Merge pull request #100 from gtbook/frank_ids

Fix ids

S10_introduction.ipynb

@@ -4,9 +4,7 @@
    "attachments": {},
    "cell_type": "markdown",
    "id": "4be934e0",
-   "metadata": {
-    "id": "Ow_ar2AAFHi0"
-   },
+   "metadata": {},
    "source": [
     "# Introduction\n"
    ]
@@ -36,9 +34,7 @@
   {
    "cell_type": "markdown",
    "id": "Ow_ar2AAFHi0",
-   "metadata": {
-    "id": "Ow_ar2AAFHi0"
-   },
+   "metadata": {},
    "source": [
     "```{index} graphical models\n",
     "```\n",

S12_reasoning.ipynb

@@ -4,9 +4,7 @@
    "attachments": {},
    "cell_type": "markdown",
    "id": "c202b3d7",
-   "metadata": {
-    "id": "Ow_ar2AAFHi0"
-   },
+   "metadata": {},
    "source": [
     "# Reasoning\n",
     "\n",
@@ -18,9 +16,7 @@
   {
    "cell_type": "markdown",
    "id": "Ow_ar2AAFHi0",
-   "metadata": {
-    "id": "Ow_ar2AAFHi0"
-   },
+   "metadata": {},
    "source": [
     "Reasoning involves manipulating data and models, ultimately reaching conclusions. This can take the form of perception, in which sensor data and models are used to make inferences about the state of the world or the robot; planning, in which action models and the perceived state are used to infer a plan that can transform the state of the world or the robot from its current state to a desired state; and, learning, in which data and models are used to improve the robot’s performance over time. In this book, reasoning ranges from very simple mathematical manipulations of probabilities to learning millions of parameters for a deep neural network."
    ]

S13_math.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "KpZHfkhiU5MF",
    "metadata": {},
    "source": [
     "# The Mathematics of Robotics\n",
@@ -13,13 +14,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ZoUDTH_CqVfJ",
    "metadata": {},
    "source": [
     "We use three different broad areas of mathematics in this book. One is *probability theory and statistics*; the next is *linear algebra*; and finally, we rely extensively on *optimization methods*. In this section, we discuss each of these in turn. We also preview how graphical models like *Bayesian networks* and *factor graphs* will be used in each of these areas as convenient representations. Since graph theory is also a branch of mathematics, these can be seen as a fourth, over-arching mathematical theme."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "TYRwFcdHKBt6",
    "metadata": {},
    "source": [
     "## Probability Theory and Statistics\n",
@@ -40,6 +43,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "VC5SwZWQVEyb",
    "metadata": {},
    "source": [
     "```{index} sampling\n",
@@ -71,6 +75,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "-6mcf7AE-Pnx",
    "metadata": {},
    "source": [
     "```{index} statistics\n",
@@ -88,6 +93,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ppmByKk0oS-Y",
    "metadata": {},
    "source": [
     "```{index} graphical model, Bayesian networks, factor graphs\n",
@@ -97,6 +103,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2rdcfXLgUyU6",
    "metadata": {},
    "source": [
     "```{index} ancestral sampling, rollout, reward, value function, reinforcement learning\n",
@@ -107,6 +114,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ZhaIlArbY815",
    "metadata": {},
    "source": [
     "##   Linear Algebra\n",
@@ -123,6 +131,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4zsLRY3S__G_",
    "metadata": {},
    "source": [
     "```{index} Gaussian, Gaussian Bayes nets, Gaussian factor graphs, GTSAM\n",
@@ -138,6 +147,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "MdkYHPdYDLio",
    "metadata": {},
    "source": [
     "```{index} rotation matrix\n",
@@ -153,6 +163,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "G4z5m8j42g41",
    "metadata": {},
    "source": [
     "```{index} Jacobian matrix\n",
@@ -163,6 +174,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "bHnLHNriF7Y8",
    "metadata": {},
    "source": [
     "##    Optimization\n",
@@ -187,6 +199,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ZIpm-kHfZagU",
    "metadata": {},
    "source": [
     "```{index} gradient descent\n",
@@ -256,5 +269,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 5
 }

S20_sorter_intro.ipynb

@@ -3,6 +3,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
+   "id": "ws1pOpThWszG",
    "metadata": {},
    "source": [
     "# A Trash Sorting Robot\n",
@@ -14,6 +15,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "wV05boTVzhjC",
    "metadata": {},
    "source": [
     "<!---TODO: get Amp Robotics photo.\n",
@@ -60,6 +62,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "blmndvFUAjqM",
    "metadata": {},
    "source": []
   }
@@ -89,5 +92,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 5
 }