprehens:[93,108,122,141,171],compress:[29,30,31,106,126,129,132],compris:[39,101,140],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,127,129,131,132,135,136,138,139,140,141,143,147,148,150,151,155,156,157,159,163,166,168,172,175,177,179,180,181,192,193,194,196,199,200],computation:[33,36,50,56,119,121,129,132,137,153],computationn:33,compute_reciproc:184,compute_target:[9,100],con:[7,47,56,101,112,160],concat:[22,30,36,38,42,54,56,66,128,132,133,134,135,137,163,176,177],concaten:[34,38,55,76,119,120,128,133,135,173,177,187,193,194,195,199],concatenated_str:176,concav:128,conceiv:[142,176],concentr:145,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,132,138,139,140,141,142,143,148,151,155,156,157,164,172,175,177,186,199],conceptu:151,concern:[7,47,54,58,59,74,106,109,112,117,139,140,143,151,171,183,196],concis:[119,147,166,176,177,199],conclud:[56,59,75,102,108,116,141,148,159],conclus:[24,50,103,112,114,171],concret:[143,159,166,167,191],concurr:[84,100,101],conda:0,condens:130,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,128,132,141,148,150,151,159,171,172,173,176,177,178,198,199],condition2:54,condorcet:147,conduct:[56,100,112,159,181],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,132,141,142,145,147,151],config:[9,38,50,66,134,137,147,150,159,191,192],configur:[10,41,45,47,99,101,136,139,140,141,166,173,174,176],confirm:[14,30,45,47,48,59,103,106,112,158,171,181,182],conflict:[93,104,112,122],conform:[113,122,139,141],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,148,150,155,157,163,176],confusingli:158,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,164,165,168,172,194,195],confusion_mtx:34,congratul:[100,101,171,172,175,178],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,130,132,133,136,141,157,167,176,177,187],connectionist:78,connecttimeouterror:130,conquer:151,consciou:7,consecut:[14,32,40,49,155],consent:[112,181],consequ:[28,102,112,130,181],conserv:[109,183],conservationstatu:[109,183],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,130,132,134,137,140,141,142,143,145,147,148,150,151,152,155,156,157,158,159,160,166,172,175,176,177,189,191,198,199],consider:[59,101,106,112,114,115,122,151,154,158,181,182],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,132,133,139,140,141,142,143,148,157,159,166,171,173,177,181,182,185,196],consol:122,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,127,128,130,131,132,136,141,151,155,167,168],constant_initi:135,constantli:114,constrain:[30,130,145],constraint:[30,101,106,119,132,140,141,145,157,159,164,191],construct:[30,50,119,122,126,128,131,132,137,139,145,147,150,151,152,155,176,177,199],constructor:[122,132,176,177,198,199],consult:[7,120],consum:[10,20,41,45,98,100,101,103,108,111,112,113,139,141,143,156,160,166,180],consumpt:[108,160,180,184],cont:54,cont_num_var:54,contact:[185,192],contagi:172,contain:[1,3,6,7,12,14,15,17,22,29,31,33,36,37,39,40,41,45,46,47,48,49,50,51,52,54,57,59,60,62,68,74,80,83,86,91,92,93,100,101,103,106,113,116,117,118,120,122,124,129,131,132,133,136,139,140,141,145,147,149,150,152,155,158,160,163,166,167,171,172,173,175,176,177,184,198,199],container:140,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,59,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,127,128,130,131,132,133,134,135,136,137,138,141,143,145,146,147,148,150,151,152,154,155,160,163,164,165,166,171,172,173,175,176,177,178,184,191,192,193,194,197,199],contest:147,context:[9,28,31,50,59,75,100,102,104,106,112,121,122,127,133,141,143,145,146,157,158,160,174,176,177,179,181,182,199],contigu:119,contin:[57,58],continu:[0,1,17,18,31,33,34,40,47,48,50,54,55,58,59,62,75,76,77,78,99,100,102,109,111,116,119,120,124,128,133,140,141,142,143,145,146,150,151,152,157,160,166,172,178,196],contour:[156,158,189],contourf:[150,158,194,195],contract:[92,93,101,140,176,177],contradictori:141,contrari:[121,122,143],contrarili:176,contrast:[7,50,90,103,119,131,141,150,156,159,187],contrib:136,contribut:[46,52,53,57,58,66,102,119,120,121,122,138,141,147,148,150,176,177,178,179],contributor:138,control:[7,11,30,43,48,56,59,61,62,63,64,65,99,103,104,106,112,114,117,119,129,130,132,134,139,140,142,150,151,157,168,177,182,192,199],controlflow:176,conv0:135,conv1:133,conv1_1:127,conv1_2:127,conv1_featur:127,conv1_pad:133,conv1d:[44,129],conv2_1:127,conv2_2:127,conv2_featur:127,conv2d:[29,30,31,32,33,34,36,37,39,127,128,129,132,133,135],conv2d_1:29,conv2d_2:29,conv2d_transpos:29,conv2d_transpose_1:29,conv2dtr:29,conv2dtranspos:[29,30,128,133,135],conv3_1:127,conv3_2:127,conv3_3:127,conv3_4:127,conv3d:129,conv4_1:127,conv4_2:127,conv4_3:127,conv4_4:127,conv5_1:127,conv5_2:127,conv5_3:127,conv5_4:127,conv:[37,127,132,133,135],conv_block:132,conv_bn:132,conv_bn_relu:132,conv_lay:127,conv_name_bas:133,convei:[104,108,182],conveni:[7,46,54,111,116,117,122,133,148,151,159,171,172,176],convent:[43,45,68,80,124,129,143,154,176,178],converg:[36,75,109,141,145,158,159,166,191],convers:[1,46,77,101,104,117,141,143,173],c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class=\"section-number\">40. </span>Self-paced assignments","<span class=\"section-number\">40.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">40.27. </span>Analyzing data","<span class=\"section-number\">40.9. </span>Analyzing text about Data Science","<span class=\"section-number\">40.13. </span>Apply your skills","<span class=\"section-number\">40.16. </span>Build your own custom vis","<span class=\"section-number\">40.17. </span>Classifying datasets","<span class=\"section-number\">40.26. </span>Data preparation","<span class=\"section-number\">40.24. </span>Data processing in Python","<span class=\"section-number\">40.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">40.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">40.10. </span>Data Science scenarios","<span class=\"section-number\">40.20. </span>Displaying airport data","<span class=\"section-number\">40.15. </span>Dive into the beehive","<span class=\"section-number\">40.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">40.25. </span>Evaluating data from a form","<span class=\"section-number\">40.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">40.37. </span>Exploring for answers","<span class=\"section-number\">40.19. </span>Introduction to probability and statistics","<span class=\"section-number\">40.12. </span>Lines, scatters and bars","<span class=\"section-number\">40.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">40.38. </span>Market research","<span class=\"section-number\">40.29. </span>Matplotlib applied","<span class=\"section-number\">40.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">40.18. </span>Small diabetes study","<span class=\"section-number\">40.21. </span>Soda profits","<span class=\"section-number\">40.35. </span>Tell a story","<span class=\"section-number\">40.14. </span>Try it in Excel","<span class=\"section-number\">40.11. </span>Write a data ethics case study","<span class=\"section-number\">40.101. </span>Intro to Autoencoders","<span class=\"section-number\">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">40.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">40.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">40.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">40.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">40.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">40.113. </span>Art by gan","<span class=\"section-number\">40.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">40.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">40.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">40.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">40.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">40.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">40.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">40.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">40.82. </span>Data engineering","<span class=\"section-number\">40.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">40.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">40.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">40.76. </span>Decision trees","<span class=\"section-number\">40.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">40.74. </span>Random forests for classification","<span class=\"section-number\">40.73. </span>Random forests intro and regression","<span class=\"section-number\">40.79. </span>Boosting with tuning","<span class=\"section-number\">40.78. </span>Gradient boosting","<span class=\"section-number\">40.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">40.67. </span>Decision Trees - Classification","<span class=\"section-number\">40.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">40.63. </span>Kernel method assignment 1","<span class=\"section-number\">40.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">40.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">40.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">40.68. </span>Model selection assignment 1","<span class=\"section-number\">40.72. </span>Regularized Linear Models","<span class=\"section-number\">40.88. </span>Build Classification Model","<span class=\"section-number\">40.87. </span>Build classification models","<span class=\"section-number\">40.57. </span>Create a regression model","<span class=\"section-number\">40.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">40.62. </span>Explore classification methods","<span class=\"section-number\">40.55. </span>Exploring visualizations","<span class=\"section-number\">40.58. </span>Linear and polynomial regression","<span class=\"section-number\">40.45. </span>Linear regression - California Housing","<span class=\"section-number\">40.48. </span>Gradient descent","<span class=\"section-number\">40.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">40.46. </span>Linear Regression Metrics","<span class=\"section-number\">40.47. </span>Loss Function","<span class=\"section-number\">40.54. </span>Managing data","<span class=\"section-number\">40.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">40.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">40.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">40.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">40.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">40.89. </span>Parameter play","<span class=\"section-number\">40.60. </span>Pumpkin varieties and color","<span class=\"section-number\">40.53. </span>Regression tools","<span class=\"section-number\">40.44. </span>Regression with Scikit-learn","<span class=\"section-number\">40.59. </span>Retrying some regression","<span class=\"section-number\">40.86. </span>Study the solvers","<span class=\"section-number\">40.56. </span>Try a different model","<span class=\"section-number\">40.8. </span>Python programming advanced","<span class=\"section-number\">40.7. </span>Python programming basics","<span class=\"section-number\">40.6. </span>Python programming introduction","<span class=\"section-number\">40.5. </span>Project Plan\u200b Template","<span class=\"section-number\">40.3. </span>First assignment","<span class=\"section-number\">40.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">5.4.2. </span>Data Selection","<span class=\"section-number\">5.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">27. </span>Autoencoder","<span class=\"section-number\">24. </span>Convolutional Neural Networks","<span class=\"section-number\">33. </span>Diffusion Model","<span class=\"section-number\">23. </span>Intro to Deep Learning","<span class=\"section-number\">30. </span>Deep Q-learning","<span class=\"section-number\">25. </span>Generative adversarial networks","<span class=\"section-number\">31. </span>Image classification","<span class=\"section-number\">32. </span>Image segmentation","<span class=\"section-number\">28. </span>Long-short term memory","<span class=\"section-number\">34. </span>Object detection","<span class=\"section-number\">26. </span>Recurrent Neural Networks","<span class=\"section-number\">29. </span>Time series","Learn AI together, for free","<span class=\"section-number\">37. </span>Data engineering","<span class=\"section-number\">39. </span>Model deployment","<span class=\"section-number\">38. </span>Model training &amp; evaluation","<span class=\"section-number\">35. </span>Overview","<span class=\"section-number\">36. </span>Problem framing","<span class=\"section-number\">16. </span>Clustering models for Machine Learning","<span class=\"section-number\">16.1. </span>Introduction to clustering","<span class=\"section-number\">16.2. </span>K-Means clustering","<span class=\"section-number\">17.1. </span>Bagging","<span class=\"section-number\">17.3. </span>Feature importance","<span class=\"section-number\">17. </span>Getting started with ensemble learning","<span class=\"section-number\">17.2. </span>Random forest","<span class=\"section-number\">18.1. </span>Gradient Boosting","<span class=\"section-number\">18.2. </span>Gradient boosting example","<span class=\"section-number\">18. </span>Introduction to Gradient Boosting","<span class=\"section-number\">18.3. </span>XGBoost","<span class=\"section-number\">18.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">20. </span>Kernel method","<span class=\"section-number\">22. </span>Model selection","<span class=\"section-number\">19. </span>Unsupervised learning","<span class=\"section-number\">21. </span>Unsupervised learning: PCA and clustering","<span class=\"section-number\">13. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">14.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">14. </span>Getting started with classification","<span class=\"section-number\">14.1. </span>Introduction to classification","<span class=\"section-number\">14.2. </span>More classifiers","<span class=\"section-number\">14.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11. </span>Summary of machine learning fundamentals","<span class=\"section-number\">15.2. </span>Gradient descent","<span class=\"section-number\">15.1. </span>Loss function","<span class=\"section-number\">15. </span>parameter-optimization","<span class=\"section-number\">12.3. </span>Linear and polynomial regression","<span class=\"section-number\">12.4. </span>Logistic regression","<span class=\"section-number\">12.2. </span>Managing data","<span class=\"section-number\">12. </span>Regression models for Machine Learning","<span class=\"section-number\">12.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">41.10. </span>Data Science in real world","<span class=\"section-number\">41.9. </span>Data Science in the cloud","<span class=\"section-number\">41.4. </span>Data Science introduction","<span class=\"section-number\">41.8. </span>Data Science lifecycle","<span class=\"section-number\">41.7. </span>Data visualization","<span class=\"section-number\">41.6. </span>NumPy and Pandas","<span class=\"section-number\">41.5. </span>Relational vs. non-relational database","<span class=\"section-number\">41.20. </span>Convolutional Neural Network","<span class=\"section-number\">41.21. </span>Generative Adversarial Network","<span class=\"section-number\">41. </span>Slides","<span class=\"section-number\">41.18. </span>Kernel method","<span class=\"section-number\">41.19. </span>Model Selection","<span class=\"section-number\">41.17. </span>Unsupervised learning","<span class=\"section-number\">41.16. </span>Build an machine learning web application","<span class=\"section-number\">41.12. </span>Linear Regression","<span class=\"section-number\">41.13. </span>Logistic Regression","<span class=\"section-number\">41.14. </span>Logistic Regression","<span class=\"section-number\">41.11. </span>Machine Learning overview","<span class=\"section-number\">41.15. </span>Neural Network","<span class=\"section-number\">41.3. </span>Python programming advanced","<span class=\"section-number\">41.2. </span>Python programming basics","<span class=\"section-number\">41.1. </span>Python programming 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diff --git a/slides/data-science/data-science-in-real-world.html b/slides/data-science/data-science-in-real-world.html
index 53f4566425..f91ec651d0 100644
--- a/slides/data-science/data-science-in-real-world.html
+++ b/slides/data-science/data-science-in-real-world.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.10. Data Science in real world &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.10. Data Science in real world &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.11. Machine Learning overview" href="../ml-fundamentals/ml-overview.html" />
-    <link rel="prev" title="38.9. Data Science in the cloud" href="data-science-in-the-cloud.html" />
+    <link rel="next" title="41.11. Machine Learning overview" href="../ml-fundamentals/ml-overview.html" />
+    <link rel="prev" title="41.9. Data Science in the cloud" href="data-science-in-the-cloud.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
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-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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-     14.2. K-Means clustering
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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-     16.3. XGBoost
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../ml-advanced/model-selection.html">
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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-   21. Convolutional Neural Networks
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   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
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-   25. Long-short term memory
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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-   35. Model training &amp; evaluation
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
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-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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-     37.8. Python programming advanced
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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-     37.11. Write a data ethics case study
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-     37.13. Apply your skills
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-     37.14. Try it in Excel
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-     37.17. Classifying datasets
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-     37.20. Displaying airport data
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-     37.21. Soda profits
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-     37.22. Analyzing COVID-19 papers
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 <section id="data-science-industry">
-<h2><span class="section-number">38.10.1. </span>1. Data Science + industry<a class="headerlink" href="#data-science-industry" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.10.1. </span>1. Data Science + industry<a class="headerlink" href="#data-science-industry" title="Permalink to this headline">#</a></h2>
 <br>
 <div class="row">
 <div class="one">
@@ -1661,7 +1693,7 @@ <h2><span class="section-number">38.10.1. </span>1. Data Science + industry<a cl
 <p><em>1. DataFlair. 6 amazing data science applications – don’t forget to check the 5th one! URL: <a class="reference external" href="https://data-flair.training/blogs/data-science-applications/">https://data-flair.training/blogs/data-science-applications/</a> (visited on 2022).</em></p>
 </font></section>
 <section id="data-science-research">
-<h2><span class="section-number">38.10.2. </span>2. Data Science + research<a class="headerlink" href="#data-science-research" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.10.2. </span>2. Data Science + research<a class="headerlink" href="#data-science-research" title="Permalink to this headline">#</a></h2>
 <p>Let’s look at one example - the <a class="reference external" href="http://gendershades.org/overview.html">MIT Gender Shades Study</a> from Joy Buolamwini (MIT Media Labs) with a <a class="reference external" href="http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf">signature research paper</a> co-authored with Timnit Gebru (then at Microsoft Research) that focused on,</p>
 <ul class="simple">
 <li><p><strong>What:</strong> The objective of the research project was to <em>evaluate bias present in automated facial analysis algorithms and datasets</em> based on gender and skin type.</p></li>
@@ -1670,7 +1702,7 @@ <h2><span class="section-number">38.10.2. </span>2. Data Science + research<a cl
 </ul>
 </section>
 <section id="data-science-humanities">
-<h2><span class="section-number">38.10.3. </span>3. Data Science + humanities<a class="headerlink" href="#data-science-humanities" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.10.3. </span>3. Data Science + humanities<a class="headerlink" href="#data-science-humanities" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671">“Emily Dickinson and the Meter of Mood”</a> - a great example from <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a> that asks how we can use data science to revisit familiar poetry and re-evaluate its meaning and the contributions of its author in new contexts.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset"><strong>Data Acquisition</strong></a> - to collect a relevant dataset for analysis. Options include using an API (e.g., <a class="reference external" href="https://poetrydb.org/index.html">Poetry DB API</a>) or scraping web pages (e.g., <a class="reference external" href="https://www.gutenberg.org/files/12242/12242-h/12242-h.htm">Project Gutenberg</a>) using tools like <a class="reference external" href="https://scrapy.org/">Scrapy</a>.</p></li>
@@ -1680,7 +1712,7 @@ <h2><span class="section-number">38.10.3. </span>3. Data Science + humanities<a
 </ul>
 </section>
 <section id="data-science-sustainability">
-<h2><span class="section-number">38.10.4. </span>4. Data Science + sustainability<a class="headerlink" href="#data-science-sustainability" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.10.4. </span>4. Data Science + sustainability<a class="headerlink" href="#data-science-sustainability" title="Permalink to this headline">#</a></h2>
 <p>The <a class="reference external" href="https://sdgs.un.org/2030agenda">2030 Agenda For Sustainable Development</a> - adopted by all United Nations members in 2015 - identifies 17 goals including ones that focus on <strong>Protecting the Planet</strong> from degradation and the impact of climate change.</p>
 <p>The <a class="reference external" href="https://planetarycomputer.microsoft.com/">Planetary Computer</a> initiative provides 4 components to help data scientists and developers in this effort:</p>
 <ul class="simple">
@@ -1691,11 +1723,11 @@ <h2><span class="section-number">38.10.4. </span>4. Data Science + sustainabilit
 </ul>
 </section>
 <section id="data-science-students">
-<h2><span class="section-number">38.10.5. </span>5. Data Science + students<a class="headerlink" href="#data-science-students" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.10.5. </span>5. Data Science + students<a class="headerlink" href="#data-science-students" title="Permalink to this headline">#</a></h2>
 <p>We’ve talked about real-world applications in industry and research, and explored Data Science application examples in digital humanities and sustainability. So how can you build your skills and share your expertise as a Data Science beginner?</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.10.6. </span>6. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.10.6. </span>6. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://builtin.com/data-science/data-science-applications-examples">17 Data Science Applications and Examples</a> - Jul 2021</p></li>
 <li><p><a class="reference external" href="https://myblindbird.com/data-science-applications-real-world/">11 Breathtaking Data Science Applications in Real World</a> - May 2021</p></li>
@@ -1704,7 +1736,7 @@ <h2><span class="section-number">38.10.6. </span>6. Your turn! 🚀<a class="hea
 </ol>
 </section>
 <section id="references">
-<h2><span class="section-number">38.10.7. </span>7. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.10.7. </span>7. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-in-the-wild.md">Data Science in the real world</a></p></li>
 </ol>
@@ -1748,13 +1780,13 @@ <h2><span class="section-number">38.10.7. </span>7. References<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.9. </span>Data Science in the cloud</p>
+            <p class="prev-next-title"><span class="section-number">41.9. </span>Data Science in the cloud</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ml-fundamentals/ml-overview.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.11. </span>Machine Learning overview</p>
+        <p class="prev-next-title"><span class="section-number">41.11. </span>Machine Learning overview</p>
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     </a>
diff --git a/slides/data-science/data-science-in-the-cloud.html b/slides/data-science/data-science-in-the-cloud.html
index 4c45a07bb4..3875c188f2 100644
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.9. Data Science in the cloud &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.9. Data Science in the cloud &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.10. Data Science in real world" href="data-science-in-real-world.html" />
-    <link rel="prev" title="38.8. Data Science lifecycle" href="data-science-lifecycle.html" />
+    <link rel="next" title="41.10. Data Science in real world" href="data-science-in-real-world.html" />
+    <link rel="prev" title="41.8. Data Science lifecycle" href="data-science-lifecycle.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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    </li>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     16.3. XGBoost
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
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-   21. Convolutional Neural Networks
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-   22. Generative adversarial networks
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-   23. Recurrent Neural Networks
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+   30. Deep Q-learning
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-   29. Image segmentation
+   32. Image segmentation
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-   30. Diffusion Model
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+   34. Object detection
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.56. Try a different model
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.57. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.58. Linear and polynomial regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.70. Dropout and Batch Normalization
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.75. Beyond random forests: more ensemble models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.80. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
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     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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+  <label for="toctree-checkbox-14">
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   <ul class="current">
    <li class="toctree-l2">
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-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-basics.html">
-     38.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
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     <a class="reference internal" href="../python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
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    </li>
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-     38.4. Data Science introduction
+     41.4. Data Science introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
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-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
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    <li class="toctree-l2 current active">
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-     38.9. Data Science in the cloud
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     <a class="reference internal" href="data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
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-     38.13. Logistic Regression
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    </li>
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+     41.14. Logistic Regression
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    </li>
    <li class="toctree-l2">
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-     38.15. Neural Network
+     41.15. Neural Network
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     <a class="reference internal" href="../ml-fundamentals/build-an-ml-web-app.html">
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+     41.16. Build an machine learning web application
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-the-cloud">
-   38.9.1. 1. What is the cloud?
+   41.9.1. 1. What is the cloud?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#why-choose-the-cloud-for-data-science">
-   38.9.2. 2. Why choose the cloud for Data Science?
+   41.9.2. 2. Why choose the cloud for Data Science?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-low-code-no-code-way">
-   38.9.3. 3. The “low code/no code” way
+   41.9.3. 3. The “low code/no code” way
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#low-code-no-code-machine-learning">
-   38.9.4. 4. Low code/no code Machine Learning
+   41.9.4. 4. Low code/no code Machine Learning
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.9.5. 5. Your turn! 🚀
+   41.9.5. 5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.9.6. 6. References
+   41.9.6. 6. References
   </a>
  </li>
 </ul>
@@ -1575,32 +1607,32 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-the-cloud">
-   38.9.1. 1. What is the cloud?
+   41.9.1. 1. What is the cloud?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#why-choose-the-cloud-for-data-science">
-   38.9.2. 2. Why choose the cloud for Data Science?
+   41.9.2. 2. Why choose the cloud for Data Science?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-low-code-no-code-way">
-   38.9.3. 3. The “low code/no code” way
+   41.9.3. 3. The “low code/no code” way
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#low-code-no-code-machine-learning">
-   38.9.4. 4. Low code/no code Machine Learning
+   41.9.4. 4. Low code/no code Machine Learning
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.9.5. 5. Your turn! 🚀
+   41.9.5. 5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.9.6. 6. References
+   41.9.6. 6. References
   </a>
  </li>
 </ul>
@@ -1629,9 +1661,9 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-science-in-the-cloud">
-<h1><span class="section-number">38.9. </span>Data Science in the cloud<a class="headerlink" href="#data-science-in-the-cloud" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.9. </span>Data Science in the cloud<a class="headerlink" href="#data-science-in-the-cloud" title="Permalink to this headline">#</a></h1>
 <section id="what-is-the-cloud">
-<h2><span class="section-number">38.9.1. </span>1. What is the cloud?<a class="headerlink" href="#what-is-the-cloud" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.9.1. </span>1. What is the cloud?<a class="headerlink" href="#what-is-the-cloud" title="Permalink to this headline">#</a></h2>
 <p>The Cloud, or Cloud Computing, is the delivery of a wide range of pay-as-you-go computing services hosted on infrastructure over the internet. Services include solutions such as storage, databases, networking, software, analytics, and intelligent services.</p>
 <ul class="simple">
 <li><p>Infrastructure as a Service (IaaS)</p></li>
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 </ul>
 </section>
 <section id="why-choose-the-cloud-for-data-science">
-<h2><span class="section-number">38.9.2. </span>2. Why choose the cloud for Data Science?<a class="headerlink" href="#why-choose-the-cloud-for-data-science" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.9.2. </span>2. Why choose the cloud for Data Science?<a class="headerlink" href="#why-choose-the-cloud-for-data-science" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Innovation</p></li>
 <li><p>Flexibility</p></li>
@@ -1665,7 +1697,7 @@ <h2><span class="section-number">38.9.2. </span>2. Why choose the cloud for Data
 </ul>
 </section>
 <section id="the-low-code-no-code-way">
-<h2><span class="section-number">38.9.3. </span>3. The “low code/no code” way<a class="headerlink" href="#the-low-code-no-code-way" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.9.3. </span>3. The “low code/no code” way<a class="headerlink" href="#the-low-code-no-code-way" title="Permalink to this headline">#</a></h2>
 <br>
 <div class="row">
 <div class="one">
@@ -1681,7 +1713,7 @@ <h2><span class="section-number">38.9.3. </span>3. The “low code/no code” wa
 <p><em>1. 45 no-code ai tools: The complete no-code ai guide(Updated December 2022). (n.d.). Akkio. Retrieved 5 March 2023, from <a class="reference external" href="https://www.akkio.com/post/45-no-code-ai-tools-complete-guide">https://www.akkio.com/post/45-no-code-ai-tools-complete-guide</a></em></p>
 </font></section>
 <section id="low-code-no-code-machine-learning">
-<h2><span class="section-number">38.9.4. </span>4. Low code/no code Machine Learning<a class="headerlink" href="#low-code-no-code-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.9.4. </span>4. Low code/no code Machine Learning<a class="headerlink" href="#low-code-no-code-machine-learning" title="Permalink to this headline">#</a></h2>
 <p>The Low code/No code way is easier to start with as it involves interacting with a GUI (Graphical User Interface), with no prior knowledge of code required.</p>
 <ul class="simple">
 <li><p>Low code/no code training of a model</p></li>
@@ -1690,7 +1722,7 @@ <h2><span class="section-number">38.9.4. </span>4. Low code/no code Machine Lear
 </ul>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.9.5. </span>5. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.9.5. </span>5. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/assignments/data-science/market-research.html">Market research</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">Low code/no code Data Science project on Azure ML</a></p></li>
@@ -1698,7 +1730,7 @@ <h2><span class="section-number">38.9.5. </span>5. Your turn! 🚀<a class="head
 </ol>
 </section>
 <section id="references">
-<h2><span class="section-number">38.9.6. </span>6. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.9.6. </span>6. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">Data Science in the cloud</a></p></li>
 </ol>
@@ -1742,13 +1774,13 @@ <h2><span class="section-number">38.9.6. </span>6. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.8. </span>Data Science lifecycle</p>
+            <p class="prev-next-title"><span class="section-number">41.8. </span>Data Science lifecycle</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-science-in-real-world.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.10. </span>Data Science in real world</p>
+        <p class="prev-next-title"><span class="section-number">41.10. </span>Data Science in real world</p>
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diff --git a/slides/data-science/data-science-introduction.html b/slides/data-science/data-science-introduction.html
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+    <title>41.4. Data Science introduction &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="38.5. Relational vs. non-relational database" href="relational-vs-non-relational-database.html" />
-    <link rel="prev" title="38.3. Python programming advanced" href="../python-programming/python-programming-advanced.html" />
+    <link rel="next" title="41.5. Relational vs. non-relational database" href="relational-vs-non-relational-database.html" />
+    <link rel="prev" title="41.3. Python programming advanced" href="../python-programming/python-programming-advanced.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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+     14.1. Introduction to classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.5. Project Plan​ Template
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@@ -1526,88 +1558,88 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   41.4.1. 1. Defining data science
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+     41.4.1.1. What’s Data Science
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     <a class="reference internal nav-link" href="#what-you-can-do-with-data">
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     <a class="reference internal nav-link" href="#related-fields">
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+     41.4.1.3. Related fields
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     </a>
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+   41.4.2. 2. Data Science ethics
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+     41.4.2.3. Ethics challenges
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+   41.4.4. 4. Introduction to statistics and probability
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+     41.4.4.1. Probability and random variables
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+     41.4.4.2. Aggregations: min, max, and everything in between
     </a>
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+   41.4.5. 5. Your turn! 🚀
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+   41.4.6. 6. References
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#defining-data-science">
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+   41.4.1. 1. Defining data science
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#whats-data-science">
-     38.4.1.1. What’s Data Science
+     41.4.1.1. What’s Data Science
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-you-can-do-with-data">
-     38.4.1.2. What you can do with data
+     41.4.1.2. What you can do with data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#related-fields">
-     38.4.1.3. Related fields
+     41.4.1.3. Related fields
     </a>
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     <a class="reference internal nav-link" href="#id1">
-     38.4.1.4. What you can do with data
+     41.4.1.4. What you can do with data
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#data-science-ethics">
-   38.4.2. 2. Data Science ethics
+   41.4.2. 2. Data Science ethics
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   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basic-definition">
-     38.4.2.1. Basic definition
+     41.4.2.1. Basic definition
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ethics-concepts">
-     38.4.2.2. Ethics concepts
+     41.4.2.2. Ethics concepts
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ethics-challenges">
-     38.4.2.3. Ethics challenges
+     41.4.2.3. Ethics challenges
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#applied-ethics">
-     38.4.2.4. Applied ethics
+     41.4.2.4. Applied ethics
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#defining-data">
-   38.4.3. 3. Defining data
+   41.4.3. 3. Defining data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-statistics-and-probability">
-   38.4.4. 4. Introduction to statistics and probability
+   41.4.4. 4. Introduction to statistics and probability
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#probability-and-random-variables">
-     38.4.4.1. Probability and random variables
+     41.4.4.1. Probability and random variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#aggregations-min-max-and-everything-in-between">
-     38.4.4.2. Aggregations: min, max, and everything in between
+     41.4.4.2. Aggregations: min, max, and everything in between
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.4.5. 5. Your turn! 🚀
+   41.4.5. 5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.4.6. 6. References
+   41.4.6. 6. References
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 </ul>
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-<h1><span class="section-number">38.4. </span>Data Science introduction<a class="headerlink" href="#data-science-introduction" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.4. </span>Data Science introduction<a class="headerlink" href="#data-science-introduction" title="Permalink to this headline">#</a></h1>
 <section id="defining-data-science">
-<h2><span class="section-number">38.4.1. </span>1. Defining data science<a class="headerlink" href="#defining-data-science" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.4.1. </span>1. Defining data science<a class="headerlink" href="#defining-data-science" title="Permalink to this headline">#</a></h2>
 <section id="whats-data-science">
-<h3><span class="section-number">38.4.1.1. </span>What’s Data Science<a class="headerlink" href="#whats-data-science" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.4.1.1. </span>What’s Data Science<a class="headerlink" href="#whats-data-science" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p><strong>Data Science</strong> is defined as a scientific field that uses scientific methods to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.</p>
 </div></blockquote>
@@ -1758,7 +1790,7 @@ <h3><span class="section-number">38.4.1.1. </span>What’s Data Science<a class=
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+<h3><span class="section-number">41.4.1.2. </span>What you can do with data<a class="headerlink" href="#what-you-can-do-with-data" title="Permalink to this headline">#</a></h3>
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@@ -1785,7 +1817,7 @@ <h3><span class="section-number">38.4.1.2. </span>What you can do with data<a cl
 <p><em>1. The data science venn diagram. URL: <a class="reference external" href="http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram">http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram</a> (visited on 2022-08-27).</em></p>
 </font></section>
 <section id="related-fields">
-<h3><span class="section-number">38.4.1.3. </span>Related fields<a class="headerlink" href="#related-fields" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.4.1.3. </span>Related fields<a class="headerlink" href="#related-fields" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
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+<h3><span class="section-number">41.4.1.4. </span>What you can do with data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
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 <section id="data-science-ethics">
-<h2><span class="section-number">38.4.2. </span>2. Data Science ethics<a class="headerlink" href="#data-science-ethics" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.4.2. </span>2. Data Science ethics<a class="headerlink" href="#data-science-ethics" title="Permalink to this headline">#</a></h2>
 <div class="row">
 <p>
 <ul class="simple">
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+<h3><span class="section-number">41.4.2.1. </span>Basic definition<a class="headerlink" href="#basic-definition" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p><strong>Data ethics</strong> is a new branch of ethics that “studies and evaluates moral problems related to data, algorithms and corresponding practices”.</p></li>
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-<h3><span class="section-number">38.4.2.2. </span>Ethics concepts<a class="headerlink" href="#ethics-concepts" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.4.2.2. </span>Ethics concepts<a class="headerlink" href="#ethics-concepts" title="Permalink to this headline">#</a></h3>
 <br>
 <div class="row">
 <div class="one">
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+<h3><span class="section-number">41.4.2.3. </span>Ethics challenges<a class="headerlink" href="#ethics-challenges" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Data ownership</p></li>
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 <ul class="simple">
 <li><p>Professional codes, e.g.</p>
 <ul>
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-<h2><span class="section-number">38.4.3. </span>3. Defining data<a class="headerlink" href="#defining-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.4.3. </span>3. Defining data<a class="headerlink" href="#defining-data" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Quantitative data</p></li>
 <li><p>Qualitative data</p></li>
@@ -1933,27 +1965,27 @@ <h2><span class="section-number">38.4.3. </span>3. Defining data<a class="header
 </ul>
 </section>
 <section id="introduction-to-statistics-and-probability">
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+<h2><span class="section-number">41.4.4. </span>4. Introduction to statistics and probability<a class="headerlink" href="#introduction-to-statistics-and-probability" title="Permalink to this headline">#</a></h2>
 <section id="probability-and-random-variables">
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+<h3><span class="section-number">41.4.4.1. </span>Probability and random variables<a class="headerlink" href="#probability-and-random-variables" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p><strong>Probability</strong> is a number between 0 and 1 that expresses how probable an <strong>event</strong> is. And when we talk about events, we use <strong>random variables</strong>.</p>
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 <section id="aggregations-min-max-and-everything-in-between">
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+<h3><span class="section-number">41.4.4.2. </span>Aggregations: min, max, and everything in between<a class="headerlink" href="#aggregations-min-max-and-everything-in-between" title="Permalink to this headline">#</a></h3>
 <p>Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question.</p>
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-<h2><span class="section-number">38.4.5. </span>5. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.4.5. </span>5. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="../../data-science/introduction/defining-data-science.html#your-turn">Word cloud</a></p></li>
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 <section id="references">
-<h2><span class="section-number">38.4.6. </span>6. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.4.6. </span>6. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/introduction/introduction.html">Data Science introduction</a></p></li>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.3. </span>Python programming advanced</p>
+            <p class="prev-next-title"><span class="section-number">41.3. </span>Python programming advanced</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="relational-vs-non-relational-database.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.5. </span>Relational vs. non-relational database</p>
+        <p class="prev-next-title"><span class="section-number">41.5. </span>Relational vs. non-relational database</p>
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     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/data-science/data-science-lifecycle.html b/slides/data-science/data-science-lifecycle.html
index 1719ba322c..25fa04ee1b 100644
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-    <link rel="next" title="38.9. Data Science in the cloud" href="data-science-in-the-cloud.html" />
-    <link rel="prev" title="38.7. Data visualization" href="data-visualization.html" />
+    <link rel="next" title="41.9. Data Science in the cloud" href="data-science-in-the-cloud.html" />
+    <link rel="prev" title="41.7. Data visualization" href="data-visualization.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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    <li class="toctree-l2">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
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  </li>
  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
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+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <label for="toctree-checkbox-12">
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-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
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    <li class="toctree-l2">
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    <li class="toctree-l2">
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-     37.7. Python programming basics
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
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-     37.14. Try it in Excel
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     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
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     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
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     </a>
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+   41.8.7. 7. Your turn! 🚀
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 </div></div>
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 <section class="tex2jax_ignore mathjax_ignore" id="data-science-lifecycle">
-<h1><span class="section-number">38.8. </span>Data Science lifecycle<a class="headerlink" href="#data-science-lifecycle" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.8. </span>Data Science lifecycle<a class="headerlink" href="#data-science-lifecycle" title="Permalink to this headline">#</a></h1>
 <section id="whats-data-science-lifecycle">
-<h2><span class="section-number">38.8.1. </span>1. What’s Data Science lifecycle<a class="headerlink" href="#whats-data-science-lifecycle" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.1. </span>1. What’s Data Science lifecycle<a class="headerlink" href="#whats-data-science-lifecycle" title="Permalink to this headline">#</a></h2>
 <p>Data Science is a process which can be broken down into 5 stages:</p>
 <br>
 <div class="row">
@@ -1721,7 +1753,7 @@ <h2><span class="section-number">38.8.1. </span>1. What’s Data Science lifecyc
 <p><em>1. What is data science? (n.d.). UCB-UMT. Retrieved 19 February 2023, from <a class="reference external" href="https://ischoolonline.berkeley.edu/data-science/what-is-data-science/">https://ischoolonline.berkeley.edu/data-science/what-is-data-science/</a></em></p>
 </font></section>
 <section id="capturing">
-<h2><span class="section-number">38.8.2. </span>2. Capturing<a class="headerlink" href="#capturing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.2. </span>2. Capturing<a class="headerlink" href="#capturing" title="Permalink to this headline">#</a></h2>
 <p>The capturing is combined by two stages: acquiring the data and defining the purpose and problems that need to be addressed.</p>
 <p>Defining the goals of the project will require deeper context into the problem or question.</p>
 <p>First, we need to identify and acquire those who need their problem solved. These may be stakeholders in a business or sponsors of the project, who can help identify who or what will benefit from this project as well as what, and why they need it. A well-defined goal should be measurable and quantifiable to define an acceptable result.</p>
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 <section id="processing">
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+<h2><span class="section-number">41.8.3. </span>3. Processing<a class="headerlink" href="#processing" title="Permalink to this headline">#</a></h2>
 <p>The processing stage of the lifecycle focuses on discovering patterns in the data as well as modeling.</p>
 <ul class="simple">
 <li><p>Classification: organizing data into categories for more efficient use.</p></li>
@@ -1748,11 +1780,11 @@ <h2><span class="section-number">38.8.3. </span>3. Processing<a class="headerlin
 </ul>
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 <section id="maintaining">
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+<h2><span class="section-number">41.8.4. </span>4. Maintaining<a class="headerlink" href="#maintaining" title="Permalink to this headline">#</a></h2>
 <p>In the diagram of the lifecycle, you may have noticed that maintenance sits between capturing and processing.</p>
 <p>Maintenance is an ongoing process of managing, storing, and securing the data throughout the process of a project and should be taken into consideration throughout the entirety of the project.</p>
 <section id="storing-data">
-<h3><span class="section-number">38.8.4.1. </span>Storing data<a class="headerlink" href="#storing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.8.4.1. </span>Storing data<a class="headerlink" href="#storing-data" title="Permalink to this headline">#</a></h3>
 <p>Considerations of how and where the data is stored can influence the cost of its storage as well as the performance of how fast the data can be accessed</p>
 <ul class="simple">
 <li><p>On premise vs off premise vs public or private cloud</p></li>
@@ -1760,12 +1792,12 @@ <h3><span class="section-number">38.8.4.1. </span>Storing data<a class="headerli
 </ul>
 </section>
 <section id="managing-data">
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+<h3><span class="section-number">41.8.4.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h3>
 <p>As you work with data you may discover that some of the data needs to be cleaned using some of the techniques covered in the section focused on <a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/data-preparation.html">data preparation</a> building accurate models.</p>
 <p>When new data arrives, it will need some of the same applications to maintain consistency in quality.</p>
 </section>
 <section id="securing-the-data">
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+<h3><span class="section-number">41.8.4.3. </span>Securing the data<a class="headerlink" href="#securing-the-data" title="Permalink to this headline">#</a></h3>
 <p>One of the main goals of securing data is ensuring that those working on it are in control of what is collected and in what context it is being used.</p>
 <ul class="simple">
 <li><p>Confirm that all data is encrypted.
@@ -1777,7 +1809,7 @@ <h3><span class="section-number">38.8.4.3. </span>Securing the data<a class="hea
 </section>
 </section>
 <section id="analyzing">
-<h2><span class="section-number">38.8.5. </span>5. Analyzing<a class="headerlink" href="#analyzing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.5. </span>5. Analyzing<a class="headerlink" href="#analyzing" title="Permalink to this headline">#</a></h2>
 <p>Analyzing the data lifecycle confirms that the data can answer the questions that are proposed or solve a particular problem.</p>
 <ul class="simple">
 <li><p>Exploratory data analysis</p></li>
@@ -1788,7 +1820,7 @@ <h2><span class="section-number">38.8.5. </span>5. Analyzing<a class="headerlink
 </ul>
 </section>
 <section id="communication">
-<h2><span class="section-number">38.8.6. </span>6. Communication<a class="headerlink" href="#communication" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.6. </span>6. Communication<a class="headerlink" href="#communication" title="Permalink to this headline">#</a></h2>
 <p>To communicate is to convey or exchange information.</p>
 <p>Information can be ideas, thoughts, feelings, messages, covert signals, data – anything that a sender (someone sending information) wants a receiver (someone receiving information) to understand.</p>
 <ul class="simple">
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 </li>
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+<h3><span class="section-number">41.8.6.1. </span>Effective communication<a class="headerlink" href="#effective-communication" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Your responsibilities as a communicator</p></li>
 <li><p>Understand your audience, your channel &amp; your communication method</p></li>
@@ -1813,7 +1845,7 @@ <h3><span class="section-number">38.8.6.1. </span>Effective communication<a clas
 </section>
 </section>
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 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-lifecycle/introduction.html#your-turn">Data science lifecycle</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-lifecycle/analyzing.html#your-turn">Analyzing</a></p></li>
@@ -1821,7 +1853,7 @@ <h2><span class="section-number">38.8.7. </span>7. Your turn! 🚀<a class="head
 </ol>
 </section>
 <section id="references">
-<h2><span class="section-number">38.8.8. </span>8. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.8. </span>8. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-lifecycle/data-science-lifecycle.html">Data Science lifecycle</a></p></li>
 </ol>
@@ -1865,13 +1897,13 @@ <h2><span class="section-number">38.8.8. </span>8. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.7. </span>Data visualization</p>
+            <p class="prev-next-title"><span class="section-number">41.7. </span>Data visualization</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">38.9. </span>Data Science in the cloud</p>
+        <p class="prev-next-title"><span class="section-number">41.9. </span>Data Science in the cloud</p>
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diff --git a/slides/data-science/data-visualization.html b/slides/data-science/data-visualization.html
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-    <title>38.7. Data visualization &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.7. Data visualization &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.8. Data Science lifecycle" href="data-science-lifecycle.html" />
-    <link rel="prev" title="38.6. NumPy and Pandas" href="numpy-and-pandas.html" />
+    <link rel="next" title="41.8. Data Science lifecycle" href="data-science-lifecycle.html" />
+    <link rel="prev" title="41.6. NumPy and Pandas" href="numpy-and-pandas.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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+     12.2. Managing data
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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  <li class="toctree-l1 has-children">
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
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-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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-     37.10. Data Science scenarios
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-     37.21. Soda profits
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-     37.45. Linear regression - California Housing
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+   41.7.1. 1. What’s data visualization
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+     41.7.2.1. Build a line plot about bird wingspan values
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+     41.7.2.2. Explore bar charts
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     <a class="reference internal nav-link" href="#pie">
-     38.7.4.2. Pie!
+     41.7.4.2. Pie!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#donuts">
-     38.7.4.3. Donuts!
+     41.7.4.3. Donuts!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#waffles">
-     38.7.4.4. Waffles!
+     41.7.4.4. Waffles!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-relationships-all-about-honey">
-   38.7.5. 5. Visualizing relationships: all about honey
+   41.7.5. 5. Visualizing relationships: all about honey
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scatterplots">
-     38.7.5.1. Scatterplots
+     41.7.5.1. Scatterplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#line-charts">
-     38.7.5.2. Line charts
+     41.7.5.2. Line charts
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#facet-grids">
-     38.7.5.3. Facet grids
+     41.7.5.3. Facet grids
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dual-line-plots">
-     38.7.5.4. Dual-line plots
+     41.7.5.4. Dual-line plots
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.7.6. 6. Your turn! 🚀
+   41.7.6. 6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.7.7. 7. References
+   41.7.7. 7. References
   </a>
  </li>
 </ul>
@@ -1663,120 +1695,120 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#whats-data-visualization">
-   38.7.1. 1. What’s data visualization
+   41.7.1. 1. What’s data visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-quantities">
-   38.7.2. 2. Visualizing quantities
+   41.7.2. 2. Visualizing quantities
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-a-line-plot-about-bird-wingspan-values">
-     38.7.2.1. Build a line plot about bird wingspan values
+     41.7.2.1. Build a line plot about bird wingspan values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-bar-charts">
-     38.7.2.2. Explore bar charts
+     41.7.2.2. Explore bar charts
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comparing-data">
-     38.7.2.3. Comparing data
+     41.7.2.3. Comparing data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-distributions">
-   38.7.3. 3. Visualizing distributions
+   41.7.3. 3. Visualizing distributions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-the-birds-dataset">
-     38.7.3.1. Explore the birds dataset
+     41.7.3.1. Explore the birds dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#working-with-histograms">
-     38.7.3.2. Working with histograms
+     41.7.3.2. Working with histograms
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-the-dataset-for-distributions-using-text-data">
-     38.7.3.3. Explore the dataset for distributions using text data
+     41.7.3.3. Explore the dataset for distributions using text data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#density-plots">
-     38.7.3.4. Density plots
+     41.7.3.4. Density plots
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-proportions">
-   38.7.4. 4. Visualizing proportions
+   41.7.4. 4. Visualizing proportions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#get-to-know-your-mushrooms">
-     38.7.4.1. Get to know your mushrooms 🍄
+     41.7.4.1. Get to know your mushrooms 🍄
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pie">
-     38.7.4.2. Pie!
+     41.7.4.2. Pie!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#donuts">
-     38.7.4.3. Donuts!
+     41.7.4.3. Donuts!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#waffles">
-     38.7.4.4. Waffles!
+     41.7.4.4. Waffles!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-relationships-all-about-honey">
-   38.7.5. 5. Visualizing relationships: all about honey
+   41.7.5. 5. Visualizing relationships: all about honey
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scatterplots">
-     38.7.5.1. Scatterplots
+     41.7.5.1. Scatterplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#line-charts">
-     38.7.5.2. Line charts
+     41.7.5.2. Line charts
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#facet-grids">
-     38.7.5.3. Facet grids
+     41.7.5.3. Facet grids
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dual-line-plots">
-     38.7.5.4. Dual-line plots
+     41.7.5.4. Dual-line plots
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.7.6. 6. Your turn! 🚀
+   41.7.6. 6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.7.7. 7. References
+   41.7.7. 7. References
   </a>
  </li>
 </ul>
@@ -1816,9 +1848,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-visualization">
-<h1><span class="section-number">38.7. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.7. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h1>
 <section id="whats-data-visualization">
-<h2><span class="section-number">38.7.1. </span>1. What’s data visualization<a class="headerlink" href="#whats-data-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.1. </span>1. What’s data visualization<a class="headerlink" href="#whats-data-visualization" title="Permalink to this headline">#</a></h2>
 <p>Visualizing data is one of the most important tasks of a data scientist. Images are worth 1000 words, and a visualization can help you identify all kinds of interesting parts of your data such as,</p>
 <ul class="simple">
 <li><p>spikes,</p></li>
@@ -1829,7 +1861,7 @@ <h2><span class="section-number">38.7.1. </span>1. What’s data visualization<a
 </ul>
 </section>
 <section id="visualizing-quantities">
-<h2><span class="section-number">38.7.2. </span>2. Visualizing quantities<a class="headerlink" href="#visualizing-quantities" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.2. </span>2. Visualizing quantities<a class="headerlink" href="#visualizing-quantities" title="Permalink to this headline">#</a></h2>
 <p>An excellent library to create both simple and sophisticated plots and charts of various kinds is <a class="reference external" href="https://matplotlib.org/stable/index.html">Matplotlib</a>.</p>
 <div class="alignRight">
   <img class="removeMargin" height="100" src="../images/matplotlib.svg"/>
@@ -1843,7 +1875,7 @@ <h2><span class="section-number">38.7.2. </span>2. Visualizing quantities<a clas
 <li><p>To show relationships between values: line, scatterplot, bubble</p></li>
 </ul>
 <section id="build-a-line-plot-about-bird-wingspan-values">
-<h3><span class="section-number">38.7.2.1. </span>Build a line plot about bird wingspan values<a class="headerlink" href="#build-a-line-plot-about-bird-wingspan-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.2.1. </span>Build a line plot about bird wingspan values<a class="headerlink" href="#build-a-line-plot-about-bird-wingspan-values" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2007,7 +2039,7 @@ <h3><span class="section-number">38.7.2.1. </span>Build a line plot about bird w
 </div>
 </section>
 <section id="explore-bar-charts">
-<h3><span class="section-number">38.7.2.2. </span>Explore bar charts<a class="headerlink" href="#explore-bar-charts" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.2.2. </span>Explore bar charts<a class="headerlink" href="#explore-bar-charts" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">birds</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
@@ -2043,7 +2075,7 @@ <h3><span class="section-number">38.7.2.2. </span>Explore bar charts<a class="he
 </div>
 </section>
 <section id="comparing-data">
-<h3><span class="section-number">38.7.2.3. </span>Comparing data<a class="headerlink" href="#comparing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.2.3. </span>Comparing data<a class="headerlink" href="#comparing-data" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_scrolled: true docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">maxlength</span> <span class="o">=</span> <span class="n">birds</span><span class="p">[</span><span class="s1">&#39;MaxLength&#39;</span><span class="p">]</span>
@@ -2077,10 +2109,10 @@ <h3><span class="section-number">38.7.2.3. </span>Comparing data<a class="header
 </section>
 </section>
 <section id="visualizing-distributions">
-<h2><span class="section-number">38.7.3. </span>3. Visualizing distributions<a class="headerlink" href="#visualizing-distributions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.3. </span>3. Visualizing distributions<a class="headerlink" href="#visualizing-distributions" title="Permalink to this headline">#</a></h2>
 <p>Another way to dig into data is by looking at its distribution, or how the data is organized along an axis.</p>
 <section id="explore-the-birds-dataset">
-<h3><span class="section-number">38.7.3.1. </span>Explore the birds dataset<a class="headerlink" href="#explore-the-birds-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.1. </span>Explore the birds dataset<a class="headerlink" href="#explore-the-birds-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">birds</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;scatter&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">&#39;MaxLength&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;Order&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
@@ -2099,7 +2131,7 @@ <h3><span class="section-number">38.7.3.1. </span>Explore the birds dataset<a cl
 </div>
 </section>
 <section id="working-with-histograms">
-<h3><span class="section-number">38.7.3.2. </span>Working with histograms<a class="headerlink" href="#working-with-histograms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.2. </span>Working with histograms<a class="headerlink" href="#working-with-histograms" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">birds</span><span class="p">[</span><span class="s1">&#39;MaxBodyMass&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
@@ -2150,7 +2182,7 @@ <h3><span class="section-number">38.7.3.2. </span>Working with histograms<a clas
 </div>
 </section>
 <section id="explore-the-dataset-for-distributions-using-text-data">
-<h3><span class="section-number">38.7.3.3. </span>Explore the dataset for distributions using text data<a class="headerlink" href="#explore-the-dataset-for-distributions-using-text-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.3. </span>Explore the dataset for distributions using text data<a class="headerlink" href="#explore-the-dataset-for-distributions-using-text-data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x1</span> <span class="o">=</span> <span class="n">filteredBirds</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">filteredBirds</span><span class="o">.</span><span class="n">ConservationStatus</span> <span class="o">==</span> <span class="s1">&#39;EX&#39;</span><span class="p">,</span> <span class="s1">&#39;MinWingspan&#39;</span><span class="p">]</span>
@@ -2180,7 +2212,7 @@ <h3><span class="section-number">38.7.3.3. </span>Explore the dataset for distri
 </div>
 </section>
 <section id="density-plots">
-<h3><span class="section-number">38.7.3.4. </span>Density plots<a class="headerlink" href="#density-plots" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.4. </span>Density plots<a class="headerlink" href="#density-plots" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
@@ -2249,7 +2281,7 @@ <h3><span class="section-number">38.7.3.4. </span>Density plots<a class="headerl
 </section>
 </section>
 <section id="visualizing-proportions">
-<h2><span class="section-number">38.7.4. </span>4. Visualizing proportions<a class="headerlink" href="#visualizing-proportions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.4. </span>4. Visualizing proportions<a class="headerlink" href="#visualizing-proportions" title="Permalink to this headline">#</a></h2>
 <p>We will use a given dataset about mushrooms to experiment with tasty visualizations such as:</p>
 <ul class="simple">
 <li><p>Pie charts 🥧</p></li>
@@ -2257,7 +2289,7 @@ <h2><span class="section-number">38.7.4. </span>4. Visualizing proportions<a cla
 <li><p>Waffle charts 🧇</p></li>
 </ul>
 <section id="get-to-know-your-mushrooms">
-<h3><span class="section-number">38.7.4.1. </span>Get to know your mushrooms 🍄<a class="headerlink" href="#get-to-know-your-mushrooms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.1. </span>Get to know your mushrooms 🍄<a class="headerlink" href="#get-to-know-your-mushrooms" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mushrooms</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../data/mushrooms.csv&#39;</span><span class="p">)</span>
@@ -2564,7 +2596,7 @@ <h3><span class="section-number">38.7.4.1. </span>Get to know your mushrooms 
 </div>
 </section>
 <section id="pie">
-<h3><span class="section-number">38.7.4.2. </span>Pie!<a class="headerlink" href="#pie" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.2. </span>Pie!<a class="headerlink" href="#pie" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Edible&#39;</span><span class="p">,</span> <span class="s1">&#39;Poisonous&#39;</span><span class="p">]</span>
@@ -2580,7 +2612,7 @@ <h3><span class="section-number">38.7.4.2. </span>Pie!<a class="headerlink" href
 </div>
 </section>
 <section id="donuts">
-<h3><span class="section-number">38.7.4.3. </span>Donuts!<a class="headerlink" href="#donuts" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.3. </span>Donuts!<a class="headerlink" href="#donuts" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">habitat</span> <span class="o">=</span> <span class="n">mushrooms</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;habitat&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
@@ -2854,7 +2886,7 @@ <h3><span class="section-number">38.7.4.3. </span>Donuts!<a class="headerlink" h
 </div>
 </section>
 <section id="waffles">
-<h3><span class="section-number">38.7.4.4. </span>Waffles!<a class="headerlink" href="#waffles" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.4. </span>Waffles!<a class="headerlink" href="#waffles" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">capcolor</span> <span class="o">=</span> <span class="n">mushrooms</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;cap-color&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
@@ -3182,10 +3214,10 @@ <h3><span class="section-number">38.7.4.4. </span>Waffles!<a class="headerlink"
 </section>
 </section>
 <section id="visualizing-relationships-all-about-honey">
-<h2><span class="section-number">38.7.5. </span>5. Visualizing relationships: all about honey<a class="headerlink" href="#visualizing-relationships-all-about-honey" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.5. </span>5. Visualizing relationships: all about honey<a class="headerlink" href="#visualizing-relationships-all-about-honey" title="Permalink to this headline">#</a></h2>
 <p>Seaborn, which we have used before, as a good library to visualize relationships between variables.</p>
 <section id="scatterplots">
-<h3><span class="section-number">38.7.5.1. </span>Scatterplots<a class="headerlink" href="#scatterplots" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.5.1. </span>Scatterplots<a class="headerlink" href="#scatterplots" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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+<h2><span class="section-number">41.7.7. </span>7. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
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 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-visualization/data-visualization.html">Data visualization</a></p></li>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.6. </span>NumPy and Pandas</p>
+            <p class="prev-next-title"><span class="section-number">41.6. </span>NumPy and Pandas</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-science-lifecycle.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.8. </span>Data Science lifecycle</p>
+        <p class="prev-next-title"><span class="section-number">41.8. </span>Data Science lifecycle</p>
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-    <link rel="next" title="38.7. Data visualization" href="data-visualization.html" />
-    <link rel="prev" title="38.5. Relational vs. non-relational database" href="relational-vs-non-relational-database.html" />
+    <link rel="next" title="41.7. Data visualization" href="data-visualization.html" />
+    <link rel="prev" title="41.5. Relational vs. non-relational database" href="relational-vs-non-relational-database.html" />
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    10. Machine Learning overview
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  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+ </li>
  <li class="toctree-l1 has-children">
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-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
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+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
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+ </li>
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+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.11. Write a data ethics case study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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-     37.56. Try a different model
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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-     37.62. Explore classification methods
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-     37.63. Kernel method assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.72. Regularized Linear Models
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-     37.75. Beyond random forests: more ensemble models
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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    <li class="toctree-l2">
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-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
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    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
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     <a class="reference internal" href="data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
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     <a class="reference internal" href="data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
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     <a class="reference internal" href="data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
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     <a class="reference internal" href="data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
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     <a class="reference internal" href="../ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
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     <a class="reference internal" href="../ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
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     <a class="reference internal" href="../ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1526,114 +1558,114 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   41.6.1. 1. NumPy
   </a>
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     <a class="reference internal nav-link" href="#the-basics-of-numpy-arrays">
-     38.6.1.1. The basics of NumPy arrays
+     41.6.1.1. The basics of NumPy arrays
     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#numpy-array-attributes">
-       38.6.1.1.1. NumPy array attributes
+       41.6.1.1.1. NumPy array attributes
       </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#computation-on-numpy-arrays-universal-functions">
-     38.6.1.2. Computation on NumPy arrays: universal functions
+     41.6.1.2. Computation on NumPy arrays: universal functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-numpys-ufuncs">
-     38.6.1.3. Exploring NumPy’s ufuncs
+     41.6.1.3. Exploring NumPy’s ufuncs
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#aggregations-min-max-and-everything-in-between">
-     38.6.1.4. Aggregations: min, max, and everything in between
+     41.6.1.4. Aggregations: min, max, and everything in between
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
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-       38.6.1.4.1. Minimum and maximum
+       41.6.1.4.1. Minimum and maximum
       </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#computation-on-arrays-broadcasting">
-     38.6.1.5. Computation on arrays: broadcasting
+     41.6.1.5. Computation on arrays: broadcasting
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-data-numpys-structured-arrays">
-     38.6.1.6. Structured data: NumPy’s structured arrays
+     41.6.1.6. Structured data: NumPy’s structured arrays
     </a>
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+     41.6.2.1. Introducing Pandas objects
     </a>
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+       41.6.2.1.1. The Pandas Series object
       </a>
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-     38.6.2.2. Data indexing and selection
+     41.6.2.2. Data indexing and selection
     </a>
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-       38.6.2.2.1. Data selection in Series
+       41.6.2.2.1. Data selection in Series
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       <a class="reference internal nav-link" href="#data-selection-in-dataframe">
-       38.6.2.2.2. Data selection in DataFrame
+       41.6.2.2.2. Data selection in DataFrame
       </a>
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       <a class="reference internal nav-link" href="#dataframe-as-two-dimensional-array">
-       38.6.2.2.3. DataFrame as two-dimensional array
+       41.6.2.2.3. DataFrame as two-dimensional array
       </a>
      </li>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#operating-on-data-in-pandas">
-     38.6.2.3. Operating on data in Pandas
+     41.6.2.3. Operating on data in Pandas
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#ufuncs-index-preservation">
-       38.6.2.3.1. Ufuncs: index preservation
+       41.6.2.3.1. Ufuncs: index preservation
       </a>
      </li>
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    <li class="toc-h3 nav-item toc-entry">
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-     38.6.2.4. Handling missing data
+     41.6.2.4. Handling missing data
     </a>
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      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nan-missing-numerical-data">
-       38.6.2.4.2. NaN: missing numerical data
+       41.6.2.4.2. NaN: missing numerical data
       </a>
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       <a class="reference internal nav-link" href="#nan-and-none-in-pandas">
-       38.6.2.4.3. NaN and None in Pandas
+       41.6.2.4.3. NaN and None in Pandas
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#operating-on-null-values">
-       38.6.2.4.4. Operating on null values
+       41.6.2.4.4. Operating on null values
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-datasets-concat-and-append">
-     38.6.2.5. Combining datasets: concat and append
+     41.6.2.5. Combining datasets: concat and append
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#simple-concatenation-with-pd-concat">
-       38.6.2.5.1. Simple concatenation with pd.concat
+       41.6.2.5.1. Simple concatenation with pd.concat
       </a>
      </li>
     </ul>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-datasets-merge-and-join">
-     38.6.2.6. Combining datasets: merge and join
+     41.6.2.6. Combining datasets: merge and join
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#your-turn">
-   38.6.3. 3. Your turn! 🚀
+   41.6.3. 3. Your turn! 🚀
   </a>
  </li>
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   <a class="reference internal nav-link" href="#references">
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+   41.6.4. 4. References
   </a>
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 </ul>
@@ -1709,114 +1741,114 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#numpy">
-   38.6.1. 1. NumPy
+   41.6.1. 1. NumPy
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-basics-of-numpy-arrays">
-     38.6.1.1. The basics of NumPy arrays
+     41.6.1.1. The basics of NumPy arrays
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#numpy-array-attributes">
-       38.6.1.1.1. NumPy array attributes
+       41.6.1.1.1. NumPy array attributes
       </a>
      </li>
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     <a class="reference internal nav-link" href="#computation-on-numpy-arrays-universal-functions">
-     38.6.1.2. Computation on NumPy arrays: universal functions
+     41.6.1.2. Computation on NumPy arrays: universal functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-numpys-ufuncs">
-     38.6.1.3. Exploring NumPy’s ufuncs
+     41.6.1.3. Exploring NumPy’s ufuncs
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#aggregations-min-max-and-everything-in-between">
-     38.6.1.4. Aggregations: min, max, and everything in between
+     41.6.1.4. Aggregations: min, max, and everything in between
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#minimum-and-maximum">
-       38.6.1.4.1. Minimum and maximum
+       41.6.1.4.1. Minimum and maximum
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#computation-on-arrays-broadcasting">
-     38.6.1.5. Computation on arrays: broadcasting
+     41.6.1.5. Computation on arrays: broadcasting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-data-numpys-structured-arrays">
-     38.6.1.6. Structured data: NumPy’s structured arrays
+     41.6.1.6. Structured data: NumPy’s structured arrays
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pandas">
-   38.6.2. 2. Pandas
+   41.6.2. 2. Pandas
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#introducing-pandas-objects">
-     38.6.2.1. Introducing Pandas objects
+     41.6.2.1. Introducing Pandas objects
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-pandas-series-object">
-       38.6.2.1.1. The Pandas Series object
+       41.6.2.1.1. The Pandas Series object
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-indexing-and-selection">
-     38.6.2.2. Data indexing and selection
+     41.6.2.2. Data indexing and selection
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-selection-in-series">
-       38.6.2.2.1. Data selection in Series
+       41.6.2.2.1. Data selection in Series
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-selection-in-dataframe">
-       38.6.2.2.2. Data selection in DataFrame
+       41.6.2.2.2. Data selection in DataFrame
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dataframe-as-two-dimensional-array">
-       38.6.2.2.3. DataFrame as two-dimensional array
+       41.6.2.2.3. DataFrame as two-dimensional array
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#operating-on-data-in-pandas">
-     38.6.2.3. Operating on data in Pandas
+     41.6.2.3. Operating on data in Pandas
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#ufuncs-index-preservation">
-       38.6.2.3.1. Ufuncs: index preservation
+       41.6.2.3.1. Ufuncs: index preservation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handling-missing-data">
-     38.6.2.4. Handling missing data
+     41.6.2.4. Handling missing data
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#none-pythonic-missing-data">
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      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nan-missing-numerical-data">
-       38.6.2.4.2. NaN: missing numerical data
+       41.6.2.4.2. NaN: missing numerical data
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nan-and-none-in-pandas">
-       38.6.2.4.3. NaN and None in Pandas
+       41.6.2.4.3. NaN and None in Pandas
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#operating-on-null-values">
-       38.6.2.4.4. Operating on null values
+       41.6.2.4.4. Operating on null values
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-datasets-concat-and-append">
-     38.6.2.5. Combining datasets: concat and append
+     41.6.2.5. Combining datasets: concat and append
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#simple-concatenation-with-pd-concat">
-       38.6.2.5.1. Simple concatenation with pd.concat
+       41.6.2.5.1. Simple concatenation with pd.concat
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-datasets-merge-and-join">
-     38.6.2.6. Combining datasets: merge and join
+     41.6.2.6. Combining datasets: merge and join
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.6.3. 3. Your turn! 🚀
+   41.6.3. 3. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.6.4. 4. References
+   41.6.4. 4. References
   </a>
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 </ul>
@@ -1908,14 +1940,14 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="numpy-and-pandas">
-<h1><span class="section-number">38.6. </span>NumPy and Pandas<a class="headerlink" href="#numpy-and-pandas" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.6. </span>NumPy and Pandas<a class="headerlink" href="#numpy-and-pandas" title="Permalink to this headline">#</a></h1>
 <section id="numpy">
-<h2><span class="section-number">38.6.1. </span>1. NumPy<a class="headerlink" href="#numpy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.1. </span>1. NumPy<a class="headerlink" href="#numpy" title="Permalink to this headline">#</a></h2>
 <p>Numpy is a library for working with tensors, i.e. multi-dimensional arrays. Array has values of the same underlying type, and it is simpler than dataframe, but it offers more mathematical operations, and creates less overhead.</p>
 <div class="alignRight">
   <img class="removeMargin" height="100" src="../images/numpy.png"/>
 </div><section id="the-basics-of-numpy-arrays">
-<h3><span class="section-number">38.6.1.1. </span>The basics of NumPy arrays<a class="headerlink" href="#the-basics-of-numpy-arrays" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.1. </span>The basics of NumPy arrays<a class="headerlink" href="#the-basics-of-numpy-arrays" title="Permalink to this headline">#</a></h3>
 <p>Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas are built around the NumPy array.</p>
 <p>We’ll cover a few categories of basic array manipulations here:</p>
 <ul class="simple">
@@ -1925,7 +1957,7 @@ <h3><span class="section-number">38.6.1.1. </span>The basics of NumPy arrays<a c
 <li><p>Reshaping of arrays: Changing the shape of a given array Joining and splitting of arrays: Combining multiple arrays into one, and splitting one array into many.</p></li>
 </ul>
 <section id="numpy-array-attributes">
-<h4><span class="section-number">38.6.1.1.1. </span>NumPy array attributes<a class="headerlink" href="#numpy-array-attributes" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.1.1.1. </span>NumPy array attributes<a class="headerlink" href="#numpy-array-attributes" title="Permalink to this headline">#</a></h4>
 <p>First let’s discuss some useful array attributes. We’ll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.</p>
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-<h3><span class="section-number">38.6.1.2. </span>Computation on NumPy arrays: universal functions<a class="headerlink" href="#computation-on-numpy-arrays-universal-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.2. </span>Computation on NumPy arrays: universal functions<a class="headerlink" href="#computation-on-numpy-arrays-universal-functions" title="Permalink to this headline">#</a></h3>
 <p>Computation on NumPy arrays can be very fast, or it can be very slow. The key to making it fast is to use vectorized operations, generally implemented through NumPy’s universal functions (ufuncs).</p>
 <div class="cell docutils container">
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@@ -2051,7 +2083,7 @@ <h3><span class="section-number">38.6.1.2. </span>Computation on NumPy arrays: u
 </div>
 </section>
 <section id="exploring-numpys-ufuncs">
-<h3><span class="section-number">38.6.1.3. </span>Exploring NumPy’s ufuncs<a class="headerlink" href="#exploring-numpys-ufuncs" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.3. </span>Exploring NumPy’s ufuncs<a class="headerlink" href="#exploring-numpys-ufuncs" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
@@ -2077,7 +2109,7 @@ <h3><span class="section-number">38.6.1.3. </span>Exploring NumPy’s ufuncs<a c
 </div>
 </section>
 <section id="aggregations-min-max-and-everything-in-between">
-<h3><span class="section-number">38.6.1.4. </span>Aggregations: min, max, and everything in between<a class="headerlink" href="#aggregations-min-max-and-everything-in-between" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.4. </span>Aggregations: min, max, and everything in between<a class="headerlink" href="#aggregations-min-max-and-everything-in-between" title="Permalink to this headline">#</a></h3>
 <p>Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2120,7 +2152,7 @@ <h3><span class="section-number">38.6.1.4. </span>Aggregations: min, max, and ev
 </div>
 </div>
 <section id="minimum-and-maximum">
-<h4><span class="section-number">38.6.1.4.1. </span>Minimum and maximum<a class="headerlink" href="#minimum-and-maximum" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.1.4.1. </span>Minimum and maximum<a class="headerlink" href="#minimum-and-maximum" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="n">big_array</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">big_array</span><span class="p">)</span>
@@ -2174,7 +2206,7 @@ <h4><span class="section-number">38.6.1.4.1. </span>Minimum and maximum<a class=
 </section>
 </section>
 <section id="computation-on-arrays-broadcasting">
-<h3><span class="section-number">38.6.1.5. </span>Computation on arrays: broadcasting<a class="headerlink" href="#computation-on-arrays-broadcasting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.5. </span>Computation on arrays: broadcasting<a class="headerlink" href="#computation-on-arrays-broadcasting" title="Permalink to this headline">#</a></h3>
 <p>Another means of vectorizing operations is to use NumPy’s broadcasting functionality. Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes.</p>
 <div class="cell docutils container">
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@@ -2233,7 +2265,7 @@ <h3><span class="section-number">38.6.1.5. </span>Computation on arrays: broadca
 </div>
 </section>
 <section id="structured-data-numpys-structured-arrays">
-<h3><span class="section-number">38.6.1.6. </span>Structured data: NumPy’s structured arrays<a class="headerlink" href="#structured-data-numpys-structured-arrays" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.6. </span>Structured data: NumPy’s structured arrays<a class="headerlink" href="#structured-data-numpys-structured-arrays" title="Permalink to this headline">#</a></h3>
 <p>While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy’s structured arrays and record arrays</p>
 <div class="cell docutils container">
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@@ -2333,16 +2365,16 @@ <h3><span class="section-number">38.6.1.6. </span>Structured data: NumPy’s str
 </section>
 </section>
 <section id="pandas">
-<h2><span class="section-number">38.6.2. </span>2. Pandas<a class="headerlink" href="#pandas" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.2. </span>2. Pandas<a class="headerlink" href="#pandas" title="Permalink to this headline">#</a></h2>
 <p>Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
 built on top of the Python programming language.</p>
 <div class="alignRight">
   <img class="removeMargin" height="100" src="../images/pandas.png"/>
 </div><section id="introducing-pandas-objects">
-<h3><span class="section-number">38.6.2.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h3>
 <p>At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.</p>
 <section id="the-pandas-series-object">
-<h4><span class="section-number">38.6.2.1.1. </span>The Pandas Series object<a class="headerlink" href="#the-pandas-series-object" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.1.1. </span>The Pandas Series object<a class="headerlink" href="#the-pandas-series-object" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2404,10 +2436,10 @@ <h4><span class="section-number">38.6.2.1.1. </span>The Pandas Series object<a c
 </section>
 </section>
 <section id="data-indexing-and-selection">
-<h3><span class="section-number">38.6.2.2. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.2. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h3>
 <p>In Pandas, we looked in detail at methods and tools to access, set, and modify values in NumPy arrays. These included indexing (e.g., <code class="docutils literal notranslate"><span class="pre">arr[2,</span> <span class="pre">1]</span></code>), slicing (e.g., <code class="docutils literal notranslate"><span class="pre">arr[:,</span> <span class="pre">1:5]</span></code>), masking (e.g., <code class="docutils literal notranslate"><span class="pre">arr[arr</span> <span class="pre">&gt;</span> <span class="pre">0]</span></code>), fancy indexing (e.g., <code class="docutils literal notranslate"><span class="pre">arr[0,</span> <span class="pre">[1,</span> <span class="pre">5]]</span></code>), and combinations thereof (e.g., <code class="docutils literal notranslate"><span class="pre">arr[:,</span> <span class="pre">[1,</span> <span class="pre">5]]</span></code>).</p>
 <section id="data-selection-in-series">
-<h4><span class="section-number">38.6.2.2.1. </span>Data selection in Series<a class="headerlink" href="#data-selection-in-series" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.2.1. </span>Data selection in Series<a class="headerlink" href="#data-selection-in-series" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
@@ -2520,7 +2552,7 @@ <h4><span class="section-number">38.6.2.2.1. </span>Data selection in Series<a c
 </div>
 </section>
 <section id="data-selection-in-dataframe">
-<h4><span class="section-number">38.6.2.2.2. </span>Data selection in DataFrame<a class="headerlink" href="#data-selection-in-dataframe" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.2.2. </span>Data selection in DataFrame<a class="headerlink" href="#data-selection-in-dataframe" title="Permalink to this headline">#</a></h4>
 <p>Recall that a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of <code class="docutils literal notranslate"><span class="pre">Series</span></code> structures sharing the same index.</p>
 <div class="cell docutils container">
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@@ -2640,7 +2672,7 @@ <h4><span class="section-number">38.6.2.2.2. </span>Data selection in DataFrame<
 </div>
 </section>
 <section id="dataframe-as-two-dimensional-array">
-<h4><span class="section-number">38.6.2.2.3. </span>DataFrame as two-dimensional array<a class="headerlink" href="#dataframe-as-two-dimensional-array" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.2.3. </span>DataFrame as two-dimensional array<a class="headerlink" href="#dataframe-as-two-dimensional-array" title="Permalink to this headline">#</a></h4>
 <p>As mentioned previously, we can also view the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> as an enhanced two-dimensional array. We can examine the raw underlying data array using the <code class="docutils literal notranslate"><span class="pre">values</span></code> attribute:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2822,10 +2854,10 @@ <h4><span class="section-number">38.6.2.2.3. </span>DataFrame as two-dimensional
 </section>
 </section>
 <section id="operating-on-data-in-pandas">
-<h3><span class="section-number">38.6.2.3. </span>Operating on data in Pandas<a class="headerlink" href="#operating-on-data-in-pandas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.3. </span>Operating on data in Pandas<a class="headerlink" href="#operating-on-data-in-pandas" title="Permalink to this headline">#</a></h3>
 <p>Pandas inherits much of this functionality from NumPy, and the ufuncs are key to this.</p>
 <section id="ufuncs-index-preservation">
-<h4><span class="section-number">38.6.2.3.1. </span>Ufuncs: index preservation<a class="headerlink" href="#ufuncs-index-preservation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.3.1. </span>Ufuncs: index preservation<a class="headerlink" href="#ufuncs-index-preservation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
@@ -2924,11 +2956,11 @@ <h4><span class="section-number">38.6.2.3.1. </span>Ufuncs: index preservation<a
 </section>
 </section>
 <section id="handling-missing-data">
-<h3><span class="section-number">38.6.2.4. </span>Handling missing data<a class="headerlink" href="#handling-missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.4. </span>Handling missing data<a class="headerlink" href="#handling-missing-data" title="Permalink to this headline">#</a></h3>
 <p>Real-world data is rarely clean and homogeneous.</p>
 <p>Pandas chose to use sentinels for missing data, and further chose to use two already-existing Python null values: the special floating-point <code class="docutils literal notranslate"><span class="pre">NaN</span></code> value, and the Python <code class="docutils literal notranslate"><span class="pre">None</span></code> object.</p>
 <section id="none-pythonic-missing-data">
-<h4><span class="section-number">38.6.2.4.1. </span><code class="docutils literal notranslate"><span class="pre">None</span></code>: pythonic missing data<a class="headerlink" href="#none-pythonic-missing-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.4.1. </span><code class="docutils literal notranslate"><span class="pre">None</span></code>: pythonic missing data<a class="headerlink" href="#none-pythonic-missing-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">vals1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
@@ -2986,7 +3018,7 @@ <h4><span class="section-number">38.6.2.4.1. </span><code class="docutils litera
 </div>
 </section>
 <section id="nan-missing-numerical-data">
-<h4><span class="section-number">38.6.2.4.2. </span>NaN: missing numerical data<a class="headerlink" href="#nan-missing-numerical-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.4.2. </span>NaN: missing numerical data<a class="headerlink" href="#nan-missing-numerical-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">vals2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> 
@@ -3028,7 +3060,7 @@ <h4><span class="section-number">38.6.2.4.2. </span>NaN: missing numerical data<
 </div>
 </section>
 <section id="nan-and-none-in-pandas">
-<h4><span class="section-number">38.6.2.4.3. </span>NaN and None in Pandas<a class="headerlink" href="#nan-and-none-in-pandas" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.4.3. </span>NaN and None in Pandas<a class="headerlink" href="#nan-and-none-in-pandas" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">])</span>
@@ -3077,7 +3109,7 @@ <h4><span class="section-number">38.6.2.4.3. </span>NaN and None in Pandas<a cla
 </div>
 </section>
 <section id="operating-on-null-values">
-<h4><span class="section-number">38.6.2.4.4. </span>Operating on null values<a class="headerlink" href="#operating-on-null-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.4.4. </span>Operating on null values<a class="headerlink" href="#operating-on-null-values" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;hello&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">])</span>
@@ -3168,7 +3200,7 @@ <h4><span class="section-number">38.6.2.4.4. </span>Operating on null values<a c
 </section>
 </section>
 <section id="combining-datasets-concat-and-append">
-<h3><span class="section-number">38.6.2.5. </span>Combining datasets: concat and append<a class="headerlink" href="#combining-datasets-concat-and-append" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.5. </span>Combining datasets: concat and append<a class="headerlink" href="#combining-datasets-concat-and-append" title="Permalink to this headline">#</a></h3>
 <p>Some of the most interesting studies of data come from combining different data sources.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3232,7 +3264,7 @@ <h3><span class="section-number">38.6.2.5. </span>Combining datasets: concat and
 </div></div></div>
 </div>
 <section id="simple-concatenation-with-pd-concat">
-<h4><span class="section-number">38.6.2.5.1. </span>Simple concatenation with pd.concat<a class="headerlink" href="#simple-concatenation-with-pd-concat" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.5.1. </span>Simple concatenation with pd.concat<a class="headerlink" href="#simple-concatenation-with-pd-concat" title="Permalink to this headline">#</a></h4>
 <p>Pandas has a function, <code class="docutils literal notranslate"><span class="pre">pd.concat()</span></code>, which has a similar syntax to <code class="docutils literal notranslate"><span class="pre">np.concatenate</span></code> but contains a number of options that we’ll discuss momentarily:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">objs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="s1">&#39;outer&#39;</span><span class="p">,</span> <span class="n">join_axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
           <span class="n">keys</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
@@ -3292,7 +3324,7 @@ <h4><span class="section-number">38.6.2.5.1. </span>Simple concatenation with pd
 </section>
 </section>
 <section id="combining-datasets-merge-and-join">
-<h3><span class="section-number">38.6.2.6. </span>Combining datasets: merge and join<a class="headerlink" href="#combining-datasets-merge-and-join" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.6. </span>Combining datasets: merge and join<a class="headerlink" href="#combining-datasets-merge-and-join" title="Permalink to this headline">#</a></h3>
 <p>One essential feature offered by Pandas is its high-performance, in-memory join and merge operations.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">pd.merge()</span></code> function implements a number of types of joins: the <em>one-to-one</em>, <em>many-to-one</em>, and <em>many-to-many</em> joins.</p>
 <div class="cell docutils container">
@@ -3474,11 +3506,11 @@ <h3><span class="section-number">38.6.2.6. </span>Combining datasets: merge and
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.6.3. </span>3. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.3. </span>3. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="../../data-science/working-with-data/numpy.html#your-turn">Data processing in Python</a></p>
 </section>
 <section id="references">
-<h2><span class="section-number">38.6.4. </span>4. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.4. </span>4. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/numpy.html">Working with data - Numpy</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/pandas.html">Working with data - Pandas</a></p></li>
@@ -3523,13 +3555,13 @@ <h2><span class="section-number">38.6.4. </span>4. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.5. </span>Relational vs. non-relational database</p>
+            <p class="prev-next-title"><span class="section-number">41.5. </span>Relational vs. non-relational database</p>
         </div>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.7. </span>Data visualization</p>
+        <p class="prev-next-title"><span class="section-number">41.7. </span>Data visualization</p>
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diff --git a/slides/data-science/relational-vs-non-relational-database.html b/slides/data-science/relational-vs-non-relational-database.html
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-    <link rel="prev" title="38.4. Data Science introduction" href="data-science-introduction.html" />
+    <link rel="next" title="41.6. NumPy and Pandas" href="numpy-and-pandas.html" />
+    <link rel="prev" title="41.4. Data Science introduction" href="data-science-introduction.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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-     13.4. Applied Machine Learning : build a web app
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.103. Fun with Variational Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal nav-link" href="#relational-databases">
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       <a class="reference internal nav-link" href="#cities">
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       <a class="reference internal nav-link" href="#rainfall">
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-     38.5.1.4. Retrieving the data
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     <a class="reference internal nav-link" href="#joining-data">
-     38.5.1.5. Joining data
+     41.5.1.5. Joining data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#non-relational-data">
-   38.5.2. 2. Non-relational data
+   41.5.2. 2. Non-relational data
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#nosql">
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+     41.5.2.1. NoSQL
     </a>
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-       38.5.2.1.1. NoSQL databaseTypes and examples
+       41.5.2.1.1. NoSQL databaseTypes and examples
        <sup>
         1
         <sup>
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  </li>
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   <a class="reference internal nav-link" href="#your-turn">
-   38.5.3. 4. Your turn! 🚀
+   41.5.3. 4. Your turn! 🚀
   </a>
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   <a class="reference internal nav-link" href="#references">
-   38.5.4. 5. References
+   41.5.4. 5. References
   </a>
  </li>
 </ul>
@@ -1623,61 +1655,61 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#relational-databases">
-   38.5.1. 1. Relational databases
+   41.5.1. 1. Relational databases
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#it-all-starts-with-tables">
-     38.5.1.1. It all starts with tables
+     41.5.1.1. It all starts with tables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-shortcomings-of-a-single-table-approach">
-     38.5.1.2. The shortcomings of a single table approach
+     41.5.1.2. The shortcomings of a single table approach
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-concepts-of-relationships">
-     38.5.1.3. The concepts of relationships
+     41.5.1.3. The concepts of relationships
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#cities">
-       38.5.1.3.1. cities
+       41.5.1.3.1. cities
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rainfall">
-       38.5.1.3.2. rainfall
+       41.5.1.3.2. rainfall
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#retrieving-the-data">
-     38.5.1.4. Retrieving the data
+     41.5.1.4. Retrieving the data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#joining-data">
-     38.5.1.5. Joining data
+     41.5.1.5. Joining data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#non-relational-data">
-   38.5.2. 2. Non-relational data
+   41.5.2. 2. Non-relational data
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#nosql">
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+     41.5.2.1. NoSQL
     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nosql-databasetypes-and-examples-sup-1-sup">
-       38.5.2.1.1. NoSQL databaseTypes and examples
+       41.5.2.1.1. NoSQL databaseTypes and examples
        <sup>
         1
         <sup>
@@ -1691,12 +1723,12 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.5.3. 4. Your turn! 🚀
+   41.5.3. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.5.4. 5. References
+   41.5.4. 5. References
   </a>
  </li>
 </ul>
@@ -1725,14 +1757,14 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="relational-vs-non-relational-database">
-<h1><span class="section-number">38.5. </span>Relational vs. non-relational database<a class="headerlink" href="#relational-vs-non-relational-database" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.5. </span>Relational vs. non-relational database<a class="headerlink" href="#relational-vs-non-relational-database" title="Permalink to this headline">#</a></h1>
 <section id="relational-databases">
-<h2><span class="section-number">38.5.1. </span>1. Relational databases<a class="headerlink" href="#relational-databases" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.1. </span>1. Relational databases<a class="headerlink" href="#relational-databases" title="Permalink to this headline">#</a></h2>
 <p>A relational database is a collection of information that organizes data in predefined relationships where data is stored in one or more tables (or “relations”) of columns and rows, making it easy to see and understand how different data structures relate to each other.<sup>[1]</sup></p>
 <font size="4">
 <p><em>1. What is a relational database (Rdbms)? (n.d.). Google Cloud. Retrieved 7 February 2023, from <a class="reference external" href="https://cloud.google.com/learn/what-is-a-relational-database">https://cloud.google.com/learn/what-is-a-relational-database</a></em></p>
 </font><section id="it-all-starts-with-tables">
-<h3><span class="section-number">38.5.1.1. </span>It all starts with tables<a class="headerlink" href="#it-all-starts-with-tables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.1. </span>It all starts with tables<a class="headerlink" href="#it-all-starts-with-tables" title="Permalink to this headline">#</a></h3>
 <p>Let’s begin our exploration by starting a table to store information about cities. We might start with their name and country. You could store this in a table as follows:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1755,7 +1787,7 @@ <h3><span class="section-number">38.5.1.1. </span>It all starts with tables<a cl
 <p>Notice the column names of <code class="docutils literal notranslate"><span class="pre">city</span></code>, <code class="docutils literal notranslate"><span class="pre">country</span></code> and <code class="docutils literal notranslate"><span class="pre">population</span></code> describe the data being stored, and each row has information about one city.</p>
 </section>
 <section id="the-shortcomings-of-a-single-table-approach">
-<h3><span class="section-number">38.5.1.2. </span>The shortcomings of a single table approach<a class="headerlink" href="#the-shortcomings-of-a-single-table-approach" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.2. </span>The shortcomings of a single table approach<a class="headerlink" href="#the-shortcomings-of-a-single-table-approach" title="Permalink to this headline">#</a></h3>
 <p>Let’s start to add some additional data to our burgeoning database  - annual rainfall (in millimeters). We’ll focus on the years 2018, 2019 and 2020. If we were to add it for Tokyo, it might look something like this:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1823,7 +1855,7 @@ <h3><span class="section-number">38.5.1.2. </span>The shortcomings of a single t
 <p>This is why we need multiple tables and relationships.</p>
 </section>
 <section id="the-concepts-of-relationships">
-<h3><span class="section-number">38.5.1.3. </span>The concepts of relationships<a class="headerlink" href="#the-concepts-of-relationships" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.3. </span>The concepts of relationships<a class="headerlink" href="#the-concepts-of-relationships" title="Permalink to this headline">#</a></h3>
 <p>Let’s return to our data and determine how we want to split things up.</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1845,7 +1877,7 @@ <h3><span class="section-number">38.5.1.3. </span>The concepts of relationships<
 </table>
 <p>We need to figure out how to reference each city. We need some form of an identifier, ID or (in technical database terms) a primary key.</p>
 <section id="cities">
-<h4><span class="section-number">38.5.1.3.1. </span>cities<a class="headerlink" href="#cities" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.5.1.3.1. </span>cities<a class="headerlink" href="#cities" title="Permalink to this headline">#</a></h4>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>city_id</p></th>
@@ -1871,7 +1903,7 @@ <h4><span class="section-number">38.5.1.3.1. </span>cities<a class="headerlink"
 <p>With our cities table created, let’s store the rainfall.</p>
 </section>
 <section id="rainfall">
-<h4><span class="section-number">38.5.1.3.2. </span>rainfall<a class="headerlink" href="#rainfall" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.5.1.3.2. </span>rainfall<a class="headerlink" href="#rainfall" title="Permalink to this headline">#</a></h4>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>rainfall_id</p></th>
@@ -1931,7 +1963,7 @@ <h4><span class="section-number">38.5.1.3.2. </span>rainfall<a class="headerlink
 </section>
 </section>
 <section id="retrieving-the-data">
-<h3><span class="section-number">38.5.1.4. </span>Retrieving the data<a class="headerlink" href="#retrieving-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.4. </span>Retrieving the data<a class="headerlink" href="#retrieving-the-data" title="Permalink to this headline">#</a></h3>
 <p>If we are using a relational database such as MySQL, SQL Server or Oracle, we can use a language called <strong>Structured Query Language</strong> or <strong>SQL</strong>.</p>
 <p>To retrieve data you use the command <code class="docutils literal notranslate"><span class="pre">SELECT</span></code>.</p>
 <div class="highlight-sql notranslate"><div class="highlight"><pre><span></span><span class="k">SELECT</span><span class="w"> </span><span class="n">city</span>
@@ -1955,7 +1987,7 @@ <h3><span class="section-number">38.5.1.4. </span>Retrieving the data<a class="h
 </div>
 </section>
 <section id="joining-data">
-<h3><span class="section-number">38.5.1.5. </span>Joining data<a class="headerlink" href="#joining-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.5. </span>Joining data<a class="headerlink" href="#joining-data" title="Permalink to this headline">#</a></h3>
 <p>Until now we’ve retrieved data from a single table. Now we want to bring the data together from both <code class="docutils literal notranslate"><span class="pre">cities</span></code> and <code class="docutils literal notranslate"><span class="pre">rainfall</span></code>. This is done by <em>joining</em> them together.</p>
 <p>Let’s retrieve the rainfall for 2019 for all our cities.</p>
 <div class="highlight-sql notranslate"><div class="highlight"><pre><span></span><span class="k">SELECT</span><span class="w"> </span><span class="n">cities</span><span class="p">.</span><span class="n">city</span>
@@ -1983,7 +2015,7 @@ <h3><span class="section-number">38.5.1.5. </span>Joining data<a class="headerli
 </section>
 </section>
 <section id="non-relational-data">
-<h2><span class="section-number">38.5.2. </span>2. Non-relational data<a class="headerlink" href="#non-relational-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.2. </span>2. Non-relational data<a class="headerlink" href="#non-relational-data" title="Permalink to this headline">#</a></h2>
 <p>A <strong>non-relational database</strong> stores data in a non-tabular form, and tends to be more flexible than the traditional, SQL-based, relational database structures. It does not follow the relational model provided by traditional relational database management systems.<sup>[1]</sup></p>
 <font size="4">
 <p><em>1. What is a non-relational database? (n.d.). MongoDB. Retrieved 9 February 2023, from <a class="reference external" href="https://www.mongodb.com/databases/non-relational">https://www.mongodb.com/databases/non-relational</a></em></p>
@@ -1995,7 +2027,7 @@ <h2><span class="section-number">38.5.2. </span>2. Non-relational data<a class="
 <font size="4">
 <p><em>1. Duca, A. L. (2021, October 5). Relational vs non relational databases. Medium. <a class="reference external" href="https://towardsdatascience.com/relational-vs-non-relational-databases-f2ac792482e3">https://towardsdatascience.com/relational-vs-non-relational-databases-f2ac792482e3</a></em></p>
 </font><section id="nosql">
-<h3><span class="section-number">38.5.2.1. </span>NoSQL<a class="headerlink" href="#nosql" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.2.1. </span>NoSQL<a class="headerlink" href="#nosql" title="Permalink to this headline">#</a></h3>
 <p><strong>NoSQL</strong> is an umbrella term for the different ways to store non-relational data and can be interpreted as “non-SQL”, “non-relational” or  “not only SQL”.</p>
 <p>And these types of database systems can be categorized into 4 types.</p>
 <p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores">Key-value</a> databases pair unique keys, which are unique identifiers associated with a value. These pairs are stored using a <a class="reference external" href="https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/">hash table</a> with an appropriate hashing function.</p>
@@ -2033,7 +2065,7 @@ <h3><span class="section-number">38.5.2.1. </span>NoSQL<a class="headerlink" hre
 </pre></div>
 </div>
 <section id="nosql-databasetypes-and-examples-sup-1-sup">
-<h4><span class="section-number">38.5.2.1.1. </span>NoSQL databaseTypes and examples<sup>1<sup><a class="headerlink" href="#nosql-databasetypes-and-examples-sup-1-sup" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.5.2.1.1. </span>NoSQL databaseTypes and examples<sup>1<sup><a class="headerlink" href="#nosql-databasetypes-and-examples-sup-1-sup" title="Permalink to this headline">#</a></h4>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Type</p></th>
@@ -2086,7 +2118,7 @@ <h4><span class="section-number">38.5.2.1.1. </span>NoSQL databaseTypes and exam
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.5.3. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.3. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/assignments/data-science/displaying-airport-data.html">Displaying airport data</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/non-relational-data.html#your-turn">Twitter data</a></p></li>
@@ -2094,7 +2126,7 @@ <h2><span class="section-number">38.5.3. </span>4. Your turn! 🚀<a class="head
 </ol>
 </section>
 <section id="references">
-<h2><span class="section-number">38.5.4. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.4. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/relational-databases.html">Relational database</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/non-relational-data.html">Non-relational data</a></p></li>
@@ -2139,13 +2171,13 @@ <h2><span class="section-number">38.5.4. </span>5. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.4. </span>Data Science introduction</p>
+            <p class="prev-next-title"><span class="section-number">41.4. </span>Data Science introduction</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="numpy-and-pandas.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.6. </span>NumPy and Pandas</p>
+        <p class="prev-next-title"><span class="section-number">41.6. </span>NumPy and Pandas</p>
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diff --git a/slides/deep-learning/cnn.html b/slides/deep-learning/cnn.html
index e1c3a02d69..a19d55708c 100644
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.20. Convolutional Neural Network &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.20. Convolutional Neural Network &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.21. Generative Adversarial Network" href="gan.html" />
-    <link rel="prev" title="38.19. Model Selection" href="../ml-advanced/model-selection.html" />
+    <link rel="next" title="41.21. Generative Adversarial Network" href="gan.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-<h1><span class="section-number">38.20. </span>Convolutional Neural Network<a class="headerlink" href="#convolutional-neural-network" title="Permalink to this headline">#</a></h1>
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 <section id="the-brain">
-<h2><span class="section-number">38.20.1. </span>The Brain<a class="headerlink" href="#the-brain" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.1. </span>The Brain<a class="headerlink" href="#the-brain" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>When we see something, we label every object based on what we have learned in the past</p></li>
 <li><p>While vision starts in the eyes, the actual interpretation of what we see happens in the brain, in the primary visual cortex</p></li>
@@ -1716,7 +1748,7 @@ <h2><span class="section-number">38.20.1. </span>The Brain<a class="headerlink"
 <p><img alt="" src="../../_images/brain.png" /></p>
 </section>
 <section id="from-brain-to-convolutional-neural-networks">
-<h2><span class="section-number">38.20.2. </span>From Brain to Convolutional Neural Networks<a class="headerlink" href="#from-brain-to-convolutional-neural-networks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.2. </span>From Brain to Convolutional Neural Networks<a class="headerlink" href="#from-brain-to-convolutional-neural-networks" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Convolutional Neural Networks are inspired by the brain</p></li>
 <li><p>The simple cells activate, for example, when they identify basic shapes as lines in a fixed area and a specific angle. The complex cells have larger receptive fields and their output is not sensitive to the specific position in the field</p></li>
@@ -1725,37 +1757,37 @@ <h2><span class="section-number">38.20.2. </span>From Brain to Convolutional Neu
 </ul>
 </section>
 <section id="how-a-computer-sees-an-image">
-<h2><span class="section-number">38.20.3. </span>How a computer sees an image<a class="headerlink" href="#how-a-computer-sees-an-image" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.3. </span>How a computer sees an image<a class="headerlink" href="#how-a-computer-sees-an-image" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/cat.png" /></p>
 </section>
 <section id="kernel-image-processing">
-<h2><span class="section-number">38.20.4. </span>Kernel (image processing)<a class="headerlink" href="#kernel-image-processing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.4. </span>Kernel (image processing)<a class="headerlink" href="#kernel-image-processing" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Kernel_(image_processing)">Wikipedia</a></p></li>
 </ul>
 <p><img alt="" src="../../_images/kernel.png" /></p>
 </section>
 <section id="cnn-architecture-1-3">
-<h2><span class="section-number">38.20.5. </span>CNN architecture (1/3)<a class="headerlink" href="#cnn-architecture-1-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.5. </span>CNN architecture (1/3)<a class="headerlink" href="#cnn-architecture-1-3" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/cnn-a1.png" /></p>
 </section>
 <section id="cnn-architecture-2-3">
-<h2><span class="section-number">38.20.6. </span>CNN architecture (2/3)<a class="headerlink" href="#cnn-architecture-2-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.6. </span>CNN architecture (2/3)<a class="headerlink" href="#cnn-architecture-2-3" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/cnn-a2.png" /></p>
 </section>
 <section id="cnn-architecture-3-3">
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+<h2><span class="section-number">41.20.7. </span>CNN architecture (3/3)<a class="headerlink" href="#cnn-architecture-3-3" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/cnn-a3.png" /></p>
 </section>
 <section id="time-to-play">
-<h2><span class="section-number">38.20.8. </span>Time to play!<a class="headerlink" href="#time-to-play" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.8. </span>Time to play!<a class="headerlink" href="#time-to-play" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://poloclub.github.io/cnn-explainer/">https://poloclub.github.io/cnn-explainer/</a></p></li>
 <li><p><a class="reference external" href="https://adamharley.com/nn_vis/">https://adamharley.com/nn_vis/</a></p></li>
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 </section>
 <section id="feature-extraction">
-<h2><span class="section-number">38.20.9. </span>Feature extraction<a class="headerlink" href="#feature-extraction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.9. </span>Feature extraction<a class="headerlink" href="#feature-extraction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/deep-learning/CNN.html">https://ocademy-ai.github.io/machine-learning/deep-learning/CNN.html</a></p></li>
 </ul>
@@ -1763,25 +1795,25 @@ <h2><span class="section-number">38.20.9. </span>Feature extraction<a class="hea
 <p><a class="reference external" href="https://medium.com/&#64;muhammadshoaibali/flattening-cnn-layers-for-neural-network-694a232eda6a">source</a></p>
 </section>
 <section id="padding">
-<h2><span class="section-number">38.20.10. </span>Padding<a class="headerlink" href="#padding" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.10. </span>Padding<a class="headerlink" href="#padding" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/padding.gif" /></p>
 </section>
 <section id="pooling">
-<h2><span class="section-number">38.20.11. </span>Pooling<a class="headerlink" href="#pooling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.11. </span>Pooling<a class="headerlink" href="#pooling" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/pooling.png" /></p>
 </section>
 <section id="flattening">
-<h2><span class="section-number">38.20.12. </span>Flattening<a class="headerlink" href="#flattening" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.12. </span>Flattening<a class="headerlink" href="#flattening" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/cnn-flattening-1.webp" /></p>
 <p><a class="reference external" href="https://medium.com/&#64;muhammadshoaibali/flattening-cnn-layers-for-neural-network-694a232eda6a">source</a></p>
 </section>
 <section id="full-connection">
-<h2><span class="section-number">38.20.13. </span>Full Connection<a class="headerlink" href="#full-connection" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.13. </span>Full Connection<a class="headerlink" href="#full-connection" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/cnn-fc-1.gif" /></p>
 <p><a class="reference external" href="https://www.linkedin.com/pulse/intro-convolutional-neural-networks-cnn-lamiae-hana/">source</a></p>
 </section>
 <section id="acknowledgement">
-<h2><span class="section-number">38.20.14. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.20.14. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://www.freecodecamp.org/news/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050/">https://www.freecodecamp.org/news/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050/</a></p>
 </section>
 </section>
@@ -1823,13 +1855,13 @@ <h2><span class="section-number">38.20.14. </span>Acknowledgement<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.19. </span>Model Selection</p>
+            <p class="prev-next-title"><span class="section-number">41.19. </span>Model Selection</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gan.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.21. </span>Generative Adversarial Network</p>
+        <p class="prev-next-title"><span class="section-number">41.21. </span>Generative Adversarial Network</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/deep-learning/gan.html b/slides/deep-learning/gan.html
index 2d0765c70d..c1d5730f63 100644
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-    <title>38.21. Generative Adversarial Network &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.21. Generative Adversarial Network &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="prev" title="38.20. Convolutional Neural Network" href="cnn.html" />
+    <link rel="prev" title="41.20. Convolutional Neural Network" href="cnn.html" />
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@@ -372,9 +372,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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    </li>
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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   <label for="toctree-checkbox-8">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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    </li>
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  <li class="toctree-l1 has-children">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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-  <label for="toctree-checkbox-10">
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-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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    <li class="toctree-l2">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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@@ -652,614 +684,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <label for="toctree-checkbox-12">
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-     37.4. Second assignment
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    <li class="toctree-l2">
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    <li class="toctree-l2">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
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-     37.14. Try it in Excel
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
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+     40.106. Neural Networks for Classification with TensorFlow
     </a>
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-     37.107. NN Classify 15 Fruits Assignment
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../introduction.html">
-   38. Slides
+   41. Slides
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-introduction.html">
-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-basics.html">
-     38.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1525,72 +1557,72 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-gerative-model-1-3">
-   38.21.1. What is a gerative model? (1/3)
+   41.21.1. What is a gerative model? (1/3)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-gerative-model-2-3">
-   38.21.2. What is a gerative model? (2/3)
+   41.21.2. What is a gerative model? (2/3)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-gerative-model-3-3">
-   38.21.3. What is a gerative model? (3/3)
+   41.21.3. What is a gerative model? (3/3)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-generative-adversarial-network-gan">
-   38.21.4. What is a generative adversarial network (GAN)?
+   41.21.4. What is a generative adversarial network (GAN)?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-does-a-gan-work">
-   38.21.5. How does a GAN work?
+   41.21.5. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   38.21.6. How does a GAN work?
+   41.21.6. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   38.21.7. How does a GAN work?
+   41.21.7. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id3">
-   38.21.8. How does a GAN work?
+   41.21.8. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id4">
-   38.21.9. How does a GAN work?
+   41.21.9. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   38.21.10. How does a GAN work?
+   41.21.10. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#time-to-play">
-   38.21.11. Time to play!
+   41.21.11. Time to play!
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lets-code-a-gan">
-   38.21.12. Let’s code a GAN!
+   41.21.12. Let’s code a GAN!
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#create-model">
-   38.21.13. Create Model
+   41.21.13. Create Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ackowledgement">
-   38.21.14. Ackowledgement
+   41.21.14. Ackowledgement
   </a>
  </li>
 </ul>
@@ -1614,72 +1646,72 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-gerative-model-1-3">
-   38.21.1. What is a gerative model? (1/3)
+   41.21.1. What is a gerative model? (1/3)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-gerative-model-2-3">
-   38.21.2. What is a gerative model? (2/3)
+   41.21.2. What is a gerative model? (2/3)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-gerative-model-3-3">
-   38.21.3. What is a gerative model? (3/3)
+   41.21.3. What is a gerative model? (3/3)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-a-generative-adversarial-network-gan">
-   38.21.4. What is a generative adversarial network (GAN)?
+   41.21.4. What is a generative adversarial network (GAN)?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-does-a-gan-work">
-   38.21.5. How does a GAN work?
+   41.21.5. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   38.21.6. How does a GAN work?
+   41.21.6. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   38.21.7. How does a GAN work?
+   41.21.7. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id3">
-   38.21.8. How does a GAN work?
+   41.21.8. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id4">
-   38.21.9. How does a GAN work?
+   41.21.9. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   38.21.10. How does a GAN work?
+   41.21.10. How does a GAN work?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#time-to-play">
-   38.21.11. Time to play!
+   41.21.11. Time to play!
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lets-code-a-gan">
-   38.21.12. Let’s code a GAN!
+   41.21.12. Let’s code a GAN!
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#create-model">
-   38.21.13. Create Model
+   41.21.13. Create Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ackowledgement">
-   38.21.14. Ackowledgement
+   41.21.14. Ackowledgement
   </a>
  </li>
 </ul>
@@ -1715,16 +1747,16 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="generative-adversarial-network">
-<h1><span class="section-number">38.21. </span>Generative Adversarial Network<a class="headerlink" href="#generative-adversarial-network" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.21. </span>Generative Adversarial Network<a class="headerlink" href="#generative-adversarial-network" title="Permalink to this headline">#</a></h1>
 <section id="what-is-a-gerative-model-1-3">
-<h2><span class="section-number">38.21.1. </span>What is a gerative model? (1/3)<a class="headerlink" href="#what-is-a-gerative-model-1-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.1. </span>What is a gerative model? (1/3)<a class="headerlink" href="#what-is-a-gerative-model-1-3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Many machine learning systems look at some kind of complicated input (say, an image) and produce a simple output (a label like, “cat”)</p></li>
 <li><p>By contrast, the goal of a generative model is something like the opposite: take a small piece of input—perhaps a few random numbers—and produce a complex output, like an image of a realistic-looking face</p></li>
 </ul>
 </section>
 <section id="what-is-a-gerative-model-2-3">
-<h2><span class="section-number">38.21.2. </span>What is a gerative model? (2/3)<a class="headerlink" href="#what-is-a-gerative-model-2-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.2. </span>What is a gerative model? (2/3)<a class="headerlink" href="#what-is-a-gerative-model-2-3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>“Generative” describes a class of statistical models that contrasts with discriminative models.</p></li>
 <li><p>Informally:</p>
@@ -1737,20 +1769,20 @@ <h2><span class="section-number">38.21.2. </span>What is a gerative model? (2/3)
 </ul>
 </section>
 <section id="what-is-a-gerative-model-3-3">
-<h2><span class="section-number">38.21.3. </span>What is a gerative model? (3/3)<a class="headerlink" href="#what-is-a-gerative-model-3-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.3. </span>What is a gerative model? (3/3)<a class="headerlink" href="#what-is-a-gerative-model-3-3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A generative model can model a distribution by producing convincing “fake” data that looks like it’s drawn from that distribution.</p></li>
 <li><p>A generative model for images might capture correlations like “things that look like boats are probably going to appear near things that look like water” and “eyes are unlikely to appear on foreheads.” These are very complicated distributions.</p></li>
 </ul>
 </section>
 <section id="what-is-a-generative-adversarial-network-gan">
-<h2><span class="section-number">38.21.4. </span>What is a generative adversarial network (GAN)?<a class="headerlink" href="#what-is-a-generative-adversarial-network-gan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.4. </span>What is a generative adversarial network (GAN)?<a class="headerlink" href="#what-is-a-generative-adversarial-network-gan" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>An especially effective type of generative model, introduced in 2014 by Ian Goodfellow, which has been a subject of intense interest in the machine learning community</p></li>
 </ul>
 </section>
 <section id="how-does-a-gan-work">
-<h2><span class="section-number">38.21.5. </span>How does a GAN work?<a class="headerlink" href="#how-does-a-gan-work" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.5. </span>How does a GAN work?<a class="headerlink" href="#how-does-a-gan-work" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A generative adversarial network (GAN) has two parts:</p>
 <ul>
@@ -1761,47 +1793,47 @@ <h2><span class="section-number">38.21.5. </span>How does a GAN work?<a class="h
 </ul>
 </section>
 <section id="id1">
-<h2><span class="section-number">38.21.6. </span>How does a GAN work?<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.6. </span>How does a GAN work?<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>When training begins, the generator produces obviously fake data, and the discriminator quickly learns to tell that it’s fake:</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g1.svg" /></p>
 </section>
 <section id="id2">
-<h2><span class="section-number">38.21.7. </span>How does a GAN work?<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.7. </span>How does a GAN work?<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>As training progresses, the generator gets closer to producing output that can fool the discriminator:</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g2.svg" /></p>
 </section>
 <section id="id3">
-<h2><span class="section-number">38.21.8. </span>How does a GAN work?<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.8. </span>How does a GAN work?<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Finally, if generator training goes well, the discriminator gets worse at telling the difference between real and fake. It starts to classify fake data as real, and its accuracy decreases.</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g3.svg" /></p>
 </section>
 <section id="id4">
-<h2><span class="section-number">38.21.9. </span>How does a GAN work?<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.9. </span>How does a GAN work?<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Here’s a picture of the whole system:</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g4.svg" /></p>
 </section>
 <section id="id5">
-<h2><span class="section-number">38.21.10. </span>How does a GAN work?<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.10. </span>How does a GAN work?<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Both the generator and the discriminator are neural networks. The generator output is connected directly to the discriminator input. Through backpropagation, the discriminator’s classification provides a signal that the generator uses to update its weights.</p></li>
 </ul>
 </section>
 <section id="time-to-play">
-<h2><span class="section-number">38.21.11. </span>Time to play!<a class="headerlink" href="#time-to-play" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.11. </span>Time to play!<a class="headerlink" href="#time-to-play" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://poloclub.github.io/ganlab/">https://poloclub.github.io/ganlab/</a></p></li>
 </ul>
 </section>
 <section id="lets-code-a-gan">
-<h2><span class="section-number">38.21.12. </span>Let’s code a GAN!<a class="headerlink" href="#lets-code-a-gan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.12. </span>Let’s code a GAN!<a class="headerlink" href="#lets-code-a-gan" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span>
@@ -1871,7 +1903,7 @@ <h2><span class="section-number">38.21.12. </span>Let’s code a GAN!<a class="h
 </div>
 </section>
 <section id="create-model">
-<h2><span class="section-number">38.21.13. </span>Create Model<a class="headerlink" href="#create-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.13. </span>Create Model<a class="headerlink" href="#create-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># create generator</span>
@@ -2016,7 +2048,7 @@ <h2><span class="section-number">38.21.13. </span>Create Model<a class="headerli
 </div>
 </section>
 <section id="ackowledgement">
-<h2><span class="section-number">38.21.14. </span>Ackowledgement<a class="headerlink" href="#ackowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.14. </span>Ackowledgement<a class="headerlink" href="#ackowledgement" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://developers.google.com/machine-learning/gan/gan_structure">https://developers.google.com/machine-learning/gan/gan_structure</a></p></li>
 <li><p><a class="reference external" href="https://www.kaggle.com/code/emreustundag/generative-adversarial-networks-gans-tutorial">https://www.kaggle.com/code/emreustundag/generative-adversarial-networks-gans-tutorial</a></p></li>
@@ -2061,7 +2093,7 @@ <h2><span class="section-number">38.21.14. </span>Ackowledgement<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.20. </span>Convolutional Neural Network</p>
+            <p class="prev-next-title"><span class="section-number">41.20. </span>Convolutional Neural Network</p>
         </div>
     </a>
 </div>
diff --git a/slides/introduction.html b/slides/introduction.html
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--- a/slides/introduction.html
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-    <title>38. Slides &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41. Slides &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="38.1. Python programming introduction" href="python-programming/python-programming-introduction.html" />
-    <link rel="prev" title="37.116. Basic classification: Classify images of clothing" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html" />
+    <link rel="next" title="41.1. Python programming introduction" href="python-programming/python-programming-introduction.html" />
+    <link rel="prev" title="40.116. Basic classification: Classify images of clothing" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
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+     12.1. Tools of the trade
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     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
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+     12.2. Managing data
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     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
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     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
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   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
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     <a class="reference internal" href="../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
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+    <a class="reference internal" href="../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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     <a class="reference internal" href="../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
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   <a class="reference internal" href="../ml-advanced/model-selection.html">
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@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../deep-learning/dl-overview.html">
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  </li>
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   <a class="reference internal" href="../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  </li>
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   <a class="reference internal" href="../deep-learning/gan.html">
-   22. Generative adversarial networks
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   <a class="reference internal" href="../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   25. Long-short term memory
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   <a class="reference internal" href="../deep-learning/difussion-model.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../machine-learning-productionization/overview.html">
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  </li>
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   <a class="reference internal" href="../machine-learning-productionization/model-training-and-evaluation.html">
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  <li class="toctree-l1 has-children">
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   </a>
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-  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../assignments/set-up-env/second-assignment.html">
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     <a class="reference internal" href="../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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-     37.7. Python programming basics
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    </li>
    <li class="toctree-l2">
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-     37.8. Python programming advanced
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
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-     37.13. Apply your skills
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
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-     37.16. Build your own custom vis
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     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
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-     37.24. Data processing in Python
+     40.24. Data processing in Python
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-     37.25. Evaluating data from a form
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-     37.29. Matplotlib applied
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-     37.43. Machine Learning overview - assignment 2
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 <p>The slides are built by using <a class="reference external" href="https://rise.readthedocs.io/en/stable/">RISE</a>. To enter live slideshow mode, please launch the slide notebook in Trust Mode through mybinder.</p>
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-<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-basics.html">38.2. Python programming basics</a></li>
-<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-advanced.html">38.3. Python programming advanced</a></li>
-<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-introduction.html">38.4. Data Science introduction</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="ml-advanced/model-selection.html">38.19. Model Selection</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="deep-learning/gan.html">38.21. Generative Adversarial Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-introduction.html">41.1. Python programming introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-basics.html">41.2. Python programming basics</a></li>
+<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-advanced.html">41.3. Python programming advanced</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-introduction.html">41.4. Data Science introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/relational-vs-non-relational-database.html">41.5. Relational vs. non-relational database</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/numpy-and-pandas.html">41.6. NumPy and Pandas</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/data-visualization.html">41.7. Data visualization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-lifecycle.html">41.8. Data Science lifecycle</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-in-the-cloud.html">41.9. Data Science in the cloud</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-in-real-world.html">41.10. Data Science in real world</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/ml-overview.html">41.11. Machine Learning overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/linear-regression.html">41.12. Linear Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/logistic-regression.html">41.13. Logistic Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/logistic-regression-condensed.html">41.14. Logistic Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/neural-network.html">41.15. Neural Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/build-an-ml-web-app.html">41.16. Build an machine learning web application</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-advanced/unsupervised-learning.html">41.17. Unsupervised learning</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-advanced/kernel-method.html">41.18. Kernel method</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-advanced/model-selection.html">41.19. Model Selection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="deep-learning/cnn.html">41.20. Convolutional Neural Network</a></li>
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@@ -1530,13 +1562,13 @@ <h1><span class="section-number">38. </span>Slides<a class="headerlink" href="#s
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         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.116. </span>Basic classification: Classify images of clothing</p>
+            <p class="prev-next-title"><span class="section-number">40.116. </span>Basic classification: Classify images of clothing</p>
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     </a>
     <a class='right-next' id="next-link" href="python-programming/python-programming-introduction.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.1. </span>Python programming introduction</p>
+        <p class="prev-next-title"><span class="section-number">41.1. </span>Python programming introduction</p>
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-    <link rel="prev" title="38.17. Unsupervised learning" href="unsupervised-learning.html" />
+    <link rel="next" title="41.19. Model Selection" href="model-selection.html" />
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    10. Machine Learning overview
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+   11. Summary of machine learning fundamentals
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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-     37.53. Regression tools
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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-     37.63. Kernel method assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.72. Regularized Linear Models
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-     37.75. Beyond random forests: more ensemble models
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-     37.76. Decision trees
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,27 +1558,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#outline">
-   38.18.1. Outline
+   41.18.1. Outline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-why-of-support-vector-machines">
-   38.18.2. The Why of Support Vector Machines
+   41.18.2. The Why of Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-to-draw-the-separator-line-1-2">
-   38.18.3. How to draw the separator  line? (1/2)
+   41.18.3. How to draw the separator  line? (1/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-to-draw-the-separator-line-2-2">
-   38.18.4. How to draw the separator  line? (2/2)
+   41.18.4. How to draw the separator  line? (2/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-maximizing-the-margin">
-   38.18.5. Support Vector Machines: Maximizing the
+   41.18.5. Support Vector Machines: Maximizing the
    <em>
     Margin
    </em>
@@ -1554,52 +1586,52 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-for-linear-classification-2d-case">
-   38.18.6. SVM for Linear classification : 2D case
+   41.18.6. SVM for Linear classification : 2D case
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-for-linear-classification-3d-case">
-   38.18.7. SVM for Linear classification : 3D case
+   41.18.7. SVM for Linear classification : 3D case
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sklearn">
-   38.18.8. sklearn
+   41.18.8. sklearn
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#insensitivity-to-distant-points">
-   38.18.9. Insensitivity to distant points
+   41.18.9. Insensitivity to distant points
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#beyond-linear-boundaries-kernel-svm">
-   38.18.10. Beyond linear boundaries: Kernel SVM
+   41.18.10. Beyond linear boundaries: Kernel SVM
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#non-linearly-separable-data">
-   38.18.11. Non-linearly separable data
+   41.18.11. Non-linearly separable data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#now-lets-do-the-kernel-trick">
-   38.18.12. Now let’s do the kernel trick
+   41.18.12. Now let’s do the kernel trick
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-v-s-logistic-regression-1-2">
-   38.18.13. SVM v.s. logistic regression (1/2)
+   41.18.13. SVM v.s. logistic regression (1/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-v-s-logistic-regression-2-2">
-   38.18.14. SVM v.s. logistic regression (2/2)
+   41.18.14. SVM v.s. logistic regression (2/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   38.18.15. Acknowledgement
+   41.18.15. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1623,27 +1655,27 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#outline">
-   38.18.1. Outline
+   41.18.1. Outline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-why-of-support-vector-machines">
-   38.18.2. The Why of Support Vector Machines
+   41.18.2. The Why of Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-to-draw-the-separator-line-1-2">
-   38.18.3. How to draw the separator  line? (1/2)
+   41.18.3. How to draw the separator  line? (1/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-to-draw-the-separator-line-2-2">
-   38.18.4. How to draw the separator  line? (2/2)
+   41.18.4. How to draw the separator  line? (2/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-maximizing-the-margin">
-   38.18.5. Support Vector Machines: Maximizing the
+   41.18.5. Support Vector Machines: Maximizing the
    <em>
     Margin
    </em>
@@ -1651,52 +1683,52 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-for-linear-classification-2d-case">
-   38.18.6. SVM for Linear classification : 2D case
+   41.18.6. SVM for Linear classification : 2D case
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-for-linear-classification-3d-case">
-   38.18.7. SVM for Linear classification : 3D case
+   41.18.7. SVM for Linear classification : 3D case
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sklearn">
-   38.18.8. sklearn
+   41.18.8. sklearn
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#insensitivity-to-distant-points">
-   38.18.9. Insensitivity to distant points
+   41.18.9. Insensitivity to distant points
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#beyond-linear-boundaries-kernel-svm">
-   38.18.10. Beyond linear boundaries: Kernel SVM
+   41.18.10. Beyond linear boundaries: Kernel SVM
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#non-linearly-separable-data">
-   38.18.11. Non-linearly separable data
+   41.18.11. Non-linearly separable data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#now-lets-do-the-kernel-trick">
-   38.18.12. Now let’s do the kernel trick
+   41.18.12. Now let’s do the kernel trick
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-v-s-logistic-regression-1-2">
-   38.18.13. SVM v.s. logistic regression (1/2)
+   41.18.13. SVM v.s. logistic regression (1/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-v-s-logistic-regression-2-2">
-   38.18.14. SVM v.s. logistic regression (2/2)
+   41.18.14. SVM v.s. logistic regression (2/2)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   38.18.15. Acknowledgement
+   41.18.15. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1738,12 +1770,12 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="kernel-method">
-<h1><span class="section-number">38.18. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.18. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
 <ul class="simple">
 <li><p>Support Vector Machines (SVM)</p></li>
 </ul>
 <section id="outline">
-<h2><span class="section-number">38.18.1. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.1. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The Why of Support Vector Machines</p>
 <ul>
@@ -1756,7 +1788,7 @@ <h2><span class="section-number">38.18.1. </span>Outline<a class="headerlink" hr
 </ul>
 </section>
 <section id="the-why-of-support-vector-machines">
-<h2><span class="section-number">38.18.2. </span>The Why of Support Vector Machines<a class="headerlink" href="#the-why-of-support-vector-machines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.2. </span>The Why of Support Vector Machines<a class="headerlink" href="#the-why-of-support-vector-machines" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1786,7 +1818,7 @@ <h2><span class="section-number">38.18.2. </span>The Why of Support Vector Machi
 </div>
 </section>
 <section id="how-to-draw-the-separator-line-1-2">
-<h2><span class="section-number">38.18.3. </span>How to draw the separator  line? (1/2)<a class="headerlink" href="#how-to-draw-the-separator-line-1-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.3. </span>How to draw the separator  line? (1/2)<a class="headerlink" href="#how-to-draw-the-separator-line-1-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A linear discriminant classifier may draw straight lines to separate two sets of data.</p></li>
 <li><p>But there may be multiple split lines that perfectly distinguish between the two categories.</p></li>
@@ -1810,7 +1842,7 @@ <h2><span class="section-number">38.18.3. </span>How to draw the separator  line
 </div>
 </section>
 <section id="how-to-draw-the-separator-line-2-2">
-<h2><span class="section-number">38.18.4. </span>How to draw the separator  line? (2/2)<a class="headerlink" href="#how-to-draw-the-separator-line-2-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.4. </span>How to draw the separator  line? (2/2)<a class="headerlink" href="#how-to-draw-the-separator-line-2-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>All three separators are good if we want to distinguish between these samples</p></li>
 <li><p>Which one is the best?</p></li>
@@ -1818,7 +1850,7 @@ <h2><span class="section-number">38.18.4. </span>How to draw the separator  line
 </ul>
 </section>
 <section id="support-vector-machines-maximizing-the-margin">
-<h2><span class="section-number">38.18.5. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.5. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>SVM proposes a new solution algorithm which finds the nearest straight line by applying an appropriate boundary fill to each line</p></li>
 <li><p>Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data</p></li>
@@ -1844,15 +1876,15 @@ <h2><span class="section-number">38.18.5. </span>Support Vector Machines: Maximi
 </div>
 </section>
 <section id="svm-for-linear-classification-2d-case">
-<h2><span class="section-number">38.18.6. </span>SVM for Linear classification : 2D case<a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this headline">#</a></h2>
-<p><img alt="" src="../../_images/svm2d1.png" /></p>
+<h2><span class="section-number">41.18.6. </span>SVM for Linear classification : 2D case<a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this headline">#</a></h2>
+<p><img alt="" src="../../_images/svm2d.png" /></p>
 </section>
 <section id="svm-for-linear-classification-3d-case">
-<h2><span class="section-number">38.18.7. </span>SVM for Linear classification : 3D case<a class="headerlink" href="#svm-for-linear-classification-3d-case" title="Permalink to this headline">#</a></h2>
-<p><img alt="" src="../../_images/svm3d1.gif" /></p>
+<h2><span class="section-number">41.18.7. </span>SVM for Linear classification : 3D case<a class="headerlink" href="#svm-for-linear-classification-3d-case" title="Permalink to this headline">#</a></h2>
+<p><img alt="" src="../../_images/svm3d.gif" /></p>
 </section>
 <section id="sklearn">
-<h2><span class="section-number">38.18.8. </span>sklearn<a class="headerlink" href="#sklearn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.8. </span>sklearn<a class="headerlink" href="#sklearn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span> <span class="c1"># &quot;Support vector classifier&quot;</span>
@@ -1916,7 +1948,7 @@ <h2><span class="section-number">38.18.8. </span>sklearn<a class="headerlink" hr
 </div>
 </section>
 <section id="insensitivity-to-distant-points">
-<h2><span class="section-number">38.18.9. </span>Insensitivity to distant points<a class="headerlink" href="#insensitivity-to-distant-points" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.9. </span>Insensitivity to distant points<a class="headerlink" href="#insensitivity-to-distant-points" title="Permalink to this headline">#</a></h2>
 <p>One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, and increases the number of training points without affecting the classification results of the model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1961,11 +1993,11 @@ <h2><span class="section-number">38.18.9. </span>Insensitivity to distant points
 </div>
 </section>
 <section id="beyond-linear-boundaries-kernel-svm">
-<h2><span class="section-number">38.18.10. </span>Beyond linear boundaries: Kernel SVM<a class="headerlink" href="#beyond-linear-boundaries-kernel-svm" title="Permalink to this headline">#</a></h2>
-<p><img alt="" src="../../_images/svm11.gif" /></p>
+<h2><span class="section-number">41.18.10. </span>Beyond linear boundaries: Kernel SVM<a class="headerlink" href="#beyond-linear-boundaries-kernel-svm" title="Permalink to this headline">#</a></h2>
+<p><img alt="" src="../../_images/svm1.gif" /></p>
 </section>
 <section id="non-linearly-separable-data">
-<h2><span class="section-number">38.18.11. </span>Non-linearly separable data<a class="headerlink" href="#non-linearly-separable-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.11. </span>Non-linearly separable data<a class="headerlink" href="#non-linearly-separable-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_circles</span>
@@ -1984,7 +2016,7 @@ <h2><span class="section-number">38.18.11. </span>Non-linearly separable data<a
 </div>
 </section>
 <section id="now-lets-do-the-kernel-trick">
-<h2><span class="section-number">38.18.12. </span>Now let’s do the kernel trick<a class="headerlink" href="#now-lets-do-the-kernel-trick" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.12. </span>Now let’s do the kernel trick<a class="headerlink" href="#now-lets-do-the-kernel-trick" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
@@ -2037,13 +2069,13 @@ <h2><span class="section-number">38.18.12. </span>Now let’s do the kernel tric
 </div>
 </section>
 <section id="svm-v-s-logistic-regression-1-2">
-<h2><span class="section-number">38.18.13. </span>SVM v.s. logistic regression (1/2)<a class="headerlink" href="#svm-v-s-logistic-regression-1-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.13. </span>SVM v.s. logistic regression (1/2)<a class="headerlink" href="#svm-v-s-logistic-regression-1-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Support vector machines only consider local points near the interval boundary, whereas logistic regression considers the global (points far from the boundary line also play a role in determining the boundary line). Support vector machines do not cause changes in the separation hyperplane by changing the non-support vector samples</p></li>
 </ul>
 </section>
 <section id="svm-v-s-logistic-regression-2-2">
-<h2><span class="section-number">38.18.14. </span>SVM v.s. logistic regression (2/2)<a class="headerlink" href="#svm-v-s-logistic-regression-2-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.14. </span>SVM v.s. logistic regression (2/2)<a class="headerlink" href="#svm-v-s-logistic-regression-2-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>SVM’s loss function is self-regularising, which is why SVM is a structural risk minimisation algorithm. And LR must additionally add a regular term to the loss function. Structural risk minimisation, meaning seeking a balance between training error and model complexity to prevent overfitting</p></li>
 <li><p>Optimization methods: LR is generally based on gradient descent, SVM on SMO</p></li>
@@ -2051,7 +2083,7 @@ <h2><span class="section-number">38.18.14. </span>SVM v.s. logistic regression (
 </ul>
 </section>
 <section id="acknowledgement">
-<h2><span class="section-number">38.18.15. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.15. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p>Some code comes from <a class="reference external" href="https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html">jakevdp</a>, for which the code part is licenced under MIT licence.</p>
 </section>
 </section>
@@ -2093,13 +2125,13 @@ <h2><span class="section-number">38.18.15. </span>Acknowledgement<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.17. </span>Unsupervised learning</p>
+            <p class="prev-next-title"><span class="section-number">41.17. </span>Unsupervised learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="model-selection.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.19. </span>Model Selection</p>
+        <p class="prev-next-title"><span class="section-number">41.19. </span>Model Selection</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/ml-advanced/model-selection.html b/slides/ml-advanced/model-selection.html
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-    <title>38.19. Model Selection &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.19. Model Selection &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="38.20. Convolutional Neural Network" href="../deep-learning/cnn.html" />
-    <link rel="prev" title="38.18. Kernel method" href="kernel-method.html" />
+    <link rel="next" title="41.20. Convolutional Neural Network" href="../deep-learning/cnn.html" />
+    <link rel="prev" title="41.18. Kernel method" href="kernel-method.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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  </li>
  <li class="toctree-l1 has-children">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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+     14.1. Introduction to classification
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-     14.1. Introduction to clustering
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <label for="toctree-checkbox-10">
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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-  <label for="toctree-checkbox-11">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
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   <a class="reference internal" href="../../deep-learning/dl-overview.html">
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-   21. Convolutional Neural Networks
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-   22. Generative adversarial networks
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-   23. Recurrent Neural Networks
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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  <li class="toctree-l1 has-children">
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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+  <label for="toctree-checkbox-13">
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-     37.9. Analyzing text about Data Science
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
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-     37.14. Try it in Excel
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@@ -1528,117 +1560,117 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   38.19.1. Introduction
+   41.19.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#outline">
-   38.19.2. Outline
+   41.19.2. Outline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#over-fitting-and-under-fitting">
-   38.19.3. Over-fitting and under-fitting
+   41.19.3. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   38.19.4. Over-fitting and under-fitting
+   41.19.4. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   38.19.5. Over-fitting and under-fitting
+   41.19.5. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id3">
-   38.19.6. Over-fitting and under-fitting
+   41.19.6. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id4">
-   38.19.7. Over-fitting and under-fitting
+   41.19.7. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   38.19.8. Over-fitting and under-fitting
+   41.19.8. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id6">
-   38.19.9. Over-fitting and under-fitting
+   41.19.9. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bias-variance-tradeoff">
-   38.19.10. Bias variance tradeoff
+   41.19.10. Bias variance tradeoff
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id7">
-   38.19.11. Bias variance tradeoff
+   41.19.11. Bias variance tradeoff
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#l1-and-l2-regularization">
-   38.19.12. L1 and L2 regularization
+   41.19.12. L1 and L2 regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id8">
-   38.19.13. L1 and L2 Regularization
+   41.19.13. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id9">
-   38.19.14. L1 and L2 Regularization
+   41.19.14. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id10">
-   38.19.15. L1 and L2 Regularization
+   41.19.15. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id11">
-   38.19.16. L1 and L2 Regularization
+   41.19.16. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id12">
-   38.19.17. L1 and L2 Regularization
+   41.19.17. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#early-stopping">
-   38.19.18. Early stopping
+   41.19.18. Early stopping
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   38.19.19. Dropout
+   41.19.19. Dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prediction-after-dropout">
-   38.19.20. Prediction after dropout
+   41.19.20. Prediction after dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusions">
-   38.19.21. Conclusions
+   41.19.21. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id13">
-   38.19.22. Conclusions
+   41.19.22. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id14">
-   38.19.23. Conclusions
+   41.19.23. Conclusions
   </a>
  </li>
 </ul>
@@ -1662,117 +1694,117 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   38.19.1. Introduction
+   41.19.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#outline">
-   38.19.2. Outline
+   41.19.2. Outline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#over-fitting-and-under-fitting">
-   38.19.3. Over-fitting and under-fitting
+   41.19.3. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   38.19.4. Over-fitting and under-fitting
+   41.19.4. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   38.19.5. Over-fitting and under-fitting
+   41.19.5. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id3">
-   38.19.6. Over-fitting and under-fitting
+   41.19.6. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id4">
-   38.19.7. Over-fitting and under-fitting
+   41.19.7. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   38.19.8. Over-fitting and under-fitting
+   41.19.8. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id6">
-   38.19.9. Over-fitting and under-fitting
+   41.19.9. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bias-variance-tradeoff">
-   38.19.10. Bias variance tradeoff
+   41.19.10. Bias variance tradeoff
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id7">
-   38.19.11. Bias variance tradeoff
+   41.19.11. Bias variance tradeoff
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#l1-and-l2-regularization">
-   38.19.12. L1 and L2 regularization
+   41.19.12. L1 and L2 regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id8">
-   38.19.13. L1 and L2 Regularization
+   41.19.13. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id9">
-   38.19.14. L1 and L2 Regularization
+   41.19.14. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id10">
-   38.19.15. L1 and L2 Regularization
+   41.19.15. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id11">
-   38.19.16. L1 and L2 Regularization
+   41.19.16. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id12">
-   38.19.17. L1 and L2 Regularization
+   41.19.17. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#early-stopping">
-   38.19.18. Early stopping
+   41.19.18. Early stopping
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   38.19.19. Dropout
+   41.19.19. Dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prediction-after-dropout">
-   38.19.20. Prediction after dropout
+   41.19.20. Prediction after dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusions">
-   38.19.21. Conclusions
+   41.19.21. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id13">
-   38.19.22. Conclusions
+   41.19.22. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id14">
-   38.19.23. Conclusions
+   41.19.23. Conclusions
   </a>
  </li>
 </ul>
@@ -1803,16 +1835,16 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="model-selection">
-<h1><span class="section-number">38.19. </span>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.19. </span>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">38.19.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Model Selection is the process of choosing the best model among all the potential candidate models for a given problem.</p></li>
 <li><p>The aim of the model selection process is to select a machine learning algorithm that evaluates to perform well against all the different parameters.</p></li>
 </ul>
 </section>
 <section id="outline">
-<h2><span class="section-number">38.19.2. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.2. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Over-fitting and under-fitting</p></li>
 <li><p>Bias variance tradeoff</p></li>
@@ -1823,14 +1855,14 @@ <h2><span class="section-number">38.19.2. </span>Outline<a class="headerlink" hr
 </ul>
 </section>
 <section id="over-fitting-and-under-fitting">
-<h2><span class="section-number">38.19.3. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.3. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p></li>
 </ul>
 <p><img alt="" src="../../_images/under_over_justalright.png" /></p>
 </section>
 <section id="id1">
-<h2><span class="section-number">38.19.4. </span>Over-fitting and under-fitting<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.4. </span>Over-fitting and under-fitting<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1841,7 +1873,7 @@ <h2><span class="section-number">38.19.4. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-datapoints.jpg" /></p>
 </section>
 <section id="id2">
-<h2><span class="section-number">38.19.5. </span>Over-fitting and under-fitting<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.5. </span>Over-fitting and under-fitting<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1852,7 +1884,7 @@ <h2><span class="section-number">38.19.5. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-overfitting.jpg" /></p>
 </section>
 <section id="id3">
-<h2><span class="section-number">38.19.6. </span>Over-fitting and under-fitting<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.6. </span>Over-fitting and under-fitting<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1863,7 +1895,7 @@ <h2><span class="section-number">38.19.6. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-overfitting-testdata.jpg" /></p>
 </section>
 <section id="id4">
-<h2><span class="section-number">38.19.7. </span>Over-fitting and under-fitting<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.7. </span>Over-fitting and under-fitting<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1874,7 +1906,7 @@ <h2><span class="section-number">38.19.7. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-underfitting.jpg" /></p>
 </section>
 <section id="id5">
-<h2><span class="section-number">38.19.8. </span>Over-fitting and under-fitting<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.8. </span>Over-fitting and under-fitting<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1885,84 +1917,84 @@ <h2><span class="section-number">38.19.8. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-underfitting-test-data.jpg" /></p>
 </section>
 <section id="id6">
-<h2><span class="section-number">38.19.9. </span>Over-fitting and under-fitting<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.9. </span>Over-fitting and under-fitting<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Classification</p></li>
 </ul>
 <p><img alt="" src="../../_images/classification.png" /></p>
 </section>
 <section id="bias-variance-tradeoff">
-<h2><span class="section-number">38.19.10. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.10. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Graphical illustration of variance and bias</p></li>
 </ul>
 <p><img alt="" src="../../_images/graphicalillustration.png" /></p>
 </section>
 <section id="id7">
-<h2><span class="section-number">38.19.11. </span>Bias variance tradeoff<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.11. </span>Bias variance tradeoff<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Model complexity v.s. error</p></li>
 </ul>
 <p><img alt="" src="../../_images/total_error.png" /></p>
 </section>
 <section id="l1-and-l2-regularization">
-<h2><span class="section-number">38.19.12. </span>L1 and L2 regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.12. </span>L1 and L2 regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
 <div class="math notranslate nohighlight">
 \[L2\ Loss = Loss + {\lambda}\sum_{i} w_i^2\]</div>
 <div class="math notranslate nohighlight">
 \[L1\ Loss = Loss +  {\lambda}\sum_{i} \lvert w \rvert\]</div>
 </section>
 <section id="id8">
-<h2><span class="section-number">38.19.13. </span>L1 and L2 Regularization<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.13. </span>L1 and L2 Regularization<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/circlesquare.png" /></p>
 </section>
 <section id="id9">
-<h2><span class="section-number">38.19.14. </span>L1 and L2 Regularization<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.14. </span>L1 and L2 Regularization<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/L1L2contour.png" /></p>
 </section>
 <section id="id10">
-<h2><span class="section-number">38.19.15. </span>L1 and L2 Regularization<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.15. </span>L1 and L2 Regularization<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/ridgelassoItayEvron.gif" /></p>
 </section>
 <section id="id11">
-<h2><span class="section-number">38.19.16. </span>L1 and L2 Regularization<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.16. </span>L1 and L2 Regularization<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/p-norm_balls.webp" /></p>
 </section>
 <section id="id12">
-<h2><span class="section-number">38.19.17. </span>L1 and L2 Regularization<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.17. </span>L1 and L2 Regularization<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span></p></li>
 </ul>
 <p><img alt="" src="../../_images/lagrange-animation.gif" /></p>
 </section>
 <section id="early-stopping">
-<h2><span class="section-number">38.19.18. </span>Early stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.18. </span>Early stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/traintestoverfitting.png" /></p>
 </section>
 <section id="dropout">
-<h2><span class="section-number">38.19.19. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.19. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/dropoutgif.gif" /></p>
 </section>
 <section id="prediction-after-dropout">
-<h2><span class="section-number">38.19.20. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.20. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/kUc8r.jpg" /></p>
 </section>
 <section id="conclusions">
-<h2><span class="section-number">38.19.21. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.21. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Training size matters</p></li>
 </ul>
 <p><img alt="" src="../../_images/ZahidHasan.png" /></p>
 </section>
 <section id="id13">
-<h2><span class="section-number">38.19.22. </span>Conclusions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.22. </span>Conclusions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>How to choose a good model</p></li>
 </ul>
 <p><img alt="" src="../../_images/steps.png" /></p>
 </section>
 <section id="id14">
-<h2><span class="section-number">38.19.23. </span>Conclusions<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.18. </span>Kernel method</p>
+            <p class="prev-next-title"><span class="section-number">41.18. </span>Kernel method</p>
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     <a class='right-next' id="next-link" href="../deep-learning/cnn.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">38.20. </span>Convolutional Neural Network</p>
+        <p class="prev-next-title"><span class="section-number">41.20. </span>Convolutional Neural Network</p>
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diff --git a/slides/ml-advanced/unsupervised-learning.html b/slides/ml-advanced/unsupervised-learning.html
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-    <link rel="prev" title="38.16. Build an machine learning web application" href="../ml-fundamentals/build-an-ml-web-app.html" />
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-   38.17.7. PCA with mnist dataset
+   41.17.7. PCA with mnist dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   38.17.8. Clustering
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+   41.17.9. K-means
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   <a class="reference internal nav-link" href="#k-means-with-sklearn">
-   38.17.10. K-means with sklearn
+   41.17.10. K-means with sklearn
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.17.11. Acknowledgments
+   41.17.11. Acknowledgments
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  </li>
 </ul>
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   38.17.1. Outline
+   41.17.1. Outline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-unsupervised-learning">
-   38.17.2. What is unsupervised learning
+   41.17.2. What is unsupervised learning
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dimension-reduction">
-   38.17.3. Dimension reduction
+   41.17.3. Dimension reduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#principal-component-analysis-pca">
-   38.17.4. Principal Component Analysis (PCA)
+   41.17.4. Principal Component Analysis (PCA)
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#quotes-from-ai-giants">
-   38.17.5. Quotes from AI giants
+   41.17.5. Quotes from AI giants
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pca-with-iris-dataset">
-   38.17.6. PCA with iris dataset
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   </a>
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-   38.17.7. PCA with mnist dataset
+   41.17.7. PCA with mnist dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#clustering">
-   38.17.8. Clustering
+   41.17.8. Clustering
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-means">
-   38.17.9. K-means
+   41.17.9. K-means
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-means-with-sklearn">
-   38.17.10. K-means with sklearn
+   41.17.10. K-means with sklearn
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.17.11. Acknowledgments
+   41.17.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1694,9 +1726,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="unsupervised-learning">
-<h1><span class="section-number">38.17. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.17. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h1>
 <section id="outline">
-<h2><span class="section-number">38.17.1. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.1. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>What is unsupervised learning</p></li>
 <li><p>Dimension reduction</p>
@@ -1712,7 +1744,7 @@ <h2><span class="section-number">38.17.1. </span>Outline<a class="headerlink" hr
 </ul>
 </section>
 <section id="what-is-unsupervised-learning">
-<h2><span class="section-number">38.17.2. </span>What is unsupervised learning<a class="headerlink" href="#what-is-unsupervised-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.2. </span>What is unsupervised learning<a class="headerlink" href="#what-is-unsupervised-learning" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Input data are unlabeled (i.e. no labels or classes given)</p></li>
 <li><p>The algorithm learns the structure of the data without any assistance</p></li>
@@ -1721,7 +1753,7 @@ <h2><span class="section-number">38.17.2. </span>What is unsupervised learning<a
 </ul>
 </section>
 <section id="dimension-reduction">
-<h2><span class="section-number">38.17.3. </span>Dimension reduction<a class="headerlink" href="#dimension-reduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.3. </span>Dimension reduction<a class="headerlink" href="#dimension-reduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Can help with data visualization</p></li>
 <li><p>Can help deal with the multicollinearity of your data and prepare the data for a supervised learning method</p></li>
@@ -1729,7 +1761,7 @@ <h2><span class="section-number">38.17.3. </span>Dimension reduction<a class="he
 </ul>
 </section>
 <section id="principal-component-analysis-pca">
-<h2><span class="section-number">38.17.4. </span>Principal Component Analysis (PCA)<a class="headerlink" href="#principal-component-analysis-pca" title="Permalink to this headline">#</a></h2>
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 <ul class="simple">
 <li><p>One of the easiest, most intuitive, and most frequently used methods for dimensionality reduction</p></li>
 <li><p>Projecting data onto its orthogonal feature subspace</p></li>
@@ -1739,13 +1771,13 @@ <h2><span class="section-number">38.17.4. </span>Principal Component Analysis (P
 </ul>
 </section>
 <section id="quotes-from-ai-giants">
-<h2><span class="section-number">38.17.5. </span>Quotes from AI giants<a class="headerlink" href="#quotes-from-ai-giants" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.5. </span>Quotes from AI giants<a class="headerlink" href="#quotes-from-ai-giants" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p>“To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say ‘fourteen’ very loudly. Everyone does it.” - Geoffrey Hinton</p>
 </div></blockquote>
 </section>
 <section id="pca-with-iris-dataset">
-<h2><span class="section-number">38.17.6. </span>PCA with iris dataset<a class="headerlink" href="#pca-with-iris-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.6. </span>PCA with iris dataset<a class="headerlink" href="#pca-with-iris-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
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-<h2><span class="section-number">38.17.7. </span>PCA with mnist dataset<a class="headerlink" href="#pca-with-mnist-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.7. </span>PCA with mnist dataset<a class="headerlink" href="#pca-with-mnist-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">digits</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">genfromtxt</span><span class="p">(</span><span class="s1">&#39;../data/mnist_8x8.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">skip_header</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -2012,13 +2044,13 @@ <h2><span class="section-number">38.17.7. </span>PCA with mnist dataset<a class=
 </div>
 </section>
 <section id="clustering">
-<h2><span class="section-number">38.17.8. </span>Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.8. </span>Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Basically, “I have these points here, and I can see that they organize into groups. It would be nice to describe these things more concretely, and, when a new point comes in, assign it to the correct group.”</p></li>
 </ul>
 </section>
 <section id="k-means">
-<h2><span class="section-number">38.17.9. </span>K-means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.9. </span>K-means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h2>
 <p>K-means algorithm is the most popular and yet simplest of all the clustering algorithms. Here is how it works:</p>
 <ol class="simple">
 <li><p>Select the number of clusters <span class="math notranslate nohighlight">\(k\)</span> that you think is the optimal number.</p></li>
@@ -2109,7 +2141,7 @@ <h2><span class="section-number">38.17.9. </span>K-means<a class="headerlink" hr
 <p>Another “feature” of this algorithm is its sensitivity to the initial positions of the cluster centroids. You can run the algorithm several times and then average all the centroid results.</p>
 </section>
 <section id="k-means-with-sklearn">
-<h2><span class="section-number">38.17.10. </span>K-means with sklearn<a class="headerlink" href="#k-means-with-sklearn" title="Permalink to this headline">#</a></h2>
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 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
@@ -2131,7 +2163,7 @@ <h2><span class="section-number">38.17.10. </span>K-means with sklearn<a class="
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">38.17.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.17.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to YURY KASHNITSKY for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this slides.</p>
 </section>
 </section>
@@ -2173,13 +2205,13 @@ <h2><span class="section-number">38.17.11. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.16. </span>Build an machine learning web application</p>
+            <p class="prev-next-title"><span class="section-number">41.16. </span>Build an machine learning web application</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="kernel-method.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.18. </span>Kernel method</p>
+        <p class="prev-next-title"><span class="section-number">41.18. </span>Kernel method</p>
     </div>
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     </a>
diff --git a/slides/ml-fundamentals/build-an-ml-web-app.html b/slides/ml-fundamentals/build-an-ml-web-app.html
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.17. Unsupervised learning" href="../ml-advanced/unsupervised-learning.html" />
-    <link rel="prev" title="38.15. Neural Network" href="neural-network.html" />
+    <link rel="next" title="41.17. Unsupervised learning" href="../ml-advanced/unsupervised-learning.html" />
+    <link rel="prev" title="41.15. Neural Network" href="neural-network.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
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-     13.3. Yet other classifiers
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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    </li>
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-     14.1. Introduction to clustering
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-     14.2. K-Means clustering
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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-     15.3. Feature importance
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-     16.2. Gradient boosting example
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   25. Generative adversarial networks
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+   26. Recurrent Neural Networks
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-   27. Deep Q-learning
+   30. Deep Q-learning
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+   32. Image segmentation
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+   34. Object detection
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-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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+   39. Model deployment
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@@ -653,614 +685,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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-     37.8. Python programming advanced
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-<h1><span class="section-number">38.16. </span>Build an machine learning web application<a class="headerlink" href="#build-an-machine-learning-web-application" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.16. </span>Build an machine learning web application<a class="headerlink" href="#build-an-machine-learning-web-application" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">38.16.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Imagine that you’re working on a machine learning project and you’ve found your champion model. What happens next?</p></li>
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@@ -1869,16 +1901,16 @@ <h2><span class="section-number">38.16.1. </span>Introduction<a class="headerlin
 </ul>
 </section>
 <section id="streamlit">
-<h2><span class="section-number">38.16.2. </span>Streamlit<a class="headerlink" href="#streamlit" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.2. </span>Streamlit<a class="headerlink" href="#streamlit" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
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 <section id="display-and-style-data">
-<h2><span class="section-number">38.16.3. </span>Display and style data<a class="headerlink" href="#display-and-style-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.3. </span>Display and style data<a class="headerlink" href="#display-and-style-data" title="Permalink to this headline">#</a></h2>
 <section id="use-magic">
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+<h3><span class="section-number">41.16.3.1. </span>Use magic<a class="headerlink" href="#use-magic" title="Permalink to this headline">#</a></h3>
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-<h2><span class="section-number">38.16.4. </span>Display and style data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.4. </span>Display and style data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
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+<h3><span class="section-number">41.16.4.1. </span><code class="docutils literal notranslate"><span class="pre">st.write()</span></code><a class="headerlink" href="#st-write" title="Permalink to this headline">#</a></h3>
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-<h2><span class="section-number">38.16.5. </span>Display and style data<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
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 <section id="styling">
-<h3><span class="section-number">38.16.5.1. </span>Styling<a class="headerlink" href="#styling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.16.5.1. </span>Styling<a class="headerlink" href="#styling" title="Permalink to this headline">#</a></h3>
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@@ -1950,7 +1982,7 @@ <h3><span class="section-number">38.16.5.1. </span>Styling<a class="headerlink"
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 <section id="draw-a-line-chart">
-<h2><span class="section-number">38.16.6. </span>Draw a line chart<a class="headerlink" href="#draw-a-line-chart" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.6. </span>Draw a line chart<a class="headerlink" href="#draw-a-line-chart" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>You can easily add a line chart to your app with
 <code class="docutils literal notranslate"><span class="pre">st.line_chart()</span></code></p></li>
@@ -1968,7 +2000,7 @@ <h2><span class="section-number">38.16.6. </span>Draw a line chart<a class="head
 </div>
 </section>
 <section id="plot-a-map">
-<h2><span class="section-number">38.16.7. </span>Plot a map<a class="headerlink" href="#plot-a-map" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.7. </span>Plot a map<a class="headerlink" href="#plot-a-map" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>With <code class="docutils literal notranslate"><span class="pre">st.map()</span></code> you can display data points on a map</p></li>
 </ul>
@@ -1985,13 +2017,13 @@ <h2><span class="section-number">38.16.7. </span>Plot a map<a class="headerlink"
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 <section id="widgets">
-<h2><span class="section-number">38.16.8. </span>Widgets<a class="headerlink" href="#widgets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.8. </span>Widgets<a class="headerlink" href="#widgets" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
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+<h2><span class="section-number">41.16.9. </span><code class="docutils literal notranslate"><span class="pre">st.slider()</span></code><a class="headerlink" href="#st-slider" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">41.16.10. </span><code class="docutils literal notranslate"><span class="pre">st.text_input()</span></code><a class="headerlink" href="#st-text-input" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">38.16.11. </span>Use checkboxes to show/hide data<a class="headerlink" href="#use-checkboxes-to-show-hide-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.11. </span>Use checkboxes to show/hide data<a class="headerlink" href="#use-checkboxes-to-show-hide-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">st</span><span class="o">.</span><span class="n">checkbox</span><span class="p">(</span><span class="s1">&#39;Show dataframe&#39;</span><span class="p">):</span>
@@ -2037,7 +2069,7 @@ <h2><span class="section-number">38.16.11. </span>Use checkboxes to show/hide da
 </div>
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 <section id="use-a-selectbox-for-options">
-<h2><span class="section-number">38.16.12. </span>Use a selectbox for options<a class="headerlink" href="#use-a-selectbox-for-options" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.12. </span>Use a selectbox for options<a class="headerlink" href="#use-a-selectbox-for-options" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
@@ -2056,13 +2088,13 @@ <h2><span class="section-number">38.16.12. </span>Use a selectbox for options<a
 </div>
 </section>
 <section id="layout">
-<h2><span class="section-number">38.16.13. </span>Layout<a class="headerlink" href="#layout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.13. </span>Layout<a class="headerlink" href="#layout" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Streamlit makes it easy to organize your widgets in a left panel sidebar with <code class="docutils literal notranslate"><span class="pre">st.sidebar</span></code>. Each element that’s passed to <code class="docutils literal notranslate"><span class="pre">st.sidebar</span></code> is pinned to the left, allowing users to focus on the content in your app while still having access to UI controls.</p></li>
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 <section id="st-sidebar">
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+<h2><span class="section-number">41.16.14. </span><code class="docutils literal notranslate"><span class="pre">st.sidebar</span></code><a class="headerlink" href="#st-sidebar" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Add a selectbox to the sidebar:</span>
@@ -2082,7 +2114,7 @@ <h2><span class="section-number">38.16.14. </span><code class="docutils literal
 </div>
 </section>
 <section id="st-columns">
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+<h2><span class="section-number">41.16.15. </span><code class="docutils literal notranslate"><span class="pre">st.columns</span></code><a class="headerlink" href="#st-columns" title="Permalink to this headline">#</a></h2>
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 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">left_column</span><span class="p">,</span> <span class="n">right_column</span> <span class="o">=</span> <span class="n">st</span><span class="o">.</span><span class="n">columns</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
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+<h2><span class="section-number">41.16.16. </span>Show progress<a class="headerlink" href="#show-progress" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
@@ -2125,7 +2157,7 @@ <h2><span class="section-number">38.16.16. </span>Show progress<a class="headerl
 </div>
 </section>
 <section id="themes">
-<h2><span class="section-number">38.16.17. </span>Themes<a class="headerlink" href="#themes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.17. </span>Themes<a class="headerlink" href="#themes" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Streamlit supports Light and Dark themes out of the box</p></li>
 <li><p>The “Settings” menu has a theme editor
@@ -2138,7 +2170,7 @@ <h2><span class="section-number">38.16.17. </span>Themes<a class="headerlink" hr
 </ul>
 </section>
 <section id="caching">
-<h2><span class="section-number">38.16.18. </span>Caching<a class="headerlink" href="#caching" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.18. </span>Caching<a class="headerlink" href="#caching" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The Streamlit cache allows your app to stay performant even when loading data from the web, manipulating large datasets, or performing expensive computations</p></li>
 <li><p>The basic idea behind caching is to store the results of expensive function calls and return the cached result when the same inputs occur again rather than calling the function on subsequent runs</p></li>
@@ -2146,7 +2178,7 @@ <h2><span class="section-number">38.16.18. </span>Caching<a class="headerlink" h
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">38.16.19. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.19. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to PRASHANT BANERJEE for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/prashant111/mnist-deep-neural-network-with-keras">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this slides.</p>
 </section>
 </section>
@@ -2188,13 +2220,13 @@ <h2><span class="section-number">38.16.19. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.15. </span>Neural Network</p>
+            <p class="prev-next-title"><span class="section-number">41.15. </span>Neural Network</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ml-advanced/unsupervised-learning.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.17. </span>Unsupervised learning</p>
+        <p class="prev-next-title"><span class="section-number">41.17. </span>Unsupervised learning</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/ml-fundamentals/linear-regression.html b/slides/ml-fundamentals/linear-regression.html
index 0587712bee..2e97a14e5a 100644
--- a/slides/ml-fundamentals/linear-regression.html
+++ b/slides/ml-fundamentals/linear-regression.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.12. Linear Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.12. Linear Regression &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.13. Logistic Regression" href="logistic-regression.html" />
-    <link rel="prev" title="38.11. Machine Learning overview" href="ml-overview.html" />
+    <link rel="next" title="41.13. Logistic Regression" href="logistic-regression.html" />
+    <link rel="prev" title="41.11. Machine Learning overview" href="ml-overview.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
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-   18. Kernel method
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+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.92. Sign Language Digits Classification with CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.103. Fun with Variational Autoencoders
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-     37.106. Neural Networks for Classification with TensorFlow
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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-     38.4. Data Science introduction
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-     38.5. Relational vs. non-relational database
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     </a>
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-     38.6. NumPy and Pandas
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@@ -1528,183 +1560,183 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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     <a class="reference internal nav-link" href="#linear-model">
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+       41.12.1.2.1. Hypothesis
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+       41.12.1.2.2. Design Matrix
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       <a class="reference internal nav-link" href="#univariate-example">
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+       41.12.1.2.3. Univariate Example
       </a>
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     <a class="reference internal nav-link" href="#what-is-a-good-fit">
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-   38.12.12. Importing the dataset
+   41.12.12. Importing the dataset
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-   38.12.13. Encoding categorical data
+   41.12.13. Encoding categorical data
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-   38.12.14. Splitting the dataset into the Training set and Test set
+   41.12.14. Splitting the dataset into the Training set and Test set
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-   38.12.15. Training the Multiple Linear Regression model on the Training set
+   41.12.15. Training the Multiple Linear Regression model on the Training set
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-   38.12.16. Predicting the Test set results
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-   38.12.17. Example 3
+   41.12.17. Example 3
   </a>
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-   38.12.18. Importing the libraries
+   41.12.18. Importing the libraries
   </a>
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-   38.12.19. Importing the dataset
+   41.12.19. Importing the dataset
   </a>
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   <a class="reference internal nav-link" href="#training-the-linear-regression-model-on-the-whole-dataset">
-   38.12.20. Training the Linear Regression model on the whole dataset
+   41.12.20. Training the Linear Regression model on the whole dataset
   </a>
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   <a class="reference internal nav-link" href="#training-the-polynomial-regression-model-on-the-whole-dataset">
-   38.12.21. Training the Polynomial Regression model on the whole dataset
+   41.12.21. Training the Polynomial Regression model on the whole dataset
   </a>
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-   38.12.22. Visualising the Polynomial Linear Regression results
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-   38.12.23. Implementation of Linear Regression for scratch
+   41.12.23. Implementation of Linear Regression for scratch
   </a>
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-   38.12.24. Traning model with our own Linear Regression
+   41.12.24. Traning model with our own Linear Regression
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-   38.12.25. Visualising the Training set results
+   41.12.25. Visualising the Training set results
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.12.26. Acknowledgments
+   41.12.26. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1728,183 +1760,183 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-linear-regression">
-   38.12.1. What is Linear Regression?
+   41.12.1. What is Linear Regression?
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#example">
-     38.12.1.1. Example
+     41.12.1.1. Example
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-model">
-     38.12.1.2. Linear Model
+     41.12.1.2. Linear Model
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#hypothesis">
-       38.12.1.2.1. Hypothesis
+       41.12.1.2.1. Hypothesis
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#design-matrix">
-       38.12.1.2.2. Design Matrix
+       41.12.1.2.2. Design Matrix
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#univariate-example">
-       38.12.1.2.3. Univariate Example
+       41.12.1.2.3. Univariate Example
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-a-good-fit">
-     38.12.1.3. What is a Good Fit?
+     41.12.1.3. What is a Good Fit?
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#optimization-problem">
-       38.12.1.3.1. Optimization Problem
+       41.12.1.3.1. Optimization Problem
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#normal-equations">
-       38.12.1.3.2. Normal Equations
+       41.12.1.3.2. Normal Equations
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#normalization">
-     38.12.1.4. Normalization
+     41.12.1.4. Normalization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-1">
-   38.12.2. Example 1
+   41.12.2. Example 1
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   38.12.3. Importing the libraries
+   41.12.3. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   38.12.4. Importing the dataset
+   41.12.4. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   38.12.5. Splitting the dataset into the Training set and Test set
+   41.12.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-simple-linear-regression-model-on-the-training-set">
-   38.12.6. Training the Simple Linear Regression model on the Training set
+   41.12.6. Training the Simple Linear Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   38.12.7. Predicting the Test set results
+   41.12.7. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   38.12.8. Visualising the Training set results
+   41.12.8. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   38.12.9. Visualising the Test set results
+   41.12.9. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-2">
-   38.12.10. Example 2
+   41.12.10. Example 2
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   38.12.11. Importing the libraries
+   41.12.11. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   38.12.12. Importing the dataset
+   41.12.12. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#encoding-categorical-data">
-   38.12.13. Encoding categorical data
+   41.12.13. Encoding categorical data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id3">
-   38.12.14. Splitting the dataset into the Training set and Test set
+   41.12.14. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-multiple-linear-regression-model-on-the-training-set">
-   38.12.15. Training the Multiple Linear Regression model on the Training set
+   41.12.15. Training the Multiple Linear Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id4">
-   38.12.16. Predicting the Test set results
+   41.12.16. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-3">
-   38.12.17. Example 3
+   41.12.17. Example 3
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   38.12.18. Importing the libraries
+   41.12.18. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id6">
-   38.12.19. Importing the dataset
+   41.12.19. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-linear-regression-model-on-the-whole-dataset">
-   38.12.20. Training the Linear Regression model on the whole dataset
+   41.12.20. Training the Linear Regression model on the whole dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-polynomial-regression-model-on-the-whole-dataset">
-   38.12.21. Training the Polynomial Regression model on the whole dataset
+   41.12.21. Training the Polynomial Regression model on the whole dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-polynomial-linear-regression-results">
-   38.12.22. Visualising the Polynomial Linear Regression results
+   41.12.22. Visualising the Polynomial Linear Regression results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementation-of-linear-regression-for-scratch">
-   38.12.23. Implementation of Linear Regression for scratch
+   41.12.23. Implementation of Linear Regression for scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#traning-model-with-our-own-linear-regression">
-   38.12.24. Traning model with our own Linear Regression
+   41.12.24. Traning model with our own Linear Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id7">
-   38.12.25. Visualising the Training set results
+   41.12.25. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.12.26. Acknowledgments
+   41.12.26. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1940,12 +1972,12 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="linear-regression">
-<h1><span class="section-number">38.12. </span>Linear Regression<a class="headerlink" href="#linear-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.12. </span>Linear Regression<a class="headerlink" href="#linear-regression" title="Permalink to this headline">#</a></h1>
 <section id="what-is-linear-regression">
-<h2><span class="section-number">38.12.1. </span>What is Linear Regression?<a class="headerlink" href="#what-is-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.1. </span>What is Linear Regression?<a class="headerlink" href="#what-is-linear-regression" title="Permalink to this headline">#</a></h2>
 <p>Finding a straight line of best fit through the data. This works well when the true underlying function is linear.</p>
 <section id="example">
-<h3><span class="section-number">38.12.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
 <p>We use features <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> to predict a “response” <span class="math notranslate nohighlight">\(y\)</span>. For example we might want to regress <code class="docutils literal notranslate"><span class="pre">num_hours_studied</span></code> onto <code class="docutils literal notranslate"><span class="pre">exam_score</span></code> - in other words we predict exam score from number of hours studied.</p>
 <p>Let’s generate some example data for this case and examine the relationship between <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> and <span class="math notranslate nohighlight">\(y\)</span>.</p>
 <div class="cell docutils container">
@@ -1968,9 +2000,9 @@ <h3><span class="section-number">38.12.1.1. </span>Example<a class="headerlink"
 <p>We will now build a linear model to fit this data.</p>
 </section>
 <section id="linear-model">
-<h3><span class="section-number">38.12.1.2. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.2. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h3>
 <section id="hypothesis">
-<h4><span class="section-number">38.12.1.2.1. </span>Hypothesis<a class="headerlink" href="#hypothesis" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.2.1. </span>Hypothesis<a class="headerlink" href="#hypothesis" title="Permalink to this headline">#</a></h4>
 <p>A linear model makes a “hypothesis” about the true nature of the underlying function - that it is linear. We express this hypothesis in the univariate case as</p>
 <div class="math notranslate nohighlight">
 \[h_\theta(x) = ax + b\]</div>
@@ -1980,7 +2012,7 @@ <h4><span class="section-number">38.12.1.2.1. </span>Hypothesis<a class="headerl
 <p>Here <span class="math notranslate nohighlight">\(\mathbf{a}\)</span> is a vector of learned parameters, and <span class="math notranslate nohighlight">\(\mathbf{X}\)</span> is the “design matrix” with all the data points. In this formulation the intercept term has been added to the design matrix as the first column (of all ones).</p>
 </section>
 <section id="design-matrix">
-<h4><span class="section-number">38.12.1.2.2. </span>Design Matrix<a class="headerlink" href="#design-matrix" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.2.2. </span>Design Matrix<a class="headerlink" href="#design-matrix" title="Permalink to this headline">#</a></h4>
 <p>In general with <span class="math notranslate nohighlight">\(n\)</span> data points and <span class="math notranslate nohighlight">\(p\)</span> features our design matrix will have <span class="math notranslate nohighlight">\(n\)</span> rows and <span class="math notranslate nohighlight">\(p\)</span> columns.</p>
 <p>Returning to our exam score regression example, let’s add one more feature - number of hours slept the night before the exam. If we have 4 data points and 2 features, then our matrix will be of shape <span class="math notranslate nohighlight">\(4 \times 3\)</span> (remember we add a bias column). It might look like</p>
 <div class="math notranslate nohighlight">
@@ -1995,7 +2027,7 @@ <h4><span class="section-number">38.12.1.2.2. </span>Design Matrix<a class="head
 <p>Notice we do <strong>not</strong> include the response (label/target) in the design matrix.</p>
 </section>
 <section id="univariate-example">
-<h4><span class="section-number">38.12.1.2.3. </span>Univariate Example<a class="headerlink" href="#univariate-example" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.2.3. </span>Univariate Example<a class="headerlink" href="#univariate-example" title="Permalink to this headline">#</a></h4>
 <p>Let’s now see what our univariate example looks like</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2033,7 +2065,7 @@ <h4><span class="section-number">38.12.1.2.3. </span>Univariate Example<a class=
 </section>
 </section>
 <section id="what-is-a-good-fit">
-<h3><span class="section-number">38.12.1.3. </span>What is a Good Fit?<a class="headerlink" href="#what-is-a-good-fit" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.3. </span>What is a Good Fit?<a class="headerlink" href="#what-is-a-good-fit" title="Permalink to this headline">#</a></h3>
 <p>Typically we use “mean squared error” to measure the goodness of fit in a regression problem.</p>
 <div class="math notranslate nohighlight">
 \[\text{MSE} = \frac{1}{n} \sum_{i=1}^n (y^{(i)} - h_\theta^{(i)})^2\]</div>
@@ -2041,14 +2073,14 @@ <h3><span class="section-number">38.12.1.3. </span>What is a Good Fit?<a class="
 <p><img alt="" src="../../_images/residual.webp" /></p>
 <p>This function is then taken to be our “loss” function - a measure of how badly we are doing. In general we want to minimize this.</p>
 <section id="optimization-problem">
-<h4><span class="section-number">38.12.1.3.1. </span>Optimization Problem<a class="headerlink" href="#optimization-problem" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.3.1. </span>Optimization Problem<a class="headerlink" href="#optimization-problem" title="Permalink to this headline">#</a></h4>
 <p>The typical recipe for machine learning algorithms is to define a loss function of the parameters of a hypothesis, then to minimize the loss function. In our case we have the optimization problem</p>
 <div class="math notranslate nohighlight">
 \[\min_{\mathbf{a}} \frac{1}{n} \sum_{i=1}^n (y^{(i)} - \mathbf{a}^\top\mathbf{X}^{(i)})^2\]</div>
 <p>Note that we have added the bias into the design matrix.</p>
 </section>
 <section id="normal-equations">
-<h4><span class="section-number">38.12.1.3.2. </span>Normal Equations<a class="headerlink" href="#normal-equations" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.3.2. </span>Normal Equations<a class="headerlink" href="#normal-equations" title="Permalink to this headline">#</a></h4>
 <p>Linear regression actually has a closed-form solution - the normal equation. It is beyond our scope to show the derivation, but here it is:</p>
 <div class="math notranslate nohighlight">
 \[\mathbf{a}^* = (\mathbf{X}^\top\mathbf{X})^{-1}\mathbf{X}^\top \mathbf{y}\]</div>
@@ -2056,18 +2088,18 @@ <h4><span class="section-number">38.12.1.3.2. </span>Normal Equations<a class="h
 </section>
 </section>
 <section id="normalization">
-<h3><span class="section-number">38.12.1.4. </span>Normalization<a class="headerlink" href="#normalization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.4. </span>Normalization<a class="headerlink" href="#normalization" title="Permalink to this headline">#</a></h3>
 <p>It is generally a good idea to normalize all the values in the design matrix. This means all values should be in the range <span class="math notranslate nohighlight">\((0, 1)\)</span> and centered around zero.</p>
 <p><img alt="" src="../../_images/normalization.jpeg" /></p>
 <p>Normalization usually helps the learning algorithm perform better.</p>
 </section>
 </section>
 <section id="example-1">
-<h2><span class="section-number">38.12.2. </span>Example 1<a class="headerlink" href="#example-1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.2. </span>Example 1<a class="headerlink" href="#example-1" title="Permalink to this headline">#</a></h2>
 <p>Simple Linear Regression</p>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">38.12.3. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.3. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2080,7 +2112,7 @@ <h2><span class="section-number">38.12.3. </span>Importing the libraries<a class
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">38.12.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Salary_Data.csv&#39;</span><span class="p">)</span>
@@ -2092,7 +2124,7 @@ <h2><span class="section-number">38.12.4. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="splitting-the-dataset-into-the-training-set-and-test-set">
-<h2><span class="section-number">38.12.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2104,7 +2136,7 @@ <h2><span class="section-number">38.12.5. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="training-the-simple-linear-regression-model-on-the-training-set">
-<h2><span class="section-number">38.12.6. </span>Training the Simple Linear Regression model on the Training set<a class="headerlink" href="#training-the-simple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.6. </span>Training the Simple Linear Regression model on the Training set<a class="headerlink" href="#training-the-simple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -2117,7 +2149,7 @@ <h2><span class="section-number">38.12.6. </span>Training the Simple Linear Regr
 </div>
 </section>
 <section id="predicting-the-test-set-results">
-<h2><span class="section-number">38.12.7. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.7. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -2127,7 +2159,7 @@ <h2><span class="section-number">38.12.7. </span>Predicting the Test set results
 </div>
 </section>
 <section id="visualising-the-training-set-results">
-<h2><span class="section-number">38.12.8. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.8. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
@@ -2142,7 +2174,7 @@ <h2><span class="section-number">38.12.8. </span>Visualising the Training set re
 </div>
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 <section id="visualising-the-test-set-results">
-<h2><span class="section-number">38.12.9. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.9. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -2157,11 +2189,11 @@ <h2><span class="section-number">38.12.9. </span>Visualising the Test set result
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-<h2><span class="section-number">38.12.10. </span>Example 2<a class="headerlink" href="#example-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.10. </span>Example 2<a class="headerlink" href="#example-2" title="Permalink to this headline">#</a></h2>
 <p>Multiple Linear Regression</p>
 </section>
 <section id="id1">
-<h2><span class="section-number">38.12.11. </span>Importing the libraries<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.11. </span>Importing the libraries<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2174,7 +2206,7 @@ <h2><span class="section-number">38.12.11. </span>Importing the libraries<a clas
 </div>
 </section>
 <section id="id2">
-<h2><span class="section-number">38.12.12. </span>Importing the dataset<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.12. </span>Importing the dataset<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/50_Startups.csv&#39;</span><span class="p">)</span>
@@ -2200,7 +2232,7 @@ <h2><span class="section-number">38.12.12. </span>Importing the dataset<a class=
 </div>
 </section>
 <section id="encoding-categorical-data">
-<h2><span class="section-number">38.12.13. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.13. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
@@ -2220,7 +2252,7 @@ <h2><span class="section-number">38.12.13. </span>Encoding categorical data<a cl
 </div>
 </section>
 <section id="id3">
-<h2><span class="section-number">38.12.14. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.14. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
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 <div class="cell_input docutils container">
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@@ -2231,7 +2263,7 @@ <h2><span class="section-number">38.12.14. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="training-the-multiple-linear-regression-model-on-the-training-set">
-<h2><span class="section-number">38.12.15. </span>Training the Multiple Linear Regression model on the Training set<a class="headerlink" href="#training-the-multiple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.15. </span>Training the Multiple Linear Regression model on the Training set<a class="headerlink" href="#training-the-multiple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -2243,7 +2275,7 @@ <h2><span class="section-number">38.12.15. </span>Training the Multiple Linear R
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 </section>
 <section id="id4">
-<h2><span class="section-number">38.12.16. </span>Predicting the Test set results<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.16. </span>Predicting the Test set results<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -2255,11 +2287,11 @@ <h2><span class="section-number">38.12.16. </span>Predicting the Test set result
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 <section id="example-3">
-<h2><span class="section-number">38.12.17. </span>Example 3<a class="headerlink" href="#example-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.17. </span>Example 3<a class="headerlink" href="#example-3" title="Permalink to this headline">#</a></h2>
 <p>Polynomial Linear Regression</p>
 </section>
 <section id="id5">
-<h2><span class="section-number">38.12.18. </span>Importing the libraries<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.18. </span>Importing the libraries<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2272,7 +2304,7 @@ <h2><span class="section-number">38.12.18. </span>Importing the libraries<a clas
 </div>
 </section>
 <section id="id6">
-<h2><span class="section-number">38.12.19. </span>Importing the dataset<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.19. </span>Importing the dataset<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
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 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Position_Salaries.csv&#39;</span><span class="p">)</span>
@@ -2284,7 +2316,7 @@ <h2><span class="section-number">38.12.19. </span>Importing the dataset<a class=
 </div>
 </section>
 <section id="training-the-linear-regression-model-on-the-whole-dataset">
-<h2><span class="section-number">38.12.20. </span>Training the Linear Regression model on the whole dataset<a class="headerlink" href="#training-the-linear-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.20. </span>Training the Linear Regression model on the whole dataset<a class="headerlink" href="#training-the-linear-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -2296,7 +2328,7 @@ <h2><span class="section-number">38.12.20. </span>Training the Linear Regression
 </div>
 </section>
 <section id="training-the-polynomial-regression-model-on-the-whole-dataset">
-<h2><span class="section-number">38.12.21. </span>Training the Polynomial Regression model on the whole dataset<a class="headerlink" href="#training-the-polynomial-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.21. </span>Training the Polynomial Regression model on the whole dataset<a class="headerlink" href="#training-the-polynomial-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">PolynomialFeatures</span>
@@ -2310,7 +2342,7 @@ <h2><span class="section-number">38.12.21. </span>Training the Polynomial Regres
 </div>
 </section>
 <section id="visualising-the-polynomial-linear-regression-results">
-<h2><span class="section-number">38.12.22. </span>Visualising the Polynomial Linear Regression results<a class="headerlink" href="#visualising-the-polynomial-linear-regression-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.22. </span>Visualising the Polynomial Linear Regression results<a class="headerlink" href="#visualising-the-polynomial-linear-regression-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
@@ -2325,7 +2357,7 @@ <h2><span class="section-number">38.12.22. </span>Visualising the Polynomial Lin
 </div>
 </section>
 <section id="implementation-of-linear-regression-for-scratch">
-<h2><span class="section-number">38.12.23. </span>Implementation of Linear Regression for scratch<a class="headerlink" href="#implementation-of-linear-regression-for-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.23. </span>Implementation of Linear Regression for scratch<a class="headerlink" href="#implementation-of-linear-regression-for-scratch" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyOwnLinearRegression</span><span class="p">:</span>
@@ -2363,7 +2395,7 @@ <h2><span class="section-number">38.12.23. </span>Implementation of Linear Regre
 </div>
 </section>
 <section id="traning-model-with-our-own-linear-regression">
-<h2><span class="section-number">38.12.24. </span>Traning model with our own Linear Regression<a class="headerlink" href="#traning-model-with-our-own-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.24. </span>Traning model with our own Linear Regression<a class="headerlink" href="#traning-model-with-our-own-linear-regression" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Salary_Data.csv&#39;</span><span class="p">)</span>
@@ -2379,7 +2411,7 @@ <h2><span class="section-number">38.12.24. </span>Traning model with our own Lin
 </div>
 </section>
 <section id="id7">
-<h2><span class="section-number">38.12.25. </span>Visualising the Training set results<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.25. </span>Visualising the Training set results<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
@@ -2394,7 +2426,7 @@ <h2><span class="section-number">38.12.25. </span>Visualising the Training set r
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">38.12.26. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.26. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TIM NIVEN for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/timniven/linear-regression-tutorial">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
 </section>
 </section>
@@ -2436,13 +2468,13 @@ <h2><span class="section-number">38.12.26. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.11. </span>Machine Learning overview</p>
+            <p class="prev-next-title"><span class="section-number">41.11. </span>Machine Learning overview</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="logistic-regression.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.13. </span>Logistic Regression</p>
+        <p class="prev-next-title"><span class="section-number">41.13. </span>Logistic Regression</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/ml-fundamentals/logistic-regression-condensed.html b/slides/ml-fundamentals/logistic-regression-condensed.html
index fe45087af1..29fa280436 100644
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-    <title>38.14. Logistic Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.14. Logistic Regression &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.15. Neural Network" href="neural-network.html" />
-    <link rel="prev" title="38.13. Logistic Regression" href="logistic-regression.html" />
+    <link rel="next" title="41.15. Neural Network" href="neural-network.html" />
+    <link rel="prev" title="41.13. Logistic Regression" href="logistic-regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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-   18. Kernel method
+   20. Kernel method
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
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-     37.5. Project Plan​ Template
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-     37.94. Object Recognition in Images using CNN
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.112. DQN On Foreign Exchange Market
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-     37.115. Generative Adversarial Networks (GANs)
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-     37.116. Basic classification: Classify images of clothing
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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-     38.3. Python programming advanced
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-     38.4. Data Science introduction
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-     38.5. Relational vs. non-relational database
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     </a>
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-     38.6. NumPy and Pandas
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     </a>
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-     38.8. Data Science lifecycle
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-     38.10. Data Science in real world
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     </a>
    </li>
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-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
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-     38.12. Linear Regression
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-     38.15. Neural Network
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-     38.16. Build an machine learning web application
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
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-     38.18. Kernel method
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     </a>
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     </a>
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     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1526,77 +1558,77 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   41.14.1. Introduction
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-   38.14.2. Importing the libraries
+   41.14.2. Importing the libraries
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+   41.14.3. Sigmoid function
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   <a class="reference internal nav-link" href="#importing-the-dataset">
-   38.14.4. Importing the dataset
+   41.14.4. Importing the dataset
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   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
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+   41.14.5. Splitting the dataset into the Training set and Test set
   </a>
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@@ -1620,77 +1652,77 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   38.14.1. Introduction
+   41.14.1. Introduction
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   <a class="reference internal nav-link" href="#importing-the-libraries">
-   38.14.2. Importing the libraries
+   41.14.2. Importing the libraries
   </a>
  </li>
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   <a class="reference internal nav-link" href="#sigmoid-function">
-   38.14.3. Sigmoid function
+   41.14.3. Sigmoid function
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   <a class="reference internal nav-link" href="#importing-the-dataset">
-   38.14.4. Importing the dataset
+   41.14.4. Importing the dataset
   </a>
  </li>
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   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   38.14.5. Splitting the dataset into the Training set and Test set
+   41.14.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
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   <a class="reference internal nav-link" href="#feature-scaling">
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+   41.14.6. Feature Scaling
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   <a class="reference internal nav-link" href="#training-the-logistic-regression-model-on-the-training-set">
-   38.14.7. Training the Logistic Regression model on the Training set
+   41.14.7. Training the Logistic Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-a-new-result">
-   38.14.8. Predicting a new result
+   41.14.8. Predicting a new result
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   38.14.9. Predicting the Test set results
+   41.14.9. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#making-the-confusion-matrix">
-   38.14.10. Making the Confusion Matrix
+   41.14.10. Making the Confusion Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   38.14.11. Visualising the Training set results
+   41.14.11. Visualising the Training set results
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   38.14.12. Visualising the Test set results
+   41.14.12. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-v-s-logistic-regression">
-   38.14.13. Linear Regression v.s. Logistic Regression
+   41.14.13. Linear Regression v.s. Logistic Regression
   </a>
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-   38.14.14. Assignment - 1
+   41.14.14. Assignment - 1
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-   38.14.15. Assignment - 2
+   41.14.15. Assignment - 2
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 </ul>
@@ -1726,9 +1758,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="logistic-regression">
-<h1><span class="section-number">38.14. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.14. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">38.14.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In fact, logistic regression is a  <u>classification</u> algorithm, unlike other regression models.</p></li>
 <li><p>Logistic Regression is very important for entering deep learning.</p></li>
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+<h2><span class="section-number">41.14.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">41.14.3. </span>Sigmoid function<a class="headerlink" href="#sigmoid-function" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -1768,7 +1800,7 @@ <h2><span class="section-number">38.14.3. </span>Sigmoid function<a class="heade
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">38.14.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Social_Network_Ads.csv&#39;</span><span class="p">)</span>
@@ -1782,7 +1814,7 @@ <h2><span class="section-number">38.14.4. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="splitting-the-dataset-into-the-training-set-and-test-set">
-<h2><span class="section-number">38.14.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -1793,7 +1825,7 @@ <h2><span class="section-number">38.14.5. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">38.14.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
@@ -1806,7 +1838,7 @@ <h2><span class="section-number">38.14.6. </span>Feature Scaling<a class="header
 </div>
 </section>
 <section id="training-the-logistic-regression-model-on-the-training-set">
-<h2><span class="section-number">38.14.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
@@ -1818,7 +1850,7 @@ <h2><span class="section-number">38.14.7. </span>Training the Logistic Regressio
 </div>
 </section>
 <section id="predicting-a-new-result">
-<h2><span class="section-number">38.14.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">transform</span><span class="p">([[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">87000</span><span class="p">],</span> <span class="p">[</span><span class="mi">65</span><span class="p">,</span> <span class="mi">990000</span><span class="p">]])))</span>
@@ -1828,7 +1860,7 @@ <h2><span class="section-number">38.14.8. </span>Predicting a new result<a class
 </div>
 </section>
 <section id="predicting-the-test-set-results">
-<h2><span class="section-number">38.14.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -1839,7 +1871,7 @@ <h2><span class="section-number">38.14.9. </span>Predicting the Test set results
 </div>
 </section>
 <section id="making-the-confusion-matrix">
-<h2><span class="section-number">38.14.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">accuracy_score</span>
@@ -1852,7 +1884,7 @@ <h2><span class="section-number">38.14.10. </span>Making the Confusion Matrix<a
 </div>
 </section>
 <section id="visualising-the-training-set-results">
-<h2><span class="section-number">38.14.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1876,7 +1908,7 @@ <h2><span class="section-number">38.14.11. </span>Visualising the Training set r
 </div>
 </section>
 <section id="visualising-the-test-set-results">
-<h2><span class="section-number">38.14.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1900,7 +1932,7 @@ <h2><span class="section-number">38.14.12. </span>Visualising the Test set resul
 </div>
 </section>
 <section id="linear-regression-v-s-logistic-regression">
-<h2><span class="section-number">38.14.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
@@ -1956,13 +1988,13 @@ <h2><span class="section-number">38.14.13. </span>Linear Regression v.s. Logisti
 </div>
 </section>
 <section id="assignment-1">
-<h2><span class="section-number">38.14.14. </span>Assignment - 1<a class="headerlink" href="#assignment-1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.14. </span>Assignment - 1<a class="headerlink" href="#assignment-1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Build classification models：Predict the price range</p></li>
 </ul>
 </section>
 <section id="assignment-2">
-<h2><span class="section-number">38.14.15. </span>Assignment - 2<a class="headerlink" href="#assignment-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.15. </span>Assignment - 2<a class="headerlink" href="#assignment-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Logistic Regression from scratch</p></li>
 </ul>
@@ -2006,13 +2038,13 @@ <h2><span class="section-number">38.14.15. </span>Assignment - 2<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.13. </span>Logistic Regression</p>
+            <p class="prev-next-title"><span class="section-number">41.13. </span>Logistic Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="neural-network.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.15. </span>Neural Network</p>
+        <p class="prev-next-title"><span class="section-number">41.15. </span>Neural Network</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/ml-fundamentals/logistic-regression.html b/slides/ml-fundamentals/logistic-regression.html
index 0a010815d4..ab4d038311 100644
--- a/slides/ml-fundamentals/logistic-regression.html
+++ b/slides/ml-fundamentals/logistic-regression.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.13. Logistic Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.13. Logistic Regression &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.14. Logistic Regression" href="logistic-regression-condensed.html" />
-    <link rel="prev" title="38.12. Linear Regression" href="linear-regression.html" />
+    <link rel="next" title="41.14. Logistic Regression" href="logistic-regression-condensed.html" />
+    <link rel="prev" title="41.12. Linear Regression" href="linear-regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -422,22 +427,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
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+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
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-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
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     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
   </a>
  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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+   30. Deep Q-learning
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+     40.90. How to choose cnn architecture mnist
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.113. Art by gan
+     40.113. Art by gan
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     37.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../introduction.html">
-   38. Slides
+   41. Slides
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
-  <label for="toctree-checkbox-13">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/>
+  <label for="toctree-checkbox-14">
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    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-introduction.html">
-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-basics.html">
-     38.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,101 +1558,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   38.13.1. Introduction
+   41.13.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   38.13.2. Importing the libraries
+   41.13.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sigmoid-function">
-   38.13.3. Sigmoid function
+   41.13.3. Sigmoid function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   38.13.4. Importing the dataset
+   41.13.4. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   38.13.5. Splitting the dataset into the Training set and Test set
+   41.13.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   38.13.6. Feature Scaling
+   41.13.6. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-logistic-regression-model-on-the-training-set">
-   38.13.7. Training the Logistic Regression model on the Training set
+   41.13.7. Training the Logistic Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-a-new-result">
-   38.13.8. Predicting a new result
+   41.13.8. Predicting a new result
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   38.13.9. Predicting the Test set results
+   41.13.9. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#making-the-confusion-matrix">
-   38.13.10. Making the Confusion Matrix
+   41.13.10. Making the Confusion Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   38.13.11. Visualising the Training set results
+   41.13.11. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   38.13.12. Visualising the Test set results
+   41.13.12. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-v-s-logistic-regression">
-   38.13.13. Linear Regression v.s. Logistic Regression
+   41.13.13. Linear Regression v.s. Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#logistic-regression-from-scratch">
-   38.13.14. Logistic Regression from scratch
+   41.13.14. Logistic Regression from scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-function-for-logistic-regression">
-   38.13.15. Loss function for logistic regression
+   41.13.15. Loss function for logistic regression
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cross-entropy">
-     38.13.15.1. Cross-entropy
+     41.13.15.1. Cross-entropy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-separability">
-   38.13.16. Linear separability
+   41.13.16. Linear separability
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#thats-when-neural-network-comes-into-play">
-     38.13.16.1. That’s when Neural Network comes into play!
+     41.13.16.1. That’s when Neural Network comes into play!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.13.17. Acknowledgments
+   41.13.17. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1644,101 +1676,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   38.13.1. Introduction
+   41.13.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   38.13.2. Importing the libraries
+   41.13.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sigmoid-function">
-   38.13.3. Sigmoid function
+   41.13.3. Sigmoid function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   38.13.4. Importing the dataset
+   41.13.4. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   38.13.5. Splitting the dataset into the Training set and Test set
+   41.13.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   38.13.6. Feature Scaling
+   41.13.6. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-logistic-regression-model-on-the-training-set">
-   38.13.7. Training the Logistic Regression model on the Training set
+   41.13.7. Training the Logistic Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-a-new-result">
-   38.13.8. Predicting a new result
+   41.13.8. Predicting a new result
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   38.13.9. Predicting the Test set results
+   41.13.9. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#making-the-confusion-matrix">
-   38.13.10. Making the Confusion Matrix
+   41.13.10. Making the Confusion Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   38.13.11. Visualising the Training set results
+   41.13.11. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   38.13.12. Visualising the Test set results
+   41.13.12. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-v-s-logistic-regression">
-   38.13.13. Linear Regression v.s. Logistic Regression
+   41.13.13. Linear Regression v.s. Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#logistic-regression-from-scratch">
-   38.13.14. Logistic Regression from scratch
+   41.13.14. Logistic Regression from scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-function-for-logistic-regression">
-   38.13.15. Loss function for logistic regression
+   41.13.15. Loss function for logistic regression
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cross-entropy">
-     38.13.15.1. Cross-entropy
+     41.13.15.1. Cross-entropy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-separability">
-   38.13.16. Linear separability
+   41.13.16. Linear separability
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#thats-when-neural-network-comes-into-play">
-     38.13.16.1. That’s when Neural Network comes into play!
+     41.13.16.1. That’s when Neural Network comes into play!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.13.17. Acknowledgments
+   41.13.17. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1774,9 +1806,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="logistic-regression">
-<h1><span class="section-number">38.13. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.13. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">38.13.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In fact, logistic regression is a  <u>classification</u> algorithm, unlike other regression models.</p></li>
 <li><p>Logistic Regression is very important for entering deep learning.</p></li>
@@ -1784,7 +1816,7 @@ <h2><span class="section-number">38.13.1. </span>Introduction<a class="headerlin
 </ul>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">38.13.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1797,7 +1829,7 @@ <h2><span class="section-number">38.13.2. </span>Importing the libraries<a class
 </div>
 </section>
 <section id="sigmoid-function">
-<h2><span class="section-number">38.13.3. </span>Sigmoid function<a class="headerlink" href="#sigmoid-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.3. </span>Sigmoid function<a class="headerlink" href="#sigmoid-function" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -1816,7 +1848,7 @@ <h2><span class="section-number">38.13.3. </span>Sigmoid function<a class="heade
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">38.13.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Social_Network_Ads.csv&#39;</span><span class="p">)</span>
@@ -1830,7 +1862,7 @@ <h2><span class="section-number">38.13.4. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="splitting-the-dataset-into-the-training-set-and-test-set">
-<h2><span class="section-number">38.13.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -1841,7 +1873,7 @@ <h2><span class="section-number">38.13.5. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">38.13.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
@@ -1854,7 +1886,7 @@ <h2><span class="section-number">38.13.6. </span>Feature Scaling<a class="header
 </div>
 </section>
 <section id="training-the-logistic-regression-model-on-the-training-set">
-<h2><span class="section-number">38.13.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
@@ -1866,7 +1898,7 @@ <h2><span class="section-number">38.13.7. </span>Training the Logistic Regressio
 </div>
 </section>
 <section id="predicting-a-new-result">
-<h2><span class="section-number">38.13.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">transform</span><span class="p">([[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">87000</span><span class="p">],</span> <span class="p">[</span><span class="mi">65</span><span class="p">,</span> <span class="mi">990000</span><span class="p">]])))</span>
@@ -1876,7 +1908,7 @@ <h2><span class="section-number">38.13.8. </span>Predicting a new result<a class
 </div>
 </section>
 <section id="predicting-the-test-set-results">
-<h2><span class="section-number">38.13.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -1887,7 +1919,7 @@ <h2><span class="section-number">38.13.9. </span>Predicting the Test set results
 </div>
 </section>
 <section id="making-the-confusion-matrix">
-<h2><span class="section-number">38.13.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">accuracy_score</span>
@@ -1900,7 +1932,7 @@ <h2><span class="section-number">38.13.10. </span>Making the Confusion Matrix<a
 </div>
 </section>
 <section id="visualising-the-training-set-results">
-<h2><span class="section-number">38.13.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1924,7 +1956,7 @@ <h2><span class="section-number">38.13.11. </span>Visualising the Training set r
 </div>
 </section>
 <section id="visualising-the-test-set-results">
-<h2><span class="section-number">38.13.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1948,7 +1980,7 @@ <h2><span class="section-number">38.13.12. </span>Visualising the Test set resul
 </div>
 </section>
 <section id="linear-regression-v-s-logistic-regression">
-<h2><span class="section-number">38.13.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
@@ -2004,7 +2036,7 @@ <h2><span class="section-number">38.13.13. </span>Linear Regression v.s. Logisti
 </div>
 </section>
 <section id="logistic-regression-from-scratch">
-<h2><span class="section-number">38.13.14. </span>Logistic Regression from scratch<a class="headerlink" href="#logistic-regression-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.14. </span>Logistic Regression from scratch<a class="headerlink" href="#logistic-regression-from-scratch" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyOwnLogisticRegression</span><span class="p">:</span>
@@ -2073,9 +2105,9 @@ <h2><span class="section-number">38.13.14. </span>Logistic Regression from scrat
 </div>
 </section>
 <section id="loss-function-for-logistic-regression">
-<h2><span class="section-number">38.13.15. </span>Loss function for logistic regression<a class="headerlink" href="#loss-function-for-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.15. </span>Loss function for logistic regression<a class="headerlink" href="#loss-function-for-logistic-regression" title="Permalink to this headline">#</a></h2>
 <section id="cross-entropy">
-<h3><span class="section-number">38.13.15.1. </span>Cross-entropy<a class="headerlink" href="#cross-entropy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.13.15.1. </span>Cross-entropy<a class="headerlink" href="#cross-entropy" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -2087,7 +2119,7 @@ <h3><span class="section-number">38.13.15.1. </span>Cross-entropy<a class="heade
 </section>
 </section>
 <section id="linear-separability">
-<h2><span class="section-number">38.13.16. </span>Linear separability<a class="headerlink" href="#linear-separability" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.16. </span>Linear separability<a class="headerlink" href="#linear-separability" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_moons</span>
@@ -2111,11 +2143,11 @@ <h2><span class="section-number">38.13.16. </span>Linear separability<a class="h
 </div>
 </div>
 <section id="thats-when-neural-network-comes-into-play">
-<h3><span class="section-number">38.13.16.1. </span>That’s when Neural Network comes into play!<a class="headerlink" href="#thats-when-neural-network-comes-into-play" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.13.16.1. </span>That’s when Neural Network comes into play!<a class="headerlink" href="#thats-when-neural-network-comes-into-play" title="Permalink to this headline">#</a></h3>
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">38.13.17. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.17. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to ERENCAN  for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/erncncbk/logistic-regression-tutorial">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
 </section>
 </section>
@@ -2157,13 +2189,13 @@ <h2><span class="section-number">38.13.17. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.12. </span>Linear Regression</p>
+            <p class="prev-next-title"><span class="section-number">41.12. </span>Linear Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="logistic-regression-condensed.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.14. </span>Logistic Regression</p>
+        <p class="prev-next-title"><span class="section-number">41.14. </span>Logistic Regression</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/ml-fundamentals/ml-overview.html b/slides/ml-fundamentals/ml-overview.html
index 1681858a43..84756bbbfd 100644
--- a/slides/ml-fundamentals/ml-overview.html
+++ b/slides/ml-fundamentals/ml-overview.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.11. Machine Learning overview &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.11. Machine Learning overview &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.12. Linear Regression" href="linear-regression.html" />
-    <link rel="prev" title="38.10. Data Science in real world" href="../data-science/data-science-in-real-world.html" />
+    <link rel="next" title="41.12. Linear Regression" href="linear-regression.html" />
+    <link rel="prev" title="41.10. Data Science in real world" href="../data-science/data-science-in-real-world.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -375,9 +375,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -387,34 +392,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -424,22 +429,44 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -453,98 +480,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     15.1. Bagging
+     17.1. Bagging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
+     17.2. Random forest
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   16. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     16.1. Gradient Boosting
+     18.1. Gradient Boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     16.2. Gradient boosting example
+     18.2. Gradient boosting example
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
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-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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+   25. Generative adversarial networks
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-   27. Deep Q-learning
+   30. Deep Q-learning
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+   32. Image segmentation
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+   34. Object detection
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+   38. Model training &amp; evaluation
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+   39. Model deployment
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@@ -655,614 +687,614 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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-     37.4. Second assignment
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-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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+     41.11.33.1. Selecting and Training a Model
     </a>
    </li>
   </ul>
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+     41.11.34.1. Performing Error Analysis
     </a>
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-     38.11.35.1. Deploying a Model
+     41.11.35.1. Deploying a Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#thank-you">
-   38.11.36. Thank you!
+   41.11.36. Thank you!
   </a>
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 </ul>
@@ -1867,322 +1899,322 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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+   41.11.1. Table of Content
   </a>
  </li>
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   <a class="reference internal nav-link" href="#what-is-machine-learning">
-   38.11.2. What is Machine Learning
+   41.11.2. What is Machine Learning
   </a>
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   </a>
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   </a>
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-   38.11.5. What is Machine Learning
+   41.11.5. What is Machine Learning
   </a>
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   <a class="reference internal nav-link" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning">
-   38.11.6. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.6. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
   </a>
  </li>
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-   38.11.7. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.7. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
   </a>
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-   38.11.8. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.8. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
   </a>
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-   38.11.9. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.9. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
   </a>
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  <li class="toc-h2 nav-item toc-entry">
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+   41.11.10. Ordinary Programming vs Machine Learning
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     38.11.10.1. Ordinary Programming
+     41.11.10.1. Ordinary Programming
     </a>
    </li>
   </ul>
  </li>
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    <li class="toc-h3 nav-item toc-entry">
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-     38.11.11.1. Machine Learning
+     41.11.11.1. Machine Learning
     </a>
    </li>
   </ul>
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   </a>
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+   41.11.13. Ordinary Programming vs Machine Learning
   </a>
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-   38.11.14. Ordinary Programming vs Machine Learning
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   </a>
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-   38.11.15. Applications of Machine Learning
+   41.11.15. Applications of Machine Learning
   </a>
  </li>
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-   38.11.16. Key terminology
+   41.11.16. Key terminology
   </a>
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+     41.11.16.1. Labels
     </a>
    </li>
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   </a>
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+     41.11.17.1. Features
     </a>
    </li>
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+     41.11.18.1. Examples
     </a>
    </li>
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-     38.11.19.1. Examples
+     41.11.19.1. Examples
     </a>
    </li>
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-   38.11.20. Key terminology
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   </a>
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     <a class="reference internal nav-link" href="#models">
-     38.11.20.1. Models
+     41.11.20.1. Models
     </a>
    </li>
   </ul>
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+   41.11.21. Types of Machine Learning Systems
   </a>
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-   38.11.22. Types of Machine Learning Systems
+   41.11.22. Types of Machine Learning Systems
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#supervised-learning">
-     38.11.22.1. Supervised Learning
+     41.11.22.1. Supervised Learning
     </a>
    </li>
   </ul>
  </li>
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-   38.11.23. Types of Machine Learning Systems
+   41.11.23. Types of Machine Learning Systems
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    <li class="toc-h3 nav-item toc-entry">
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-     38.11.23.1. Supervised Learning
+     41.11.23.1. Supervised Learning
     </a>
    </li>
   </ul>
  </li>
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-   38.11.24. Types of Machine Learning Systems
+   41.11.24. Types of Machine Learning Systems
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    <li class="toc-h3 nav-item toc-entry">
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-     38.11.24.1. Supervised Learning
+     41.11.24.1. Supervised Learning
     </a>
    </li>
   </ul>
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-   38.11.25. Types of Machine Learning Systems
+   41.11.25. Types of Machine Learning Systems
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#unsupervised-learning">
-     38.11.25.1. Unsupervised Learning
+     41.11.25.1. Unsupervised Learning
     </a>
    </li>
   </ul>
  </li>
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-   38.11.26. Types of Machine Learning Systems
+   41.11.26. Types of Machine Learning Systems
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reinforcement-learning">
-     38.11.26.1. Reinforcement Learning
+     41.11.26.1. Reinforcement Learning
     </a>
    </li>
   </ul>
  </li>
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-   38.11.27. Typical Machine Learning Workflow
+   41.11.27. Typical Machine Learning Workflow
   </a>
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-   38.11.28. Typical Machine Learning Workflow
+   41.11.28. Typical Machine Learning Workflow
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-     38.11.28.1. Defining and formulating a problem
+     41.11.28.1. Defining and formulating a problem
     </a>
    </li>
   </ul>
  </li>
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-   38.11.29. Typical Machine Learning Workflow
+   41.11.29. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#collecting-data">
-     38.11.29.1. Collecting Data
+     41.11.29.1. Collecting Data
     </a>
    </li>
   </ul>
  </li>
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-   38.11.30. Typical Machine Learning Workflow
+   41.11.30. Typical Machine Learning Workflow
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#establishing-a-baseline">
-     38.11.30.1. Establishing a Baseline
+     41.11.30.1. Establishing a Baseline
     </a>
    </li>
   </ul>
  </li>
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-   38.11.31. Typical Machine Learning Workflow
+   41.11.31. Typical Machine Learning Workflow
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis-eda">
-     38.11.31.1. Exploratory data analysis(EDA)
+     41.11.31.1. Exploratory data analysis(EDA)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   38.11.32. Typical Machine Learning Workflow
+   41.11.32. Typical Machine Learning Workflow
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     38.11.32.1. Data Preprocessing
+     41.11.32.1. Data Preprocessing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id28">
-   38.11.33. Typical Machine Learning Workflow
+   41.11.33. Typical Machine Learning Workflow
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#selecting-and-training-a-model">
-     38.11.33.1. Selecting and Training a Model
+     41.11.33.1. Selecting and Training a Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id29">
-   38.11.34. Typical Machine Learning Workflow
+   41.11.34. Typical Machine Learning Workflow
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#performing-error-analysis">
-     38.11.34.1. Performing Error Analysis
+     41.11.34.1. Performing Error Analysis
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id30">
-   38.11.35. Typical Machine Learning Workflow
+   41.11.35. Typical Machine Learning Workflow
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#deploying-a-model">
-     38.11.35.1. Deploying a Model
+     41.11.35.1. Deploying a Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#thank-you">
-   38.11.36. Thank you!
+   41.11.36. Thank you!
   </a>
  </li>
 </ul>
@@ -2211,9 +2243,9 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="machine-learning-overview">
-<h1><span class="section-number">38.11. </span>Machine Learning overview<a class="headerlink" href="#machine-learning-overview" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.11. </span>Machine Learning overview<a class="headerlink" href="#machine-learning-overview" title="Permalink to this headline">#</a></h1>
 <section id="table-of-content">
-<h2><span class="section-number">38.11.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>What is machine learning?</p></li>
 <li><p>Difference between AI, data science, ML and DL</p></li>
@@ -2225,14 +2257,14 @@ <h2><span class="section-number">38.11.1. </span>Table of Content<a class="heade
 </ul>
 </section>
 <section id="what-is-machine-learning">
-<h2><span class="section-number">38.11.2. </span>What is Machine Learning<a class="headerlink" href="#what-is-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.2. </span>What is Machine Learning<a class="headerlink" href="#what-is-machine-learning" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Machine learning is a new programming paradigm in which instead of explicitly programming computers to perform some tasks, we let them learn from data in order to find the underlying patterns in the data</p></li>
 <li><p>In few words, machine learning is the science of giving the machine the ability to reason about the data</p></li>
 </ul>
 </section>
 <section id="id1">
-<h2><span class="section-number">38.11.3. </span>What is Machine Learning<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.3. </span>What is Machine Learning<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The term <a class="reference external" href="https://en.wikipedia.org/wiki/Machine_learning">machine learning</a> was coined by Arthur Samuel in 1959. At that time, Arthur defined machine learning as a:</p></li>
 </ul>
@@ -2241,7 +2273,7 @@ <h2><span class="section-number">38.11.3. </span>What is Machine Learning<a clas
 </div></blockquote>
 </section>
 <section id="id2">
-<h2><span class="section-number">38.11.4. </span>What is Machine Learning<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.4. </span>What is Machine Learning<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A more technical definition of machine learning was provided by Tom M. Mitchell in 1997. Here is how Tom defined machine learning:</p></li>
 </ul>
@@ -2250,7 +2282,7 @@ <h2><span class="section-number">38.11.4. </span>What is Machine Learning<a clas
 </div></blockquote>
 </section>
 <section id="id3">
-<h2><span class="section-number">38.11.5. </span>What is Machine Learning<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.5. </span>What is Machine Learning<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Wikipedia provides a much clearer definition of machine learning:</p></li>
 </ul>
@@ -2262,34 +2294,34 @@ <h2><span class="section-number">38.11.5. </span>What is Machine Learning<a clas
 </ul>
 </section>
 <section id="artificial-intelligence-data-science-machine-learning-and-deep-learning">
-<h2><span class="section-number">38.11.6. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.6. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>AI is a branch of computer science concerned with building intelligent machines capable of performing tasks at the level of human. AI seeks to mimic human. AI is an interdisciplinary field that involves machine learning, programming, robotics, data science, etc…</p></li>
 </ul>
 </section>
 <section id="id4">
-<h2><span class="section-number">38.11.7. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.7. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Machine learning on the other hand is the branch of AI and as we saw, it is concerned with giving the machine the ability to learn from data. Machine learning algorithms consists of shallow or classical algorithms such as decision trees and deep learning algorithms such as convolutional neural networks</p></li>
 </ul>
 </section>
 <section id="id5">
-<h2><span class="section-number">38.11.8. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.8. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Deep learning is a branch of machine learning that deals with the study of artificial neural networks and it was inspired by the human brain. Classical machine learning algorithms needs a lot of feature engineering, but deep learning algorithms can extract features in huge amount of data such as images themselves</p></li>
 </ul>
 </section>
 <section id="id6">
-<h2><span class="section-number">38.11.9. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.9. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The difference between AI, machine learning and deep learning</p></li>
 </ul>
-<p><img alt="" src="../../_images/ai-ml-dl1.png" /></p>
+<p><img alt="" src="../../_images/ai-ml-dl.png" /></p>
 </section>
 <section id="ordinary-programming-vs-machine-learning">
-<h2><span class="section-number">38.11.10. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.10. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
 <section id="ordinary-programming">
-<h3><span class="section-number">38.11.10.1. </span>Ordinary Programming<a class="headerlink" href="#ordinary-programming" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.10.1. </span>Ordinary Programming<a class="headerlink" href="#ordinary-programming" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>In ordinary programming, the job of the programmer is to clearly write every single rule that makes up the task he/she is trying to accomplish</p></li>
 <li><p>In order to get the results, she/he must write all rules that acts up on the data</p></li>
@@ -2297,55 +2329,55 @@ <h3><span class="section-number">38.11.10.1. </span>Ordinary Programming<a class
 </section>
 </section>
 <section id="id7">
-<h2><span class="section-number">38.11.11. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.11. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <section id="machine-learning">
-<h3><span class="section-number">38.11.11.1. </span>Machine Learning<a class="headerlink" href="#machine-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.11.1. </span>Machine Learning<a class="headerlink" href="#machine-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Machine learning flips that. Instead of having to write the rules that makes up a particular application, we can feed data and results(or labels) to the machine learning model, and its job can be to determine the set of rules that map the data and labels</p></li>
 </ul>
 </section>
 </section>
 <section id="id8">
-<h2><span class="section-number">38.11.12. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.12. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Traditional Programming vs Deep Learning</p></li>
 </ul>
-<p><img alt="" src="../../_images/traditional-ml1.png" /></p>
+<p><img alt="" src="../../_images/traditional-ml.png" /></p>
 </section>
 <section id="id9">
-<h2><span class="section-number">38.11.13. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.13. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Let’s take a real world example. If you wanted to build an application that given a picture of person can determine if he/she is wearing or not wearing a facemask, you can just feed a bunch of images of people with and without facemasks to the machine learning model, and the model can learn the rules or patterns that map the images to whether they have a facemask or not</p></li>
 <li><p>You can even extend that further and use those learned rules to recognize facemasks in the images that were never seen by the that model</p></li>
 </ul>
 </section>
 <section id="id10">
-<h2><span class="section-number">38.11.14. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.14. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Approaching a facemask recognition problem with rule based programming would really be a hard problem. You would have to write lots of code that would later turn out to not work typically because your program will be tested on different kinds of facemasks and people in various scenarios and it’s almost impossible to express that in rules</p></li>
 <li><p>Whereas with machine learning, all you need is a bunch of images of people with and without facemasks, and there you are few steps away from getting an effective facemask recognizer</p></li>
 </ul>
 </section>
 <section id="applications-of-machine-learning">
-<h2><span class="section-number">38.11.15. </span>Applications of Machine Learning<a class="headerlink" href="#applications-of-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.15. </span>Applications of Machine Learning<a class="headerlink" href="#applications-of-machine-learning" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Machine learning has transformed many industries, from banking, manufacturing, streaming, autonomous vehicles, agriculture, etc…In fact, most of the tech products and services we use daily possess some sorts of machine learning algorithms running in their backgrounds</p></li>
 <li><p>Fraud detection, Loan repayment prediction, Diagnozing diseases and predicting the survival rate, Detecting defects in industry, Churn prediction, Spam detection, Autonomous vehicles, etc.</p></li>
 </ul>
 </section>
 <section id="key-terminology">
-<h2><span class="section-number">38.11.16. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.16. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
 <section id="labels">
-<h3><span class="section-number">38.11.16.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.16.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A <code class="docutils literal notranslate"><span class="pre">label</span></code> is the thing we’re predicting—the <code class="docutils literal notranslate"><span class="pre">y</span></code> variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.</p></li>
 </ul>
 </section>
 </section>
 <section id="id11">
-<h2><span class="section-number">38.11.17. </span>Key terminology<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.17. </span>Key terminology<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <section id="features">
-<h3><span class="section-number">38.11.17.1. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.17.1. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A <code class="docutils literal notranslate"><span class="pre">feature</span></code> is an input variable—the <code class="docutils literal notranslate"><span class="pre">x</span></code> variable in simple linear regression. A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features, specified as:</p></li>
 </ul>
@@ -2354,9 +2386,9 @@ <h3><span class="section-number">38.11.17.1. </span>Features<a class="headerlink
 </section>
 </section>
 <section id="id12">
-<h2><span class="section-number">38.11.18. </span>Key terminology<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.18. </span>Key terminology<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <section id="examples">
-<h3><span class="section-number">38.11.18.1. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.18.1. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>An <code class="docutils literal notranslate"><span class="pre">example</span></code> is a particular instance of data, <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong>. (We put <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong> in boldface to indicate that it is a vector.) We break examples into two categories:</p>
 <ul>
@@ -2368,9 +2400,9 @@ <h3><span class="section-number">38.11.18.1. </span>Examples<a class="headerlink
 </section>
 </section>
 <section id="id13">
-<h2><span class="section-number">38.11.19. </span>Key terminology<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.19. </span>Key terminology<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <section id="id14">
-<h3><span class="section-number">38.11.19.1. </span>Examples<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.19.1. </span>Examples<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A <strong><code class="docutils literal notranslate"><span class="pre">labeled</span> <span class="pre">example</span></code></strong> includes both feature(s) and the label. That is:</p></li>
 </ul>
@@ -2383,9 +2415,9 @@ <h3><span class="section-number">38.11.19.1. </span>Examples<a class="headerlink
 </section>
 </section>
 <section id="id15">
-<h2><span class="section-number">38.11.20. </span>Key terminology<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.20. </span>Key terminology<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
 <section id="models">
-<h3><span class="section-number">38.11.20.1. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.20.1. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with “spam”. Let’s highlight two phases of a model’s life:</p>
 <ul>
@@ -2397,7 +2429,7 @@ <h3><span class="section-number">38.11.20.1. </span>Models<a class="headerlink"
 </section>
 </section>
 <section id="types-of-machine-learning-systems">
-<h2><span class="section-number">38.11.21. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.21. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In broad, there are 3 main types of machine learning systems that are:</p>
 <ul>
@@ -2409,9 +2441,9 @@ <h2><span class="section-number">38.11.21. </span>Types of Machine Learning Syst
 </ul>
 </section>
 <section id="id16">
-<h2><span class="section-number">38.11.22. </span>Types of Machine Learning Systems<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.22. </span>Types of Machine Learning Systems<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
 <section id="supervised-learning">
-<h3><span class="section-number">38.11.22.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.22.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Most machine learning tasks fall into supervised learning type</p></li>
 <li><p>Trained with input data along with some form of guidance that we can call labels</p></li>
@@ -2421,9 +2453,9 @@ <h3><span class="section-number">38.11.22.1. </span>Supervised Learning<a class=
 </section>
 </section>
 <section id="id17">
-<h2><span class="section-number">38.11.23. </span>Types of Machine Learning Systems<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.23. </span>Types of Machine Learning Systems<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
 <section id="id18">
-<h3><span class="section-number">38.11.23.1. </span>Supervised Learning<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.23.1. </span>Supervised Learning<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>there are 2 main kinds of supervised learning problems:</p>
 <ul>
@@ -2435,16 +2467,16 @@ <h3><span class="section-number">38.11.23.1. </span>Supervised Learning<a class=
 </section>
 </section>
 <section id="id19">
-<h2><span class="section-number">38.11.24. </span>Types of Machine Learning Systems<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.24. </span>Types of Machine Learning Systems<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
 <section id="id20">
-<h3><span class="section-number">38.11.24.1. </span>Supervised Learning<a class="headerlink" href="#id20" title="Permalink to this headline">#</a></h3>
-<p><img alt="" src="../../_images/class-reg1.png" /></p>
+<h3><span class="section-number">41.11.24.1. </span>Supervised Learning<a class="headerlink" href="#id20" title="Permalink to this headline">#</a></h3>
+<p><img alt="" src="../../_images/class-reg.png" /></p>
 </section>
 </section>
 <section id="id21">
-<h2><span class="section-number">38.11.25. </span>Types of Machine Learning Systems<a class="headerlink" href="#id21" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.25. </span>Types of Machine Learning Systems<a class="headerlink" href="#id21" title="Permalink to this headline">#</a></h2>
 <section id="unsupervised-learning">
-<h3><span class="section-number">38.11.25.1. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.25.1. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Unsupervised learning algorithms are trained on unlabelled data</p></li>
 <li><p>Unsupervised learning algorithms are primarily used for:</p>
@@ -2457,9 +2489,9 @@ <h3><span class="section-number">38.11.25.1. </span>Unsupervised Learning<a clas
 </section>
 </section>
 <section id="id22">
-<h2><span class="section-number">38.11.26. </span>Types of Machine Learning Systems<a class="headerlink" href="#id22" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.26. </span>Types of Machine Learning Systems<a class="headerlink" href="#id22" title="Permalink to this headline">#</a></h2>
 <section id="reinforcement-learning">
-<h3><span class="section-number">38.11.26.1. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.26.1. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Mostly applied in robotics and games</p></li>
 <li><p>A learning system called <em>an agent</em> can perceives the environment, performs some actions, and gets <em>rewarded</em> or <em>penalized</em> depending on how it is performing. The main goal of the agent is to accumulate as much as rewards as possible.</p></li>
@@ -2468,7 +2500,7 @@ <h3><span class="section-number">38.11.26.1. </span>Reinforcement Learning<a cla
 </section>
 </section>
 <section id="typical-machine-learning-workflow">
-<h2><span class="section-number">38.11.27. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.27. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Overall, a typical machine learning project workflow consists of:</p>
 <ul>
@@ -2484,9 +2516,9 @@ <h2><span class="section-number">38.11.27. </span>Typical Machine Learning Workf
 </ul>
 </section>
 <section id="id23">
-<h2><span class="section-number">38.11.28. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id23" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.28. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id23" title="Permalink to this headline">#</a></h2>
 <section id="defining-and-formulating-a-problem">
-<h3><span class="section-number">38.11.28.1. </span>Defining and formulating a problem<a class="headerlink" href="#defining-and-formulating-a-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.28.1. </span>Defining and formulating a problem<a class="headerlink" href="#defining-and-formulating-a-problem" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Understanding the problem is all about diving deep into the details of the problem at hand and asking the right questions</p></li>
 <li><p>First, it’s important to narrow down the problem until you can have a simple and a well-defined goal. Here are examples of simple goals: To <em>classify</em> products into different categories, to <em>predict</em> the price of a used car given its features (such as brand, age, etc…), to <em>recognize</em> if a person is wearing a facemask, to <em>divide</em> customers into different groups that share similar behaviors, etc…</p></li>
@@ -2495,9 +2527,9 @@ <h3><span class="section-number">38.11.28.1. </span>Defining and formulating a p
 </section>
 </section>
 <section id="id24">
-<h2><span class="section-number">38.11.29. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id24" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.29. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id24" title="Permalink to this headline">#</a></h2>
 <section id="collecting-data">
-<h3><span class="section-number">38.11.29.1. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.29.1. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>There are 2 main types of data that are:</p>
 <ul>
@@ -2511,9 +2543,9 @@ <h3><span class="section-number">38.11.29.1. </span>Collecting Data<a class="hea
 </section>
 </section>
 <section id="id25">
-<h2><span class="section-number">38.11.30. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id25" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.30. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id25" title="Permalink to this headline">#</a></h2>
 <section id="establishing-a-baseline">
-<h3><span class="section-number">38.11.30.1. </span>Establishing a Baseline<a class="headerlink" href="#establishing-a-baseline" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.30.1. </span>Establishing a Baseline<a class="headerlink" href="#establishing-a-baseline" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Without a benchmark, you won’t know how to evaluate your results properly</p></li>
 <li><p>The single most purpose of a baseline is to act as a reference point when comparing the actual model with the baseline</p></li>
@@ -2522,9 +2554,9 @@ <h3><span class="section-number">38.11.30.1. </span>Establishing a Baseline<a cl
 </section>
 </section>
 <section id="id26">
-<h2><span class="section-number">38.11.31. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id26" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.31. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id26" title="Permalink to this headline">#</a></h2>
 <section id="exploratory-data-analysis-eda">
-<h3><span class="section-number">38.11.31.1. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.31.1. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Before manipulating the data, it is quite important to learn about the dataset</p></li>
 <li><p>Go through some values, plot some features, and try to understand the correlation between them</p></li>
@@ -2534,9 +2566,9 @@ <h3><span class="section-number">38.11.31.1. </span>Exploratory data analysis(ED
 </section>
 </section>
 <section id="id27">
-<h2><span class="section-number">38.11.32. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id27" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.32. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id27" title="Permalink to this headline">#</a></h2>
 <section id="data-preprocessing">
-<h3><span class="section-number">38.11.32.1. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.32.1. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Perhaps the biggest part of any machine learning project</p></li>
 <li><p>Convert the raw data to go in a format that can be accepted by the machine learning algorithms</p></li>
@@ -2547,9 +2579,9 @@ <h3><span class="section-number">38.11.32.1. </span>Data Preprocessing<a class="
 </section>
 </section>
 <section id="id28">
-<h2><span class="section-number">38.11.33. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id28" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.33. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id28" title="Permalink to this headline">#</a></h2>
 <section id="selecting-and-training-a-model">
-<h3><span class="section-number">38.11.33.1. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.33.1. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Depending on your problem, you can choose different relevant model</p></li>
 <li><p>The scope of the problem</p></li>
@@ -2560,9 +2592,9 @@ <h3><span class="section-number">38.11.33.1. </span>Selecting and Training a Mod
 </section>
 </section>
 <section id="id29">
-<h2><span class="section-number">38.11.34. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id29" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.34. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id29" title="Permalink to this headline">#</a></h2>
 <section id="performing-error-analysis">
-<h3><span class="section-number">38.11.34.1. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.34.1. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Performing error analysis will guide you throughout the process of improving the results of the model</p></li>
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 </section>
 </section>
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-<h2><span class="section-number">38.11.35. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id30" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.35. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id30" title="Permalink to this headline">#</a></h2>
 <section id="deploying-a-model">
-<h3><span class="section-number">38.11.35.1. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.35.1. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Model deployment is the last part in this workflow.</p></li>
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 </section>
 </section>
 <section id="thank-you">
-<h2><span class="section-number">38.11.36. </span>Thank you!<a class="headerlink" href="#thank-you" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.36. </span>Thank you!<a class="headerlink" href="#thank-you" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Any questions?</p></li>
 </ul>
@@ -2625,13 +2657,13 @@ <h2><span class="section-number">38.11.36. </span>Thank you!<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.10. </span>Data Science in real world</p>
+            <p class="prev-next-title"><span class="section-number">41.10. </span>Data Science in real world</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="linear-regression.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.12. </span>Linear Regression</p>
+        <p class="prev-next-title"><span class="section-number">41.12. </span>Linear Regression</p>
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diff --git a/slides/ml-fundamentals/neural-network.html b/slides/ml-fundamentals/neural-network.html
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-    <link rel="next" title="38.16. Build an machine learning web application" href="build-an-ml-web-app.html" />
-    <link rel="prev" title="38.14. Logistic Regression" href="logistic-regression-condensed.html" />
+    <link rel="next" title="41.16. Build an machine learning web application" href="build-an-ml-web-app.html" />
+    <link rel="prev" title="41.14. Logistic Regression" href="logistic-regression-condensed.html" />
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    10. Machine Learning overview
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+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
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+     14.4. Applied Machine Learning : build a web app
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+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
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-     37.24. Data processing in Python
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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-     37.72. Regularized Linear Models
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.101. Intro to Autoencoders
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,107 +1558,107 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   38.15.1. Introduction
+   41.15.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   38.15.2. Importing the libraries
+   41.15.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   38.15.3. Importing the dataset
+   41.15.3. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-visualization">
-   38.15.4. Data visualization
+   41.15.4. Data visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#designing-model-architecture-using-keras">
-   38.15.5. Designing model architecture using Keras
+   41.15.5. Designing model architecture using Keras
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-keras-layers">
-   38.15.6. Import Keras layers
+   41.15.6. Import Keras layers
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#compute-the-number-of-labels">
-   38.15.7. Compute the number of labels
+   41.15.7. Compute the number of labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#one-hot-encoding">
-   38.15.8. One-Hot Encoding
+   41.15.8. One-Hot Encoding
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   38.15.9. Data Preprocessing
+   41.15.9. Data Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#setting-network-parameters">
-   38.15.10. Setting network parameters
+   41.15.10. Setting network parameters
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#designing-the-model-architecture">
-   38.15.11. Designing the model architecture
+   41.15.11. Designing the model architecture
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#view-model-summary">
-   38.15.12. View model summary
+   41.15.12. View model summary
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-big-is-our-model-number-of-parameters">
-   38.15.13. How big is our model (number of parameters)?
+   41.15.13. How big is our model (number of parameters)?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#compile-the-model-with-compile-method">
-   38.15.14. Compile the model with compile() method
+   41.15.14. Compile the model with compile() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-function-categorical-crossentropy">
-   38.15.15. Loss function (categorical_crossentropy)
+   41.15.15. Loss function (categorical_crossentropy)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optimization-optimizer-adam">
-   38.15.16. Optimization (optimizer adam)
+   41.15.16. Optimization (optimizer adam)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#metrics-accuracy">
-   38.15.17. Metrics (accuracy)
+   41.15.17. Metrics (accuracy)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-the-model-with-fit-method">
-   38.15.18. Train the model with fit() method
+   41.15.18. Train the model with fit() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-model-performance-with-evaluate-method">
-   38.15.19. Evaluating model performance with evaluate() method
+   41.15.19. Evaluating model performance with evaluate() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#neural-network-from-scratch">
-   38.15.20. Neural Network from scratch
+   41.15.20. Neural Network from scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.15.21. Acknowledgments
+   41.15.21. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1650,107 +1682,107 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   38.15.1. Introduction
+   41.15.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   38.15.2. Importing the libraries
+   41.15.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   38.15.3. Importing the dataset
+   41.15.3. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-visualization">
-   38.15.4. Data visualization
+   41.15.4. Data visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#designing-model-architecture-using-keras">
-   38.15.5. Designing model architecture using Keras
+   41.15.5. Designing model architecture using Keras
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-keras-layers">
-   38.15.6. Import Keras layers
+   41.15.6. Import Keras layers
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#compute-the-number-of-labels">
-   38.15.7. Compute the number of labels
+   41.15.7. Compute the number of labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#one-hot-encoding">
-   38.15.8. One-Hot Encoding
+   41.15.8. One-Hot Encoding
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   38.15.9. Data Preprocessing
+   41.15.9. Data Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#setting-network-parameters">
-   38.15.10. Setting network parameters
+   41.15.10. Setting network parameters
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#designing-the-model-architecture">
-   38.15.11. Designing the model architecture
+   41.15.11. Designing the model architecture
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#view-model-summary">
-   38.15.12. View model summary
+   41.15.12. View model summary
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-big-is-our-model-number-of-parameters">
-   38.15.13. How big is our model (number of parameters)?
+   41.15.13. How big is our model (number of parameters)?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#compile-the-model-with-compile-method">
-   38.15.14. Compile the model with compile() method
+   41.15.14. Compile the model with compile() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-function-categorical-crossentropy">
-   38.15.15. Loss function (categorical_crossentropy)
+   41.15.15. Loss function (categorical_crossentropy)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optimization-optimizer-adam">
-   38.15.16. Optimization (optimizer adam)
+   41.15.16. Optimization (optimizer adam)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#metrics-accuracy">
-   38.15.17. Metrics (accuracy)
+   41.15.17. Metrics (accuracy)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-the-model-with-fit-method">
-   38.15.18. Train the model with fit() method
+   41.15.18. Train the model with fit() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-model-performance-with-evaluate-method">
-   38.15.19. Evaluating model performance with evaluate() method
+   41.15.19. Evaluating model performance with evaluate() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#neural-network-from-scratch">
-   38.15.20. Neural Network from scratch
+   41.15.20. Neural Network from scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   38.15.21. Acknowledgments
+   41.15.21. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1786,9 +1818,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="neural-network">
-<h1><span class="section-number">38.15. </span>Neural Network<a class="headerlink" href="#neural-network" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.15. </span>Neural Network<a class="headerlink" href="#neural-network" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">38.15.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In fact, Logistic Regression (that have learned in our last session) is the simplest form of Neural Network; Artificial neural networks can be viewed as an extension of Logistic Regression</p></li>
 <li><p>Logistic Regression: results in decision boundaries that are a straight line</p></li>
@@ -1798,7 +1830,7 @@ <h2><span class="section-number">38.15.1. </span>Introduction<a class="headerlin
 </ul>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">38.15.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1815,7 +1847,7 @@ <h2><span class="section-number">38.15.2. </span>Importing the libraries<a class
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">38.15.3. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.3. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>MNIST is a collection of handwritten digits ranging from the number 0 to 9.</p></li>
 <li><p>It has a training set of 60,000 images, and 10,000 test images that are classified into corresponding categories or labels.</p></li>
@@ -1846,7 +1878,7 @@ <h2><span class="section-number">38.15.3. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="data-visualization">
-<h2><span class="section-number">38.15.4. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.4. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Let’s sample the 25 random MNIST digits and visualize them.</p></li>
 </ul>
@@ -1872,10 +1904,10 @@ <h2><span class="section-number">38.15.4. </span>Data visualization<a class="hea
 </div>
 </section>
 <section id="designing-model-architecture-using-keras">
-<h2><span class="section-number">38.15.5. </span>Designing model architecture using Keras<a class="headerlink" href="#designing-model-architecture-using-keras" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.5. </span>Designing model architecture using Keras<a class="headerlink" href="#designing-model-architecture-using-keras" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="import-keras-layers">
-<h2><span class="section-number">38.15.6. </span>Import Keras layers<a class="headerlink" href="#import-keras-layers" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.6. </span>Import Keras layers<a class="headerlink" href="#import-keras-layers" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span>
@@ -1887,7 +1919,7 @@ <h2><span class="section-number">38.15.6. </span>Import Keras layers<a class="he
 </div>
 </section>
 <section id="compute-the-number-of-labels">
-<h2><span class="section-number">38.15.7. </span>Compute the number of labels<a class="headerlink" href="#compute-the-number-of-labels" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.7. </span>Compute the number of labels<a class="headerlink" href="#compute-the-number-of-labels" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_labels</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span>
@@ -1897,7 +1929,7 @@ <h2><span class="section-number">38.15.7. </span>Compute the number of labels<a
 </div>
 </section>
 <section id="one-hot-encoding">
-<h2><span class="section-number">38.15.8. </span>One-Hot Encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.8. </span>One-Hot Encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>At this point, the labels are in digits format, 0 to 9.</p></li>
 <li><p>A more suitable format is called a one-hot vector, a 10-dim vector with all elements 0, except for the index of the digit class.</p></li>
@@ -1914,7 +1946,7 @@ <h2><span class="section-number">38.15.8. </span>One-Hot Encoding<a class="heade
 </div>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">38.15.9. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.9. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># image dimensions (assumed square)</span>
@@ -1938,7 +1970,7 @@ <h2><span class="section-number">38.15.9. </span>Data Preprocessing<a class="hea
 </div>
 </section>
 <section id="setting-network-parameters">
-<h2><span class="section-number">38.15.10. </span>Setting network parameters<a class="headerlink" href="#setting-network-parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.10. </span>Setting network parameters<a class="headerlink" href="#setting-network-parameters" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The <strong>batch_size</strong> argument indicates the number of data that we will use for each update of the model parameters.</p></li>
 <li><p><strong>Hidden_units</strong> shows the number of hidden units.</p></li>
@@ -1956,7 +1988,7 @@ <h2><span class="section-number">38.15.10. </span>Setting network parameters<a c
 </div>
 </section>
 <section id="designing-the-model-architecture">
-<h2><span class="section-number">38.15.11. </span>Designing the model architecture<a class="headerlink" href="#designing-the-model-architecture" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.11. </span>Designing the model architecture<a class="headerlink" href="#designing-the-model-architecture" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is a 3-layer MLP with ReLU and dropout after each layer</span>
@@ -1975,7 +2007,7 @@ <h2><span class="section-number">38.15.11. </span>Designing the model architectu
 </div>
 </section>
 <section id="view-model-summary">
-<h2><span class="section-number">38.15.12. </span>View model summary<a class="headerlink" href="#view-model-summary" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.12. </span>View model summary<a class="headerlink" href="#view-model-summary" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Keras library provides us <code class="docutils literal notranslate"><span class="pre">summary()</span></code> method to check the model description.</p></li>
 </ul>
@@ -1988,7 +2020,7 @@ <h2><span class="section-number">38.15.12. </span>View model summary<a class="he
 </div>
 </section>
 <section id="how-big-is-our-model-number-of-parameters">
-<h2><span class="section-number">38.15.13. </span>How big is our model (number of parameters)?<a class="headerlink" href="#how-big-is-our-model-number-of-parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.13. </span>How big is our model (number of parameters)?<a class="headerlink" href="#how-big-is-our-model-number-of-parameters" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>From input to Dense layer: 784 × 256 + 256 = 200,960.</p></li>
 <li><p>From first Dense to second Dense: 256 × 256 + 256 = 65,792.</p></li>
@@ -1997,7 +2029,7 @@ <h2><span class="section-number">38.15.13. </span>How big is our model (number o
 </ul>
 </section>
 <section id="compile-the-model-with-compile-method">
-<h2><span class="section-number">38.15.14. </span>Compile the model with compile() method<a class="headerlink" href="#compile-the-model-with-compile-method" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.14. </span>Compile the model with compile() method<a class="headerlink" href="#compile-the-model-with-compile-method" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> 
@@ -2009,7 +2041,7 @@ <h2><span class="section-number">38.15.14. </span>Compile the model with compile
 </div>
 </section>
 <section id="loss-function-categorical-crossentropy">
-<h2><span class="section-number">38.15.15. </span>Loss function (categorical_crossentropy)<a class="headerlink" href="#loss-function-categorical-crossentropy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.15. </span>Loss function (categorical_crossentropy)<a class="headerlink" href="#loss-function-categorical-crossentropy" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>How far the predicted tensor is from the one-hot ground truth vector is called <strong>loss</strong>.</p></li>
 <li><p>In this example, we use <strong>categorical_crossentropy</strong> as the loss function. It is the negative of the sum of the product of the target and the logarithm of the prediction.</p></li>
@@ -2018,7 +2050,7 @@ <h2><span class="section-number">38.15.15. </span>Loss function (categorical_cro
 </ul>
 </section>
 <section id="optimization-optimizer-adam">
-<h2><span class="section-number">38.15.16. </span>Optimization (optimizer adam)<a class="headerlink" href="#optimization-optimizer-adam" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.16. </span>Optimization (optimizer adam)<a class="headerlink" href="#optimization-optimizer-adam" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>With optimization, the objective is to minimize the loss function. The idea is that if the loss is reduced to an acceptable level, the model has indirectly learned the function mapping input to output.</p></li>
 <li><p>In Keras, there are several choices for optimizers. The most commonly used optimizers are; <strong>Stochastic Gradient Descent (SGD)</strong>, <strong>Adaptive Moments (Adam)</strong> and <strong>Root Mean Squared Propagation (RMSprop)</strong>.</p></li>
@@ -2027,7 +2059,7 @@ <h2><span class="section-number">38.15.16. </span>Optimization (optimizer adam)<
 </ul>
 </section>
 <section id="metrics-accuracy">
-<h2><span class="section-number">38.15.17. </span>Metrics (accuracy)<a class="headerlink" href="#metrics-accuracy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.17. </span>Metrics (accuracy)<a class="headerlink" href="#metrics-accuracy" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Performance metrics are used to determine if a model has learned the underlying data distribution. The default metric in Keras is loss.</p></li>
 <li><p>During training, validation, and testing, other metrics such as <strong>accuracy</strong> can also be included.</p></li>
@@ -2035,7 +2067,7 @@ <h2><span class="section-number">38.15.17. </span>Metrics (accuracy)<a class="he
 </ul>
 </section>
 <section id="train-the-model-with-fit-method">
-<h2><span class="section-number">38.15.18. </span>Train the model with fit() method<a class="headerlink" href="#train-the-model-with-fit-method" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.18. </span>Train the model with fit() method<a class="headerlink" href="#train-the-model-with-fit-method" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
@@ -2045,7 +2077,7 @@ <h2><span class="section-number">38.15.18. </span>Train the model with fit() met
 </div>
 </section>
 <section id="evaluating-model-performance-with-evaluate-method">
-<h2><span class="section-number">38.15.19. </span>Evaluating model performance with evaluate() method<a class="headerlink" href="#evaluating-model-performance-with-evaluate-method" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.19. </span>Evaluating model performance with evaluate() method<a class="headerlink" href="#evaluating-model-performance-with-evaluate-method" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">loss</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
@@ -2056,13 +2088,13 @@ <h2><span class="section-number">38.15.19. </span>Evaluating model performance w
 </div>
 </section>
 <section id="neural-network-from-scratch">
-<h2><span class="section-number">38.15.20. </span>Neural Network from scratch<a class="headerlink" href="#neural-network-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.20. </span>Neural Network from scratch<a class="headerlink" href="#neural-network-from-scratch" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>It’s for tomorrow!</p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">38.15.21. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.21. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to PRASHANT BANERJEE for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/prashant111/mnist-deep-neural-network-with-keras">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this slides.</p>
 </section>
 </section>
@@ -2104,13 +2136,13 @@ <h2><span class="section-number">38.15.21. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.14. </span>Logistic Regression</p>
+            <p class="prev-next-title"><span class="section-number">41.14. </span>Logistic Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-an-ml-web-app.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.16. </span>Build an machine learning web application</p>
+        <p class="prev-next-title"><span class="section-number">41.16. </span>Build an machine learning web application</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/python-programming/python-programming-advanced.html b/slides/python-programming/python-programming-advanced.html
index f5b926bbbc..b43f791f5a 100644
--- a/slides/python-programming/python-programming-advanced.html
+++ b/slides/python-programming/python-programming-advanced.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.3. Python programming advanced &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.3. Python programming advanced &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.4. Data Science introduction" href="../data-science/data-science-introduction.html" />
-    <link rel="prev" title="38.2. Python programming basics" href="python-programming-basics.html" />
+    <link rel="next" title="41.4. Data Science introduction" href="../data-science/data-science-introduction.html" />
+    <link rel="prev" title="41.2. Python programming basics" href="python-programming-basics.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
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   <label for="toctree-checkbox-8">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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+  <label for="toctree-checkbox-12">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
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  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
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   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
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-   24. Autoencoder
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   <a class="reference internal" href="../../deep-learning/lstm.html">
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   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
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   <a class="reference internal" href="../../deep-learning/image-classification.html">
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-   29. Image segmentation
+   32. Image segmentation
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   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
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+   34. Object detection
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   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
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+   35. Overview
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  </li>
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   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
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   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
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   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../assignments/README.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
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     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
+     40.6. Python programming introduction
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
+     40.10. Data Science scenarios
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     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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-     37.12. Lines, scatters and bars
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-     37.13. Apply your skills
+     40.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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-     37.54. Managing data
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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     </a>
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-     37.62. Explore classification methods
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-     37.63. Kernel method assignment 1
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.66. Decision Trees - Intro and Regression
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-     37.67. Decision Trees - Classification
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-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.70. Dropout and Batch Normalization
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-     37.72. Regularized Linear Models
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-     37.75. Beyond random forests: more ensemble models
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.101. Intro to Autoencoders
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-     37.112. DQN On Foreign Exchange Market
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+     41.15. Neural Network
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+     41.16. Build an machine learning web application
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-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     38.18. Kernel method
+     41.18. Kernel method
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-     38.19. Model Selection
+     41.19. Model Selection
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-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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+     41.21. Generative Adversarial Network
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+   41.3.3. Control flow
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-     38.3.11.1. Defining a function
+     41.3.11.1. Defining a function
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+     41.3.11.2. Calling the function
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-     38.3.12.1. Scopes of variables inside functions
+     41.3.12.1. Scopes of variables inside functions
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+     41.3.13.1. Scopes of variables inside functions
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+     41.3.14.1. Code output:
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-     38.3.16.1. Documentation strings (docstring)
+     41.3.16.1. Documentation strings (docstring)
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   </ul>
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-     38.3.17.1. Function annotations
+     41.3.17.1. Function annotations
     </a>
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+   41.3.18. Functions
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-     38.3.18.1. Function decorators
+     41.3.18.1. Function decorators
     </a>
    </li>
   </ul>
  </li>
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+   41.3.19. Classes
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-   38.3.20. Classes
+   41.3.20. Classes
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     <a class="reference internal nav-link" href="#inheritance">
-     38.3.20.1. Inheritance
+     41.3.20.1. Inheritance
     </a>
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   </ul>
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-   38.3.21. Modules
+   41.3.21. Modules
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-   38.3.22. Let’s try with our own module
+   41.3.22. Let’s try with our own module
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#errors-and-exceptions">
-   38.3.23. Errors and exceptions
+   41.3.23. Errors and exceptions
   </a>
  </li>
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   <a class="reference internal nav-link" href="#your-turn">
-   38.3.24. Your turn! 🚀
+   41.3.24. Your turn! 🚀
   </a>
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-   38.3.25. References:
+   41.3.25. References:
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+     41.3.11.1. Defining a function
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+     41.3.11.2. Calling the function
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+     41.3.13.1. Scopes of variables inside functions
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#documentation-strings-docstring">
-     38.3.16.1. Documentation strings (docstring)
+     41.3.16.1. Documentation strings (docstring)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id17">
-   38.3.17. Functions
+   41.3.17. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#function-annotations">
-     38.3.17.1. Function annotations
+     41.3.17.1. Function annotations
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id18">
-   38.3.18. Functions
+   41.3.18. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#function-decorators">
-     38.3.18.1. Function decorators
+     41.3.18.1. Function decorators
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classes">
-   38.3.19. Classes
+   41.3.19. Classes
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id19">
-   38.3.20. Classes
+   41.3.20. Classes
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#inheritance">
-     38.3.20.1. Inheritance
+     41.3.20.1. Inheritance
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modules">
-   38.3.21. Modules
+   41.3.21. Modules
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lets-try-with-our-own-module">
-   38.3.22. Let’s try with our own module
+   41.3.22. Let’s try with our own module
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#errors-and-exceptions">
-   38.3.23. Errors and exceptions
+   41.3.23. Errors and exceptions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.3.24. Your turn! 🚀
+   41.3.24. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.3.25. References:
+   41.3.25. References:
   </a>
  </li>
 </ul>
@@ -2209,9 +2241,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="python-programming-advanced">
-<h1><span class="section-number">38.3. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.3. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
 <section id="table-of-content">
-<h2><span class="section-number">38.3.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Control flow</p>
 <ul>
@@ -2238,9 +2270,9 @@ <h2><span class="section-number">38.3.1. </span>Table of Content<a class="header
 </ul>
 </section>
 <section id="control-flow">
-<h2><span class="section-number">38.3.2. </span>Control flow<a class="headerlink" href="#control-flow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.2. </span>Control flow<a class="headerlink" href="#control-flow" title="Permalink to this headline">#</a></h2>
 <section id="the-if-statement">
-<h3><span class="section-number">38.3.2.1. </span>The <code class="docutils literal notranslate"><span class="pre">if</span></code> statement<a class="headerlink" href="#the-if-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.2.1. </span>The <code class="docutils literal notranslate"><span class="pre">if</span></code> statement<a class="headerlink" href="#the-if-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Used for conditional execution</p></li>
 <li><p>If a condition is true, we run a block of statements</p></li>
@@ -2271,9 +2303,9 @@ <h3><span class="section-number">38.3.2.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id1">
-<h2><span class="section-number">38.3.3. </span>Control flow<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.3. </span>Control flow<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <section id="the-for-statement">
-<h3><span class="section-number">38.3.3.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#the-for-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.3.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#the-for-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Sequence: an ordered collection of items</p></li>
 <li><p>A looping statement which iterates over a sequence of objects, i.e. go through each item in a sequence</p></li>
@@ -2320,9 +2352,9 @@ <h3><span class="section-number">38.3.3.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id2">
-<h2><span class="section-number">38.3.4. </span>Control flow<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.4. </span>Control flow<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <section id="id3">
-<h3><span class="section-number">38.3.4.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.4.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement can be used in tandem with:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">range()</span></code>: to iterate over a sequence of numbers</p></li>
@@ -2347,9 +2379,9 @@ <h3><span class="section-number">38.3.4.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id4">
-<h2><span class="section-number">38.3.5. </span>Control flow<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.5. </span>Control flow<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <section id="id5">
-<h3><span class="section-number">38.3.5.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.5.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement can be used in tandem with:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">enumerate()</span></code>: to get a counter and the value from the iterable at the same time</p></li>
@@ -2375,9 +2407,9 @@ <h3><span class="section-number">38.3.5.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id6">
-<h2><span class="section-number">38.3.6. </span>Control flow<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.6. </span>Control flow<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <section id="id7">
-<h3><span class="section-number">38.3.6.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.6.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement can be used in tandem with:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">items()</span></code>: to loop through dictionaries</p></li>
@@ -2408,9 +2440,9 @@ <h3><span class="section-number">38.3.6.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id8">
-<h2><span class="section-number">38.3.7. </span>Control flow<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.7. </span>Control flow<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
 <section id="the-while-statement">
-<h3><span class="section-number">38.3.7.1. </span>The <code class="docutils literal notranslate"><span class="pre">while</span></code> statement<a class="headerlink" href="#the-while-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.7.1. </span>The <code class="docutils literal notranslate"><span class="pre">while</span></code> statement<a class="headerlink" href="#the-while-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Repeatedly executes a block of statements as long as a condition is true</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">True</span></code> and <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
@@ -2435,9 +2467,9 @@ <h3><span class="section-number">38.3.7.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id9">
-<h2><span class="section-number">38.3.8. </span>Control flow<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.8. </span>Control flow<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <section id="the-break-statement">
-<h3><span class="section-number">38.3.8.1. </span>The <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#the-break-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.8.1. </span>The <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#the-break-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Breaks out of the innermost enclosing “for” or “while” loop</p></li>
 </ul>
@@ -2463,9 +2495,9 @@ <h3><span class="section-number">38.3.8.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id10">
-<h2><span class="section-number">38.3.9. </span>Control flow<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.9. </span>Control flow<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <section id="the-continue-statement">
-<h3><span class="section-number">38.3.9.1. </span>The <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#the-continue-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.9.1. </span>The <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#the-continue-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Continues with the next iteration of the loop</p></li>
 </ul>
@@ -2491,7 +2523,7 @@ <h3><span class="section-number">38.3.9.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="functions">
-<h2><span class="section-number">38.3.10. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.10. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A function is a group of statements that exist within a program for the purpose of performing a specific task</p></li>
 <li><p>Since the beginning of the session, we have been using a number of Python’s built-in functions, including:</p>
@@ -2505,9 +2537,9 @@ <h2><span class="section-number">38.3.10. </span>Functions<a class="headerlink"
 </ul>
 </section>
 <section id="id11">
-<h2><span class="section-number">38.3.11. </span>Functions<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.11. </span>Functions<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <section id="defining-a-function">
-<h3><span class="section-number">38.3.11.1. </span>Defining a function<a class="headerlink" href="#defining-a-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.11.1. </span>Defining a function<a class="headerlink" href="#defining-a-function" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">myfunction</span><span class="p">():</span>
@@ -2518,7 +2550,7 @@ <h3><span class="section-number">38.3.11.1. </span>Defining a function<a class="
 </div>
 </section>
 <section id="calling-the-function">
-<h3><span class="section-number">38.3.11.2. </span>Calling the function<a class="headerlink" href="#calling-the-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.11.2. </span>Calling the function<a class="headerlink" href="#calling-the-function" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myfunction</span><span class="p">()</span>
@@ -2613,9 +2645,9 @@ <h3><span class="section-number">38.3.11.2. </span>Calling the function<a class=
 </section>
 </section>
 <section id="id12">
-<h2><span class="section-number">38.3.12. </span>Functions<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.12. </span>Functions<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <section id="scopes-of-variables-inside-functions">
-<h3><span class="section-number">38.3.12.1. </span>Scopes of variables inside functions<a class="headerlink" href="#scopes-of-variables-inside-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.12.1. </span>Scopes of variables inside functions<a class="headerlink" href="#scopes-of-variables-inside-functions" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Local variable:</p>
 <ul>
@@ -2645,9 +2677,9 @@ <h3><span class="section-number">38.3.12.1. </span>Scopes of variables inside fu
 </section>
 </section>
 <section id="id13">
-<h2><span class="section-number">38.3.13. </span>Functions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.13. </span>Functions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <section id="id14">
-<h3><span class="section-number">38.3.13.1. </span>Scopes of variables inside functions<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.13.1. </span>Scopes of variables inside functions<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Global Variables:</p>
 <ul>
@@ -2833,7 +2865,7 @@ <h3><span class="section-number">38.3.13.1. </span>Scopes of variables inside fu
 </section>
 </section>
 <section id="unpacking-argument-lists-and-statements">
-<h2><span class="section-number">38.3.14. </span>Unpacking argument lists (<code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> statements)<a class="headerlink" href="#unpacking-argument-lists-and-statements" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.14. </span>Unpacking argument lists (<code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> statements)<a class="headerlink" href="#unpacking-argument-lists-and-statements" title="Permalink to this headline">#</a></h2>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fun</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
 
@@ -2844,12 +2876,12 @@ <h2><span class="section-number">38.3.14. </span>Unpacking argument lists (<code
 </pre></div>
 </div>
 <section id="code-output">
-<h3><span class="section-number">38.3.14.1. </span>Code output:<a class="headerlink" href="#code-output" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.14.1. </span>Code output:<a class="headerlink" href="#code-output" title="Permalink to this headline">#</a></h3>
 <p><strong>TypeError</strong>: <code class="docutils literal notranslate"><span class="pre">fun()</span></code> takes exactly 4 arguments (1 given)</p>
 </section>
 </section>
 <section id="id15">
-<h2><span class="section-number">38.3.15. </span>Unpacking argument lists (<code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> statements)<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.15. </span>Unpacking argument lists (<code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> statements)<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">*args</span></code> (Non-keyword arguments)</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> (Keyword arguments)</p></li>
@@ -3083,9 +3115,9 @@ <h2><span class="section-number">38.3.15. </span>Unpacking argument lists (<code
 </div>
 </section>
 <section id="id16">
-<h2><span class="section-number">38.3.16. </span>Functions<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.16. </span>Functions<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
 <section id="documentation-strings-docstring">
-<h3><span class="section-number">38.3.16.1. </span>Documentation strings (docstring)<a class="headerlink" href="#documentation-strings-docstring" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.16.1. </span>Documentation strings (docstring)<a class="headerlink" href="#documentation-strings-docstring" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p>“Code is more often read than written.”</p>
 <p>— Guido van Rossum</p>
@@ -3195,9 +3227,9 @@ <h3><span class="section-number">38.3.16.1. </span>Documentation strings (docstr
 </section>
 </section>
 <section id="id17">
-<h2><span class="section-number">38.3.17. </span>Functions<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.17. </span>Functions<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
 <section id="function-annotations">
-<h3><span class="section-number">38.3.17.1. </span>Function annotations<a class="headerlink" href="#function-annotations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.17.1. </span>Function annotations<a class="headerlink" href="#function-annotations" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Function annotations are completely optional metadata information about the types used by user-defined functions.</p></li>
 </ul>
@@ -3221,9 +3253,9 @@ <h3><span class="section-number">38.3.17.1. </span>Function annotations<a class=
 </section>
 </section>
 <section id="id18">
-<h2><span class="section-number">38.3.18. </span>Functions<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.18. </span>Functions<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h2>
 <section id="function-decorators">
-<h3><span class="section-number">38.3.18.1. </span>Function decorators<a class="headerlink" href="#function-decorators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.18.1. </span>Function decorators<a class="headerlink" href="#function-decorators" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Wrappers to existing functions</p></li>
 <li><p>Dynamically alter the functionality of a function</p></li>
@@ -3312,7 +3344,7 @@ <h3><span class="section-number">38.3.18.1. </span>Function decorators<a class="
 </section>
 </section>
 <section id="classes">
-<h2><span class="section-number">38.3.19. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.19. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>To group data and functions belonging together to form custom data types</p></li>
 <li><p>A Class is like an object constructor or a “blueprint” for creating objects</p></li>
@@ -3350,9 +3382,9 @@ <h2><span class="section-number">38.3.19. </span>Classes<a class="headerlink" hr
 </div>
 </section>
 <section id="id19">
-<h2><span class="section-number">38.3.20. </span>Classes<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.20. </span>Classes<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
 <section id="inheritance">
-<h3><span class="section-number">38.3.20.1. </span>Inheritance<a class="headerlink" href="#inheritance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.20.1. </span>Inheritance<a class="headerlink" href="#inheritance" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Allows a derived class to reuse the same code and modify it accordingly</p></li>
 <li><p>Derived classes may override methods of their base classes.</p></li>
@@ -3420,7 +3452,7 @@ <h3><span class="section-number">38.3.20.1. </span>Inheritance<a class="headerli
 </section>
 </section>
 <section id="modules">
-<h2><span class="section-number">38.3.21. </span>Modules<a class="headerlink" href="#modules" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.21. </span>Modules<a class="headerlink" href="#modules" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>As your program gets longer, you may want to split it into several files for easier maintenance</p></li>
 <li><p>Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter</p></li>
@@ -3494,7 +3526,7 @@ <h2><span class="section-number">38.3.21. </span>Modules<a class="headerlink" hr
 </div>
 </section>
 <section id="lets-try-with-our-own-module">
-<h2><span class="section-number">38.3.22. </span>Let’s try with our own module<a class="headerlink" href="#lets-try-with-our-own-module" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.22. </span>Let’s try with our own module<a class="headerlink" href="#lets-try-with-our-own-module" title="Permalink to this headline">#</a></h2>
 <p>We write those code into the file <code class="docutils literal notranslate"><span class="pre">my_module.py</span></code>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">my_sum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
@@ -3538,7 +3570,7 @@ <h2><span class="section-number">38.3.22. </span>Let’s try with our own module
 </div>
 </section>
 <section id="errors-and-exceptions">
-<h2><span class="section-number">38.3.23. </span>Errors and exceptions<a class="headerlink" href="#errors-and-exceptions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.23. </span>Errors and exceptions<a class="headerlink" href="#errors-and-exceptions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Even if a statement or expression is syntactically correct, it may cause an error when an attempt is made to execute it</p></li>
 <li><p>Errors detected during execution are called exceptions and are not unconditionally fatal</p></li>
@@ -3583,13 +3615,13 @@ <h2><span class="section-number">38.3.23. </span>Errors and exceptions<a class="
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.3.24. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.24. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Practice the Python programming (advanced) by following the assignment</p></li>
 </ul>
 </section>
 <section id="references">
-<h2><span class="section-number">38.3.25. </span>References:<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.25. </span>References:<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.geeksforgeeks.org/packing-and-unpacking-arguments-in-python/">https://www.geeksforgeeks.org/packing-and-unpacking-arguments-in-python/</a></p></li>
 <li><p><a class="reference external" href="https://cs.nyu.edu/courses/summer16/CSCI-UA.0002-002/slides/Python_Functions.pdf">https://cs.nyu.edu/courses/summer16/CSCI-UA.0002-002/slides/Python_Functions.pdf</a></p></li>
@@ -3636,13 +3668,13 @@ <h2><span class="section-number">38.3.25. </span>References:<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.2. </span>Python programming basics</p>
+            <p class="prev-next-title"><span class="section-number">41.2. </span>Python programming basics</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../data-science/data-science-introduction.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.4. </span>Data Science introduction</p>
+        <p class="prev-next-title"><span class="section-number">41.4. </span>Data Science introduction</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/python-programming/python-programming-basics.html b/slides/python-programming/python-programming-basics.html
index ff1d09decd..8c4e7fa2f9 100644
--- a/slides/python-programming/python-programming-basics.html
+++ b/slides/python-programming/python-programming-basics.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>38.2. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.2. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.3. Python programming advanced" href="python-programming-advanced.html" />
-    <link rel="prev" title="38.1. Python programming introduction" href="python-programming-introduction.html" />
+    <link rel="next" title="41.3. Python programming advanced" href="python-programming-advanced.html" />
+    <link rel="prev" title="41.1. Python programming introduction" href="python-programming-introduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <label for="toctree-checkbox-7">
@@ -385,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     11.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   13. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     13.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     13.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     13.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -451,98 +478,103 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   14. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
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    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   15. Getting started with ensemble learning
+   17. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     15.2. Random forest
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     15.3. Feature importance
+     17.3. Feature importance
     </a>
    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     16.3. XGBoost
+     18.3. XGBoost
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    </li>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     16.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   17. Unsupervised learning
+   19. Unsupervised learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/model-selection.html">
-   19. Model selection
+   22. Model selection
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  </li>
 </ul>
@@ -554,62 +586,62 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
+   23. Intro to Deep Learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
+   24. Convolutional Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
+   25. Generative adversarial networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
+   26. Recurrent Neural Networks
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
+   27. Autoencoder
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
+   28. Long-short term memory
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
+   29. Time series
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
+   30. Deep Q-learning
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
+   31. Image classification
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
+   32. Image segmentation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
+   33. Diffusion Model
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
+   34. Object detection
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
+   35. Overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
+   36. Problem framing
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
+   38. Model training &amp; evaluation
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
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 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../assignments/README.html">
-   37. Self-paced assignments
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     37.3. First assignment
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     <a class="reference internal" href="../../assignments/set-up-env/second-assignment.html">
-     37.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     37.5. Project Plan​ Template
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-     37.6. Python programming introduction
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-     37.7. Python programming basics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     37.13. Apply your skills
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     37.17. Classifying datasets
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     37.21. Soda profits
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.53. Regression tools
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     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.54. Managing data
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.59. Retrying some regression
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.61. Delicious asian and indian cuisines
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     </a>
    </li>
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-     37.62. Explore classification methods
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.63. Kernel method assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.66. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.67. Decision Trees - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.68. Model selection assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.72. Regularized Linear Models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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   <ul class="current">
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     <a class="reference internal" href="python-programming-introduction.html">
-     38.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     38.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="python-programming-advanced.html">
-     38.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-introduction.html">
-     38.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/relational-vs-non-relational-database.html">
-     38.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/numpy-and-pandas.html">
-     38.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization.html">
-     38.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-lifecycle.html">
-     38.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud.html">
-     38.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-real-world.html">
-     38.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-overview.html">
-     38.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression.html">
-     38.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/logistic-regression.html">
-     38.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/logistic-regression-condensed.html">
-     38.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/neural-network.html">
-     38.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,118 +1558,118 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#python-syntax">
-   38.2.1. 1. Python syntax
+   41.2.1. 1. Python syntax
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-indentations">
-     38.2.1.1. Python indentations
+     41.2.1.1. Python indentations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     38.2.1.2. Comments
+     41.2.1.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#docstrings">
-     38.2.1.3. Docstrings
+     41.2.1.3. Docstrings
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variables">
-   38.2.2. 2. Variables
+   41.2.2. 2. Variables
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   38.2.3. 3. Operators
+   41.2.3. 3. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#arithmetic-operators">
-     38.2.3.1. Arithmetic operators
+     41.2.3.1. Arithmetic operators
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comparison-operators">
-     38.2.3.2. Comparison operators
+     41.2.3.2. Comparison operators
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-types">
-   38.2.4. 4. Data types
+   41.2.4. 4. Data types
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#numbers-including-booleans">
-     38.2.4.1. Numbers (including booleans)
+     41.2.4.1. Numbers (including booleans)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#integers">
-       38.2.4.1.1. Integers
+       41.2.4.1.1. Integers
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       38.2.4.1.2. Booleans
+       41.2.4.1.2. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#floats">
-       38.2.4.1.3. Floats
+       41.2.4.1.3. Floats
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#complexes">
-       38.2.4.1.4. Complexes
+       41.2.4.1.4. Complexes
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-operation">
-       38.2.4.1.5. Number operation
+       41.2.4.1.5. Number operation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#strings-and-their-methods">
-     38.2.4.2. Strings and their methods
+     41.2.4.2. Strings and their methods
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-type">
-       38.2.4.2.1. String type
+       41.2.4.2.1. String type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-operations">
-       38.2.4.2.2. String operations
+       41.2.4.2.2. String operations
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-methods">
-       38.2.4.2.3. String methods
+       41.2.4.2.3. String methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-formatting">
-       38.2.4.2.4. String formatting
+       41.2.4.2.4. String formatting
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#formatted-string-literals">
-         38.2.4.2.4.1. Formatted string literals
+         41.2.4.2.4.1. Formatted string literals
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#the-string-format-method">
-         38.2.4.2.4.2. The string format() method
+         41.2.4.2.4.2. The string format() method
         </a>
        </li>
       </ul>
@@ -1646,56 +1678,56 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lists-and-their-methods-including-list-comprehensions">
-     38.2.4.3. Lists and their methods (including list comprehensions)
+     41.2.4.3. Lists and their methods (including list comprehensions)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-type">
-       38.2.4.3.1. List type
+       41.2.4.3.1. List type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-methods">
-       38.2.4.3.2. List methods
+       41.2.4.3.2. List methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-del-statement">
-       38.2.4.3.3. The del statement
+       41.2.4.3.3. The del statement
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-comprehensions">
-       38.2.4.3.4. List comprehensions
+       41.2.4.3.4. List comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nested-list-comprehensions">
-       38.2.4.3.5. Nested list comprehensions
+       41.2.4.3.5. Nested list comprehensions
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dictionaries">
-     38.2.4.4. Dictionaries
+     41.2.4.4. Dictionaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#type-casting">
-     38.2.4.5. Type casting
+     41.2.4.5. Type casting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.2.5. 4. Your turn! 🚀
+   41.2.5. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.2.6. 5. References
+   41.2.6. 5. References
   </a>
  </li>
 </ul>
@@ -1719,118 +1751,118 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#python-syntax">
-   38.2.1. 1. Python syntax
+   41.2.1. 1. Python syntax
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-indentations">
-     38.2.1.1. Python indentations
+     41.2.1.1. Python indentations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     38.2.1.2. Comments
+     41.2.1.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#docstrings">
-     38.2.1.3. Docstrings
+     41.2.1.3. Docstrings
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variables">
-   38.2.2. 2. Variables
+   41.2.2. 2. Variables
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   38.2.3. 3. Operators
+   41.2.3. 3. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#arithmetic-operators">
-     38.2.3.1. Arithmetic operators
+     41.2.3.1. Arithmetic operators
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comparison-operators">
-     38.2.3.2. Comparison operators
+     41.2.3.2. Comparison operators
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-types">
-   38.2.4. 4. Data types
+   41.2.4. 4. Data types
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#numbers-including-booleans">
-     38.2.4.1. Numbers (including booleans)
+     41.2.4.1. Numbers (including booleans)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#integers">
-       38.2.4.1.1. Integers
+       41.2.4.1.1. Integers
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       38.2.4.1.2. Booleans
+       41.2.4.1.2. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#floats">
-       38.2.4.1.3. Floats
+       41.2.4.1.3. Floats
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#complexes">
-       38.2.4.1.4. Complexes
+       41.2.4.1.4. Complexes
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-operation">
-       38.2.4.1.5. Number operation
+       41.2.4.1.5. Number operation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#strings-and-their-methods">
-     38.2.4.2. Strings and their methods
+     41.2.4.2. Strings and their methods
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-type">
-       38.2.4.2.1. String type
+       41.2.4.2.1. String type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-operations">
-       38.2.4.2.2. String operations
+       41.2.4.2.2. String operations
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-methods">
-       38.2.4.2.3. String methods
+       41.2.4.2.3. String methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-formatting">
-       38.2.4.2.4. String formatting
+       41.2.4.2.4. String formatting
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#formatted-string-literals">
-         38.2.4.2.4.1. Formatted string literals
+         41.2.4.2.4.1. Formatted string literals
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#the-string-format-method">
-         38.2.4.2.4.2. The string format() method
+         41.2.4.2.4.2. The string format() method
         </a>
        </li>
       </ul>
@@ -1839,56 +1871,56 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lists-and-their-methods-including-list-comprehensions">
-     38.2.4.3. Lists and their methods (including list comprehensions)
+     41.2.4.3. Lists and their methods (including list comprehensions)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-type">
-       38.2.4.3.1. List type
+       41.2.4.3.1. List type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-methods">
-       38.2.4.3.2. List methods
+       41.2.4.3.2. List methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-del-statement">
-       38.2.4.3.3. The del statement
+       41.2.4.3.3. The del statement
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-comprehensions">
-       38.2.4.3.4. List comprehensions
+       41.2.4.3.4. List comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nested-list-comprehensions">
-       38.2.4.3.5. Nested list comprehensions
+       41.2.4.3.5. Nested list comprehensions
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dictionaries">
-     38.2.4.4. Dictionaries
+     41.2.4.4. Dictionaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#type-casting">
-     38.2.4.5. Type casting
+     41.2.4.5. Type casting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.2.5. 4. Your turn! 🚀
+   41.2.5. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.2.6. 5. References
+   41.2.6. 5. References
   </a>
  </li>
 </ul>
@@ -1917,16 +1949,16 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="python-programming-basics">
-<h1><span class="section-number">38.2. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.2. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
 <section id="python-syntax">
-<h2><span class="section-number">38.2.1. </span>1. Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.1. </span>1. Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Python was designed for readability and has some similarities to the English language.</p></li>
 <li><p>Python uses new lines to complete a command, not semicolons or parentheses.</p></li>
 <li><p>Python relies on indentation, using whitespace, to define scope, not curly brackets.</p></li>
 </ul>
 <section id="python-indentations">
-<h3><span class="section-number">38.2.1.1. </span>Python indentations<a class="headerlink" href="#python-indentations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.1.1. </span>Python indentations<a class="headerlink" href="#python-indentations" title="Permalink to this headline">#</a></h3>
 <p>Python uses indentation to indicate a block of code.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="mi">5</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Five is greater than two!&quot;</span><span class="p">)</span>
@@ -1934,7 +1966,7 @@ <h3><span class="section-number">38.2.1.1. </span>Python indentations<a class="h
 </div>
 </section>
 <section id="comments">
-<h3><span class="section-number">38.2.1.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.1.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>Python has the commenting capability for the purpose of in-code documentation.</p>
 <p>Comments start with a <code class="docutils literal notranslate"><span class="pre">#</span></code>, and Python will render the rest of the line as a comment:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># This is a comment.</span>
@@ -1943,7 +1975,7 @@ <h3><span class="section-number">38.2.1.2. </span>Comments<a class="headerlink"
 </div>
 </section>
 <section id="docstrings">
-<h3><span class="section-number">38.2.1.3. </span>Docstrings<a class="headerlink" href="#docstrings" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.1.3. </span>Docstrings<a class="headerlink" href="#docstrings" title="Permalink to this headline">#</a></h3>
 <p>Python also has extended capability for multiline documentation, called docstrings.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;This is a </span>
 <span class="sd">multiline docstring.&quot;&quot;&quot;</span>
@@ -1953,7 +1985,7 @@ <h3><span class="section-number">38.2.1.3. </span>Docstrings<a class="headerlink
 </section>
 </section>
 <section id="variables">
-<h2><span class="section-number">38.2.2. </span>2. Variables<a class="headerlink" href="#variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.2. </span>2. Variables<a class="headerlink" href="#variables" title="Permalink to this headline">#</a></h2>
 <p>Python is completely <strong>object-oriented</strong>, and <strong>not “statically typed”</strong>. You <strong>do not need to declare</strong> variables or their type before using them. Every variable in Python is an <strong>object</strong>.</p>
 <p>Rules for Python variables:</p>
 <ul class="simple">
@@ -1971,7 +2003,7 @@ <h2><span class="section-number">38.2.2. </span>2. Variables<a class="headerlink
 </div>
 </section>
 <section id="operators">
-<h2><span class="section-number">38.2.3. </span>3. Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.3. </span>3. Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
 <p>Operators are used to performing operations on variables and values. Python divides the operators into the following groups:</p>
 <ul class="simple">
 <li><p>Arithmetic operators</p></li>
@@ -1983,7 +2015,7 @@ <h2><span class="section-number">38.2.3. </span>3. Operators<a class="headerlink
 <li><p>Bitwise operators</p></li>
 </ul>
 <section id="arithmetic-operators">
-<h3><span class="section-number">38.2.3.1. </span>Arithmetic operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.3.1. </span>Arithmetic operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this headline">#</a></h3>
 <p>Arithmetic operators are used with numeric values to perform common mathematical operations.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># Addition.</span>
 <span class="k">assert</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">==</span> <span class="mi">8</span>
@@ -2014,7 +2046,7 @@ <h3><span class="section-number">38.2.3.1. </span>Arithmetic operators<a class="
 </div>
 </section>
 <section id="comparison-operators">
-<h3><span class="section-number">38.2.3.2. </span>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.3.2. </span>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">#</a></h3>
 <p>Comparison operators are used to compare two values.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># Equal.</span>
 <span class="n">number</span> <span class="o">=</span> <span class="mi">5</span>
@@ -2046,7 +2078,7 @@ <h3><span class="section-number">38.2.3.2. </span>Comparison operators<a class="
 </section>
 </section>
 <section id="data-types">
-<h2><span class="section-number">38.2.4. </span>4. Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.4. </span>4. Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h2>
 <p>In programming, data type is an important concept. Variables can store data of different types, and different types can do different things. Python has the following data types built in by default, in these categories:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -2082,7 +2114,7 @@ <h2><span class="section-number">38.2.4. </span>4. Data types<a class="headerlin
 </tbody>
 </table>
 <section id="numbers-including-booleans">
-<h3><span class="section-number">38.2.4.1. </span>Numbers (including booleans)<a class="headerlink" href="#numbers-including-booleans" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.1. </span>Numbers (including booleans)<a class="headerlink" href="#numbers-including-booleans" title="Permalink to this headline">#</a></h3>
 <p>There are three numeric types in Python:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">int</span></code></p></li>
@@ -2090,7 +2122,7 @@ <h3><span class="section-number">38.2.4.1. </span>Numbers (including booleans)<a
 <li><p><code class="docutils literal notranslate"><span class="pre">complex</span></code></p></li>
 </ul>
 <section id="integers">
-<h4><span class="section-number">38.2.4.1.1. </span>Integers<a class="headerlink" href="#integers" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.1. </span>Integers<a class="headerlink" href="#integers" title="Permalink to this headline">#</a></h4>
 <p>Int, or integer, is a whole number, positive or negative, without decimals, of unlimited length.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">positive_integer</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="n">negative_integer</span> <span class="o">=</span> <span class="o">-</span><span class="mi">3255522</span>
@@ -2103,7 +2135,7 @@ <h4><span class="section-number">38.2.4.1.1. </span>Integers<a class="headerlink
 </div>
 </section>
 <section id="booleans">
-<h4><span class="section-number">38.2.4.1.2. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.2. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
 <p>Booleans represent the truth values <code class="docutils literal notranslate"><span class="pre">False</span></code> and <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">true_boolean</span> <span class="o">=</span> <span class="kc">True</span>
 <span class="n">false_boolean</span> <span class="o">=</span> <span class="kc">False</span>
@@ -2121,7 +2153,7 @@ <h4><span class="section-number">38.2.4.1.2. </span>Booleans<a class="headerlink
 </div>
 </section>
 <section id="floats">
-<h4><span class="section-number">38.2.4.1.3. </span>Floats<a class="headerlink" href="#floats" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.3. </span>Floats<a class="headerlink" href="#floats" title="Permalink to this headline">#</a></h4>
 <p>Float, or “floating point number” is a number, positive or negative, containing one or more decimals.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">float_number</span> <span class="o">=</span> <span class="mf">7.0</span>
 <span class="c1"># Another way of declaring float is using float() function.</span>
@@ -2144,7 +2176,7 @@ <h4><span class="section-number">38.2.4.1.3. </span>Floats<a class="headerlink"
 </div>
 </section>
 <section id="complexes">
-<h4><span class="section-number">38.2.4.1.4. </span>Complexes<a class="headerlink" href="#complexes" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.4. </span>Complexes<a class="headerlink" href="#complexes" title="Permalink to this headline">#</a></h4>
 <p>A complex number has two parts, a real part and an imaginary part.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">complex_number_1</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">6</span><span class="n">j</span>
 <span class="n">complex_number_2</span> <span class="o">=</span> <span class="mi">3</span> <span class="o">-</span> <span class="mi">2</span><span class="n">j</span>
@@ -2156,7 +2188,7 @@ <h4><span class="section-number">38.2.4.1.4. </span>Complexes<a class="headerlin
 </div>
 </section>
 <section id="number-operation">
-<h4><span class="section-number">38.2.4.1.5. </span>Number operation<a class="headerlink" href="#number-operation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.5. </span>Number operation<a class="headerlink" href="#number-operation" title="Permalink to this headline">#</a></h4>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># Addition.</span>
 <span class="k">assert</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">==</span> <span class="mi">6</span>
 
@@ -2182,7 +2214,7 @@ <h4><span class="section-number">38.2.4.1.5. </span>Number operation<a class="he
 </section>
 </section>
 <section id="strings-and-their-methods">
-<h3><span class="section-number">38.2.4.2. </span>Strings and their methods<a class="headerlink" href="#strings-and-their-methods" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.2. </span>Strings and their methods<a class="headerlink" href="#strings-and-their-methods" title="Permalink to this headline">#</a></h3>
 <p>Besides numbers, Python can also manipulate strings, which can be expressed in several ways. They can be enclosed in single quotes <code class="docutils literal notranslate"><span class="pre">''</span></code> or double quotes <code class="docutils literal notranslate"><span class="pre">&quot;&quot;</span></code> with the same result.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># String with double quotes.</span>
 <span class="n">name_1</span> <span class="o">=</span> <span class="s2">&quot;John&quot;</span>
@@ -2196,7 +2228,7 @@ <h3><span class="section-number">38.2.4.2. </span>Strings and their methods<a cl
 </pre></div>
 </div>
 <section id="string-type">
-<h4><span class="section-number">38.2.4.2.1. </span>String type<a class="headerlink" href="#string-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.1. </span>String type<a class="headerlink" href="#string-type" title="Permalink to this headline">#</a></h4>
 <p><code class="docutils literal notranslate"><span class="pre">\</span></code> can be used to escape quotes. Use <code class="docutils literal notranslate"><span class="pre">\'</span></code> to escape the single quote or use double quotes instead.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">single_quote_string</span> <span class="o">=</span> <span class="s1">&#39;doesn</span><span class="se">\&#39;</span><span class="s1">t&#39;</span>
 <span class="n">double_quote_string</span> <span class="o">=</span> <span class="s2">&quot;doesn&#39;t&quot;</span>
@@ -2262,7 +2294,7 @@ <h4><span class="section-number">38.2.4.2.1. </span>String type<a class="headerl
 </div>
 </section>
 <section id="string-operations">
-<h4><span class="section-number">38.2.4.2.2. </span>String operations<a class="headerlink" href="#string-operations" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.2. </span>String operations<a class="headerlink" href="#string-operations" title="Permalink to this headline">#</a></h4>
 <p>Strings can be concatenated (glued together) with the <code class="docutils literal notranslate"><span class="pre">+</span></code> operator, and repeated with <code class="docutils literal notranslate"><span class="pre">*</span></code>.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="mi">3</span> <span class="o">*</span> <span class="s1">&#39;un&#39;</span> <span class="o">+</span> <span class="s1">&#39;ium&#39;</span> <span class="o">==</span> <span class="s1">&#39;unununium&#39;</span>
 <span class="k">assert</span> <span class="s1">&#39;Py&#39;</span> <span class="s1">&#39;thon&#39;</span> <span class="o">==</span> <span class="s1">&#39;Python&#39;</span>
@@ -2283,7 +2315,7 @@ <h4><span class="section-number">38.2.4.2.2. </span>String operations<a class="h
 </div>
 </section>
 <section id="string-methods">
-<h4><span class="section-number">38.2.4.2.3. </span>String methods<a class="headerlink" href="#string-methods" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.3. </span>String methods<a class="headerlink" href="#string-methods" title="Permalink to this headline">#</a></h4>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">hello_world_string</span> <span class="o">=</span> <span class="s2">&quot;Hello, World!&quot;</span>
 </pre></div>
 </div>
@@ -2315,10 +2347,10 @@ <h4><span class="section-number">38.2.4.2.3. </span>String methods<a class="head
 </div>
 </section>
 <section id="string-formatting">
-<h4><span class="section-number">38.2.4.2.4. </span>String formatting<a class="headerlink" href="#string-formatting" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.4. </span>String formatting<a class="headerlink" href="#string-formatting" title="Permalink to this headline">#</a></h4>
 <p>Often you’ll want more control over the formatting of your output than simply printing space-separated values. There are several ways to format output.</p>
 <section id="formatted-string-literals">
-<h5><span class="section-number">38.2.4.2.4.1. </span>Formatted string literals<a class="headerlink" href="#formatted-string-literals" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">41.2.4.2.4.1. </span>Formatted string literals<a class="headerlink" href="#formatted-string-literals" title="Permalink to this headline">#</a></h5>
 <p>Formatted string literals (also called f-strings for short) let you include the value of Python expressions inside a string by prefixing the string with <code class="docutils literal notranslate"><span class="pre">f</span></code> or <code class="docutils literal notranslate"><span class="pre">F</span></code> and writing expressions as <code class="docutils literal notranslate"><span class="pre">{expression}</span></code>.</p>
 <p>An optional format specifier can follow the expression. This allows greater control over how the value is formatted. The following example rounds pi to three places after the decimal.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">pi_value</span> <span class="o">=</span> <span class="mf">3.14159</span>
@@ -2338,7 +2370,7 @@ <h5><span class="section-number">38.2.4.2.4.1. </span>Formatted string literals<
 </div>
 </section>
 <section id="the-string-format-method">
-<h5><span class="section-number">38.2.4.2.4.2. </span>The string format() method<a class="headerlink" href="#the-string-format-method" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">41.2.4.2.4.2. </span>The string format() method<a class="headerlink" href="#the-string-format-method" title="Permalink to this headline">#</a></h5>
 <p>Basic usage of the <code class="docutils literal notranslate"><span class="pre">str.format()</span></code> method looks like this:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="s1">&#39;We are </span><span class="si">{}</span><span class="s1"> who say &quot;</span><span class="si">{}</span><span class="s1">!&quot;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;knights&#39;</span><span class="p">,</span> <span class="s1">&#39;Ni&#39;</span><span class="p">)</span> <span class="o">==</span> <span class="s1">&#39;We are knights who say &quot;Ni!&quot;&#39;</span>
 </pre></div>
@@ -2384,10 +2416,10 @@ <h5><span class="section-number">38.2.4.2.4.2. </span>The string format() method
 </section>
 </section>
 <section id="lists-and-their-methods-including-list-comprehensions">
-<h3><span class="section-number">38.2.4.3. </span>Lists and their methods (including list comprehensions)<a class="headerlink" href="#lists-and-their-methods-including-list-comprehensions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.3. </span>Lists and their methods (including list comprehensions)<a class="headerlink" href="#lists-and-their-methods-including-list-comprehensions" title="Permalink to this headline">#</a></h3>
 <p>Python knows several compound data types, used to group together other values. The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets.</p>
 <section id="list-type">
-<h4><span class="section-number">38.2.4.3.1. </span>List type<a class="headerlink" href="#list-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.1. </span>List type<a class="headerlink" href="#list-type" title="Permalink to this headline">#</a></h4>
 <p>Lists are very similar to arrays. They can contain any type of variable, and they can contain as many variables as you wish. Lists can also be iterated over in a very simple manner.</p>
 <p>Here is an example of how to build a list.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">squares</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">25</span><span class="p">]</span>
@@ -2445,7 +2477,7 @@ <h4><span class="section-number">38.2.4.3.1. </span>List type<a class="headerlin
 </div>
 </section>
 <section id="list-methods">
-<h4><span class="section-number">38.2.4.3.2. </span>List methods<a class="headerlink" href="#list-methods" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.2. </span>List methods<a class="headerlink" href="#list-methods" title="Permalink to this headline">#</a></h4>
 <p><code class="docutils literal notranslate"><span class="pre">list.remove(x)</span></code> removes the first item from the list whose value is equal to x. It raises a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> if there is no such item.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">fruits</span> <span class="o">==</span> <span class="p">[</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="s1">&#39;apple&#39;</span><span class="p">,</span> <span class="s1">&#39;pear&#39;</span><span class="p">,</span> <span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="s1">&#39;kiwi&#39;</span><span class="p">,</span> <span class="s1">&#39;apple&#39;</span><span class="p">,</span> <span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="s1">&#39;grape&#39;</span><span class="p">]</span>
 
@@ -2522,7 +2554,7 @@ <h4><span class="section-number">38.2.4.3.2. </span>List methods<a class="header
 </div>
 </section>
 <section id="the-del-statement">
-<h4><span class="section-number">38.2.4.3.3. </span>The del statement<a class="headerlink" href="#the-del-statement" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.3. </span>The del statement<a class="headerlink" href="#the-del-statement" title="Permalink to this headline">#</a></h4>
 <p>There is a way to remove an item from a list given its index instead of its value: the <code class="docutils literal notranslate"><span class="pre">del</span></code> statement.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pytest</span>
 
@@ -2537,7 +2569,7 @@ <h4><span class="section-number">38.2.4.3.3. </span>The del statement<a class="h
 </div>
 </section>
 <section id="list-comprehensions">
-<h4><span class="section-number">38.2.4.3.4. </span>List comprehensions<a class="headerlink" href="#list-comprehensions" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.4. </span>List comprehensions<a class="headerlink" href="#list-comprehensions" title="Permalink to this headline">#</a></h4>
 <p>List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable or to create a subsequence of those elements that satisfy a certain condition.</p>
 <p>For example, assume we want to create a list of squares, like:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">squares</span> <span class="o">=</span> <span class="p">[]</span>
@@ -2610,7 +2642,7 @@ <h4><span class="section-number">38.2.4.3.4. </span>List comprehensions<a class=
 </div>
 </section>
 <section id="nested-list-comprehensions">
-<h4><span class="section-number">38.2.4.3.5. </span>Nested list comprehensions<a class="headerlink" href="#nested-list-comprehensions" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.5. </span>Nested list comprehensions<a class="headerlink" href="#nested-list-comprehensions" title="Permalink to this headline">#</a></h4>
 <p>The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.</p>
 <p>Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">matrix</span> <span class="o">=</span> <span class="p">[</span>
@@ -2672,7 +2704,7 @@ <h4><span class="section-number">38.2.4.3.5. </span>Nested list comprehensions<a
 </section>
 </section>
 <section id="dictionaries">
-<h3><span class="section-number">38.2.4.4. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.4. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h3>
 <p>A dictionary is a collection that is unordered, changeable and indexed. In Python, dictionaries are written with curly brackets, and they have keys and values.</p>
 <p>It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: <code class="docutils literal notranslate"><span class="pre">{}</span></code>. Placing a comma-separated list of key-value pairs within the braces adds initial key-value pairs to the dictionary; this is also the way dictionaries are written on output.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">fruits_dictionary</span> <span class="o">=</span> <span class="p">{</span>
@@ -2738,7 +2770,7 @@ <h3><span class="section-number">38.2.4.4. </span>Dictionaries<a class="headerli
 </div>
 </section>
 <section id="type-casting">
-<h3><span class="section-number">38.2.4.5. </span>Type casting<a class="headerlink" href="#type-casting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.5. </span>Type casting<a class="headerlink" href="#type-casting" title="Permalink to this headline">#</a></h3>
 <p>There may be times when you want to specify a type on to a variable. This can be done with <strong>casting</strong>. Python is an object-orientated language, and as such it uses classes to define data types, including its primitive types.</p>
 <p>Casting in python is therefore done using constructor functions.</p>
 <ul class="simple">
@@ -2772,11 +2804,11 @@ <h3><span class="section-number">38.2.4.5. </span>Type casting<a class="headerli
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.2.5. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.5. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-basics.html#your-turn">Python programming basics - Your turn</a></p>
 </section>
 <section id="references">
-<h2><span class="section-number">38.2.6. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.6. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-basic.html">Python programming basic</a></p></li>
 </ol>
@@ -2820,13 +2852,13 @@ <h2><span class="section-number">38.2.6. </span>5. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">38.1. </span>Python programming introduction</p>
+            <p class="prev-next-title"><span class="section-number">41.1. </span>Python programming introduction</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="python-programming-advanced.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">38.3. </span>Python programming advanced</p>
+        <p class="prev-next-title"><span class="section-number">41.3. </span>Python programming advanced</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/python-programming/python-programming-introduction.html b/slides/python-programming/python-programming-introduction.html
index 97743a3efa..4b6566d6a0 100644
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-    <title>38.1. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.1. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="38.2. Python programming basics" href="python-programming-basics.html" />
-    <link rel="prev" title="38. Slides" href="../introduction.html" />
+    <link rel="next" title="41.2. Python programming basics" href="python-programming-basics.html" />
+    <link rel="prev" title="41. Slides" href="../introduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -373,9 +373,14 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    10. Machine Learning overview
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
+   11. Summary of machine learning fundamentals
+  </a>
+ </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   11. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     11.1. Tools of the trade
+     12.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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+     12.2. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     11.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     11.4. Logistic regression
+     12.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   12. Build a web app to use a Machine Learning model
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  <li class="toctree-l1 has-children">
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     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
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     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     13.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
+   15. parameter-optimization
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+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
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+   </i>
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
+     15.1. Loss function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
+     15.2. Gradient descent
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     14.1. Introduction to clustering
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    </li>
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     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     14.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
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-  <label for="toctree-checkbox-11">
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   <a class="reference internal" href="../../ml-advanced/kernel-method.html">
-   18. Kernel method
+   20. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../ml-advanced/unsupervised-learning-pca-and-clustering.html">
+   21. Unsupervised learning: PCA and clustering
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
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     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     37.26. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     37.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
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-     37.29. Matplotlib applied
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     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     37.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     37.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.47. Loss Function
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.50. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
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-     37.53. Regression tools
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-     37.55. Exploring visualizations
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-     37.56. Try a different model
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-     37.57. Create a regression model
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-     37.58. Linear and polynomial regression
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-     37.59. Retrying some regression
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-     37.60. Pumpkin varieties and color
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-     37.61. Delicious asian and indian cuisines
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-     37.62. Explore classification methods
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-     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.66. Decision Trees - Intro and Regression
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-     37.68. Model selection assignment 1
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-     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.70. Dropout and Batch Normalization
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-     37.72. Regularized Linear Models
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-     37.75. Beyond random forests: more ensemble models
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-     37.76. Decision trees
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-     37.77. Hyperparameter tuning gradient boosting
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-     37.78. Gradient boosting
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-     37.80. Random Forest Classifier with Feature Importance
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-     37.82. Data engineering
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-     37.83. Counterintuitive Challenges in ML Debugging
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-     37.84. Case Study: Debugging in Classification
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-     37.85. Case Study: Debugging in Regression
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-     37.86. Study the solvers
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-     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Google Stock Price Prediction RNN
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-     37.101. Intro to Autoencoders
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-     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.107. NN Classify 15 Fruits Assignment
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-     37.112. DQN On Foreign Exchange Market
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+     41.1. Python programming introduction
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-an-ml-web-app.html">
-     38.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     38.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method.html">
-     38.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     38.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     38.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     38.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1526,96 +1558,96 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#whats-python">
-   38.1.1. 1. What’s Python?
+   41.1.1. 1. What’s Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#why-python">
-   38.1.2. 2. Why Python?
+   41.1.2. 2. Why Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#basic-python">
-   38.1.3. 3. Basic Python
+   41.1.3. 3. Basic Python
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#indentation">
-     38.1.3.1. Indentation
+     41.1.3.1. Indentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     38.1.3.2. Comments
+     41.1.3.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-types">
-     38.1.3.3. Data types
+     41.1.3.3. Data types
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number">
-       38.1.3.3.1. Number
+       41.1.3.3.1. Number
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string">
-       38.1.3.3.2. String
+       41.1.3.3.2. String
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       38.1.3.3.3. Booleans
+       41.1.3.3.3. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list">
-       38.1.3.3.4. List
+       41.1.3.3.4. List
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dictionary">
-       38.1.3.3.5. Dictionary
+       41.1.3.3.5. Dictionary
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#tuple">
-       38.1.3.3.6. Tuple
+       41.1.3.3.6. Tuple
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set">
-       38.1.3.3.7. Set
+       41.1.3.3.7. Set
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-data-types">
-     38.1.3.4. Checking data types
+     41.1.3.4. Checking data types
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-file">
-     38.1.3.5. Python file
+     41.1.3.5. Python file
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     38.1.3.6. Python file
+     41.1.3.6. Python file
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.1.4. 4. Your turn! 🚀
+   41.1.4. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.1.5. 5. References
+   41.1.5. 5. References
   </a>
  </li>
 </ul>
@@ -1639,96 +1671,96 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#whats-python">
-   38.1.1. 1. What’s Python?
+   41.1.1. 1. What’s Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#why-python">
-   38.1.2. 2. Why Python?
+   41.1.2. 2. Why Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#basic-python">
-   38.1.3. 3. Basic Python
+   41.1.3. 3. Basic Python
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#indentation">
-     38.1.3.1. Indentation
+     41.1.3.1. Indentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     38.1.3.2. Comments
+     41.1.3.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-types">
-     38.1.3.3. Data types
+     41.1.3.3. Data types
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number">
-       38.1.3.3.1. Number
+       41.1.3.3.1. Number
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string">
-       38.1.3.3.2. String
+       41.1.3.3.2. String
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       38.1.3.3.3. Booleans
+       41.1.3.3.3. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list">
-       38.1.3.3.4. List
+       41.1.3.3.4. List
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dictionary">
-       38.1.3.3.5. Dictionary
+       41.1.3.3.5. Dictionary
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#tuple">
-       38.1.3.3.6. Tuple
+       41.1.3.3.6. Tuple
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set">
-       38.1.3.3.7. Set
+       41.1.3.3.7. Set
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-data-types">
-     38.1.3.4. Checking data types
+     41.1.3.4. Checking data types
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-file">
-     38.1.3.5. Python file
+     41.1.3.5. Python file
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     38.1.3.6. Python file
+     41.1.3.6. Python file
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   38.1.4. 4. Your turn! 🚀
+   41.1.4. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   38.1.5. 5. References
+   41.1.5. 5. References
   </a>
  </li>
 </ul>
@@ -1757,9 +1789,9 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="python-programming-introduction">
-<h1><span class="section-number">38.1. </span>Python programming introduction<a class="headerlink" href="#python-programming-introduction" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.1. </span>Python programming introduction<a class="headerlink" href="#python-programming-introduction" title="Permalink to this headline">#</a></h1>
 <section id="whats-python">
-<h2><span class="section-number">38.1.1. </span>1. What’s Python?<a class="headerlink" href="#whats-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.1. </span>1. What’s Python?<a class="headerlink" href="#whats-python" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Python is a high-level programming language for general-purpose programming.</p></li>
 <li><p>Python is open-source.</p></li>
@@ -1770,7 +1802,7 @@ <h2><span class="section-number">38.1.1. </span>1. What’s Python?<a class="hea
   <img class="removeMargin" src="https://www.python.org/static/img/python-logo.png"/>
 </div></section>
 <section id="why-python">
-<h2><span class="section-number">38.1.2. </span>2. Why Python?<a class="headerlink" href="#why-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.2. </span>2. Why Python?<a class="headerlink" href="#why-python" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Easy to learn.</p></li>
 <li><p>Well adopted.</p></li>
@@ -1784,13 +1816,13 @@ <h2><span class="section-number">38.1.2. </span>2. Why Python?<a class="headerli
     </div>
 </div></section>
 <section id="basic-python">
-<h2><span class="section-number">38.1.3. </span>3. Basic Python<a class="headerlink" href="#basic-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.3. </span>3. Basic Python<a class="headerlink" href="#basic-python" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A Python script can be written in Python interactive shell or the code editor.</p></li>
 <li><p>A Python file has an extension <code class="docutils literal notranslate"><span class="pre">.py</span></code>.</p></li>
 </ul>
 <section id="indentation">
-<h3><span class="section-number">38.1.3.1. </span>Indentation<a class="headerlink" href="#indentation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.1. </span>Indentation<a class="headerlink" href="#indentation" title="Permalink to this headline">#</a></h3>
 <p>An indentation is a white space in a text. Indentation in many languages is used to increase code readability, however, Python uses indentation to create blocks of codes.</p>
 <div class="cell tag_raises-exception docutils container">
 <div class="cell_input docutils container">
@@ -1807,7 +1839,7 @@ <h3><span class="section-number">38.1.3.1. </span>Indentation<a class="headerlin
 </div>
 </section>
 <section id="comments">
-<h3><span class="section-number">38.1.3.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>Comments are very important to make the code more readable and to leave remarks in our code. Python does not run comment parts of our code.</p>
 <p><strong>Example: Single Line Comment</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># This is the first comment</span>
@@ -1824,9 +1856,9 @@ <h3><span class="section-number">38.1.3.2. </span>Comments<a class="headerlink"
 </div>
 </section>
 <section id="data-types">
-<h3><span class="section-number">38.1.3.3. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.3. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h3>
 <section id="number">
-<h4><span class="section-number">38.1.3.3.1. </span>Number<a class="headerlink" href="#number" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.1. </span>Number<a class="headerlink" href="#number" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>Integer: integer(negative, zero and positive) numbers.
 Example: … -3, -2, -1, 0, 1, 2, 3 …</p></li>
@@ -1837,7 +1869,7 @@ <h4><span class="section-number">38.1.3.3.1. </span>Number<a class="headerlink"
 </ul>
 </section>
 <section id="string">
-<h4><span class="section-number">38.1.3.3.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h4>
 <p>A collection of one or more characters under a single or double quote. If a string is more than one sentence then we use a triple quote.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="s1">&#39;Asabeneh&#39;</span>
 <span class="s1">&#39;Finland&#39;</span>
@@ -1848,14 +1880,14 @@ <h4><span class="section-number">38.1.3.3.2. </span>String<a class="headerlink"
 </div>
 </section>
 <section id="booleans">
-<h4><span class="section-number">38.1.3.3.3. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.3. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kc">True</span>  <span class="c1">#  Is the light on? If it is on, then the value is True</span>
 <span class="kc">False</span> <span class="c1"># I</span>
 </pre></div>
 </div>
 </section>
 <section id="list">
-<h4><span class="section-number">38.1.3.3.4. </span>List<a class="headerlink" href="#list" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.4. </span>List<a class="headerlink" href="#list" title="Permalink to this headline">#</a></h4>
 <p>Python list is an ordered collection that allows to store items of different data types. A list is similar to an array in JavaScript.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>  <span class="c1"># all are the same data types - a list of numbers</span>
@@ -1866,7 +1898,7 @@ <h4><span class="section-number">38.1.3.3.4. </span>List<a class="headerlink" hr
 </div>
 </section>
 <section id="dictionary">
-<h4><span class="section-number">38.1.3.3.5. </span>Dictionary<a class="headerlink" href="#dictionary" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.5. </span>Dictionary<a class="headerlink" href="#dictionary" title="Permalink to this headline">#</a></h4>
 <p>A Python dictionary object is an unordered collection of data in a key-value pair format.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
@@ -1881,7 +1913,7 @@ <h4><span class="section-number">38.1.3.3.5. </span>Dictionary<a class="headerli
 </div>
 </section>
 <section id="tuple">
-<h4><span class="section-number">38.1.3.3.6. </span>Tuple<a class="headerlink" href="#tuple" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.6. </span>Tuple<a class="headerlink" href="#tuple" title="Permalink to this headline">#</a></h4>
 <p>A tuple is an ordered collection of different data types like a list, but tuples can not be modified once they are created. They are immutable.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="s1">&#39;Asabeneh&#39;</span><span class="p">,</span> <span class="s1">&#39;Pawel&#39;</span><span class="p">,</span> <span class="s1">&#39;Brook&#39;</span><span class="p">,</span> <span class="s1">&#39;Abraham&#39;</span><span class="p">,</span> <span class="s1">&#39;Lidiya&#39;</span><span class="p">)</span> <span class="c1"># Names</span>
@@ -1892,7 +1924,7 @@ <h4><span class="section-number">38.1.3.3.6. </span>Tuple<a class="headerlink" h
 </div>
 </section>
 <section id="set">
-<h4><span class="section-number">38.1.3.3.7. </span>Set<a class="headerlink" href="#set" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.7. </span>Set<a class="headerlink" href="#set" title="Permalink to this headline">#</a></h4>
 <p>A set is a collection of data types similar to a list and tuple. Unlike the list and the tuple, a set is not an ordered collection of items. Like in Mathematics, set in Python only stores unique items. In later sections, we will go into detail about every Python data type.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">}</span>
@@ -1902,7 +1934,7 @@ <h4><span class="section-number">38.1.3.3.7. </span>Set<a class="headerlink" hre
 </section>
 </section>
 <section id="checking-data-types">
-<h3><span class="section-number">38.1.3.4. </span>Checking data types<a class="headerlink" href="#checking-data-types" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.4. </span>Checking data types<a class="headerlink" href="#checking-data-types" title="Permalink to this headline">#</a></h3>
 <p>To check the data type of certain data/variables we use the <code class="docutils literal notranslate"><span class="pre">type</span></code> function.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1942,7 +1974,7 @@ <h3><span class="section-number">38.1.3.4. </span>Checking data types<a class="h
 </div>
 </section>
 <section id="python-file">
-<h3><span class="section-number">38.1.3.5. </span>Python file<a class="headerlink" href="#python-file" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.5. </span>Python file<a class="headerlink" href="#python-file" title="Permalink to this headline">#</a></h3>
 <p>A sample Python file <code class="docutils literal notranslate"><span class="pre">helloworld.py</span></code>.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">3</span><span class="p">)</span>             <span class="c1"># addition(+)</span>
 <span class="nb">print</span><span class="p">(</span><span class="mi">3</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>             <span class="c1"># subtraction(-)</span>
@@ -1965,15 +1997,15 @@ <h3><span class="section-number">38.1.3.5. </span>Python file<a class="headerlin
 </div>
 </section>
 <section id="id1">
-<h3><span class="section-number">38.1.3.6. </span>Python file<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.6. </span>Python file<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.1.4. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.4. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-introduction.html#your-turn">Python programming introduction - Your turn</a></p>
 </section>
 <section id="references">
-<h2><span class="section-number">38.1.5. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.5. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
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@@ -2017,13 +2049,13 @@ <h2><span class="section-number">38.1.5. </span>5. References<a class="headerlin
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