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a/_sources/assignments/deep-learning/cnn/image-classification.ipynb b/_sources/assignments/deep-learning/cnn/image-classification.ipynb new file mode 100644 index 0000000000..7f3a631caf --- /dev/null +++ b/_sources/assignments/deep-learning/cnn/image-classification.ipynb @@ -0,0 +1,1396 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Install the necessary dependencies\n", + "\n", + "import os\n", + "import sys\n", + "!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn 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": { + "id": "FE7KNzPPVrVV" + }, + "source": [ + "# Image classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gN7G9GFmVrVY" + }, + "source": [ + "This tutorial shows how to classify images of flowers using a `tf.keras.Sequential` model and load data using `tf.keras.utils.image_dataset_from_directory`. It demonstrates the following concepts:\n", + "\n", + "\n", + "* Efficiently loading a dataset off disk.\n", + "* Identifying overfitting and applying techniques to mitigate it, including data augmentation and dropout.\n", + "\n", + "This tutorial follows a basic machine learning workflow:\n", + "\n", + "1. Examine and understand data\n", + "2. Build an input pipeline\n", + "3. Build the model\n", + "4. Train the model\n", + "5. Test the model\n", + "6. Improve the model and repeat the process\n", + "\n", + "In addition, the notebook demonstrates how to convert a saved model to a TensorFlow Lite model for on-device machine learning on mobile, embedded, and IoT devices." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zF9uvbXNVrVY" + }, + "source": [ + "## Setup\n", + "\n", + "Import TensorFlow and other necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:37.838594Z", + "iopub.status.busy": "2023-10-27T06:06:37.838012Z", + "iopub.status.idle": "2023-10-27T06:06:40.429734Z", + "shell.execute_reply": "2023-10-27T06:06:40.429024Z" + }, + "id": "L1WtoaOHVrVh" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import PIL\n", + "import tensorflow as tf\n", + "\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from tensorflow.keras.models import Sequential" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UZZI6lNkVrVm" + }, + "source": [ + "## Download and explore the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DPHx8-t-VrVo" + }, + "source": [ + "This tutorial uses a dataset of about 3,700 photos of flowers. The dataset contains five sub-directories, one per class:\n", + "\n", + "```\n", + "flower_photo/\n", + " daisy/\n", + " dandelion/\n", + " roses/\n", + " sunflowers/\n", + " tulips/\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:40.434690Z", + "iopub.status.busy": "2023-10-27T06:06:40.433828Z", + "iopub.status.idle": "2023-10-27T06:06:43.613076Z", + "shell.execute_reply": "2023-10-27T06:06:43.612374Z" + }, + "id": "57CcilYSG0zv" + }, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "dataset_url = \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/flower_photos.tgz\"\n", + "data_dir = tf.keras.utils.get_file('flower_photos.tar', origin=dataset_url, extract=True)\n", + "data_dir = pathlib.Path(data_dir).with_suffix('')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VpmywIlsVrVx" + }, + "source": [ + "After downloading, you should now have a copy of the dataset available. There are 3,670 total images:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:43.617178Z", + "iopub.status.busy": "2023-10-27T06:06:43.616929Z", + "iopub.status.idle": "2023-10-27T06:06:43.630586Z", + "shell.execute_reply": "2023-10-27T06:06:43.629991Z" + }, + "id": "SbtTDYhOHZb6" + }, + "outputs": [], + "source": [ + "image_count = len(list(data_dir.glob('*/*.jpg')))\n", + "print(image_count)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PVmwkOSdHZ5A" + }, + "source": [ + "Here are some roses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:43.633873Z", + "iopub.status.busy": "2023-10-27T06:06:43.633337Z", + "iopub.status.idle": "2023-10-27T06:06:43.663732Z", + "shell.execute_reply": "2023-10-27T06:06:43.663159Z" + }, + "id": "N1loMlbYHeiJ" + }, + "outputs": [], + "source": [ + "roses = list(data_dir.glob('roses/*'))\n", + "PIL.Image.open(str(roses[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:43.669205Z", + "iopub.status.busy": "2023-10-27T06:06:43.668553Z", + "iopub.status.idle": "2023-10-27T06:06:43.706526Z", + "shell.execute_reply": "2023-10-27T06:06:43.705970Z" + }, + "id": "RQbZBOTLHiUP" + }, + "outputs": [], + "source": [ + "PIL.Image.open(str(roses[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DGEqiBbRHnyI" + }, + "source": [ + "And some tulips:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:43.713651Z", + "iopub.status.busy": "2023-10-27T06:06:43.712988Z", + "iopub.status.idle": "2023-10-27T06:06:43.732316Z", + "shell.execute_reply": "2023-10-27T06:06:43.731754Z" + }, + "id": "HyQkfPGdHilw" + }, + "outputs": [], + "source": [ + "tulips = list(data_dir.glob('tulips/*'))\n", + "PIL.Image.open(str(tulips[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:43.736431Z", + "iopub.status.busy": "2023-10-27T06:06:43.735919Z", + "iopub.status.idle": "2023-10-27T06:06:43.758890Z", + "shell.execute_reply": "2023-10-27T06:06:43.758290Z" + }, + "id": "wtlhWJPAHivf" + }, + "outputs": [], + "source": [ + "PIL.Image.open(str(tulips[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gIjgz7_JIo_m" + }, + "source": [ + "## Load data using a Keras utility\n", + "\n", + "Next, load these images off disk using the helpful `tf.keras.utils.image_dataset_from_directory` utility. This will take you from a directory of images on disk to a `tf.data.Dataset` in just a couple lines of code. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xyDNn9MbIzfT" + }, + "source": [ + "### Create a dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "anqiK_AGI086" + }, + "source": [ + "Define some parameters for the loader:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:43.764214Z", + "iopub.status.busy": "2023-10-27T06:06:43.763806Z", + "iopub.status.idle": "2023-10-27T06:06:43.766863Z", + "shell.execute_reply": "2023-10-27T06:06:43.766310Z" + }, + "id": "H74l2DoDI2XD" + }, + "outputs": [], + "source": [ + "batch_size = 32\n", + "img_height = 180\n", + "img_width = 180" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pFBhRrrEI49z" + }, + "source": [ + "It's good practice to use a validation split when developing your model. Use 80% of the images for training and 20% for validation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:43.770270Z", + "iopub.status.busy": "2023-10-27T06:06:43.769868Z", + "iopub.status.idle": "2023-10-27T06:06:47.127119Z", + "shell.execute_reply": "2023-10-27T06:06:47.126368Z" + }, + "id": "fIR0kRZiI_AT" + }, + "outputs": [], + "source": [ + "train_ds = tf.keras.utils.image_dataset_from_directory(\n", + " data_dir,\n", + " validation_split=0.2,\n", + " subset=\"training\",\n", + " seed=123,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:47.131037Z", + "iopub.status.busy": "2023-10-27T06:06:47.130765Z", + "iopub.status.idle": "2023-10-27T06:06:47.287395Z", + "shell.execute_reply": "2023-10-27T06:06:47.286778Z" + }, + "id": "iscU3UoVJBXj" + }, + "outputs": [], + "source": [ + "val_ds = tf.keras.utils.image_dataset_from_directory(\n", + " data_dir,\n", + " validation_split=0.2,\n", + " subset=\"validation\",\n", + " seed=123,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WLQULyAvJC3X" + }, + "source": [ + "You can find the class names in the `class_names` attribute on these datasets. These correspond to the directory names in alphabetical order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:47.291072Z", + "iopub.status.busy": "2023-10-27T06:06:47.290824Z", + "iopub.status.idle": "2023-10-27T06:06:47.294420Z", + "shell.execute_reply": "2023-10-27T06:06:47.293799Z" + }, + "id": "ZHAxkHX5JD3k" + }, + "outputs": [], + "source": [ + "class_names = train_ds.class_names\n", + "print(class_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_uoVvxSLJW9m" + }, + "source": [ + "## Visualize the data\n", + "\n", + "Here are the first nine images from the training dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:47.297749Z", + "iopub.status.busy": "2023-10-27T06:06:47.297476Z", + "iopub.status.idle": "2023-10-27T06:06:48.298425Z", + "shell.execute_reply": "2023-10-27T06:06:48.297546Z" + }, + "id": "wBmEA9c0JYes" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(10, 10))\n", + "for images, labels in train_ds.take(1):\n", + " for i in range(9):\n", + " ax = plt.subplot(3, 3, i + 1)\n", + " plt.imshow(images[i].numpy().astype(\"uint8\"))\n", + " plt.title(class_names[labels[i]])\n", + " plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5M6BXtXFJdW0" + }, + "source": [ + "You will pass these datasets to the Keras `Model.fit` method for training later in this tutorial. If you like, you can also manually iterate over the dataset and retrieve batches of images:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:48.314518Z", + "iopub.status.busy": "2023-10-27T06:06:48.314267Z", + "iopub.status.idle": "2023-10-27T06:06:48.383826Z", + "shell.execute_reply": "2023-10-27T06:06:48.383086Z" + }, + "id": "2-MfMoenJi8s" + }, + "outputs": [], + "source": [ + "for image_batch, labels_batch in train_ds:\n", + " print(image_batch.shape)\n", + " print(labels_batch.shape)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wj4FrKxxJkoW" + }, + "source": [ + "The `image_batch` is a tensor of the shape `(32, 180, 180, 3)`. This is a batch of 32 images of shape `180x180x3` (the last dimension refers to color channels RGB). The `label_batch` is a tensor of the shape `(32,)`, these are corresponding labels to the 32 images.\n", + "\n", + "You can call `.numpy()` on the `image_batch` and `labels_batch` tensors to convert them to a `numpy.ndarray`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Dr0at41KcAU" + }, + "source": [ + "## Configure the dataset for performance\n", + "\n", + "Make sure to use buffered prefetching, so you can yield data from disk without having I/O become blocking. These are two important methods you should use when loading data:\n", + "\n", + "- `Dataset.cache` keeps the images in memory after they're loaded off disk during the first epoch. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache.\n", + "- `Dataset.prefetch` overlaps data preprocessing and model execution while training.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:48.387326Z", + "iopub.status.busy": "2023-10-27T06:06:48.387053Z", + "iopub.status.idle": "2023-10-27T06:06:48.399552Z", + "shell.execute_reply": "2023-10-27T06:06:48.398861Z" + }, + "id": "nOjJSm7DKoZA" + }, + "outputs": [], + "source": [ + "AUTOTUNE = tf.data.AUTOTUNE\n", + "\n", + "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n", + "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8GUnmPF4JvEf" + }, + "source": [ + "## Standardize the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e56VXHMWJxYT" + }, + "source": [ + "The RGB channel values are in the `[0, 255]` range. This is not ideal for a neural network; in general you should seek to make your input values small.\n", + "\n", + "Here, you will standardize values to be in the `[0, 1]` range by using `tf.keras.layers.Rescaling`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:48.403081Z", + "iopub.status.busy": "2023-10-27T06:06:48.402855Z", + "iopub.status.idle": "2023-10-27T06:06:48.409847Z", + "shell.execute_reply": "2023-10-27T06:06:48.409235Z" + }, + "id": "PEYxo2CTJvY9" + }, + "outputs": [], + "source": [ + "normalization_layer = layers.Rescaling(1./255)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bl4RmanbJ4g0" + }, + "source": [ + "There are two ways to use this layer. You can apply it to the dataset by calling `Dataset.map`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:48.413451Z", + "iopub.status.busy": "2023-10-27T06:06:48.412874Z", + "iopub.status.idle": "2023-10-27T06:06:48.874337Z", + "shell.execute_reply": "2023-10-27T06:06:48.873622Z" + }, + "id": "X9o9ESaJJ502" + }, + "outputs": [], + "source": [ + "normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))\n", + "image_batch, labels_batch = next(iter(normalized_ds))\n", + "first_image = image_batch[0]\n", + "# Notice the pixel values are now in `[0,1]`.\n", + "print(np.min(first_image), np.max(first_image))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XWEOmRSBJ9J8" + }, + "source": [ + "Or, you can include the layer inside your model definition, which can simplify deployment. Use the second approach here." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XsRk1xCwKZR4" + }, + "source": [ + "Note: You previously resized images using the `image_size` argument of `tf.keras.utils.image_dataset_from_directory`. If you want to include the resizing logic in your model as well, you can use the `tf.keras.layers.Resizing` layer." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WcUTyDOPKucd" + }, + "source": [ + "## A basic Keras model\n", + "\n", + "### Create the model\n", + "\n", + "The Keras [Sequential](https://www.tensorflow.org/guide/keras/sequential_model) model consists of three convolution blocks (`tf.keras.layers.Conv2D`) with a max pooling layer (`tf.keras.layers.MaxPooling2D`) in each of them. There's a fully-connected layer (`tf.keras.layers.Dense`) with 128 units on top of it that is activated by a ReLU activation function (`'relu'`). This model has not been tuned for high accuracy; the goal of this tutorial is to show a standard approach." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:48.878528Z", + "iopub.status.busy": "2023-10-27T06:06:48.878247Z", + "iopub.status.idle": "2023-10-27T06:06:48.977446Z", + "shell.execute_reply": "2023-10-27T06:06:48.976699Z" + }, + "id": "QR6argA1K074" + }, + "outputs": [], + "source": [ + "num_classes = len(class_names)\n", + "\n", + "model = Sequential([\n", + " layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),\n", + " layers.Conv2D(16, 3, padding='same', activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Conv2D(32, 3, padding='same', activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Conv2D(64, 3, padding='same', activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Flatten(),\n", + " layers.Dense(128, activation='relu'),\n", + " layers.Dense(num_classes)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EaKFzz72Lqpg" + }, + "source": [ + "### Compile the model\n", + "\n", + "For this tutorial, choose the `tf.keras.optimizers.Adam` optimizer and `tf.keras.losses.SparseCategoricalCrossentropy` loss function. To view training and validation accuracy for each training epoch, pass the `metrics` argument to `Model.compile`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:48.981686Z", + "iopub.status.busy": "2023-10-27T06:06:48.981407Z", + "iopub.status.idle": "2023-10-27T06:06:49.130037Z", + "shell.execute_reply": "2023-10-27T06:06:49.129313Z" + }, + "id": "jloGNS1MLx3A" + }, + "outputs": [], + "source": [ + "model.compile(optimizer='adam',\n", + " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aMJ4DnuJL55A" + }, + "source": [ + "### Model summary\n", + "\n", + "View all the layers of the network using the Keras `Model.summary` method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:49.134092Z", + "iopub.status.busy": "2023-10-27T06:06:49.133829Z", + "iopub.status.idle": "2023-10-27T06:06:49.152889Z", + "shell.execute_reply": "2023-10-27T06:06:49.152297Z" + }, + "id": "llLYH-BXL7Xe" + }, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NiYHcbvaL9H-" + }, + "source": [ + "### Train the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j30F69T4sIVN" + }, + "source": [ + "Train the model for 10 epochs with the Keras `Model.fit` method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:06:49.166383Z", + "iopub.status.busy": "2023-10-27T06:06:49.166146Z", + "iopub.status.idle": "2023-10-27T06:07:12.242819Z", + "shell.execute_reply": "2023-10-27T06:07:12.242097Z" + }, + "id": "5fWToCqYMErH" + }, + "outputs": [], + "source": [ + "epochs=10\n", + "history = model.fit(\n", + " train_ds,\n", + " validation_data=val_ds,\n", + " epochs=epochs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SyFKdQpXMJT4" + }, + "source": [ + "## Visualize training results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dFvOvmAmMK9w" + }, + "source": [ + "Create plots of the loss and accuracy on the training and validation sets:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:12.247018Z", + "iopub.status.busy": "2023-10-27T06:07:12.246357Z", + "iopub.status.idle": "2023-10-27T06:07:12.534186Z", + "shell.execute_reply": "2023-10-27T06:07:12.533543Z" + }, + "id": "jWnopEChMMCn" + }, + "outputs": [], + "source": [ + "acc = history.history['accuracy']\n", + "val_acc = history.history['val_accuracy']\n", + "\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs_range = range(epochs)\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epochs_range, acc, label='Training Accuracy')\n", + "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n", + "plt.legend(loc='lower right')\n", + "plt.title('Training and Validation Accuracy')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epochs_range, loss, label='Training Loss')\n", + "plt.plot(epochs_range, val_loss, label='Validation Loss')\n", + "plt.legend(loc='upper right')\n", + "plt.title('Training and Validation Loss')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hO_jT7HwMrEn" + }, + "source": [ + "The plots show that training accuracy and validation accuracy are off by large margins, and the model has achieved only around 60% accuracy on the validation set.\n", + "\n", + "The following tutorial sections show how to inspect what went wrong and try to increase the overall performance of the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hqtyGodAMvNV" + }, + "source": [ + "## Overfitting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ixsz9XFfMxcu" + }, + "source": [ + "In the plots above, the training accuracy is increasing linearly over time, whereas validation accuracy stalls around 60% in the training process. Also, the difference in accuracy between training and validation accuracy is noticeable—a sign of [overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit).\n", + "\n", + "When there are a small number of training examples, the model sometimes learns from noises or unwanted details from training examples—to an extent that it negatively impacts the performance of the model on new examples. This phenomenon is known as overfitting. It means that the model will have a difficult time generalizing on a new dataset.\n", + "\n", + "There are multiple ways to fight overfitting in the training process. In this tutorial, you'll use *data augmentation* and add *dropout* to your model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BDMfYqwmM1C-" + }, + "source": [ + "## Data augmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GxYwix81M2YO" + }, + "source": [ + "Overfitting generally occurs when there are a small number of training examples. **Data augmentation** takes the approach of generating additional training data from your existing examples by augmenting them using random transformations that yield believable-looking images. This helps expose the model to more aspects of the data and generalize better.\n", + "\n", + "You will implement data augmentation using the following Keras preprocessing layers: `tf.keras.layers.RandomFlip`, `tf.keras.layers.RandomRotation`, and `tf.keras.layers.RandomZoom`. These can be included inside your model like other layers, and run on the GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:12.537963Z", + "iopub.status.busy": "2023-10-27T06:07:12.537729Z", + "iopub.status.idle": "2023-10-27T06:07:12.684248Z", + "shell.execute_reply": "2023-10-27T06:07:12.683621Z" + }, + "id": "9J80BAbIMs21" + }, + "outputs": [], + "source": [ + "data_augmentation = keras.Sequential(\n", + " [\n", + " layers.RandomFlip(\"horizontal\",\n", + " input_shape=(img_height,\n", + " img_width,\n", + " 3)),\n", + " layers.RandomRotation(0.1),\n", + " layers.RandomZoom(0.1),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PN4k1dK3S6eV" + }, + "source": [ + "Visualize a few augmented examples by applying data augmentation to the same image several times:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:12.687482Z", + "iopub.status.busy": "2023-10-27T06:07:12.687256Z", + "iopub.status.idle": "2023-10-27T06:07:13.738634Z", + "shell.execute_reply": "2023-10-27T06:07:13.737939Z" + }, + "id": "7Z90k539S838" + }, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 10))\n", + "for images, _ in train_ds.take(1):\n", + " for i in range(9):\n", + " augmented_images = data_augmentation(images)\n", + " ax = plt.subplot(3, 3, i + 1)\n", + " plt.imshow(augmented_images[0].numpy().astype(\"uint8\"))\n", + " plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tsjXCBLYYNs5" + }, + "source": [ + "You will add data augmentation to your model before training in the next step." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZeD3bXepYKXs" + }, + "source": [ + "## Dropout\n", + "\n", + "Another technique to reduce overfitting is to introduce [dropout](https://developers.google.com/machine-learning/glossary#dropout_regularization) regularization to the network.\n", + "\n", + "When you apply dropout to a layer, it randomly drops out (by setting the activation to zero) a number of output units from the layer during the training process. Dropout takes a fractional number as its input value, in the form such as 0.1, 0.2, 0.4, etc. This means dropping out 10%, 20% or 40% of the output units randomly from the applied layer.\n", + "\n", + "Create a new neural network with `tf.keras.layers.Dropout` before training it using the augmented images:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:13.750960Z", + "iopub.status.busy": "2023-10-27T06:07:13.750684Z", + "iopub.status.idle": "2023-10-27T06:07:13.952738Z", + "shell.execute_reply": "2023-10-27T06:07:13.952076Z" + }, + "id": "2Zeg8zsqXCsm" + }, + "outputs": [], + "source": [ + "model = Sequential([\n", + " data_augmentation,\n", + " layers.Rescaling(1./255),\n", + " layers.Conv2D(16, 3, padding='same', activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Conv2D(32, 3, padding='same', activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Conv2D(64, 3, padding='same', activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Dropout(0.2),\n", + " layers.Flatten(),\n", + " layers.Dense(128, activation='relu'),\n", + " layers.Dense(num_classes, name=\"outputs\")\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L4nEcuqgZLbi" + }, + "source": [ + "## Compile and train the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:13.956252Z", + "iopub.status.busy": "2023-10-27T06:07:13.955872Z", + "iopub.status.idle": "2023-10-27T06:07:13.965125Z", + "shell.execute_reply": "2023-10-27T06:07:13.964525Z" + }, + "id": "EvyAINs9ZOmJ" + }, + "outputs": [], + "source": [ + "model.compile(optimizer='adam',\n", + " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:13.968257Z", + "iopub.status.busy": "2023-10-27T06:07:13.967902Z", + "iopub.status.idle": "2023-10-27T06:07:13.987989Z", + "shell.execute_reply": "2023-10-27T06:07:13.987423Z" + }, + "id": "wWLkKoKjZSoC" + }, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:14.004029Z", + "iopub.status.busy": "2023-10-27T06:07:14.003575Z", + "iopub.status.idle": "2023-10-27T06:07:55.642484Z", + "shell.execute_reply": "2023-10-27T06:07:55.641736Z" + }, + "id": "LWS-vvNaZDag" + }, + "outputs": [], + "source": [ + "epochs = 15\n", + "history = model.fit(\n", + " train_ds,\n", + " validation_data=val_ds,\n", + " epochs=epochs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lkdl8VsBbZOu" + }, + "source": [ + "## Visualize training results\n", + "\n", + "After applying data augmentation and `tf.keras.layers.Dropout`, there is less overfitting than before, and training and validation accuracy are closer aligned:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:55.646327Z", + "iopub.status.busy": "2023-10-27T06:07:55.646069Z", + "iopub.status.idle": "2023-10-27T06:07:55.930762Z", + "shell.execute_reply": "2023-10-27T06:07:55.930040Z" + }, + "id": "dduoLfKsZVIA" + }, + "outputs": [], + "source": [ + "acc = history.history['accuracy']\n", + "val_acc = history.history['val_accuracy']\n", + "\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs_range = range(epochs)\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epochs_range, acc, label='Training Accuracy')\n", + "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n", + "plt.legend(loc='lower right')\n", + "plt.title('Training and Validation Accuracy')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epochs_range, loss, label='Training Loss')\n", + "plt.plot(epochs_range, val_loss, label='Validation Loss')\n", + "plt.legend(loc='upper right')\n", + "plt.title('Training and Validation Loss')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dtv5VbaVb-3W" + }, + "source": [ + "## Predict on new data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "10buWpJbcCQz" + }, + "source": [ + "Use your model to classify an image that wasn't included in the training or validation sets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NKgMZ4bDcHf7" + }, + "source": [ + "Note: Data augmentation and dropout layers are inactive at inference time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:55.935151Z", + "iopub.status.busy": "2023-10-27T06:07:55.934521Z", + "iopub.status.idle": "2023-10-27T06:07:56.295539Z", + "shell.execute_reply": "2023-10-27T06:07:56.294784Z" + }, + "id": "dC40sRITBSsQ" + }, + "outputs": [], + "source": [ + "sunflower_url = \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/592px-Red_sunflower.jpg\"\n", + "sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)\n", + "\n", + "img = tf.keras.utils.load_img(\n", + " sunflower_path, target_size=(img_height, img_width)\n", + ")\n", + "img_array = tf.keras.utils.img_to_array(img)\n", + "img_array = tf.expand_dims(img_array, 0) # Create a batch\n", + "\n", + "predictions = model.predict(img_array)\n", + "score = tf.nn.softmax(predictions[0])\n", + "\n", + "print(\n", + " \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n", + " .format(class_names[np.argmax(score)], 100 * np.max(score))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aOc3PZ2N2r18" + }, + "source": [ + "## Use TensorFlow Lite\n", + "\n", + "TensorFlow Lite is a set of tools that enables on-device machine learning by helping developers run their models on mobile, embedded, and edge devices." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cThu25rh4LPP" + }, + "source": [ + "### Convert the Keras Sequential model to a TensorFlow Lite model\n", + "\n", + "To use the trained model with on-device applications, first [convert it](https://www.tensorflow.org/lite/models/convert) to a smaller and more efficient model format called a [TensorFlow Lite](https://www.tensorflow.org/lite/) model.\n", + "\n", + "In this example, take the trained Keras Sequential model and use `tf.lite.TFLiteConverter.from_keras_model` to generate a [TensorFlow Lite](https://www.tensorflow.org/lite/) model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:56.299091Z", + "iopub.status.busy": "2023-10-27T06:07:56.298832Z", + "iopub.status.idle": "2023-10-27T06:07:59.418375Z", + "shell.execute_reply": "2023-10-27T06:07:59.417577Z" + }, + "id": "mXo6ftuL2ufx" + }, + "outputs": [], + "source": [ + "# Convert the model.\n", + "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n", + "tflite_model = converter.convert()\n", + "\n", + "# Save the model.\n", + "with open('model.tflite', 'wb') as f:\n", + " f.write(tflite_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4R26OU4gGKhh" + }, + "source": [ + "The TensorFlow Lite model you saved in the previous step can contain several function signatures. The Keras model converter API uses the default signature automatically. Learn more about [TensorFlow Lite signatures](https://www.tensorflow.org/lite/guide/signatures)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7fjQfXaV2l-5" + }, + "source": [ + "### Run the TensorFlow Lite model\n", + "\n", + "You can access the TensorFlow Lite saved model signatures in Python via the `tf.lite.Interpreter` class.\n", + "\n", + "Load the model with the `Interpreter`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:59.422492Z", + "iopub.status.busy": "2023-10-27T06:07:59.421837Z", + "iopub.status.idle": "2023-10-27T06:07:59.426824Z", + "shell.execute_reply": "2023-10-27T06:07:59.426189Z" + }, + "id": "cHYcip_FOaHq" + }, + "outputs": [], + "source": [ + "TF_MODEL_FILE_PATH = 'model.tflite' # The default path to the saved TensorFlow Lite model\n", + "\n", + "interpreter = tf.lite.Interpreter(model_path=TF_MODEL_FILE_PATH)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nPUXY6BdHDHo" + }, + "source": [ + "Print the signatures from the converted model to obtain the names of the inputs (and outputs):\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:59.430202Z", + "iopub.status.busy": "2023-10-27T06:07:59.429564Z", + "iopub.status.idle": "2023-10-27T06:07:59.434156Z", + "shell.execute_reply": "2023-10-27T06:07:59.433471Z" + }, + "id": "ZdDl00E2OaHq" + }, + "outputs": [], + "source": [ + "interpreter.get_signature_list()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4eVFqT0je3YG" + }, + "source": [ + "In this example, you have one default signature called `serving_default`. In addition, the name of the `'inputs'` is `'sequential_1_input'`, while the `'outputs'` are called `'outputs'`. You can look up these first and last Keras layer names when running `Model.summary`, as demonstrated earlier in this tutorial.\n", + "\n", + "Now you can test the loaded TensorFlow Model by performing inference on a sample image with `tf.lite.Interpreter.get_signature_runner` by passing the signature name as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:59.437162Z", + "iopub.status.busy": "2023-10-27T06:07:59.436928Z", + "iopub.status.idle": "2023-10-27T06:07:59.441228Z", + "shell.execute_reply": "2023-10-27T06:07:59.440621Z" + }, + "id": "yFoT_7W_OaHq" + }, + "outputs": [], + "source": [ + "classify_lite = interpreter.get_signature_runner('serving_default')\n", + "classify_lite" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b1mfRcBOnEx0" + }, + "source": [ + "Similar to what you did earlier in the tutorial, you can use the TensorFlow Lite model to classify images that weren't included in the training or validation sets.\n", + "\n", + "You have already tensorized that image and saved it as `img_array`. Now, pass it to the first argument (the name of the `'inputs'`) of the loaded TensorFlow Lite model (`predictions_lite`), compute softmax activations, and then print the prediction for the class with the highest computed probability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:59.444546Z", + "iopub.status.busy": "2023-10-27T06:07:59.444034Z", + "iopub.status.idle": "2023-10-27T06:07:59.455152Z", + "shell.execute_reply": "2023-10-27T06:07:59.454445Z" + }, + "id": "sEqR27YcnFvc" + }, + "outputs": [], + "source": [ + "predictions_lite = classify_lite(sequential_1_input=img_array)['outputs']\n", + "score_lite = tf.nn.softmax(predictions_lite)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:59.458120Z", + "iopub.status.busy": "2023-10-27T06:07:59.457878Z", + "iopub.status.idle": "2023-10-27T06:07:59.462008Z", + "shell.execute_reply": "2023-10-27T06:07:59.461371Z" + }, + "id": "ZKP_GFeKUWb5" + }, + "outputs": [], + "source": [ + "print(\n", + " \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n", + " .format(class_names[np.argmax(score_lite)], 100 * np.max(score_lite))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Poz_iYgeUg_U" + }, + "source": [ + "The prediction generated by the lite model should be almost identical to the predictions generated by the original model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T06:07:59.465227Z", + "iopub.status.busy": "2023-10-27T06:07:59.464844Z", + "iopub.status.idle": "2023-10-27T06:07:59.468274Z", + "shell.execute_reply": "2023-10-27T06:07:59.467738Z" + }, + "id": "InXXDJL8UYC1" + }, + "outputs": [], + "source": [ + "print(np.max(np.abs(predictions - predictions_lite)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5hJzY8XijM7N" + }, + "source": [ + "Of the five classes—`'daisy'`, `'dandelion'`, `'roses'`, `'sunflowers'`, and `'tulips'`—the model should predict the image belongs to sunflowers, which is the same result as before the TensorFlow Lite conversion.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1RlfCY9v2_ir" + }, + "source": [ + "## Acknowledgments\n", + "\n", + "Thanks to Tensorflow for creating the tutorial notebook [Image classification](https://www.tensorflow.org/tutorials/images/classification). It inspires the majority of the content in this chapter." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "classification.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/_sources/assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.ipynb b/_sources/assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.ipynb new file mode 100644 index 0000000000..3fe2073a9c --- /dev/null +++ b/_sources/assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# Install the necessary dependencies\n", + "\n", + "import os\n", + "import sys\n", + "!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython skimage" + ] + }, + { + "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": [ + "# Comparing edge-based and region-based segmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we will try how to segment objects from a background. We use the coins image from skimage.data, which shows several coins outlined against a darker background." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from skimage import data\n", + "from skimage.exposure import histogram\n", + "\n", + "coins = data.coins()\n", + "hist, hist_centers = histogram(coins)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n", + "axes[0].imshow(coins, cmap=plt.cm.gray)\n", + "axes[0].axis('off')\n", + "axes[1].plot(hist_centers, hist, lw=2)\n", + "axes[1].set_title('histogram of gray values')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thresholding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A simple way to segment the coins is to choose a threshold based on the histogram of gray values. Unfortunately, thresholding this image gives a binary image that either misses significant parts of the coins or merges parts of the background with the coins:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n", + "\n", + "axes[0].imshow(coins > 100, cmap=plt.cm.gray)\n", + "axes[0].set_title('coins > 100')\n", + "\n", + "axes[1].imshow(coins > 150, cmap=plt.cm.gray)\n", + "axes[1].set_title('coins > 150')\n", + "\n", + "for a in axes:\n", + " a.axis('off')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Edge-based segmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we try to delineate the contours of the coins using edge-based segmentation. To do this, we first get the edges of features using the Canny edge-detector." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from skimage.feature import canny\n", + "\n", + "edges = canny(coins)\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 3))\n", + "ax.imshow(edges, cmap=plt.cm.gray)\n", + "ax.set_title('Canny detector')\n", + "ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These contours are then filled using mathematical morphology." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import ndimage as ndi\n", + "\n", + "fill_coins = ndi.binary_fill_holes(edges)\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 3))\n", + "ax.imshow(fill_coins, cmap=plt.cm.gray)\n", + "ax.set_title('filling the holes')\n", + "ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Small spurious objects are easily removed by setting a minimum size for valid objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from skimage import morphology\n", + "\n", + "coins_cleaned = morphology.remove_small_objects(fill_coins, 21)\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 3))\n", + "ax.imshow(coins_cleaned, cmap=plt.cm.gray)\n", + "ax.set_title('removing small objects')\n", + "ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, this method is not very robust, since contours that are not perfectly closed are not filled correctly, as is the case for one unfilled coin above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Region-based segmentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We therefore try a region-based method using the watershed transform. First, we find an elevation map using the Sobel gradient of the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from skimage.filters import sobel\n", + "\n", + "elevation_map = sobel(coins)\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 3))\n", + "ax.imshow(elevation_map, cmap=plt.cm.gray)\n", + "ax.set_title('elevation map')\n", + "ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we find markers of the background and the coins based on the extreme parts of the histogram of gray values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "markers = np.zeros_like(coins)\n", + "markers[coins < 30] = 1\n", + "markers[coins > 150] = 2\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 3))\n", + "ax.imshow(markers, cmap=plt.cm.nipy_spectral)\n", + "ax.set_title('markers')\n", + "ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from skimage import segmentation\n", + "\n", + "segmentation_coins = segmentation.watershed(elevation_map, markers)\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 3))\n", + "ax.imshow(segmentation_coins, cmap=plt.cm.gray)\n", + "ax.set_title('segmentation')\n", + "ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This last method works even better, and the coins can be segmented and labeled individually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from skimage.color import label2rgb\n", + "\n", + "segmentation_coins = ndi.binary_fill_holes(segmentation_coins - 1)\n", + "labeled_coins, _ = ndi.label(segmentation_coins)\n", + "image_label_overlay = label2rgb(labeled_coins, image=coins, bg_label=0)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n", + "axes[0].imshow(coins, cmap=plt.cm.gray)\n", + "axes[0].contour(segmentation_coins, [0.5], linewidths=1.2, colors='y')\n", + "axes[1].imshow(image_label_overlay)\n", + "\n", + "for a in axes:\n", + " a.axis('off')\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acknowledgments\n", + "\n", + "Thanks to [Scikit-image](https://scikit-image.org/) for creating the open-source project [Comparing edge-based and region-based segmentation](https://scikit-image.org/docs/stable/auto_examples/applications/plot_coins_segmentation.html). This inspires the majority of the content in this chapter." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "open-machine-learning-jupyter-book", + "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": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/assignments/deep-learning/nlp/getting-start-nlp-with-classification-task.ipynb b/_sources/assignments/deep-learning/nlp/getting-start-nlp-with-classification-task.ipynb new file mode 100644 index 0000000000..457d84b138 --- /dev/null +++ b/_sources/assignments/deep-learning/nlp/getting-start-nlp-with-classification-task.ipynb @@ -0,0 +1,1152 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6e14052d-5eec-449a-b6d5-bcc100456aba", + "metadata": {}, + "source": [ + "# Getting Start NLP with classification task" + ] + }, + { + "cell_type": "markdown", + "id": "259d0ecc-b734-44ef-b989-49a6e127a944", + "metadata": {}, + "source": [ + "One area where deep learning has dramatically improved in the last couple of years is natural language processing (NLP). Computers can now generate text, translate automatically from one language to another, analyze comments, label words in sentences, and much more.\n", + "\n", + "Perhaps the most widely practically useful application of NLP is classification -- that is, classifying a document automatically into some category. This can be used, for instance, for:\n", + "\n", + "- Sentiment analysis (e.g are people saying positive or negative things about your product)\n", + "- Author identification (what author most likely wrote some document)\n", + "- Legal discovery (which documents are in scope for a trial)\n", + "- Organizing documents by topic\n", + "- Triaging inbound emails\n", + "- ...and much more!\n", + "\n", + "Today, we are tasked with comparing two words or short phrases, and scoring them based on whether they're similar or not, based on which patent class they were used in. With a score of 1 it is considered that the two inputs have identical meaning, and 0 means they have totally different meaning. For instance, abatement and eliminating process have a score of 0.5, meaning they're somewhat similar, but not identical.\n", + "\n", + "It turns out that this can be represented as a classification problem. How? By representing the question like this:\n", + "\n", + "> For the following text...: \"TEXT1: abatement; TEXT2: eliminating process\" ...chose a category of meaning similarity: \"Different; Similar; Identical\".\n", + "\n", + "In this assignment section we'll see how to solve the Patent Phrase Matching problem by treating it as a classification task, by representing it in a very similar way to that shown above." + ] + }, + { + "cell_type": "markdown", + "id": "12389d2f-c08d-4941-a759-d8bbd8fa44ba", + "metadata": {}, + "source": [ + "## Import and EDA" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4e4e3c06-4292-40a7-bb1a-66207159c604", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from datasets import Dataset,DatasetDict\n", + "from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "f3085076-3dbc-4eab-941c-9c7e2a4b740e", + "metadata": {}, + "source": [ + "First of all, let's import the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cbd450f6-31dd-46c9-918e-e0f3984d1436", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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    " + ], + "text/plain": [ + " id anchor target context score \\\n", + "0 37d61fd2272659b1 abatement abatement of pollution A47 0.50 \n", + "1 7b9652b17b68b7a4 abatement act of abating A47 0.75 \n", + "2 36d72442aefd8232 abatement active catalyst A47 0.25 \n", + "3 5296b0c19e1ce60e abatement eliminating process A47 0.50 \n", + "4 54c1e3b9184cb5b6 abatement forest region A47 0.00 \n", + "\n", + " input \n", + "0 TEXT1: A47; TEXT2: abatement of pollution; ANC... \n", + "1 TEXT1: A47; TEXT2: act of abating; ANC1: abate... \n", + "2 TEXT1: A47; TEXT2: active catalyst; ANC1: abat... \n", + "3 TEXT1: A47; TEXT2: eliminating process; ANC1: ... \n", + "4 TEXT1: A47; TEXT2: forest region; ANC1: abatement " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['input'] = 'TEXT1: ' + df.context + '; TEXT2: ' + df.target + '; ANC1: ' + df.anchor\n", + "df.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "0fc9e71f-aed8-40b0-821d-c352331f4a89", + "metadata": {}, + "source": [ + "## Tokenization" + ] + }, + { + "cell_type": "markdown", + "id": "0d9c20bb-a146-497b-a503-edfc6372c084", + "metadata": {}, + "source": [ + "Transformers uses a `Dataset` object for storing their dataset, of course! We can create one like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a3dcc05b-e170-43ed-92f5-8805d3dbac7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['id', 'anchor', 'target', 'context', 'score', 'input'],\n", + " num_rows: 36473\n", + "})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = Dataset.from_pandas(df)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "107fe0be-b9a4-4c30-a141-41fcfb3beab4", + "metadata": {}, + "source": [ + "But we can't pass the texts directly into a model. A deep learning model expects numbers as inputs, not English sentences! So we need to do two things:\n", + "\n", + "- Tokenization: Split each text up into words (or actually, as we'll see, into tokens)\n", + "- Numericalization: Convert each word (or token) into a number.\n", + "\n", + "The details about how this is done actually depend on the particular model we use. So first we'll need to pick a model. There are thousands of models available, but a reasonable starting point for nearly any NLP problem is to use this (replace \"small\" with \"large\" for a slower but more accurate model, once you've finished exploring):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9bd4fdd3-f881-486b-9d75-ccbb6286b525", + "metadata": {}, + "outputs": [], + "source": [ + "model_nm = 'microsoft/deberta-v3-small'" + ] + }, + { + "cell_type": "markdown", + "id": "3f60c3b5-a0a5-4dd5-b512-549cb4242579", + "metadata": {}, + "source": [ + "`AutoTokenizer` will create a tokenizer appropriate for a given model:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "96aca66f-0663-4dfd-97f6-169f8a0fcb1d", + "metadata": {}, + "outputs": [], + "source": [ + "tokz = AutoTokenizer.from_pretrained(model_nm)" + ] + }, + { + "cell_type": "markdown", + "id": "5335cfec-7a71-481a-8c18-e934a90194ad", + "metadata": {}, + "source": [ + "Here's an example of how the tokenizer splits a text into \"tokens\" (which are like words, but can be sub-word pieces, as you see below):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6fd61beb-a5a8-4f89-9659-8470faf62fb4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['▁G',\n", + " \"'\",\n", + " 'day',\n", + " '▁folks',\n", + " ',',\n", + " '▁I',\n", + " \"'\",\n", + " 'm',\n", + " '▁Jeremy',\n", + " '▁from',\n", + " '▁fast',\n", + " '.',\n", + " 'ai',\n", + " '!']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokz.tokenize(\"G'day folks, I'm Jeremy from fast.ai!\")" + ] + }, + { + "cell_type": "markdown", + "id": "0a2a6a56-2e10-403a-9533-b65afb974425", + "metadata": {}, + "source": [ + "Uncommon words will be split into pieces just like `ornithorhynchus`. The start of a new word is represented by `▁`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "92e0189f-c8f1-405f-83fb-bd01c483710c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['▁A',\n", + " '▁platypus',\n", + " '▁is',\n", + " '▁an',\n", + " '▁or',\n", + " 'ni',\n", + " 'tho',\n", + " 'rhynch',\n", + " 'us',\n", + " '▁an',\n", + " 'at',\n", + " 'inus',\n", + " '.']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokz.tokenize(\"A platypus is an ornithorhynchus anatinus.\")" + ] + }, + { + "cell_type": "markdown", + "id": "a4e253e5-e098-4253-b401-57f0cf753325", + "metadata": {}, + "source": [ + "## Numericalization" + ] + }, + { + "cell_type": "markdown", + "id": "83ee57e9-54d7-45cb-a23d-68156b0350f8", + "metadata": {}, + "source": [ + "After completing Tokenization, we need to convert each token into a number, because the model only accepts numbers as input. But ... how to do it?\n", + "We need a large token dictionary to map each token to a number!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7d56000f-9137-4f7f-b3b6-93624efddc50", + "metadata": {}, + "outputs": [], + "source": [ + "vocab = tokz.get_vocab()" + ] + }, + { + "cell_type": "markdown", + "id": "f062775c-83b5-48cb-be62-70d9e1355d05", + "metadata": {}, + "source": [ + "The above is the token dictionary that comes with the `deberta-v3-small` model. You can print it out to check." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dd749eeb-17da-4bd9-b229-c96270a0d415", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input_ids': [1, 336, 114224, 269, 299, 289, 4840, 34765, 102530, 1867, 299, 2401, 26835, 260, 2], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokz(\"A platypus is an ornithorhynchus anatinus.\")" + ] + }, + { + "cell_type": "markdown", + "id": "95028988-a530-46d2-96a9-b11c2e9ab3d7", + "metadata": {}, + "source": [ + "According to this token dictionary, we can convert the original token sequence into a digital sequence. Input_ids is the number we need, token_type_ids represents whether all tokens belong to the same sentence, and attention_mask represents whether the token exists in the token dictionary.\n", + "\n", + "Here's a simple function which tokenizes our inputs:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8c6da089-a612-499b-8a00-e448a0eee212", + "metadata": {}, + "outputs": [], + "source": [ + "def tok_func(x): return tokz(x[\"input\"])" + ] + }, + { + "cell_type": "markdown", + "id": "681d176d-f055-42fd-87de-88dcee8ec18a", + "metadata": {}, + "source": [ + "To run this quickly in parallel on every row in our dataset, use map:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fec0c346-3726-42d2-87c6-2260b7d4c80c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "42f7bd84ff414f0cb23b9b1bb44b8ad5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/36473 [00:00\n", + " \n", + " \n", + " [856/856 00:53, Epoch 4/4]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    EpochTraining LossValidation Loss
    1No log0.026275
    2No log0.021973
    30.0396000.022443
    40.0396000.023286

    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainer.train();" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3c35b83e-0320-4ea6-89df-342f9d3fb36e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([[-1.50489807e-03],\n", + " [ 4.90570068e-03],\n", + " [-5.05447388e-04],\n", + " [ 2.69412994e-04],\n", + " [-1.44767761e-03],\n", + " [ 4.85897064e-04],\n", + " [-1.81484222e-03],\n", + " [ 8.22067261e-04],\n", + " [ 4.36019897e-03],\n", + " [ 4.40216064e-03],\n", + " [-6.16550446e-04],\n", + " [-4.18424606e-05],\n", + " [-1.20639801e-03],\n", + " [ 3.18288803e-04],\n", + " [-6.15119934e-04],\n", + " [-8.05377960e-04],\n", + " [-2.66265869e-03],\n", + " [ 2.60114670e-04],\n", + " [ 3.48281860e-03],\n", + " [ 1.68323517e-03],\n", + " [ 1.38378143e-03],\n", + " [-2.48527527e-03],\n", + " [ 7.53879547e-04],\n", + " [ 8.55922699e-04],\n", + " [-2.27355957e-03],\n", + " [-2.88581848e-03],\n", + " [ 3.29780579e-03],\n", + " [ 9.42707062e-04],\n", + " [ 4.26769257e-04],\n", + " [-1.19447708e-04],\n", + " [-2.77519226e-03],\n", + " [ 5.27381897e-04],\n", + " [-8.44001770e-04],\n", + " [ 4.88281250e-04],\n", + " [-2.11715698e-04],\n", + " [-1.00421906e-03]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds = trainer.predict(eval_ds).predictions.astype(float)\n", + "preds" + ] + }, + { + "cell_type": "markdown", + "id": "138c6cd1-e0cb-405e-8b15-952e90c6a954", + "metadata": {}, + "source": [ + "Look out - some of our predictions are <0, or >1! Let's fix those out-of-bounds predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d6cfff69-3178-4afb-858e-5653a938e3af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. ],\n", + " [0.0049057 ],\n", + " [0. ],\n", + " [0.00026941],\n", + " [0. ],\n", + " [0.0004859 ],\n", + " [0. ],\n", + " [0.00082207],\n", + " [0.0043602 ],\n", + " [0.00440216],\n", + " [0. ],\n", + " [0. ],\n", + " [0. ],\n", + " [0.00031829],\n", + " [0. ],\n", + " [0. ],\n", + " [0. ],\n", + " [0.00026011],\n", + " [0.00348282],\n", + " [0.00168324],\n", + " [0.00138378],\n", + " [0. ],\n", + " [0.00075388],\n", + " [0.00085592],\n", + " [0. ],\n", + " [0. ],\n", + " [0.00329781],\n", + " [0.00094271],\n", + " [0.00042677],\n", + " [0. ],\n", + " [0. ],\n", + " [0.00052738],\n", + " [0. ],\n", + " [0.00048828],\n", + " [0. ],\n", + " [0. ]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preds = np.clip(preds, 0, 1)\n", + "preds" + ] + }, + { + "cell_type": "markdown", + "id": "464c75ad-e4c3-4c6a-a776-c98011ed5eba", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# Acknowledgments\n", + "\n", + "Thanks to [Jeremy Howard](https://www.kaggle.com/jhoward) for creating [Getting started with NLP for absolute beginners](https://www.kaggle.com/code/jhoward/getting-started-with-nlp-for-absolute-beginners). It inspires the majority of the content in this chapter." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vmamba", + "language": "python", + "name": "vmamba" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/assignments/machine-learning-productionization/random-forest-classifier.ipynb b/_sources/assignments/machine-learning-productionization/random-forest-classifier.ipynb new file mode 100644 index 0000000000..16f232bd58 --- /dev/null +++ b/_sources/assignments/machine-learning-productionization/random-forest-classifier.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["

    LICENSE\n","\n","Copyright 2018 Google LLC.\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License.\n","You may obtain a copy of the License at\n","\n","https://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software\n","distributed under the License is distributed on an \"AS IS\" BASIS,\n","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","See the License for the specific language governing permissions and\n","limitations under the License.\n","
    "]},{"cell_type":"markdown","metadata":{},"source":["# Introduction"]},{"cell_type":"markdown","metadata":{},"source":["Climate Prediction-Random Forest is a model that uses a combination of climate variables and machine learning algorithms to predict future climate conditions. The model is trained on a large dataset of climate observations and uses a random forest approach to generate predictions. The predictions are based on the relationships between the climate variables and the random forest algorithm is able to capture complex patterns in the data."]},{"cell_type":"markdown","metadata":{},"source":["## Importing Libraries"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:09.977471Z","iopub.status.busy":"2021-09-12T07:42:09.976692Z","iopub.status.idle":"2021-09-12T07:42:11.175857Z","shell.execute_reply":"2021-09-12T07:42:11.174872Z","shell.execute_reply.started":"2021-09-12T07:42:09.977341Z"},"hideCode":false,"hidePrompt":false,"id":"d4a2uASN7jbm","outputId":"a31fc9d2-6d41-4f32-f935-4de1392fb75d","trusted":true},"outputs":[],"source":["# Pandas is used for data manipulation\n","import pandas as pd\n","\n","# Use numpy to convert to arrays\n","import numpy as np\n","\n","# Import tools needed for visualization\n","\n","import matplotlib.pyplot as plt\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"execution":{"iopub.execute_input":"2021-06-05T03:15:54.659441Z","iopub.status.busy":"2021-06-05T03:15:54.658886Z","iopub.status.idle":"2021-06-05T03:15:54.679235Z","shell.execute_reply":"2021-06-05T03:15:54.677744Z","shell.execute_reply.started":"2021-06-05T03:15:54.659396Z"}},"source":["## Data Exploration"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.178015Z","iopub.status.busy":"2021-09-12T07:42:11.177625Z","iopub.status.idle":"2021-09-12T07:42:11.198022Z","shell.execute_reply":"2021-09-12T07:42:11.197162Z","shell.execute_reply.started":"2021-09-12T07:42:11.177971Z"},"trusted":true},"outputs":[],"source":["# Reading the data to a dataframe \n","df = pd.read_csv('https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/classification/temps.csv')"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.201049Z","iopub.status.busy":"2021-09-12T07:42:11.200281Z","iopub.status.idle":"2021-09-12T07:42:11.230628Z","shell.execute_reply":"2021-09-12T07:42:11.229917Z","shell.execute_reply.started":"2021-09-12T07:42:11.200999Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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    "],"text/plain":[" year month day week temp_2 temp_1 average actual friend\n","0 2019 1 1 Fri 45 45 45.6 45 29\n","1 2019 1 2 Sat 44 45 45.7 44 61\n","2 2019 1 3 Sun 45 44 45.8 41 56\n","3 2019 1 4 Mon 44 41 45.9 40 53\n","4 2019 1 5 Tues 41 40 46.0 44 41"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# displaying first 5 rows\n","df.head(5)"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.232535Z","iopub.status.busy":"2021-09-12T07:42:11.232032Z","iopub.status.idle":"2021-09-12T07:42:11.237917Z","shell.execute_reply":"2021-09-12T07:42:11.236766Z","shell.execute_reply.started":"2021-09-12T07:42:11.232503Z"},"hideCode":false,"hidePrompt":false,"id":"5aXM1w987jbq","outputId":"c9eabdf4-30d9-4df4-b890-b28df3c5287b","trusted":true},"outputs":[{"data":{"text/plain":["(348, 9)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# the shape of our features\n","df.shape"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.239954Z","iopub.status.busy":"2021-09-12T07:42:11.239514Z","iopub.status.idle":"2021-09-12T07:42:11.253434Z","shell.execute_reply":"2021-09-12T07:42:11.252149Z","shell.execute_reply.started":"2021-09-12T07:42:11.239913Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Index(['year', 'month', 'day', 'week', 'temp_2', 'temp_1', 'average', 'actual',\n"," 'friend'],\n"," dtype='object')"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# column names\n","df.columns"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.256082Z","iopub.status.busy":"2021-09-12T07:42:11.255489Z","iopub.status.idle":"2021-09-12T07:42:11.271869Z","shell.execute_reply":"2021-09-12T07:42:11.270748Z","shell.execute_reply.started":"2021-09-12T07:42:11.256038Z"},"trusted":true},"outputs":[{"data":{"text/plain":["year 0\n","month 0\n","day 0\n","week 0\n","temp_2 0\n","temp_1 0\n","average 0\n","actual 0\n","friend 0\n","dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# checking for null values\n","df.isnull().sum()"]},{"cell_type":"markdown","metadata":{},"source":["There are no null values"]},{"cell_type":"markdown","metadata":{"id":"Nzu0v5mQ7jbs"},"source":["## One-Hot Encoding"]},{"cell_type":"markdown","metadata":{"execution":{"iopub.execute_input":"2021-06-05T03:26:04.246284Z","iopub.status.busy":"2021-06-05T03:26:04.245896Z","iopub.status.idle":"2021-06-05T03:26:04.252279Z","shell.execute_reply":"2021-06-05T03:26:04.250937Z","shell.execute_reply.started":"2021-06-05T03:26:04.246247Z"}},"source":["A one hot encoding allows the representation of categorical data to be more expressive. "]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.273448Z","iopub.status.busy":"2021-09-12T07:42:11.273117Z","iopub.status.idle":"2021-09-12T07:42:11.308893Z","shell.execute_reply":"2021-09-12T07:42:11.307365Z","shell.execute_reply.started":"2021-09-12T07:42:11.273418Z"},"hideCode":false,"hidePrompt":false,"id":"VURjcTE27jbu","outputId":"12cc15a3-072a-4e40-89c8-009ea27c2622","trusted":true},"outputs":[{"data":{"text/html":["
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    4201915414046.04441FalseFalseFalseFalseFalseTrueFalse
    \n","
    "],"text/plain":[" year month day temp_2 temp_1 average actual friend week_Fri \\\n","0 2019 1 1 45 45 45.6 45 29 True \n","1 2019 1 2 44 45 45.7 44 61 False \n","2 2019 1 3 45 44 45.8 41 56 False \n","3 2019 1 4 44 41 45.9 40 53 False \n","4 2019 1 5 41 40 46.0 44 41 False \n","\n"," week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed \n","0 False False False False False False \n","1 False True False False False False \n","2 False False True False False False \n","3 True False False False False False \n","4 False False False False True False "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["# One-hot encode categorical features\n","df = pd.get_dummies(df)\n","df.head(5)"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.312525Z","iopub.status.busy":"2021-09-12T07:42:11.312019Z","iopub.status.idle":"2021-09-12T07:42:11.320042Z","shell.execute_reply":"2021-09-12T07:42:11.318836Z","shell.execute_reply.started":"2021-09-12T07:42:11.312458Z"},"id":"zgYBtUrr7jbv","outputId":"69df322f-2e24-4576-9fd2-d34773ac406c","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Shape of features after one-hot encoding: (348, 15)\n"]}],"source":["print('Shape of features after one-hot encoding:', df.shape)"]},{"cell_type":"markdown","metadata":{"id":"mtd7DqrQ7jbw"},"source":["## Features and Labels"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.322293Z","iopub.status.busy":"2021-09-12T07:42:11.321937Z","iopub.status.idle":"2021-09-12T07:42:11.33496Z","shell.execute_reply":"2021-09-12T07:42:11.333645Z","shell.execute_reply.started":"2021-09-12T07:42:11.322261Z"},"id":"2rYCVrfV7jbx","trusted":true},"outputs":[],"source":["# Labels are the values we want to predict\n","labels = df['actual']\n","\n","# Remove the labels from the features\n","df = df.drop('actual', axis = 1)\n","\n","# Saving feature names for later use\n","feature_list = list(df.columns)"]},{"cell_type":"markdown","metadata":{"id":"Q6SSjx5p7jb0"},"source":["## Train Test Split"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.336918Z","iopub.status.busy":"2021-09-12T07:42:11.336569Z","iopub.status.idle":"2021-09-12T07:42:11.348348Z","shell.execute_reply":"2021-09-12T07:42:11.347294Z","shell.execute_reply.started":"2021-09-12T07:42:11.336886Z"},"id":"11BJUq0s7jb0","trusted":true},"outputs":[],"source":["# Using Skicit-learn to split data into training and testing sets\n","from sklearn.model_selection import train_test_split\n","\n","# Split the data into training and testing sets\n","train_features, test_features, train_labels, test_labels = train_test_split(df,\n"," labels,\n"," test_size = 0.20,\n"," random_state = 42)"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.350455Z","iopub.status.busy":"2021-09-12T07:42:11.350066Z","iopub.status.idle":"2021-09-12T07:42:11.358556Z","shell.execute_reply":"2021-09-12T07:42:11.357489Z","shell.execute_reply.started":"2021-09-12T07:42:11.350426Z"},"id":"KkVnZf4H7jb2","outputId":"3c0a9db7-0f71-44be-bd0a-946fddc7d048","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Training Features Shape: (278, 14)\n","Training Labels Shape: (278,)\n","Testing Features Shape: (70, 14)\n","Testing Labels Shape: (70,)\n"]}],"source":["print('Training Features Shape:', train_features.shape)\n","print('Training Labels Shape:', train_labels.shape)\n","print('Testing Features Shape:', test_features.shape)\n","print('Testing Labels Shape:', test_labels.shape)"]},{"cell_type":"markdown","metadata":{"id":"ny3qdq-i7jb4"},"source":["## Training the Forest"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:11.360258Z","iopub.status.busy":"2021-09-12T07:42:11.359962Z","iopub.status.idle":"2021-09-12T07:42:13.842601Z","shell.execute_reply":"2021-09-12T07:42:13.841175Z","shell.execute_reply.started":"2021-09-12T07:42:11.360229Z"},"hideCode":false,"hidePrompt":false,"id":"d_Vboxs77jb5","trusted":true},"outputs":[],"source":["# Import the model we are using\n","from sklearn.ensemble import RandomForestRegressor\n","\n","# Instantiate model \n","rf = RandomForestRegressor(n_estimators= 1000, random_state=42)\n","\n","# Train the model on training data\n","rf.fit(train_features, train_labels);"]},{"cell_type":"markdown","metadata":{"id":"rJz8X7b77jb6"},"source":["## Make Predictions on Test Data"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:13.844914Z","iopub.status.busy":"2021-09-12T07:42:13.844471Z","iopub.status.idle":"2021-09-12T07:42:13.975596Z","shell.execute_reply":"2021-09-12T07:42:13.974317Z","shell.execute_reply.started":"2021-09-12T07:42:13.84487Z"},"id":"pssgaBC67jb6","outputId":"5a3a9029-c98b-4ac8-c081-2f7e17c3ca86","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean Absolute Error: 3.78 degrees.\n"]}],"source":["# Use the forest's predict method on the test data\n","predictions = rf.predict(test_features)\n","\n","# Calculate the absolute errors\n","errors = abs(predictions - test_labels)\n","\n","# Print out the mean absolute error (mae)\n","print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2021-09-12T07:42:13.978583Z","iopub.status.busy":"2021-09-12T07:42:13.97822Z","iopub.status.idle":"2021-09-12T07:42:13.985832Z","shell.execute_reply":"2021-09-12T07:42:13.984493Z","shell.execute_reply.started":"2021-09-12T07:42:13.978549Z"},"id":"fDaM3Z677jb7","outputId":"2307bab3-cb96-4a7a-f57d-3a9d80cec129","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy: 94.02 %.\n"]}],"source":["# Calculate mean absolute percentage error (MAPE)\n","mape = 100 * (errors / test_labels)\n","\n","# Calculate and display accuracy\n","accuracy = 100 - np.mean(mape)\n","print('Accuracy:', round(accuracy, 2), '%.')"]},{"cell_type":"markdown","metadata":{"id":"9U2KQYmS7jb7"},"source":["## Visualizing a Single Decision Tree"]},{"cell_type":"markdown","metadata":{"id":"Cnbb-pTt7jb9"},"source":["![Decision Tree](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/assignment/deep-learning/nn/tree.png)"]},{"cell_type":"markdown","metadata":{},"source":["## Your turn! 🚀\n","You can practice your random-forest skills by following the assignment [Climate Prediction-Random Forest](../../assignments/machine-learning-productionization/random-forest-classifier.ipynb)."]},{"cell_type":"markdown","metadata":{},"source":["## Acknowledgments\n","\n","Thanks to Kaggle for creating the open source course [Climate Prediction-Random Forest](https://www.kaggle.com/code/anandhuh/climate-prediction-random-forest-94-accuracy?scriptVersionId=74560159&cellId=26). It contributes some of the content in this chapter."]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":1018620,"sourceId":1717426,"sourceType":"datasetVersion"}],"dockerImageVersionId":30096,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","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":"3.11.5"}},"nbformat":4,"nbformat_minor":4} diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb index a5ecb1db0a..5b96cb622c 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": "ee2af449", + "id": "6e4ad8e0", "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 1f287345e2..2b91d3a104 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": "70c3fb3a", + "id": "8633a20a", "metadata": {}, "source": [ "# Exploring visualizations\n", diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb index 7290324945..8c808e78eb 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": "b62869bf", + "id": "ccc592ea", "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 4b58aa3f21..48bcd3b714 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": "2812ba9a", + "id": "d5e8515c", "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 7db5432d06..1c55b67f0e 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": "4e729fc1", + "id": "441cd1b1", "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 13cc19f4b8..26b6ec9650 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": "1816b909", + "id": "1824344d", "metadata": {}, "source": [ "# NumPy\n", @@ -19,7 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "16db2570", + "id": "524f2024", "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ }, { "cell_type": "markdown", - "id": "0ea8e4d9", + "id": "0ecaa4a1", "metadata": {}, "source": [ "### Create a basic array\n", @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "f3a97a93", + "id": "fdbbbc0b", "metadata": {}, "outputs": [ { @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "db97ae62", + "id": "52e94fbd", "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": "03b948d2", + "id": "e00c2e09", "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ }, { "cell_type": "markdown", - "id": "9d11a2b8", + "id": "754873dd", "metadata": {}, "source": [ "Or an array filled with 1’s:" @@ -113,7 +113,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "f31d9d52", + "id": "a4fcd2e2", "metadata": {}, "outputs": [ { @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "a44b0e87", + "id": "4c6327c9", "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": "d02a7123", + "id": "453ab575", "metadata": {}, "outputs": [ { @@ -162,7 +162,7 @@ }, { "cell_type": "markdown", - "id": "c4546a55", + "id": "ef83f671", "metadata": {}, "source": [ "You can create an array with a range of elements:" @@ -171,7 +171,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "cbad5e9e", + "id": "a3d25666", "metadata": {}, "outputs": [ { @@ -191,7 +191,7 @@ }, { "cell_type": "markdown", - "id": "c4ae3281", + "id": "a1e4a408", "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": "796953a0", + "id": "6851463b", "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "41299cfe", + "id": "214052a9", "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": "411e39bd", + "id": "392ba480", "metadata": {}, "outputs": [ { @@ -249,7 +249,7 @@ }, { "cell_type": "markdown", - "id": "d416d0be", + "id": "758d340d", "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": "0cfcf49d", + "id": "4956e399", "metadata": {}, "outputs": [ { @@ -278,7 +278,7 @@ }, { "cell_type": "markdown", - "id": "58557c57", + "id": "424fc3cf", "metadata": {}, "source": [ "### Adding, removing, and sorting elements\n", @@ -291,7 +291,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "c5680f41", + "id": "82acaac3", "metadata": {}, "outputs": [], "source": [ @@ -300,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "03ca1110", + "id": "d1b1a4a4", "metadata": {}, "source": [ "You can quickly sort the numbers in ascending order with:" @@ -309,7 +309,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "6bfee14a", + "id": "33f1d0f9", "metadata": {}, "outputs": [ { @@ -329,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "312788c4", + "id": "290dc695", "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": "3b8ccf7d", + "id": "59a365e9", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "e0b12155", + "id": "abfb3a12", "metadata": {}, "source": [ "You can concatenate them with `np.concatenate()`." @@ -364,7 +364,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "47e4d2d7", + "id": "178dc1a9", "metadata": {}, "outputs": [ { @@ -384,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "14f32192", + "id": "81c2d2cc", "metadata": {}, "source": [ "Or, if you start with these arrays:" @@ -393,7 +393,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "03035494", + "id": "0041f9cb", "metadata": {}, "outputs": [], "source": [ @@ -403,7 +403,7 @@ }, { "cell_type": "markdown", - "id": "f1177f1f", + "id": "62a5fda3", "metadata": {}, "source": [ "You can concatenate them with:" @@ -412,7 +412,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "b55eb0c6", + "id": "c5b2ad7f", "metadata": {}, "outputs": [ { @@ -434,7 +434,7 @@ }, { "cell_type": "markdown", - "id": "bebaef59", + "id": "023eed3d", "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": "e0ae6518", + "id": "7e0508c9", "metadata": {}, "outputs": [ { @@ -475,7 +475,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "d88c14eb", + "id": "72bcb260", "metadata": {}, "outputs": [ { @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "42651f1a", + "id": "a64eea9d", "metadata": {}, "source": [ "- ndarray.shape\n", @@ -505,7 +505,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "034505ee", + "id": "6f45f950", "metadata": {}, "outputs": [ { @@ -525,7 +525,7 @@ }, { "cell_type": "markdown", - "id": "6319a33f", + "id": "68585758", "metadata": {}, "source": [ "- ndarray.size\n", @@ -535,7 +535,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "b02dad23", + "id": "7e313856", "metadata": {}, "outputs": [ { @@ -555,7 +555,7 @@ }, { "cell_type": "markdown", - "id": "40df55da", + "id": "3a2d0042", "metadata": {}, "source": [ "- ndarray.dtype\n", @@ -565,7 +565,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "d0ee25cc", + "id": "a23efb5c", "metadata": {}, "outputs": [ { @@ -586,7 +586,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "ce5388d2", + "id": "587a63a9", "metadata": {}, "outputs": [ { @@ -606,7 +606,7 @@ }, { "cell_type": "markdown", - "id": "13f9ecb1", + "id": "5c23cb2e", "metadata": {}, "source": [ "- ndarray.itemsize\n", @@ -616,7 +616,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "a37e6226", + "id": "00a5ec05", "metadata": {}, "outputs": [ { @@ -636,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "2f645661", + "id": "021373f2", "metadata": {}, "source": [ "- ndarray.data\n", @@ -646,13 +646,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "230eb0a8", + "id": "246a9ae5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -666,7 +666,7 @@ }, { "cell_type": "markdown", - "id": "c1391da9", + "id": "c8c621a6", "metadata": {}, "source": [ "### Reshape an array\n", @@ -679,7 +679,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "eb269161", + "id": "f303592f", "metadata": {}, "outputs": [ { @@ -700,7 +700,7 @@ }, { "cell_type": "markdown", - "id": "e1799fdd", + "id": "9966b1c7", "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": "474b905b", + "id": "ca2f6ae0", "metadata": {}, "outputs": [ { @@ -732,7 +732,7 @@ }, { "cell_type": "markdown", - "id": "b652bd10", + "id": "3cba7acb", "metadata": {}, "source": [ "With `np.reshape`, you can specify a few optional parameters:" @@ -741,7 +741,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "2111b540", + "id": "91843f24", "metadata": {}, "outputs": [ { @@ -761,7 +761,7 @@ }, { "cell_type": "markdown", - "id": "3fe6825e", + "id": "1a0a17fc", "metadata": {}, "source": [ "`a` is the array to be reshaped.\n", @@ -782,7 +782,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "9c7d4be6", + "id": "dc26bcaa", "metadata": {}, "outputs": [ { @@ -803,7 +803,7 @@ }, { "cell_type": "markdown", - "id": "b8f5cdba", + "id": "c9c3ad13", "metadata": {}, "source": [ "You can use `np.newaxis` to add a new axis:" @@ -812,7 +812,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "ecc8a5a1", + "id": "222e3bbc", "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "2136fbc5", + "id": "5aca6723", "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": "5f5c0256", + "id": "78686b80", "metadata": {}, "outputs": [ { @@ -863,7 +863,7 @@ }, { "cell_type": "markdown", - "id": "f446042c", + "id": "1880a2bc", "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": "085bcaea", + "id": "79e0afad", "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ }, { "cell_type": "markdown", - "id": "e14b2290", + "id": "39c555ff", "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": "43063c3a", + "id": "2702b9e5", "metadata": {}, "outputs": [ { @@ -925,7 +925,7 @@ }, { "cell_type": "markdown", - "id": "f1ca9224", + "id": "02d4d8ad", "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": "54426fa5", + "id": "f399bc85", "metadata": {}, "outputs": [ { @@ -955,7 +955,7 @@ }, { "cell_type": "markdown", - "id": "ad62b973", + "id": "1d3afbaa", "metadata": {}, "source": [ "You can add an axis at index position 0 with:" @@ -964,7 +964,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "e5b4e1db", + "id": "8b5f3d80", "metadata": {}, "outputs": [ { @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "a7d628d0", + "id": "f5643a8d", "metadata": {}, "source": [ "### Indexing and slicing\n", @@ -996,7 +996,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "7744b04c", + "id": "989b5895", "metadata": {}, "outputs": [], "source": [ @@ -1006,7 +1006,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "e57b89ed", + "id": "5b691c28", "metadata": {}, "outputs": [ { @@ -1027,7 +1027,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "871490cb", + "id": "0215274d", "metadata": {}, "outputs": [ { @@ -1048,7 +1048,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "b1edf739", + "id": "bf45b2af", "metadata": {}, "outputs": [ { @@ -1069,7 +1069,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "4f445c63", + "id": "e7bdb1d6", "metadata": {}, "outputs": [ { @@ -1089,7 +1089,7 @@ }, { "cell_type": "markdown", - "id": "abe25bce", + "id": "b6be28c4", "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": "4ff5af74", + "id": "6c11e978", "metadata": {}, "outputs": [], "source": [ @@ -1111,7 +1111,7 @@ }, { "cell_type": "markdown", - "id": "46e9a9e4", + "id": "71c0d6dd", "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": "315c2a6d", + "id": "d8a5e5b6", "metadata": {}, "outputs": [ { @@ -1140,7 +1140,7 @@ }, { "cell_type": "markdown", - "id": "48eb2ce0", + "id": "62ca2bd5", "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": "72f77806", + "id": "88160ef6", "metadata": {}, "outputs": [ { @@ -1170,7 +1170,7 @@ }, { "cell_type": "markdown", - "id": "a9d85724", + "id": "93c0082f", "metadata": {}, "source": [ "You can select elements that are divisible by 2:" @@ -1179,7 +1179,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "2eb963d0", + "id": "bd315251", "metadata": {}, "outputs": [ { @@ -1200,7 +1200,7 @@ }, { "cell_type": "markdown", - "id": "e413ed52", + "id": "f0317012", "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": "3cbbcb78", + "id": "b232c963", "metadata": {}, "outputs": [ { @@ -1230,7 +1230,7 @@ }, { "cell_type": "markdown", - "id": "15d9f972", + "id": "c19ab5ad", "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": "71107b1d", + "id": "cc3ee644", "metadata": {}, "outputs": [ { @@ -1262,7 +1262,7 @@ }, { "cell_type": "markdown", - "id": "f33cf954", + "id": "c1edf600", "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": "4c70a74d", + "id": "cfa0a483", "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "ffe2a170", + "id": "8af96f8e", "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": "8b4b45b8", + "id": "f27a3cb8", "metadata": {}, "outputs": [ { @@ -1312,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "55e88a39", + "id": "2334c12d", "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": "b66242da", + "id": "8b52d868", "metadata": {}, "outputs": [ { @@ -1345,7 +1345,7 @@ }, { "cell_type": "markdown", - "id": "0b59a31e", + "id": "97416f45", "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": "dbb5def2", + "id": "7f6736f7", "metadata": {}, "outputs": [ { @@ -1374,7 +1374,7 @@ }, { "cell_type": "markdown", - "id": "e8e58650", + "id": "fa8447ee", "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": "9c70546d", + "id": "30f0472b", "metadata": {}, "outputs": [ { @@ -1404,7 +1404,7 @@ }, { "cell_type": "markdown", - "id": "9453b1a1", + "id": "dabfad61", "metadata": {}, "source": [ "### Create an array from existing data\n", @@ -1417,7 +1417,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "e78ed6f3", + "id": "a6842649", "metadata": {}, "outputs": [], "source": [ @@ -1426,7 +1426,7 @@ }, { "cell_type": "markdown", - "id": "acda1ab2", + "id": "8b614389", "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": "94e882ca", + "id": "6989fa4f", "metadata": {}, "outputs": [ { @@ -1456,7 +1456,7 @@ }, { "cell_type": "markdown", - "id": "2f306341", + "id": "f289eda6", "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": "da7cf18e", + "id": "e210134f", "metadata": {}, "outputs": [], "source": [ @@ -1479,7 +1479,7 @@ }, { "cell_type": "markdown", - "id": "defa57a1", + "id": "2afdac4f", "metadata": {}, "source": [ "You can stack them vertically with `vstack`:" @@ -1488,7 +1488,7 @@ { "cell_type": "code", "execution_count": 53, - "id": "c5e69457", + "id": "26755e66", "metadata": {}, "outputs": [ { @@ -1511,7 +1511,7 @@ }, { "cell_type": "markdown", - "id": "012e163b", + "id": "fef26743", "metadata": {}, "source": [ "Or stack them horizontally with hstack:" @@ -1520,7 +1520,7 @@ { "cell_type": "code", "execution_count": 54, - "id": "570dc433", + "id": "6644a131", "metadata": {}, "outputs": [ { @@ -1541,7 +1541,7 @@ }, { "cell_type": "markdown", - "id": "4ff18130", + "id": "9c076954", "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": "1347c4a4", + "id": "bc4467b4", "metadata": {}, "outputs": [ { @@ -1574,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "0191a5a0", + "id": "0c66f90e", "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": "c5c5352f", + "id": "c085a4c4", "metadata": {}, "outputs": [ { @@ -1608,7 +1608,7 @@ }, { "cell_type": "markdown", - "id": "4553dec2", + "id": "09d10de4", "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": "bff5cc21", + "id": "6f735a83", "metadata": {}, "outputs": [ { @@ -1642,7 +1642,7 @@ }, { "cell_type": "markdown", - "id": "6672a1d9", + "id": "10d069a1", "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": "84d6807a", + "id": "681e767a", "metadata": {}, "outputs": [], "source": [ @@ -1664,7 +1664,7 @@ }, { "cell_type": "markdown", - "id": "7743a4b6", + "id": "d793698b", "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": "fb7cea7e", + "id": "03f7af11", "metadata": {}, "outputs": [ { @@ -1695,7 +1695,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "a14c10fa", + "id": "29abfd98", "metadata": {}, "outputs": [ { @@ -1717,7 +1717,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "e21fbebe", + "id": "1cb28f3b", "metadata": {}, "outputs": [ { @@ -1739,7 +1739,7 @@ }, { "cell_type": "markdown", - "id": "2fa50f1c", + "id": "6fa60f89", "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": "ab07c948", + "id": "8dd1dbb7", "metadata": {}, "outputs": [], "source": [ @@ -1757,7 +1757,7 @@ }, { "cell_type": "markdown", - "id": "5bfb0e9a", + "id": "3c1bf5b9", "metadata": {}, "source": [ "## Array operations\n", @@ -1770,7 +1770,7 @@ { "cell_type": "code", "execution_count": 63, - "id": "f63f7a98", + "id": "0df0d146", "metadata": {}, "outputs": [], "source": [ @@ -1780,7 +1780,7 @@ }, { "cell_type": "markdown", - "id": "edb26bed", + "id": "6096c489", "metadata": {}, "source": [ "You can add the arrays together with the plus sign." @@ -1789,7 +1789,7 @@ { "cell_type": "code", "execution_count": 64, - "id": "a12628ca", + "id": "a3da3376", "metadata": {}, "outputs": [ { @@ -1809,7 +1809,7 @@ }, { "cell_type": "markdown", - "id": "10a78453", + "id": "857ceeca", "metadata": {}, "source": [ "You can, of course, do more than just addition!" @@ -1818,7 +1818,7 @@ { "cell_type": "code", "execution_count": 65, - "id": "0a1ab57e", + "id": "c34fbb3d", "metadata": {}, "outputs": [ { @@ -1839,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "3a7fe8c5", + "id": "db7b0009", "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": "4765fbea", + "id": "3ccef6f8", "metadata": {}, "outputs": [ { @@ -1869,7 +1869,7 @@ }, { "cell_type": "markdown", - "id": "c795d2ae", + "id": "92cbcee9", "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": "22774bc3", + "id": "c97288df", "metadata": {}, "outputs": [], "source": [ @@ -1889,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "ca4b6372", + "id": "17f5f516", "metadata": {}, "source": [ "You can sum over the axis of rows with:" @@ -1898,7 +1898,7 @@ { "cell_type": "code", "execution_count": 68, - "id": "8547d72c", + "id": "4eea7e0a", "metadata": {}, "outputs": [ { @@ -1918,7 +1918,7 @@ }, { "cell_type": "markdown", - "id": "0ac60ab0", + "id": "be1de269", "metadata": {}, "source": [ "You can sum over the axis of columns with:" @@ -1927,7 +1927,7 @@ { "cell_type": "code", "execution_count": 69, - "id": "85ac759f", + "id": "3a987410", "metadata": {}, "outputs": [ { @@ -1947,7 +1947,7 @@ }, { "cell_type": "markdown", - "id": "25b5d4f4", + "id": "369fb67d", "metadata": {}, "source": [ "### Universal functions(ufunc)\n", @@ -2038,7 +2038,7 @@ { "cell_type": "code", "execution_count": 70, - "id": "e169b883", + "id": "bf6e23ee", "metadata": {}, "outputs": [ { @@ -2060,7 +2060,7 @@ }, { "cell_type": "markdown", - "id": "2cc7a971", + "id": "434fd9bf", "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": "3741d36a", + "id": "a469d37a", "metadata": {}, "outputs": [ { @@ -2091,7 +2091,7 @@ }, { "cell_type": "markdown", - "id": "c0dac40e", + "id": "1ee6b52f", "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": "76014f7f", + "id": "35e1c26c", "metadata": {}, "outputs": [ { @@ -2154,7 +2154,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "12e9b800", + "id": "26aefd82", "metadata": {}, "outputs": [ { @@ -2175,7 +2175,7 @@ { "cell_type": "code", "execution_count": 74, - "id": "538e8b0f", + "id": "d37ad687", "metadata": {}, "outputs": [ { @@ -2195,7 +2195,7 @@ }, { "cell_type": "markdown", - "id": "6c450279", + "id": "273886fa", "metadata": {}, "source": [ "Let’s start with this array, called “a”." @@ -2204,7 +2204,7 @@ { "cell_type": "code", "execution_count": 75, - "id": "df7e5735", + "id": "34183eda", "metadata": {}, "outputs": [], "source": [ @@ -2215,7 +2215,7 @@ }, { "cell_type": "markdown", - "id": "80b524b9", + "id": "c0f61cfa", "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": "66c67ac4", + "id": "cb0a6d59", "metadata": {}, "outputs": [ { @@ -2244,7 +2244,7 @@ }, { "cell_type": "markdown", - "id": "5c440ff4", + "id": "9e3b702e", "metadata": {}, "source": [ "Or:" @@ -2253,7 +2253,7 @@ { "cell_type": "code", "execution_count": 77, - "id": "0064a683", + "id": "ab98f982", "metadata": {}, "outputs": [ { @@ -2273,7 +2273,7 @@ }, { "cell_type": "markdown", - "id": "77adcfae", + "id": "e4b23885", "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": "406f149a", + "id": "555a5494", "metadata": {}, "outputs": [ { @@ -2302,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "c6150b00", + "id": "3cf31cb7", "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": "c82893ba", + "id": "b8db9ba4", "metadata": {}, "outputs": [], "source": [ @@ -2335,7 +2335,7 @@ { "cell_type": "code", "execution_count": 80, - "id": "5bbf1fa8", + "id": "c38ea3cb", "metadata": {}, "outputs": [ { @@ -2356,7 +2356,7 @@ { "cell_type": "code", "execution_count": 81, - "id": "046bdefe", + "id": "6ff1d893", "metadata": {}, "outputs": [ { @@ -2376,7 +2376,7 @@ }, { "cell_type": "markdown", - "id": "a5569c55", + "id": "d7c505e0", "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": "6d2ddea2", + "id": "73771785", "metadata": {}, "outputs": [], "source": [ @@ -2395,7 +2395,7 @@ { "cell_type": "code", "execution_count": 83, - "id": "94aa2b0d", + "id": "c087f055", "metadata": {}, "outputs": [ { @@ -2416,7 +2416,7 @@ { "cell_type": "code", "execution_count": 84, - "id": "8f19e665", + "id": "55e3a5ae", "metadata": {}, "outputs": [ { @@ -2436,7 +2436,7 @@ }, { "cell_type": "markdown", - "id": "b5948b7d", + "id": "254c4056", "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": "707ba14b", + "id": "bc3f0df1", "metadata": {}, "outputs": [ { @@ -2465,7 +2465,7 @@ }, { "cell_type": "markdown", - "id": "03ccae92", + "id": "60e0fea3", "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": "0d7943d1", + "id": "17ca9933", "metadata": {}, "outputs": [ { @@ -2494,7 +2494,7 @@ }, { "cell_type": "markdown", - "id": "9bfe60fd", + "id": "b1540b46", "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": "39736de3", + "id": "5d59ce09", "metadata": {}, "outputs": [ { @@ -2542,7 +2542,7 @@ }, { "cell_type": "markdown", - "id": "cae23acf", + "id": "0ad4af78", "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": "9b7f09b1", + "id": "0b8d903a", "metadata": {}, "outputs": [ { @@ -2572,7 +2572,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "a917329c", + "id": "0dccd35c", "metadata": {}, "outputs": [ { @@ -2592,7 +2592,7 @@ }, { "cell_type": "markdown", - "id": "817f1318", + "id": "fdf27673", "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": "aa2480ef", + "id": "9c58ff87", "metadata": {}, "outputs": [ { @@ -2621,7 +2621,7 @@ }, { "cell_type": "markdown", - "id": "cdb21dd3", + "id": "36552e98", "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": "c3dcf26b", + "id": "a50bd245", "metadata": {}, "outputs": [ { @@ -2652,7 +2652,7 @@ { "cell_type": "code", "execution_count": 92, - "id": "e3dc5a91", + "id": "0486952f", "metadata": {}, "outputs": [ { @@ -2674,7 +2674,7 @@ }, { "cell_type": "markdown", - "id": "24d6064c", + "id": "fddbb9d3", "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": "d93749f7", + "id": "12c63082", "metadata": {}, "outputs": [ { @@ -2720,7 +2720,7 @@ }, { "cell_type": "markdown", - "id": "ca8798f4", + "id": "0078053e", "metadata": {}, "source": [ "This is equivalent to:" @@ -2729,7 +2729,7 @@ { "cell_type": "code", "execution_count": 94, - "id": "cd4c7a2e", + "id": "340be6f0", "metadata": {}, "outputs": [ { @@ -2750,7 +2750,7 @@ }, { "cell_type": "markdown", - "id": "32625800", + "id": "9d18f154", "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": "20fe2be5", + "id": "6d7c70b3", "metadata": {}, "outputs": [ { @@ -2780,7 +2780,7 @@ { "cell_type": "code", "execution_count": 96, - "id": "63240821", + "id": "2ac0fede", "metadata": {}, "outputs": [ { @@ -2800,7 +2800,7 @@ }, { "cell_type": "markdown", - "id": "0a220928", + "id": "c33c4d8f", "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": "20ae9d4b", + "id": "af1e4760", "metadata": {}, "outputs": [ { @@ -2834,7 +2834,7 @@ }, { "cell_type": "markdown", - "id": "8616dbbc", + "id": "a682dfca", "metadata": {}, "source": [ "### Advanced indexing\n", @@ -2857,7 +2857,7 @@ { "cell_type": "code", "execution_count": 98, - "id": "4cade501", + "id": "09f60290", "metadata": {}, "outputs": [], "source": [ @@ -2867,7 +2867,7 @@ { "cell_type": "code", "execution_count": 99, - "id": "8cb265f3", + "id": "c6215f55", "metadata": {}, "outputs": [ { @@ -2888,7 +2888,7 @@ { "cell_type": "code", "execution_count": 100, - "id": "6a678867", + "id": "4c78bcbf", "metadata": {}, "outputs": [ { @@ -2909,7 +2909,7 @@ { "cell_type": "code", "execution_count": 101, - "id": "76ffa3ab", + "id": "712d9208", "metadata": {}, "outputs": [ { @@ -2929,7 +2929,7 @@ }, { "cell_type": "markdown", - "id": "3046f1aa", + "id": "e5d75aa2", "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": "69ad05d6", + "id": "3f885a84", "metadata": {}, "outputs": [], "source": [ @@ -2948,7 +2948,7 @@ { "cell_type": "code", "execution_count": 103, - "id": "a94a5db2", + "id": "eed86f9e", "metadata": {}, "outputs": [ { @@ -2969,7 +2969,7 @@ }, { "cell_type": "markdown", - "id": "fe1eb07f", + "id": "6dd9de97", "metadata": {}, "source": [ "```py\n", @@ -2999,7 +2999,7 @@ { "cell_type": "code", "execution_count": 104, - "id": "534eaca5", + "id": "804a6e1c", "metadata": {}, "outputs": [], "source": [ @@ -3009,7 +3009,7 @@ { "cell_type": "code", "execution_count": 105, - "id": "48bf6028", + "id": "2a4c6d27", "metadata": {}, "outputs": [ { @@ -3034,7 +3034,7 @@ { "cell_type": "code", "execution_count": 106, - "id": "3d30a489", + "id": "34937c0b", "metadata": {}, "outputs": [ { @@ -3054,7 +3054,7 @@ }, { "cell_type": "markdown", - "id": "237662eb", + "id": "72afe642", "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": "4af2f4e9", + "id": "746a8a2a", "metadata": {}, "outputs": [ { @@ -3097,7 +3097,7 @@ }, { "cell_type": "markdown", - "id": "cfb6a961", + "id": "c806293b", "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": "970c577f", + "id": "8d24f98b", "metadata": {}, "outputs": [ { @@ -3128,7 +3128,7 @@ }, { "cell_type": "markdown", - "id": "8f83cd68", + "id": "75a6a88c", "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": "7845e084", + "id": "843d2d9d", "metadata": {}, "outputs": [ { @@ -3164,7 +3164,7 @@ }, { "cell_type": "markdown", - "id": "9d716b3d", + "id": "b53bec85", "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": "9d028c17", + "id": "8b3486a3", "metadata": {}, "outputs": [ { @@ -3206,7 +3206,7 @@ }, { "cell_type": "markdown", - "id": "758b4a35", + "id": "6b649b71", "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": "ec7b51df", + "id": "e499d3c0", "metadata": {}, "outputs": [], "source": [ @@ -3226,7 +3226,7 @@ { "cell_type": "code", "execution_count": 112, - "id": "edbf9768", + "id": "8c259488", "metadata": {}, "outputs": [ { @@ -3248,7 +3248,7 @@ { "cell_type": "code", "execution_count": 113, - "id": "eb31dd1f", + "id": "162b4cf4", "metadata": {}, "outputs": [ { @@ -3269,7 +3269,7 @@ }, { "cell_type": "markdown", - "id": "7ff7c435", + "id": "6804676b", "metadata": {}, "source": [ "This broadcasting can also be achieved using the function `ix_`:" @@ -3278,7 +3278,7 @@ { "cell_type": "code", "execution_count": 114, - "id": "b5b937a5", + "id": "9be46052", "metadata": {}, "outputs": [ { @@ -3299,7 +3299,7 @@ }, { "cell_type": "markdown", - "id": "0a8f070c", + "id": "e9ff09f5", "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": "05619cd7", + "id": "e796de41", "metadata": {}, "outputs": [ { @@ -3328,7 +3328,7 @@ }, { "cell_type": "markdown", - "id": "9d77ccdb", + "id": "12406154", "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": "d73844ae", + "id": "0c1a1859", "metadata": {}, "outputs": [ { @@ -3370,7 +3370,7 @@ }, { "cell_type": "markdown", - "id": "985d5493", + "id": "cfda58c9", "metadata": {}, "source": [ "Or wish to add a constant to all negative elements:" @@ -3379,7 +3379,7 @@ { "cell_type": "code", "execution_count": 117, - "id": "75ee7fe3", + "id": "84b58448", "metadata": {}, "outputs": [ { @@ -3401,7 +3401,7 @@ }, { "cell_type": "markdown", - "id": "d9247246", + "id": "ac73a2b4", "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": "e691a204", + "id": "030806a6", "metadata": {}, "outputs": [], "source": [ @@ -3425,7 +3425,7 @@ { "cell_type": "code", "execution_count": 119, - "id": "6730d9e8", + "id": "419f7088", "metadata": {}, "outputs": [ { @@ -3446,7 +3446,7 @@ { "cell_type": "code", "execution_count": 120, - "id": "716092f9", + "id": "356547dd", "metadata": {}, "outputs": [ { @@ -3467,7 +3467,7 @@ }, { "cell_type": "markdown", - "id": "657b732f", + "id": "5ead70ba", "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": "24e80f01", + "id": "59f2584c", "metadata": {}, "outputs": [ { @@ -3503,7 +3503,7 @@ }, { "cell_type": "markdown", - "id": "2e9c7a50", + "id": "fde47f24", "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": "0aee987f", + "id": "ad475641", "metadata": {}, "outputs": [], "source": [ @@ -3530,7 +3530,7 @@ { "cell_type": "code", "execution_count": 123, - "id": "43d73458", + "id": "57e1caef", "metadata": {}, "outputs": [ { @@ -3551,7 +3551,7 @@ { "cell_type": "code", "execution_count": 124, - "id": "4544e60c", + "id": "f711ea9e", "metadata": {}, "outputs": [], "source": [ @@ -3561,7 +3561,7 @@ { "cell_type": "code", "execution_count": 125, - "id": "084a72fa", + "id": "afb6793d", "metadata": {}, "outputs": [ { @@ -3582,7 +3582,7 @@ }, { "cell_type": "markdown", - "id": "54d115d4", + "id": "20bb0f03", "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": "492e0749", + "id": "fec999aa", "metadata": {}, "outputs": [ { @@ -3615,7 +3615,7 @@ }, { "cell_type": "markdown", - "id": "9794683e", + "id": "5f069d0e", "metadata": {}, "source": [ "##### Example 3\n", @@ -3626,7 +3626,7 @@ { "cell_type": "code", "execution_count": 127, - "id": "27a94206", + "id": "78014573", "metadata": {}, "outputs": [ { @@ -3654,7 +3654,7 @@ { "cell_type": "code", "execution_count": 128, - "id": "602decb2", + "id": "5f18d1f1", "metadata": {}, "outputs": [ { @@ -3678,7 +3678,7 @@ }, { "cell_type": "markdown", - "id": "822f10a9", + "id": "a0d3b147", "metadata": {}, "source": [ "#### Combining advanced and basic indexing\n", @@ -3691,7 +3691,7 @@ { "cell_type": "code", "execution_count": 129, - "id": "2fd94f53", + "id": "9313ab71", "metadata": {}, "outputs": [ { @@ -3714,7 +3714,7 @@ }, { "cell_type": "markdown", - "id": "61fb52dd", + "id": "2ae88709", "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": "6b417598", + "id": "56b1f695", "metadata": {}, "outputs": [ { @@ -3745,7 +3745,7 @@ }, { "cell_type": "markdown", - "id": "e1c85d79", + "id": "0e6dddf9", "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": "0ff42fd8", + "id": "607ce968", "metadata": {}, "outputs": [], "source": [ @@ -3767,7 +3767,7 @@ { "cell_type": "code", "execution_count": 132, - "id": "58013488", + "id": "f04d4d21", "metadata": {}, "outputs": [ { @@ -3788,7 +3788,7 @@ { "cell_type": "code", "execution_count": 133, - "id": "5e4c2001", + "id": "7bdee1ce", "metadata": {}, "outputs": [ { @@ -3808,7 +3808,7 @@ }, { "cell_type": "markdown", - "id": "381fc0be", + "id": "df8272ed", "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": "1731247e", + "id": "e665163b", "metadata": {}, "outputs": [], "source": [ @@ -3845,7 +3845,7 @@ { "cell_type": "code", "execution_count": 135, - "id": "baefc549", + "id": "8a0eeb3f", "metadata": {}, "outputs": [ { @@ -3870,7 +3870,7 @@ { "cell_type": "code", "execution_count": 136, - "id": "e6e1c108", + "id": "211ae299", "metadata": {}, "outputs": [ { @@ -3891,7 +3891,7 @@ }, { "cell_type": "markdown", - "id": "eaa10183", + "id": "ac489faf", "metadata": {}, "source": [ "### Field access\n", @@ -3908,7 +3908,7 @@ { "cell_type": "code", "execution_count": 137, - "id": "8c715588", + "id": "87082d2b", "metadata": {}, "outputs": [], "source": [ @@ -3918,7 +3918,7 @@ { "cell_type": "code", "execution_count": 138, - "id": "7b5d745d", + "id": "25797dbf", "metadata": {}, "outputs": [ { @@ -3939,7 +3939,7 @@ { "cell_type": "code", "execution_count": 139, - "id": "473385a9", + "id": "d362383b", "metadata": {}, "outputs": [ { @@ -3960,7 +3960,7 @@ { "cell_type": "code", "execution_count": 140, - "id": "38df66f1", + "id": "d277a47b", "metadata": {}, "outputs": [ { @@ -3981,7 +3981,7 @@ { "cell_type": "code", "execution_count": 141, - "id": "6b405005", + "id": "bb59101a", "metadata": {}, "outputs": [ { @@ -4001,7 +4001,7 @@ }, { "cell_type": "markdown", - "id": "ceaa6265", + "id": "81e86d2a", "metadata": {}, "source": [ "### Flat Iterator indexing\n", @@ -4016,7 +4016,7 @@ { "cell_type": "code", "execution_count": 142, - "id": "60df60bc", + "id": "651d8796", "metadata": {}, "outputs": [], "source": [ @@ -4026,7 +4026,7 @@ }, { "cell_type": "markdown", - "id": "e35ad0a7", + "id": "f0bf23cc", "metadata": {}, "source": [ "Or an array of the right size:" @@ -4035,7 +4035,7 @@ { "cell_type": "code", "execution_count": 143, - "id": "36c7a028", + "id": "3f734843", "metadata": {}, "outputs": [], "source": [ @@ -4044,7 +4044,7 @@ }, { "cell_type": "markdown", - "id": "d6bb68ea", + "id": "7577483a", "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": "9f3a644a", + "id": "4a2f112c", "metadata": {}, "outputs": [ { @@ -4074,7 +4074,7 @@ }, { "cell_type": "markdown", - "id": "a9daf9ca", + "id": "47e09f84", "metadata": {}, "source": [ "```py\n", @@ -4093,7 +4093,7 @@ { "cell_type": "code", "execution_count": 145, - "id": "152f86ff", + "id": "b1b83b6d", "metadata": {}, "outputs": [ { @@ -4115,7 +4115,7 @@ { "cell_type": "code", "execution_count": 146, - "id": "8a4b69d7", + "id": "c2ed584e", "metadata": {}, "outputs": [ { @@ -4136,7 +4136,7 @@ }, { "cell_type": "markdown", - "id": "94071121", + "id": "26367fa1", "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": "f8f02799", + "id": "361dad28", "metadata": {}, "outputs": [ { @@ -4171,7 +4171,7 @@ }, { "cell_type": "markdown", - "id": "0c35b2c7", + "id": "e214f199", "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": "b05c46a5", + "id": "956f5a69", "metadata": {}, "outputs": [ { @@ -4203,7 +4203,7 @@ }, { "cell_type": "markdown", - "id": "32f2990f", + "id": "7d68302b", "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": "0c52b1c9", + "id": "8e19d081", "metadata": {}, "outputs": [ { @@ -4235,7 +4235,7 @@ }, { "cell_type": "markdown", - "id": "2cf9e8ae", + "id": "30b1c106", "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": "0e5cedd0", + "id": "bc878720", "metadata": {}, "outputs": [ { @@ -4316,7 +4316,7 @@ { "cell_type": "code", "execution_count": 151, - "id": "93461eca", + "id": "8d0c8879", "metadata": {}, "outputs": [ { @@ -4336,7 +4336,7 @@ }, { "cell_type": "markdown", - "id": "860ceada", + "id": "b900ab4b", "metadata": {}, "source": [ "## Structured arrays\n", @@ -4349,7 +4349,7 @@ { "cell_type": "code", "execution_count": 152, - "id": "bbccc513", + "id": "731cfdd6", "metadata": {}, "outputs": [ { @@ -4372,7 +4372,7 @@ }, { "cell_type": "markdown", - "id": "599c24ca", + "id": "148197cf", "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": "745967eb", + "id": "9616caaa", "metadata": {}, "outputs": [ { @@ -4403,7 +4403,7 @@ }, { "cell_type": "markdown", - "id": "d2f00026", + "id": "de516267", "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": "fec68e48", + "id": "d43c6311", "metadata": {}, "outputs": [ { @@ -4433,7 +4433,7 @@ { "cell_type": "code", "execution_count": 155, - "id": "25eb0464", + "id": "0be2975e", "metadata": {}, "outputs": [ { @@ -4454,7 +4454,7 @@ { "cell_type": "code", "execution_count": 156, - "id": "d1f77685", + "id": "80a499fb", "metadata": {}, "outputs": [ { @@ -4475,7 +4475,7 @@ }, { "cell_type": "markdown", - "id": "d8d5a893", + "id": "3b028ec2", "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": "797dd050", + "id": "74ebd158", "metadata": {}, "outputs": [ { @@ -4518,7 +4518,7 @@ }, { "cell_type": "markdown", - "id": "32712a39", + "id": "e34b6ce2", "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": "09d05c4f", + "id": "0f6e526b", "metadata": {}, "outputs": [ { @@ -4547,7 +4547,7 @@ }, { "cell_type": "markdown", - "id": "4f6bb0fd", + "id": "53d95bbc", "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": "28c8a822", + "id": "b2287306", "metadata": {}, "outputs": [ { @@ -4581,7 +4581,7 @@ { "cell_type": "code", "execution_count": 160, - "id": "f7b83bcf", + "id": "79eff3e6", "metadata": {}, "outputs": [ { @@ -4601,7 +4601,7 @@ }, { "cell_type": "markdown", - "id": "5c960b91", + "id": "257bd476", "metadata": {}, "source": [ "- A dictionary of field parameter arrays\n", @@ -4614,7 +4614,7 @@ { "cell_type": "code", "execution_count": 161, - "id": "434ca05a", + "id": "ab42e01a", "metadata": {}, "outputs": [ { @@ -4635,7 +4635,7 @@ { "cell_type": "code", "execution_count": 162, - "id": "e2a4528d", + "id": "9fd55ad4", "metadata": {}, "outputs": [ { @@ -4658,7 +4658,7 @@ }, { "cell_type": "markdown", - "id": "36a35aeb", + "id": "64c2856f", "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": "d9f02eba", + "id": "d9c1f88a", "metadata": {}, "outputs": [ { @@ -4693,7 +4693,7 @@ }, { "cell_type": "markdown", - "id": "a436abe7", + "id": "86448c29", "metadata": {}, "source": [ "#### Manipulating and Displaying Structured Datatypes\n", @@ -4704,7 +4704,7 @@ { "cell_type": "code", "execution_count": 164, - "id": "f69590d4", + "id": "b9033bda", "metadata": {}, "outputs": [ { @@ -4725,7 +4725,7 @@ }, { "cell_type": "markdown", - "id": "4e7b9a5d", + "id": "178065ea", "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": "43f8a0f5", + "id": "2ab6061b", "metadata": {}, "outputs": [ { @@ -4756,7 +4756,7 @@ }, { "cell_type": "markdown", - "id": "1232b323", + "id": "ec92a2d5", "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": "a0ed1548", + "id": "655b8383", "metadata": {}, "outputs": [ { @@ -4793,7 +4793,7 @@ }, { "cell_type": "markdown", - "id": "7828e2b3", + "id": "6ff6765b", "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": "a92fe7b9", + "id": "054229c3", "metadata": {}, "outputs": [ { @@ -4822,7 +4822,7 @@ }, { "cell_type": "markdown", - "id": "a03df07f", + "id": "8cb7b0c3", "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": "7d79d449", + "id": "46fa0789", "metadata": {}, "outputs": [ { @@ -4850,7 +4850,7 @@ }, { "cell_type": "markdown", - "id": "f326f48c", + "id": "90027ceb", "metadata": {}, "source": [ "### Indexing and Assignment to Structured arrays\n", @@ -4867,7 +4867,7 @@ { "cell_type": "code", "execution_count": 169, - "id": "0bcff25e", + "id": "3acd25ac", "metadata": {}, "outputs": [ { @@ -4890,7 +4890,7 @@ }, { "cell_type": "markdown", - "id": "c6036a27", + "id": "71abc223", "metadata": {}, "source": [ "##### Assignment from Scalars\n", @@ -4901,7 +4901,7 @@ { "cell_type": "code", "execution_count": 170, - "id": "5f8180c1", + "id": "09ad3720", "metadata": {}, "outputs": [], "source": [ @@ -4911,7 +4911,7 @@ { "cell_type": "code", "execution_count": 171, - "id": "1d2f184e", + "id": "b4e9d99e", "metadata": {}, "outputs": [ { @@ -4934,7 +4934,7 @@ { "cell_type": "code", "execution_count": 172, - "id": "38fa378b", + "id": "17e3e19a", "metadata": {}, "outputs": [ { @@ -4956,7 +4956,7 @@ }, { "cell_type": "markdown", - "id": "31872854", + "id": "bfc7d2c2", "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": "2ad8f17d", + "id": "e27b36d3", "metadata": {}, "outputs": [], "source": [ @@ -4976,7 +4976,7 @@ { "cell_type": "code", "execution_count": 174, - "id": "59d06d12", + "id": "bbda13b9", "metadata": {}, "outputs": [], "source": [ @@ -4985,7 +4985,7 @@ }, { "cell_type": "markdown", - "id": "254ad34d", + "id": "ce6b3e7e", "metadata": {}, "source": [ "```py\n", @@ -5006,7 +5006,7 @@ { "cell_type": "code", "execution_count": 175, - "id": "a1e63a89", + "id": "4c1918c7", "metadata": {}, "outputs": [], "source": [ @@ -5017,7 +5017,7 @@ { "cell_type": "code", "execution_count": 176, - "id": "ac4e538d", + "id": "00d0a5f3", "metadata": {}, "outputs": [ { @@ -5039,7 +5039,7 @@ }, { "cell_type": "markdown", - "id": "695a3ae3", + "id": "2c3d7598", "metadata": {}, "source": [ "##### Assignment involving subarrays\n", @@ -5056,7 +5056,7 @@ { "cell_type": "code", "execution_count": 177, - "id": "78eb2d91", + "id": "095efc01", "metadata": {}, "outputs": [ { @@ -5078,7 +5078,7 @@ { "cell_type": "code", "execution_count": 178, - "id": "bd39cd14", + "id": "4aba895c", "metadata": {}, "outputs": [ { @@ -5099,7 +5099,7 @@ }, { "cell_type": "markdown", - "id": "65aa6b1c", + "id": "b41a3c7a", "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": "98b4398f", + "id": "748ea7c0", "metadata": {}, "outputs": [ { @@ -5130,7 +5130,7 @@ }, { "cell_type": "markdown", - "id": "eb5964c9", + "id": "dd634134", "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": "f1fc17d5", + "id": "da053c07", "metadata": {}, "outputs": [ { @@ -5159,7 +5159,7 @@ }, { "cell_type": "markdown", - "id": "2ce4b583", + "id": "527b2bfe", "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": "511e4a07", + "id": "4aab2047", "metadata": {}, "outputs": [], "source": [ @@ -5178,7 +5178,7 @@ { "cell_type": "code", "execution_count": 182, - "id": "6a2b3394", + "id": "aa1fe97d", "metadata": {}, "outputs": [ { @@ -5199,7 +5199,7 @@ { "cell_type": "code", "execution_count": 183, - "id": "13a0bed3", + "id": "347a151d", "metadata": {}, "outputs": [ { @@ -5219,7 +5219,7 @@ }, { "cell_type": "markdown", - "id": "2fcb6413", + "id": "16ef12a4", "metadata": {}, "source": [ "##### Accessing multiple fields\n", @@ -5232,7 +5232,7 @@ { "cell_type": "code", "execution_count": 184, - "id": "1fbf406d", + "id": "7d571104", "metadata": {}, "outputs": [ { @@ -5254,7 +5254,7 @@ }, { "cell_type": "markdown", - "id": "446cb17a", + "id": "fd414843", "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": "9b1533fb", + "id": "63fc9305", "metadata": {}, "outputs": [ { @@ -5287,7 +5287,7 @@ }, { "cell_type": "markdown", - "id": "1ab777da", + "id": "3bebf5bb", "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": "92f9299f", + "id": "d012c47a", "metadata": {}, "outputs": [], "source": [ @@ -5305,7 +5305,7 @@ }, { "cell_type": "markdown", - "id": "ff11badf", + "id": "3a06e1be", "metadata": {}, "source": [ "##### Indexing with an integer to get a structured scalar\n", @@ -5316,7 +5316,7 @@ { "cell_type": "code", "execution_count": 187, - "id": "3c4458fb", + "id": "57a2317e", "metadata": {}, "outputs": [ { @@ -5339,7 +5339,7 @@ { "cell_type": "code", "execution_count": 188, - "id": "9d02156e", + "id": "68e825bd", "metadata": {}, "outputs": [ { @@ -5359,7 +5359,7 @@ }, { "cell_type": "markdown", - "id": "091a49db", + "id": "76996871", "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": "fce3539a", + "id": "7717de35", "metadata": {}, "outputs": [ { @@ -5391,7 +5391,7 @@ }, { "cell_type": "markdown", - "id": "ec2ee51b", + "id": "0c60396e", "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": "7892151e", + "id": "f15636e2", "metadata": {}, "outputs": [ { @@ -5422,7 +5422,7 @@ { "cell_type": "code", "execution_count": 191, - "id": "1f5b258b", + "id": "f36c37e1", "metadata": {}, "outputs": [], "source": [ @@ -5431,7 +5431,7 @@ }, { "cell_type": "markdown", - "id": "fc2974b3", + "id": "902b6b77", "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": "75900109", + "id": "501e7ddc", "metadata": {}, "outputs": [ { @@ -5460,7 +5460,7 @@ }, { "cell_type": "markdown", - "id": "675d25e0", + "id": "701d3646", "metadata": {}, "source": [ "#### Viewing structured arrays containing objects\n", @@ -5475,7 +5475,7 @@ { "cell_type": "code", "execution_count": 193, - "id": "4e0d5322", + "id": "3a2ffa4c", "metadata": {}, "outputs": [ { @@ -5497,7 +5497,7 @@ }, { "cell_type": "markdown", - "id": "bf1f00b0", + "id": "bd9211ed", "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": "afc92f87", + "id": "06be69c9", "metadata": {}, "outputs": [ { @@ -5527,7 +5527,7 @@ }, { "cell_type": "markdown", - "id": "580de1f6", + "id": "b6da70a8", "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": "ca2a66a5", + "id": "337df658", "metadata": {}, "outputs": [ { @@ -5557,7 +5557,7 @@ { "cell_type": "code", "execution_count": 196, - "id": "ef7f384f", + "id": "51141e9f", "metadata": {}, "outputs": [ { @@ -5577,7 +5577,7 @@ }, { "cell_type": "markdown", - "id": "93698d5e", + "id": "04f4a521", "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": "fb8d6547", + "id": "a4837e21", "metadata": {}, "outputs": [ { @@ -5608,7 +5608,7 @@ { "cell_type": "code", "execution_count": 198, - "id": "76a68ed4", + "id": "b4e5acc1", "metadata": {}, "outputs": [ { @@ -5628,7 +5628,7 @@ }, { "cell_type": "markdown", - "id": "3c61f81d", + "id": "2635d41b", "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": "c471ee7c", + "id": "ec117194", "metadata": {}, "outputs": [ { @@ -5659,7 +5659,7 @@ { "cell_type": "code", "execution_count": 200, - "id": "cf6d9f72", + "id": "c20a5317", "metadata": {}, "outputs": [ { @@ -5680,7 +5680,7 @@ { "cell_type": "code", "execution_count": 201, - "id": "77b637fe", + "id": "0648deaa", "metadata": {}, "outputs": [ { @@ -5700,7 +5700,7 @@ }, { "cell_type": "markdown", - "id": "f7bae0a4", + "id": "462d8b57", "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": "56b42f94", + "id": "53ca495a", "metadata": {}, "outputs": [ { @@ -5729,7 +5729,7 @@ }, { "cell_type": "markdown", - "id": "7253081d", + "id": "fab01908", "metadata": {}, "source": [ "The `<` and `>` operators always return `False` when comparing void structured arrays, and arithmetic and bitwise operations are not supported.\n", @@ -5816,7 +5816,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.19" }, "source_map": [ 14, diff --git a/_sources/deep-learning/cnn/cnn-deepdream.ipynb b/_sources/deep-learning/cnn/cnn-deepdream.ipynb index 49ef8353bf..bc578fe39e 100644 --- a/_sources/deep-learning/cnn/cnn-deepdream.ipynb +++ b/_sources/deep-learning/cnn/cnn-deepdream.ipynb @@ -352,7 +352,7 @@ "source": [ "## Your turn! 🚀\n", "\n", - "TBD." + "You can practice your cnn skills by following the assignment [sign language digits classification with cnn](../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.ipynb)" ] }, { diff --git a/_sources/deep-learning/cnn/cnn-vgg.ipynb b/_sources/deep-learning/cnn/cnn-vgg.ipynb index 4e67c7e724..188b31cdbd 100644 --- a/_sources/deep-learning/cnn/cnn-vgg.ipynb +++ b/_sources/deep-learning/cnn/cnn-vgg.ipynb @@ -440,8 +440,7 @@ "metadata": {}, "source": [ "## Your turn! 🚀\n", - "\n", - "TBD." + "You can practice your cnn skills by following the assignment [object recognition in images using cnn](../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.ipynb)." ] }, { diff --git a/_sources/deep-learning/cnn/cnn.ipynb b/_sources/deep-learning/cnn/cnn.ipynb index 29bb544d7b..5ac8402b46 100644 --- a/_sources/deep-learning/cnn/cnn.ipynb +++ b/_sources/deep-learning/cnn/cnn.ipynb @@ -162,6 +162,42 @@ "\"\"\"))" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "

    \n", + "\n", + "A demo of CNN. [source]\n", + "

    \n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import HTML\n", + "display(HTML(\"\"\"\n", + "

    \n", + "\n", + "A demo of CNN. [source]\n", + "

    \n", + "\"\"\"))" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -861,8 +897,7 @@ "metadata": {}, "source": [ "## Your turn! 🚀\n", - "\n", - "TBD." + "You can practice your cnn skills by following the assignment [how to choose cnn architecture mnist](../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.ipynb)." ] }, { diff --git a/_sources/deep-learning/image-classification.ipynb b/_sources/deep-learning/image-classification.ipynb index 56f4d51c5e..2486078d3d 100644 --- a/_sources/deep-learning/image-classification.ipynb +++ b/_sources/deep-learning/image-classification.ipynb @@ -219,8 +219,8 @@ "source": [ "- Using small 3×3 filters to replace large convolutional kernels.\n", "- After replacing the convolution kernel, the convolution layers have the same perceptual field. \n", - "- Each layer is trained by Re LU activation function and batch gradient descent after convolution operation.\n", - "- It is verified that increasing the network depth can improve the model performance Although, VGG has achieved good results in image classification and localization problems in 2014 due to its deeper network structure and low computational complexity, it uses 140 million parameters and is computationally intensive, which is its shortcoming." + "- Each layer is trained by ReLU activation function and batch gradient descent after convolution operation.\n", + "- It is verified that increasing the network depth can improve the model performance. Although, VGG has achieved good results in image classification and localization problems in 2014 due to its deeper network structure and low computational complexity, it uses 140 million parameters and is computationally intensive, which is its shortcoming." ] }, { @@ -1156,7 +1156,7 @@ "id": "b4552758", "metadata": {}, "source": [ - "TBD." + "Assignment - [Image classification](../../assignments/deep-learning/cnn/image-classification.ipynb)" ] }, { diff --git a/_sources/deep-learning/image-segmentation.ipynb b/_sources/deep-learning/image-segmentation.ipynb index 7c2d3556dc..abbe6c89a3 100644 --- a/_sources/deep-learning/image-segmentation.ipynb +++ b/_sources/deep-learning/image-segmentation.ipynb @@ -2045,7 +2045,7 @@ "id": "a0994ed1", "metadata": {}, "source": [ - "TBD." + "Assignment - [Comparing edge-based and region-based segmentation](../../assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.ipynb)" ] }, { diff --git a/_sources/deep-learning/lstm.ipynb b/_sources/deep-learning/lstm.ipynb index c920b28b04..6ea5b6e8f9 100644 --- a/_sources/deep-learning/lstm.ipynb +++ b/_sources/deep-learning/lstm.ipynb @@ -4770,7 +4770,7 @@ "source": [ "## Your turn! 🚀\n", "\n", - "Practice the Long-Short Term Memory Networks by following this TBD.\n", + "Assignment - [Bitcoin lstm model with tweet volume and sentiment](../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.ipynb)\n", "\n", "## Acknowledgments\n", "\n", diff --git a/_sources/deep-learning/nlp.ipynb b/_sources/deep-learning/nlp.ipynb index 839b75e674..efcff10fd5 100644 --- a/_sources/deep-learning/nlp.ipynb +++ b/_sources/deep-learning/nlp.ipynb @@ -786,8 +786,7 @@ }, "source": [ "## Your turn! 🚀\n", - "\n", - "TBD." + "You can practice your nlp skills by following the assignment [getting start nlp with classification task](../assignments/deep-learning/nlp/getting-start-nlp-with-classification-task.ipynb)." ] }, { diff --git a/_sources/deep-learning/rnn.ipynb b/_sources/deep-learning/rnn.ipynb index d436b6048b..7c1bf555d2 100644 --- a/_sources/deep-learning/rnn.ipynb +++ b/_sources/deep-learning/rnn.ipynb @@ -2,14 +2,23 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4f92eda8", "metadata": { "tags": [ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Ignoring invalid distribution -fds-nightly (c:\\users\\16111\\.conda\\envs\\open-machine-learning-jupyter-book\\lib\\site-packages)\n", + "WARNING: Ignoring invalid distribution -fds-nightly (c:\\users\\16111\\.conda\\envs\\open-machine-learning-jupyter-book\\lib\\site-packages)\n" + ] + } + ], "source": [ "# Install the necessary dependencies\n", "\n", @@ -159,9 +168,21 @@ "## Code" ] }, + { + "cell_type": "markdown", + "id": "6b52f47d", + "metadata": {}, + "source": [ + "A text classifier implemented in TensorFlow to classify SMS spam messages.\n", + "Code first downloads and processes the SMS Spam Collection dataset from the UCI Machine Learning Repository and then builds a basic Recurrent neural network (RNN) for text classification using TensorFlow.\n", + "The code first cleans and preprocesses the text, then splits it into training and test sets, followed by tokenizing and padding the training set. Next, the code uses an embedding layer to convert the tokenized text into a vector representation, which is then fed into a recurrent neural network and finally classified using a Softmax loss function.\n", + "The output of the # code is the accuracy of the classifier along with some statistics\n", + "We implement an RNN in TensorFlow to predict spam/ham from texts" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "b8241ee1", "metadata": { "attributes": { @@ -171,294 +192,303 @@ "id": "" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat', 'ok lar joking wif u oni', 'free entry in a wkly comp to win fa cup final tkts st may text fa to to receive entry questionstd txt ratetcs apply overs', 'u dun say so early hor u c already then say', 'nah i dont think he goes to usf he lives around here though']\n", - "(5574, 25)\n", - "[[ 191 3 17 ... 2725 0 0]\n", - " [ 365 1206 41 ... 0 0 0]\n", - " [ 81 40 4 ... 0 0 0]\n", - " ...\n", - " [7814 7815 533 ... 0 0 0]\n", - " [ 2 28 1439 ... 7 161 3]\n", - " [ 51 20 3 ... 0 0 0]]\n", - "Vocabulary Size: 8629\n", - "80-20 Train Test split: 4459 -- 1115\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\fuqiongying\\AppData\\Local\\Temp\\ipykernel_1816\\2116000173.py:115: UserWarning: `tf.nn.rnn_cell.BasicRNNCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.SimpleRNNCell`, and will be replaced by that in Tensorflow 2.0.\n", - " cell = tf.nn.rnn_cell.BasicRNNCell(num_units=rnn_size)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tensor(\"Mean:0\", shape=(), dtype=float32)\n", - "Tensor(\"Mean_1:0\", shape=(), dtype=float32)\n", - "Epoch: 1, Test Loss: 0.69, Test Acc: 0.81\n", - "Epoch: 2, Test Loss: 0.65, Test Acc: 0.82\n", - "Epoch: 3, Test Loss: 0.6, Test Acc: 0.82\n", - "Epoch: 4, Test Loss: 0.55, Test Acc: 0.82\n", - "Epoch: 5, Test Loss: 0.51, Test Acc: 0.83\n", - "Epoch: 6, Test Loss: 0.47, Test Acc: 0.83\n", - "Epoch: 7, Test Loss: 0.45, Test Acc: 0.83\n", - "Epoch: 8, Test Loss: 0.43, Test Acc: 0.84\n", - "Epoch: 9, Test Loss: 0.42, Test Acc: 0.84\n", - "Epoch: 10, Test Loss: 0.41, Test Acc: 0.84\n", - "Epoch: 11, Test Loss: 0.41, Test Acc: 0.84\n", - "Epoch: 12, Test Loss: 0.4, Test Acc: 0.85\n", - "Epoch: 13, Test Loss: 0.4, Test Acc: 0.85\n", - "Epoch: 14, Test Loss: 0.4, Test Acc: 0.85\n", - "Epoch: 15, Test Loss: 0.4, Test Acc: 0.86\n", - "Epoch: 16, Test Loss: 0.39, Test Acc: 0.86\n", - "Epoch: 17, Test Loss: 0.39, Test Acc: 0.87\n", - "Epoch: 18, Test Loss: 0.38, Test Acc: 0.87\n", - "Epoch: 19, Test Loss: 0.38, Test Acc: 0.87\n", - "Epoch: 20, Test Loss: 0.37, Test Acc: 0.87\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# A text classifier implemented in TensorFlow to classify SMS spam messages.\n", - "\n", - "# Code first downloads and processes the SMS Spam Collection dataset from the UCI Machine Learning Repository and then builds a basic Recurrent neural network (RNN) for text classification using TensorFlow.\n", - "\n", - "# The code first cleans and preprocesses the text, then splits it into training and test sets, followed by tokenizing and padding the training set. Next, the code uses an embedding layer to convert the tokenized text into a vector representation, which is then fed into a recurrent neural network and finally classified using a Softmax loss function.\n", - "\n", - "#The output of the # code is the accuracy of the classifier along with some statistics\n", - "\n", - "# We implement an RNN in TensorFlow to predict spam/ham from texts\n", - "\n", "import os\n", "import re\n", "import io\n", "import requests\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import tensorflow.compat.v1 as tf\n", + "import tensorflow as tf\n", "from zipfile import ZipFile\n", - "from tensorflow.python.framework import ops\n", - "tf.disable_v2_behavior()\n", - "ops.reset_default_graph()\n", - "\n", - "# Start a graph\n", - "sess = tf.Session()\n", - "\n", - "# Set RNN parameters\n", - "epochs = 20\n", - "batch_size = 250\n", - "max_sequence_length =25 \n", - "rnn_size = 10\n", - "embedding_size = 50\n", - "min_word_frequency = 10\n", - "learning_rate = 0.0005\n", - "dropout_keep_prob = tf.placeholder(tf.float32,name='dropout_keep_prob')\n", - "\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b3fa5d09", + "metadata": {}, + "outputs": [], + "source": [ + "# Set random seed for reproducibility\n", + "tf.random.set_seed(42)\n", "\n", "# Download or open data\n", - "data_dir = 'tmp'\n", - "data_file = 'text_data.txt'\n", + "data_dir = \"tmp\"\n", + "data_file = \"text_data.txt\"\n", "if not os.path.exists(data_dir):\n", " os.makedirs(data_dir)\n", "\n", "if not os.path.isfile(os.path.join(data_dir, data_file)):\n", - " zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip'\n", + " zip_url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\"\n", " r = requests.get(zip_url)\n", " z = ZipFile(io.BytesIO(r.content))\n", - " file = z.read('SMSSpamCollection')\n", + " file = z.read(\"SMSSpamCollection\")\n", " # Format Data\n", " text_data = file.decode()\n", - " text_data = text_data.encode('ascii', errors='ignore')\n", - " text_data = text_data.decode().split('\\n')\n", + " text_data = text_data.encode(\"ascii\", errors=\"ignore\")\n", + " text_data = text_data.decode().split(\"\\n\")\n", "\n", " # Save data to text file\n", - " with open(os.path.join(data_dir, data_file), 'w') as file_conn:\n", + " with open(os.path.join(data_dir, data_file), \"w\") as file_conn:\n", " for text in text_data:\n", " file_conn.write(\"{}\\n\".format(text))\n", "else:\n", " # Open data from text file\n", " text_data = []\n", - " with open(os.path.join(data_dir, data_file), 'r') as file_conn:\n", + " with open(os.path.join(data_dir, data_file), \"r\") as file_conn:\n", " for row in file_conn:\n", " text_data.append(row)\n", " text_data = text_data[:-1]\n", "\n", - "text_data = [x.split('\\t') for x in text_data if len(x) >= 1]\n", - "[text_data_target, text_data_train] = [list(x) for x in zip(*text_data)]\n", - "\n", - "\n", + "text_data = [x.split(\"\\t\") for x in text_data if len(x) >= 1]\n", + "[text_data_target, text_data_train] = [list(x) for x in zip(*text_data)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d2d94450", + "metadata": {}, + "outputs": [], + "source": [ "# Create a text cleaning function\n", "def clean_text(text_string):\n", - " text_string = re.sub(r'([^\\s\\w]|_|[0-9])+', '', text_string)\n", + " text_string = re.sub(r\"([^\\s\\w]|_|[0-9])+\", \"\", text_string)\n", " text_string = \" \".join(text_string.split())\n", " text_string = text_string.lower()\n", - " return text_string\n", - "\n", + " return text_string" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7c01c5b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat', 'ok lar joking wif u oni', 'free entry in a wkly comp to win fa cup final tkts st may text fa to to receive entry questionstd txt ratetcs apply overs', 'u dun say so early hor u c already then say', 'nah i dont think he goes to usf he lives around here though']\n", + "(5574, 25)\n" + ] + } + ], + "source": [ "# Clean texts\n", "text_data_train = [clean_text(x) for x in text_data_train]\n", - "#print(text_data[:5])\n", "print(text_data_train[:5])\n", "\n", "# Tokenize and pad sequences\n", - "vocab_processor = tf.keras.preprocessing.text.Tokenizer()\n", - "vocab_processor.fit_on_texts(text_data_train)\n", - "text_processed = vocab_processor.texts_to_sequences(text_data_train)\n", - "max_document_length = max([len(x) for x in text_processed])\n", - "#pads the text data to ensure all sequences have the same length (max_sequence_length).\n", - "text_processed = tf.keras.preprocessing.sequence.pad_sequences(text_processed, maxlen=max_sequence_length, padding='post')\n", - "print(text_processed.shape)\n", + "tokenizer = Tokenizer()\n", + "tokenizer.fit_on_texts(text_data_train)\n", + "text_processed = tokenizer.texts_to_sequences(text_data_train)\n", + "max_sequence_length = 25\n", + "text_processed = pad_sequences(\n", + " text_processed, maxlen=max_sequence_length, padding=\"post\"\n", + ")\n", + "print(text_processed.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fe65852c", + "metadata": {}, + "outputs": [], + "source": [ "# Shuffle and split data\n", "text_processed = np.array(text_processed)\n", - "text_data_target = np.array([1 if x == 'ham' else 0 for x in text_data_target])\n", + "text_data_target = np.array([1 if x == \"ham\" else 0 for x in text_data_target])\n", "shuffled_ix = np.random.permutation(np.arange(len(text_data_target)))\n", "x_shuffled = text_processed[shuffled_ix]\n", - "y_shuffled = text_data_target[shuffled_ix]\n", - "\n", + "y_shuffled = text_data_target[shuffled_ix]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b107bb0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vocabulary Size: 8630\n", + "80-20 Train Test split: 4459 -- 1115\n" + ] + } + ], + "source": [ "# Split train/test set\n", - "ix_cutoff = int(len(y_shuffled)*0.80)\n", + "ix_cutoff = int(len(y_shuffled) * 0.80)\n", "x_train, x_test = x_shuffled[:ix_cutoff], x_shuffled[ix_cutoff:]\n", - "print(x_train)\n", "y_train, y_test = y_shuffled[:ix_cutoff], y_shuffled[ix_cutoff:]\n", - "vocab_size = len(vocab_processor.word_counts)\n", + "vocab_size = len(tokenizer.word_index) + 1\n", "print(\"Vocabulary Size: {:d}\".format(vocab_size))\n", - "print(\"80-20 Train Test split: {:d} -- {:d}\".format(len(y_train), len(y_test)))\n", - "\n", - "# Create placeholders\n", - "x_data = tf.placeholder(tf.int32, [None, max_sequence_length])\n", - "y_output = tf.placeholder(tf.int32, [None])\n", - "\n", - "# Create embedding\n", - "embedding_mat = tf.Variable(tf.random_uniform([vocab_size+1, embedding_size], -1.0, 1.0))\n", - "embedding_output = tf.nn.embedding_lookup(embedding_mat, x_data)\n", - "\n", - "# Define the RNN cell\n", - "# tensorflow change >= 1.0, rnn is put into tensorflow.contrib directory. Prior version not test.\n", - "cell = tf.nn.rnn_cell.BasicRNNCell(num_units=rnn_size)\n", - "\n", - "output, state = tf.nn.dynamic_rnn(cell, embedding_output, dtype=tf.float32)\n", - "output = tf.nn.dropout(output, dropout_keep_prob)\n", - "\n", - "# Get output of RNN sequence\n", - "output = tf.transpose(output, [1, 0, 2])\n", - "last = tf.gather(output, int(output.get_shape()[0]) - 1)\n", - "\n", - "weight = tf.Variable(tf.truncated_normal([rnn_size, 2], stddev=0.1))\n", - "bias = tf.Variable(tf.constant(0.1, shape=[2]))\n", - "logits_out = tf.matmul(last, weight) + bias\n", - "\n", - "\n", - "# Loss function\n", - "losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_out, labels=y_output)\n", - "loss = tf.reduce_mean(losses)\n", - "print(loss)\n", - "accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits_out, 1), tf.cast(y_output, tf.int64)), tf.float32))\n", - "print(accuracy)\n", - "optimizer = tf.train.RMSPropOptimizer(learning_rate)\n", - "train_step = optimizer.minimize(loss)\n", - "init = tf.global_variables_initializer()\n", - "sess.run(init)\n", - "\n", - "train_loss = []\n", - "test_loss = []\n", - "train_accuracy = []\n", - "test_accuracy = []\n", - "# Start training\n", - "for epoch in range(epochs):\n", - " # Shuffle training data\n", - " shuffled_ix = np.random.permutation(np.arange(len(x_train)))\n", - " x_train = x_train[shuffled_ix]\n", - " y_train = y_train[shuffled_ix]\n", - " num_batches = int(len(x_train)/batch_size) + 1\n", - " # TO DO CALCULATE GENERATIONS ExACTLY\n", - " for i in range(num_batches):\n", - " # Select train data\n", - " min_ix = i * batch_size\n", - " max_ix = np.min([len(x_train), ((i+1) * batch_size)])\n", - " x_train_batch = x_train[min_ix:max_ix]\n", - " y_train_batch = y_train[min_ix:max_ix]\n", - " max_len = max([len(x) for x in x_train_batch])\n", - " x_train_batch = np.array([np.pad(x, (0, max_len - len(x)), 'constant') for x in x_train_batch])\n", - " # Run train step\n", - " train_dict = {x_data: x_train_batch, y_output: y_train_batch, dropout_keep_prob:0.5}\n", - " sess.run(train_step, feed_dict=train_dict)\n", - " # Run loss and accuracy for training\n", - " train_dict = {x_data: x_train, y_output: y_train, dropout_keep_prob:1.0}\n", - " temp_train_loss, temp_train_acc = sess.run([loss, accuracy], feed_dict=train_dict)\n", - " train_loss.append(temp_train_loss)\n", - " train_accuracy.append(temp_train_acc)\n", - " # Run Eval Step\n", - " test_dict = {x_data: x_test, y_output: y_test, dropout_keep_prob:1.0}\n", - " temp_test_loss, temp_test_acc = sess.run([loss, accuracy], feed_dict=test_dict)\n", - " test_loss.append(temp_test_loss)\n", - " test_accuracy.append(temp_test_acc)\n", - " print('Epoch: {}, Test Loss: {:.2}, Test Acc: {:.2}'.format(epoch+1, temp_test_loss, temp_test_acc))\n", - " \n", - "# Plot loss over time\n", - "epoch_seq = np.arange(1, epochs+1)\n", - "plt.plot(epoch_seq, train_loss, 'k--', label='Train Set')\n", - "plt.plot(epoch_seq, test_loss, 'r-', label='Test Set')\n", - "plt.title('Softmax Loss')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Softmax Loss')\n", - "plt.legend(loc='upper left')\n", - "plt.show()\n", + "print(\"80-20 Train Test split: {:d} -- {:d}\".format(len(y_train), len(y_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fdd3ca65", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the model using the Sequential API\n", + "embedding_size = 50\n", + "model = tf.keras.Sequential(\n", + " [\n", + " tf.keras.layers.Embedding(\n", + " input_dim=vocab_size,\n", + " output_dim=embedding_size,\n", + " input_length=max_sequence_length,\n", + " ),\n", + " tf.keras.layers.SimpleRNN(units=10),\n", + " tf.keras.layers.Dropout(0.5),\n", + " tf.keras.layers.Dense(units=2, activation=\"softmax\"),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "34b221e2", + "metadata": {}, + "outputs": [], + "source": [ + "# Compile the model\n", + "model.compile(\n", + " optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0005),\n", + " loss=\"sparse_categorical_crossentropy\",\n", + " metrics=[\"accuracy\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e7bef8d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "15/15 [==============================] - 3s 65ms/step - loss: 0.5753 - accuracy: 0.7575 - val_loss: 0.4707 - val_accuracy: 0.8756\n", + "Epoch 2/20\n", + "15/15 [==============================] - 0s 33ms/step - loss: 0.4656 - accuracy: 0.8433 - val_loss: 0.3906 - val_accuracy: 0.9283\n", + "Epoch 3/20\n", + "15/15 [==============================] - 0s 24ms/step - loss: 0.3762 - accuracy: 0.9162 - val_loss: 0.3093 - val_accuracy: 0.9574\n", + "Epoch 4/20\n", + "15/15 [==============================] - 0s 9ms/step - loss: 0.3103 - accuracy: 0.9422 - val_loss: 0.2595 - val_accuracy: 0.9652\n", + "Epoch 5/20\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.2693 - accuracy: 0.9498 - val_loss: 0.2225 - val_accuracy: 0.9664\n", + "Epoch 6/20\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.2285 - accuracy: 0.9686 - val_loss: 0.1987 - val_accuracy: 0.9664\n", + "Epoch 7/20\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.2024 - accuracy: 0.9795 - val_loss: 0.1820 - val_accuracy: 0.9619\n", + "Epoch 8/20\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.1825 - accuracy: 0.9748 - val_loss: 0.1675 - val_accuracy: 0.9630\n", + "Epoch 9/20\n", + "15/15 [==============================] - 0s 9ms/step - loss: 0.1647 - accuracy: 0.9821 - val_loss: 0.1631 - val_accuracy: 0.9608\n", + "Epoch 10/20\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1546 - accuracy: 0.9837 - val_loss: 0.1623 - val_accuracy: 0.9574\n", + "Epoch 11/20\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1400 - accuracy: 0.9865 - val_loss: 0.1622 - val_accuracy: 0.9552\n", + "Epoch 12/20\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1302 - accuracy: 0.9868 - val_loss: 0.1632 - val_accuracy: 0.9552\n", + "Epoch 13/20\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1285 - accuracy: 0.9865 - val_loss: 0.1640 - val_accuracy: 0.9540\n", + "Epoch 14/20\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.1194 - accuracy: 0.9871 - val_loss: 0.1579 - val_accuracy: 0.9552\n", + "Epoch 15/20\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1190 - accuracy: 0.9874 - val_loss: 0.1647 - val_accuracy: 0.9518\n", + "Epoch 16/20\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.1103 - accuracy: 0.9874 - val_loss: 0.1596 - val_accuracy: 0.9563\n", + "Epoch 17/20\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1033 - accuracy: 0.9879 - val_loss: 0.1530 - val_accuracy: 0.9585\n", + "Epoch 18/20\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0954 - accuracy: 0.9905 - val_loss: 0.1611 - val_accuracy: 0.9552\n", + "Epoch 19/20\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0937 - accuracy: 0.9896 - val_loss: 0.1640 - val_accuracy: 0.9552\n", + "Epoch 20/20\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.0924 - accuracy: 0.9907 - val_loss: 0.1848 - val_accuracy: 0.9484\n" + ] + } + ], + "source": [ + "# Train the model\n", + "epochs = 20\n", + "batch_size = 250\n", "\n", - "# Plot accuracy over time\n", - "plt.plot(epoch_seq, train_accuracy, 'k--', label='Train Set')\n", - "plt.plot(epoch_seq, test_accuracy, 'r-', label='Test Set')\n", - "plt.title('Test Accuracy')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Accuracy')\n", - "plt.legend(loc='upper left')\n", - "plt.show()" + "history = model.fit(\n", + " x_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.2\n", + ")" ] }, { - "cell_type": "markdown", - "id": "3bc90f40", + "cell_type": "code", + "execution_count": 11, + "id": "e8d94dcc", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## Your turn! 🚀\n", + "# Plot loss and accuracy over time\n", + "plt.plot(history.history[\"loss\"], label=\"Train Set\")\n", + "plt.plot(history.history[\"val_loss\"], label=\"Validation Set\")\n", + "plt.title(\"Softmax Loss\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"Softmax Loss\")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.show()\n", "\n", - "Practice the Recurrent Neural Networks by following this TBD." + "plt.plot(history.history[\"accuracy\"], label=\"Train Set\")\n", + "plt.plot(history.history[\"val_accuracy\"], label=\"Validation Set\")\n", + "plt.title(\"Test Accuracy\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "863d18f5", + "id": "3bc90f40", "metadata": {}, "source": [ - "## Self study\n", + "## Your turn! 🚀\n", "\n", - "TBD" + "You can practice your rnn skills by following the assignment [google stock price prediction rnn](../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.ipynb)" ] }, { @@ -488,7 +518,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/_sources/deep-learning/time-series.ipynb b/_sources/deep-learning/time-series.ipynb index 2947c75e1b..35bd8f304b 100644 --- a/_sources/deep-learning/time-series.ipynb +++ b/_sources/deep-learning/time-series.ipynb @@ -1700,12 +1700,18 @@ "\n", "## Your turn! 🚀\n", "\n", - "TBD.\n", + "You can practice your time series skills by following the assignment [time series forecasting assignment](../assignments/deep-learning/time-series-forecasting-assignment.ipynb)\n", "\n", "## Acknowledgments\n", "\n", "Thanks to [kaggle](https://www.kaggle.com/) for creating the open-source course [Time Series](https://www.kaggle.com/learn/time-series). It inspires the majority of the content in this chapter.\n" ] + }, + { + "cell_type": "markdown", + "id": "f258c933", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/assignments/README.html b/assignments/README.html index 96b07d8a02..91b898373f 100644 --- a/assignments/README.html +++ b/assignments/README.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1. + + 40.89. Introduction + +
  2. - 40.89. Study the solvers + 40.90. Study the solvers
  3. - 40.90. Build classification models + 40.91. Build classification models
  4. - 40.91. Build Classification Model + 40.92. Build Classification Model
  5. - 40.92. Parameter play + 40.93. Parameter play
  6. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  7. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  8. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    9. +
  9. + + 40.99. Image classification
  10. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  11. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  12. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  13. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  14. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  15. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  16. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  17. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  18. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  19. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  20. - 40.116. Art by gan + 40.118. Art by gan
  21. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    22. +
  22. + + 40.121. Comparing edge-based and region-based segmentation
  23. - 40.119. Summary + 40.122. Summary
  24. - 40.120. Car Object Detection + 40.123. Car Object Detection
  25. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    26. +
  26. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  27. + + 40.89. Introduction + +
  28. - 40.89. Study the solvers + 40.90. Study the solvers
  29. - 40.90. Build classification models + 40.91. Build classification models
  30. - 40.91. Build Classification Model + 40.92. Build Classification Model
  31. - 40.92. Parameter play + 40.93. Parameter play
  32. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  33. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  34. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    35. +
  35. + + 40.99. Image classification
  36. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  37. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  38. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  39. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  40. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  41. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  42. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  43. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  44. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  45. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  46. - 40.116. Art by gan + 40.118. Art by gan
  47. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    48. +
  48. + + 40.121. Comparing edge-based and region-based segmentation
  49. - 40.119. Summary + 40.122. Summary
  50. - 40.120. Car Object Detection + 40.123. Car Object Detection
  51. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    52. +
  52. + + 40.126. Getting Start NLP with classification task
    53. diff --git a/assignments/data-science/analyzing-data.html b/assignments/data-science/analyzing-data.html index 77a97545c8..17478978d9 100644 --- a/assignments/data-science/analyzing-data.html +++ b/assignments/data-science/analyzing-data.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  53. + + 40.89. Introduction + +
  54. - 40.89. Study the solvers + 40.90. Study the solvers
  55. - 40.90. Build classification models + 40.91. Build classification models
  56. - 40.91. Build Classification Model + 40.92. Build Classification Model
  57. - 40.92. Parameter play + 40.93. Parameter play
  58. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  59. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  60. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    61. +
  61. + + 40.99. Image classification
  62. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  63. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  64. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  65. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  66. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  67. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  68. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  69. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  70. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  71. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  72. - 40.116. Art by gan + 40.118. Art by gan
  73. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    74. +
  74. + + 40.121. Comparing edge-based and region-based segmentation
  75. - 40.119. Summary + 40.122. Summary
  76. - 40.120. Car Object Detection + 40.123. Car Object Detection
  77. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    78. +
  78. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  79. + + 40.89. Introduction + +
  80. - 40.89. Study the solvers + 40.90. Study the solvers
  81. - 40.90. Build classification models + 40.91. Build classification models
  82. - 40.91. Build Classification Model + 40.92. Build Classification Model
  83. - 40.92. Parameter play + 40.93. Parameter play
  84. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  85. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  86. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    87. +
  87. + + 40.99. Image classification
  88. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  89. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  90. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  91. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  92. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  93. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  94. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  95. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  96. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  97. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  98. - 40.116. Art by gan + 40.118. Art by gan
  99. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    100. +
  100. + + 40.121. Comparing edge-based and region-based segmentation
  101. - 40.119. Summary + 40.122. Summary
  102. - 40.120. Car Object Detection + 40.123. Car Object Detection
  103. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    104. +
  104. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  105. + + 40.89. Introduction + +
  106. - 40.89. Study the solvers + 40.90. Study the solvers
  107. - 40.90. Build classification models + 40.91. Build classification models
  108. - 40.91. Build Classification Model + 40.92. Build Classification Model
  109. - 40.92. Parameter play + 40.93. Parameter play
  110. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  111. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  112. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    113. +
  113. + + 40.99. Image classification
  114. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  115. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  116. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  117. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  118. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  119. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  120. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  121. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  122. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  123. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  124. - 40.116. Art by gan + 40.118. Art by gan
  125. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    126. +
  126. + + 40.121. Comparing edge-based and region-based segmentation
  127. - 40.119. Summary + 40.122. Summary
  128. - 40.120. Car Object Detection + 40.123. Car Object Detection
  129. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    130. +
  130. + + 40.126. Getting Start NLP with classification task
    131. diff --git a/assignments/data-science/build-your-own-custom-vis.html b/assignments/data-science/build-your-own-custom-vis.html index 702a95fe68..a5e53858c3 100644 --- a/assignments/data-science/build-your-own-custom-vis.html +++ b/assignments/data-science/build-your-own-custom-vis.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  131. + + 40.89. Introduction + +
  132. - 40.89. Study the solvers + 40.90. Study the solvers
  133. - 40.90. Build classification models + 40.91. Build classification models
  134. - 40.91. Build Classification Model + 40.92. Build Classification Model
  135. - 40.92. Parameter play + 40.93. Parameter play
  136. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  137. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  138. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    139. +
  139. + + 40.99. Image classification
  140. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  141. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  142. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  143. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  144. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  145. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  146. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  147. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  148. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  149. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  150. - 40.116. Art by gan + 40.118. Art by gan
  151. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    152. +
  152. + + 40.121. Comparing edge-based and region-based segmentation
  153. - 40.119. Summary + 40.122. Summary
  154. - 40.120. Car Object Detection + 40.123. Car Object Detection
  155. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    156. +
  156. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  157. + + 40.89. Introduction + +
  158. - 40.89. Study the solvers + 40.90. Study the solvers
  159. - 40.90. Build classification models + 40.91. Build classification models
  160. - 40.91. Build Classification Model + 40.92. Build Classification Model
  161. - 40.92. Parameter play + 40.93. Parameter play
  162. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  163. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  164. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    165. +
  165. + + 40.99. Image classification
  166. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  167. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  168. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  169. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  170. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  171. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  172. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  173. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  174. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  175. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  176. - 40.116. Art by gan + 40.118. Art by gan
  177. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    178. +
  178. + + 40.121. Comparing edge-based and region-based segmentation
  179. - 40.119. Summary + 40.122. Summary
  180. - 40.120. Car Object Detection + 40.123. Car Object Detection
  181. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    182. +
  182. + + 40.126. Getting Start NLP with classification task
    183. diff --git a/assignments/data-science/data-preparation.html b/assignments/data-science/data-preparation.html index 196b614e7e..e8501dbc15 100644 --- a/assignments/data-science/data-preparation.html +++ b/assignments/data-science/data-preparation.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  183. + + 40.89. Introduction + +
  184. - 40.89. Study the solvers + 40.90. Study the solvers
  185. - 40.90. Build classification models + 40.91. Build classification models
  186. - 40.91. Build Classification Model + 40.92. Build Classification Model
  187. - 40.92. Parameter play + 40.93. Parameter play
  188. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  189. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  190. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    191. +
  191. + + 40.99. Image classification
  192. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  193. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  194. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  195. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  196. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  197. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  198. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  199. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  200. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  201. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  202. - 40.116. Art by gan + 40.118. Art by gan
  203. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    204. +
  204. + + 40.121. Comparing edge-based and region-based segmentation
  205. - 40.119. Summary + 40.122. Summary
  206. - 40.120. Car Object Detection + 40.123. Car Object Detection
  207. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    208. +
  208. + + 40.126. Getting Start NLP with classification task
    209. diff --git a/assignments/data-science/data-processing-in-python.html b/assignments/data-science/data-processing-in-python.html index 66f846f4cc..bf225e8b7d 100644 --- a/assignments/data-science/data-processing-in-python.html +++ b/assignments/data-science/data-processing-in-python.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  209. + + 40.89. Introduction + +
  210. - 40.89. Study the solvers + 40.90. Study the solvers
  211. - 40.90. Build classification models + 40.91. Build classification models
  212. - 40.91. Build Classification Model + 40.92. Build Classification Model
  213. - 40.92. Parameter play + 40.93. Parameter play
  214. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  215. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  216. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    217. +
  217. + + 40.99. Image classification
  218. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  219. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  220. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  221. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  222. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  223. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  224. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  225. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  226. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  227. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  228. - 40.116. Art by gan + 40.118. Art by gan
  229. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    230. +
  230. + + 40.121. Comparing edge-based and region-based segmentation
  231. - 40.119. Summary + 40.122. Summary
  232. - 40.120. Car Object Detection + 40.123. Car Object Detection
  233. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    234. +
  234. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  235. + + 40.89. Introduction + +
  236. - 40.89. Study the solvers + 40.90. Study the solvers
  237. - 40.90. Build classification models + 40.91. Build classification models
  238. - 40.91. Build Classification Model + 40.92. Build Classification Model
  239. - 40.92. Parameter play + 40.93. Parameter play
  240. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  241. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  242. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    243. +
  243. + + 40.99. Image classification
  244. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  245. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  246. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  247. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  248. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  249. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  250. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  251. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  252. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  253. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  254. - 40.116. Art by gan + 40.118. Art by gan
  255. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    256. +
  256. + + 40.121. Comparing edge-based and region-based segmentation
  257. - 40.119. Summary + 40.122. Summary
  258. - 40.120. Car Object Detection + 40.123. Car Object Detection
  259. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    260. +
  260. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  261. + + 40.89. Introduction + +
  262. - 40.89. Study the solvers + 40.90. Study the solvers
  263. - 40.90. Build classification models + 40.91. Build classification models
  264. - 40.91. Build Classification Model + 40.92. Build Classification Model
  265. - 40.92. Parameter play + 40.93. Parameter play
  266. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  267. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  268. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    269. +
  269. + + 40.99. Image classification
  270. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  271. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  272. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  273. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  274. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  275. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  276. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  277. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  278. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  279. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  280. - 40.116. Art by gan + 40.118. Art by gan
  281. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    282. +
  282. + + 40.121. Comparing edge-based and region-based segmentation
  283. - 40.119. Summary + 40.122. Summary
  284. - 40.120. Car Object Detection + 40.123. Car Object Detection
  285. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    286. +
  286. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  287. + + 40.89. Introduction + +
  288. - 40.89. Study the solvers + 40.90. Study the solvers
  289. - 40.90. Build classification models + 40.91. Build classification models
  290. - 40.91. Build Classification Model + 40.92. Build Classification Model
  291. - 40.92. Parameter play + 40.93. Parameter play
  292. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  293. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  294. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    295. +
  295. + + 40.99. Image classification
  296. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  297. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  298. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  299. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  300. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  301. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  302. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  303. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  304. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  305. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  306. - 40.116. Art by gan + 40.118. Art by gan
  307. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    308. +
  308. + + 40.121. Comparing edge-based and region-based segmentation
  309. - 40.119. Summary + 40.122. Summary
  310. - 40.120. Car Object Detection + 40.123. Car Object Detection
  311. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    312. +
  312. + + 40.126. Getting Start NLP with classification task
    313. diff --git a/assignments/data-science/displaying-airport-data.html b/assignments/data-science/displaying-airport-data.html index d4111b697b..6192f3ae34 100644 --- a/assignments/data-science/displaying-airport-data.html +++ b/assignments/data-science/displaying-airport-data.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  313. + + 40.89. Introduction + +
  314. - 40.89. Study the solvers + 40.90. Study the solvers
  315. - 40.90. Build classification models + 40.91. Build classification models
  316. - 40.91. Build Classification Model + 40.92. Build Classification Model
  317. - 40.92. Parameter play + 40.93. Parameter play
  318. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  319. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  320. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    321. +
  321. + + 40.99. Image classification
  322. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  323. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  324. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  325. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  326. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  327. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  328. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  329. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  330. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  331. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  332. - 40.116. Art by gan + 40.118. Art by gan
  333. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    334. +
  334. + + 40.121. Comparing edge-based and region-based segmentation
  335. - 40.119. Summary + 40.122. Summary
  336. - 40.120. Car Object Detection + 40.123. Car Object Detection
  337. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
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  338. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

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  339. + + 40.89. Introduction + +
  340. - 40.89. Study the solvers + 40.90. Study the solvers
  341. - 40.90. Build classification models + 40.91. Build classification models
  342. - 40.91. Build Classification Model + 40.92. Build Classification Model
  343. - 40.92. Parameter play + 40.93. Parameter play
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  345. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  346. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    347. +
  347. + + 40.99. Image classification
  348. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  349. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  350. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  351. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  352. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  353. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  354. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  355. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  356. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  357. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  358. - 40.116. Art by gan + 40.118. Art by gan
  359. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    360. +
  360. + + 40.121. Comparing edge-based and region-based segmentation
  361. - 40.119. Summary + 40.122. Summary
  362. - 40.120. Car Object Detection + 40.123. Car Object Detection
  363. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    364. +
  364. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  365. + + 40.89. Introduction + +
  366. - 40.89. Study the solvers + 40.90. Study the solvers
  367. - 40.90. Build classification models + 40.91. Build classification models
  368. - 40.91. Build Classification Model + 40.92. Build Classification Model
  369. - 40.92. Parameter play + 40.93. Parameter play
  370. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  371. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  372. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    373. +
  373. + + 40.99. Image classification
  374. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  375. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  376. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  377. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  378. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  379. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  380. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  381. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  382. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  383. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  384. - 40.116. Art by gan + 40.118. Art by gan
  385. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    386. +
  386. + + 40.121. Comparing edge-based and region-based segmentation
  387. - 40.119. Summary + 40.122. Summary
  388. - 40.120. Car Object Detection + 40.123. Car Object Detection
  389. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    390. +
  390. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  391. + + 40.89. Introduction + +
  392. - 40.89. Study the solvers + 40.90. Study the solvers
  393. - 40.90. Build classification models + 40.91. Build classification models
  394. - 40.91. Build Classification Model + 40.92. Build Classification Model
  395. - 40.92. Parameter play + 40.93. Parameter play
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  397. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  398. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    399. +
  399. + + 40.99. Image classification
  400. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  401. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  402. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  403. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  404. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  405. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  406. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  407. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  408. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  409. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  410. - 40.116. Art by gan + 40.118. Art by gan
  411. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    412. +
  412. + + 40.121. Comparing edge-based and region-based segmentation
  413. - 40.119. Summary + 40.122. Summary
  414. - 40.120. Car Object Detection + 40.123. Car Object Detection
  415. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    416. +
  416. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  417. + + 40.89. Introduction + +
  418. - 40.89. Study the solvers + 40.90. Study the solvers
  419. - 40.90. Build classification models + 40.91. Build classification models
  420. - 40.91. Build Classification Model + 40.92. Build Classification Model
  421. - 40.92. Parameter play + 40.93. Parameter play
  422. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  423. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  424. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    425. +
  425. + + 40.99. Image classification
  426. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  427. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  428. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  429. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  430. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  431. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  432. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  433. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  434. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  435. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  436. - 40.116. Art by gan + 40.118. Art by gan
  437. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    438. +
  438. + + 40.121. Comparing edge-based and region-based segmentation
  439. - 40.119. Summary + 40.122. Summary
  440. - 40.120. Car Object Detection + 40.123. Car Object Detection
  441. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    442. +
  442. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  443. + + 40.89. Introduction + +
  444. - 40.89. Study the solvers + 40.90. Study the solvers
  445. - 40.90. Build classification models + 40.91. Build classification models
  446. - 40.91. Build Classification Model + 40.92. Build Classification Model
  447. - 40.92. Parameter play + 40.93. Parameter play
  448. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  449. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  450. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    451. +
  451. + + 40.99. Image classification
  452. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  453. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  454. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  455. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  456. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  457. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  458. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  459. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  460. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  461. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  462. - 40.116. Art by gan + 40.118. Art by gan
  463. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    464. +
  464. + + 40.121. Comparing edge-based and region-based segmentation
  465. - 40.119. Summary + 40.122. Summary
  466. - 40.120. Car Object Detection + 40.123. Car Object Detection
  467. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    468. +
  468. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  469. + + 40.89. Introduction + +
  470. - 40.89. Study the solvers + 40.90. Study the solvers
  471. - 40.90. Build classification models + 40.91. Build classification models
  472. - 40.91. Build Classification Model + 40.92. Build Classification Model
  473. - 40.92. Parameter play + 40.93. Parameter play
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  475. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  476. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    477. +
  477. + + 40.99. Image classification
  478. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  479. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  480. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  481. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  482. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  483. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  484. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  485. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  486. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  487. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  488. - 40.116. Art by gan + 40.118. Art by gan
  489. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    490. +
  490. + + 40.121. Comparing edge-based and region-based segmentation
  491. - 40.119. Summary + 40.122. Summary
  492. - 40.120. Car Object Detection + 40.123. Car Object Detection
  493. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    494. +
  494. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  495. + + 40.89. Introduction + +
  496. - 40.89. Study the solvers + 40.90. Study the solvers
  497. - 40.90. Build classification models + 40.91. Build classification models
  498. - 40.91. Build Classification Model + 40.92. Build Classification Model
  499. - 40.92. Parameter play + 40.93. Parameter play
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  501. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  502. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    503. +
  503. + + 40.99. Image classification
  504. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  505. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  506. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  507. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  508. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  509. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  510. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  511. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  512. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  513. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  514. - 40.116. Art by gan + 40.118. Art by gan
  515. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    516. +
  516. + + 40.121. Comparing edge-based and region-based segmentation
  517. - 40.119. Summary + 40.122. Summary
  518. - 40.120. Car Object Detection + 40.123. Car Object Detection
  519. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    520. +
  520. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  521. + + 40.89. Introduction + +
  522. - 40.89. Study the solvers + 40.90. Study the solvers
  523. - 40.90. Build classification models + 40.91. Build classification models
  524. - 40.91. Build Classification Model + 40.92. Build Classification Model
  525. - 40.92. Parameter play + 40.93. Parameter play
  526. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  527. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  528. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    529. +
  529. + + 40.99. Image classification
  530. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  531. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  532. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  533. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  534. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  535. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  536. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  537. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  538. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  539. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  540. - 40.116. Art by gan + 40.118. Art by gan
  541. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    542. +
  542. + + 40.121. Comparing edge-based and region-based segmentation
  543. - 40.119. Summary + 40.122. Summary
  544. - 40.120. Car Object Detection + 40.123. Car Object Detection
  545. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    546. +
  546. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  547. + + 40.89. Introduction + +
  548. - 40.89. Study the solvers + 40.90. Study the solvers
  549. - 40.90. Build classification models + 40.91. Build classification models
  550. - 40.91. Build Classification Model + 40.92. Build Classification Model
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  552. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  553. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  554. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    555. +
  555. + + 40.99. Image classification
  556. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  557. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  558. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  559. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  560. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  561. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  562. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  563. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  564. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  565. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  566. - 40.116. Art by gan + 40.118. Art by gan
  567. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    568. +
  568. + + 40.121. Comparing edge-based and region-based segmentation
  569. - 40.119. Summary + 40.122. Summary
  570. - 40.120. Car Object Detection + 40.123. Car Object Detection
  571. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
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  572. + + 40.126. Getting Start NLP with classification task
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    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  573. + + 40.89. Introduction + +
  574. - 40.89. Study the solvers + 40.90. Study the solvers
  575. - 40.90. Build classification models + 40.91. Build classification models
  576. - 40.91. Build Classification Model + 40.92. Build Classification Model
  577. - 40.92. Parameter play + 40.93. Parameter play
  578. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  579. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  580. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    581. +
  581. + + 40.99. Image classification
  582. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  583. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  584. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  585. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  586. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  587. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  588. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  589. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  590. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  591. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  592. - 40.116. Art by gan + 40.118. Art by gan
  593. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    594. +
  594. + + 40.121. Comparing edge-based and region-based segmentation
  595. - 40.119. Summary + 40.122. Summary
  596. - 40.120. Car Object Detection + 40.123. Car Object Detection
  597. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    598. +
  598. + + 40.126. Getting Start NLP with classification task
    599. diff --git a/assignments/data-science/nyc-taxi-data-in-winter-and-summer.html b/assignments/data-science/nyc-taxi-data-in-winter-and-summer.html index 09e96c6e8c..c9a1a493aa 100644 --- a/assignments/data-science/nyc-taxi-data-in-winter-and-summer.html +++ b/assignments/data-science/nyc-taxi-data-in-winter-and-summer.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  599. + + 40.89. Introduction + +
  600. - 40.89. Study the solvers + 40.90. Study the solvers
  601. - 40.90. Build classification models + 40.91. Build classification models
  602. - 40.91. Build Classification Model + 40.92. Build Classification Model
  603. - 40.92. Parameter play + 40.93. Parameter play
  604. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  605. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  606. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    607. +
  607. + + 40.99. Image classification
  608. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  609. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  610. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  611. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  612. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  613. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  614. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  615. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  616. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  617. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  618. - 40.116. Art by gan + 40.118. Art by gan
  619. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    620. +
  620. + + 40.121. Comparing edge-based and region-based segmentation
  621. - 40.119. Summary + 40.122. Summary
  622. - 40.120. Car Object Detection + 40.123. Car Object Detection
  623. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    624. +
  624. + + 40.126. Getting Start NLP with classification task
    625. diff --git a/assignments/data-science/small-diabetes-study.html b/assignments/data-science/small-diabetes-study.html index 393a1a7bfb..d89a37a0e2 100644 --- a/assignments/data-science/small-diabetes-study.html +++ b/assignments/data-science/small-diabetes-study.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  625. + + 40.89. Introduction + +
  626. - 40.89. Study the solvers + 40.90. Study the solvers
  627. - 40.90. Build classification models + 40.91. Build classification models
  628. - 40.91. Build Classification Model + 40.92. Build Classification Model
  629. - 40.92. Parameter play + 40.93. Parameter play
  630. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  631. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  632. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    633. +
  633. + + 40.99. Image classification
  634. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  635. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  636. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  637. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  638. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  639. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  640. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  641. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  642. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  643. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  644. - 40.116. Art by gan + 40.118. Art by gan
  645. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    646. +
  646. + + 40.121. Comparing edge-based and region-based segmentation
  647. - 40.119. Summary + 40.122. Summary
  648. - 40.120. Car Object Detection + 40.123. Car Object Detection
  649. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    650. +
  650. + + 40.126. Getting Start NLP with classification task
    651. diff --git a/assignments/data-science/soda-profits.html b/assignments/data-science/soda-profits.html index 781f096a1d..5732e0d8d7 100644 --- a/assignments/data-science/soda-profits.html +++ b/assignments/data-science/soda-profits.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  651. + + 40.89. Introduction + +
  652. - 40.89. Study the solvers + 40.90. Study the solvers
  653. - 40.90. Build classification models + 40.91. Build classification models
  654. - 40.91. Build Classification Model + 40.92. Build Classification Model
  655. - 40.92. Parameter play + 40.93. Parameter play
  656. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  657. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  658. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    659. +
  659. + + 40.99. Image classification
  660. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  661. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  662. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  663. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  664. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  665. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  666. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  667. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  668. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  669. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  670. - 40.116. Art by gan + 40.118. Art by gan
  671. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    672. +
  672. + + 40.121. Comparing edge-based and region-based segmentation
  673. - 40.119. Summary + 40.122. Summary
  674. - 40.120. Car Object Detection + 40.123. Car Object Detection
  675. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    676. +
  676. + + 40.126. Getting Start NLP with classification task
    677. diff --git a/assignments/data-science/tell-a-story.html b/assignments/data-science/tell-a-story.html index 92afd1d949..e89a7530ca 100644 --- a/assignments/data-science/tell-a-story.html +++ b/assignments/data-science/tell-a-story.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  677. + + 40.89. Introduction + +
  678. - 40.89. Study the solvers + 40.90. Study the solvers
  679. - 40.90. Build classification models + 40.91. Build classification models
  680. - 40.91. Build Classification Model + 40.92. Build Classification Model
  681. - 40.92. Parameter play + 40.93. Parameter play
  682. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  683. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  684. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    685. +
  685. + + 40.99. Image classification
  686. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  687. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  688. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  689. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  690. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  691. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  692. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  693. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  694. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  695. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  696. - 40.116. Art by gan + 40.118. Art by gan
  697. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    698. +
  698. + + 40.121. Comparing edge-based and region-based segmentation
  699. - 40.119. Summary + 40.122. Summary
  700. - 40.120. Car Object Detection + 40.123. Car Object Detection
  701. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    702. +
  702. + + 40.126. Getting Start NLP with classification task
    703. diff --git a/assignments/data-science/try-it-in-excel.html b/assignments/data-science/try-it-in-excel.html index 8f741a6da6..03bafa4f22 100644 --- a/assignments/data-science/try-it-in-excel.html +++ b/assignments/data-science/try-it-in-excel.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  703. + + 40.89. Introduction + +
  704. - 40.89. Study the solvers + 40.90. Study the solvers
  705. - 40.90. Build classification models + 40.91. Build classification models
  706. - 40.91. Build Classification Model + 40.92. Build Classification Model
  707. - 40.92. Parameter play + 40.93. Parameter play
  708. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  709. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  710. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    711. +
  711. + + 40.99. Image classification
  712. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  713. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  714. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  715. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  716. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  717. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  718. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  719. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  720. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  721. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  722. - 40.116. Art by gan + 40.118. Art by gan
  723. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    724. +
  724. + + 40.121. Comparing edge-based and region-based segmentation
  725. - 40.119. Summary + 40.122. Summary
  726. - 40.120. Car Object Detection + 40.123. Car Object Detection
  727. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    728. +
  728. + + 40.126. Getting Start NLP with classification task
    729. diff --git a/assignments/data-science/write-a-data-ethics-case-study.html b/assignments/data-science/write-a-data-ethics-case-study.html index 17e93fcf77..7e819dfdc0 100644 --- a/assignments/data-science/write-a-data-ethics-case-study.html +++ b/assignments/data-science/write-a-data-ethics-case-study.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  729. + + 40.89. Introduction + +
  730. - 40.89. Study the solvers + 40.90. Study the solvers
  731. - 40.90. Build classification models + 40.91. Build classification models
  732. - 40.91. Build Classification Model + 40.92. Build Classification Model
  733. - 40.92. Parameter play + 40.93. Parameter play
  734. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  735. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  736. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    737. +
  737. + + 40.99. Image classification
  738. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  739. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  740. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  741. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  742. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  743. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  744. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  745. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  746. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  747. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  748. - 40.116. Art by gan + 40.118. Art by gan
  749. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    750. +
  750. + + 40.121. Comparing edge-based and region-based segmentation
  751. - 40.119. Summary + 40.122. Summary
  752. - 40.120. Car Object Detection + 40.123. Car Object Detection
  753. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    754. +
  754. + + 40.126. Getting Start NLP with classification task
    755. diff --git a/assignments/deep-learning/autoencoder/autoencoder.html b/assignments/deep-learning/autoencoder/autoencoder.html index e42ae9eeeb..f81f57c752 100644 --- a/assignments/deep-learning/autoencoder/autoencoder.html +++ b/assignments/deep-learning/autoencoder/autoencoder.html @@ -6,7 +6,7 @@ - 40.104. Intro to Autoencoders — Ocademy Open Machine Learning Book + 40.106. Intro to Autoencoders — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  755. + + 40.89. Introduction + +
  756. - 40.89. Study the solvers + 40.90. Study the solvers
  757. - 40.90. Build classification models + 40.91. Build classification models
  758. - 40.91. Build Classification Model + 40.92. Build Classification Model
  759. - 40.92. Parameter play + 40.93. Parameter play
  760. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  761. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  762. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    763. +
  763. + + 40.99. Image classification
  764. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  765. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  766. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  767. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  768. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  769. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  770. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  771. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  772. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  773. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  774. - 40.116. Art by gan + 40.118. Art by gan
  775. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    776. +
  776. + + 40.121. Comparing edge-based and region-based segmentation
  777. - 40.119. Summary + 40.122. Summary
  778. - 40.120. Car Object Detection + 40.123. Car Object Detection
  779. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    780. +
  780. + + 40.126. Getting Start NLP with classification task
    781. @@ -1600,71 +1620,71 @@

    Ocademy Open Machine Learning Book

    @@ -1688,71 +1708,71 @@

    Contents

    @@ -1766,12 +1786,12 @@

    Contents

    -

    40.104. Intro to Autoencoders#

    +

    40.106. Intro to Autoencoders#

    This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.

    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.

    To learn more about autoencoders, please consider reading chapter 14 from Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

    -

    40.104.1. Import TensorFlow and other libraries#

    +

    40.106.1. Import TensorFlow and other libraries#

    import matplotlib.pyplot as plt
@@ -1790,7 +1810,7 @@ 
    40.104.1. Import TensorFlow and other li
    -

    40.104.2. Download models#

    +

    40.106.2. Download models#

    import os
@@ -1875,7 +1895,7 @@ 
    40.104.2. Download models
-
    40.104.3. Load the dataset#
    
+
    40.106.3. Load the dataset#
    
 
    To start, you will train the basic autoencoder using the Fashion MNIST dataset. Each image in this dataset is 28x28 pixels.
    
 
    
 
    
@@ -1898,7 +1918,7 @@ 
    40.104.3. Load the dataset
-
    40.104.4. First example: Basic autoencoder#
    
+
    40.106.4. First example: Basic autoencoder#
    
 
    Basic autoencoder results
    
 
    Define an autoencoder with two Dense layers: an encoder, which compresses the images into a 64 dimensional latent vector, and a decoder, that reconstructs the original image from the latent space.
    
 
    To define your model, use the Keras Model Subclassing API.
    
@@ -1995,7 +2015,7 @@ 
    40.104.4. First example: Basic autoencod
    -

    40.104.5. Second example: Image denoising#

    +

    40.106.5. Second example: Image denoising#

    Image denoising results

    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.

    Let’s reimport the dataset to omit the modifications made earlier.

    @@ -2056,7 +2076,7 @@

    40.104.5. Second example: Image denoisin

    -

    40.104.5.1. Define a convolutional autoencoder#

    +

    40.106.5.1. Define a convolutional autoencoder#

    In this example, you will train a convolutional autoencoder using Conv2D layers in the encoder, and Conv2DTranspose layers in the decoder.

    @@ -2204,15 +2224,15 @@

    40.104.5.1. Define a convolutional autoe

    -

    40.104.6. Third example: Anomaly detection#

    +

    40.106.6. Third example: Anomaly detection#

    -

    40.104.7. Overview#

    +

    40.106.7. Overview#

    In this example, you will train an autoencoder to detect anomalies on the ECG5000 dataset. This dataset contains 5,000 Electrocardiograms, each with 140 data points. You will use a simplified version of the dataset, where each example has been labeled either 0 (corresponding to an abnormal rhythm), or 1 (corresponding to a normal rhythm). You are interested in identifying the abnormal rhythms.

    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).

    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.

    -

    40.104.7.1. Load ECG data#

    +

    40.106.7.1. Load ECG data#

    The dataset you will use is based on one from timeseriesclassification.com.

    @@ -2510,7 +2530,7 @@

    40.104.7.1. Load ECG data -

    40.104.7.2. Build the model#

    +

    40.106.7.2. Build the model#

    class AnomalyDetector(Model):
@@ -2602,7 +2622,7 @@ 
    40.104.7.2. Build the model
-
    40.104.7.3. Detect anomalies#
    
+
    40.106.7.3. Detect anomalies#
    
 
    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.
    
 
    Plot the reconstruction error on normal ECGs from the training set
    
 
    
@@ -2693,11 +2713,11 @@ 
    40.104.7.3. Detect anomalies
-
    40.104.8. Next steps#
    
+
    40.106.8. Next steps#
    
 
    To learn more about anomaly detection with autoencoders, check out this excellent interactive example built with TensorFlow.js by Victor Dibia. For a real-world use case, you can learn how Airbus Detects Anomalies in ISS Telemetry Data using TensorFlow. To learn more about the basics, consider reading this blog post by François Chollet. For more details, check out chapter 14 from Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
    -

    40.104.9. Acknowledgments#

    +

    40.106.9. Acknowledgments#

    Thanks to TensorFlow Core for creating the open-source course autoencoder. It inspires the majority of the content in this chapter.

    @@ -2739,13 +2759,13 @@

    40.104.9. Acknowledgments

    previous

    -

    40.102. Google Stock Price Prediction RNN

    +

    40.104. Google Stock Price Prediction RNN

    next

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    40.105. Base/Denoising Autoencoder & Dimension Reduction

    +

    40.107. Base/Denoising Autoencoder & Dimension Reduction

     diff --git a/assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html b/assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html index 401488de1f..798090dc80 100644 --- a/assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html +++ b/assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html @@ -6,7 +6,7 @@ - 40.105. Base/Denoising Autoencoder & Dimension Reduction — Ocademy Open Machine Learning Book + 40.107. Base/Denoising Autoencoder & Dimension Reduction — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -100,8 +100,8 @@ - - + + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  781. + + 40.89. Introduction + +
  782. - 40.89. Study the solvers + 40.90. Study the solvers
  783. - 40.90. Build classification models + 40.91. Build classification models
  784. - 40.91. Build Classification Model + 40.92. Build Classification Model
  785. - 40.92. Parameter play + 40.93. Parameter play
  786. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  787. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  788. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    789. +
  789. + + 40.99. Image classification
  790. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  791. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  792. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  793. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  794. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  795. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  796. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  797. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  798. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  799. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  800. - 40.116. Art by gan + 40.118. Art by gan
  801. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    802. +
  802. + + 40.121. Comparing edge-based and region-based segmentation
  803. - 40.119. Summary + 40.122. Summary
  804. - 40.120. Car Object Detection + 40.123. Car Object Detection
  805. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    806. +
  806. + + 40.126. Getting Start NLP with classification task
    807. @@ -1602,110 +1622,110 @@

    Ocademy Open Machine Learning Book

    @@ -1729,110 +1749,110 @@

    Contents

    @@ -1846,9 +1866,9 @@

    Contents

    -

    40.105. Base/Denoising Autoencoder & Dimension Reduction#

    +

    40.107. Base/Denoising Autoencoder & Dimension Reduction#

    -

    40.105.1. Introduction#

    +

    40.107.1. Introduction#

    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.

    In this notebook, we will cover two autoencoders:

    @@ -1883,7 +1903,7 @@

    40.105.1. Introduction -

    40.105.2. Loading and Scaling Datasets#

    +

    40.107.2. Loading and Scaling Datasets#

    Train a basic autoencoder using the Fashon MNIST dataset. Each image in this dataset is 28x28 pixels. Inputs are scaled for training.

    @@ -1907,7 +1927,7 @@

    40.105.2. Loading and Scaling Datasets

    -

    40.105.3. Load model#

    +

    40.107.3. Load model#

    import os
@@ -1963,7 +1983,7 @@ 
    40.105.3. Load model
-
    40.105.3.1. Checking dataset by 2D plot#
    
+
    40.107.3.1. Checking dataset by 2D plot#
    
 
    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.
    
 
    
 
    
@@ -2023,7 +2043,7 @@ 
    40.105.3.1. Checking dataset by 2D plot<
 
    Ref: https://umap-learn.readthedocs.io/en
    -

    40.105.3.2. Checking dataset by 3D plot#

    +

    40.107.3.2. Checking dataset by 3D plot#

    import plotly.express as px
@@ -2054,9 +2074,9 @@ 
    40.105.3.2. Checking dataset by 3D plot<
    -

    40.105.4. Base Autoencoder#

    +

    40.107.4. Base Autoencoder#

    -

    40.105.4.1. Modeling#

    +

    40.107.4.1. Modeling#

    An autoencoder always consists of two parts: an encoder and a decoder.

    • Encoder (Recognition network): it transforms an input into an internal representation.

      • @@ -2100,7 +2120,7 @@

      40.105.4.1. Modeling -

      40.105.4.2. Training#

      +

      40.107.4.2. Training#

      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.

      @@ -2131,7 +2151,7 @@

      40.105.4.2. Training -

      40.105.4.3. Plotting the latent space after Dimension Reduction#

      +

      40.107.4.3. Plotting the latent space after Dimension Reduction#

      y_test = pd.DataFrame(y_test,columns=['class'])
@@ -2157,7 +2177,7 @@ 
      40.105.4.3. Plotting the latent space af
 
      The \(28*28\) dimension input is compressed into the \(7*7\) 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.
    -

    40.105.4.4. Checking results#

    +

    40.107.4.4. Checking results#

    n = 4
@@ -2190,7 +2210,7 @@ 
    40.105.4.4. Checking results
-
    40.105.5. Denoising Autoencoder#
    
+
    40.107.5. Denoising Autoencoder#
    
 
    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.
    
 
    
 
    
@@ -2218,7 +2238,7 @@ 
    40.105.5. Denoising Autoencoder
 
    
 
    
-
    40.105.5.1. Adding random noise to the image.#
    
+
    40.107.5.1. Adding random noise to the image.#
    
 
    
 
    
 
    noise_factor = 0.2
@@ -2233,7 +2253,7 @@ 
    40.105.5.1. Adding random noise to the i
 
    
 
    
 
    
-
    40.105.5.2. Plotting a noisy image.#
    
+
    40.107.5.2. Plotting a noisy image.#
    
 
    
 
    
 
    n = 4
@@ -2253,9 +2273,9 @@ 
    40.105.5.2. Plotting a noisy image.
 
    
 
    
-
    40.105.5.3. Checking Noisy Dataset using Demension Reduction#
    
+
    40.107.5.3. Checking Noisy Dataset using Demension Reduction#
    
 
    
-
    40.105.5.3.1. 1) Noisy Dataset#
    
+
    40.107.5.3.1. 1) Noisy Dataset#
    
 
    
 
    
 
    x_train_noisy_flat = x_train.reshape(x_train_noisy.shape[0], -1)
@@ -2287,7 +2307,7 @@ 
    40.105.5.3.1. 1) Noisy Dataset
-
    40.105.5.3.2. 2) Orignal Dataset#
    
+
    40.107.5.3.2. 2) Orignal Dataset#
    
 
    
 
    
 
    x_train_flat = x_train.reshape(x_train.shape[0], -1)
@@ -2316,7 +2336,7 @@ 
    40.105.5.3.2. 2) Orignal Dataset
 
    
 
    
-
    40.105.5.4. Modeling#
    
+
    40.107.5.4. Modeling#
    
 
    
 
    
 
    class Denoise(Model):
@@ -2351,7 +2371,7 @@ 
    40.105.5.4. Modeling
-
    40.105.5.5. Training#
    
+
    40.107.5.5. Training#
    
 
    
 
    
 
    autoencoder.fit(x_train_noisy, x_train,
@@ -2471,7 +2491,7 @@ 
    40.105.5.5. Training
-
    40.105.5.6. Checking results#
    
+
    40.107.5.6. Checking results#
    
 
    Plots both the noisy and denoised images generated by the autoencoder.
    
 
    
 
    
@@ -2513,7 +2533,7 @@ 
    40.105.5.6. Checking results
-
    40.105.6. Acknowledgments#
    
+
    40.107.6. Acknowledgments#
    
 
    Thanks to TOH SEOK KIM for creating the Kaggle open-source project Base/Denoising Autoencoder + Dimension Reduction. It inspires the majority of the content in this chapter.
    
 
    
 
    
@@ -2555,13 +2575,13 @@ 
    40.105.6. Acknowledgments
         
    
             
    previous
    
-            
    40.104. Intro to Autoencoders
    
+            
    40.106. Intro to Autoencoders
    
         
    
         
     
     
    
         
    next
    
-        
    40.106. Fun with Variational Autoencoders
    
+        
    40.108. Fun with Variational Autoencoders
    
     
    
     
         
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 2175d5259f..2e446a58c2 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 @@
     
     
 
-    40.106. Fun with Variational Autoencoders — Ocademy Open Machine Learning Book
+    40.108. Fun with Variational Autoencoders — Ocademy Open Machine Learning Book
     
   
   
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -98,8 +98,8 @@
     
     
     
-    
-    
+    
+    
     
     
     
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  807. 
+    
+     40.89. Introduction
+    
+   
    808. 
    
  808. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    809. 
    
  809. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    810. 
    
  810. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    811. 
    
  811. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    812. 
    
  812. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    813. 
    
  813. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    814. 
    
  814. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    815. 
+   
  815. 
+    
+     40.99. Image classification
     
    
    816. 
    
  816. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    817. 
    
  817. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    818. 
    
  818. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    819. 
    
  819. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    820. 
    
  820. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    821. 
    
  821. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    822. 
    
  822. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    823. 
    
  823. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    824. 
    
  824. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    825. 
    
  825. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    826. 
    
  826. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    827. 
    
  827. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    828. 
+   
  828. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    829. 
    
  829. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    830. 
    
  830. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    831. 
    
  831. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    832. 
+   
  832. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    833. 
   
@@ -1600,62 +1620,62 @@ 
    Ocademy Open Machine Learning Book
    
         
@@ -1679,62 +1699,62 @@ 
     Contents 
    
                             
@@ -1748,10 +1768,10 @@ 
     Contents 
    
               
    
                 
   
    
-
    40.106. Fun with Variational Autoencoders#
    
+
    40.108. Fun with Variational Autoencoders#
    
 
    This is a starter kernel to use Labelled Faces in the Wild (LFW) Dataset in order to maintain knowledge about main Autoencoder principles. PyTorch will be used for modelling.
    
 
    
-
    40.106.1. Fork it and give it an upvote.#
    
+
    40.108.1. Fork it and give it an upvote.#
    
 
    architecture
    
 
    Useful links:
    
 
      
@@ -1764,7 +1784,7 @@ 
      40.106.1. Fork it and give it an upvote.
 
    
 
    
 
    
-
    40.106.2. A bit of theory#
    
+
    40.108.2. A bit of theory#
    
 
    “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.
    
 
      
 
    1. 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.
      2. 
@@ -1806,7 +1826,7 @@ 
      40.106.2. A bit of theory
-
      40.106.3. Load datasets#
      
+
      40.108.3. Load datasets#
      
 
      
 
      
 
      datasets_url = "https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/variational-autoencoder-and-faces-generation/lfw-deepfunneled.zip"
@@ -1882,7 +1902,7 @@ 
      40.106.3. Load datasets
-
      40.106.4. Explore the data#
      
+
      40.108.4. Explore the data#
      
 
      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.
      
 
      
 
      
@@ -1927,7 +1947,7 @@ 
      40.106.4. Explore the data
-
      40.106.5. Prepare the dataset#
      
+
      40.108.5. Prepare the dataset#
      
 
      
 
        
 
      • 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.
        • 
@@ -2010,7 +2030,7 @@ 
        40.106.5. Prepare the dataset
-
        40.106.6. Building simple autoencoder#
        
+
        40.108.6. Building simple autoencoder#
        
 
        
 
        
 
        dim_z=100
@@ -2116,7 +2136,7 @@ 
        40.106.6. Building simple autoencoder
 
    
 
    
-
    40.106.7. Train autoencoder#
    
+
    40.108.7. Train autoencoder#
    
 
    
 
      
 
    • 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.
      • 
@@ -2282,7 +2302,7 @@ 
      40.106.7. Train autoencoder
-
      40.106.8. Sampling#
      
+
      40.108.8. Sampling#
      
 
      Let’s generate some samples from random vectors
      
 
      
 
      
@@ -2301,7 +2321,7 @@ 
      40.106.8. Sampling
 
    
 
    
-
    40.106.9. Adding smile and glasses#
    
+
    40.108.9. Adding smile and glasses#
    
 
    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.
    
 
    
 
    
@@ -2431,7 +2451,7 @@ 
    40.106.9. Adding smile and glassesWhile the concept is pretty straightforward the simple autoencoder have some disadvantages. Let’s explore them and try to do better.
    
 
    
 
    
-
    40.106.10. Variational autoencoder#
    
+
    40.108.10. Variational autoencoder#
    
 
    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 generate new image from the point decoder never met we’re getting the best image it can produce, but the quelity is not good enough.
    
 
    
 
    In other words the encoded vectors may not be continuous in the latent space.
    
@@ -2607,12 +2627,12 @@ 
    40.106.10. Variational autoencoder
 
    
 
    
-
    40.106.11. Conclusion#
    
+
    40.108.11. Conclusion#
    
 
    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.
    
 
    
 
    
-
    40.106.12. Acknowledgments#
    
+
    40.108.12. Acknowledgments#
    
 
    Thanks to SERGEI AVERKIEV for creating the Kaggle open-source project Variational Autoencoder and Faces Generation. It inspires the majority of the content in this chapter.
    
 
    
 
    
@@ -2654,13 +2674,13 @@ 
    40.106.12. Acknowledgments
         
    
             
    previous
    
-            
    40.105. Base/Denoising Autoencoder & Dimension Reduction
    
+            
    40.107. Base/Denoising Autoencoder & Dimension Reduction
    
         
    
         
     
     
    
         
    next
    
-        
    40.107. Time Series Forecasting Assignment
    
+        
    40.109. Time Series Forecasting Assignment
    
     
    
     
         
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 710634ff2d..85badd7a0f 100644
--- a/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html
+++ b/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html
@@ -6,7 +6,7 @@
     
     
 
-    40.93. How to choose cnn architecture mnist — Ocademy Open Machine Learning Book
+    40.94. How to choose cnn architecture mnist — Ocademy Open Machine Learning Book
     
   
   
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -98,8 +98,8 @@
     
     
     
-    
-    
+    
+    
     
     
     
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  833. 
+    
+     40.89. Introduction
+    
+   
    834. 
    
  834. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    835. 
    
  835. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    836. 
    
  836. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    837. 
    
  837. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    838. 
    
  838. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    839. 
    
  839. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    840. 
    
  840. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    841. 
+   
  841. 
+    
+     40.99. Image classification
     
    
    842. 
    
  842. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    843. 
    
  843. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    844. 
    
  844. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    845. 
    
  845. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    846. 
    
  846. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    847. 
    
  847. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    848. 
    
  848. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    849. 
    
  849. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    850. 
    
  850. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    851. 
    
  851. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    852. 
    
  852. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    853. 
    
  853. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    854. 
+   
  854. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    855. 
    
  855. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    856. 
    
  856. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    857. 
    
  857. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    858. 
+   
  858. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    859. 
   
@@ -1600,207 +1620,207 @@ 
    Ocademy Open Machine Learning Book
    
         
@@ -1824,207 +1844,207 @@ 
     Contents 
    
                             
@@ -2038,19 +2058,19 @@ 
     Contents 
    
               
    
                 
   
    
-
    40.93. How to choose cnn architecture mnist#
    
+
    40.94. How to choose cnn architecture mnist#
    
 
    
-
    40.93.1. What is the best CNN architecture for MNIST?#
    
+
    40.94.1. What is the best CNN architecture for MNIST?#
    
 
    There are so many choices for CNN architecture. How do we choose the best one? First we must define what best 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.
    
 
    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][https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist]. This kernel demostrates the experiments used to determine that kernel’s CNN architecture.
    
 
    
 
    
-
    40.93.2. Basic CNN structure#
    
+
    40.94.2. Basic CNN structure#
    
 
    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.
 extract
    
 
    
 
    
-
    40.93.3. Load libraries#
    
+
    40.94.3. Load libraries#
    
 
    
 
    
 
    import pandas as pd
@@ -2077,7 +2097,7 @@ 
    40.93.3. Load libraries
-
    40.93.4. Load the data#
    
+
    40.94.4. Load the data#
    
 
    
 
    
 
    train = pd.read_csv("https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/mnist_train.csv")
@@ -2088,7 +2108,7 @@ 
    40.93.4. Load the data
-
    40.93.5. Prepare data for neural network#
    
+
    40.94.5. Prepare data for neural network#
    
 
    
 
    
 
    Y_train = train["label"]
@@ -2109,7 +2129,7 @@ 
    40.93.5. Prepare data for neural network
 
    
 
    
 
    
-
    40.93.6. Global variables#
    
+
    40.94.6. Global variables#
    
 
    
 
    
 
    annealer = LearningRateScheduler(lambda x: 1e-3 * 0.95**x, verbose=0)
@@ -2120,7 +2140,7 @@ 
    40.93.6. Global variables
-
    40.93.7. 1. How many convolution-subsambling pairs?#
    
+
    40.94.7. 1. How many convolution-subsambling pairs?#
    
 
    First question, how many pairs of convolution-subsampling should we use? For example, our network could have 1, 2, or 3:
    
 
      
 
    • 784 - [24C5-P2] - 256 - 10
      • 
@@ -2130,10 +2150,10 @@ 
      40.93.7. 1. How many convolution-subsamb
 
      It’s typical to increase the number of feature maps for each subsequent pair as shown here.
      
 
    
 
    
-
    40.93.8. Experiment 1#
    
+
    40.94.8. Experiment 1#
    
 
    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.
    
 
    
-
    40.93.8.1. Build convolutional neural networks#
    
+
    40.94.8.1. Build convolutional neural networks#
    
 
    
 
    
 
    nets = 3
@@ -2169,7 +2189,7 @@ 
    40.93.8.1. Build convolutional neural ne
 
    
 
    
 
    
-
    40.93.8.2. Create validation set and train networks#
    
+
    40.94.8.2. Create validation set and train networks#
    
 
    
 
    
 
    X_train2, X_val2, Y_train2, Y_val2 = train_test_split(X_train, Y_train, test_size=0.333)
@@ -2200,7 +2220,7 @@ 
    40.93.8.2. Create validation set and tra
 
    
 
    
 
    
-
    40.93.8.3. Plot accuracies#
    
+
    40.94.8.3. Plot accuracies#
    
 
    
 
    
 
    plt.figure(figsize=(15, 5))
@@ -2219,12 +2239,12 @@ 
    40.93.8.3. Plot accuracies
-
    40.93.8.4. Summary#
    
+
    40.94.8.4. Summary#
    
 
    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.
    
 
    
 
    
 
    
-
    40.93.9. 2. How many feature maps?#
    
+
    40.94.9. 2. How many feature maps?#
    
 
    In the previous experiement, we decided that two pairs is sufficient. How many feature maps should we include? For example, we could do
    
 
      
 
    • 784 - [8C5-P2] - [16C5-P2] - 256 - 10
      • 
@@ -2236,9 +2256,9 @@ 
      40.93.9. 2. How many feature maps?
 
    
 
    
-
    40.93.10. Experiment 2#
    
+
    40.94.10. Experiment 2#
    
 
    
-
    40.93.10.1. Build convolutional neural networks#
    
+
    40.94.10.1. Build convolutional neural networks#
    
 
    
 
    
 
    nets = 6
@@ -2263,7 +2283,7 @@ 
    40.93.10.1. Build convolutional neural n
 
    
 
    
 
    
-
    40.93.10.2. Create validation set and train networks#
    
+
    40.94.10.2. Create validation set and train networks#
    
 
    
 
    
 
    X_train2, X_val2, Y_train2, Y_val2 = train_test_split(X_train, Y_train, test_size=0.333)
@@ -2294,7 +2314,7 @@ 
    40.93.10.2. Create validation set and tr
 
    
 
    
 
    
-
    40.93.10.3. Plot accuracies#
    
+
    40.94.10.3. Plot accuracies#
    
 
    
 
    
 
    plt.figure(figsize=(15, 5))
@@ -2313,12 +2333,12 @@ 
    40.93.10.3. Plot accuracies
-
    40.93.10.4. Summary#
    
+
    40.94.10.4. Summary#
    
 
    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.
    
 
    
 
    
 
    
-
    40.93.11. 3. How large a dense layer?#
    
+
    40.94.11. 3. How large a dense layer?#
    
 
    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
    
 
      
 
    • 784 - [32C5-P2] - [64C5-P2] - 0 - 10
      • 
@@ -2332,10 +2352,10 @@ 
      40.93.11. 3. How large a dense layer?
 
    
 
    
-
    40.93.12. Experiment 3#
    
+
    40.94.12. Experiment 3#
    
 
    
 
    
-
    40.93.13. Build convolutional neural networks#
    
+
    40.94.13. Build convolutional neural networks#
    
 
    
 
    
 
    nets = 8
@@ -2360,7 +2380,7 @@ 
    40.93.13. Build convolutional neural net
 
    
 
    
 
    
-
    40.93.14. Create validation set and train networks#
    
+
    40.94.14. Create validation set and train networks#
    
 
    
 
    
 
    X_train2, X_val2, Y_train2, Y_val2 = train_test_split(X_train, Y_train, test_size=0.333)
@@ -2392,7 +2412,7 @@ 
    40.93.14. Create validation set and trai
 
    
 
    
 
    
-
    40.93.15. Plot accuracies#
    
+
    40.94.15. Plot accuracies#
    
 
    
 
    
 
    plt.figure(figsize=(15, 5))
@@ -2410,22 +2430,22 @@ 
    40.93.15. Plot accuracies
-
    40.93.15.1. Summary#
    
+
    40.94.15.1. Summary#
    
 
    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.)
    
 
    
 
    
 
    
-
    40.93.16. 4. How much dropout?#
    
+
    40.94.16. 4. How much dropout?#
    
 
    Dropout will prevent our network from overfitting thus helping our network generalize better. How much dropout should we add after each layer?
    
 
      
 
    • 0%, 10%, 20%, 30%, 40%, 50%, 60%, or 70%
      • 
 
    
 
    
 
    
-
    40.93.17. Experiment 4#
    
+
    40.94.17. Experiment 4#
    
 
    
 
    
-
    40.93.18. Build convolutional neural networks#
    
+
    40.94.18. Build convolutional neural networks#
    
 
    
 
    
 
    nets = 8
@@ -2452,7 +2472,7 @@ 
    40.93.18. Build convolutional neural net
 
    
 
    
 
    
-
    40.93.19. Create validation set and train networks#
    
+
    40.94.19. Create validation set and train networks#
    
 
    
 
    
 
    X_train2, X_val2, Y_train2, Y_val2 = train_test_split(X_train, Y_train, test_size=0.333)
@@ -2484,7 +2504,7 @@ 
    40.93.19. Create validation set and trai
 
    
 
    
 
    
-
    40.93.20. Plot accuracies#
    
+
    40.94.20. Plot accuracies#
    
 
    
 
    
 
    plt.figure(figsize=(15, 5))
@@ -2503,11 +2523,11 @@ 
    40.93.20. Plot accuracies
-
    40.93.21. Summary#
    
+
    40.94.21. Summary#
    
 
    From this experiment, it appears that 40% dropout is the best.
    
 
    
 
    
-
    40.93.22. 5. Advanced features#
    
+
    40.94.22. 5. Advanced features#
    
 
    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
    
 
      
 
    • replace ‘32C5’ with ‘32C3-32C3’
      • 
@@ -2517,7 +2537,7 @@ 
      40.93.22. 5. Advanced features
-
      40.93.23. Build convolutional neural networks#
      
+
      40.94.23. Build convolutional neural networks#
      
 
      
 
      
 
      nets = 5
@@ -2636,7 +2656,7 @@ 
      40.93.23. Build convolutional neural net
 
      
 
    
 
    
-
    40.93.24. Create validation set and train networks#
    
+
    40.94.24. Create validation set and train networks#
    
 
    
 
    
 
    X_train2, X_val2, Y_train2, Y_val2 = train_test_split(X_train, Y_train, test_size=0.2)
@@ -2668,7 +2688,7 @@ 
    40.93.24. Create validation set and trai
 
    
 
    
 
    
-
    40.93.25. Create more training images via data augmentation and train network#
    
+
    40.94.25. Create more training images via data augmentation and train network#
    
 
    
 
    
 
    datagen = ImageDataGenerator(
@@ -2698,7 +2718,7 @@ 
    40.93.25. Create more training images vi
 
    
 
    
 
    
-
    40.93.26. Plot accuracies#
    
+
    40.94.26. Plot accuracies#
    
 
    
 
    
 
    plt.figure(figsize=(15, 5))
@@ -2716,12 +2736,12 @@ 
    40.93.26. Plot accuracies
-
    40.93.26.1. Summary#
    
+
    40.94.26.1. Summary#
    
 
    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 here.)
    -

    40.93.27. Conclusion#

    +

    40.94.27. Conclusion#

    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

    @@ -2771,13 +2791,13 @@

    40.94. Acknowledgments

    previous

    -

    40.92. Parameter play

    +

    40.93. Parameter play

    next

    -

    40.95. Sign Language Digits Classification with CNN

    +

    40.96. Sign Language Digits Classification with CNN

     diff --git a/assignments/deep-learning/cnn/image-classification.html b/assignments/deep-learning/cnn/image-classification.html new file mode 100644 index 0000000000..df11facb29 --- /dev/null +++ b/assignments/deep-learning/cnn/image-classification.html @@ -0,0 +1,2554 @@ + + + + + + + + + 40.99. Image classification — Ocademy Open Machine Learning Book + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Learn AI together, for free! At Ocademy.
    +
    + + + + + + +
    +
    + + + + + + + + + + +
    + +
    + +
    + + + + +
    +
    + + + + +
    +
    +
    + +
    + +
    +
    + + + +
    + +
    + +
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    + +
    + +
    +
    + + +
    +
    + + +
    +
    + Contents +
    + +
    +
    +
    +
    + +
    +

    Image classification

    + +
    + +
    +
    +
    + +
    + +
    +
    +
    # Install the necessary dependencies
+
+import os
+import sys
+!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython
+
    +
    +
    +
    +
    +

    40.99. Image classification#

    +

    This tutorial shows how to classify images of flowers using a tf.keras.Sequential model and load data using tf.keras.utils.image_dataset_from_directory. It demonstrates the following concepts:

    +
      +

    • Efficiently loading a dataset off disk.

      • +

    • Identifying overfitting and applying techniques to mitigate it, including data augmentation and dropout.

      • +
    +

    This tutorial follows a basic machine learning workflow:

    +
      +

    1. Examine and understand data

      2. +

    2. Build an input pipeline

      3. +

    3. Build the model

      4. +

    4. Train the model

      5. +

    5. Test the model

      6. +

    6. Improve the model and repeat the process

      7. +
    +

    In addition, the notebook demonstrates how to convert a saved model to a TensorFlow Lite model for on-device machine learning on mobile, embedded, and IoT devices.

    +
    +

    40.99.1. Setup#

    +

    Import TensorFlow and other necessary libraries:

    +
    +
    +
    import matplotlib.pyplot as plt
+import numpy as np
+import PIL
+import tensorflow as tf
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+
    +
    +
    +
    +
    +
    +

    40.99.2. Download and explore the dataset#

    +

    This tutorial uses a dataset of about 3,700 photos of flowers. The dataset contains five sub-directories, one per class:

    +
    flower_photo/
+  daisy/
+  dandelion/
+  roses/
+  sunflowers/
+  tulips/
+
    +
    +
    +
    +
    import pathlib
+
+dataset_url = "https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/flower_photos.tgz"
+data_dir = tf.keras.utils.get_file('flower_photos.tar', origin=dataset_url, extract=True)
+data_dir = pathlib.Path(data_dir).with_suffix('')
+
    +
    +
    +
    +

    After downloading, you should now have a copy of the dataset available. There are 3,670 total images:

    +
    +
    +
    image_count = len(list(data_dir.glob('*/*.jpg')))
+print(image_count)
+
    +
    +
    +
    +

    Here are some roses:

    +
    +
    +
    roses = list(data_dir.glob('roses/*'))
+PIL.Image.open(str(roses[0]))
+
    +
    +
    +
    +
    +
    +
    PIL.Image.open(str(roses[1]))
+
    +
    +
    +
    +

    And some tulips:

    +
    +
    +
    tulips = list(data_dir.glob('tulips/*'))
+PIL.Image.open(str(tulips[0]))
+
    +
    +
    +
    +
    +
    +
    PIL.Image.open(str(tulips[1]))
+
    +
    +
    +
    +
    +
    +

    40.99.3. Load data using a Keras utility#

    +

    Next, load these images off disk using the helpful tf.keras.utils.image_dataset_from_directory utility. This will take you from a directory of images on disk to a tf.data.Dataset in just a couple lines of code.

    +
    +

    40.99.3.1. Create a dataset#

    +

    Define some parameters for the loader:

    +
    +
    +
    batch_size = 32
+img_height = 180
+img_width = 180
+
    +
    +
    +
    +

    It’s good practice to use a validation split when developing your model. Use 80% of the images for training and 20% for validation.

    +
    +
    +
    train_ds = tf.keras.utils.image_dataset_from_directory(
+  data_dir,
+  validation_split=0.2,
+  subset="training",
+  seed=123,
+  image_size=(img_height, img_width),
+  batch_size=batch_size)
+
    +
    +
    +
    +
    +
    +
    val_ds = tf.keras.utils.image_dataset_from_directory(
+  data_dir,
+  validation_split=0.2,
+  subset="validation",
+  seed=123,
+  image_size=(img_height, img_width),
+  batch_size=batch_size)
+
    +
    +
    +
    +

    You can find the class names in the class_names attribute on these datasets. These correspond to the directory names in alphabetical order.

    +
    +
    +
    class_names = train_ds.class_names
+print(class_names)
+
    +
    +
    +
    +
    +
    +
    +

    40.99.4. Visualize the data#

    +

    Here are the first nine images from the training dataset:

    +
    +
    +
    import matplotlib.pyplot as plt
+
+plt.figure(figsize=(10, 10))
+for images, labels in train_ds.take(1):
+  for i in range(9):
+    ax = plt.subplot(3, 3, i + 1)
+    plt.imshow(images[i].numpy().astype("uint8"))
+    plt.title(class_names[labels[i]])
+    plt.axis("off")
+
    +
    +
    +
    +

    You will pass these datasets to the Keras Model.fit method for training later in this tutorial. If you like, you can also manually iterate over the dataset and retrieve batches of images:

    +
    +
    +
    for image_batch, labels_batch in train_ds:
+  print(image_batch.shape)
+  print(labels_batch.shape)
+  break
+
    +
    +
    +
    +

    The image_batch is a tensor of the shape (32, 180, 180, 3). This is a batch of 32 images of shape 180x180x3 (the last dimension refers to color channels RGB). The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images.

    +

    You can call .numpy() on the image_batch and labels_batch tensors to convert them to a numpy.ndarray.

    +
    +
    +

    40.99.5. Configure the dataset for performance#

    +

    Make sure to use buffered prefetching, so you can yield data from disk without having I/O become blocking. These are two important methods you should use when loading data:

    +
      +

    • Dataset.cache keeps the images in memory after they’re loaded off disk during the first epoch. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache.

      • +

    • Dataset.prefetch overlaps data preprocessing and model execution while training.

      • +
    +
    +
    +
    AUTOTUNE = tf.data.AUTOTUNE
+
+train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
+val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
+
    +
    +
    +
    +
    +
    +

    40.99.6. Standardize the data#

    +

    The RGB channel values are in the [0, 255] range. This is not ideal for a neural network; in general you should seek to make your input values small.

    +

    Here, you will standardize values to be in the [0, 1] range by using tf.keras.layers.Rescaling:

    +
    +
    +
    normalization_layer = layers.Rescaling(1./255)
+
    +
    +
    +
    +

    There are two ways to use this layer. You can apply it to the dataset by calling Dataset.map:

    +
    +
    +
    normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
+image_batch, labels_batch = next(iter(normalized_ds))
+first_image = image_batch[0]
+# Notice the pixel values are now in `[0,1]`.
+print(np.min(first_image), np.max(first_image))
+
    +
    +
    +
    +

    Or, you can include the layer inside your model definition, which can simplify deployment. Use the second approach here.

    +

    Note: You previously resized images using the image_size argument of tf.keras.utils.image_dataset_from_directory. If you want to include the resizing logic in your model as well, you can use the tf.keras.layers.Resizing layer.

    +
    +
    +

    40.99.7. A basic Keras model#

    +
    +

    40.99.7.1. Create the model#

    +

    The Keras Sequential model consists of three convolution blocks (tf.keras.layers.Conv2D) with a max pooling layer (tf.keras.layers.MaxPooling2D) in each of them. There’s a fully-connected layer (tf.keras.layers.Dense) with 128 units on top of it that is activated by a ReLU activation function ('relu'). This model has not been tuned for high accuracy; the goal of this tutorial is to show a standard approach.

    +
    +
    +
    num_classes = len(class_names)
+
+model = Sequential([
+  layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
+  layers.Conv2D(16, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(32, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(64, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Flatten(),
+  layers.Dense(128, activation='relu'),
+  layers.Dense(num_classes)
+])
+
    +
    +
    +
    +
    +
    +

    40.99.7.2. Compile the model#

    +

    For this tutorial, choose the tf.keras.optimizers.Adam optimizer and tf.keras.losses.SparseCategoricalCrossentropy loss function. To view training and validation accuracy for each training epoch, pass the metrics argument to Model.compile.

    +
    +
    +
    model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
    +
    +
    +
    +
    +
    +

    40.99.7.3. Model summary#

    +

    View all the layers of the network using the Keras Model.summary method:

    +
    +
    +
    model.summary()
+
    +
    +
    +
    +
    +
    +

    40.99.7.4. Train the model#

    +

    Train the model for 10 epochs with the Keras Model.fit method:

    +
    +
    +
    epochs=10
+history = model.fit(
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
+)
+
    +
    +
    +
    +
    +
    +
    +

    40.99.8. Visualize training results#

    +

    Create plots of the loss and accuracy on the training and validation sets:

    +
    +
    +
    acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+epochs_range = range(epochs)
+
+plt.figure(figsize=(8, 8))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
+
    +
    +
    +
    +

    The plots show that training accuracy and validation accuracy are off by large margins, and the model has achieved only around 60% accuracy on the validation set.

    +

    The following tutorial sections show how to inspect what went wrong and try to increase the overall performance of the model.

    +
    +
    +

    40.99.9. Overfitting#

    +

    In the plots above, the training accuracy is increasing linearly over time, whereas validation accuracy stalls around 60% in the training process. Also, the difference in accuracy between training and validation accuracy is noticeable—a sign of overfitting.

    +

    When there are a small number of training examples, the model sometimes learns from noises or unwanted details from training examples—to an extent that it negatively impacts the performance of the model on new examples. This phenomenon is known as overfitting. It means that the model will have a difficult time generalizing on a new dataset.

    +

    There are multiple ways to fight overfitting in the training process. In this tutorial, you’ll use data augmentation and add dropout to your model.

    +
    +
    +

    40.99.10. Data augmentation#

    +

    Overfitting generally occurs when there are a small number of training examples. Data augmentation takes the approach of generating additional training data from your existing examples by augmenting them using random transformations that yield believable-looking images. This helps expose the model to more aspects of the data and generalize better.

    +

    You will implement data augmentation using the following Keras preprocessing layers: tf.keras.layers.RandomFlip, tf.keras.layers.RandomRotation, and tf.keras.layers.RandomZoom. These can be included inside your model like other layers, and run on the GPU.

    +
    +
    +
    data_augmentation = keras.Sequential(
+  [
+    layers.RandomFlip("horizontal",
+                      input_shape=(img_height,
+                                  img_width,
+                                  3)),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+  ]
+)
+
    +
    +
    +
    +

    Visualize a few augmented examples by applying data augmentation to the same image several times:

    +
    +
    +
    plt.figure(figsize=(10, 10))
+for images, _ in train_ds.take(1):
+  for i in range(9):
+    augmented_images = data_augmentation(images)
+    ax = plt.subplot(3, 3, i + 1)
+    plt.imshow(augmented_images[0].numpy().astype("uint8"))
+    plt.axis("off")
+
    +
    +
    +
    +

    You will add data augmentation to your model before training in the next step.

    +
    +
    +

    40.99.11. Dropout#

    +

    Another technique to reduce overfitting is to introduce dropout regularization to the network.

    +

    When you apply dropout to a layer, it randomly drops out (by setting the activation to zero) a number of output units from the layer during the training process. Dropout takes a fractional number as its input value, in the form such as 0.1, 0.2, 0.4, etc. This means dropping out 10%, 20% or 40% of the output units randomly from the applied layer.

    +

    Create a new neural network with tf.keras.layers.Dropout before training it using the augmented images:

    +
    +
    +
    model = Sequential([
+  data_augmentation,
+  layers.Rescaling(1./255),
+  layers.Conv2D(16, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(32, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(64, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Dropout(0.2),
+  layers.Flatten(),
+  layers.Dense(128, activation='relu'),
+  layers.Dense(num_classes, name="outputs")
+])
+
    +
    +
    +
    +
    +
    +

    40.99.12. Compile and train the model#

    +
    +
    +
    model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
    +
    +
    +
    +
    +
    +
    model.summary()
+
    +
    +
    +
    +
    +
    +
    epochs = 15
+history = model.fit(
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
+)
+
    +
    +
    +
    +
    +
    +

    40.99.13. Visualize training results#

    +

    After applying data augmentation and tf.keras.layers.Dropout, there is less overfitting than before, and training and validation accuracy are closer aligned:

    +
    +
    +
    acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+epochs_range = range(epochs)
+
+plt.figure(figsize=(8, 8))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
+
    +
    +
    +
    +
    +
    +

    40.99.14. Predict on new data#

    +

    Use your model to classify an image that wasn’t included in the training or validation sets.

    +

    Note: Data augmentation and dropout layers are inactive at inference time.

    +
    +
    +
    sunflower_url = "https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/592px-Red_sunflower.jpg"
+sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)
+
+img = tf.keras.utils.load_img(
+    sunflower_path, target_size=(img_height, img_width)
+)
+img_array = tf.keras.utils.img_to_array(img)
+img_array = tf.expand_dims(img_array, 0) # Create a batch
+
+predictions = model.predict(img_array)
+score = tf.nn.softmax(predictions[0])
+
+print(
+    "This image most likely belongs to {} with a {:.2f} percent confidence."
+    .format(class_names[np.argmax(score)], 100 * np.max(score))
+)
+
    +
    +
    +
    +
    +
    +

    40.99.15. Use TensorFlow Lite#

    +

    TensorFlow Lite is a set of tools that enables on-device machine learning by helping developers run their models on mobile, embedded, and edge devices.

    +
    +

    40.99.15.1. Convert the Keras Sequential model to a TensorFlow Lite model#

    +

    To use the trained model with on-device applications, first convert it to a smaller and more efficient model format called a TensorFlow Lite model.

    +

    In this example, take the trained Keras Sequential model and use tf.lite.TFLiteConverter.from_keras_model to generate a TensorFlow Lite model:

    +
    +
    +
    # Convert the model.
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+tflite_model = converter.convert()
+
+# Save the model.
+with open('model.tflite', 'wb') as f:
+  f.write(tflite_model)
+
    +
    +
    +
    +

    The TensorFlow Lite model you saved in the previous step can contain several function signatures. The Keras model converter API uses the default signature automatically. Learn more about TensorFlow Lite signatures.

    +
    +
    +

    40.99.15.2. Run the TensorFlow Lite model#

    +

    You can access the TensorFlow Lite saved model signatures in Python via the tf.lite.Interpreter class.

    +

    Load the model with the Interpreter:

    +
    +
    +
    TF_MODEL_FILE_PATH = 'model.tflite' # The default path to the saved TensorFlow Lite model
+
+interpreter = tf.lite.Interpreter(model_path=TF_MODEL_FILE_PATH)
+
    +
    +
    +
    +

    Print the signatures from the converted model to obtain the names of the inputs (and outputs):

    +
    +
    +
    interpreter.get_signature_list()
+
    +
    +
    +
    +

    In this example, you have one default signature called serving_default. In addition, the name of the 'inputs' is 'sequential_1_input', while the 'outputs' are called 'outputs'. You can look up these first and last Keras layer names when running Model.summary, as demonstrated earlier in this tutorial.

    +

    Now you can test the loaded TensorFlow Model by performing inference on a sample image with tf.lite.Interpreter.get_signature_runner by passing the signature name as follows:

    +
    +
    +
    classify_lite = interpreter.get_signature_runner('serving_default')
+classify_lite
+
    +
    +
    +
    +

    Similar to what you did earlier in the tutorial, you can use the TensorFlow Lite model to classify images that weren’t included in the training or validation sets.

    +

    You have already tensorized that image and saved it as img_array. Now, pass it to the first argument (the name of the 'inputs') of the loaded TensorFlow Lite model (predictions_lite), compute softmax activations, and then print the prediction for the class with the highest computed probability.

    +
    +
    +
    predictions_lite = classify_lite(sequential_1_input=img_array)['outputs']
+score_lite = tf.nn.softmax(predictions_lite)
+
    +
    +
    +
    +
    +
    +
    print(
+    "This image most likely belongs to {} with a {:.2f} percent confidence."
+    .format(class_names[np.argmax(score_lite)], 100 * np.max(score_lite))
+)
+
    +
    +
    +
    +

    The prediction generated by the lite model should be almost identical to the predictions generated by the original model:

    +
    +
    +
    print(np.max(np.abs(predictions - predictions_lite)))
+
    +
    +
    +
    +

    Of the five classes—'daisy', 'dandelion', 'roses', 'sunflowers', and 'tulips'—the model should predict the image belongs to sunflowers, which is the same result as before the TensorFlow Lite conversion.

    +
    +
    +
    +

    40.99.16. Acknowledgments#

    +

    Thanks to Tensorflow for creating the tutorial notebook Image classification. It inspires the majority of the content in this chapter.

    +
    +
    + + + + + +
    + +
    + +
    +
    +
    + +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file 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 7dc0f2e259..6634627100 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 @@ -6,7 +6,7 @@ - 40.97. Object Recognition in Images using CNN — Ocademy Open Machine Learning Book + 40.98. Object Recognition in Images using CNN — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  859. + + 40.89. Introduction + +
  860. - 40.89. Study the solvers + 40.90. Study the solvers
  861. - 40.90. Build classification models + 40.91. Build classification models
  862. - 40.91. Build Classification Model + 40.92. Build Classification Model
  863. - 40.92. Parameter play + 40.93. Parameter play
  864. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  865. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  866. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    867. +
  867. + + 40.99. Image classification
  868. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  869. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  870. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  871. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  872. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  873. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  874. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  875. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  876. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  877. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  878. - 40.116. Art by gan + 40.118. Art by gan
  879. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    880. +
  880. + + 40.121. Comparing edge-based and region-based segmentation
  881. - 40.119. Summary + 40.122. Summary
  882. - 40.120. Car Object Detection + 40.123. Car Object Detection
  883. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    884. +
  884. + + 40.126. Getting Start NLP with classification task
    885. @@ -1600,63 +1620,63 @@

    Ocademy Open Machine Learning Book

    @@ -1680,63 +1700,63 @@

    Contents

    @@ -1750,18 +1770,18 @@

    Contents

    -

    40.97. Object Recognition in Images using CNN#

    +

    40.98. Object Recognition in Images using CNN#

    -

    40.97.1. About Dataset#

    +

    40.98.1. About Dataset#

    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.

    cifar10

    -

    40.97.2. Table of Contents#

    +

    40.98.2. Table of Contents#

    -

    40.97.2.1. Import Libaries#

    +

    40.98.2.1. Import Libaries#

    import os
@@ -1784,7 +1804,7 @@ 
    40.97.2.1. Import Libaries
-
    40.97.2.2. Exploring the Data#
    
+
    40.98.2.2. Exploring the Data#
    
 
    Extract from tar archive file
    
 
    The dataset is extracted to the directory tmp/object-recognition-in-images-using-cnn/cifar10. It contains 2 folders train and test, 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 os.listdir.
    
 
    
@@ -1955,7 +1975,7 @@ 
    40.97.2.2. Exploring the Data
-
    40.97.3. Training and Validation Datasets#
    
+
    40.98.3. Training and Validation Datasets#
    
 
    While building real world machine learning models, it is quite common to split the dataset into 3 parts:
    
 
      
 
    1. Training set - used to train the model i.e. compute the loss and adjust the weights of the model using gradient descent.
      2. 
@@ -2015,7 +2035,7 @@ 
      40.97.3. Training and Validation Dataset
    -

    40.97.3.1. training data single batch images#

    +

    40.98.3.1. training data single batch images#

    show_images_batch(train_dl)
@@ -2033,9 +2053,9 @@ 
    40.97.3.1. training data single batch im
    -

    40.97.4. Convolutional Neural Network#

    +

    40.98.4. Convolutional Neural Network#

    -

    40.97.4.1. Defining the Model (Convolutional Neural Network)#

    +

    40.98.4.1. Defining the Model (Convolutional Neural Network)#

    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.

    Let us implement a convolution operation on a 1 channel image with a 3x3 kernel.

    @@ -2244,7 +2264,7 @@

    40.97.4.1. Defining the Model (Convoluti

    -

    40.97.5. Training the Model#

    +

    40.98.5. Training the Model#

    In this mode, the result of every computation will have requires_grad=False, even when the inputs have requires_grad=True.

    @@ -2360,7 +2380,7 @@

    40.97.5. Training the Model -

    40.97.6. Testing with individual images#

    +

    40.98.6. Testing with individual images#

    test_dataset = ImageFolder(data_dir+'/test', transform=ToTensor())
@@ -2465,7 +2485,7 @@ 
    40.97.6. Testing with individual images<
    -

    40.97.7. Acknowledgments#

    +

    40.98.7. Acknowledgments#

    Thanks to datajameson for creating the Kaggle notebook Cifar-10 Object Recognition(CNN) Explained. It inspires the majority of the content in this chapter.

    @@ -2507,13 +2527,13 @@

    40.97.7. Acknowledgments

    previous

    -

    40.95. Sign Language Digits Classification with CNN

    +

    40.96. Sign Language Digits Classification with CNN

     - +

    next

    -

    40.98. Intro to TensorFlow for Deep Learning

    +

    40.99. Image classification

     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 f5cb56e29c..1c7cb30616 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 @@ - 40.95. Sign Language Digits Classification with CNN — Ocademy Open Machine Learning Book + 40.96. Sign Language Digits Classification with CNN — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  885. + + 40.89. Introduction + +
  886. - 40.89. Study the solvers + 40.90. Study the solvers
  887. - 40.90. Build classification models + 40.91. Build classification models
  888. - 40.91. Build Classification Model + 40.92. Build Classification Model
  889. - 40.92. Parameter play + 40.93. Parameter play
  890. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  891. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  892. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    893. +
  893. + + 40.99. Image classification
  894. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  895. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  896. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  897. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  898. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  899. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  900. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  901. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  902. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  903. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  904. - 40.116. Art by gan + 40.118. Art by gan
  905. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    906. +
  906. + + 40.121. Comparing edge-based and region-based segmentation
  907. - 40.119. Summary + 40.122. Summary
  908. - 40.120. Car Object Detection + 40.123. Car Object Detection
  909. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    910. +
  910. + + 40.126. Getting Start NLP with classification task
    911. @@ -1600,69 +1620,69 @@

    Ocademy Open Machine Learning Book

    @@ -1686,69 +1706,69 @@

    Contents

    @@ -1762,9 +1782,9 @@

    Contents

    -

    40.95. Sign Language Digits Classification with CNN#

    +

    40.96. Sign Language Digits Classification with CNN#

    -

    40.95.1. Load libraries#

    +

    40.96.1. Load libraries#

    import numpy as np
@@ -1783,7 +1803,7 @@ 
    40.95.1. Load libraries
-
    40.95.2. Load data from numpy file#
    
+
    40.96.2. Load data from numpy file#
    
 
    
 
    
 
    X = np.load(
@@ -1848,7 +1868,7 @@ 
    40.95.2. Load data from numpy file
    -

    40.95.3. Preparing Data#

    +

    40.96.3. Preparing Data#

    We will re-organize data to match labels and images correctly.

    -

    40.95.6. Data Augmentation With Keras API#

    +

    40.96.6. Data Augmentation With Keras API#

    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.

    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.

      @@ -1961,7 +1981,7 @@

      40.95.6. Data Augmentation With Keras AP

    -

    40.95.7. Changin zoom level#

    +

    40.96.7. Changin zoom level#

    datagen = ImageDataGenerator(zoom_range=0.5)
@@ -1973,7 +1993,7 @@ 
    40.95.7. Changin zoom level
-
    40.95.8. Changing rotaion#
    
+
    40.96.8. Changing rotaion#
    
 
    
 
    
 
    datagen = ImageDataGenerator(rotation_range=45)
@@ -1985,7 +2005,7 @@ 
    40.95.8. Changing rotaion
-
    40.95.9. Changing rotaion, zoom#
    
+
    40.96.9. Changing rotaion, zoom#
    
 
    
 
    
 
    datagen = ImageDataGenerator(zoom_range=0.5, rotation_range=45)
@@ -1997,7 +2017,7 @@ 
    40.95.9. Changing rotaion, zoom
    -

    40.95.10. Model Implementation#

    +

    40.96.10. Model Implementation#

    model = Sequential()
@@ -2047,7 +2067,7 @@ 
    40.95.10. Model Implementation
-
    40.95.11. Conclusion#
    
+
    40.96.11. Conclusion#
    
 
    
 
    
 
    plt.figure(figsize=(10, 5))
@@ -2080,7 +2100,7 @@ 
    40.95.11. Conclusion
-
    40.96. Acknowledgments#
    
+
    40.97. Acknowledgments#
    
 
    Thanks to Görkem Günay for creating sign-language-digits-classification-with-cnn. It inspires the majority of the content in this chapter.
    @@ -2121,13 +2141,13 @@

    40.96. Acknowledgments

    previous

    -

    40.93. How to choose cnn architecture mnist

    +

    40.94. How to choose cnn architecture mnist

    next

    -

    40.97. Object Recognition in Images using CNN

    +

    40.98. Object Recognition in Images using CNN

     diff --git a/assignments/deep-learning/difussion-model/denoising-difussion-model.html b/assignments/deep-learning/difussion-model/denoising-difussion-model.html index 0862a234be..786f14823f 100644 --- a/assignments/deep-learning/difussion-model/denoising-difussion-model.html +++ b/assignments/deep-learning/difussion-model/denoising-difussion-model.html @@ -6,7 +6,7 @@ - 40.119. Summary — Ocademy Open Machine Learning Book + 40.122. Summary — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  911. + + 40.89. Introduction + +
  912. - 40.89. Study the solvers + 40.90. Study the solvers
  913. - 40.90. Build classification models + 40.91. Build classification models
  914. - 40.91. Build Classification Model + 40.92. Build Classification Model
  915. - 40.92. Parameter play + 40.93. Parameter play
  916. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  917. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  918. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    919. +
  919. + + 40.99. Image classification
  920. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  921. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  922. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  923. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  924. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  925. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  926. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  927. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  928. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  929. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  930. - 40.116. Art by gan + 40.118. Art by gan
  931. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    932. +
  932. + + 40.121. Comparing edge-based and region-based segmentation
  933. - 40.119. Summary + 40.122. Summary
  934. - 40.120. Car Object Detection + 40.123. Car Object Detection
  935. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    936. +
  936. + + 40.126. Getting Start NLP with classification task
    937. @@ -1600,52 +1620,52 @@

    Ocademy Open Machine Learning Book

    @@ -1669,52 +1689,52 @@

    Contents

    @@ -1728,11 +1748,11 @@

    Contents

    -

    40.119. Summary#

    +

    40.122. Summary#

    This notebook shows how to train Denoising Difussion Models.

    The code has been adapted and curated from this tutorial by Andras Beres.

    -

    40.119.1. Hyperparams#

    +

    40.122.1. Hyperparams#

    import numpy as np
@@ -1763,7 +1783,7 @@ 
    40.119.1. Hyperparams
-
    40.119.2. Dataset#
    
+
    40.122.2. Dataset#
    
 
    
 
    
 
    def preprocess_image(data):
@@ -1803,11 +1823,11 @@ 
    40.119.2. Dataset
    -

    40.119.3. Denoising Network#

    +

    40.122.3. Denoising Network#

    We will use the Residual U-Net model.

    -

    40.119.4. TODO: can we use something simpler?#

    +

    40.122.4. TODO: can we use something simpler?#

    embedding_max_frequency = 1000.0
@@ -1833,7 +1853,7 @@ 
    40.119.4. TODO: can we use something sim
    -

    40.119.5. Custom Residual Network#

    +

    40.122.5. Custom Residual Network#

    def get_network_custom(image_size, block_depth=17, output_channels=1):
@@ -1864,7 +1884,7 @@ 
    40.119.5. Custom Residual Network
    -

    40.119.6. Residual U-Net#

    +

    40.122.6. Residual U-Net#

    widths = [32, 64, 96, 128]
@@ -1942,7 +1962,7 @@ 
    40.119.6. Residual U-Net
-
    40.119.7. Difussion Model#
    
+
    40.122.7. Difussion Model#
    
 
    
 
    
 
    class DiffusionModel(tf.keras.Model):
@@ -2059,7 +2079,7 @@ 
    40.119.7. Difussion Model
-
    40.119.8. Complete Model#
    
+
    40.122.8. Complete Model#
    
 
    Chose one of the residual networks.
    
 
    
 
    
@@ -2191,7 +2211,7 @@ 
    40.119.8. Complete Model
-
    40.119.9. Visualize#
    
+
    40.122.9. Visualize#
    
 
    
 
    
 
    num_rows = 2
@@ -2220,7 +2240,7 @@ 
    40.119.9. Visualize
    -

    40.119.10. Acknowledgments#

    +

    40.122.10. Acknowledgments#

    Thanks to Maciej Skorski for creating Denoising Difussion Model. It inspires the majority of the content in this chapter.

    @@ -2258,17 +2278,17 @@

    40.119.10. Acknowledgments

    previous

    -

    40.118. Generative Adversarial Networks (GANs)

    +

    40.121. Comparing edge-based and region-based segmentation

    next

    -

    40.120. Car Object Detection

    +

    40.123. Car Object Detection

     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 43814b4075..419886796d 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 @@ - 40.115. DQN On Foreign Exchange Market — Ocademy Open Machine Learning Book + 40.117. DQN On Foreign Exchange Market — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  937. + + 40.89. Introduction + +
  938. - 40.89. Study the solvers + 40.90. Study the solvers
  939. - 40.90. Build classification models + 40.91. Build classification models
  940. - 40.91. Build Classification Model + 40.92. Build Classification Model
  941. - 40.92. Parameter play + 40.93. Parameter play
  942. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  943. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  944. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    945. +
  945. + + 40.99. Image classification
  946. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  947. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  948. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  949. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  950. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  951. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  952. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  953. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  954. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  955. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  956. - 40.116. Art by gan + 40.118. Art by gan
  957. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    958. +
  958. + + 40.121. Comparing edge-based and region-based segmentation
  959. - 40.119. Summary + 40.122. Summary
  960. - 40.120. Car Object Detection + 40.123. Car Object Detection
  961. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    962. +
  962. + + 40.126. Getting Start NLP with classification task
    963. @@ -1600,27 +1620,27 @@

    Ocademy Open Machine Learning Book

    @@ -1644,27 +1664,27 @@

    Contents

    @@ -1678,9 +1698,9 @@

    Contents

    -

    40.115. DQN On Foreign Exchange Market#

    +

    40.117. DQN On Foreign Exchange Market#

    -

    40.115.1. Load dataset#

    +

    40.117.1. Load dataset#

    # This Python 3 environment comes with many helpful analytics libraries installed
@@ -1884,7 +1904,7 @@ 
    40.115.1. Load dataset
-
    40.115.2. Define envireonment#
    
+
    40.117.2. Define envireonment#
    
 
    
 
    
 
    class Environment:
@@ -1977,7 +1997,7 @@ 
    40.115.2. Define envireonment
-
    40.115.3. Agent class#
    
+
    40.117.3. Agent class#
    
 
    
 
    
 
    # Deep Q-learning Agent
@@ -2041,7 +2061,7 @@ 
    40.115.3. Agent class
-
    40.115.4. Train the DQN#
    
+
    40.117.4. Train the DQN#
    
 
    
 
    
 
    if __name__ == "__main__":
@@ -2498,7 +2518,7 @@ 
    40.115.4. Train the DQN
-
    40.115.5. Acknowledgement#
    
+
    40.117.5. Acknowledgement#
    
 
    Thanks to emrebulbul23 for creating DQN on foreign exchange market. It inspired the majority of the content in this article.
    @@ -2540,13 +2560,13 @@

    40.115.5. Acknowledgement

    previous

    -

    40.110. NN Classify 15 Fruits Assignment

    +

    40.112. NN Classify 15 Fruits Assignment

    next

    -

    40.116. Art by gan

    +

    40.118. Art by gan

     diff --git a/assignments/deep-learning/gan/art-by-gan.html b/assignments/deep-learning/gan/art-by-gan.html index 5487735130..365a38cf65 100644 --- a/assignments/deep-learning/gan/art-by-gan.html +++ b/assignments/deep-learning/gan/art-by-gan.html @@ -6,7 +6,7 @@ - 40.116. Art by gan — Ocademy Open Machine Learning Book + 40.118. Art by gan — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  963. + + 40.89. Introduction + +
  964. - 40.89. Study the solvers + 40.90. Study the solvers
  965. - 40.90. Build classification models + 40.91. Build classification models
  966. - 40.91. Build Classification Model + 40.92. Build Classification Model
  967. - 40.92. Parameter play + 40.93. Parameter play
  968. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  969. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  970. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    971. +
  971. + + 40.99. Image classification
  972. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  973. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  974. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  975. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  976. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  977. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  978. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  979. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  980. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  981. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  982. - 40.116. Art by gan + 40.118. Art by gan
  983. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    984. +
  984. + + 40.121. Comparing edge-based and region-based segmentation
  985. - 40.119. Summary + 40.122. Summary
  986. - 40.120. Car Object Detection + 40.123. Car Object Detection
  987. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    988. +
  988. + + 40.126. Getting Start NLP with classification task
    989. @@ -1600,79 +1620,79 @@

    Ocademy Open Machine Learning Book

    @@ -1696,79 +1716,79 @@

    Contents

    @@ -1782,7 +1802,7 @@

    Contents

    -

    40.116. Art by gan#

    +

    40.118. Art by gan#

    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.

    @@ -1819,11 +1839,11 @@

    40.116. Art by gan

    -

    40.116.1. Data loading & Prepreprocessing#

    +

    40.118.1. Data loading & Prepreprocessing#

    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.

    -

    40.116.2. Loading the data#

    +

    40.118.2. Loading the data#

    import os
@@ -1891,7 +1911,7 @@ 
    40.116.2. Loading the data
-
    40.116.3. Preprocessing the data#
    
+
    40.118.3. Preprocessing the data#
    
 
    Normalization: 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.
    
 
    
@@ -1911,7 +1931,7 @@ 
    40.116.3. Preprocessing the dataNow the data is up as a tensorflow Dataset object and is Prepocessed. Next up is building the GAN.
    -

    40.116.4. Building GAN#

    +

    40.118.4. Building GAN#

    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.

    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.

    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.

    @@ -1922,7 +1942,7 @@

    40.116.4. Building GAN -

    40.116.5. The generator#

    +

    40.118.5. The generator#

    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.

    Following steps are involved in the models building

      @@ -1931,7 +1951,7 @@

      40.116.5. The generator -

      40.116.6. Building a generator#

      +

      40.118.6. Building a generator#

      latent_dim = 100
@@ -2046,12 +2066,12 @@ 
      40.116.6. Building a generator
-
      40.116.7. The discriminator#
      
+
      40.118.7. The discriminator#
      
 
      In GANs the Generator works along with the Discriminator.
      
 
      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.
    -

    40.116.8. Building a discriminator#

    +

    40.118.8. Building a discriminator#

    discriminator = Sequential()
@@ -2167,7 +2187,7 @@ 
    40.116.8. Building a discriminatorLet us proceed and build the GAN architecture to train.
    -

    40.116.9. GAN compilation#

    +

    40.118.9. GAN compilation#

    GAN training has two sections:

    Section 1: 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.

    @@ -2244,7 +2264,7 @@

    40.116.9. GAN compilation -

    40.116.10. Training the model#

    +

    40.118.10. Training the model#

    Calling the above created GAN function trains the generator and discriminator simultaneously. To implement the GAN we must define:

      @@ -2303,7 +2323,7 @@

      40.116.10. Training the model -

      40.116.11. Ploting the Learning Curves#

      +

      40.118.11. Ploting the Learning Curves#

      import pandas as pd
@@ -2320,7 +2340,7 @@ 
      40.116.11. Ploting the Learning Curves
    -

    40.116.12. AI makes artwork#

    +

    40.118.12. AI makes artwork#

    # Number of images to be generate
@@ -2366,12 +2386,12 @@ 
    40.116.12. AI makes artwork
-
    40.116.13. Conculsion#
    
+
    40.118.13. Conculsion#
    
 
    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.
    -

    40.117. Acknowledgments#

    +

    40.119. Acknowledgments#

    Thanks to Karnika Kapoor for creating art-by-gan. It inspires the majority of the content in this chapter.

    @@ -2412,13 +2432,13 @@

    40.117. Acknowledgments

    previous

    -

    40.115. DQN On Foreign Exchange Market

    +

    40.117. DQN On Foreign Exchange Market

    next

    -

    40.118. Generative Adversarial Networks (GANs)

    +

    40.120. Generative Adversarial Networks (GANs)

     diff --git a/assignments/deep-learning/gan/gan-introduction.html b/assignments/deep-learning/gan/gan-introduction.html index 4916345a13..44a7df05d7 100644 --- a/assignments/deep-learning/gan/gan-introduction.html +++ b/assignments/deep-learning/gan/gan-introduction.html @@ -6,7 +6,7 @@ - 40.118. Generative Adversarial Networks (GANs) — Ocademy Open Machine Learning Book + 40.120. Generative Adversarial Networks (GANs) — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  989. + + 40.89. Introduction + +
  990. - 40.89. Study the solvers + 40.90. Study the solvers
  991. - 40.90. Build classification models + 40.91. Build classification models
  992. - 40.91. Build Classification Model + 40.92. Build Classification Model
  993. - 40.92. Parameter play + 40.93. Parameter play
  994. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  995. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  996. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    997. +
  997. + + 40.99. Image classification
  998. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  999. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1000. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1001. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1002. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1003. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1004. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1005. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1006. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1007. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1008. - 40.116. Art by gan + 40.118. Art by gan
  1009. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1010. +
  1010. + + 40.121. Comparing edge-based and region-based segmentation
  1011. - 40.119. Summary + 40.122. Summary
  1012. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1013. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1014. +
  1014. + + 40.126. Getting Start NLP with classification task
    1015. @@ -1600,91 +1620,91 @@

    Ocademy Open Machine Learning Book

    @@ -1708,91 +1728,91 @@

    Contents

    @@ -1806,9 +1826,9 @@

    Contents

    -

    40.118. Generative Adversarial Networks (GANs)#

    +

    40.120. Generative Adversarial Networks (GANs)#

    -

    40.118.1. Loading data#

    +

    40.120.1. Loading data#

    import os
@@ -1843,7 +1863,7 @@ 
    40.118.1. Loading data
-
    40.118.2. Importing the libraries#
    
+
    40.120.2. Importing the libraries#
    
 
    
 
    
 
    from __future__ import print_function
@@ -1872,7 +1892,7 @@ 
    40.118.2. Importing the libraries
    -

    40.118.3. Some dogs#

    +

    40.120.3. Some dogs#

    The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world.

    @@ -1895,7 +1915,7 @@

    40.118.3. Some dogs

    -

    40.118.4. Image Preprocessing#

    +

    40.120.4. Image Preprocessing#

    batch_size = 32
@@ -1930,7 +1950,7 @@ 
    40.118.4. Image Preprocessing
-
    40.118.5. Weights#
    
+
    40.120.5. Weights#
    
 
    
 
    
 
    def weights_init(m):
@@ -1946,7 +1966,7 @@ 
    40.118.5. Weights
    -

    40.118.6. Generator#

    +

    40.120.6. Generator#

    class G(nn.Module):
@@ -1984,7 +2004,7 @@ 
    40.118.6. Generator
    -

    40.118.7. Discriminator#

    +

    40.120.7. Discriminator#

    class D(nn.Module):
@@ -2017,7 +2037,7 @@ 
    40.118.7. Discriminator
-
    40.118.8. Another setup#
    
+
    40.120.8. Another setup#
    
 
    
 
    
 
    class Generator(nn.Module):
@@ -2083,7 +2103,7 @@ 
    40.118.8. Another setup
-
    40.118.9. Training#
    
+
    40.120.9. Training#
    
 
    
 
    
 
    EPOCH = 0
@@ -2141,9 +2161,9 @@ 
    40.118.9. Training
    -

    40.118.10. Best public training#

    +

    40.120.10. Best public training#

    -

    40.118.10.1. Parameters#

    +

    40.120.10.1. Parameters#

    batch_size = 32
@@ -2164,7 +2184,7 @@ 
    40.118.10.1. Parameters
-
    40.118.10.2. Initialize models and optimizers#
    
+
    40.120.10.2. Initialize models and optimizers#
    
 
    
 
    
 
    netG = Generator(nz).to(device)
@@ -2202,7 +2222,7 @@ 
    40.118.10.2. Initialize models and optim
    -

    40.118.11. Show generated images#

    +

    40.120.11. Show generated images#

    def show_generated_img(n_images=5):
@@ -2227,7 +2247,7 @@ 
    40.118.11. Show generated images
    -

    40.118.12. Training Loop#

    +

    40.120.12. Training Loop#

    for epoch in range(epochs):
@@ -2299,7 +2319,7 @@ 
    40.118.12. Training Loop
-
    40.118.13. Generation example#
    
+
    40.120.13. Generation example#
    
 
    
 
    
 
    show_generated_img(7)
@@ -2338,7 +2358,7 @@ 
    40.118.13. Generation example
-
    40.118.13.1. Save models#
    
+
    40.120.13.1. Save models#
    
 
    
 
    
 
    torch.save(netG.state_dict(), 'generator.pth')
@@ -2350,7 +2370,7 @@ 
    40.118.13.1. Save models
-
    40.118.14. Acknowledgement#
    
+
    40.120.14. Acknowledgement#
    
 
    Thanks to jesucristo for creating GAN Introduction. It inspired the majority of the content in this article.
    @@ -2392,13 +2412,13 @@

    40.118.14. Acknowledgement

    previous

    -

    40.116. Art by gan

    +

    40.118. Art by gan

     - +

    next

    -

    40.119. Summary

    +

    40.121. Comparing edge-based and region-based segmentation

     diff --git a/assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.html b/assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.html new file mode 100644 index 0000000000..15d4c1c215 --- /dev/null +++ b/assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.html @@ -0,0 +1,1949 @@ + + + + + + + + + 40.121. Comparing edge-based and region-based segmentation — Ocademy Open Machine Learning Book + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + Contents +
    + +
    +
    +
    +
    + +
    +

    Comparing edge-based and region-based segmentation

    + +
    + +
    +
    +
    + +
    + +
    +
    +
    # Install the necessary dependencies
+
+import os
+import sys
+!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython skimage
+
    +
    +
    +
    +
    +

    40.121. Comparing edge-based and region-based segmentation#

    +

    In this example, we will try how to segment objects from a background. We use the coins image from skimage.data, which shows several coins outlined against a darker background.

    +
    +
    +
    import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data
+from skimage.exposure import histogram
+
+coins = data.coins()
+hist, hist_centers = histogram(coins)
+
+fig, axes = plt.subplots(1, 2, figsize=(8, 3))
+axes[0].imshow(coins, cmap=plt.cm.gray)
+axes[0].axis('off')
+axes[1].plot(hist_centers, hist, lw=2)
+axes[1].set_title('histogram of gray values')
+
    +
    +
    +
    +
    +

    40.121.1. Thresholding#

    +

    A simple way to segment the coins is to choose a threshold based on the histogram of gray values. Unfortunately, thresholding this image gives a binary image that either misses significant parts of the coins or merges parts of the background with the coins:

    +
    +
    +
    fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
+
+axes[0].imshow(coins > 100, cmap=plt.cm.gray)
+axes[0].set_title('coins > 100')
+
+axes[1].imshow(coins > 150, cmap=plt.cm.gray)
+axes[1].set_title('coins > 150')
+
+for a in axes:
+    a.axis('off')
+
+plt.tight_layout()
+
    +
    +
    +
    +
    +
    +

    40.121.2. Edge-based segmentation#

    +

    Next, we try to delineate the contours of the coins using edge-based segmentation. To do this, we first get the edges of features using the Canny edge-detector.

    +
    +
    +
    from skimage.feature import canny
+
+edges = canny(coins)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(edges, cmap=plt.cm.gray)
+ax.set_title('Canny detector')
+ax.axis('off')
+
    +
    +
    +
    +

    These contours are then filled using mathematical morphology.

    +
    +
    +
    from scipy import ndimage as ndi
+
+fill_coins = ndi.binary_fill_holes(edges)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(fill_coins, cmap=plt.cm.gray)
+ax.set_title('filling the holes')
+ax.axis('off')
+
    +
    +
    +
    +

    Small spurious objects are easily removed by setting a minimum size for valid objects.

    +
    +
    +
    from skimage import morphology
+
+coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(coins_cleaned, cmap=plt.cm.gray)
+ax.set_title('removing small objects')
+ax.axis('off')
+
    +
    +
    +
    +

    However, this method is not very robust, since contours that are not perfectly closed are not filled correctly, as is the case for one unfilled coin above.

    +
    +
    +

    40.121.3. Region-based segmentation#

    +

    We therefore try a region-based method using the watershed transform. First, we find an elevation map using the Sobel gradient of the image.

    +
    +
    +
    from skimage.filters import sobel
+
+elevation_map = sobel(coins)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(elevation_map, cmap=plt.cm.gray)
+ax.set_title('elevation map')
+ax.axis('off')
+
    +
    +
    +
    +

    Next we find markers of the background and the coins based on the extreme parts of the histogram of gray values.

    +
    +
    +
    markers = np.zeros_like(coins)
+markers[coins < 30] = 1
+markers[coins > 150] = 2
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(markers, cmap=plt.cm.nipy_spectral)
+ax.set_title('markers')
+ax.axis('off')
+
    +
    +
    +
    +

    Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above:

    +
    +
    +
    from skimage import segmentation
+
+segmentation_coins = segmentation.watershed(elevation_map, markers)
+
+fig, ax = plt.subplots(figsize=(4, 3))
+ax.imshow(segmentation_coins, cmap=plt.cm.gray)
+ax.set_title('segmentation')
+ax.axis('off')
+
    +
    +
    +
    +

    This last method works even better, and the coins can be segmented and labeled individually.

    +
    +
    +
    from skimage.color import label2rgb
+
+segmentation_coins = ndi.binary_fill_holes(segmentation_coins - 1)
+labeled_coins, _ = ndi.label(segmentation_coins)
+image_label_overlay = label2rgb(labeled_coins, image=coins, bg_label=0)
+
+fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
+axes[0].imshow(coins, cmap=plt.cm.gray)
+axes[0].contour(segmentation_coins, [0.5], linewidths=1.2, colors='y')
+axes[1].imshow(image_label_overlay)
+
+for a in axes:
+    a.axis('off')
+
+plt.tight_layout()
+
+plt.show()
+
    +
    +
    +
    +
    +
    +

    40.121.4. Acknowledgments#

    +

    Thanks to Scikit-image for creating the open-source project Comparing edge-based and region-based segmentation. This inspires the majority of the content in this chapter.

    +
    +
    + + + + + +
    + +
    + +
    +
    +
    + +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html b/assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html index 7e8bff53c3..e4b3468e84 100644 --- a/assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html +++ b/assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html @@ -6,7 +6,7 @@ - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment — Ocademy Open Machine Learning Book + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1015. + + 40.89. Introduction + +
  1016. - 40.89. Study the solvers + 40.90. Study the solvers
  1017. - 40.90. Build classification models + 40.91. Build classification models
  1018. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1019. - 40.92. Parameter play + 40.93. Parameter play
  1020. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1021. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1022. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1023. +
  1023. + + 40.99. Image classification
  1024. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1025. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1026. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1027. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1028. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1029. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1030. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1031. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1032. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1033. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1034. - 40.116. Art by gan + 40.118. Art by gan
  1035. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1036. +
  1036. + + 40.121. Comparing edge-based and region-based segmentation
  1037. - 40.119. Summary + 40.122. Summary
  1038. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1039. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1040. +
  1040. + + 40.126. Getting Start NLP with classification task
    1041. @@ -1600,34 +1620,34 @@

    Ocademy Open Machine Learning Book

    @@ -1651,34 +1671,34 @@

    Contents

    @@ -1692,9 +1712,9 @@

    Contents

    -

    40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment#

    +

    40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment#

    -

    40.100.1. Load libraries#

    +

    40.102.1. Load libraries#

    import pandas as pd
@@ -1725,7 +1745,7 @@ 
    40.100.1. Load libraries
-
    40.100.2. Data Pre-processing#
    
+
    40.102.2. Data Pre-processing#
    
 
    
 
    
 
    notclean = pd.read_csv(
@@ -3424,7 +3444,7 @@ 
    40.100.2. Data Pre-processing
-
    40.100.3. Exploratory Analysis#
    
+
    40.102.3. Exploratory Analysis#
    
 
    
 
    
 
    # --------------Analysis----------------------------#
@@ -4764,7 +4784,7 @@ 
    40.100.3. Exploratory Analysis
-
    40.100.4. LSTM Model#
    
+
    40.102.4. LSTM Model#
    
 
    
 
    
 
    from math import sqrt
@@ -5585,7 +5605,7 @@ 
    40.100.4. LSTM Model
-
    40.101. Acknowledgements#
    
+
    40.103. Acknowledgements#
    
 
    Thanks to Paul Simpson for creating Bitcoin Lstm Model with Tweet Volume and Sentiment. It inspires the majority of the content in this chapter.
    @@ -5626,13 +5646,13 @@

    40.101. Acknowledgements

    previous

    -

    40.98. Intro to TensorFlow for Deep Learning

    +

    40.100. Intro to TensorFlow for Deep Learning

    next

    -

    40.102. Google Stock Price Prediction RNN

    +

    40.104. Google Stock Price Prediction RNN

     diff --git a/assignments/deep-learning/nlp/getting-start-nlp-with-classification-task.html b/assignments/deep-learning/nlp/getting-start-nlp-with-classification-task.html new file mode 100644 index 0000000000..adb93071a3 --- /dev/null +++ b/assignments/deep-learning/nlp/getting-start-nlp-with-classification-task.html @@ -0,0 +1,2504 @@ + + + + + + + + + 40.126. Getting Start NLP with classification task — Ocademy Open Machine Learning Book + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Learn AI together, for free! At Ocademy.
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    + + + + + + +
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    + Contents +
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    +
    +
    +
    + +
    +

    Getting Start NLP with classification task

    + +
    + +
    +
    +
    + +
    + +
    +

    40.126. Getting Start NLP with classification task#

    +

    One area where deep learning has dramatically improved in the last couple of years is natural language processing (NLP). Computers can now generate text, translate automatically from one language to another, analyze comments, label words in sentences, and much more.

    +

    Perhaps the most widely practically useful application of NLP is classification – that is, classifying a document automatically into some category. This can be used, for instance, for:

    +
      +

    • Sentiment analysis (e.g are people saying positive or negative things about your product)

      • +

    • Author identification (what author most likely wrote some document)

      • +

    • Legal discovery (which documents are in scope for a trial)

      • +

    • Organizing documents by topic

      • +

    • Triaging inbound emails

      • +

    • …and much more!

      • +
    +

    Today, we are tasked with comparing two words or short phrases, and scoring them based on whether they’re similar or not, based on which patent class they were used in. With a score of 1 it is considered that the two inputs have identical meaning, and 0 means they have totally different meaning. For instance, abatement and eliminating process have a score of 0.5, meaning they’re somewhat similar, but not identical.

    +

    It turns out that this can be represented as a classification problem. How? By representing the question like this:

    +
    +

    For the following text…: “TEXT1: abatement; TEXT2: eliminating process” …chose a category of meaning similarity: “Different; Similar; Identical”.

    +
    +

    In this assignment section we’ll see how to solve the Patent Phrase Matching problem by treating it as a classification task, by representing it in a very similar way to that shown above.

    +
    +

    40.126.1. Import and EDA#

    +
    +
    +
    import pandas as pd
+import numpy as np
+from datasets import Dataset,DatasetDict
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer
+import warnings
+
+warnings.filterwarnings("ignore")
+
    +
    +
    +
    +

    First of all, let’s import the dataset.

    +
    +
    +
    df = pd.read_csv('https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/nlp/phrase_matching_train.csv')
+df.head()
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    idanchortargetcontextscore
    037d61fd2272659b1abatementabatement of pollutionA470.50
    17b9652b17b68b7a4abatementact of abatingA470.75
    236d72442aefd8232abatementactive catalystA470.25
    35296b0c19e1ce60eabatementeliminating processA470.50
    454c1e3b9184cb5b6abatementforest regionA470.00
    +
    +
    +

    As you see, there are 5 columns, where anchor and target are a pair phrases, context is the common context they are in, score is the similarity score of anchor and target.

    +
    +
    +
    df.describe(include='object')
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    idanchortargetcontext
    count36473364733647336473
    unique3647373329340106
    top37d61fd2272659b1component composite coatingcompositionH01
    freq1152242186
    +
    +
    +

    We can see that in the 36473 rows, there are 733 unique anchors, 106 contexts, and nearly 30000 targets. Some anchors are very common, with “component composite coating” for instance appearing 152 times.

    +

    Earlier, I suggested we could represent the input to the model as something like “TEXT1: abatement; TEXT2: eliminating process”. We’ll need to add the context to this too. In Pandas, we just use + to concatenate, like so:

    +
    +
    +
    df['input'] = 'TEXT1: ' + df.context + '; TEXT2: ' + df.target + '; ANC1: ' + df.anchor
+df.head(5)
+
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    idanchortargetcontextscoreinput
    037d61fd2272659b1abatementabatement of pollutionA470.50TEXT1: A47; TEXT2: abatement of pollution; ANC...
    17b9652b17b68b7a4abatementact of abatingA470.75TEXT1: A47; TEXT2: act of abating; ANC1: abate...
    236d72442aefd8232abatementactive catalystA470.25TEXT1: A47; TEXT2: active catalyst; ANC1: abat...
    35296b0c19e1ce60eabatementeliminating processA470.50TEXT1: A47; TEXT2: eliminating process; ANC1: ...
    454c1e3b9184cb5b6abatementforest regionA470.00TEXT1: A47; TEXT2: forest region; ANC1: abatement
    +
    +
    +
    +
    +

    40.126.2. Tokenization#

    +

    Transformers uses a Dataset object for storing their dataset, of course! We can create one like so:

    +
    +
    +
    ds = Dataset.from_pandas(df)
+ds
+
    +
    +
    +
    +
    Dataset({
+    features: ['id', 'anchor', 'target', 'context', 'score', 'input'],
+    num_rows: 36473
+})
+
    +
    +
    +
    +

    But we can’t pass the texts directly into a model. A deep learning model expects numbers as inputs, not English sentences! So we need to do two things:

    +
      +

    • Tokenization: Split each text up into words (or actually, as we’ll see, into tokens)

      • +

    • Numericalization: Convert each word (or token) into a number.

      • +
    +

    The details about how this is done actually depend on the particular model we use. So first we’ll need to pick a model. There are thousands of models available, but a reasonable starting point for nearly any NLP problem is to use this (replace “small” with “large” for a slower but more accurate model, once you’ve finished exploring):

    +
    +
    +
    model_nm = 'microsoft/deberta-v3-small'
+
    +
    +
    +
    +

    AutoTokenizer will create a tokenizer appropriate for a given model:

    +
    +
    +
    tokz = AutoTokenizer.from_pretrained(model_nm)
+
    +
    +
    +
    +

    Here’s an example of how the tokenizer splits a text into “tokens” (which are like words, but can be sub-word pieces, as you see below):

    +
    +
    +
    tokz.tokenize("G'day folks, I'm Jeremy from fast.ai!")
+
    +
    +
    +
    +
    ['▁G',
+ "'",
+ 'day',
+ '▁folks',
+ ',',
+ '▁I',
+ "'",
+ 'm',
+ '▁Jeremy',
+ '▁from',
+ '▁fast',
+ '.',
+ 'ai',
+ '!']
+
    +
    +
    +
    +

    Uncommon words will be split into pieces just like ornithorhynchus. The start of a new word is represented by ▁:

    +
    +
    +
    tokz.tokenize("A platypus is an ornithorhynchus anatinus.")
+
    +
    +
    +
    +
    ['▁A',
+ '▁platypus',
+ '▁is',
+ '▁an',
+ '▁or',
+ 'ni',
+ 'tho',
+ 'rhynch',
+ 'us',
+ '▁an',
+ 'at',
+ 'inus',
+ '.']
+
    +
    +
    +
    +
    +
    +

    40.126.3. Numericalization#

    +

    After completing Tokenization, we need to convert each token into a number, because the model only accepts numbers as input. But … how to do it? +We need a large token dictionary to map each token to a number!

    +
    +
    +
    vocab = tokz.get_vocab()
+
    +
    +
    +
    +

    The above is the token dictionary that comes with the deberta-v3-small model. You can print it out to check.

    +
    +
    +
    tokz("A platypus is an ornithorhynchus anatinus.")
+
    +
    +
    +
    +
    {'input_ids': [1, 336, 114224, 269, 299, 289, 4840, 34765, 102530, 1867, 299, 2401, 26835, 260, 2], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+
    +
    +
    +
    +

    According to this token dictionary, we can convert the original token sequence into a digital sequence. Input_ids is the number we need, token_type_ids represents whether all tokens belong to the same sentence, and attention_mask represents whether the token exists in the token dictionary.

    +

    Here’s a simple function which tokenizes our inputs:

    +
    +
    +
    def tok_func(x): return tokz(x["input"])
+
    +
    +
    +
    +

    To run this quickly in parallel on every row in our dataset, use map:

    +
    +
    +
    tok_ds = ds.map(tok_func, batched=True)
+tok_ds
+
    +
    +
    +
    +
    Dataset({
+    features: ['id', 'anchor', 'target', 'context', 'score', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],
+    num_rows: 36473
+})
+
    +
    +
    +
    +

    This adds a new item to our dataset called input_ids. For instance, here is the input and IDs for the first row of our data:

    +
    +
    +
    row = tok_ds[0]
+row['input'], row['input_ids']
+
    +
    +
    +
    +
    ('TEXT1: A47; TEXT2: abatement of pollution; ANC1: abatement',
+ [1,
+  54453,
+  435,
+  294,
+  336,
+  5753,
+  346,
+  54453,
+  445,
+  294,
+  47284,
+  265,
+  6435,
+  346,
+  23702,
+  435,
+  294,
+  47284,
+  2])
+
    +
    +
    +
    +

    Finally, we need to prepare our labels. Transformers always assumes that your labels has the column name labels, but in our dataset it’s currently score. Therefore, we need to rename it:

    +
    +
    +
    tok_ds = tok_ds.rename_columns({'score':'labels'})
+tok_ds
+
    +
    +
    +
    +
    Dataset({
+    features: ['id', 'anchor', 'target', 'context', 'labels', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],
+    num_rows: 36473
+})
+
    +
    +
    +
    +

    Now that we’ve prepared our tokens and labels, we need to create our validation set.

    +
    +
    +

    40.126.4. Test and validation sets#

    +
    +
    +
    eval_df = pd.read_csv('https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/nlp/phrase_matching_test.csv')
+eval_df['input'] = 'TEXT1: ' + eval_df.context + '; TEXT2: ' + eval_df.target + '; ANC1: ' + eval_df.anchor
+eval_ds = Dataset.from_pandas(eval_df).map(tok_func, batched=True)
+eval_ds
+
    +
    +
    +
    +
    Dataset({
+    features: ['id', 'anchor', 'target', 'context', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],
+    num_rows: 36
+})
+
    +
    +
    +
    +

    This is the test set. Possibly the most important idea in machine learning is that of having separate training, validation, and test data sets.

    +
    +
    +
    dds = tok_ds.train_test_split(0.25, seed=42)
+dds
+
    +
    +
    +
    +
    DatasetDict({
+    train: Dataset({
+        features: ['id', 'anchor', 'target', 'context', 'labels', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],
+        num_rows: 27354
+    })
+    test: Dataset({
+        features: ['id', 'anchor', 'target', 'context', 'labels', 'input', 'input_ids', 'token_type_ids', 'attention_mask'],
+        num_rows: 9119
+    })
+})
+
    +
    +
    +
    +

    This is the validation set. We use train_test_split to separate it from the training set with a separation ratio of 25%.

    +
    +
    +

    40.126.5. Training our model#

    +

    Before starting training, we need to set some hyperparameters for our model. Here’s a concise explanation:

    +
      +

    • Batch Size (bs): 128 examples processed in each iteration.

      • +

    • Epochs (epochs): The model will be trained through the entire dataset 4 times.

      • +

    • Learning Rate (lr): The step size for adjusting model weights during optimization is set to 8e-5.

      • +

    • TrainingArguments (args):

      +
        +

      • Warmup Ratio: 10% of training steps used for learning rate warm-up.

        • +

      • Learning Rate Scheduler: Cosine learning rate scheduler.

        • +

      • Mixed Precision (fp16): Training with mixed-precision for faster computation.

        • +

      • Evaluation Strategy: Model evaluation after each epoch.

        • +

      • Batch Sizes: 128 examples per training device, 256 for evaluation.

        • +

      • Number of Training Epochs: Training for 4 epochs.

        • +

      • Weight Decay: L2 regularization with a rate of 0.01.

        • +

      • Report To: No reports sent during training (set to ‘none’). to ‘none’).

        • +
      +
      • +
    +
    +
    +
    bs = 128
+epochs = 4
+lr = 8e-5
+args = TrainingArguments('outputs', learning_rate=lr, warmup_ratio=0.1, lr_scheduler_type='cosine', fp16=True,
+    evaluation_strategy="epoch", per_device_train_batch_size=bs, per_device_eval_batch_size=bs*2,
+    num_train_epochs=epochs, weight_decay=0.01, report_to='none')
+
    +
    +
    +
    +

    Now, we can initialize a pre-trained sequence classification model and sets up a training environment using Hugging Face’s Trainer. The model is loaded with AutoModelForSequenceClassification.from_pretrained and configured with training parameters in the Trainer object.

    +
    +
    +
    model = AutoModelForSequenceClassification.from_pretrained(model_nm, num_labels=1)
+trainer = Trainer(model, args, train_dataset=dds['train'], eval_dataset=dds['test'],
+                  tokenizer=tokz)
+
    +
    +
    +
    +
    Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at deberta-v3-small and are newly initialized: ['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+
    +
    +
    +
    +
    +
    +
    trainer.train();
+
    +
    +
    +
    +
    +
    + + + [856/856 00:53, Epoch 4/4] +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    EpochTraining LossValidation Loss
    1No log0.026275
    2No log0.021973
    30.0396000.022443
    40.0396000.023286

    +
    +
    +
    +
    preds = trainer.predict(eval_ds).predictions.astype(float)
+preds
+
    +
    +
    +
    +
    array([[-1.50489807e-03],
+       [ 4.90570068e-03],
+       [-5.05447388e-04],
+       [ 2.69412994e-04],
+       [-1.44767761e-03],
+       [ 4.85897064e-04],
+       [-1.81484222e-03],
+       [ 8.22067261e-04],
+       [ 4.36019897e-03],
+       [ 4.40216064e-03],
+       [-6.16550446e-04],
+       [-4.18424606e-05],
+       [-1.20639801e-03],
+       [ 3.18288803e-04],
+       [-6.15119934e-04],
+       [-8.05377960e-04],
+       [-2.66265869e-03],
+       [ 2.60114670e-04],
+       [ 3.48281860e-03],
+       [ 1.68323517e-03],
+       [ 1.38378143e-03],
+       [-2.48527527e-03],
+       [ 7.53879547e-04],
+       [ 8.55922699e-04],
+       [-2.27355957e-03],
+       [-2.88581848e-03],
+       [ 3.29780579e-03],
+       [ 9.42707062e-04],
+       [ 4.26769257e-04],
+       [-1.19447708e-04],
+       [-2.77519226e-03],
+       [ 5.27381897e-04],
+       [-8.44001770e-04],
+       [ 4.88281250e-04],
+       [-2.11715698e-04],
+       [-1.00421906e-03]])
+
    +
    +
    +
    +

    Look out - some of our predictions are <0, or >1! Let’s fix those out-of-bounds predictions:

    +
    +
    +
    preds = np.clip(preds, 0, 1)
+preds
+
    +
    +
    +
    +
    array([[0.        ],
+       [0.0049057 ],
+       [0.        ],
+       [0.00026941],
+       [0.        ],
+       [0.0004859 ],
+       [0.        ],
+       [0.00082207],
+       [0.0043602 ],
+       [0.00440216],
+       [0.        ],
+       [0.        ],
+       [0.        ],
+       [0.00031829],
+       [0.        ],
+       [0.        ],
+       [0.        ],
+       [0.00026011],
+       [0.00348282],
+       [0.00168324],
+       [0.00138378],
+       [0.        ],
+       [0.00075388],
+       [0.00085592],
+       [0.        ],
+       [0.        ],
+       [0.00329781],
+       [0.00094271],
+       [0.00042677],
+       [0.        ],
+       [0.        ],
+       [0.00052738],
+       [0.        ],
+       [0.00048828],
+       [0.        ],
+       [0.        ]])
+
    +
    +
    +
    +
    +
    +
    +

    40.127. Acknowledgments#

    +

    Thanks to Jeremy Howard for creating Getting started with NLP for absolute beginners. It inspires the majority of the content in this chapter.

    +
    + + + + + +
    + +
    + +
    +
    +
    + +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/assignments/deep-learning/nn-classify-15-fruits-assignment.html b/assignments/deep-learning/nn-classify-15-fruits-assignment.html index 7879be1f52..780b82d6e1 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 @@ - 40.110. NN Classify 15 Fruits Assignment — Ocademy Open Machine Learning Book + 40.112. NN Classify 15 Fruits Assignment — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1041. + + 40.89. Introduction + +
  1042. - 40.89. Study the solvers + 40.90. Study the solvers
  1043. - 40.90. Build classification models + 40.91. Build classification models
  1044. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1045. - 40.92. Parameter play + 40.93. Parameter play
  1046. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1047. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1048. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1049. +
  1049. + + 40.99. Image classification
  1050. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1051. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1052. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1053. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1054. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1055. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1056. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1057. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1058. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1059. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1060. - 40.116. Art by gan + 40.118. Art by gan
  1061. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1062. +
  1062. + + 40.121. Comparing edge-based and region-based segmentation
  1063. - 40.119. Summary + 40.122. Summary
  1064. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1065. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1066. +
  1066. + + 40.126. Getting Start NLP with classification task
    1067. @@ -1600,46 +1620,46 @@

    Ocademy Open Machine Learning Book

    @@ -1663,46 +1683,46 @@

    Contents

    @@ -1716,11 +1736,11 @@

    Contents

    -

    40.110. NN Classify 15 Fruits Assignment#

    +

    40.112. NN Classify 15 Fruits Assignment#

    Fruit Example

    -

    40.111. Data collection#

    +

    40.113. Data collection#

    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 @@ -1759,7 +1779,7 @@

    40.111. Data collection -

    40.112. Load and visualize the dataset#

    +

    40.114. Load and visualize the dataset#

    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.

    Type of fruits in the dataset:

      @@ -1935,7 +1955,7 @@

      40.112. Load and visualize the dataset

    -

    40.113. Train the neural network from scratch with Keras and w/o generator#

    +

    40.115. Train the neural network from scratch with Keras and w/o generator#

    # The pictures will be resized to have the same size for the neural network
@@ -1952,7 +1972,7 @@ 
    40.113. Train the neural network from sc
    -

    40.113.1. Create and train the NN Model#

    +

    40.115.1. Create and train the NN Model#

    def cut_df(df, number_of_parts, part):
@@ -2131,7 +2151,7 @@ 
    40.113.1. Create and train the NN Model<
    -

    40.113.2. Predictions#

    +

    40.115.2. Predictions#

    import warnings
@@ -2182,7 +2202,7 @@ 
    40.113.2. Predictions
    -

    40.113.3. Visualize the result with pictures of fruits#

    +

    40.115.3. Visualize the result with pictures of fruits#

    fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(10, 10),
@@ -2201,7 +2221,7 @@ 
    40.113.3. Visualize the result with pict
    -

    40.114. Acknowledgments#

    +

    40.116. Acknowledgments#

    Thanks to DATALIRA for creating the open-source course Classify 15 Fruits with TensorFlow . It inspires the majority of the content in this chapter.

    @@ -2242,13 +2262,13 @@

    40.114. Acknowledgments

    previous

    -

    40.109. Neural Networks for Classification with TensorFlow

    +

    40.111. Neural Networks for Classification with TensorFlow

    next

    -

    40.115. DQN On Foreign Exchange Market

    +

    40.117. DQN On Foreign Exchange Market

     diff --git a/assignments/deep-learning/nn-for-classification-assignment.html b/assignments/deep-learning/nn-for-classification-assignment.html index fba1decde4..767fcdbb70 100644 --- a/assignments/deep-learning/nn-for-classification-assignment.html +++ b/assignments/deep-learning/nn-for-classification-assignment.html @@ -6,7 +6,7 @@ - 40.109. Neural Networks for Classification with TensorFlow — Ocademy Open Machine Learning Book + 40.111. Neural Networks for Classification with TensorFlow — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1067. + + 40.89. Introduction + +
  1068. - 40.89. Study the solvers + 40.90. Study the solvers
  1069. - 40.90. Build classification models + 40.91. Build classification models
  1070. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1071. - 40.92. Parameter play + 40.93. Parameter play
  1072. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1073. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1074. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1075. +
  1075. + + 40.99. Image classification
  1076. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1077. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1078. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1079. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1080. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1081. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1082. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1083. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1084. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1085. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1086. - 40.116. Art by gan + 40.118. Art by gan
  1087. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1088. +
  1088. + + 40.121. Comparing edge-based and region-based segmentation
  1089. - 40.119. Summary + 40.122. Summary
  1090. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1091. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1092. +
  1092. + + 40.126. Getting Start NLP with classification task
    1093. @@ -1600,101 +1620,101 @@

    Ocademy Open Machine Learning Book

    @@ -1718,101 +1738,101 @@

    Contents

    @@ -1826,14 +1846,14 @@

    Contents

    -

    40.109. Neural Networks for Classification with TensorFlow#

    +

    40.111. Neural Networks for Classification with TensorFlow#

    -

    40.109.1. Getting Started: Binary Classifier#

    +

    40.111.1. Getting Started: Binary Classifier#

    We will first practice building neural networks for binary classifier. In binary classification, we have two classes.

    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.

    The dataset contains two labels: malignant, benign.

    -

    40.109.1.1. Getting the data#

    +

    40.111.1.1. Getting the data#

    We will get the data from sklearn datasets.

    @@ -1912,7 +1932,7 @@

    40.109.1.1. Getting the data -

    40.109.2. Taking a look in the data#

    +

    40.111.2. Taking a look in the data#

    # Looking from the head 
@@ -1942,7 +1962,7 @@ 
    40.109.2. Taking a look in the data
    -

    40.109.3. Preparing the Data#

    +

    40.111.3. Preparing the Data#

    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.

    @@ -1985,7 +2005,7 @@

    40.109.3. Preparing the Data -

    40.109.4. Creating, Compiling and Training a Model#

    +

    40.111.4. Creating, Compiling and Training a Model#

    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.

    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.

    Also, if we are interested in tracking other metrics during training, we can specify them in metric.

    @@ -2038,7 +2058,7 @@

    40.109.4. Creating, Compiling and Traini

    ‼️ 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.

    -

    40.109.5. Visualizing the Results#

    +

    40.111.5. Visualizing the Results#

    Visualizing the model results after training is always a good way to learn what you can do to improve the performance.

    Let’s get a Pandas dataframe containing training loss and accuracy, and validation loss and accuracy.

    @@ -2056,7 +2076,7 @@

    40.109.5. Visualizing the ResultsLet’s evaluate the model on the test set.

    -

    40.109.6. Evaluating the Model#

    +

    40.111.6. Evaluating the Model#

    Quite often, you will want to test your model on the data that it never saw. This data is normally called test set and in more applied practice, you will only feed the test to the model after you have done your best to improve it.

    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.

    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.

    @@ -2174,13 +2194,13 @@

    40.109.6. Evaluating the Model -

    40.109.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier#

    +

    40.111.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier#

    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).

    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.

    That is going to be cool! Let’s get started!

    -

    40.109.8. Getting the Fashion data#

    +

    40.111.8. Getting the Fashion data#

    Let’s get the dataset from Keras.

    @@ -2195,7 +2215,7 @@

    40.109.8. Getting the Fashion data

    -

    40.109.9. Looking in the Fashion Data#

    +

    40.111.9. Looking in the Fashion Data#

    As always, it is a best practice to peep into the images to see how they like.

    Let’s display the pixels values of a given image, image, and its corresponding label.

    @@ -2266,7 +2286,7 @@

    40.109.9. Looking in the Fashion DataNow that we know the dataset that we are working with, let us do some few proprocessing before building a model.

    -

    40.109.10. Preparing the Data#

    +

    40.111.10. Preparing the Data#

    In many cases, real world images datasets are not that clean like fashion mnist.

    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.

    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!!

    @@ -2285,7 +2305,7 @@

    40.109.10. Preparing the Data -

    40.109.11. Creating, Compiling, and Training a Model#

    +

    40.111.11. Creating, Compiling, and Training a Model#

    There are few points to note before creating a model:

    • 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).

      • @@ -2335,7 +2355,7 @@

      40.109.11. Creating, Compiling, and Trai

      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.

    -

    40.109.12. Visualizing the Model Results#

    +

    40.111.12. Visualizing the Model Results#

    Let’s visualize the model results to see how training went.

    @@ -2354,7 +2374,7 @@

    40.109.12. Visualizing the Model Results

    Let’s see how the model performs on unseed data: test set.

    -

    40.109.13. Model Evaluation#

    +

    40.111.13. Model Evaluation#

    # if you need a model trained, you can use this cell
@@ -2385,11 +2405,11 @@ 
    40.109.13. Model Evaluation
-
    40.109.14. Controlling Training with Callbacks#
    
+
    40.111.14. Controlling Training with Callbacks#
    
 
    We can use Callbacks functions to control the training.
    
 
    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.
    
 
    
-
    40.109.14.1. Implementing Callbacks#
    
+
    40.111.14.1. Implementing Callbacks#
    
 
    There are various functionalities available in Keras Callbacks.
    
 
    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.
    
 
    I will rebuild a same model again.
    
@@ -2565,7 +2585,7 @@ 
    40.109.14.1. Implementing Callbacks
 
    
 
    
-
    40.109.14.2. Custom Callback#
    
+
    40.111.14.2. Custom Callback#
    
 
    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.
    
 
    You can do certain actions on almost every step of the training. Let’s stop the training when the accuracy is 95%.
    
 
    
@@ -2630,7 +2650,7 @@ 
    40.109.14.2. Custom Callback
-
    40.109.15. Using TensorBoard for Model Visualization#
    
+
    40.111.15. Using TensorBoard for Model Visualization#
    
 
    Tensorboard is incredible tool used by many people (and not just only TensorFlow developers) to experiment with machine learning.
    
 
    With TensorBoard, you can:
    
 
      
@@ -2721,7 +2741,7 @@ 
      40.109.15. Using TensorBoard for Model V
 
      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.
      
 
    
 
    
-
    40.109.16. Acknowledgments#
    
+
    40.111.16. Acknowledgments#
    
 
    Thanks to Jean de Dieu Nyandwi for creating the open-source course  machine learning complete . It inspires the majority of the content in this chapter.
    @@ -2763,13 +2783,13 @@

    40.109.16. Acknowledgments

    previous

    -

    40.107. Time Series Forecasting Assignment

    +

    40.109. Time Series Forecasting Assignment

    next

    -

    40.110. NN Classify 15 Fruits Assignment

    +

    40.112. NN Classify 15 Fruits Assignment

     diff --git a/assignments/deep-learning/object-detection/car-object-detection.html b/assignments/deep-learning/object-detection/car-object-detection.html index e7c303b30f..a73140b98c 100644 --- a/assignments/deep-learning/object-detection/car-object-detection.html +++ b/assignments/deep-learning/object-detection/car-object-detection.html @@ -6,7 +6,7 @@ - 40.120. Car Object Detection — Ocademy Open Machine Learning Book + 40.123. Car Object Detection — Ocademy Open Machine Learning Book @@ -27,8 +27,8 @@ - + @@ -98,8 +98,8 @@ - - + + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1093. + + 40.89. Introduction + +
  1094. - 40.89. Study the solvers + 40.90. Study the solvers
  1095. - 40.90. Build classification models + 40.91. Build classification models
  1096. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1097. - 40.92. Parameter play + 40.93. Parameter play
  1098. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1099. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1100. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1101. +
  1101. + + 40.99. Image classification
  1102. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1103. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1104. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1105. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1106. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1107. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1108. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1109. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1110. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1111. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1112. - 40.116. Art by gan + 40.118. Art by gan
  1113. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1114. +
  1114. + + 40.121. Comparing edge-based and region-based segmentation
  1115. - 40.119. Summary + 40.122. Summary
  1116. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1117. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1118. +
  1118. + + 40.126. Getting Start NLP with classification task
    1119. @@ -1600,44 +1620,44 @@

    Ocademy Open Machine Learning Book

    diff --git a/data-science/working-with-data/relational-databases.html b/data-science/working-with-data/relational-databases.html index 16b8b2eb89..de4bb8a83d 100644 --- a/data-science/working-with-data/relational-databases.html +++ b/data-science/working-with-data/relational-databases.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1119. + + 40.89. Introduction + +
  1120. - 40.89. Study the solvers + 40.90. Study the solvers
  1121. - 40.90. Build classification models + 40.91. Build classification models
  1122. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1123. - 40.92. Parameter play + 40.93. Parameter play
  1124. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1125. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1126. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1127. +
  1127. + + 40.99. Image classification
  1128. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1129. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1130. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1131. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1132. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1133. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1134. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1135. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1136. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1137. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1138. - 40.116. Art by gan + 40.118. Art by gan
  1139. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1140. +
  1140. + + 40.121. Comparing edge-based and region-based segmentation
  1141. - 40.119. Summary + 40.122. Summary
  1142. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1143. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1144. +
  1144. + + 40.126. Getting Start NLP with classification task
    1145. diff --git a/data-science/working-with-data/working-with-data.html b/data-science/working-with-data/working-with-data.html index bfdd8f9710..a8666c714d 100644 --- a/data-science/working-with-data/working-with-data.html +++ b/data-science/working-with-data/working-with-data.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1145. + + 40.89. Introduction + +
  1146. - 40.89. Study the solvers + 40.90. Study the solvers
  1147. - 40.90. Build classification models + 40.91. Build classification models
  1148. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1149. - 40.92. Parameter play + 40.93. Parameter play
  1150. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1151. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1152. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1153. +
  1153. + + 40.99. Image classification
  1154. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1155. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1156. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1157. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1158. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1159. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1160. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1161. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1162. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1163. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1164. - 40.116. Art by gan + 40.118. Art by gan
  1165. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1166. +
  1166. + + 40.121. Comparing edge-based and region-based segmentation
  1167. - 40.119. Summary + 40.122. Summary
  1168. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1169. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1170. +
  1170. + + 40.126. Getting Start NLP with classification task
    1171. diff --git a/deep-learning/autoencoder.html b/deep-learning/autoencoder.html index 568f2e2a81..3b55978560 100644 --- a/deep-learning/autoencoder.html +++ b/deep-learning/autoencoder.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1171. + + 40.89. Introduction + +
  1172. - 40.89. Study the solvers + 40.90. Study the solvers
  1173. - 40.90. Build classification models + 40.91. Build classification models
  1174. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1175. - 40.92. Parameter play + 40.93. Parameter play
  1176. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1177. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1178. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1179. +
  1179. + + 40.99. Image classification
  1180. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1181. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1182. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1183. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1184. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1185. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1186. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1187. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1188. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1189. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1190. - 40.116. Art by gan + 40.118. Art by gan
  1191. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1192. +
  1192. + + 40.121. Comparing edge-based and region-based segmentation
  1193. - 40.119. Summary + 40.122. Summary
  1194. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1195. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1196. +
  1196. + + 40.126. Getting Start NLP with classification task
    1197. diff --git a/deep-learning/cnn/cnn-deepdream.html b/deep-learning/cnn/cnn-deepdream.html index 5b03d3d5c2..75eb8d9c4f 100644 --- a/deep-learning/cnn/cnn-deepdream.html +++ b/deep-learning/cnn/cnn-deepdream.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1197. + + 40.89. Introduction + +
  1198. - 40.89. Study the solvers + 40.90. Study the solvers
  1199. - 40.90. Build classification models + 40.91. Build classification models
  1200. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1201. - 40.92. Parameter play + 40.93. Parameter play
  1202. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1203. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1204. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1205. +
  1205. + + 40.99. Image classification
  1206. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1207. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1208. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1209. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1210. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1211. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1212. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1213. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1214. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1215. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1216. - 40.116. Art by gan + 40.118. Art by gan
  1217. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1218. +
  1218. + + 40.121. Comparing edge-based and region-based segmentation
  1219. - 40.119. Summary + 40.122. Summary
  1220. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1221. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1222. +
  1222. + + 40.126. Getting Start NLP with classification task
    1223. @@ -1899,7 +1919,7 @@

    23.3.1.2.1. Code

    23.3.1.2.2. Your turn! 🚀#

    -

    TBD.

    +

    You can practice your cnn skills by following the assignment sign language digits classification with cnn

    23.3.1.2.3. Acknowledgments#

    diff --git a/deep-learning/cnn/cnn-vgg.html b/deep-learning/cnn/cnn-vgg.html index 224cd5e214..75b4faf4ce 100644 --- a/deep-learning/cnn/cnn-vgg.html +++ b/deep-learning/cnn/cnn-vgg.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1223. + + 40.89. Introduction + +
  1224. - 40.89. Study the solvers + 40.90. Study the solvers
  1225. - 40.90. Build classification models + 40.91. Build classification models
  1226. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1227. - 40.92. Parameter play + 40.93. Parameter play
  1228. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1229. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1230. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1231. +
  1231. + + 40.99. Image classification
  1232. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1233. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1234. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1235. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1236. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1237. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1238. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1239. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1240. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1241. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1242. - 40.116. Art by gan + 40.118. Art by gan
  1243. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1244. +
  1244. + + 40.121. Comparing edge-based and region-based segmentation
  1245. - 40.119. Summary + 40.122. Summary
  1246. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1247. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1248. +
  1248. + + 40.126. Getting Start NLP with classification task
    1249. @@ -2038,7 +2058,7 @@

    23.3.1.1.1.4. Style Transfer Implementat

    23.3.1.1.2. Your turn! 🚀#

    -

    TBD.

    +

    You can practice your cnn skills by following the assignment object recognition in images using cnn.

    23.3.1.1.3. Self study#

    diff --git a/deep-learning/cnn/cnn.html b/deep-learning/cnn/cnn.html index cbb5d79d26..08dc345f39 100644 --- a/deep-learning/cnn/cnn.html +++ b/deep-learning/cnn/cnn.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1249. + + 40.89. Introduction + +
  1250. - 40.89. Study the solvers + 40.90. Study the solvers
  1251. - 40.90. Build classification models + 40.91. Build classification models
  1252. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1253. - 40.92. Parameter play + 40.93. Parameter play
  1254. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1255. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1256. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1257. +
  1257. + + 40.99. Image classification
  1258. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1259. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1260. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1261. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1262. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1263. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1264. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1265. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1266. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1267. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1268. - 40.116. Art by gan + 40.118. Art by gan
  1269. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1270. +
  1270. + + 40.121. Comparing edge-based and region-based segmentation
  1271. - 40.119. Summary + 40.122. Summary
  1272. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1273. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1274. +
  1274. + + 40.126. Getting Start NLP with classification task
    1275. @@ -1827,6 +1847,30 @@

    23.1. MNIST handwritten digits
    from IPython.display import HTML
 display(HTML("""
 <p style="text-align: center;">
+<iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/cnn-vis-3/index.html" width="105%" height="600px;"
+style="border:none;" scrolling="auto"></iframe>
+A demo of CNN. <a
+href="https://poloclub.github.io/cnn-explainer/"> [source]</a>
+</p>
+"""))
+
    +

    +
    +
    +
    +

    + +A demo of CNN. [source] +

    +
    +
    +
    +
    +
    from IPython.display import HTML
+display(HTML("""
+<p style="text-align: center;">
 <iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/cnn-vis-2/index.html" width="105%" height="600px;"
 style="border:none;" scrolling="auto"></iframe>
 A demo of CNN. <a
@@ -1907,7 +1951,7 @@ 
    23.1.1. Code
+../../_images/cnn_11_1.png
    @@ -2125,12 +2169,12 @@

    23.1.1. Code -../../_images/cnn_11_2.png +../../_images/cnn_12_1.png +../../_images/cnn_12_2.png 
    1/1 [==============================] - 0s 97ms/step
    -../../_images/cnn_11_4.png +../../_images/cnn_12_4.png

    @@ -2410,7 +2454,7 @@

    23.3.1. Code

    23.4. Your turn! 🚀#

    -

    TBD.

    +

    You can practice your cnn skills by following the assignment how to choose cnn architecture mnist.

    23.5. Self study#

    diff --git a/deep-learning/difussion-model.html b/deep-learning/difussion-model.html index a2a13748a6..6e0a59ed76 100644 --- a/deep-learning/difussion-model.html +++ b/deep-learning/difussion-model.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1275. + + 40.89. Introduction + +
  1276. - 40.89. Study the solvers + 40.90. Study the solvers
  1277. - 40.90. Build classification models + 40.91. Build classification models
  1278. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1279. - 40.92. Parameter play + 40.93. Parameter play
  1280. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1281. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1282. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1283. +
  1283. + + 40.99. Image classification
  1284. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1285. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1286. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1287. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1288. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1289. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1290. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1291. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1292. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1293. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1294. - 40.116. Art by gan + 40.118. Art by gan
  1295. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1296. +
  1296. + + 40.121. Comparing edge-based and region-based segmentation
  1297. - 40.119. Summary + 40.122. Summary
  1298. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1299. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1300. +
  1300. + + 40.126. Getting Start NLP with classification task
    1301. diff --git a/deep-learning/dl-overview.html b/deep-learning/dl-overview.html index c7d80d0a18..e9e82d8ce9 100644 --- a/deep-learning/dl-overview.html +++ b/deep-learning/dl-overview.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1301. + + 40.89. Introduction + +
  1302. - 40.89. Study the solvers + 40.90. Study the solvers
  1303. - 40.90. Build classification models + 40.91. Build classification models
  1304. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1305. - 40.92. Parameter play + 40.93. Parameter play
  1306. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1307. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1308. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1309. +
  1309. + + 40.99. Image classification
  1310. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1311. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1312. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1313. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1314. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1315. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1316. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1317. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1318. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1319. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1320. - 40.116. Art by gan + 40.118. Art by gan
  1321. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1322. +
  1322. + + 40.121. Comparing edge-based and region-based segmentation
  1323. - 40.119. Summary + 40.122. Summary
  1324. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1325. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1326. +
  1326. + + 40.126. Getting Start NLP with classification task
    1327. diff --git a/deep-learning/dqn.html b/deep-learning/dqn.html index fd7c3e81e4..88c4942f14 100644 --- a/deep-learning/dqn.html +++ b/deep-learning/dqn.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1327. + + 40.89. Introduction + +
  1328. - 40.89. Study the solvers + 40.90. Study the solvers
  1329. - 40.90. Build classification models + 40.91. Build classification models
  1330. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1331. - 40.92. Parameter play + 40.93. Parameter play
  1332. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1333. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1334. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1335. +
  1335. + + 40.99. Image classification
  1336. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1337. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1338. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1339. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1340. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1341. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1342. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1343. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1344. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1345. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1346. - 40.116. Art by gan + 40.118. Art by gan
  1347. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1348. +
  1348. + + 40.121. Comparing edge-based and region-based segmentation
  1349. - 40.119. Summary + 40.122. Summary
  1350. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1351. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1352. +
  1352. + + 40.126. Getting Start NLP with classification task
    1353. diff --git a/deep-learning/gan.html b/deep-learning/gan.html index 2eced21695..6b2eafca88 100644 --- a/deep-learning/gan.html +++ b/deep-learning/gan.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1353. + + 40.89. Introduction + +
  1354. - 40.89. Study the solvers + 40.90. Study the solvers
  1355. - 40.90. Build classification models + 40.91. Build classification models
  1356. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1357. - 40.92. Parameter play + 40.93. Parameter play
  1358. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1359. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1360. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1361. +
  1361. + + 40.99. Image classification
  1362. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1363. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1364. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1365. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1366. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1367. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1368. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1369. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1370. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1371. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1372. - 40.116. Art by gan + 40.118. Art by gan
  1373. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1374. +
  1374. + + 40.121. Comparing edge-based and region-based segmentation
  1375. - 40.119. Summary + 40.122. Summary
  1376. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1377. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1378. +
  1378. + + 40.126. Getting Start NLP with classification task
    1379. diff --git a/deep-learning/image-classification.html b/deep-learning/image-classification.html index 90c77a6fbe..f91d94831e 100644 --- a/deep-learning/image-classification.html +++ b/deep-learning/image-classification.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1379. + + 40.89. Introduction + +
  1380. - 40.89. Study the solvers + 40.90. Study the solvers
  1381. - 40.90. Build classification models + 40.91. Build classification models
  1382. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1383. - 40.92. Parameter play + 40.93. Parameter play
  1384. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1385. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1386. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1387. +
  1387. + + 40.99. Image classification
  1388. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1389. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1390. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1391. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1392. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1393. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1394. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1395. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1396. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1397. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1398. - 40.116. Art by gan + 40.118. Art by gan
  1399. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1400. +
  1400. + + 40.121. Comparing edge-based and region-based segmentation
  1401. - 40.119. Summary + 40.122. Summary
  1402. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1403. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1404. +
  1404. + + 40.126. Getting Start NLP with classification task
    1405. @@ -1956,8 +1976,8 @@

    31.4.1. VGGNet

  1405. Using small 3×3 filters to replace large convolutional kernels.

  1406. After replacing the convolution kernel, the convolution layers have the same perceptual field.

    1407. -

  1407. Each layer is trained by Re LU activation function and batch gradient descent after convolution operation.

    1408. -

  1408. It is verified that increasing the network depth can improve the model performance Although, VGG has achieved good results in image classification and localization problems in 2014 due to its deeper network structure and low computational complexity, it uses 140 million parameters and is computationally intensive, which is its shortcoming.

    1409. +

  1409. Each layer is trained by ReLU activation function and batch gradient descent after convolution operation.

    1410. +

  1410. It is verified that increasing the network depth can improve the model performance. Although, VGG has achieved good results in image classification and localization problems in 2014 due to its deeper network structure and low computational complexity, it uses 140 million parameters and is computationally intensive, which is its shortcoming.

    1411. https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/02_VGG.png @@ -2585,7 +2605,7 @@

    31.6.2. Top-5 accuracy

    31.7. Your turn! 🚀#

    -

    TBD.

    +

    Assignment - Image classification

    31.8. Acknowledgments#

    diff --git a/deep-learning/image-segmentation.html b/deep-learning/image-segmentation.html index c1f29672f9..edf0f5ca61 100644 --- a/deep-learning/image-segmentation.html +++ b/deep-learning/image-segmentation.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1411. + + 40.89. Introduction + +
  1412. - 40.89. Study the solvers + 40.90. Study the solvers
  1413. - 40.90. Build classification models + 40.91. Build classification models
  1414. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1415. - 40.92. Parameter play + 40.93. Parameter play
  1416. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1417. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1418. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1419. +
  1419. + + 40.99. Image classification
  1420. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1421. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1422. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1423. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1424. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1425. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1426. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1427. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1428. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1429. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1430. - 40.116. Art by gan + 40.118. Art by gan
  1431. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1432. +
  1432. + + 40.121. Comparing edge-based and region-based segmentation
  1433. - 40.119. Summary + 40.122. Summary
  1434. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1435. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1436. +
  1436. + + 40.126. Getting Start NLP with classification task
    1437. @@ -3338,7 +3358,7 @@

    32.3.4.1. Code

    32.4. Your turn! 🚀#

    -

    TBD.

    +

    Assignment - Comparing edge-based and region-based segmentation

    32.5. Acknowledgments#

    diff --git a/deep-learning/lstm.html b/deep-learning/lstm.html index cc046448ab..d348ce152e 100644 --- a/deep-learning/lstm.html +++ b/deep-learning/lstm.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1437. + + 40.89. Introduction + +
  1438. - 40.89. Study the solvers + 40.90. Study the solvers
  1439. - 40.90. Build classification models + 40.91. Build classification models
  1440. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1441. - 40.92. Parameter play + 40.93. Parameter play
  1442. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1443. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1444. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1445. +
  1445. + + 40.99. Image classification
  1446. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1447. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1448. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1449. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1450. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1451. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1452. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1453. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1454. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1455. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1456. - 40.116. Art by gan + 40.118. Art by gan
  1457. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1458. +
  1458. + + 40.121. Comparing edge-based and region-based segmentation
  1459. - 40.119. Summary + 40.122. Summary
  1460. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1461. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1462. +
  1462. + + 40.126. Getting Start NLP with classification task
    1463. @@ -6248,7 +6268,7 @@

    28.2.1. Code

    28.3. Your turn! 🚀#

    -

    Practice the Long-Short Term Memory Networks by following this TBD.

    +

    Assignment - Bitcoin lstm model with tweet volume and sentiment

    28.4. Acknowledgments#

    diff --git a/deep-learning/nlp.html b/deep-learning/nlp.html index 565d92a3c5..7696ec0256 100644 --- a/deep-learning/nlp.html +++ b/deep-learning/nlp.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1463. + + 40.89. Introduction + +
  1464. - 40.89. Study the solvers + 40.90. Study the solvers
  1465. - 40.90. Build classification models + 40.91. Build classification models
  1466. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1467. - 40.92. Parameter play + 40.93. Parameter play
  1468. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1469. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1470. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1471. +
  1471. + + 40.99. Image classification
  1472. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1473. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1474. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1475. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1476. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1477. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1478. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1479. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1480. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1481. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1482. - 40.116. Art by gan + 40.118. Art by gan
  1483. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1484. +
  1484. + + 40.121. Comparing edge-based and region-based segmentation
  1485. - 40.119. Summary + 40.122. Summary
  1486. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1487. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1488. +
  1488. + + 40.126. Getting Start NLP with classification task
    1489. @@ -2206,7 +2226,7 @@

    26.5.2. Model

    26.6. Your turn! 🚀#

    -

    TBD.

    +

    You can practice your nlp skills by following the assignment getting start nlp with classification task.

    26.7. Acknowledgments#

    diff --git a/deep-learning/nn.html b/deep-learning/nn.html index 94e8a40e0e..c96b6d24d5 100644 --- a/deep-learning/nn.html +++ b/deep-learning/nn.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1489. + + 40.89. Introduction + +
  1490. - 40.89. Study the solvers + 40.90. Study the solvers
  1491. - 40.90. Build classification models + 40.91. Build classification models
  1492. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1493. - 40.92. Parameter play + 40.93. Parameter play
  1494. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1495. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1496. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1497. +
  1497. + + 40.99. Image classification
  1498. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1499. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1500. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1501. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1502. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1503. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1504. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1505. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1506. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1507. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1508. - 40.116. Art by gan + 40.118. Art by gan
  1509. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1510. +
  1510. + + 40.121. Comparing edge-based and region-based segmentation
  1511. - 40.119. Summary + 40.122. Summary
  1512. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1513. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1514. +
  1514. + + 40.126. Getting Start NLP with classification task
    1515. diff --git a/deep-learning/object-detection.html b/deep-learning/object-detection.html index e4ea5a56f5..cde938572f 100644 --- a/deep-learning/object-detection.html +++ b/deep-learning/object-detection.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1515. + + 40.89. Introduction + +
  1516. - 40.89. Study the solvers + 40.90. Study the solvers
  1517. - 40.90. Build classification models + 40.91. Build classification models
  1518. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1519. - 40.92. Parameter play + 40.93. Parameter play
  1520. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1521. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1522. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1523. +
  1523. + + 40.99. Image classification
  1524. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1525. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1526. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1527. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1528. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1529. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1530. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1531. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1532. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1533. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1534. - 40.116. Art by gan + 40.118. Art by gan
  1535. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1536. +
  1536. + + 40.121. Comparing edge-based and region-based segmentation
  1537. - 40.119. Summary + 40.122. Summary
  1538. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1539. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1540. +
  1540. + + 40.126. Getting Start NLP with classification task
    1541. diff --git a/deep-learning/rnn.html b/deep-learning/rnn.html index c7631b9f19..aa492b2ef4 100644 --- a/deep-learning/rnn.html +++ b/deep-learning/rnn.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1541. + + 40.89. Introduction + +
  1542. - 40.89. Study the solvers + 40.90. Study the solvers
  1543. - 40.90. Build classification models + 40.91. Build classification models
  1544. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1545. - 40.92. Parameter play + 40.93. Parameter play
  1546. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1547. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1548. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1549. +
  1549. + + 40.99. Image classification
  1550. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1551. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1552. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1553. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1554. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1555. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1556. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1557. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1558. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1559. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1560. - 40.116. Art by gan + 40.118. Art by gan
  1561. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1562. +
  1562. + + 40.121. Comparing edge-based and region-based segmentation
  1563. - 40.119. Summary + 40.122. Summary
  1564. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1565. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1566. +
  1566. + + 40.126. Getting Start NLP with classification task
    1567. @@ -1610,14 +1630,9 @@

    Ocademy Open Machine Learning Book

    25.2. Your turn! 🚀 -
  1567. - - 25.3. Self study - -
  1568. - 25.4. Acknowledgments + 25.3. Acknowledgments
    1569. @@ -1649,14 +1664,9 @@

    Contents

    25.2. Your turn! 🚀 -
  1569. - - 25.3. Self study - -
  1570. - 25.4. Acknowledgments + 25.3. Acknowledgments
    1571. @@ -1679,6 +1689,12 @@

    Contents

    +
    +
    WARNING: Ignoring invalid distribution -fds-nightly (c:\users\16111\.conda\envs\open-machine-learning-jupyter-book\lib\site-packages)
+WARNING: Ignoring invalid distribution -fds-nightly (c:\users\16111\.conda\envs\open-machine-learning-jupyter-book\lib\site-packages)
+
    +
    +

    25. Recurrent Neural Networks#

    @@ -1750,260 +1766,256 @@

    25. Recurrent Neural Networks

    25.1. Code#

    +

    A text classifier implemented in TensorFlow to classify SMS spam messages. +Code first downloads and processes the SMS Spam Collection dataset from the UCI Machine Learning Repository and then builds a basic Recurrent neural network (RNN) for text classification using TensorFlow. +The code first cleans and preprocesses the text, then splits it into training and test sets, followed by tokenizing and padding the training set. Next, the code uses an embedding layer to convert the tokenized text into a vector representation, which is then fed into a recurrent neural network and finally classified using a Softmax loss function. +The output of the # code is the accuracy of the classifier along with some statistics +We implement an RNN in TensorFlow to predict spam/ham from texts

    -
    # A text classifier implemented in TensorFlow to classify SMS spam messages.
-
-# Code first downloads and processes the SMS Spam Collection dataset from the UCI Machine Learning Repository and then builds a basic Recurrent neural network (RNN) for text classification using TensorFlow.
-
-# The code first cleans and preprocesses the text, then splits it into training and test sets, followed by tokenizing and padding the training set. Next, the code uses an embedding layer to convert the tokenized text into a vector representation, which is then fed into a recurrent neural network and finally classified using a Softmax loss function.
-
-#The output of the # code is the accuracy of the classifier along with some statistics
-
-# We implement an RNN in TensorFlow to predict spam/ham from texts
-
-import os
+
    import os
 import re
 import io
 import requests
 import numpy as np
 import matplotlib.pyplot as plt
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 from zipfile import ZipFile
-from tensorflow.python.framework import ops
-tf.disable_v2_behavior()
-ops.reset_default_graph()
-
-# Start a graph
-sess = tf.Session()
-
-# Set RNN parameters
-epochs = 20
-batch_size = 250
-max_sequence_length =25 
-rnn_size = 10
-embedding_size = 50
-min_word_frequency = 10
-learning_rate = 0.0005
-dropout_keep_prob = tf.placeholder(tf.float32,name='dropout_keep_prob')
-
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
    
+
    
+
    +
    +
    +
    +
    # Set random seed for reproducibility
+tf.random.set_seed(42)
 
 # Download or open data
-data_dir = 'tmp'
-data_file = 'text_data.txt'
+data_dir = "tmp"
+data_file = "text_data.txt"
 if not os.path.exists(data_dir):
     os.makedirs(data_dir)
 
 if not os.path.isfile(os.path.join(data_dir, data_file)):
-    zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip'
+    zip_url = "http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip"
     r = requests.get(zip_url)
     z = ZipFile(io.BytesIO(r.content))
-    file = z.read('SMSSpamCollection')
+    file = z.read("SMSSpamCollection")
     # Format Data
     text_data = file.decode()
-    text_data = text_data.encode('ascii', errors='ignore')
-    text_data = text_data.decode().split('\n')
+    text_data = text_data.encode("ascii", errors="ignore")
+    text_data = text_data.decode().split("\n")
 
     # Save data to text file
-    with open(os.path.join(data_dir, data_file), 'w') as file_conn:
+    with open(os.path.join(data_dir, data_file), "w") as file_conn:
         for text in text_data:
             file_conn.write("{}\n".format(text))
 else:
     # Open data from text file
     text_data = []
-    with open(os.path.join(data_dir, data_file), 'r') as file_conn:
+    with open(os.path.join(data_dir, data_file), "r") as file_conn:
         for row in file_conn:
             text_data.append(row)
     text_data = text_data[:-1]
 
-text_data = [x.split('\t') for x in text_data if len(x) >= 1]
+text_data = [x.split("\t") for x in text_data if len(x) >= 1]
 [text_data_target, text_data_train] = [list(x) for x in zip(*text_data)]
-
-
-# Create a text cleaning function
+
    +
    +
    +
    +
    +
    +
    # Create a text cleaning function
 def clean_text(text_string):
-    text_string = re.sub(r'([^\s\w]|_|[0-9])+', '', text_string)
+    text_string = re.sub(r"([^\s\w]|_|[0-9])+", "", text_string)
     text_string = " ".join(text_string.split())
     text_string = text_string.lower()
     return text_string
-
-# Clean texts
+
    +
    +
    +
    +
    +
    +
    # Clean texts
 text_data_train = [clean_text(x) for x in text_data_train]
-#print(text_data[:5])
 print(text_data_train[:5])
 
 # Tokenize and pad sequences
-vocab_processor = tf.keras.preprocessing.text.Tokenizer()
-vocab_processor.fit_on_texts(text_data_train)
-text_processed = vocab_processor.texts_to_sequences(text_data_train)
-max_document_length = max([len(x) for x in text_processed])
-#pads the text data to ensure all sequences have the same length (max_sequence_length).
-text_processed = tf.keras.preprocessing.sequence.pad_sequences(text_processed, maxlen=max_sequence_length, padding='post')
+tokenizer = Tokenizer()
+tokenizer.fit_on_texts(text_data_train)
+text_processed = tokenizer.texts_to_sequences(text_data_train)
+max_sequence_length = 25
+text_processed = pad_sequences(
+    text_processed, maxlen=max_sequence_length, padding="post"
+)
 print(text_processed.shape)
-# Shuffle and split data
+
    +
    +
    +
    +
    ['go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat', 'ok lar joking wif u oni', 'free entry in a wkly comp to win fa cup final tkts st may text fa to to receive entry questionstd txt ratetcs apply overs', 'u dun say so early hor u c already then say', 'nah i dont think he goes to usf he lives around here though']
+(5574, 25)
+
    +
    +
    +
    +
    +
    +
    # Shuffle and split data
 text_processed = np.array(text_processed)
-text_data_target = np.array([1 if x == 'ham' else 0 for x in text_data_target])
+text_data_target = np.array([1 if x == "ham" else 0 for x in text_data_target])
 shuffled_ix = np.random.permutation(np.arange(len(text_data_target)))
 x_shuffled = text_processed[shuffled_ix]
 y_shuffled = text_data_target[shuffled_ix]
-
-# Split train/test set
-ix_cutoff = int(len(y_shuffled)*0.80)
+
    +
    +
    +
    +
    +
    +
    # Split train/test set
+ix_cutoff = int(len(y_shuffled) * 0.80)
 x_train, x_test = x_shuffled[:ix_cutoff], x_shuffled[ix_cutoff:]
-print(x_train)
 y_train, y_test = y_shuffled[:ix_cutoff], y_shuffled[ix_cutoff:]
-vocab_size = len(vocab_processor.word_counts)
+vocab_size = len(tokenizer.word_index) + 1
 print("Vocabulary Size: {:d}".format(vocab_size))
 print("80-20 Train Test split: {:d} -- {:d}".format(len(y_train), len(y_test)))
-
-# Create placeholders
-x_data = tf.placeholder(tf.int32, [None, max_sequence_length])
-y_output = tf.placeholder(tf.int32, [None])
-
-# Create embedding
-embedding_mat = tf.Variable(tf.random_uniform([vocab_size+1, embedding_size], -1.0, 1.0))
-embedding_output = tf.nn.embedding_lookup(embedding_mat, x_data)
-
-# Define the RNN cell
-# tensorflow change >= 1.0, rnn is put into tensorflow.contrib directory. Prior version not test.
-cell = tf.nn.rnn_cell.BasicRNNCell(num_units=rnn_size)
-
-output, state = tf.nn.dynamic_rnn(cell, embedding_output, dtype=tf.float32)
-output = tf.nn.dropout(output, dropout_keep_prob)
-
-# Get output of RNN sequence
-output = tf.transpose(output, [1, 0, 2])
-last = tf.gather(output, int(output.get_shape()[0]) - 1)
-
-weight = tf.Variable(tf.truncated_normal([rnn_size, 2], stddev=0.1))
-bias = tf.Variable(tf.constant(0.1, shape=[2]))
-logits_out = tf.matmul(last, weight) + bias
-
-
-# Loss function
-losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_out, labels=y_output)
-loss = tf.reduce_mean(losses)
-print(loss)
-accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits_out, 1), tf.cast(y_output, tf.int64)), tf.float32))
-print(accuracy)
-optimizer = tf.train.RMSPropOptimizer(learning_rate)
-train_step = optimizer.minimize(loss)
-init = tf.global_variables_initializer()
-sess.run(init)
-
-train_loss = []
-test_loss = []
-train_accuracy = []
-test_accuracy = []
-# Start training
-for epoch in range(epochs):
-    # Shuffle training data
-    shuffled_ix = np.random.permutation(np.arange(len(x_train)))
-    x_train = x_train[shuffled_ix]
-    y_train = y_train[shuffled_ix]
-    num_batches = int(len(x_train)/batch_size) + 1
-    # TO DO CALCULATE GENERATIONS ExACTLY
-    for i in range(num_batches):
-        # Select train data
-        min_ix = i * batch_size
-        max_ix = np.min([len(x_train), ((i+1) * batch_size)])
-        x_train_batch = x_train[min_ix:max_ix]
-        y_train_batch = y_train[min_ix:max_ix]
-        max_len = max([len(x) for x in x_train_batch])
-        x_train_batch = np.array([np.pad(x, (0, max_len - len(x)), 'constant') for x in x_train_batch])
-        # Run train step
-        train_dict = {x_data: x_train_batch, y_output: y_train_batch, dropout_keep_prob:0.5}
-        sess.run(train_step, feed_dict=train_dict)
-    # Run loss and accuracy for training
-    train_dict = {x_data: x_train, y_output: y_train, dropout_keep_prob:1.0}
-    temp_train_loss, temp_train_acc = sess.run([loss, accuracy], feed_dict=train_dict)
-    train_loss.append(temp_train_loss)
-    train_accuracy.append(temp_train_acc)
-    # Run Eval Step
-    test_dict = {x_data: x_test, y_output: y_test, dropout_keep_prob:1.0}
-    temp_test_loss, temp_test_acc = sess.run([loss, accuracy], feed_dict=test_dict)
-    test_loss.append(temp_test_loss)
-    test_accuracy.append(temp_test_acc)
-    print('Epoch: {}, Test Loss: {:.2}, Test Acc: {:.2}'.format(epoch+1, temp_test_loss, temp_test_acc))
-    
-# Plot loss over time
-epoch_seq = np.arange(1, epochs+1)
-plt.plot(epoch_seq, train_loss, 'k--', label='Train Set')
-plt.plot(epoch_seq, test_loss, 'r-', label='Test Set')
-plt.title('Softmax Loss')
-plt.xlabel('Epochs')
-plt.ylabel('Softmax Loss')
-plt.legend(loc='upper left')
-plt.show()
-
-# Plot accuracy over time
-plt.plot(epoch_seq, train_accuracy, 'k--', label='Train Set')
-plt.plot(epoch_seq, test_accuracy, 'r-', label='Test Set')
-plt.title('Test Accuracy')
-plt.xlabel('Epochs')
-plt.ylabel('Accuracy')
-plt.legend(loc='upper left')
-plt.show()
    -
    ['go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat', 'ok lar joking wif u oni', 'free entry in a wkly comp to win fa cup final tkts st may text fa to to receive entry questionstd txt ratetcs apply overs', 'u dun say so early hor u c already then say', 'nah i dont think he goes to usf he lives around here though']
-(5574, 25)
-[[ 191    3   17 ... 2725    0    0]
- [ 365 1206   41 ...    0    0    0]
- [  81   40    4 ...    0    0    0]
- ...
- [7814 7815  533 ...    0    0    0]
- [   2   28 1439 ...    7  161    3]
- [  51   20    3 ...    0    0    0]]
-Vocabulary Size: 8629
+
    Vocabulary Size: 8630
 80-20 Train Test split: 4459 -- 1115
 
    
 
    
-
    C:\Users\fuqiongying\AppData\Local\Temp\ipykernel_1816\2116000173.py:115: UserWarning: `tf.nn.rnn_cell.BasicRNNCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.SimpleRNNCell`, and will be replaced by that in Tensorflow 2.0.
-  cell = tf.nn.rnn_cell.BasicRNNCell(num_units=rnn_size)
+
    
+
    
+
    
+
    
+
    # Create the model using the Sequential API
+embedding_size = 50
+model = tf.keras.Sequential(
+    [
+        tf.keras.layers.Embedding(
+            input_dim=vocab_size,
+            output_dim=embedding_size,
+            input_length=max_sequence_length,
+        ),
+        tf.keras.layers.SimpleRNN(units=10),
+        tf.keras.layers.Dropout(0.5),
+        tf.keras.layers.Dense(units=2, activation="softmax"),
+    ]
+)
+
    
+
    
+
    
+
    
+
    
+
    
+
    # Compile the model
+model.compile(
+    optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0005),
+    loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"],
+)
 
    
 
    
-
    Tensor("Mean:0", shape=(), dtype=float32)
-Tensor("Mean_1:0", shape=(), dtype=float32)
-Epoch: 1, Test Loss: 0.69, Test Acc: 0.81
-Epoch: 2, Test Loss: 0.65, Test Acc: 0.82
-Epoch: 3, Test Loss: 0.6, Test Acc: 0.82
-Epoch: 4, Test Loss: 0.55, Test Acc: 0.82
-Epoch: 5, Test Loss: 0.51, Test Acc: 0.83
-Epoch: 6, Test Loss: 0.47, Test Acc: 0.83
-Epoch: 7, Test Loss: 0.45, Test Acc: 0.83
-Epoch: 8, Test Loss: 0.43, Test Acc: 0.84
-Epoch: 9, Test Loss: 0.42, Test Acc: 0.84
-Epoch: 10, Test Loss: 0.41, Test Acc: 0.84
-Epoch: 11, Test Loss: 0.41, Test Acc: 0.84
-Epoch: 12, Test Loss: 0.4, Test Acc: 0.85
-Epoch: 13, Test Loss: 0.4, Test Acc: 0.85
-Epoch: 14, Test Loss: 0.4, Test Acc: 0.85
-Epoch: 15, Test Loss: 0.4, Test Acc: 0.86
-Epoch: 16, Test Loss: 0.39, Test Acc: 0.86
-Epoch: 17, Test Loss: 0.39, Test Acc: 0.87
-Epoch: 18, Test Loss: 0.38, Test Acc: 0.87
-Epoch: 19, Test Loss: 0.38, Test Acc: 0.87
-Epoch: 20, Test Loss: 0.37, Test Acc: 0.87
+
    
+
    
+
    
+
    
+
    # Train the model
+epochs = 20
+batch_size = 250
+
+history = model.fit(
+    x_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.2
+)
 
    
 
    
-../_images/rnn_7_3.png
-../_images/rnn_7_4.png
+
    
+
    
+
    Epoch 1/20
+15/15 [==============================] - 3s 65ms/step - loss: 0.5753 - accuracy: 0.7575 - val_loss: 0.4707 - val_accuracy: 0.8756
+Epoch 2/20
+15/15 [==============================] - 0s 33ms/step - loss: 0.4656 - accuracy: 0.8433 - val_loss: 0.3906 - val_accuracy: 0.9283
+Epoch 3/20
+15/15 [==============================] - 0s 24ms/step - loss: 0.3762 - accuracy: 0.9162 - val_loss: 0.3093 - val_accuracy: 0.9574
+Epoch 4/20
+15/15 [==============================] - 0s 9ms/step - loss: 0.3103 - accuracy: 0.9422 - val_loss: 0.2595 - val_accuracy: 0.9652
+Epoch 5/20
+15/15 [==============================] - 0s 10ms/step - loss: 0.2693 - accuracy: 0.9498 - val_loss: 0.2225 - val_accuracy: 0.9664
+Epoch 6/20
+15/15 [==============================] - 0s 10ms/step - loss: 0.2285 - accuracy: 0.9686 - val_loss: 0.1987 - val_accuracy: 0.9664
+Epoch 7/20
+15/15 [==============================] - 0s 10ms/step - loss: 0.2024 - accuracy: 0.9795 - val_loss: 0.1820 - val_accuracy: 0.9619
+Epoch 8/20
+15/15 [==============================] - 0s 10ms/step - loss: 0.1825 - accuracy: 0.9748 - val_loss: 0.1675 - val_accuracy: 0.9630
+Epoch 9/20
+15/15 [==============================] - 0s 9ms/step - loss: 0.1647 - accuracy: 0.9821 - val_loss: 0.1631 - val_accuracy: 0.9608
+Epoch 10/20
+15/15 [==============================] - 0s 14ms/step - loss: 0.1546 - accuracy: 0.9837 - val_loss: 0.1623 - val_accuracy: 0.9574
+Epoch 11/20
+15/15 [==============================] - 0s 12ms/step - loss: 0.1400 - accuracy: 0.9865 - val_loss: 0.1622 - val_accuracy: 0.9552
+Epoch 12/20
+15/15 [==============================] - 0s 13ms/step - loss: 0.1302 - accuracy: 0.9868 - val_loss: 0.1632 - val_accuracy: 0.9552
+Epoch 13/20
+15/15 [==============================] - 0s 14ms/step - loss: 0.1285 - accuracy: 0.9865 - val_loss: 0.1640 - val_accuracy: 0.9540
+Epoch 14/20
+15/15 [==============================] - 0s 17ms/step - loss: 0.1194 - accuracy: 0.9871 - val_loss: 0.1579 - val_accuracy: 0.9552
+Epoch 15/20
+15/15 [==============================] - 0s 15ms/step - loss: 0.1190 - accuracy: 0.9874 - val_loss: 0.1647 - val_accuracy: 0.9518
+Epoch 16/20
+15/15 [==============================] - 0s 17ms/step - loss: 0.1103 - accuracy: 0.9874 - val_loss: 0.1596 - val_accuracy: 0.9563
+Epoch 17/20
+15/15 [==============================] - 0s 11ms/step - loss: 0.1033 - accuracy: 0.9879 - val_loss: 0.1530 - val_accuracy: 0.9585
+Epoch 18/20
+15/15 [==============================] - 0s 11ms/step - loss: 0.0954 - accuracy: 0.9905 - val_loss: 0.1611 - val_accuracy: 0.9552
+Epoch 19/20
+15/15 [==============================] - 0s 17ms/step - loss: 0.0937 - accuracy: 0.9896 - val_loss: 0.1640 - val_accuracy: 0.9552
+Epoch 20/20
+15/15 [==============================] - 0s 10ms/step - loss: 0.0924 - accuracy: 0.9907 - val_loss: 0.1848 - val_accuracy: 0.9484
+
    
+
    
+
    
+
    
+
    
+
    
+
    # Plot loss and accuracy over time
+plt.plot(history.history["loss"], label="Train Set")
+plt.plot(history.history["val_loss"], label="Validation Set")
+plt.title("Softmax Loss")
+plt.xlabel("Epochs")
+plt.ylabel("Softmax Loss")
+plt.legend(loc="upper left")
+plt.show()
+
+plt.plot(history.history["accuracy"], label="Train Set")
+plt.plot(history.history["val_accuracy"], label="Validation Set")
+plt.title("Test Accuracy")
+plt.xlabel("Epochs")
+plt.ylabel("Accuracy")
+plt.legend(loc="upper left")
+plt.show()
+
    
+
    
+
    
+
    
+../_images/rnn_17_0.png
+../_images/rnn_17_1.png

    25.2. Your turn! 🚀#

    -

    Practice the Recurrent Neural Networks by following this TBD.

    -
    -
    -

    25.3. Self study#

    -

    TBD

    +

    You can practice your rnn skills by following the assignment google stock price prediction rnn

    -

    25.4. Acknowledgments#

    +

    25.3. Acknowledgments#

    Thanks to Nick for creating the open-source course tensorflow_cookbook and Sebastian Raschka for creating the open-sourse stat453-deep-learning-ss20. It inspires the majority of the content in this chapter.

    diff --git a/deep-learning/time-series.html b/deep-learning/time-series.html index 895fb24ffa..fc37d26ba3 100644 --- a/deep-learning/time-series.html +++ b/deep-learning/time-series.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1571. + + 40.89. Introduction + +
  1572. - 40.89. Study the solvers + 40.90. Study the solvers
  1573. - 40.90. Build classification models + 40.91. Build classification models
  1574. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1575. - 40.92. Parameter play + 40.93. Parameter play
  1576. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1577. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1578. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1579. +
  1579. + + 40.99. Image classification
  1580. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1581. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1582. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1583. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1584. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1585. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1586. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1587. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1588. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1589. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1590. - 40.116. Art by gan + 40.118. Art by gan
  1591. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1592. +
  1592. + + 40.121. Comparing edge-based and region-based segmentation
  1593. - 40.119. Summary + 40.122. Summary
  1594. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1595. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1596. +
  1596. + + 40.126. Getting Start NLP with classification task
    1597. @@ -2956,7 +2976,7 @@

    29.5.4.2. Direct strategy

    29.6. Your turn! 🚀#

    -

    TBD.

    +

    You can practice your time series skills by following the assignment time series forecasting assignment

    29.7. Acknowledgments#

    diff --git a/genindex.html b/genindex.html index 41bc5a1ea2..90986e64cf 100644 --- a/genindex.html +++ b/genindex.html @@ -26,8 +26,8 @@ - + @@ -1107,114 +1107,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1597. + + 40.89. Introduction + +
  1598. - 40.89. Study the solvers + 40.90. Study the solvers
  1599. - 40.90. Build classification models + 40.91. Build classification models
  1600. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1601. - 40.92. Parameter play + 40.93. Parameter play
  1602. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1603. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1604. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1605. +
  1605. + + 40.99. Image classification
  1606. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1607. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1608. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1609. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1610. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1611. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1612. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1613. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1614. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1615. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1616. - 40.116. Art by gan + 40.118. Art by gan
  1617. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1618. +
  1618. + + 40.121. Comparing edge-based and region-based segmentation
  1619. - 40.119. Summary + 40.122. Summary
  1620. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1621. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1622. +
  1622. + + 40.126. Getting Start NLP with classification task
    1623. diff --git a/intro.html b/intro.html index 6ef1449c1d..ad1dd28f2c 100644 --- a/intro.html +++ b/intro.html @@ -27,8 +27,8 @@ - + @@ -1109,114 +1109,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1623. + + 40.89. Introduction + +
  1624. - 40.89. Study the solvers + 40.90. Study the solvers
  1625. - 40.90. Build classification models + 40.91. Build classification models
  1626. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1627. - 40.92. Parameter play + 40.93. Parameter play
  1628. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1629. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1630. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1631. +
  1631. + + 40.99. Image classification
  1632. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1633. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1634. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1635. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1636. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1637. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1638. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1639. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1640. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1641. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1642. - 40.116. Art by gan + 40.118. Art by gan
  1643. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1644. +
  1644. + + 40.121. Comparing edge-based and region-based segmentation
  1645. - 40.119. Summary + 40.122. Summary
  1646. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1647. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1648. +
  1648. + + 40.126. Getting Start NLP with classification task
    1649. diff --git a/machine-learning-productionization/data-engineering.html b/machine-learning-productionization/data-engineering.html index 246cde3689..3b8c685dfe 100644 --- a/machine-learning-productionization/data-engineering.html +++ b/machine-learning-productionization/data-engineering.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1649. + + 40.89. Introduction + +
  1650. - 40.89. Study the solvers + 40.90. Study the solvers
  1651. - 40.90. Build classification models + 40.91. Build classification models
  1652. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1653. - 40.92. Parameter play + 40.93. Parameter play
  1654. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1655. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1656. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1657. +
  1657. + + 40.99. Image classification
  1658. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1659. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1660. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1661. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1662. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1663. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1664. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1665. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1666. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1667. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1668. - 40.116. Art by gan + 40.118. Art by gan
  1669. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1670. +
  1670. + + 40.121. Comparing edge-based and region-based segmentation
  1671. - 40.119. Summary + 40.122. Summary
  1672. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1673. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1674. +
  1674. + + 40.126. Getting Start NLP with classification task
    1675. diff --git a/machine-learning-productionization/model-deployment.html b/machine-learning-productionization/model-deployment.html index e46fba8b34..fc455fccdf 100644 --- a/machine-learning-productionization/model-deployment.html +++ b/machine-learning-productionization/model-deployment.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1675. + + 40.89. Introduction + +
  1676. - 40.89. Study the solvers + 40.90. Study the solvers
  1677. - 40.90. Build classification models + 40.91. Build classification models
  1678. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1679. - 40.92. Parameter play + 40.93. Parameter play
  1680. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1681. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1682. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1683. +
  1683. + + 40.99. Image classification
  1684. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1685. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1686. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1687. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1688. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1689. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1690. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1691. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1692. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1693. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1694. - 40.116. Art by gan + 40.118. Art by gan
  1695. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1696. +
  1696. + + 40.121. Comparing edge-based and region-based segmentation
  1697. - 40.119. Summary + 40.122. Summary
  1698. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1699. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1700. +
  1700. + + 40.126. Getting Start NLP with classification task
    1701. diff --git a/machine-learning-productionization/model-training-and-evaluation.html b/machine-learning-productionization/model-training-and-evaluation.html index a45a9f49c1..6308a50773 100644 --- a/machine-learning-productionization/model-training-and-evaluation.html +++ b/machine-learning-productionization/model-training-and-evaluation.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1701. + + 40.89. Introduction + +
  1702. - 40.89. Study the solvers + 40.90. Study the solvers
  1703. - 40.90. Build classification models + 40.91. Build classification models
  1704. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1705. - 40.92. Parameter play + 40.93. Parameter play
  1706. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1707. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1708. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1709. +
  1709. + + 40.99. Image classification
  1710. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1711. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1712. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1713. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1714. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1715. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1716. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1717. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1718. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1719. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1720. - 40.116. Art by gan + 40.118. Art by gan
  1721. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1722. +
  1722. + + 40.121. Comparing edge-based and region-based segmentation
  1723. - 40.119. Summary + 40.122. Summary
  1724. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1725. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1726. +
  1726. + + 40.126. Getting Start NLP with classification task
    1727. diff --git a/machine-learning-productionization/overview.html b/machine-learning-productionization/overview.html index 4aafc03a50..b2e99231be 100644 --- a/machine-learning-productionization/overview.html +++ b/machine-learning-productionization/overview.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1727. + + 40.89. Introduction + +
  1728. - 40.89. Study the solvers + 40.90. Study the solvers
  1729. - 40.90. Build classification models + 40.91. Build classification models
  1730. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1731. - 40.92. Parameter play + 40.93. Parameter play
  1732. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1733. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1734. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1735. +
  1735. + + 40.99. Image classification
  1736. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1737. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1738. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1739. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1740. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1741. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1742. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1743. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1744. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1745. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1746. - 40.116. Art by gan + 40.118. Art by gan
  1747. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1748. +
  1748. + + 40.121. Comparing edge-based and region-based segmentation
  1749. - 40.119. Summary + 40.122. Summary
  1750. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1751. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1752. +
  1752. + + 40.126. Getting Start NLP with classification task
    1753. diff --git a/machine-learning-productionization/problem-framing.html b/machine-learning-productionization/problem-framing.html index 04862a0b8d..fef9e34520 100644 --- a/machine-learning-productionization/problem-framing.html +++ b/machine-learning-productionization/problem-framing.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1753. + + 40.89. Introduction + +
  1754. - 40.89. Study the solvers + 40.90. Study the solvers
  1755. - 40.90. Build classification models + 40.91. Build classification models
  1756. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1757. - 40.92. Parameter play + 40.93. Parameter play
  1758. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1759. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1760. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1761. +
  1761. + + 40.99. Image classification
  1762. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1763. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1764. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1765. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1766. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1767. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1768. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1769. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1770. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1771. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1772. - 40.116. Art by gan + 40.118. Art by gan
  1773. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1774. +
  1774. + + 40.121. Comparing edge-based and region-based segmentation
  1775. - 40.119. Summary + 40.122. Summary
  1776. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1777. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1778. +
  1778. + + 40.126. Getting Start NLP with classification task
    1779. diff --git a/ml-advanced/clustering/clustering-models-for-machine-learning.html b/ml-advanced/clustering/clustering-models-for-machine-learning.html index 947bbaba84..88f25394bb 100644 --- a/ml-advanced/clustering/clustering-models-for-machine-learning.html +++ b/ml-advanced/clustering/clustering-models-for-machine-learning.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1779. + + 40.89. Introduction + +
  1780. - 40.89. Study the solvers + 40.90. Study the solvers
  1781. - 40.90. Build classification models + 40.91. Build classification models
  1782. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1783. - 40.92. Parameter play + 40.93. Parameter play
  1784. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1785. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1786. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1787. +
  1787. + + 40.99. Image classification
  1788. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1789. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1790. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1791. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1792. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1793. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1794. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1795. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1796. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1797. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1798. - 40.116. Art by gan + 40.118. Art by gan
  1799. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1800. +
  1800. + + 40.121. Comparing edge-based and region-based segmentation
  1801. - 40.119. Summary + 40.122. Summary
  1802. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1803. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1804. +
  1804. + + 40.126. Getting Start NLP with classification task
    1805. diff --git a/ml-advanced/clustering/introduction-to-clustering.html b/ml-advanced/clustering/introduction-to-clustering.html index 831af03dec..683116dc99 100644 --- a/ml-advanced/clustering/introduction-to-clustering.html +++ b/ml-advanced/clustering/introduction-to-clustering.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1805. + + 40.89. Introduction + +
  1806. - 40.89. Study the solvers + 40.90. Study the solvers
  1807. - 40.90. Build classification models + 40.91. Build classification models
  1808. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1809. - 40.92. Parameter play + 40.93. Parameter play
  1810. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1811. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1812. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1813. +
  1813. + + 40.99. Image classification
  1814. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1815. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1816. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1817. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1818. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1819. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1820. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1821. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1822. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1823. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1824. - 40.116. Art by gan + 40.118. Art by gan
  1825. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1826. +
  1826. + + 40.121. Comparing edge-based and region-based segmentation
  1827. - 40.119. Summary + 40.122. Summary
  1828. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1829. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1830. +
  1830. + + 40.126. Getting Start NLP with classification task
    1831. diff --git a/ml-advanced/clustering/k-means-clustering.html b/ml-advanced/clustering/k-means-clustering.html index 6dad1580cc..f94a948d84 100644 --- a/ml-advanced/clustering/k-means-clustering.html +++ b/ml-advanced/clustering/k-means-clustering.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1831. + + 40.89. Introduction + +
  1832. - 40.89. Study the solvers + 40.90. Study the solvers
  1833. - 40.90. Build classification models + 40.91. Build classification models
  1834. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1835. - 40.92. Parameter play + 40.93. Parameter play
  1836. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1837. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1838. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1839. +
  1839. + + 40.99. Image classification
  1840. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1841. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1842. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1843. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1844. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1845. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1846. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1847. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1848. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1849. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1850. - 40.116. Art by gan + 40.118. Art by gan
  1851. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1852. +
  1852. + + 40.121. Comparing edge-based and region-based segmentation
  1853. - 40.119. Summary + 40.122. Summary
  1854. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1855. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1856. +
  1856. + + 40.126. Getting Start NLP with classification task
    1857. diff --git a/ml-advanced/ensemble-learning/bagging.html b/ml-advanced/ensemble-learning/bagging.html index 08883c2f06..e00168729e 100644 --- a/ml-advanced/ensemble-learning/bagging.html +++ b/ml-advanced/ensemble-learning/bagging.html @@ -27,8 +27,8 @@ - + @@ -1112,114 +1112,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  1857. + + 40.89. Introduction + +
  1858. - 40.89. Study the solvers + 40.90. Study the solvers
  1859. - 40.90. Build classification models + 40.91. Build classification models
  1860. - 40.91. Build Classification Model + 40.92. Build Classification Model
  1861. - 40.92. Parameter play + 40.93. Parameter play
  1862. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  1863. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  1864. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    1865. +
  1865. + + 40.99. Image classification
  1866. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  1867. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  1868. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  1869. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  1870. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  1871. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  1872. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  1873. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  1874. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  1875. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  1876. - 40.116. Art by gan + 40.118. Art by gan
  1877. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    1878. +
  1878. + + 40.121. Comparing edge-based and region-based segmentation
  1879. - 40.119. Summary + 40.122. Summary
  1880. - 40.120. Car Object Detection + 40.123. Car Object Detection
  1881. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    1882. +
  1882. + + 40.126. Getting Start NLP with classification task
    1883. @@ -1938,7 +1958,7 @@

    15.1.2. Bootstrapping -
    <matplotlib.legend.Legend at 0x7fda965d69d0>
+
    <matplotlib.legend.Legend at 0x7f9b21c61670>
 
    
 
    
 ../../_images/bagging_10_1.png
diff --git a/ml-advanced/ensemble-learning/feature-importance.html b/ml-advanced/ensemble-learning/feature-importance.html
index cf5ac634f6..b4236acc2b 100644
--- a/ml-advanced/ensemble-learning/feature-importance.html
+++ b/ml-advanced/ensemble-learning/feature-importance.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
         
    
+   
  1883. 
+    
+     40.89. Introduction
+    
+   
    1884. 
    
  1884. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    1885. 
    
  1885. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    1886. 
    
  1886. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    1887. 
    
  1887. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    1888. 
    
  1888. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    1889. 
    
  1889. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    1890. 
    
  1890. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    1891. 
+   
  1891. 
+    
+     40.99. Image classification
     
    
    1892. 
    
  1892. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    1893. 
    
  1893. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    1894. 
    
  1894. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    1895. 
    
  1895. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    1896. 
    
  1896. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    1897. 
    
  1897. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    1898. 
    
  1898. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    1899. 
    
  1899. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    1900. 
    
  1900. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    1901. 
    
  1901. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    1902. 
    
  1902. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    1903. 
    
  1903. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    1904. 
+   
  1904. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    1905. 
    
  1905. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    1906. 
    
  1906. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    1907. 
    
  1907. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    1908. 
+   
  1908. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    1909. 
   
diff --git a/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html b/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html
index 19f75b3e59..6f64e577d6 100644
--- a/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html
+++ b/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
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    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  1909. 
+    
+     40.89. Introduction
+    
+   
    1910. 
    
  1910. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    1911. 
    
  1911. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    1912. 
    
  1912. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    1913. 
    
  1913. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    1914. 
    
  1914. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    1915. 
    
  1915. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    1916. 
    
  1916. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    1917. 
+   
  1917. 
+    
+     40.99. Image classification
     
    
    1918. 
    
  1918. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    1919. 
    
  1919. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    1920. 
    
  1920. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    1921. 
    
  1921. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    1922. 
    
  1922. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    1923. 
    
  1923. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    1924. 
    
  1924. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    1925. 
    
  1925. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    1926. 
    
  1926. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    1927. 
    
  1927. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    1928. 
    
  1928. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    1929. 
    
  1929. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    1930. 
+   
  1930. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    1931. 
    
  1931. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    1932. 
    
  1932. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    1933. 
    
  1933. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    1934. 
+   
  1934. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    1935. 
   
diff --git a/ml-advanced/ensemble-learning/random-forest.html b/ml-advanced/ensemble-learning/random-forest.html
index 47556b5e7f..107cc09f8e 100644
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@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  1935. 
+    
+     40.89. Introduction
+    
+   
    1936. 
    
  1936. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    1937. 
    
  1937. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    1938. 
    
  1938. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    1939. 
    
  1939. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    1940. 
    
  1940. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    1941. 
    
  1941. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    1942. 
    
  1942. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    1943. 
+   
  1943. 
+    
+     40.99. Image classification
     
    
    1944. 
    
  1944. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    1945. 
    
  1945. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    1946. 
    
  1946. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    1947. 
    
  1947. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    1948. 
    
  1948. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    1949. 
    
  1949. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    1950. 
    
  1950. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    1951. 
    
  1951. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    1952. 
    
  1952. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    1953. 
    
  1953. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    1954. 
    
  1954. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    1955. 
    
  1955. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    1956. 
+   
  1956. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    1957. 
    
  1957. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    1958. 
    
  1958. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    1959. 
    
  1959. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    1960. 
+   
  1960. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    1961. 
   
diff --git a/ml-advanced/gradient-boosting/gradient-boosting-example.html b/ml-advanced/gradient-boosting/gradient-boosting-example.html
index 4c4d684ce1..82a41519ca 100644
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+    
     
     
   
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    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  1961. 
+    
+     40.89. Introduction
+    
+   
    1962. 
    
  1962. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    1963. 
    
  1963. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    1964. 
    
  1964. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    1965. 
    
  1965. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    1966. 
    
  1966. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    1967. 
    
  1967. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    1968. 
    
  1968. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    1969. 
+   
  1969. 
+    
+     40.99. Image classification
     
    
    1970. 
    
  1970. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    1971. 
    
  1971. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    1972. 
    
  1972. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    1973. 
    
  1973. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    1974. 
    
  1974. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    1975. 
    
  1975. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    1976. 
    
  1976. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    1977. 
    
  1977. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    1978. 
    
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-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    1979. 
    
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-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    1980. 
    
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-     40.116. Art by gan
+     40.118. Art by gan
     
    
    1981. 
    
  1981. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    1982. 
+   
  1982. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    1983. 
    
  1983. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    1984. 
    
  1984. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    1985. 
    
  1985. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    1986. 
+   
  1986. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    1987. 
   
diff --git a/ml-advanced/gradient-boosting/gradient-boosting.html b/ml-advanced/gradient-boosting/gradient-boosting.html
index debd4517ea..59970fa598 100644
--- a/ml-advanced/gradient-boosting/gradient-boosting.html
+++ b/ml-advanced/gradient-boosting/gradient-boosting.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  1987. 
+    
+     40.89. Introduction
+    
+   
    1988. 
    
  1988. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    1989. 
    
  1989. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    1990. 
    
  1990. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    1991. 
    
  1991. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    1992. 
    
  1992. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    1993. 
    
  1993. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    1994. 
    
  1994. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    1995. 
+   
  1995. 
+    
+     40.99. Image classification
     
    
    1996. 
    
  1996. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    1997. 
    
  1997. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    1998. 
    
  1998. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    1999. 
    
  1999. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2000. 
    
  2000. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2001. 
    
  2001. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2002. 
    
  2002. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2003. 
    
  2003. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2004. 
    
  2004. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2005. 
    
  2005. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2006. 
    
  2006. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2007. 
    
  2007. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2008. 
+   
  2008. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2009. 
    
  2009. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2010. 
    
  2010. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2011. 
    
  2011. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2012. 
+   
  2012. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2013. 
   
diff --git a/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html b/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html
index 5c9a6ec64e..b0088f2cae 100644
--- a/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html
+++ b/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2013. 
+    
+     40.89. Introduction
+    
+   
    2014. 
    
  2014. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2015. 
    
  2015. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2016. 
    
  2016. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2017. 
    
  2017. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2018. 
    
  2018. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2019. 
    
  2019. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2020. 
    
  2020. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2021. 
+   
  2021. 
+    
+     40.99. Image classification
     
    
    2022. 
    
  2022. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2023. 
    
  2023. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2024. 
    
  2024. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2025. 
    
  2025. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2026. 
    
  2026. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2027. 
    
  2027. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2028. 
    
  2028. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2029. 
    
  2029. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2030. 
    
  2030. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2031. 
    
  2031. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2032. 
    
  2032. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2033. 
    
  2033. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2034. 
+   
  2034. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2035. 
    
  2035. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2036. 
    
  2036. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2037. 
    
  2037. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2038. 
+   
  2038. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2039. 
   
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
index 78594ec788..3381625259 100644
--- a/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html
+++ b/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2039. 
+    
+     40.89. Introduction
+    
+   
    2040. 
    
  2040. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2041. 
    
  2041. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2042. 
    
  2042. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2043. 
    
  2043. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2044. 
    
  2044. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2045. 
    
  2045. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2046. 
    
  2046. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2047. 
+   
  2047. 
+    
+     40.99. Image classification
     
    
    2048. 
    
  2048. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2049. 
    
  2049. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2050. 
    
  2050. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2051. 
    
  2051. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2052. 
    
  2052. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2053. 
    
  2053. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2054. 
    
  2054. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2055. 
    
  2055. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2056. 
    
  2056. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2057. 
    
  2057. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2058. 
    
  2058. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2059. 
    
  2059. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2060. 
+   
  2060. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2061. 
    
  2061. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2062. 
    
  2062. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2063. 
    
  2063. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2064. 
+   
  2064. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2065. 
   
diff --git a/ml-advanced/gradient-boosting/xgboost.html b/ml-advanced/gradient-boosting/xgboost.html
index 3f4ec55989..094be52aab 100644
--- a/ml-advanced/gradient-boosting/xgboost.html
+++ b/ml-advanced/gradient-boosting/xgboost.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2065. 
+    
+     40.89. Introduction
+    
+   
    2066. 
    
  2066. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2067. 
    
  2067. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2068. 
    
  2068. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2069. 
    
  2069. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2070. 
    
  2070. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2071. 
    
  2071. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2072. 
    
  2072. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2073. 
+   
  2073. 
+    
+     40.99. Image classification
     
    
    2074. 
    
  2074. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2075. 
    
  2075. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2076. 
    
  2076. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2077. 
    
  2077. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2078. 
    
  2078. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2079. 
    
  2079. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2080. 
    
  2080. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2081. 
    
  2081. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2082. 
    
  2082. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2083. 
    
  2083. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2084. 
    
  2084. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2085. 
    
  2085. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2086. 
+   
  2086. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2087. 
    
  2087. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2088. 
    
  2088. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2089. 
    
  2089. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2090. 
+   
  2090. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2091. 
   
@@ -1854,7 +1874,7 @@ 
    16.3.2. Example
    -
    Mean Absolute Error : 17878.656881421233
+
    Mean Absolute Error : 19123.337178938356
    diff --git a/ml-advanced/kernel-method.html b/ml-advanced/kernel-method.html index 569c005a3f..142f4b0f2b 100644 --- a/ml-advanced/kernel-method.html +++ b/ml-advanced/kernel-method.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression     +
  2091. + + 40.89. Introduction + +
  2092. - 40.89. Study the solvers + 40.90. Study the solvers
  2093. - 40.90. Build classification models + 40.91. Build classification models
  2094. - 40.91. Build Classification Model + 40.92. Build Classification Model
  2095. - 40.92. Parameter play + 40.93. Parameter play
  2096. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  2097. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  2098. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    2099. +
  2099. + + 40.99. Image classification
  2100. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  2101. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  2102. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  2103. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  2104. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  2105. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  2106. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  2107. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  2108. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  2109. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  2110. - 40.116. Art by gan + 40.118. Art by gan
  2111. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    2112. +
  2112. + + 40.121. Comparing edge-based and region-based segmentation
  2113. - 40.119. Summary + 40.122. Summary
  2114. - 40.120. Car Object Detection + 40.123. Car Object Detection
  2115. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    2116. +
  2116. + + 40.126. Getting Start NLP with classification task
    2117. @@ -1882,7 +1902,7 @@

    18.1. Motivating Support Vector Machines

    -
    <matplotlib.collections.PathCollection at 0x7f5e393fbf70>
+
    <matplotlib.collections.PathCollection at 0x7fe83267c070>
 
    
 
    
 ../_images/kernel-method_9_1.png
@@ -2069,7 +2089,7 @@ 
    18.3. Fitting a support vector machine
 
    
 
    <function __main__.plot_svm(N=10, ax=None)>
 
    
 
    
@@ -2127,111 +2147,111 @@ 
    18.4. Beyond linear boundaries: Kernel S
 
    
 
    
 
    <function __main__.plot_3D(elev=30, azim=30, X=array([[ 5.95705480e-02, -4.38163860e-02],
-       [-1.62477454e-01,  8.65543325e-02],
-       [-2.40660274e-03, -2.98693918e-02],
-       [-6.52889119e-01,  5.69147803e-01],
-       [-4.65872906e-02, -1.08106401e-01],
-       [-8.01057247e-01,  7.61571016e-01],
-       [ 3.12592925e-01,  1.08662089e+00],
-       [ 1.09721540e-01,  5.76700042e-02],
-       [-7.51393182e-02, -7.31722824e-02],
-       [-1.42577697e-01,  4.20453655e-02],
-       [-8.96984417e-02,  2.13129353e-02],
-       [-1.60611176e-04,  1.41867679e-01],
-       [ 7.74871473e-01,  5.33837964e-01],
-       [-1.47178350e-01,  3.92483335e-02],
-       [-6.85461521e-01, -6.37446555e-01],
-       [ 5.15858217e-02, -1.71140282e-01],
-       [ 6.07499715e-01, -6.69710373e-01],
-       [ 8.07161290e-01, -6.22003671e-01],
-       [-1.06568076e+00, -3.71069972e-02],
-       [ 1.03681898e+00, -9.26129238e-02],
-       [ 5.32911502e-03,  1.27811128e-01],
-       [ 7.66867670e-02,  5.83709456e-02],
-       [-5.21018933e-02,  1.38616702e-01],
-       [-7.28924647e-02,  6.37284549e-02],
-       [ 1.63424116e-01,  5.22065768e-02],
-       [ 1.03328308e-02, -2.02673422e-01],
-       [-1.36178972e-01,  1.76014705e-01],
-       [ 8.81485948e-01,  4.87699381e-01],
-       [ 6.44468627e-02, -4.96658114e-02],
-       [ 5.74038093e-01, -7.00584401e-01],
-       [ 2.05311586e-02, -1.68543687e-01],
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-       [ 6.29098517e-02,  8.71327164e-02],
-       [ 9.72645393e-01, -4.06293699e-01],
-       [ 9.10711052e-01,  3.24153448e-01],
-       [-1.50409232e-01, -9.06474761e-02],
-       [-5.59908764e-01, -9.82774910e-01],
-       [-9.55588338e-01,  3.89779848e-01],
-       [ 2.13957641e-01,  1.10059785e-01],
-       [ 1.16272558e+00, -1.17338595e-01],
-       [ 1.88114261e-01,  1.04690013e+00],
-       [ 6.83892432e-01,  5.17684239e-01],
-       [-1.61510254e-02, -8.64061041e-01],
-       [ 2.21178094e-02,  1.03265407e-01],
-       [ 8.01461712e-01, -4.27437630e-01],
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@@ -2264,7 +2284,7 @@ 
    18.4. Beyond linear boundaries: Kernel S
 
    
 
    
 
    
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    18.15. Acknowledgement
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"}}]}}, 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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+hHQ6BQCwWCzo6OiEKIbh83XBbrebXCEREZWjogeWm2++GQ8++CA+/OEP44Mf/CDGx8fxuc99DjfffDP3YClzuq5jdHQUr7wyju3b9wAoBJTubj+mpiYgioV1KRUVHpMrJSKiclf0wFJdXY2vfe1r+PSnP40Pf/jD8Hg8uP7663H77bcX+6WpSBKJWSiKDEWJYG4uAbfbgYaGFtTVNQIALrnkMt7hQ0RE62pD/qoEAgF89atf3YiXoiLJZDKIxaKQ5QhGR0eMcYfDgR07dsBme/VSD8MKERGtN/5loVWZmBjHL37xOABAEAS0tbVDFMMQxSCammoRjye5Ap+IiIqGgYVW0HUdU1OTUBQZbrcb5513PgCgra0d7e0daG/vRCgkHreXCnePJCKi4mNgIQDA/Pw8VFWGLEcQj08DACoqKrB373mwWCywWCx461v/zNwiiYhoy2Jg2eJisR68/PJLGB4egr60o4/NZoPP1w1JkkyujoiIqICBZYvRNA2CIEBY6kA1MjKMoaFBAEBLSyskKQy/Pwin02lmmURERCswsGwR09PHIMsRqKqMK674E7S1tQMAtm/fAZfLBVGU4PWeXasEIiKiYmFg2cQWFpKIRlUoSgSTk5PGeE+PagSW+voG1Nc3mFUiERHRqjCwbELpdBqPPfYzDA0NQNMKtxpbrVZ0dvogimF0dvpMrpCIiOjMMLBsArquI5GYRXV1DQDA6XRidjYOTdPQ3LwNoZCEYDAEt9ttbqFERERrxMBSxmZm4ktb5MtYXMzg0KH3wGazQRAEvOENB1BR4UFtLbshExFR+WNgKTOpVAo9PVEoSgTj42PGuMPhwPT0MTQ1FRpKLq9RISIi2gwYWMqIosj4xS8eQz6fB1DojNze3glJkuDzdcNut5/mGYiIiMoTA0uJ0nUdExPjsFqtaGgodEFubGxCPp9HY2MjRDGMYDCEigqPyZUSEREVHwNLiUkkElBVGYoSwczMDAKBEP7kT64CANTW1uLtb38namq4LoWIiLYWBpYSkMlkEItFIcsRjI6OGOM2mx0OhwO6rhs70zKsEBHRVsTAUgJ+9KMfGAtoBUFAW1s7RDEMv98Pu91hcnVERETmY2DZQLquY2pqEqoq44IL9hlhxO8PYHFxEZIURigkobKy0uRKiYiISgsDywaYn5+HqipQlAimp48BAOrrGyFJYQDA7t17sWfPucZlHyIiIlqJgaVIstksYrEeKEoEw8ND0HUdAGCz2eDzdaO6+tVGgxaLxawyiYiIygIDS5Gk02k8/vjPjK9bWlohihICgRCcTqeJlREREZUfBpZ1MD19DLIcQSaTwRvfeAAAUFVVhXB4B6qqqiCKErze6tM8CxEREZ0MA8sapVILxrqUyclJAIVLOxdeuM/YzO3yy68ws0QiIqJNg4HlDA0PD+HIkRcwODgATdMAAFarFZ2dPohiGA4HL/cQERGtNwaW09B1HZqmwWq1Aih0SO7v7wMANDU1G1vku91uE6skIiLa3BhYTmJmJg5VlSHLMvbuPQ+7dp0DAAgEQkgmkwiFJNTWctdZIiKijcDAcpx0Oo1oVIWiRIydZwEgFosagcXlcuHCCy8yq0QiIqItiYEFhcs+P//5T9DbG0M+nwdQWEDb3t4JSZLg83WbXCEREdHWtiUDi67riMenUVdXD6DQvyebzSKfz6OhoRGiKCEUEo27fYiIiMhcWyqwJBIJqKoMRYlgZmYGt9xyyNgf5cILL8K+fftRX99gcpVERET0xzZ9YMlkMojFopDlCEZHR4xxm82OqakpI7A0NDSaVSIRERGdxqYOLKOjI/jhD7+PXC4HoHDpp7W1DaIYht8fgMPhMLlCIiIiWo1NE1h0XcfU1CQWFxfR1tYOAGhoaIAgCKitrYMkhREMiqiqqjK5UiIiIjpTZR9Yksl5KEphi/zp6WOora3DTTe9A4IgwG534KabbkFlZSUEQTC7VCIiIlqjsg0si4uL+MEP/h0DAwPQdR0AYLPZUFdXj1wuC7u9cLmHZ1SIiIjKX9kGloWFBQwOFsJKS0srRFFCIBCC08lePkRERJtN2QaWQmfkixAIhIw7fYiIiGhzKtvAUlVVhQsuuBC5nGZ2KURERFRkFrMLWCsuoiUiIto6yjawEBER0dbBwEJEREQlr+iB5emnn8ZHP/pRHDx4EJIk4e///u+L/ZJERES0yRQ9sDz11FOIRCK44IIL4PV6i/1yREREtAkV/S6hv/mbv8Edd9wBAPj1r39d7JcjIiKiTajoZ1gsFi6TISIiorNTtvuwAEB1tRtLu/KTSZbvLuexMB+PRengsSgdPBalxWJZ+5YkZR1YePamdPBYlA4ei9LBY1E6eCzK3xkHlrm5OUxMTJz2+zo6OuBwONZUFBEREdHxzjiwHD58GHfeeedpv+/RRx9FIBBYU1FERERExzvjwHLDDTfghhtuKEYtRERERCfEi3pERERU8oq+6HZ4eBgvvfQSACCVSmFgYACHDx8GAFx11VXFfnkiIiLaBARdL+6NXo888gg+/vGPn/AxWZaL+dJERES0SRQ9sBARERGdLa5hISIiopLHwEJEREQlj4GFiIiISh4DCxEREZU8BhYiIiIqeQwsREREVPLKPrA8/fTT+OhHP4qDBw9CkiT8/d//vdklbQk9PT1497vfjb179+Liiy/G5z73OSwuLppd1pbU39+PT37yk7j22muxY8cOXHPNNWaXtCX9+Mc/xl/+5V/isssuw969e3HttdfiO9/5DrhzhDmefPJJvPOd78RFF12EXbt24YorrsBnP/tZzM3NmV3alpdMJnHZZZdBkiRjY9nVKPpOt8X21FNPIRKJ4IILLsDs7KzZ5WwJs7OzeNe73oWuri7cc889GB8fx9133410Oo1PfvKTZpe35aiqiieffBJ79uyBpmn8A2mSr371q2hra8Mdd9yB2tpaPPPMM/jEJz6BsbEx3HbbbWaXt+XMzMxg9+7duPXWW1FTUwNVVXHPPfdAVVU88MADZpe3pX3hC19APp8/8x/Uy1w+nzf++fLLL9fvuusuE6vZGu677z597969ejweN8a++c1v6tu3b9fHxsbMK2yLOn4OfOxjH9Pf8pa3mFjN1nXs2LHXjN155536eeedt+IYkXm+9a1v6aIo8n3KRNFoVN+7d6/+jW98QxdFUf/973+/6p8t+0tCFkvZ/yuUnV/+8pfYv38/ampqjLGrr74amqbh6aefNq+wLYpzoDTU1dW9Zmz79u2Yn5/HwsKCCRXRH1t+z8pms+YWsoV95jOfwc0334zu7u4z/lm+09EZi8Vi8Pv9K8a8Xi8aGxsRi8VMqoqo9Dz//PNobm5GZWWl2aVsWfl8HplMBi+//DI+//nP48CBA2hvbze7rC3p8OHDUBQFH/7wh9f082W/hoU2XiKRgNfrfc14dXU11xERLXnuuefw6KOP4mMf+5jZpWxpl19+OcbHxwEAl156Kf7X//pfJle0NaVSKdx99924/fbb1xzgSy6wzM3NYWJi4rTf19HRAYfDsQEVERGdmbGxMdx+++3Yt28fDh06ZHY5W9r999+PVCqFaDSKe++9Fx/60Ifwla98BVar1ezStpR7770X9fX1uO6669b8HCUXWA4fPow777zztN/36KOPIhAIbEBF9Me8Xu8Jbw2cnZ1FdXW1CRURlY5EIoH3v//9qKmpwT333MM1RiYLh8MAgHPPPRfnnHMOrr32WvzsZz/DVVddZXJlW8fw8DAeeOABfP7znzf+diyv61pYWEAymYTH4znt85RcYLnhhhtwww03mF0GnYLf73/NWpW5uTlMTk6+Zm0L0VaSTqfxwQ9+EHNzc/jWt76Fqqoqs0ui40iSBLvdjoGBAbNL2VKGhoaQzWbxgQ984DWPHTp0CHv27MHDDz982ucpucBCpe+yyy7Dfffdt2Ity+HDh2GxWHDxxRebXB2ROXK5HD7ykY8gFovhoYceQnNzs9kl0R85cuQIstksF91usO3bt+PrX//6irGjR4/is5/9LO666y6cc845q3qesg8sw8PDxk55qVQKAwMDOHz4MADwlF+R3HzzzXjwwQfx4Q9/GB/84AcxPj6Oz33uc7j55pv5Jm2CVCqFJ598EkBhPszPzxtz4MILLzzh7ba0/u666y488cQTuOOOOzA/P48XX3zReGzHjh1cc7fBbrvtNuzatQuSJMHlciESieDLX/4yJEnCwYMHzS5vS/F6vdi3b98JH9u5cyd27ty5qucRdL28t8V85JFH8PGPf/yEj8myvMHVbB09PT349Kc/jRdeeAEejwfXXnstbr/9dr4pm2BoaAhXXHHFCR/7+te/ftI3ClpfBw4cwPDw8Akfe+yxx/ipfoPdf//9ePTRRzEwMABd19HW1oYrr7wS733ve3mbeQn49a9/jUOHDuE73/nOqs+wlH1gISIios2Py9eJiIio5DGwEBERUcljYCEiIqKSx8BCREREJY+BhYiIiEoeAwsRERGVPAYWIiIiKnkMLERERFTyGFiIiIio5DGwEBERUcljYCEiIqKS9/8DQLeYnUD1XxEAAAAASUVORK5CYII="}}]}}, 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"}}]}}}, "version_major": 2, "version_minor": 0}
 
 
 
diff --git a/ml-advanced/model-selection.html b/ml-advanced/model-selection.html
index 96b1c54348..1dd82ced85 100644
--- a/ml-advanced/model-selection.html
+++ b/ml-advanced/model-selection.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
         
    
+   
  2117. 
+    
+     40.89. Introduction
+    
+   
    2118. 
    
  2118. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2119. 
    
  2119. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2120. 
    
  2120. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2121. 
    
  2121. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2122. 
    
  2122. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2123. 
    
  2123. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2124. 
    
  2124. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2125. 
+   
  2125. 
+    
+     40.99. Image classification
     
    
    2126. 
    
  2126. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2127. 
    
  2127. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2128. 
    
  2128. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2129. 
    
  2129. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2130. 
    
  2130. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2131. 
    
  2131. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2132. 
    
  2132. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2133. 
    
  2133. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2134. 
    
  2134. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2135. 
    
  2135. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2136. 
    
  2136. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2137. 
    
  2137. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2138. 
+   
  2138. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2139. 
    
  2139. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2140. 
    
  2140. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2141. 
    
  2141. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2142. 
+   
  2142. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2143. 
   
diff --git a/ml-advanced/unsupervised-learning-pca-and-clustering.html b/ml-advanced/unsupervised-learning-pca-and-clustering.html
index e53fea58bc..228a6fcfc0 100644
--- a/ml-advanced/unsupervised-learning-pca-and-clustering.html
+++ b/ml-advanced/unsupervised-learning-pca-and-clustering.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2143. 
+    
+     40.89. Introduction
+    
+   
    2144. 
    
  2144. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2145. 
    
  2145. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2146. 
    
  2146. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2147. 
    
  2147. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2148. 
    
  2148. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2149. 
    
  2149. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2150. 
    
  2150. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2151. 
+   
  2151. 
+    
+     40.99. Image classification
     
    
    2152. 
    
  2152. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2153. 
    
  2153. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2154. 
    
  2154. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2155. 
    
  2155. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2156. 
    
  2156. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2157. 
    
  2157. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2158. 
    
  2158. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2159. 
    
  2159. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2160. 
    
  2160. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2161. 
    
  2161. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2162. 
    
  2162. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2163. 
    
  2163. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2164. 
+   
  2164. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2165. 
    
  2165. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2166. 
    
  2166. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2167. 
    
  2167. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2168. 
+   
  2168. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2169. 
   
diff --git a/ml-advanced/unsupervised-learning.html b/ml-advanced/unsupervised-learning.html
index 1211428054..3d81dab62d 100644
--- a/ml-advanced/unsupervised-learning.html
+++ b/ml-advanced/unsupervised-learning.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2169. 
+    
+     40.89. Introduction
+    
+   
    2170. 
    
  2170. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2171. 
    
  2171. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2172. 
    
  2172. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2173. 
    
  2173. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2174. 
    
  2174. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2175. 
    
  2175. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2176. 
    
  2176. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2177. 
+   
  2177. 
+    
+     40.99. Image classification
     
    
    2178. 
    
  2178. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2179. 
    
  2179. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2180. 
    
  2180. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2181. 
    
  2181. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2182. 
    
  2182. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2183. 
    
  2183. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2184. 
    
  2184. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2185. 
    
  2185. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2186. 
    
  2186. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2187. 
    
  2187. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2188. 
    
  2188. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2189. 
    
  2189. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2190. 
+   
  2190. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2191. 
    
  2191. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2192. 
    
  2192. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2193. 
    
  2193. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2194. 
+   
  2194. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2195. 
   
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 443a9d98d4..370dc0494c 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
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2195. 
+    
+     40.89. Introduction
+    
+   
    2196. 
    
  2196. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2197. 
    
  2197. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2198. 
    
  2198. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2199. 
    
  2199. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2200. 
    
  2200. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2201. 
    
  2201. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2202. 
    
  2202. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2203. 
+   
  2203. 
+    
+     40.99. Image classification
     
    
    2204. 
    
  2204. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2205. 
    
  2205. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2206. 
    
  2206. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2207. 
    
  2207. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2208. 
    
  2208. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2209. 
    
  2209. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2210. 
    
  2210. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2211. 
    
  2211. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2212. 
    
  2212. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2213. 
    
  2213. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2214. 
    
  2214. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2215. 
    
  2215. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2216. 
+   
  2216. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2217. 
    
  2217. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2218. 
    
  2218. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2219. 
    
  2219. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2220. 
+   
  2220. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2221. 
   
@@ -2079,7 +2099,7 @@ 
    12.4.4. Exercise - build your model
                  precision    recall  f1-score   support
 
            0       1.00      1.00      1.00        41
-           1       0.86      0.31      0.45       250
+           1       0.85      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
@@ -2089,7 +2109,7 @@ 
    12.4.4. Exercise - build your model
    
 
    
 
    
diff --git a/ml-fundamentals/classification/getting-started-with-classification.html b/ml-fundamentals/classification/getting-started-with-classification.html
index 9fb929a987..0d1c0b8448 100644
--- a/ml-fundamentals/classification/getting-started-with-classification.html
+++ b/ml-fundamentals/classification/getting-started-with-classification.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
         
    
+   
  2221. 
+    
+     40.89. Introduction
+    
+   
    2222. 
    
  2222. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2223. 
    
  2223. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2224. 
    
  2224. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2225. 
    
  2225. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2226. 
    
  2226. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2227. 
    
  2227. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2228. 
    
  2228. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2229. 
+   
  2229. 
+    
+     40.99. Image classification
     
    
    2230. 
    
  2230. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2231. 
    
  2231. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2232. 
    
  2232. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2233. 
    
  2233. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2234. 
    
  2234. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2235. 
    
  2235. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2236. 
    
  2236. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2237. 
    
  2237. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2238. 
    
  2238. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2239. 
    
  2239. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2240. 
    
  2240. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2241. 
    
  2241. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2242. 
+   
  2242. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2243. 
    
  2243. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2244. 
    
  2244. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2245. 
    
  2245. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2246. 
+   
  2246. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2247. 
   
diff --git a/ml-fundamentals/classification/introduction-to-classification.html b/ml-fundamentals/classification/introduction-to-classification.html
index 9032c775d4..a60d80157b 100644
--- a/ml-fundamentals/classification/introduction-to-classification.html
+++ b/ml-fundamentals/classification/introduction-to-classification.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2247. 
+    
+     40.89. Introduction
+    
+   
    2248. 
    
  2248. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2249. 
    
  2249. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2250. 
    
  2250. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2251. 
    
  2251. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2252. 
    
  2252. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2253. 
    
  2253. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2254. 
    
  2254. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2255. 
+   
  2255. 
+    
+     40.99. Image classification
     
    
    2256. 
    
  2256. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2257. 
    
  2257. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2258. 
    
  2258. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2259. 
    
  2259. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2260. 
    
  2260. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2261. 
    
  2261. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2262. 
    
  2262. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2263. 
    
  2263. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2264. 
    
  2264. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2265. 
    
  2265. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2266. 
    
  2266. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2267. 
    
  2267. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2268. 
+   
  2268. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2269. 
    
  2269. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2270. 
    
  2270. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2271. 
    
  2271. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2272. 
+   
  2272. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2273. 
   
diff --git a/ml-fundamentals/classification/more-classifiers.html b/ml-fundamentals/classification/more-classifiers.html
index 2d317cccad..d636dcf70b 100644
--- a/ml-fundamentals/classification/more-classifiers.html
+++ b/ml-fundamentals/classification/more-classifiers.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2273. 
+    
+     40.89. Introduction
+    
+   
    2274. 
    
  2274. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2275. 
    
  2275. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2276. 
    
  2276. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2277. 
    
  2277. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2278. 
    
  2278. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2279. 
    
  2279. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2280. 
    
  2280. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2281. 
+   
  2281. 
+    
+     40.99. Image classification
     
    
    2282. 
    
  2282. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2283. 
    
  2283. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2284. 
    
  2284. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2285. 
    
  2285. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2286. 
    
  2286. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2287. 
    
  2287. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2288. 
    
  2288. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2289. 
    
  2289. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2290. 
    
  2290. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2291. 
    
  2291. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2292. 
    
  2292. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2293. 
    
  2293. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2294. 
+   
  2294. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2295. 
    
  2295. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2296. 
    
  2296. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2297. 
    
  2297. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2298. 
+   
  2298. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2299. 
   
@@ -2245,7 +2265,7 @@ 
    12.2.4. Exercise - apply logistic regres
    -
    Accuracy is 0.8006672226855713
+
    Accuracy is 0.7798165137614679
    @@ -2268,10 +2288,10 @@

    12.2.4. Exercise - apply logistic regres

    -
    ingredients: Index(['beef', 'black_pepper', 'mustard', 'sesame_oil', 'soy_sauce',
-       'vegetable_oil', 'vinegar'],
+
    ingredients: Index(['barley', 'cane_molasses', 'fish', 'sake', 'scallion', 'soy_sauce',
+       'soybean', 'wine'],
       dtype='object')
-cuisine: korean
+cuisine: japanese
    @@ -2320,25 +2340,25 @@

    12.2.4. Exercise - apply logistic regres + + japanese + 0.940964 + korean - 0.685323 + 0.053126 chinese - 0.209312 + 0.004265 - japanese - 0.102098 + thai + 0.001630 indian - 0.001763 - - - thai - 0.001504 + 0.000014 @@ -2359,15 +2379,15 @@

    12.2.4. Exercise - apply logistic regres 
                  precision    recall  f1-score   support
 
-     chinese       0.73      0.74      0.74       244
-      indian       0.92      0.91      0.92       233
-    japanese       0.75      0.76      0.75       232
-      korean       0.86      0.78      0.82       256
-        thai       0.75      0.82      0.78       234
+     chinese       0.69      0.69      0.69       231
+      indian       0.92      0.88      0.90       250
+    japanese       0.64      0.80      0.71       233
+      korean       0.85      0.78      0.81       236
+        thai       0.84      0.75      0.79       249
 
-    accuracy                           0.80      1199
-   macro avg       0.80      0.80      0.80      1199
-weighted avg       0.80      0.80      0.80      1199
+    accuracy                           0.78      1199
+   macro avg       0.79      0.78      0.78      1199
+weighted avg       0.79      0.78      0.78      1199
    diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html index c9e4855ef1..08b1900344 100644 --- a/ml-fundamentals/classification/yet-other-classifiers.html +++ b/ml-fundamentals/classification/yet-other-classifiers.html @@ -27,8 +27,8 @@ - + @@ -1110,114 +1110,134 @@

    Ocademy Open Machine Learning Book

    40.88. Case Study: Debugging in Regression +
  2299. + + 40.89. Introduction + +
  2300. - 40.89. Study the solvers + 40.90. Study the solvers
  2301. - 40.90. Build classification models + 40.91. Build classification models
  2302. - 40.91. Build Classification Model + 40.92. Build Classification Model
  2303. - 40.92. Parameter play + 40.93. Parameter play
  2304. - 40.93. How to choose cnn architecture mnist + 40.94. How to choose cnn architecture mnist
  2305. - 40.95. Sign Language Digits Classification with CNN + 40.96. Sign Language Digits Classification with CNN
  2306. - 40.97. Object Recognition in Images using CNN + 40.98. Object Recognition in Images using CNN + +
    2307. +
  2307. + + 40.99. Image classification
  2308. - 40.98. Intro to TensorFlow for Deep Learning + 40.100. Intro to TensorFlow for Deep Learning
  2309. - 40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment + 40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
  2310. - 40.102. Google Stock Price Prediction RNN + 40.104. Google Stock Price Prediction RNN
  2311. - 40.104. Intro to Autoencoders + 40.106. Intro to Autoencoders
  2312. - 40.105. Base/Denoising Autoencoder & Dimension Reduction + 40.107. Base/Denoising Autoencoder & Dimension Reduction
  2313. - 40.106. Fun with Variational Autoencoders + 40.108. Fun with Variational Autoencoders
  2314. - 40.107. Time Series Forecasting Assignment + 40.109. Time Series Forecasting Assignment
  2315. - 40.109. Neural Networks for Classification with TensorFlow + 40.111. Neural Networks for Classification with TensorFlow
  2316. - 40.110. NN Classify 15 Fruits Assignment + 40.112. NN Classify 15 Fruits Assignment
  2317. - 40.115. DQN On Foreign Exchange Market + 40.117. DQN On Foreign Exchange Market
  2318. - 40.116. Art by gan + 40.118. Art by gan
  2319. - 40.118. Generative Adversarial Networks (GANs) + 40.120. Generative Adversarial Networks (GANs) + +
    2320. +
  2320. + + 40.121. Comparing edge-based and region-based segmentation
  2321. - 40.119. Summary + 40.122. Summary
  2322. - 40.120. Car Object Detection + 40.123. Car Object Detection
  2323. - 40.122. Basic classification: Classify images of clothing + 40.125. Basic classification: Classify images of clothing + +
    2324. +
  2324. + + 40.126. Getting Start NLP with classification task
    2325. @@ -1903,11 +1923,11 @@

    12.3.4.1. Exercise - apply a linear SVC< 
    Accuracy (train) for Linear SVC: 78.1% 
               precision    recall  f1-score   support
 
-     chinese       0.64      0.72      0.68       238
-      indian       0.92      0.85      0.88       236
-    japanese       0.77      0.73      0.75       269
-      korean       0.85      0.74      0.80       230
-        thai       0.76      0.87      0.81       226
+     chinese       0.63      0.74      0.68       230
+      indian       0.91      0.89      0.90       251
+    japanese       0.78      0.78      0.78       242
+      korean       0.87      0.64      0.73       231
+        thai       0.77      0.84      0.80       245
 
     accuracy                           0.78      1199
    macro avg       0.79      0.78      0.78      1199
@@ -1938,28 +1958,28 @@ 
    12.3.5.1. Exercise - apply the K-Neighbo
 
    Accuracy (train) for Linear SVC: 78.1% 
               precision    recall  f1-score   support
 
-     chinese       0.64      0.72      0.68       238
-      indian       0.92      0.85      0.88       236
-    japanese       0.77      0.73      0.75       269
-      korean       0.85      0.74      0.80       230
-        thai       0.76      0.87      0.81       226
+     chinese       0.63      0.74      0.68       230
+      indian       0.91      0.89      0.90       251
+    japanese       0.78      0.78      0.78       242
+      korean       0.87      0.64      0.73       231
+        thai       0.77      0.84      0.80       245
 
     accuracy                           0.78      1199
    macro avg       0.79      0.78      0.78      1199
 weighted avg       0.79      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 73.6% 
+Accuracy (train) for KNN classifier: 70.7% 
               precision    recall  f1-score   support
 
-     chinese       0.62      0.78      0.69       238
-      indian       0.86      0.75      0.81       236
-    japanese       0.70      0.87      0.77       269
-      korean       0.93      0.58      0.72       230
-        thai       0.71      0.67      0.69       226
+     chinese       0.61      0.73      0.67       230
+      indian       0.83      0.79      0.81       251
+    japanese       0.62      0.85      0.71       242
+      korean       0.91      0.47      0.62       231
+        thai       0.72      0.69      0.70       245
 
-    accuracy                           0.74      1199
-   macro avg       0.76      0.73      0.73      1199
-weighted avg       0.76      0.74      0.74      1199
+    accuracy                           0.71      1199
+   macro avg       0.74      0.70      0.70      1199
+weighted avg       0.74      0.71      0.70      1199
    @@ -1990,42 +2010,42 @@

    12.3.6.1. Exercise - apply a Support Vec 
    Accuracy (train) for Linear SVC: 78.1% 
               precision    recall  f1-score   support
 
-     chinese       0.64      0.72      0.68       238
-      indian       0.92      0.85      0.88       236
-    japanese       0.77      0.73      0.75       269
-      korean       0.85      0.74      0.80       230
-        thai       0.76      0.87      0.81       226
+     chinese       0.63      0.74      0.68       230
+      indian       0.91      0.89      0.90       251
+    japanese       0.78      0.78      0.78       242
+      korean       0.87      0.64      0.73       231
+        thai       0.77      0.84      0.80       245
 
     accuracy                           0.78      1199
    macro avg       0.79      0.78      0.78      1199
 weighted avg       0.79      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 73.6% 
+Accuracy (train) for KNN classifier: 70.7% 
               precision    recall  f1-score   support
 
-     chinese       0.62      0.78      0.69       238
-      indian       0.86      0.75      0.81       236
-    japanese       0.70      0.87      0.77       269
-      korean       0.93      0.58      0.72       230
-        thai       0.71      0.67      0.69       226
+     chinese       0.61      0.73      0.67       230
+      indian       0.83      0.79      0.81       251
+    japanese       0.62      0.85      0.71       242
+      korean       0.91      0.47      0.62       231
+        thai       0.72      0.69      0.70       245
 
-    accuracy                           0.74      1199
-   macro avg       0.76      0.73      0.73      1199
-weighted avg       0.76      0.74      0.74      1199
+    accuracy                           0.71      1199
+   macro avg       0.74      0.70      0.70      1199
+weighted avg       0.74      0.71      0.70      1199
    -
    Accuracy (train) for SVC: 82.9% 
+
    Accuracy (train) for SVC: 81.3% 
               precision    recall  f1-score   support
 
-     chinese       0.74      0.81      0.77       238
-      indian       0.92      0.87      0.90       236
-    japanese       0.84      0.78      0.81       269
-      korean       0.90      0.79      0.84       230
-        thai       0.78      0.90      0.84       226
+     chinese       0.73      0.74      0.74       230
+      indian       0.88      0.92      0.90       251
+    japanese       0.80      0.82      0.81       242
+      korean       0.91      0.72      0.80       231
+        thai       0.76      0.84      0.80       245
 
-    accuracy                           0.83      1199
-   macro avg       0.83      0.83      0.83      1199
-weighted avg       0.83      0.83      0.83      1199
+    accuracy                           0.81      1199
+   macro avg       0.82      0.81      0.81      1199
+weighted avg       0.82      0.81      0.81      1199
    @@ -2053,70 +2073,70 @@

    12.3.7. Ensemble Classifiers
    Accuracy (train) for Linear SVC: 78.1% 
               precision    recall  f1-score   support
 
-     chinese       0.64      0.72      0.68       238
-      indian       0.92      0.85      0.88       236
-    japanese       0.77      0.73      0.75       269
-      korean       0.85      0.74      0.80       230
-        thai       0.76      0.87      0.81       226
+     chinese       0.63      0.74      0.68       230
+      indian       0.91      0.89      0.90       251
+    japanese       0.78      0.78      0.78       242
+      korean       0.87      0.64      0.73       231
+        thai       0.77      0.84      0.80       245
 
     accuracy                           0.78      1199
    macro avg       0.79      0.78      0.78      1199
 weighted avg       0.79      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 73.6% 
+Accuracy (train) for KNN classifier: 70.7% 
               precision    recall  f1-score   support
 
-     chinese       0.62      0.78      0.69       238
-      indian       0.86      0.75      0.81       236
-    japanese       0.70      0.87      0.77       269
-      korean       0.93      0.58      0.72       230
-        thai       0.71      0.67      0.69       226
+     chinese       0.61      0.73      0.67       230
+      indian       0.83      0.79      0.81       251
+    japanese       0.62      0.85      0.71       242
+      korean       0.91      0.47      0.62       231
+        thai       0.72      0.69      0.70       245
 
-    accuracy                           0.74      1199
-   macro avg       0.76      0.73      0.73      1199
-weighted avg       0.76      0.74      0.74      1199
+    accuracy                           0.71      1199
+   macro avg       0.74      0.70      0.70      1199
+weighted avg       0.74      0.71      0.70      1199

    -
    Accuracy (train) for SVC: 82.9% 
+
    Accuracy (train) for SVC: 81.3% 
               precision    recall  f1-score   support
 
-     chinese       0.74      0.81      0.77       238
-      indian       0.92      0.87      0.90       236
-    japanese       0.84      0.78      0.81       269
-      korean       0.90      0.79      0.84       230
-        thai       0.78      0.90      0.84       226
+     chinese       0.73      0.74      0.74       230
+      indian       0.88      0.92      0.90       251
+    japanese       0.80      0.82      0.81       242
+      korean       0.91      0.72      0.80       231
+        thai       0.76      0.84      0.80       245
 
-    accuracy                           0.83      1199
-   macro avg       0.83      0.83      0.83      1199
-weighted avg       0.83      0.83      0.83      1199
+    accuracy                           0.81      1199
+   macro avg       0.82      0.81      0.81      1199
+weighted avg       0.82      0.81      0.81      1199
 
    
 
    
-
    Accuracy (train) for RFST: 84.7% 
+
    Accuracy (train) for RFST: 82.7% 
               precision    recall  f1-score   support
 
-     chinese       0.78      0.84      0.81       238
-      indian       0.94      0.88      0.91       236
-    japanese       0.89      0.81      0.85       269
-      korean       0.86      0.80      0.83       230
-        thai       0.78      0.91      0.84       226
+     chinese       0.77      0.79      0.78       230
+      indian       0.91      0.90      0.91       251
+    japanese       0.80      0.81      0.80       242
+      korean       0.86      0.75      0.80       231
+        thai       0.79      0.87      0.83       245
 
-    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      0.82      0.83      1199
+weighted avg       0.83      0.83      0.83      1199
 
    
 
    
-
    Accuracy (train) for ADA: 72.8% 
+
    Accuracy (train) for ADA: 70.0% 
               precision    recall  f1-score   support
 
-     chinese       0.65      0.61      0.63       238
-      indian       0.91      0.81      0.85       236
-    japanese       0.66      0.68      0.67       269
-      korean       0.75      0.70      0.73       230
-        thai       0.70      0.85      0.77       226
+     chinese       0.60      0.50      0.55       230
+      indian       0.83      0.86      0.85       251
+    japanese       0.64      0.70      0.67       242
+      korean       0.75      0.71      0.73       231
+        thai       0.67      0.71      0.69       245
 
-    accuracy                           0.73      1199
-   macro avg       0.73      0.73      0.73      1199
-weighted avg       0.73      0.73      0.73      1199
+    accuracy                           0.70      1199
+   macro avg       0.70      0.70      0.70      1199
+weighted avg       0.70      0.70      0.70      1199
 
    
 
    
 
    
diff --git a/ml-fundamentals/ml-overview.html b/ml-fundamentals/ml-overview.html
index 613f0f3e02..9a7bb4c2ee 100644
--- a/ml-fundamentals/ml-overview.html
+++ b/ml-fundamentals/ml-overview.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
         
    
+   
  2325. 
+    
+     40.89. Introduction
+    
+   
    2326. 
    
  2326. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2327. 
    
  2327. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2328. 
    
  2328. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2329. 
    
  2329. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2330. 
    
  2330. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2331. 
    
  2331. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2332. 
    
  2332. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2333. 
+   
  2333. 
+    
+     40.99. Image classification
     
    
    2334. 
    
  2334. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2335. 
    
  2335. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2336. 
    
  2336. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2337. 
    
  2337. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2338. 
    
  2338. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2339. 
    
  2339. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2340. 
    
  2340. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2341. 
    
  2341. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2342. 
    
  2342. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2343. 
    
  2343. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2344. 
    
  2344. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2345. 
    
  2345. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2346. 
+   
  2346. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2347. 
    
  2347. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2348. 
    
  2348. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2349. 
    
  2349. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2350. 
+   
  2350. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2351. 
   
diff --git a/ml-fundamentals/parameter-optimization/gradient-descent.html b/ml-fundamentals/parameter-optimization/gradient-descent.html
index 84019e2e10..6adc36c765 100644
--- a/ml-fundamentals/parameter-optimization/gradient-descent.html
+++ b/ml-fundamentals/parameter-optimization/gradient-descent.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2351. 
+    
+     40.89. Introduction
+    
+   
    2352. 
    
  2352. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2353. 
    
  2353. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2354. 
    
  2354. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2355. 
    
  2355. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2356. 
    
  2356. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2357. 
    
  2357. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2358. 
    
  2358. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2359. 
+   
  2359. 
+    
+     40.99. Image classification
     
    
    2360. 
    
  2360. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2361. 
    
  2361. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2362. 
    
  2362. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2363. 
    
  2363. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2364. 
    
  2364. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2365. 
    
  2365. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2366. 
    
  2366. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2367. 
    
  2367. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2368. 
    
  2368. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2369. 
    
  2369. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2370. 
    
  2370. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2371. 
    
  2371. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2372. 
+   
  2372. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2373. 
    
  2373. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2374. 
    
  2374. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2375. 
    
  2375. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2376. 
+   
  2376. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2377. 
   
@@ -2083,19 +2103,19 @@ 
    13.2.5.1. Linear regression With gradien
 
    
 
    
 
    
-
    {'lr': 0.0003, 'n_iters': 5000, 'weights': array([ 0.44776251,  0.22740589,  0.01286507,  0.09545673,  0.14355988,
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+
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diff --git a/ml-fundamentals/parameter-optimization/loss-function.html b/ml-fundamentals/parameter-optimization/loss-function.html
index 1c8cef0a84..4c93234e54 100644
--- a/ml-fundamentals/parameter-optimization/loss-function.html
+++ b/ml-fundamentals/parameter-optimization/loss-function.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2377. 
+    
+     40.89. Introduction
+    
+   
    2378. 
    
  2378. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2379. 
    
  2379. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2380. 
    
  2380. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2381. 
    
  2381. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2382. 
    
  2382. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2383. 
    
  2383. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2384. 
    
  2384. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2385. 
+   
  2385. 
+    
+     40.99. Image classification
     
    
    2386. 
    
  2386. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2387. 
    
  2387. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2388. 
    
  2388. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2389. 
    
  2389. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2390. 
    
  2390. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2391. 
    
  2391. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2392. 
    
  2392. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2393. 
    
  2393. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2394. 
    
  2394. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2395. 
    
  2395. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2396. 
    
  2396. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2397. 
    
  2397. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2398. 
+   
  2398. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2399. 
    
  2399. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2400. 
    
  2400. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2401. 
    
  2401. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2402. 
+   
  2402. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2403. 
   
diff --git a/ml-fundamentals/parameter-optimization/parameter-optimization.html b/ml-fundamentals/parameter-optimization/parameter-optimization.html
index e7b15cc4ff..c413b56bd7 100644
--- a/ml-fundamentals/parameter-optimization/parameter-optimization.html
+++ b/ml-fundamentals/parameter-optimization/parameter-optimization.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2403. 
+    
+     40.89. Introduction
+    
+   
    2404. 
    
  2404. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2405. 
    
  2405. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2406. 
    
  2406. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2407. 
    
  2407. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2408. 
    
  2408. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2409. 
    
  2409. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2410. 
    
  2410. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2411. 
+   
  2411. 
+    
+     40.99. Image classification
     
    
    2412. 
    
  2412. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2413. 
    
  2413. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2414. 
    
  2414. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2415. 
    
  2415. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2416. 
    
  2416. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2417. 
    
  2417. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2418. 
    
  2418. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2419. 
    
  2419. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2420. 
    
  2420. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2421. 
    
  2421. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2422. 
    
  2422. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2423. 
    
  2423. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2424. 
+   
  2424. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2425. 
    
  2425. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2426. 
    
  2426. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2427. 
    
  2427. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2428. 
+   
  2428. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2429. 
   
diff --git a/ml-fundamentals/regression/linear-and-polynomial-regression.html b/ml-fundamentals/regression/linear-and-polynomial-regression.html
index b00837b842..790629ab00 100644
--- a/ml-fundamentals/regression/linear-and-polynomial-regression.html
+++ b/ml-fundamentals/regression/linear-and-polynomial-regression.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2429. 
+    
+     40.89. Introduction
+    
+   
    2430. 
    
  2430. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2431. 
    
  2431. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2432. 
    
  2432. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2433. 
    
  2433. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2434. 
    
  2434. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2435. 
    
  2435. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2436. 
    
  2436. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2437. 
+   
  2437. 
+    
+     40.99. Image classification
     
    
    2438. 
    
  2438. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2439. 
    
  2439. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2440. 
    
  2440. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2441. 
    
  2441. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2442. 
    
  2442. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2443. 
    
  2443. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2444. 
    
  2444. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2445. 
    
  2445. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2446. 
    
  2446. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2447. 
    
  2447. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2448. 
    
  2448. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2449. 
    
  2449. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2450. 
+   
  2450. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2451. 
    
  2451. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2452. 
    
  2452. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2453. 
    
  2453. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2454. 
+   
  2454. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2455. 
   
@@ -2287,7 +2307,7 @@ 
    11.3.3. Correlation
 
    
 
    
-
    <matplotlib.collections.PathCollection at 0x7fee359bd610>
+
    <matplotlib.collections.PathCollection at 0x7f88170fc6a0>
 
    
 
    
 ../../_images/linear-and-polynomial-regression_12_1.png
@@ -2473,7 +2493,7 @@ 
    11.3.5. Simple Linear Regression
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x7fee24c9a220>]
+
    [<matplotlib.lines.Line2D at 0x7f880645a550>]
 
    
 
    
 ../../_images/linear-and-polynomial-regression_34_1.png
diff --git a/ml-fundamentals/regression/logistic-regression.html b/ml-fundamentals/regression/logistic-regression.html
index b91ed11235..fbaab8f014 100644
--- a/ml-fundamentals/regression/logistic-regression.html
+++ b/ml-fundamentals/regression/logistic-regression.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
         
    
+   
  2455. 
+    
+     40.89. Introduction
+    
+   
    2456. 
    
  2456. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2457. 
    
  2457. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2458. 
    
  2458. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2459. 
    
  2459. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2460. 
    
  2460. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2461. 
    
  2461. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2462. 
    
  2462. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2463. 
+   
  2463. 
+    
+     40.99. Image classification
     
    
    2464. 
    
  2464. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2465. 
    
  2465. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2466. 
    
  2466. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2467. 
    
  2467. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2468. 
    
  2468. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2469. 
    
  2469. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2470. 
    
  2470. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2471. 
    
  2471. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2472. 
    
  2472. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2473. 
    
  2473. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2474. 
    
  2474. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2475. 
    
  2475. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2476. 
+   
  2476. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2477. 
    
  2477. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2478. 
    
  2478. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2479. 
    
  2479. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2480. 
+   
  2480. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2481. 
   
@@ -2166,7 +2186,7 @@ 
    11.4.5.1. Visualization - side-by-side g
 
    
 
    
 
    
-
    <seaborn.axisgrid.PairGrid at 0x7f2ed47b3070>
+
    <seaborn.axisgrid.PairGrid at 0x7ff23cb12580>
 
    
 
    
 ../../_images/logistic-regression_19_1.png
@@ -2225,7 +2245,7 @@ 
    11.4.5.3. Violin plot
-
    <seaborn.axisgrid.FacetGrid at 0x7f2eaf733550>
+
    <seaborn.axisgrid.FacetGrid at 0x7ff214796dc0>
 
    
 
    
 ../../_images/logistic-regression_24_1.png
diff --git a/ml-fundamentals/regression/managing-data.html b/ml-fundamentals/regression/managing-data.html
index 48bdf75c71..30468fe624 100644
--- a/ml-fundamentals/regression/managing-data.html
+++ b/ml-fundamentals/regression/managing-data.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
         
    
+   
  2481. 
+    
+     40.89. Introduction
+    
+   
    2482. 
    
  2482. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2483. 
    
  2483. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2484. 
    
  2484. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2485. 
    
  2485. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2486. 
    
  2486. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2487. 
    
  2487. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2488. 
    
  2488. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2489. 
+   
  2489. 
+    
+     40.99. Image classification
     
    
    2490. 
    
  2490. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2491. 
    
  2491. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2492. 
    
  2492. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2493. 
    
  2493. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2494. 
    
  2494. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2495. 
    
  2495. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2496. 
    
  2496. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2497. 
    
  2497. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2498. 
    
  2498. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2499. 
    
  2499. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2500. 
    
  2500. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2501. 
    
  2501. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2502. 
+   
  2502. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2503. 
    
  2503. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2504. 
    
  2504. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2505. 
    
  2505. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2506. 
+   
  2506. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2507. 
   
diff --git a/ml-fundamentals/regression/regression-models-for-machine-learning.html b/ml-fundamentals/regression/regression-models-for-machine-learning.html
index 58a64f40ba..feb5c0cb41 100644
--- a/ml-fundamentals/regression/regression-models-for-machine-learning.html
+++ b/ml-fundamentals/regression/regression-models-for-machine-learning.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2507. 
+    
+     40.89. Introduction
+    
+   
    2508. 
    
  2508. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2509. 
    
  2509. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2510. 
    
  2510. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2511. 
    
  2511. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2512. 
    
  2512. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2513. 
    
  2513. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2514. 
    
  2514. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2515. 
+   
  2515. 
+    
+     40.99. Image classification
     
    
    2516. 
    
  2516. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2517. 
    
  2517. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2518. 
    
  2518. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2519. 
    
  2519. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2520. 
    
  2520. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2521. 
    
  2521. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2522. 
    
  2522. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2523. 
    
  2523. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2524. 
    
  2524. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2525. 
    
  2525. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2526. 
    
  2526. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2527. 
    
  2527. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2528. 
+   
  2528. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2529. 
    
  2529. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2530. 
    
  2530. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2531. 
    
  2531. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2532. 
+   
  2532. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2533. 
   
diff --git a/ml-fundamentals/regression/tools-of-the-trade.html b/ml-fundamentals/regression/tools-of-the-trade.html
index a727510f6c..d6640a7f2a 100644
--- a/ml-fundamentals/regression/tools-of-the-trade.html
+++ b/ml-fundamentals/regression/tools-of-the-trade.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2533. 
+    
+     40.89. Introduction
+    
+   
    2534. 
    
  2534. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2535. 
    
  2535. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2536. 
    
  2536. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2537. 
    
  2537. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2538. 
    
  2538. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2539. 
    
  2539. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2540. 
    
  2540. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2541. 
+   
  2541. 
+    
+     40.99. Image classification
     
    
    2542. 
    
  2542. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2543. 
    
  2543. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2544. 
    
  2544. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2545. 
    
  2545. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2546. 
    
  2546. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2547. 
    
  2547. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2548. 
    
  2548. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2549. 
    
  2549. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2550. 
    
  2550. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2551. 
    
  2551. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2552. 
    
  2552. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2553. 
    
  2553. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2554. 
+   
  2554. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2555. 
    
  2555. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2556. 
    
  2556. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2557. 
    
  2557. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2558. 
+   
  2558. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2559. 
   
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diff --git a/prerequisites/python-programming-advanced.html b/prerequisites/python-programming-advanced.html
index 6022ef77d2..6466aea631 100644
--- a/prerequisites/python-programming-advanced.html
+++ b/prerequisites/python-programming-advanced.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2559. 
+    
+     40.89. Introduction
+    
+   
    2560. 
    
  2560. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2561. 
    
  2561. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2562. 
    
  2562. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2563. 
    
  2563. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2564. 
    
  2564. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2565. 
    
  2565. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2566. 
    
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183,184,185],BUT:[97,98,183,184,185],BY:[106,150],Be:[90,96,109,113,124,169],Being:[45,64,106,109,127],But:[33,39,41,42,45,50,51,52,54,55,58,59,60,61,63,66,71,79,85,105,109,114,117,129,134,136,143,146,148,149,150,155,159,160,161,163,165,166,167,168,174,179,182,183,184,185,196,200],By:[7,18,29,41,43,48,51,54,55,56,59,61,71,79,81,82,83,85,104,106,114,119,122,124,127,129,135,146,148,149,151,154,156,157,164,167,171,174,175,178,179,183,194],FOR:[97,98,183,184,185],For:[7,19,29,30,31,32,36,37,39,40,41,42,43,45,47,48,49,50,51,52,53,56,61,62,63,66,68,69,70,71,72,74,76,77,79,80,89,90,91,93,94,95,96,105,106,107,116,117,118,119,121,122,124,125,126,127,131,136,137,140,143,144,146,148,149,150,151,152,154,155,156,157,159,160,161,164,165,166,167,168,169,171,172,173,174,175,178,179,180,182,183,184,185,186,196,200,203,204,206],IN:[25,87,97,98,183,184,185],IS:[22,47,49,50,56,97,98,102,154,183,184,185],IT:[56,104,149],If:[1,7,14,16,18,29,30,33,34,36,40,41,42,43,45,47,50,51,52,53,54,55,56,59,60,61,62,64,66,68,71,74,79,81,82,85,87,97,98,100,105,106,108,109,111,113,114,115,116,117,118,119,121,122,123,124,125,126,127,129,131,135,136,137,139,141,142,145,148,149,150,152,154,156,159,160,161,163,165,166,167,168,169,171,173,174,175,178,179,180,182,183,184,185,192,198,200,203,205,206,207],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,37,40,41,42,43,45,47,48,49,50,51,52,54,55,56,59,60,61,62,63,64,66,68,69,71,74,76,79,80,81,82,83,85,87,88,93,94,95,97,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,129,130,131,132,133,135,136,137,138,139,140,142,143,144,146,148,149,150,151,152,154,155,156,157,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,189,191,192,193,198,200,201,202,203,204,206,207],Is:[52,98,102,104,108,111,116,117,118,135,143,149,150,154,169,174,179,180,207],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,48,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,73,74,76,77,79,81,82,83,85,87,88,89,90,91,93,94,95,96,104,105,106,107,108,109,111,113,114,115,116,117,118,119,121,122,123,124,125,126,127,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,148,149,150,151,152,153,154,155,156,157,159,160,161,162,163,164,165,166,167,168,169,171,172,173,174,175,176,178,180,182,183,184,185,187,188,194,198,199,200,201,203,204,206],Its:[136,143,159,164,179],NEAR:[63,79],NO:[97,98,183,184,185],NOT:[87,97,98,124,138,142,183,184,185],Near:[114,190],No:[7,20,29,33,52,56,58,66,73,87,93,98,103,105,106,115,137,156,165,167,172,183,184,187,190],Not:[7,41,45,51,54,56,58,71,85,106,116,122,123,141,160,172,178,179,184,199,206],OF:[22,47,49,50,97,98,138,183,184,185],ON:192,ONE:7,OR:[22,47,49,50,97,98,124,183,184,185],Of:[52,69,106,107,109,119,167,185,186],On:[51,52,54,59,60,61,62,63,68,69,71,79,85,88,106,109,111,143,150,156,159,160,163,164,165,167,168,169,174,178,182,183,189,199],One:[1,7,11,28,41,45,51,52,54,55,56,57,59,60,61,68,88,103,108,109,111,113,115,119,121,124,132,136,144,150,155,157,159,165,168,174,178,180,183,184,185,189,190,191,196,198,203,206],Or:[32,41,52,60,79,107,109,124,136,137,150,154,157,174,183,184,199,203,206],Such:[1,7,30,41,45,51,52,56,121,150,151,178,183,205],THAT:87,THE:[97,98,183,184,185],TO:[56,97,98,145,183,184,185],That:[31,32,41,45,50,51,52,54,59,63,64,69,71,79,85,109,114,121,124,129,137,155,157,160,161,167,169,174,175,179,184,185,203],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,36,38,40,41,42,43,47,48,49,50,51,52,54,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,76,79,81,83,85,88,89,95,96,97,98,104,107,108,109,111,113,114,115,117,118,119,120,121,123,124,125,126,127,131,132,133,134,135,136,137,138,139,140,141,142,144,145,146,147,148,149,150,151,152,154,155,156,157,159,160,161,162,163,164,165,168,169,171,172,174,175,176,178,179,185,186,188,189,190,194,195,197,198,199,200,201,202,203,204,207],Their:143,Then:[7,31,42,47,49,52,58,68,82,87,106,109,121,124,132,135,141,142,145,150,151,152,155,156,157,159,160,164,168,169,178,182,183,184,185,199],There:[0,1,3,7,18,28,29,30,32,34,37,38,40,41,43,45,48,49,51,52,53,56,59,61,62,64,66,69,71,77,79,85,90,105,106,109,111,113,114,115,118,119,121,122,123,124,125,129,131,132,133,136,139,141,142,143,145,146,148,149,150,151,152,154,155,157,159,160,163,167,168,169,170,171,173,174,175,178,179,181,182,183,184,185,203,204,205,206],These:[7,30,31,40,42,43,47,51,56,58,61,62,69,79,80,82,83,104,106,109,111,114,117,118,123,124,126,127,139,142,143,149,155,159,160,164,166,167,168,183,185,187,189,191,192,194,205],To:[0,1,7,14,18,22,29,30,33,34,37,41,42,43,47,48,50,51,52,54,56,60,63,64,68,69,71,79,80,81,87,97,104,105,106,107,109,114,115,116,117,119,121,122,124,125,127,129,134,135,136,139,140,141,142,146,147,148,149,150,151,152,156,157,160,161,163,164,165,167,168,169,171,174,175,178,179,180,182,183,184,185,189,190,192,195,198,199,203,205,206,207],WITH:[97,98,183,184,185],Will:[152,179,185],With:[7,41,43,45,49,52,58,62,63,64,89,104,108,109,113,114,116,117,118,119,123,124,126,127,129,136,149,150,157,159,160,165,167,168,174,176,183,192,199,204],_0:160,_1:157,_2:157,_:[18,29,31,33,35,38,42,43,53,58,73,86,131,132,135,137,138,139,140,141,145,146,157,160,167,175,183,184,194,200,201,206],____:[3,12,22,24,25,48,97,98,102],_____:[24,97],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:[29,30,37,42],__________________________________________________________________________________________________:35,____i:98,__abs__:97,__add__:97,__all__:183,__annotations__:[183,205],__builtins__:183,__cached__:183,__call__:[65,67,133,135,140],__class__:[38,183],__dict__:[175,201,202],__doc__:[183,205],__eq__:97,__file__:[73,183],__finalize__:[125,126],__future__:38,__get__:125,__getitem__:[126,127],__init__:[29,30,31,33,35,36,37,38,45,57,65,67,86,87,99,125,133,135,137,138,139,140,141,144,161,175,183,200,201,205],__iter__:[33,138],__len__:33,__loader__:183,__main__:[36,137,165,168,169],__mul__:97,__name__:[36,38,137,168,169,183],__operators__:141,__package__:183,__repr__:57,__spec__:183,__str__:97,__sub__:97,__truediv__:97,__version__:[43,137,167,205],_aspp:140,_attach:[123,192],_bin:56,_branch:140,_build_model:36,_bunch:[59,60],_caller:125,_check_indexing_error:127,_concaten:125,_consolidate_inplac:126,_constructor:126,_conv_block:140,_conv_bn_relu:140,_conv_relu:140,_data:126,_decor:125,_deeplabv3:140,_deprecate_mismatched_index:126,_deprecated_arg:126,_engin:127,_etag:[123,192],_fcn_16:140,_fcn_32:140,_fcn_8:140,_format_argument_list:125,_fuse_bn_tensor:139,_get_axi:126,_get_block_manager_axi:126,_get_comb_axi:125,_get_concat_axi:125,_get_join_info:125,_get_list_axi:126,_get_new_ax:125,_get_result_dim:125,_get_slice_axi:126,_get_valu:[126,127],_get_values_for_loc:127,_getbool_axi:126,_getitem_axi:126,_getitem_lowerdim:126,_getitem_tupl:126,_i:[82,83,157,167],_identity_block:140,_ilocindex:126,_index:58,_indicator_pre_merg:125,_info_axi:125,_invalid_index:126,_is_copi:126,_is_scalar_access:126,_items_overlap_with_suffix:125,_j:[157,167],_k:137,_kmean:168,_label:59,_left:125,_lib:[125,127],_locationindex:126,_locindex:126,_m:137,_make_concat_multiindex:125,_make_stag:139,_mapdataset:37,_maybe_cast_for_get_loc:126,_maybe_cast_slice_bound:126,_maybe_check_integr:125,_merge_doc:125,_merge_typ:125,_mergeoper:125,_method:191,_mgr:[125,126],_novalu:191,_oldcor:88,_other:125,_pad_1x1_to_3x3_tensor:139,_recognized_scalar:126,_reindex_and_concat:125,_rid:[123,192],_right:125,_sec_1:98,_segnet:140,_self:[123,192],_sigmoid:[86,175,201],_skip:3,_slice:126,_static:[126,127],_subplot:[69,79],_sum:191,_t:[123,192],_t_sne:[168,198],_take:126,_take_with_is_copi:126,_takeabl:126,_valid_typ:126,_validate_integ:126,_validate_kei:126,_validate_tuple_index:126,_valu:127,a0958ad901d7:123,a0:[125,191],a10:127,a1:[124,125,191],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:61,a21453:184,a23:[183,205],a2:[124,125,191],a3:125,a3z5kdkfn3tbq:61,a4:125,a5:125,a7yia1n5fo6efhugqfis3dhueyjsa:61,a_:[87,168],a_dict:184,a_i:[87,156],a_list:184,a_n:159,aaaaaa:[165,196],aafter:166,aaron:[29,52,83,138,176],ab:[52,65,67,73,82,97,98,126,134,138,144,167,183,184,206],abadi:138,abbrevi:[129,135],abc:[98,126,127,184,191,207],abcd:[7,122,126,191],abcdef:126,abcmous:[117,188],abil:[45,54,56,71,81,82,85,113,136,142,148,159,165,169,174,177,183,185,196,203],abl:[3,7,10,11,14,16,20,31,41,51,52,54,55,56,59,63,64,79,81,106,109,115,117,119,123,124,127,136,143,146,149,151,154,160,163,166,168,169,171,178,179,180,182,188,198,201,202,205],abnorm:29,abnorml:68,abo:39,aboslut:166,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,41,42,43,45,48,49,50,51,52,54,55,56,59,60,61,62,63,64,68,69,71,82,83,85,88,89,95,104,105,106,107,108,109,111,113,114,115,117,119,120,121,122,123,124,125,127,129,130,136,137,140,142,143,144,146,147,148,149,150,151,153,154,155,156,159,160,161,163,164,166,167,168,169,170,172,173,174,175,176,178,180,181,182,183,184,185,186,188,189,192,200,203,205,207],abov:[0,1,7,11,14,19,26,29,32,37,41,45,47,48,49,50,51,52,53,54,55,56,59,60,61,62,66,68,69,71,79,85,97,98,109,113,115,119,121,124,125,126,127,129,132,135,136,137,138,139,140,142,144,146,148,149,150,151,154,155,156,157,159,163,164,166,167,168,171,174,177,178,179,180,181,182,183,184,185,190,199,200],above_cutoff:167,abracadabra:184,abraham:207,abs_vector:[184,206],absenc:[56,168,198],absent:[126,175],absolut:[49,81,83,88,97,121,124,150,159,163,166,183,184,185,206],absolute_error:82,absolute_percentage_error:82,abspath:73,absurd:69,abund:[115,190],ac:[69,143,167],academ:[117,120,147,188],academi:205,acc:[33,40,49,51,54,59,132,145,204],acc_and_loss:132,acceler:[105,115,116,150,178,190],acceleromet:[121,168],accept:[16,41,42,59,69,71,85,88,105,108,111,117,124,126,127,139,150,160,164,174,183,189,203,204],acceptedcmp1:69,acceptedcmp2:69,acceptedcmp3:69,acceptedcmp4:69,acceptedcmp5:69,access:[6,14,16,39,43,71,79,85,104,106,107,109,111,113,117,123,126,142,148,151,152,169,176,178,183,184,188,189,191,199,205,206],accessor:127,accident:160,acclaim:164,accommod:[7,37,49,122,184],accompani:[121,150,178],accomplish:[93,150,160,168,174,198,203],accord:[18,47,52,56,65,67,69,104,113,114,115,116,121,124,126,139,148,151,154,156,159,164,168,169,172,174,178,179,180,182,198],accordingli:[35,43,57,132,154,183,205],account:[0,6,8,14,16,42,52,81,97,106,107,117,121,123,124,143,152,156,160,178,179,188,191],accumul:[1,52,97,111,146,157,174,184,203],accur:[15,32,33,43,52,56,61,71,74,82,85,95,106,111,117,118,121,136,140,142,144,148,151,156,163,166,168,171,174,177,178,179,189],accuraci:[29,33,34,40,41,42,50,51,52,53,54,56,58,59,62,66,71,72,74,81,82,83,85,87,88,89,107,117,122,132,140,142,143,145,150,151,152,155,156,157,159,160,162,163,165,167,169,172,173,178,179,186,188,194,198],accuracy_metr:132,accuracy_scor:[29,40,51,52,53,54,58,59,61,62,71,72,85,88,143,159,164,168,169,172,173,179,198,201,202],achiev:[32,33,41,50,52,56,58,61,69,108,111,124,137,139,140,148,149,150,151,152,159,160,161,162,164,165,166,167,183],achitectur:138,aci_servic:[9,105],aci_service_nam:[9,105],aciconfig:[9,105],acid:50,aciwebservic:[9,105],acm:[117,188],aco:35,acoust:[153,154,155],acquir:[6,108,111,150,189],acquisit:[3,107,111,119,146,186,188],acro:169,acronym:114,across:[33,45,49,56,71,85,107,117,119,121,124,125,127,129,132,142,146,148,149,150,160,164,167,183,184,186,188],act:[3,14,22,24,36,55,64,98,109,117,124,127,132,133,137,142,144,174,178,191,203],act_greedi:36,act_valu:36,action:[0,7,36,41,47,48,97,98,104,108,109,117,119,122,123,124,149,151,169,172,174,183,184,185,188,203],action_prob:137,action_s:36,actions_count:36,activ:[0,29,30,32,33,34,35,36,37,40,41,42,43,45,46,47,49,50,58,59,64,72,73,87,117,131,132,133,135,137,138,139,140,141,143,144,145,151,160,166,167,171,188,193,194,199,204],activateion:141,activation_1:37,activation_2:37,activation_3:37,activation_4:37,activespac:192,actor:183,actual:[7,39,41,42,45,48,49,50,52,53,54,58,59,61,62,68,71,73,79,80,81,82,83,85,87,88,89,97,106,116,118,119,121,122,123,124,126,127,132,135,136,146,148,154,160,162,164,166,167,171,174,176,178,179,183,185,187,189,193,200,203],actual_result:[3,14,22,24,55,98],actual_valu:[39,82],acut:159,ad:[1,7,18,22,29,32,37,39,43,45,47,50,52,54,56,61,66,69,71,81,85,97,98,116,118,121,123,133,135,139,147,149,150,159,160,161,163,165,168,174,175,178,184,200,206],ada:173,adaboost:[160,173],adaboost_clf:51,adaboostclassifi:[51,58,172,173],adagradoptim:150,adam:[29,30,31,32,33,34,36,37,38,39,40,41,42,43,44,46,47,49,50,64,72,131,132,135,138,140,141,143,194],adamax:37,adamharlei:[132,193],adamoptim:[134,150],adamw:35,adapt:[35,49,60,64,104,117,139,146,150,160,176,188,204],adaptiveaveragepooling2d:139,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,37,39,40,43,44,45,46,47,48,49,52,54,56,63,64,65,67,68,72,81,97,98,113,115,118,121,123,125,129,131,134,135,139,140,141,143,144,149,159,160,161,163,164,165,166,169,173,174,178,179,180,182,183,184,185,192,194,196,199,200,203,204,206],add_1:141,add_:31,add_artist:[115,190],add_ax:168,add_legend:154,add_selectbox:199,add_slid:199,add_subplot:[36,38,49,69,72,80,137],add_trick:183,add_weight:139,addit:[1,7,18,23,32,43,48,56,61,66,68,79,80,81,97,108,109,111,113,117,118,121,122,123,124,129,137,139,140,141,144,146,150,152,156,160,161,163,164,165,167,175,178,184,185,191,192,206,207],addition:[31,122,124,129,149,152,154,156,160,165,192,196],additon:32,address:[80,81,95,108,109,111,117,143,146,148,149,152,156,163,174,183,188,189],adel:159,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,74,76,77,90,93,94,95,96,121,150],adher:[50,111,142,149],adjac:[168,183],adject:[184,206],adjunct:174,adjust:[29,33,37,42,47,57,59,60,80,139,149,150,159,160,165,168,173],adjusted_mutual_info_scor:168,adjusted_rand_scor:168,admin2:151,admin:192,administr:[152,185],admonit:[125,126,127],adobe_premier:39,adolesc:183,adopt:[61,90,107,117,149,150,156,160,186,188,207],adult:[183,205],advai:42,advanc:[36,45,68,107,117,121,127,128,130,142,147,149,150,152,156,168,174,184,192,195],advantag:[41,52,56,71,85,127,139,143,149,152,159,162,163,165,184],advent:[51,136,149],advers:28,adversari:[37,152,195],advertis:109,advic:150,advis:[7,48,55,60,63,122,159],advoc:117,ae5:168,ae:[31,135],aebf:[123,192],aeroplan:7,aerospik:192,aesthet:22,afb:169,affect:[7,17,33,40,51,54,56,58,60,71,81,85,87,107,109,111,117,121,122,137,139,150,157,163,165,166,175,183,186,188,196],affer:145,affin:[87,154,168],affinity_matrix_:167,affinitypropag:168,afford:[7,79,122,174],african:[117,152,188],afro:[154,155],afropop:[154,155],after:[0,7,14,29,32,33,34,36,37,40,41,43,49,50,51,52,53,56,57,58,59,62,64,66,69,72,79,81,87,109,113,119,121,122,124,129,131,132,139,140,141,145,146,149,150,151,154,155,157,159,160,163,164,168,173,174,178,180,183,184,185,199,201,202,204,205,206],afterward:[32,124],ag:[9,18,22,52,53,69,79,93,97,98,105,106,119,121,123,124,125,149,154,156,157,161,164,171,174,181,182,183,184,185,191,192,201,202,203,205,206,207],again:[7,14,17,41,43,49,51,52,53,54,55,59,60,61,71,85,87,122,127,135,155,160,164,166,168,179,180,183,184,185,198,199],against:[0,18,43,49,52,61,82,83,109,117,119,121,125,126,146,150,152,160,166,169,182,197],agaricu:115,age_distribut:24,age_median_imput:22,age_sal_tre:52,age_tre:52,agefil:22,agenc:109,agenda:[107,186],agent:[117,174,203],ageron:167,agg:[18,39,167],aggfunc:125,agglom:[69,154],agglomerativeclust:[69,167,168],aggreg:[7,14,51,111,116,132,156,159,164],agil:[148,149],agnost:149,ago:[136,160],agre:[22,47,49,50,117],agreement:142,agricultur:[107,116,180,186,203],ahead:[51,54,59,109,146],ahnjovq9nfghs6fj4piqib3brpgnscyflm6riahdtaeyfclwo1cf:61,ai:[12,18,25,105,106,107,113,117,119,125,126,127,137,148,149,151,152,169,174,182,187,188,193,203,205],aid:[56,64,83,142,143,154,175,178],aim:[56,80,81,109,137,139,142,144,157,168,174,176,197],air:118,airbu:29,airflow:149,airlin:7,airplan:132,airport:[107,129,186,192],aka:[37,69,149],akkio:187,al:[31,71,85,116,117,152,190],alabama:151,alacazam:184,albeit:[47,179],albifron:[114,190],album:154,alcohol:[50,106],alekseynp:167,alert:148,alex:[33,135],alexa:[142,150],alexand:[130,133],alexandru:68,alexei:61,alexi:161,alexnet:139,alfredo:182,alg:58,algebra:[44,53,56,61,89,124,204],algo:[160,168],algorithm:[3,31,43,51,53,54,55,57,58,59,60,61,62,63,76,80,81,87,88,89,95,97,104,105,106,107,117,124,134,135,136,139,142,144,146,147,148,149,150,152,155,156,157,161,162,163,165,166,168,171,172,174,175,180,183,186,188,196,197,198,200,201,202,203],algoritm:160,algorythm:88,alia:[124,168,198],alic:[184,191],align:[22,82,117,124,132,139,140,144,146,154,155,157,159,165,167,168,169,171,175,176,178,179,198],alik:[0,152,159],aliz:35,all:[0,1,3,6,7,8,11,12,14,16,18,19,22,25,26,27,29,31,32,33,34,37,38,39,40,41,42,43,45,48,50,51,52,53,54,56,58,59,60,61,62,64,66,68,69,71,81,85,87,89,94,97,98,105,106,107,108,109,111,112,113,115,117,119,121,122,123,124,125,127,135,136,137,138,139,140,141,142,143,145,148,149,150,151,154,155,156,157,159,160,161,162,163,164,165,166,167,168,169,171,172,174,175,176,179,180,182,183,184,185,186,188,189,191,196,197,198,200,203,204,205,206,207],all_attr:31,all_clfs_acc:51,all_data:68,all_nod:1,all_photo:31,allah:142,allbeit:87,allclos:87,allegrograph:192,allei:[56,68],allevi:[51,54,56],allianc:111,alloc:[41,52,117,168,194,198],allow:[1,3,14,18,50,52,56,61,80,81,82,104,105,106,108,116,117,118,119,121,123,124,125,126,127,129,131,137,139,140,142,143,148,149,150,154,160,163,164,165,168,171,172,178,182,183,184,185,198,199,205,206,207],allow_arg:125,allowed_arg:125,allowfullscreen:[121,158,171,180],allpub:68,allud:52,almeida:50,almond:[70,115,171,172,190],almost:[7,31,37,41,45,52,59,64,73,107,109,122,129,159,160,174,175,178,183,184,199,203],alon:[69,111],along:[1,7,33,37,41,50,53,56,61,71,80,81,85,108,109,114,122,123,124,125,126,127,145,149,151,154,159,168,172,173,174,178,183,190,203],alongsid:[76,114,150],alot:[56,136],alpha:[37,57,68,69,80,82,88,114,135,138,143,156,159,160,164,165,166,167,168,184,190,196,198,201,202,206],alpha_:135,alpha_t:[135,160],alpha_t_bar:135,alpha_tb_t:160,alphabet:[118,123],alphago:[136,174],alphas_cumprod:135,alphas_cumprod_prev:135,alphas_t:135,alq:56,alreadi:[41,45,51,52,54,56,62,65,67,71,83,85,87,98,105,111,119,126,127,135,140,145,150,156,160,167,168,175,176,180,182,183,185,189,191],alright:[37,87],also:[0,1,3,7,14,16,18,20,23,28,29,30,31,32,33,34,37,40,41,45,47,48,49,50,51,52,54,55,56,57,58,59,61,62,63,64,65,66,67,68,69,71,73,79,81,82,83,85,87,88,103,104,106,107,108,109,111,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,129,131,132,133,135,136,137,139,143,144,146,147,148,149,150,151,152,156,159,160,163,164,165,166,167,168,169,171,172,173,174,176,178,179,180,182,183,184,185,191,196,198,203,204,206],altair:199,altavista:160,alter:[80,89,111,118,183,189,205],alter_imag:89,altern:[7,16,32,47,56,61,64,116,117,122,124,143,150,154,165,166,183],although:[30,31,51,52,54,56,57,62,68,81,89,136,139,145,148,149,156,160,164,167,174,183,184],altogeh:149,altogeth:[14,166],altunyan:107,alwai:[7,14,30,33,34,35,37,41,45,47,49,50,51,54,56,57,59,60,61,63,71,85,109,114,121,124,125,127,129,135,136,137,139,146,149,150,151,152,159,160,164,165,166,167,171,174,179,183,184,185,205,206],am:[0,41,61,98,184,205],amalgam:83,amax:36,amaz:[32,106,113,140,185,186],amazon:[104,148,149,150,152,192],ambigu:[33,111,126,142,183],america:[113,181],american:[117,152,188],ami:168,aml:[9,105],aml_comput:[9,105],aml_config:[9,105],aml_nam:[9,105],amlb:150,amlcomput:[9,105],among:[7,58,61,66,69,121,124,139,149,150,159,160,164,168,174,179,197],amongst:[69,154],amor:145,amount:[7,17,31,58,61,69,80,104,105,106,115,116,119,125,129,132,136,139,142,148,150,160,161,165,166,168,169,174,180,183,184,187,188,190,191,192,196,198,203],amp:[154,169],amplifi:[107,117],amus:154,an:[1,5,7,14,16,18,20,22,23,27,28,29,30,32,33,34,37,41,42,43,45,47,48,49,50,51,52,54,56,58,59,60,61,64,69,71,72,73,79,80,82,83,85,87,88,89,95,96,97,98,104,107,108,111,113,114,115,116,117,118,119,121,122,125,126,127,12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4,200,201,202,206],concatenated_str:183,concav:135,conceiv:[151,183],concentr:154,concept:[3,18,29,31,49,52,61,80,81,82,106,107,119,121,123,124,127,132,139,147,148,149,150,151,152,157,160,164,165,166,172,177,179,182,184,193,206],conceptu:160,concern:[7,42,49,56,60,61,69,79,111,114,117,122,142,148,149,152,160,178,190,203],concis:[124,156,174,183,184,206],conclud:[58,61,69,80,107,113,121,150,157,168],conclus:[24,52,108,117,119,178],concret:[152,168,174,198],concurr:[89,105,106],conda:[0,30,36,37,114],condens:137,condit:[3,22,31,40,41,47,49,50,52,56,98,107,117,124,135,139,143,150,157,159,160,168,178,179,180,183,184,185,205,206],condition2:56,condorcet:156,conduct:[58,105,117,168,188],conf:18,conf_matrix:[54,59],confer:[109,113,125],confid:[41,43,50,71,85,116,139,150,151,154,156,160],config:[9,39,52,68,73,141,146,156,159,168,198,199],configur:[10,43,47,49,104,106,145,148,149,150,174,180,181,183],confirm:[14,30,47,49,50,61,108,111,117,167,178,188,189],conflict:[98,109,117,127],conform:[118,127,148,150],confus:[7,34,41,52,54,59,62,71,85,88,109,122,124,143,157,159,164,166,171,183],confusingli:167,confusion_matrix:[34,40,41,53,54,59,61,62,71,72,85,88,89,143,172,173,175,179,201,202],confusion_mtx:34,congratul:[105,106,178,179,182,185],conjug:97,conjunct:119,connect:[6,30,32,33,35,43,45,47,50,64,87,97,98,107,109,117,121,139,140,142,143,145,150,166,183,184,185,194],connectionist:83,conquer:160,consciou:7,consecut:[14,32,41,51,164],consent:[117,188],consequ:[28,107,117,137,188],conserv:[114,190],conservationstatu:[114,190],consid:[1,3,7,8,11,14,18,22,24,29,37,40,41,43,47,48,51,52,55,58,64,66,80,81,98,106,108,109,111,113,118,119,120,121,122,131,132,137,139,141,142,143,146,149,150,151,152,154,156,157,159,160,161,164,165,166,167,168,169,174,179,182,183,184,196,198,205,206],consider:[61,69,106,111,117,119,120,127,160,163,167,188,189],consist:[0,1,3,8,15,30,33,34,37,43,47,51,52,54,56,61,93,111,117,122,124,131,139,140,148,149,150,151,152,157,166,168,174,178,180,184,188,189,192,203],consol:127,consolid:80,conspiraci:64,constant:[52,65,67,80,83,124,133,134,135,137,138,139,143,145,150,160,164,175],constant_initi:144,constantli:119,constitut:143,constrain:[30,137,154],constraint:[30,106,111,124,139,149,150,154,166,168,172,198],construct:[30,52,124,127,131,135,138,139,143,146,148,154,156,159,160,161,164,183,184,206],constructor:[127,139,183,184,205,206],consult:[7,125],consum:[10,20,43,47,103,105,106,108,113,116,117,118,148,150,152,165,169,174,187],consumpt:[113,169,187,191],cont:56,cont_num_var:56,contact:[192,199],contagi:179,contain:[1,3,6,7,12,14,15,17,22,29,31,33,37,38,40,41,42,43,47,48,49,50,51,52,53,54,56,59,61,62,64,71,79,85,88,91,96,97,98,105,106,108,111,118,121,122,123,125,127,129,136,138,139,140,142,143,145,148,149,150,154,156,158,159,161,164,167,169,171,174,178,179,180,182,183,184,191,205,206],container:149,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,45,46,48,51,52,53,54,55,56,57,58,61,64,65,66,67,68,69,70,71,74,76,77,79,85,87,88,89,90,91,93,94,95,96,104,105,106,107,108,109,111,113,114,115,116,117,118,119,121,122,123,124,125,126,127,129,131,132,133,134,135,137,138,139,140,141,142,143,144,145,146,147,150,152,154,155,156,157,159,160,161,163,164,169,171,172,173,174,178,179,180,182,183,184,185,191,198,199,200,201,204,206],contest:156,context:[9,28,31,52,61,80,105,107,109,111,117,126,127,134,140,142,150,152,154,155,166,167,169,181,183,184,186,188,189,206],contigu:124,contin:[59,60],continu:[0,1,17,18,31,33,34,41,49,50,52,56,57,60,61,64,80,81,82,83,104,105,107,114,116,121,124,125,129,135,140,142,143,149,150,151,152,154,155,159,160,161,166,169,174,179,185,203],contour:[165,167,196],contourf:[159,167,201,202],contract:[97,98,106,149,183,184,185],contradictori:150,contrari:[126,127,152],contrarili:183,contrast:[7,52,95,108,124,138,150,159,165,168,194],contrib:145,contribut:[48,54,55,59,60,68,107,124,125,126,127,142,143,147,150,156,157,159,183,184,185,186],contributor:147,control:[7,11,30,45,50,58,61,63,64,65,66,67,73,104,108,109,111,117,119,122,124,136,137,139,141,148,149,151,159,160,166,175,184,189,199,206],controlflow:183,conv0:144,conv10:35,conv1:140,conv1_1:134,conv1_2:134,conv1_featur:132,conv1_pad:140,conv1d:[46,136],conv2:35,conv2_1:134,conv2_2:134,conv2_featur:132,conv2d:[29,30,31,32,33,34,35,37,38,40,42,73,132,134,135,136,139,140,144],conv2d_1:[29,30,35,37],conv2d_20:42,conv2d_21:42,conv2d_22:42,conv2d_23:42,conv2d_24:42,conv2d_25:42,conv2d_26:42,conv2d_27:42,conv2d_28:42,conv2d_29:42,conv2d_2:[29,30,35,37],conv2d_3:37,conv2d_4:37,conv2d_5:37,conv2d_6:37,conv2d_7:37,conv2d_8:37,conv2d_9:37,conv2d_transpos:[29,30],conv2d_transpose_1:[29,30],conv2dt:30,conv2dtr:29,conv2dtra:30,conv2dtranspos:[29,30,135,140,144],conv3:35,conv3_1:134,conv3_2:134,conv3_3:134,conv3_4:134,conv3d:136,conv4:35,conv4_1:134,conv4_2:134,conv4_3:134,conv4_4:134,conv5:35,conv5_1:134,conv5_2:134,conv5_3:134,conv5_4:134,conv6:35,conv7:35,conv8:35,conv9:35,conv:[35,38,132,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Self-paced assignments","40.22. Analyzing COVID-19 papers","40.27. Analyzing data","40.9. Analyzing text about Data Science","40.13. Apply your skills","40.16. Build your own custom vis","40.17. Classifying datasets","40.26. Data preparation","40.24. Data processing in Python","40.41. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","40.40. Data Science project using Azure ML SDK","40.10. Data Science scenarios","40.20. Displaying airport data","40.15. Dive into the beehive","40.23. Estimation of COVID-19 pandemic","40.25. Evaluating data from a form","40.36. Explore a planetary computer dataset","40.37. Exploring for answers","40.19. Introduction to probability and statistics","40.12. Lines, scatters and bars","40.39. Low code/no code Data Science project on Azure ML","40.38. Market research","40.29. Matplotlib applied","40.28. NYC taxi data in winter and summer","40.18. Small diabetes study","40.21. Soda profits","40.35. Tell a story","40.14. Try it in Excel","40.11. Write a data ethics case study","40.104. Intro to Autoencoders","40.105. Base/Denoising Autoencoder & Dimension Reduction","40.106. Fun with Variational Autoencoders","40.93. How to choose cnn architecture mnist","40.97. Object Recognition in Images using CNN","40.95. Sign Language Digits Classification with CNN","40.119. Summary","40.115. DQN On Foreign Exchange Market","40.116. Art by gan","40.118. Generative Adversarial Networks (GANs)","40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment","40.110. NN Classify 15 Fruits Assignment","40.109. Neural Networks for Classification with TensorFlow","40.120. Car Object Detection","40.122. Basic classification: Classify images of clothing","40.102. Google Stock Price Prediction RNN","40.98. Intro to TensorFlow for Deep Learning","40.107. Time Series Forecasting Assignment","40.86. Counterintuitive Challenges in ML Debugging","40.85. Data engineering","40.87. Case Study: Debugging in Classification","40.88. Case Study: Debugging in Regression","40.78. Beyond random forests: more ensemble models","40.79. Decision trees","40.83. Random Forest Classifier with Feature Importance","40.77. Random forests for classification","40.76. Random forests intro and regression","40.82. Boosting with tuning","40.81. Gradient boosting","40.80. Hyperparameter tuning gradient boosting","40.69. Decision Trees - Classification","40.68. Decision Trees - Intro and Regression","40.65. Kernel method assignment 1","40.67. Support Vector Machines (SVM) - Classification","40.66. Support Vector Machines (SVM) - Intro and SVM for Regression","40.72. Dropout and Batch Normalization","40.73. Lasso and Ridge Regression","40.71. Learning Curve To Identify Overfit & Underfit","40.70. Model selection assignment 1","40.74. Regularized Linear Models","40.75. Customer segmentation: clustering - assignment 3","40.91. Build Classification Model","40.90. Build classification models","40.53. Build ML web app - assignment 1","40.54. Build ML web app - assignment 2","40.59. Create a regression model","40.63. Delicious asian and indian cuisines","40.64. Explore classification methods","40.57. Exploring visualizations","40.60. Linear and polynomial regression","40.45. Linear regression - California Housing","40.48. Gradient descent","40.49. Linear Regression Implementation from Scratch","40.46. Linear Regression Metrics","40.47. Loss Function","40.56. Managing data","40.50. ML logistic regression - assignment 1","40.51. ML logistic regression - assignment 2","40.52. ML neural network - Assignment 1","40.42. Machine Learning overview - assignment 1","40.43. Machine Learning overview - assignment 2","40.92. Parameter play","40.62. Pumpkin varieties and color","40.55. Regression tools","40.44. Regression with Scikit-learn","40.61. Retrying some regression","40.89. Study the solvers","40.58. Try a different model","40.8. Python programming advanced","40.7. Python programming basics","40.6. Python programming introduction","40.5. Project Plan\u200b Template","40.3. First assignment","40.4. Second assignment","8. Data Science in the cloud","8.1. Introduction","8.3. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","8.2. The \u201clow code/no code\u201d way","9. Data Science in the real world","7.2. Analyzing","7.3. Communication","7. Data Science lifecycle","7.1. Introduction to the Data Science lifecycle","6. Data visualization","6.4. Making meaningful visualizations","6.1. Visualizing distributions","6.2. Visualizing proportions","6.3. Visualizing relationships: all about honey \ud83c\udf6f","4.2. Data Science ethics","4.3. Defining data","4.1. Defining data science","4. Introduction","4.4. Introduction to statistics and probability","5.5. Data preparation","5.2. Non-relational data","5.3. NumPy","5.4.3. Advanced Pandas Techniques","5.4.2. Data Selection","5.4.1. Introduction and Data Structures","5.4. Pandas","5.1. Relational databases","5. Working with data","27. Autoencoder","23. Convolutional Neural Networks","23.3.1.2. Deepdream in TensorFlow","23.3.1.1. Stylenet / Neural-Style","33. Diffusion Model","21. Intro to Deep Learning","30. Deep Q-learning","24. Generative adversarial networks","31. Image classification","32. Image segmentation","28. Long-short term memory","26. Natural Language Processing Overview","22. Neural Networks","34. Object detection","25. Recurrent Neural Networks","29. Time series","Learn AI together, for free","37. Data engineering","39. Model deployment","38. Model training & evaluation","35. Overview","36. Problem framing","14. Clustering models for Machine Learning","14.1. Introduction to clustering","14.2. K-Means clustering","15.1. Bagging","15.3. Feature importance","15. Getting started with ensemble learning","15.2. Random forest","16.1. Gradient Boosting","16.2. Gradient boosting example","16. Introduction to Gradient Boosting","16.3. XGBoost","16.4. 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Convolutional Neural Network","41.21. Generative Adversarial Network","41. Slides","41.18. Kernel method","41.19. Model Selection","41.17. Unsupervised learning","41.16. Build an machine learning web application","41.12. Linear Regression","41.13. Logistic Regression","41.14. Logistic Regression","41.11. Machine Learning overview","41.15. Neural Network","41.3. Python programming advanced","41.2. Python programming basics","41.1. Python programming 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7,38,39,41,42,43,44,45,46,47,48,49,51,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,77,78,80,81,83,85,86,87,89,91,92,93,94,95,97,98,99,100,108,109,110,111,112,113,115,117,118,119,120,121,122,123,125,126,127,128,129,130,131,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,163,164,165,166,167,168,169,170,171,172,173,175,176,177,178,179,180,182,184,186,187,188,189,191,192,198,202,203,204,205,207,208,210],Its:[140,147,163,168,183],NEAR:[67,83],NO:[101,102,187,188,189],NOT:[91,101,102,128,142,146,187,188,189],Near:[118,194],No:[7,20,29,34,42,56,60,62,70,77,91,97,102,107,109,110,119,141,160,169,171,176,187,188,191,194],Not:[7,44,48,55,58,60,62,75,89,110,120,126,127,145,164,176,182,183,188,203,210],OF:[22,50,52,53,54,101,102,142,187,188,189],ON:196,ONE:7,OR:[22,50,52,53,54,101,102,128,187,188,189],Of:[33,56,73,110,111,113,123,171,189,190],On:[55,56,58,63,64,65,66,67,72,73,75,83,89,92,110,113,115,147,154,160,163,164,167,168,169,171,172,173,178,182,186,187,193,203],One:[1,7,11,28,42,44,48,55,56,58,59,60,61,63,64,65,72,92,107,112,113,115,117,119,123,125,128,136,140,148,154,159,161,163,169,172,178,182,184,187,188,189,193,194,195,200,202,207,210],Or:[32,33,44,56,64,83,111,113,128,140,141,154,158,161,178,187,188,203,207,210],Such:[1,7,30,44,48,55,56,60,125,154,155,182,187,209],THAT:91,THE:[101,102,187,188,189],TO:[60,101,102,187,188,189],That:[31,32,44,48,53,55,56,58,63,67,68,73,75,83,89,113,118,125,128,133,141,159,161,164,165,171,173,178,179,183,188,189,207],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,36,37,39,42,43,44,45,46,50,51,52,53,54,55,56,58,60,61,62,63,64,65,66,67,68,69,70,71,72,73,75,80,83,85,87,89,92,93,99,100,101,102,108,111,112,113,115,117,118,119,121,122,123,124,125,127,128,129,130,131,135,136,137,138,139,140,141,142,143,144,145,146,148,149,150,151,152,153,154,155,156,158,159,160,161,163,164,165,166,167,168,169,172,173,175,176,178,179,180,182,183,189,190,192,193,194,198,199,201,202,203,204,205,206,207,208,211],Their:147,Then:[7,31,45,50,52,56,62,72,86,91,110,113,125,128,136,139,145,146,149,154,155,156,159,160,161,163,164,168,172,173,182,186,187,188,189,203],There:[0,1,3,7,18,28,29,30,32,33,35,38,39,42,43,44,46,48,51,52,54,55,56,57,60,63,65,66,68,70,73,75,81,83,89,94,109,110,113,115,117,118,119,122,123,125,126,127,128,129,133,135,136,137,140,143,145,146,147,149,150,152,153,154,155,156,158,159,161,163,164,167,171,172,173,174,175,177,178,179,182,183,185,186,187,188,189,207,208,209,210],These:[7,30,31,33,40,43,45,46,50,55,60,62,65,66,73,83,84,86,87,108,110,113,115,118,121,122,127,128,130,131,143,146,147,153,159,163,164,168,170,171,172,187,189,191,193,195,196,198,209],To:[0,1,7,14,18,22,29,30,33,34,35,38,40,42,44,45,46,50,51,53,55,56,58,60,64,67,68,72,73,75,83,84,85,91,101,108,109,110,111,113,118,119,120,121,123,125,126,128,129,131,133,138,139,140,143,144,145,146,150,151,152,153,154,155,156,160,161,164,165,167,168,169,171,172,173,175,178,179,182,183,184,186,187,188,189,193,194,196,199,202,203,207,209,210,211],WITH:[101,102,187,188,189],Will:[156,183,189],With:[7,42,44,46,48,52,56,62,66,67,68,93,108,112,113,117,118,120,121,122,123,127,128,130,131,133,140,153,154,161,163,164,169,171,172,178,180,187,196,203,208],_0:164,_1:161,_2:161,_:[18,29,31,33,34,36,39,40,45,46,57,62,77,90,135,136,139,141,142,143,144,145,149,150,161,164,171,179,187,188,198,204,205,210],____:[3,12,22,24,25,51,101,102,106],_____:[24,101],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:[29,30,38,45],__________________________________________________________________________________________________:36,____i:102,__abs__:101,__add__:101,__all__:187,__annotations__:[187,209],__builtins__:187,__cached__:187,__call__:[69,71,137,139,144],__class__:[39,187],__dict__:[179,205,206],__doc__:[187,209],__eq__:101,__file__:[77,187],__finalize__:[129,130],__future__:39,__get__:129,__getitem__:[130,131],__init__:[29,30,31,34,36,37,38,39,48,61,69,71,90,91,103,129,137,139,141,142,143,144,145,148,165,179,187,204,205,209],__iter__:[34,142],__len__:34,__loader__:187,__main__:[37,141,169,172,173],__mul__:101,__name__:[37,39,141,172,173,187],__operators__:145,__package__:187,__repr__:61,__spec__:187,__str__:101,__sub__:101,__truediv__:101,__version__:[46,141,171,209],_aspp:144,_attach:[127,196],_bin:60,_branch:144,_build_model:37,_bunch:[63,64],_caller:129,_check_indexing_error:131,_concaten:129,_consolidate_inplac:130,_constructor:130,_conv_block:144,_conv_bn_relu:144,_conv_relu:144,_data:130,_decor:129,_deeplabv3:144,_deprecate_mismatched_index:130,_deprecated_arg:130,_engin:131,_etag:[127,196],_fcn_16:144,_fcn_32:144,_fcn_8:144,_format_argument_list:129,_fuse_bn_tensor:143,_get_axi:130,_get_block_manager_axi:130,_get_comb_axi:129,_get_concat_axi:129,_get_join_info:129,_get_list_axi:130,_get_new_ax:129,_get_result_dim:129,_get_slice_axi:130,_get_valu:[130,131],_get_values_for_loc:131,_getbool_axi:130,_getitem_axi:130,_getitem_lowerdim:130,_getitem_tupl:130,_i:[86,87,161,171],_identity_block:144,_ilocindex:130,_index:62,_indicator_pre_merg:129,_info_axi:129,_invalid_index:130,_is_copi:130,_is_scalar_access:130,_items_overlap_with_suffix:129,_j:[161,171],_k:141,_kmean:172,_label:63,_left:129,_lib:[129,131],_locationindex:130,_locindex:130,_m:141,_make_concat_multiindex:129,_make_stag:143,_mapdataset:38,_maybe_cast_for_get_loc:130,_maybe_cast_slice_bound:130,_maybe_check_integr:129,_merge_doc:129,_merge_typ:129,_mergeoper:129,_method:195,_mgr:[129,130],_novalu:195,_oldcor:92,_other:129,_pad_1x1_to_3x3_tensor:143,_recognized_scalar:130,_reindex_and_concat:129,_rid:[127,196],_right:129,_sec_1:102,_segnet:144,_self:[127,196],_sigmoid:[90,179,205],_skip:3,_slice:130,_static:[130,131],_subplot:[73,83],_sum:195,_t:[127,196],_t_sne:[172,202],_take:130,_take_with_is_copi:130,_takeabl:130,_valid_typ:130,_validate_integ:130,_validate_kei:130,_validate_tuple_index:130,_valu:131,a0958ad901d7:127,a0:[129,195],a10:131,a1:[128,129,195],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:65,a21453:188,a23:[187,209],a2:[128,129,195],a3:129,a3z5kdkfn3tbq:65,a47:42,a4:129,a5:129,a7yia1n5fo6efhugqfis3dhueyjsa:65,a_:[91,172],a_dict:188,a_i:[91,160],a_list:188,a_n:163,aaaaaa:[169,200],aafter:170,aaron:[29,56,87,142,180],ab:[33,54,56,69,71,77,86,101,102,130,138,142,148,171,187,188,210],abadi:142,abat:42,abbrevi:[133,139],abc:[102,130,131,188,195,211],abcd:[7,126,130,195],abcdef:130,abcmous:[121,192],abil:[48,58,60,75,85,86,89,117,140,146,152,163,169,173,178,181,187,189,200,207],abl:[3,7,10,11,14,16,20,31,42,44,54,55,56,58,59,60,63,67,68,83,85,110,113,119,121,123,127,128,131,140,147,150,153,155,158,164,167,170,172,173,175,182,183,184,186,192,202,205,206,209],abnorm:29,abnorml:72,abo:41,aboslut:170,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,33,42,44,45,46,48,51,52,53,55,56,58,59,60,63,64,65,66,67,68,72,73,75,86,87,89,92,93,99,108,109,110,111,112,113,115,117,118,119,121,123,124,125,126,127,128,129,131,133,134,140,141,144,146,147,148,150,151,152,153,154,155,157,158,159,160,163,164,165,167,168,170,171,172,173,174,176,177,178,179,180,182,184,185,186,187,188,189,190,192,193,196,204,207,209,211],abov:[0,1,7,11,14,19,26,29,32,33,38,40,42,44,48,50,51,52,53,55,56,57,58,59,60,63,64,65,66,70,72,73,75,83,89,101,102,113,117,119,123,125,128,129,130,131,133,136,139,140,141,142,143,144,146,148,150,152,153,154,155,158,159,160,161,163,167,168,170,171,172,175,178,181,182,183,184,185,186,187,188,189,194,203,204],above_cutoff:171,abracadabra:188,abraham:211,abs_vector:[188,210],absenc:[60,172,202],absent:[130,179],absolut:[42,52,54,85,87,92,101,125,128,154,163,167,170,187,188,189,210],absolute_error:86,absolute_percentage_error:86,abspath:77,absurd:73,abund:[119,194],ac:[73,147,171],academ:[121,124,151,192],academi:209,acc:[33,34,43,52,55,58,63,136,208],acc_and_loss:136,acceler:[109,119,120,154,182,194],acceleromet:[125,172],accept:[16,42,44,45,63,73,75,89,92,109,112,115,121,128,130,131,143,154,164,168,178,187,193,207,208],acceptedcmp1:73,acceptedcmp2:73,acceptedcmp3:73,acceptedcmp4:73,acceptedcmp5:73,access:[6,14,16,33,41,46,75,83,89,108,110,111,113,115,117,121,127,130,146,152,155,156,173,180,182,187,188,192,193,195,203,209,210],accessor:131,accident:164,acclaim:168,accommod:[7,38,52,126,188],accompani:[125,154,182],accomplish:[97,154,164,172,178,202,207],accord:[18,42,50,56,60,69,71,73,108,117,118,119,120,125,128,130,143,152,155,158,160,163,168,172,173,176,178,182,183,184,186,202],accordingli:[36,46,61,136,158,187,209],account:[0,6,8,14,16,45,56,85,101,110,111,121,125,127,128,147,156,160,164,182,183,192,195],accumul:[1,56,101,115,150,161,178,188,207],accur:[15,32,34,42,46,56,60,65,75,78,86,89,99,110,115,121,122,125,140,144,146,148,152,155,160,167,170,172,175,178,181,182,183,193],accuraci:[29,33,34,35,43,44,45,53,54,55,56,57,58,60,62,63,66,70,75,76,78,85,86,87,89,91,92,93,111,121,126,136,144,146,147,149,154,155,156,159,160,161,163,164,166,167,169,171,173,176,177,182,183,190,192,198,202],accuracy_metr:136,accuracy_scor:[29,43,55,56,57,58,62,63,65,66,75,76,89,92,147,163,168,172,173,176,177,183,202,205,206],achiev:[32,33,34,44,53,56,60,62,65,73,112,115,128,141,143,144,152,153,154,155,156,163,164,165,166,168,169,170,171,187],achitectur:142,aci_servic:[9,109],aci_service_nam:[9,109],aciconfig:[9,109],acid:53,aciwebservic:[9,109],acm:[121,192],aco:36,acoust:[157,158,159],acquir:[6,112,115,154,193],acquisit:[3,111,115,123,150,190,192],acro:173,acronym:118,across:[34,48,52,60,75,89,111,121,123,125,128,129,131,133,136,146,150,152,153,154,164,168,171,187,188,190,192],act:[3,14,22,24,37,42,59,68,102,113,121,128,131,136,137,141,146,148,178,182,195,207],act_greedi:37,act_valu:37,action:[0,7,37,44,50,51,101,102,108,112,113,121,123,126,127,128,153,155,173,176,178,187,188,189,192,207],action_prob:141,action_s:37,actions_count:37,activ:[0,29,30,32,33,34,35,36,37,38,42,43,44,45,46,48,49,50,52,53,62,63,68,76,77,91,121,135,136,137,139,141,142,143,144,145,147,148,149,155,164,170,171,175,192,197,198,203,208],activateion:145,activation_1:38,activation_2:38,activation_3:38,activation_4:38,activespac:196,actor:187,actual:[7,41,42,44,45,48,51,52,53,54,56,57,58,62,63,65,66,72,75,77,83,84,85,86,87,89,91,92,93,101,110,120,122,123,125,126,127,128,130,131,136,139,140,150,152,158,164,166,168,170,171,175,178,180,182,183,187,189,191,193,197,204,207],actual_result:[3,14,22,24,59,102],actual_valu:[41,86],acut:163,ad:[1,7,18,22,29,32,38,41,46,48,50,53,56,58,60,65,70,73,75,85,89,101,102,120,122,125,127,137,139,143,151,153,154,163,164,165,167,169,172,178,179,182,188,204,210],ada:177,adaboost:[164,177],adaboost_clf:55,adaboostclassifi:[55,62,176,177],adagradoptim:154,adam:[29,30,31,32,33,34,35,37,38,39,41,43,44,45,46,47,49,50,52,53,68,76,135,136,139,142,144,145,147,198],adamax:38,adamharlei:[136,197],adamoptim:[138,154],adamw:36,adapt:[36,52,64,68,108,121,143,150,154,164,180,192,208],adaptiveaveragepooling2d:143,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,37,38,41,42,43,46,47,48,49,50,51,52,56,58,60,67,68,69,71,72,76,85,101,102,117,119,122,125,127,129,133,135,138,139,143,144,145,147,148,153,163,164,165,167,168,169,170,173,177,178,182,183,184,186,187,188,189,196,198,200,203,204,207,208,210],add_1:145,add_:31,add_artist:[119,194],add_ax:172,add_legend:158,add_selectbox:203,add_slid:203,add_subplot:[37,39,52,73,76,84,141],add_trick:187,add_weight:143,addit:[1,7,18,23,32,33,46,51,60,65,70,72,83,84,85,101,112,113,115,117,121,122,125,126,127,128,133,141,143,144,145,148,150,154,156,160,164,165,167,168,169,171,179,182,188,189,195,196,210,211],addition:[31,126,128,133,153,156,158,160,164,169,196,200],additon:32,address:[84,85,99,112,113,115,121,147,150,152,153,156,160,167,178,187,192,193],adel:163,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,78,80,81,94,97,98,99,100,125,154],adher:[53,115,146,153],adjac:[172,187],adject:[188,210],adjunct:178,adjust:[29,34,38,42,45,50,61,63,64,84,143,153,154,163,164,169,172,177],adjusted_mutual_info_scor:172,adjusted_rand_scor:172,admin2:155,admin:196,administr:[156,189],admonit:[129,130,131],adobe_premier:41,adolesc:187,adopt:[65,94,111,121,153,154,160,164,190,192,211],adult:[187,209],advai:45,advanc:[37,48,72,111,121,125,131,132,134,146,151,153,154,156,160,172,178,188,196,199],advantag:[44,56,60,75,89,131,143,147,153,156,163,166,167,169,188],advent:[55,140,153],advers:28,adversari:[38,156,199],advertis:113,advic:154,advis:[7,51,59,64,67,126,163],advoc:121,ae5:172,ae:[31,139],aebf:[127,196],aeroplan:7,aerospik:196,aesthet:22,afb:173,affect:[7,17,34,43,55,58,60,62,64,75,85,89,91,111,113,115,121,125,126,141,143,154,161,167,169,170,179,187,190,192,200],affer:149,affin:[91,158,172],affinity_matrix_:171,affinitypropag:172,afford:[7,83,126,178],african:[121,156,192],afro:[158,159],afropop:[158,159],after:[0,7,14,29,32,33,34,35,37,38,42,43,44,46,52,53,54,55,56,57,60,61,62,63,66,68,70,73,76,83,85,91,113,117,123,125,126,128,133,135,136,143,144,145,149,150,153,154,155,158,159,161,163,164,167,168,172,177,178,182,184,187,188,189,203,205,206,208,209,210],afterward:[32,128],ag:[9,18,22,56,57,73,83,97,101,102,109,110,123,125,127,128,129,153,158,160,161,165,168,175,178,185,186,187,188,189,195,196,205,206,207,209,210,211],again:[7,14,17,44,46,52,55,56,57,58,59,63,64,65,75,89,91,126,131,139,159,164,168,170,172,183,184,187,188,189,202,203],against:[0,18,40,46,52,56,65,86,87,113,121,123,125,129,130,150,154,156,164,170,173,186,201],agaricu:119,age_distribut:24,age_median_imput:22,age_sal_tre:56,age_tre:56,agefil:22,agenc:113,agenda:[111,190],agent:[121,178,207],ageron:171,agg:[18,41,171],aggfunc:129,agglom:[73,158],agglomerativeclust:[73,171,172],aggreg:[7,14,55,115,120,136,160,163,168],agil:[152,153],agnost:153,ago:[140,164],agre:[22,50,52,53,54,121],agreement:146,agricultur:[111,120,184,190,207],ahead:[55,58,63,113,150],ahnjovq9nfghs6fj4piqib3brpgnscyflm6riahdtaeyfclwo1cf:65,ai:[12,18,25,42,109,110,111,117,121,123,129,130,131,141,152,153,155,156,173,178,186,191,192,197,207,209],aid:[60,68,87,146,147,158,179,182],aim:[60,84,85,113,141,143,146,148,161,172,178,180,201],air:122,airbu:29,airflow:153,airlin:7,airplan:136,airport:[111,133,190,196],aka:[38,73,153],akkio:191,al:[31,75,89,120,121,156,194],alabama:155,alacazam:188,albeit:[50,183],albifron:[118,194],album:158,alcohol:[53,110],alekseynp:171,alert:152,alex:[34,139],alexa:[146,154],alexand:[134,137],alexandru:72,alexei:65,alexi:165,alexnet:143,alfredo:186,alg:62,algebra:[47,57,60,65,93,128,208],algo:[164,172],algorithm:[3,31,46,54,55,57,58,59,61,62,63,64,65,66,67,80,84,85,91,92,93,99,101,108,109,110,111,121,128,138,139,140,143,146,148,150,151,152,153,154,156,159,160,161,165,166,167,169,170,172,175,176,178,179,184,187,190,192,200,201,202,204,205,206,207],algoritm:164,algorythm:92,alia:[128,172,202],alic:[188,195],align:[22,33,86,121,128,136,143,144,148,150,158,159,161,163,169,171,172,173,175,179,180,182,183,202],alik:[0,156,163],aliz:36,all:[0,1,3,6,7,8,11,12,14,16,18,19,22,25,26,27,29,31,32,33,34,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5,121,171,182,192,193],conflict:[102,113,121,131],conform:[122,131,152,154],confus:[7,35,44,56,58,63,66,75,89,92,113,126,128,147,161,163,168,170,175,187],confusingli:171,confusion_matrix:[35,43,44,57,58,63,65,66,75,76,89,92,93,147,176,177,179,183,205,206],confusion_mtx:35,congratul:[109,110,182,183,186,189],conjug:101,conjunct:123,connect:[6,30,32,33,34,36,46,48,50,53,68,91,101,102,111,113,121,125,143,144,146,147,149,154,170,187,188,189,198],connectionist:87,conquer:164,consciou:7,consecut:[14,32,44,55,168],consent:[121,192],consequ:[28,111,121,141,192],conserv:[118,194],conservationstatu:[118,194],consid:[1,3,7,8,11,14,18,22,24,29,38,42,43,44,46,50,51,55,56,59,62,68,70,84,85,102,110,112,113,115,117,122,123,124,125,126,135,136,141,143,145,146,147,150,153,154,155,156,158,160,161,163,164,165,168,169,170,171,172,173,178,183,186,187,188,200,202,209,210],consider:[65,73,110,115,121,123,124,131,164,167,171,192,193],consist:[0,1,3,8,15,30,33,34,35,38,46,50,55,56,58,60,65,97,115,121,126,128,135,143,144,152,153,154,155,156,161,170,172,178,182,184,188,192,193,196,207],consol:131,consolid:84,conspiraci:68,constant:[56,69,71,84,87,128,137,138,139,141,142,143,147,154,164,168,179],constant_initi:148,constantli:123,constitut:147,constrain:[30,141,158],constraint:[30,110,115,128,143,153,154,158,170,172,176,202],construct:[30,56,128,131,135,139,142,143,147,150,152,158,160,163,164,165,168,187,188,210],constructor:[131,143,187,188,209,210],consult:[7,129],consum:[10,20,46,50,107,109,110,112,117,120,121,122,152,154,156,169,173,178,191],consumpt:[117,173,191,195],cont:60,cont_num_var:60,contact:[196,203],contagi:183,contain:[1,3,6,7,12,14,15,17,22,29,31,33,34,38,39,43,44,45,46,50,51,52,53,55,56,57,58,60,63,65,66,68,75,83,89,92,95,100,101,102,109,110,112,115,122,125,126,127,129,131,133,140,142,143,144,146,147,149,152,153,154,158,160,162,163,165,168,171,173,175,178,182,183,184,186,187,188,195,209,210],container:153,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,51,54,55,56,57,58,59,60,61,62,65,68,69,70,71,72,73,74,75,78,80,81,83,89,91,92,93,94,95,97,98,99,100,108,109,110,111,112,113,115,117,118,119,120,121,122,123,125,126,127,128,129,130,131,133,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,151,154,156,158,159,160,161,163,164,165,167,168,173,175,176,177,178,182,183,184,186,187,188,189,195,202,203,204,205,208,210],contest:160,context:[9,28,31,42,56,65,84,109,111,113,115,121,130,131,138,144,146,154,156,158,159,170,171,173,185,187,188,190,192,193,210],contigu:128,contin:[63,64],continu:[0,1,17,18,31,34,35,44,52,53,56,60,61,64,65,68,84,85,86,87,108,109,111,118,120,125,128,129,133,139,144,146,147,153,154,155,156,158,159,163,164,165,170,173,178,183,189,207],contour:[40,169,171,200],contourf:[163,171,205,206],contract:[101,102,110,153,187,188,189],contradictori:154,contrari:[130,131,156],contrarili:187,contrast:[7,56,99,112,128,142,154,163,169,172,198],contribut:[51,54,58,59,63,64,72,111,128,129,130,131,146,147,151,154,160,161,163,187,188,189,190],contributor:151,control:[7,11,30,48,53,62,65,67,68,69,70,71,77,108,112,113,115,121,123,126,128,140,141,143,145,152,153,155,163,164,170,179,188,193,203,210],controlflow:187,conv0:148,conv10:36,conv1:144,conv1_1:138,conv1_2:138,conv1_featur:136,conv1_pad:144,conv1d:[49,140],conv2:36,conv2_1:138,conv2_2:138,conv2_featur:136,conv2d:[29,30,31,32,33,34,35,36,38,39,43,45,77,136,138,139,140,143,144,148],conv2d_1:[29,30,36,38],conv2d_20:45,conv2d_21:45,conv2d_22:45,conv2d_23:45,conv2d_24:45,conv2d_25:45,conv2d_26:45,conv2d_27:45,conv2d_28:45,conv2d_29:45,conv2d_2:[29,30,36,38],conv2d_3:38,conv2d_4:38,conv2d_5:38,conv2d_6:38,conv2d_7:38,conv2d_8:38,conv2d_9:38,conv2d_transpos:[29,30],conv2d_transpose_1:[29,30],conv2dt:30,conv2dtr:29,conv2dtra:30,conv2dtranspos:[29,30,139,144,148],conv3:36,conv3_1:138,conv3_2:138,conv3_3:138,conv3_4:138,conv3d:140,conv4:36,conv4_1:138,conv4_2:138,conv4_3:138,conv4_4:138,conv5:36,conv5_1:138,conv5_2:138,conv5_3:138,conv5_4:138,conv6:36,conv7:36,conv8:36,conv9:36,conv:[36,39,136,143,144,148],conv_block:143,conv_bn:143,conv_bn_relu:143,conv_lay:138,conv_name_bas:144,convei:[113,117,146,193],conveni:[7,51,60,120,125,126,131,144,161,164,172,182,183,187],convent:[45,48,50,75,89,133,140,156,167,187,189],converg:[38,84,118,154,158,171,172,178,202],convers:[1,33,51,86,110,113,126,146,154,156,184],convert:[1,3,7,14,31,38,41,42,44,45,46,48,50,52,54,55,62,63,65,70,83,92,93,95,101,102,109,117,119,121,123,130,131,135,136,137,138,143,145,147,149,168,173,178,182,184,188,203,207,208,210],convert_image_dtyp:137,convert_indic:130,convert_to_tensor:[137,141,143],convex:[119,139,194],convinc:[113,189,198],convlay:39,convnet:[139,140],convolut:[33,45,138,139,143,144,148,178,199,207],convolutional_autoencoder_model:29,convolutional_autoencoder_model_nam:29,convolutional_autoencoder_model_respons:29,convolutional_autoencoder_model_save_path:29,convolutional_autoencoder_model_url:29,convtran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Write a data ethics case study","40.106. Intro to Autoencoders","40.107. Base/Denoising Autoencoder & Dimension Reduction","40.108. Fun with Variational Autoencoders","40.94. How to choose cnn architecture mnist","40.99. Image classification","40.98. Object Recognition in Images using CNN","40.96. Sign Language Digits Classification with CNN","40.122. Summary","40.117. DQN On Foreign Exchange Market","40.118. Art by gan","40.120. Generative Adversarial Networks (GANs)","40.121. Comparing edge-based and region-based segmentation","40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment","40.126. Getting Start NLP with classification task","40.112. NN Classify 15 Fruits Assignment","40.111. Neural Networks for Classification with TensorFlow","40.123. Car Object Detection","40.125. Basic classification: Classify images of clothing","40.104. Google Stock Price Prediction RNN","40.100. Intro to TensorFlow for Deep Learning","40.109. Time Series Forecasting Assignment","40.86. Counterintuitive Challenges in ML Debugging","40.85. Data engineering","40.87. Case Study: Debugging in Classification","40.88. Case Study: Debugging in Regression","40.89. Introduction","40.78. Beyond random forests: more ensemble models","40.79. Decision trees","40.83. Random Forest Classifier with Feature Importance","40.77. Random forests for classification","40.76. Random forests intro and regression","40.82. Boosting with tuning","40.81. Gradient boosting","40.80. Hyperparameter tuning gradient boosting","40.69. Decision Trees - Classification","40.68. Decision Trees - Intro and Regression","40.65. Kernel method assignment 1","40.67. Support Vector Machines (SVM) - Classification","40.66. Support Vector Machines (SVM) - Intro and SVM for Regression","40.72. Dropout and Batch Normalization","40.73. Lasso and Ridge Regression","40.71. Learning Curve To Identify Overfit & Underfit","40.70. Model selection assignment 1","40.74. Regularized Linear Models","40.75. Customer segmentation: clustering - assignment 3","40.92. Build Classification Model","40.91. Build classification models","40.53. Build ML web app - assignment 1","40.54. Build ML web app - assignment 2","40.59. Create a regression model","40.63. Delicious asian and indian cuisines","40.64. Explore classification methods","40.57. Exploring visualizations","40.60. Linear and polynomial regression","40.45. Linear regression - California Housing","40.48. Gradient descent","40.49. Linear Regression Implementation from Scratch","40.46. Linear Regression Metrics","40.47. Loss Function","40.56. Managing data","40.50. ML logistic regression - assignment 1","40.51. ML logistic regression - assignment 2","40.52. ML neural network - Assignment 1","40.42. Machine Learning overview - assignment 1","40.43. Machine Learning overview - assignment 2","40.93. Parameter play","40.62. Pumpkin varieties and color","40.55. Regression tools","40.44. Regression with Scikit-learn","40.61. Retrying some regression","40.90. Study the solvers","40.58. Try a different model","40.8. Python programming advanced","40.7. Python programming basics","40.6. Python programming introduction","40.5. Project Plan\u200b Template","40.3. First assignment","40.4. Second assignment","8. Data Science in the cloud","8.1. Introduction","8.3. Data Science in the cloud: The \u201cAzure ML SDK\u201d way","8.2. The \u201clow code/no code\u201d way","9. Data Science in the real world","7.2. Analyzing","7.3. Communication","7. Data Science lifecycle","7.1. Introduction to the Data Science lifecycle","6. Data visualization","6.4. Making meaningful visualizations","6.1. Visualizing distributions","6.2. Visualizing proportions","6.3. Visualizing relationships: all about honey \ud83c\udf6f","4.2. Data Science ethics","4.3. Defining data","4.1. Defining data science","4. Introduction","4.4. Introduction to statistics and probability","5.5. Data preparation","5.2. Non-relational data","5.3. NumPy","5.4.3. Advanced Pandas Techniques","5.4.2. Data Selection","5.4.1. Introduction and Data Structures","5.4. Pandas","5.1. Relational databases","5. Working with data","27. Autoencoder","23. Convolutional Neural Networks","23.3.1.2. Deepdream in TensorFlow","23.3.1.1. Stylenet / Neural-Style","33. Diffusion Model","21. Intro to Deep Learning","30. Deep Q-learning","24. Generative adversarial networks","31. Image classification","32. Image segmentation","28. Long-short term memory","26. Natural Language Processing Overview","22. Neural Networks","34. Object detection","25. Recurrent Neural Networks","29. Time series","Learn AI together, for free","37. Data engineering","39. Model deployment","38. Model training & evaluation","35. Overview","36. Problem framing","14. Clustering models for Machine Learning","14.1. Introduction to clustering","14.2. K-Means clustering","15.1. Bagging","15.3. Feature importance","15. Getting started with ensemble learning","15.2. Random forest","16.1. Gradient Boosting","16.2. Gradient boosting example","16. Introduction to Gradient Boosting","16.3. XGBoost","16.4. XGBoost + k-fold CV + Feature Importance","18. Kernel method","20. Model selection","17. Unsupervised learning","19. Unsupervised learning: PCA and clustering","12.4. Build a web app to use a Machine Learning model","12. Getting started with classification","12.1. Introduction to classification","12.2. More classifiers","12.3. Yet other classifiers","10. Machine Learning overview","13.2. Gradient descent","13.1. Loss function","13. Parameter Optimization","11.3. Linear and polynomial regression","11.4. Logistic regression","11.2. Managing data","11. Regression models for Machine Learning","11.1. Tools of the trade","3. Python programming advanced","2. Python programming basics","1. Python programming introduction","41.10. Data Science in real world","41.9. Data Science in the cloud","41.4. Data Science introduction","41.8. Data Science lifecycle","41.7. Data visualization","41.6. NumPy and Pandas","41.5. Relational vs. non-relational database","41.20. Convolutional Neural Network","41.21. Generative Adversarial Network","41. Slides","41.18. Kernel method","41.19. Model Selection","41.17. Unsupervised learning","41.16. Build an machine learning web application","41.12. Linear Regression","41.13. Logistic Regression","41.14. Logistic Regression","41.11. Machine Learning overview","41.15. Neural Network","41.3. Python programming advanced","41.2. Python programming basics","41.1. Python programming introduction"],titleterms:{"0":65,"1":[3,24,30,32,48,55,56,58,59,60,62,63,64,65,66,67,71,75,76,83,84,89,91,92,113,122,128,143,172,190,191,192,193,194,195,196,197,198,200,204,206,210,211],"10":[44,62,136,143],"100":[57,65,143],"1000":[65,143],"11":62,"12":62,"13":62,"1300131294":169,"15":43,"19":[1,8,14,138],"1d":128,"2":[3,24,30,32,48,49,55,56,57,58,59,60,62,63,64,66,67,75,77,84,89,90,93,113,122,128,172,190,191,192,193,194,195,196,197,198,200,204,206,210,211],"2d":[30,128,200],"3":[3,24,32,43,48,55,56,58,59,60,62,63,64,66,67,73,75,83,89,113,128,190,191,192,193,194,195,197,198,204,210,211],"3d":[30,92,117,200],"4":[3,24,32,48,55,56,57,58,59,60,62,63,64,66,67,75,89,113,172,190,191,192,193,194,195,196,210,211],"5":[24,32,48,55,56,59,60,62,63,64,66,67,75,89,92,113,143,172,190,191,192,193,194,196,210,211],"50":62,"500":62,"6":[48,56,58,59,60,62,63,64,66,67,75,89,190,191,192,193,194],"7":[48,56,58,59,62,63,64,66,67,190,193,194],"8":[58,62,193],"9":62,"90":169,"abstract":179,"boolean":[128,188,189,210,211],"break":[101,187,209],"case":[28,50,52,53,56,113,121,184,200],"class":[37,43,52,56,57,69,71,101,180,187,209],"default":[57,65,138,187],"do":[52,123,183,184,188,192,200],"final":[55,77,83,84,179],"float":[7,188,210],"function":[48,57,65,84,85,87,91,101,102,128,140,141,164,179,180,187,189,195,205,206,208,209],"import":[9,29,34,39,42,46,49,54,55,57,58,59,60,62,63,64,65,66,67,70,72,73,92,126,138,139,154,161,168,186,187,204,205,206,208],"long":145,"new":[33,62,103,128,131,205,206],"null":[7,65,195],"public":39,"return":[103,141,187],"short":145,"static":169,"true":65,"try":[0,27,53,75,84,89,100,187,209],"while":[101,187,209],A:[31,33,140,170,176,177,182],And:178,At:[51,179,180],But:184,By:164,For:[101,146],Is:156,It:[133,183,196],NOT:183,Not:178,On:37,One:[54,83,84,208],That:205,The:[9,38,48,57,59,84,86,91,109,110,126,133,147,170,171,177,184,186,187,188,191,195,196,197,200,209,210],There:184,To:[70,170],With:[35,150,179],about:[3,34,120,175,183,194],absolut:[86,180],acceler:171,access:[102,128,131],accuraci:[32,46,52,65,143,168,172,208],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,51,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,78,80,81,83,89,91,92,93,94,95,97,98,99,100,101,102,108,109,110,111,112,113,115,117,118,119,120,121,122,123,125,126,127,128,129,130,131,133,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,151,154,158,159,160,161,163,164,165,167,168,169,173,175,176,177,178,182,183,184,186,187,188,189,197,200,202,203,204,205,208],ackowledg:198,action:141,activ:140,actual:113,ad:[30,31,52,68,72,128,170],adaboost:55,adam:208,add:[53,128,154],addit:131,advanc:[32,101,128,129,187,209],adversari:[39,142,198],after:[30,170,201],ag:24,against:53,agent:[37,141],agglom:[171,172],aggreg:[128,129,192,195],ahead:91,ai:[38,46,151,202],airport:12,aka:70,algorithm:[56,60,141,155,158,163,164,168,171],align:131,all:[24,52,83,120,133,182,194,196],an:[9,31,70,72,109,110,113,127,128,173,197,203],anagram:188,analysi:[8,41,55,57,58,59,60,62,63,64,65,66,67,83,86,108,112,172,178,202,207],analyz:[1,2,3,18,112,128,184,193],anchor:[43,57],anim:117,ann:49,annot:[187,209],anomal:52,anomali:29,anoth:39,answer:17,ap:169,api:[35,48],app:[76,77,173],append:[102,195],appli:[4,22,52,121,176,177,192],applic:[56,135,146,172,203,207],approach:[57,113,133,176,196],ar:188,arbitrari:187,architectur:[32,136,140,147,197,208],argument:[138,187,209],arithmet:[131,188,210],arrai:[128,131,195],art:38,artifici:207,artwork:38,ascend:[102,103],asian:79,ask:184,assert:57,assign:[0,12,25,43,49,65,71,73,76,77,89,90,91,92,93,105,106,128,129,131,172,206],assist:144,attribut:[65,128,131,195],auc:[62,65],audienc:113,augment:[32,33,35,139],author:186,autoencod:[29,30,31,135],automl:[9,109,110,154],avail:128,averag:[101,184],avoid:[117,170],axi:128,azur:[9,10,20,109,110,127],b:[84,179],babylonian:102,backend:76,background:139,backprop:91,bag:[55,160,163,168],balanc:175,bar:[19,22,194],base:[30,40,101,130,168],basebal:18,baselin:[52,53,178,207],basi:65,basic:[29,32,33,46,48,102,121,128,131,141,188,189,192,195,210,211],batch:[34,68,171],beehiv:13,begin:113,behind:61,best:[9,32,39,109,156],beta:139,better:[176,183],between:[24,60,62,75,83,89,128,163,180,192,195],beyond:[44,55,169,200],bi:60,bia:[70,163,170,201],bibliographi:[25,179,180],big:208,binai:60,binari:[44,180,183],binder:0,bird:[118,194],bit:[31,128],bitcoin:41,blend:60,bmi:24,boost:[55,60,61,62,164,165,166,168],bootstrap:160,bound:139,boundari:[169,171,200],boxplot:[24,92],bp:24,brain:197,broadcast:[128,195],bug:53,build:[5,29,31,32,38,45,46,56,57,74,75,76,77,89,117,146,159,173,182,183,184,194,203],c:65,cach:203,calcul:[52,101,102,188],california:83,call:209,callabl:130,callback:44,can:[36,56,62,123,192],candid:62,capac:170,captur:[115,193],car:45,cardin:57,cast:[188,210],catalog:152,catboost:60,categor:[7,57,60,63,75,83,89,182,204],categori:102,categorical_crossentropi:208,caus:50,c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    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2663. 
+    
+     40.89. Introduction
+    
+   
    2664. 
    
  2664. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2665. 
    
  2665. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2666. 
    
  2666. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2667. 
    
  2667. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2668. 
    
  2668. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2669. 
    
  2669. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2670. 
    
  2670. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2671. 
+   
  2671. 
+    
+     40.99. Image classification
     
    
    2672. 
    
  2672. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2673. 
    
  2673. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2674. 
    
  2674. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2675. 
    
  2675. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2676. 
    
  2676. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2677. 
    
  2677. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2678. 
    
  2678. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2679. 
    
  2679. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2680. 
    
  2680. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2681. 
    
  2681. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2682. 
    
  2682. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2683. 
    
  2683. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2684. 
+   
  2684. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2685. 
    
  2685. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2686. 
    
  2686. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2687. 
    
  2687. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2688. 
+   
  2688. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2689. 
   
diff --git a/slides/data-science/data-science-in-the-cloud.html b/slides/data-science/data-science-in-the-cloud.html
index eb59a710e2..65963a2ef1 100644
--- a/slides/data-science/data-science-in-the-cloud.html
+++ b/slides/data-science/data-science-in-the-cloud.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2689. 
+    
+     40.89. Introduction
+    
+   
    2690. 
    
  2690. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2691. 
    
  2691. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2692. 
    
  2692. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2693. 
    
  2693. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2694. 
    
  2694. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2695. 
    
  2695. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2696. 
    
  2696. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2697. 
+   
  2697. 
+    
+     40.99. Image classification
     
    
    2698. 
    
  2698. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2699. 
    
  2699. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2700. 
    
  2700. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2701. 
    
  2701. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2702. 
    
  2702. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2703. 
    
  2703. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2704. 
    
  2704. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2705. 
    
  2705. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2706. 
    
  2706. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2707. 
    
  2707. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2708. 
    
  2708. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2709. 
    
  2709. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2710. 
+   
  2710. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2711. 
    
  2711. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2712. 
    
  2712. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2713. 
    
  2713. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2714. 
+   
  2714. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2715. 
   
diff --git a/slides/data-science/data-science-introduction.html b/slides/data-science/data-science-introduction.html
index c7bd8bbd19..b694d62ca1 100644
--- a/slides/data-science/data-science-introduction.html
+++ b/slides/data-science/data-science-introduction.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2715. 
+    
+     40.89. Introduction
+    
+   
    2716. 
    
  2716. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2717. 
    
  2717. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2718. 
    
  2718. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2719. 
    
  2719. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2720. 
    
  2720. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2721. 
    
  2721. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2722. 
    
  2722. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2723. 
+   
  2723. 
+    
+     40.99. Image classification
     
    
    2724. 
    
  2724. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2725. 
    
  2725. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2726. 
    
  2726. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2727. 
    
  2727. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2728. 
    
  2728. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2729. 
    
  2729. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2730. 
    
  2730. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2731. 
    
  2731. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2732. 
    
  2732. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2733. 
    
  2733. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2734. 
    
  2734. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2735. 
    
  2735. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2736. 
+   
  2736. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2737. 
    
  2737. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2738. 
    
  2738. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2739. 
    
  2739. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2740. 
+   
  2740. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2741. 
   
diff --git a/slides/data-science/data-science-lifecycle.html b/slides/data-science/data-science-lifecycle.html
index 8ad8795726..6f450b6df9 100644
--- a/slides/data-science/data-science-lifecycle.html
+++ b/slides/data-science/data-science-lifecycle.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2741. 
+    
+     40.89. Introduction
+    
+   
    2742. 
    
  2742. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2743. 
    
  2743. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2744. 
    
  2744. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2745. 
    
  2745. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2746. 
    
  2746. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2747. 
    
  2747. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2748. 
    
  2748. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2749. 
+   
  2749. 
+    
+     40.99. Image classification
     
    
    2750. 
    
  2750. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2751. 
    
  2751. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2752. 
    
  2752. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2753. 
    
  2753. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2754. 
    
  2754. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2755. 
    
  2755. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2756. 
    
  2756. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2757. 
    
  2757. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2758. 
    
  2758. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2759. 
    
  2759. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2760. 
    
  2760. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2761. 
    
  2761. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2762. 
+   
  2762. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2763. 
    
  2763. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2764. 
    
  2764. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2765. 
    
  2765. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2766. 
+   
  2766. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2767. 
   
diff --git a/slides/data-science/data-visualization.html b/slides/data-science/data-visualization.html
index f7fb5da01d..363cb98353 100644
--- a/slides/data-science/data-visualization.html
+++ b/slides/data-science/data-visualization.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2767. 
+    
+     40.89. Introduction
+    
+   
    2768. 
    
  2768. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2769. 
    
  2769. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2770. 
    
  2770. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2771. 
    
  2771. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2772. 
    
  2772. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2773. 
    
  2773. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2774. 
    
  2774. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2775. 
+   
  2775. 
+    
+     40.99. Image classification
     
    
    2776. 
    
  2776. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2777. 
    
  2777. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2778. 
    
  2778. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2779. 
    
  2779. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2780. 
    
  2780. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2781. 
    
  2781. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2782. 
    
  2782. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2783. 
    
  2783. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2784. 
    
  2784. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2785. 
    
  2785. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2786. 
    
  2786. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2787. 
    
  2787. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2788. 
+   
  2788. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2789. 
    
  2789. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2790. 
    
  2790. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2791. 
    
  2791. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2792. 
+   
  2792. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2793. 
   
diff --git a/slides/data-science/numpy-and-pandas.html b/slides/data-science/numpy-and-pandas.html
index 440523b973..24dc257020 100644
--- a/slides/data-science/numpy-and-pandas.html
+++ b/slides/data-science/numpy-and-pandas.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2793. 
+    
+     40.89. Introduction
+    
+   
    2794. 
    
  2794. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2795. 
    
  2795. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2796. 
    
  2796. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2797. 
    
  2797. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2798. 
    
  2798. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2799. 
    
  2799. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2800. 
    
  2800. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2801. 
+   
  2801. 
+    
+     40.99. Image classification
     
    
    2802. 
    
  2802. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2803. 
    
  2803. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2804. 
    
  2804. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2805. 
    
  2805. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2806. 
    
  2806. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2807. 
    
  2807. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2808. 
    
  2808. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2809. 
    
  2809. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2810. 
    
  2810. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2811. 
    
  2811. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2812. 
    
  2812. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2813. 
    
  2813. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2814. 
+   
  2814. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2815. 
    
  2815. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2816. 
    
  2816. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2817. 
    
  2817. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2818. 
+   
  2818. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2819. 
   
diff --git a/slides/data-science/relational-vs-non-relational-database.html b/slides/data-science/relational-vs-non-relational-database.html
index f3a2f34949..a628546f02 100644
--- a/slides/data-science/relational-vs-non-relational-database.html
+++ b/slides/data-science/relational-vs-non-relational-database.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2819. 
+    
+     40.89. Introduction
+    
+   
    2820. 
    
  2820. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2821. 
    
  2821. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2822. 
    
  2822. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2823. 
    
  2823. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2824. 
    
  2824. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2825. 
    
  2825. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2826. 
    
  2826. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2827. 
+   
  2827. 
+    
+     40.99. Image classification
     
    
    2828. 
    
  2828. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2829. 
    
  2829. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2830. 
    
  2830. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2831. 
    
  2831. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2832. 
    
  2832. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2833. 
    
  2833. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2834. 
    
  2834. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2835. 
    
  2835. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2836. 
    
  2836. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2837. 
    
  2837. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2838. 
    
  2838. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2839. 
    
  2839. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2840. 
+   
  2840. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2841. 
    
  2841. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2842. 
    
  2842. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2843. 
    
  2843. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2844. 
+   
  2844. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2845. 
   
diff --git a/slides/deep-learning/cnn.html b/slides/deep-learning/cnn.html
index 0467d048b3..9a01b2949c 100644
--- a/slides/deep-learning/cnn.html
+++ b/slides/deep-learning/cnn.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2845. 
+    
+     40.89. Introduction
+    
+   
    2846. 
    
  2846. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2847. 
    
  2847. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2848. 
    
  2848. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2849. 
    
  2849. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2850. 
    
  2850. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2851. 
    
  2851. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2852. 
    
  2852. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2853. 
+   
  2853. 
+    
+     40.99. Image classification
     
    
    2854. 
    
  2854. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2855. 
    
  2855. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2856. 
    
  2856. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2857. 
    
  2857. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2858. 
    
  2858. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2859. 
    
  2859. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2860. 
    
  2860. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2861. 
    
  2861. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2862. 
    
  2862. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2863. 
    
  2863. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2864. 
    
  2864. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2865. 
    
  2865. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2866. 
+   
  2866. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2867. 
    
  2867. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2868. 
    
  2868. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2869. 
    
  2869. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2870. 
+   
  2870. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2871. 
   
diff --git a/slides/deep-learning/gan.html b/slides/deep-learning/gan.html
index a691486fdf..f5d4dcf7ab 100644
--- a/slides/deep-learning/gan.html
+++ b/slides/deep-learning/gan.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1109,114 +1109,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2871. 
+    
+     40.89. Introduction
+    
+   
    2872. 
    
  2872. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2873. 
    
  2873. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2874. 
    
  2874. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2875. 
    
  2875. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2876. 
    
  2876. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2877. 
    
  2877. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2878. 
    
  2878. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2879. 
+   
  2879. 
+    
+     40.99. Image classification
     
    
    2880. 
    
  2880. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2881. 
    
  2881. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2882. 
    
  2882. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2883. 
    
  2883. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2884. 
    
  2884. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2885. 
    
  2885. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2886. 
    
  2886. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2887. 
    
  2887. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2888. 
    
  2888. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2889. 
    
  2889. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2890. 
    
  2890. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2891. 
    
  2891. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2892. 
+   
  2892. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2893. 
    
  2893. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2894. 
    
  2894. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2895. 
    
  2895. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2896. 
+   
  2896. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2897. 
   
diff --git a/slides/introduction.html b/slides/introduction.html
index ac6b9671f3..72773e9230 100644
--- a/slides/introduction.html
+++ b/slides/introduction.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -99,7 +99,7 @@
     
     
     
-    
+    
     
     
     
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2897. 
+    
+     40.89. Introduction
+    
+   
    2898. 
    
  2898. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2899. 
    
  2899. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2900. 
    
  2900. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2901. 
    
  2901. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2902. 
    
  2902. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2903. 
    
  2903. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2904. 
    
  2904. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2905. 
+   
  2905. 
+    
+     40.99. Image classification
     
    
    2906. 
    
  2906. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2907. 
    
  2907. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2908. 
    
  2908. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2909. 
    
  2909. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2910. 
    
  2910. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2911. 
    
  2911. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2912. 
    
  2912. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2913. 
    
  2913. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2914. 
    
  2914. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2915. 
    
  2915. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2916. 
    
  2916. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2917. 
    
  2917. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2918. 
+   
  2918. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2919. 
    
  2919. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2920. 
    
  2920. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2921. 
    
  2921. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2922. 
+   
  2922. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2923. 
   
@@ -1600,11 +1620,11 @@ 
    41. Slides
+    
         
         
    
             
    previous
    
-            
    40.122. Basic classification: Classify images of clothing
    
+            
    40.126. Getting Start NLP with classification task
    
         
    
         
     
diff --git a/slides/ml-advanced/kernel-method.html b/slides/ml-advanced/kernel-method.html
index 42468f6b9f..65022cb6c5 100644
--- a/slides/ml-advanced/kernel-method.html
+++ b/slides/ml-advanced/kernel-method.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
         
    
+   
  2923. 
+    
+     40.89. Introduction
+    
+   
    2924. 
    
  2924. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2925. 
    
  2925. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2926. 
    
  2926. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2927. 
    
  2927. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2928. 
    
  2928. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2929. 
    
  2929. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2930. 
    
  2930. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2931. 
+   
  2931. 
+    
+     40.99. Image classification
     
    
    2932. 
    
  2932. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2933. 
    
  2933. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2934. 
    
  2934. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2935. 
    
  2935. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2936. 
    
  2936. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2937. 
    
  2937. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2938. 
    
  2938. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2939. 
    
  2939. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2940. 
    
  2940. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2941. 
    
  2941. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2942. 
    
  2942. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2943. 
    
  2943. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2944. 
+   
  2944. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2945. 
    
  2945. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2946. 
    
  2946. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2947. 
    
  2947. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2948. 
+   
  2948. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2949. 
   
diff --git a/slides/ml-advanced/model-selection.html b/slides/ml-advanced/model-selection.html
index 8980d3ebed..c5f4028edc 100644
--- a/slides/ml-advanced/model-selection.html
+++ b/slides/ml-advanced/model-selection.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2949. 
+    
+     40.89. Introduction
+    
+   
    2950. 
    
  2950. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2951. 
    
  2951. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2952. 
    
  2952. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2953. 
    
  2953. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2954. 
    
  2954. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2955. 
    
  2955. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2956. 
    
  2956. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2957. 
+   
  2957. 
+    
+     40.99. Image classification
     
    
    2958. 
    
  2958. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2959. 
    
  2959. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2960. 
    
  2960. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2961. 
    
  2961. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2962. 
    
  2962. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2963. 
    
  2963. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2964. 
    
  2964. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2965. 
    
  2965. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2966. 
    
  2966. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2967. 
    
  2967. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2968. 
    
  2968. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2969. 
    
  2969. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2970. 
+   
  2970. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2971. 
    
  2971. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2972. 
    
  2972. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2973. 
    
  2973. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    2974. 
+   
  2974. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    2975. 
   
diff --git a/slides/ml-advanced/unsupervised-learning.html b/slides/ml-advanced/unsupervised-learning.html
index 60b3d93e9c..d7c21cd172 100644
--- a/slides/ml-advanced/unsupervised-learning.html
+++ b/slides/ml-advanced/unsupervised-learning.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  2975. 
+    
+     40.89. Introduction
+    
+   
    2976. 
    
  2976. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    2977. 
    
  2977. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    2978. 
    
  2978. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    2979. 
    
  2979. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    2980. 
    
  2980. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    2981. 
    
  2981. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    2982. 
    
  2982. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    2983. 
+   
  2983. 
+    
+     40.99. Image classification
     
    
    2984. 
    
  2984. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    2985. 
    
  2985. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    2986. 
    
  2986. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    2987. 
    
  2987. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    2988. 
    
  2988. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    2989. 
    
  2989. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    2990. 
    
  2990. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    2991. 
    
  2991. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    2992. 
    
  2992. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    2993. 
    
  2993. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    2994. 
    
  2994. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    2995. 
    
  2995. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    2996. 
+   
  2996. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    2997. 
    
  2997. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    2998. 
    
  2998. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    2999. 
    
  2999. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3000. 
+   
  3000. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3001. 
   
diff --git a/slides/ml-fundamentals/build-an-ml-web-app.html b/slides/ml-fundamentals/build-an-ml-web-app.html
index f083e60ff7..6908400ee9 100644
--- a/slides/ml-fundamentals/build-an-ml-web-app.html
+++ b/slides/ml-fundamentals/build-an-ml-web-app.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3001. 
+    
+     40.89. Introduction
+    
+   
    3002. 
    
  3002. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3003. 
    
  3003. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3004. 
    
  3004. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3005. 
    
  3005. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3006. 
    
  3006. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3007. 
    
  3007. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3008. 
    
  3008. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3009. 
+   
  3009. 
+    
+     40.99. Image classification
     
    
    3010. 
    
  3010. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3011. 
    
  3011. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3012. 
    
  3012. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3013. 
    
  3013. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3014. 
    
  3014. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3015. 
    
  3015. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3016. 
    
  3016. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3017. 
    
  3017. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3018. 
    
  3018. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3019. 
    
  3019. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3020. 
    
  3020. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3021. 
    
  3021. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3022. 
+   
  3022. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3023. 
    
  3023. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3024. 
    
  3024. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3025. 
    
  3025. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3026. 
+   
  3026. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3027. 
   
diff --git a/slides/ml-fundamentals/linear-regression.html b/slides/ml-fundamentals/linear-regression.html
index 27b667c13b..cdbb32d237 100644
--- a/slides/ml-fundamentals/linear-regression.html
+++ b/slides/ml-fundamentals/linear-regression.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3027. 
+    
+     40.89. Introduction
+    
+   
    3028. 
    
  3028. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3029. 
    
  3029. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3030. 
    
  3030. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3031. 
    
  3031. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3032. 
    
  3032. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3033. 
    
  3033. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3034. 
    
  3034. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3035. 
+   
  3035. 
+    
+     40.99. Image classification
     
    
    3036. 
    
  3036. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3037. 
    
  3037. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3038. 
    
  3038. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3039. 
    
  3039. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3040. 
    
  3040. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3041. 
    
  3041. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3042. 
    
  3042. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3043. 
    
  3043. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3044. 
    
  3044. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3045. 
    
  3045. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3046. 
    
  3046. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3047. 
    
  3047. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3048. 
+   
  3048. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3049. 
    
  3049. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3050. 
    
  3050. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3051. 
    
  3051. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3052. 
+   
  3052. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3053. 
   
diff --git a/slides/ml-fundamentals/logistic-regression-condensed.html b/slides/ml-fundamentals/logistic-regression-condensed.html
index 4fe9e8eb26..f9bf7eeccc 100644
--- a/slides/ml-fundamentals/logistic-regression-condensed.html
+++ b/slides/ml-fundamentals/logistic-regression-condensed.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3053. 
+    
+     40.89. Introduction
+    
+   
    3054. 
    
  3054. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3055. 
    
  3055. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3056. 
    
  3056. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3057. 
    
  3057. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3058. 
    
  3058. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3059. 
    
  3059. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3060. 
    
  3060. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3061. 
+   
  3061. 
+    
+     40.99. Image classification
     
    
    3062. 
    
  3062. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3063. 
    
  3063. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3064. 
    
  3064. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3065. 
    
  3065. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3066. 
    
  3066. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3067. 
    
  3067. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3068. 
    
  3068. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3069. 
    
  3069. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3070. 
    
  3070. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3071. 
    
  3071. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3072. 
    
  3072. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3073. 
    
  3073. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3074. 
+   
  3074. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3075. 
    
  3075. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3076. 
    
  3076. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3077. 
    
  3077. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3078. 
+   
  3078. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3079. 
   
diff --git a/slides/ml-fundamentals/logistic-regression.html b/slides/ml-fundamentals/logistic-regression.html
index a8e0072a55..e51a27547f 100644
--- a/slides/ml-fundamentals/logistic-regression.html
+++ b/slides/ml-fundamentals/logistic-regression.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
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    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3079. 
+    
+     40.89. Introduction
+    
+   
    3080. 
    
  3080. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3081. 
    
  3081. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3082. 
    
  3082. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3083. 
    
  3083. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3084. 
    
  3084. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3085. 
    
  3085. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3086. 
    
  3086. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3087. 
+   
  3087. 
+    
+     40.99. Image classification
     
    
    3088. 
    
  3088. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3089. 
    
  3089. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3090. 
    
  3090. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3091. 
    
  3091. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3092. 
    
  3092. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3093. 
    
  3093. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3094. 
    
  3094. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3095. 
    
  3095. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3096. 
    
  3096. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3097. 
    
  3097. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3098. 
    
  3098. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3099. 
    
  3099. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3100. 
+   
  3100. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3101. 
    
  3101. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3102. 
    
  3102. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3103. 
    
  3103. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3104. 
+   
  3104. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3105. 
   
diff --git a/slides/ml-fundamentals/ml-overview.html b/slides/ml-fundamentals/ml-overview.html
index 19f8093b81..b635773838 100644
--- a/slides/ml-fundamentals/ml-overview.html
+++ b/slides/ml-fundamentals/ml-overview.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1112,114 +1112,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3105. 
+    
+     40.89. Introduction
+    
+   
    3106. 
    
  3106. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3107. 
    
  3107. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3108. 
    
  3108. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3109. 
    
  3109. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3110. 
    
  3110. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3111. 
    
  3111. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3112. 
    
  3112. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3113. 
+   
  3113. 
+    
+     40.99. Image classification
     
    
    3114. 
    
  3114. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3115. 
    
  3115. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3116. 
    
  3116. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3117. 
    
  3117. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3118. 
    
  3118. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3119. 
    
  3119. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3120. 
    
  3120. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3121. 
    
  3121. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3122. 
    
  3122. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3123. 
    
  3123. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3124. 
    
  3124. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3125. 
    
  3125. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3126. 
+   
  3126. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3127. 
    
  3127. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3128. 
    
  3128. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3129. 
    
  3129. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3130. 
+   
  3130. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3131. 
   
diff --git a/slides/ml-fundamentals/neural-network.html b/slides/ml-fundamentals/neural-network.html
index 5c51366ae6..6d901a9f49 100644
--- a/slides/ml-fundamentals/neural-network.html
+++ b/slides/ml-fundamentals/neural-network.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3131. 
+    
+     40.89. Introduction
+    
+   
    3132. 
    
  3132. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3133. 
    
  3133. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3134. 
    
  3134. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3135. 
    
  3135. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3136. 
    
  3136. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3137. 
    
  3137. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3138. 
    
  3138. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3139. 
+   
  3139. 
+    
+     40.99. Image classification
     
    
    3140. 
    
  3140. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3141. 
    
  3141. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3142. 
    
  3142. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3143. 
    
  3143. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3144. 
    
  3144. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3145. 
    
  3145. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3146. 
    
  3146. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3147. 
    
  3147. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3148. 
    
  3148. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3149. 
    
  3149. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3150. 
    
  3150. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3151. 
    
  3151. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3152. 
+   
  3152. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3153. 
    
  3153. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3154. 
    
  3154. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3155. 
    
  3155. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3156. 
+   
  3156. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3157. 
   
diff --git a/slides/python-programming/python-programming-advanced.html b/slides/python-programming/python-programming-advanced.html
index 462acbcca1..778ad0faba 100644
--- a/slides/python-programming/python-programming-advanced.html
+++ b/slides/python-programming/python-programming-advanced.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3157. 
+    
+     40.89. Introduction
+    
+   
    3158. 
    
  3158. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3159. 
    
  3159. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3160. 
    
  3160. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3161. 
    
  3161. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3162. 
    
  3162. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3163. 
    
  3163. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3164. 
    
  3164. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3165. 
+   
  3165. 
+    
+     40.99. Image classification
     
    
    3166. 
    
  3166. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3167. 
    
  3167. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3168. 
    
  3168. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3169. 
    
  3169. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3170. 
    
  3170. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3171. 
    
  3171. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3172. 
    
  3172. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3173. 
    
  3173. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3174. 
    
  3174. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3175. 
    
  3175. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3176. 
    
  3176. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3177. 
    
  3177. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3178. 
+   
  3178. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3179. 
    
  3179. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3180. 
    
  3180. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3181. 
    
  3181. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3182. 
+   
  3182. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3183. 
   
diff --git a/slides/python-programming/python-programming-basics.html b/slides/python-programming/python-programming-basics.html
index 08b2d3e2c6..4d254ab20d 100644
--- a/slides/python-programming/python-programming-basics.html
+++ b/slides/python-programming/python-programming-basics.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3183. 
+    
+     40.89. Introduction
+    
+   
    3184. 
    
  3184. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3185. 
    
  3185. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3186. 
    
  3186. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3187. 
    
  3187. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3188. 
    
  3188. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3189. 
    
  3189. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3190. 
    
  3190. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3191. 
+   
  3191. 
+    
+     40.99. Image classification
     
    
    3192. 
    
  3192. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3193. 
    
  3193. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3194. 
    
  3194. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3195. 
    
  3195. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3196. 
    
  3196. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3197. 
    
  3197. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3198. 
    
  3198. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3199. 
    
  3199. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3200. 
    
  3200. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3201. 
    
  3201. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3202. 
    
  3202. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3203. 
    
  3203. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3204. 
+   
  3204. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3205. 
    
  3205. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3206. 
    
  3206. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3207. 
    
  3207. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3208. 
+   
  3208. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3209. 
   
diff --git a/slides/python-programming/python-programming-introduction.html b/slides/python-programming/python-programming-introduction.html
index ba25787f4b..b9edb44f4f 100644
--- a/slides/python-programming/python-programming-introduction.html
+++ b/slides/python-programming/python-programming-introduction.html
@@ -27,8 +27,8 @@
     
     
     
-    
     
+    
     
     
   
@@ -1110,114 +1110,134 @@ 
    Ocademy Open Machine Learning Book
    
      40.88. Case Study: Debugging in Regression
     
    
+   
  3209. 
+    
+     40.89. Introduction
+    
+   
    3210. 
    
  3210. 
     
-     40.89. Study the solvers
+     40.90. Study the solvers
     
    
    3211. 
    
  3211. 
     
-     40.90. Build classification models
+     40.91. Build classification models
     
    
    3212. 
    
  3212. 
     
-     40.91. Build Classification Model
+     40.92. Build Classification Model
     
    
    3213. 
    
  3213. 
     
-     40.92. Parameter play
+     40.93. Parameter play
     
    
    3214. 
    
  3214. 
     
-     40.93. How to choose cnn architecture mnist
+     40.94. How to choose cnn architecture mnist
     
    
    3215. 
    
  3215. 
     
-     40.95. Sign Language Digits Classification with CNN
+     40.96. Sign Language Digits Classification with CNN
     
    
    3216. 
    
  3216. 
     
-     40.97. Object Recognition in Images using CNN
+     40.98. Object Recognition in Images using CNN
+    
+   
    3217. 
+   
  3217. 
+    
+     40.99. Image classification
     
    
    3218. 
    
  3218. 
     
-     40.98. Intro to TensorFlow for Deep Learning
+     40.100. Intro to TensorFlow for Deep Learning
     
    
    3219. 
    
  3219. 
     
-     40.100. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.102. Bitcoin LSTM Model with Tweet Volume and Sentiment
     
    
    3220. 
    
  3220. 
     
-     40.102. Google Stock Price Prediction RNN
+     40.104. Google Stock Price Prediction RNN
     
    
    3221. 
    
  3221. 
     
-     40.104. Intro to Autoencoders
+     40.106. Intro to Autoencoders
     
    
    3222. 
    
  3222. 
     
-     40.105. Base/Denoising Autoencoder & Dimension Reduction
+     40.107. Base/Denoising Autoencoder & Dimension Reduction
     
    
    3223. 
    
  3223. 
     
-     40.106. Fun with Variational Autoencoders
+     40.108. Fun with Variational Autoencoders
     
    
    3224. 
    
  3224. 
     
-     40.107. Time Series Forecasting Assignment
+     40.109. Time Series Forecasting Assignment
     
    
    3225. 
    
  3225. 
     
-     40.109. Neural Networks for Classification with TensorFlow
+     40.111. Neural Networks for Classification with TensorFlow
     
    
    3226. 
    
  3226. 
     
-     40.110. NN Classify 15 Fruits Assignment
+     40.112. NN Classify 15 Fruits Assignment
     
    
    3227. 
    
  3227. 
     
-     40.115. DQN On Foreign Exchange Market
+     40.117. DQN On Foreign Exchange Market
     
    
    3228. 
    
  3228. 
     
-     40.116. Art by gan
+     40.118. Art by gan
     
    
    3229. 
    
  3229. 
     
-     40.118. Generative Adversarial Networks (GANs)
+     40.120. Generative Adversarial Networks (GANs)
+    
+   
    3230. 
+   
  3230. 
+    
+     40.121. Comparing edge-based and region-based segmentation
     
    
    3231. 
    
  3231. 
     
-     40.119. Summary
+     40.122. Summary
     
    
    3232. 
    
  3232. 
     
-     40.120. Car Object Detection
+     40.123. Car Object Detection
     
    
    3233. 
    
  3233. 
     
-     40.122. Basic classification: Classify images of clothing
+     40.125. Basic classification: Classify images of clothing
+    
+   
    3234. 
+   
  3234. 
+    
+     40.126. Getting Start NLP with classification task
     
    
    3235.

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