Add Colab button, fixes #346

extra_autodiff.ipynb

@@ -14,6 +14,17 @@
     "_This notebook contains toy implementations of various autodiff techniques, to explain how they works._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/extra_autodiff.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

extra_gradient_descent_comparison.ipynb

@@ -14,6 +14,17 @@
     "This notebook displays an animation comparing Batch, Mini-Batch and Stochastic Gradient Descent (introduced in Chapter 4). Thanks to [Daniel Ingram](https://github.com/daniel-s-ingram) who contributed this notebook."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/extra_gradient_descent_comparison.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -257,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

index.ipynb

@@ -10,6 +10,17 @@
     "\n",
     "[Prerequisites](#Prerequisites) (see below)\n",
     "\n",
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/index.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Notebooks\n",
     "1. [The Machine Learning landscape](01_the_machine_learning_landscape.ipynb)\n",
     "2. [End-to-end Machine Learning project](02_end_to_end_machine_learning_project.ipynb)\n",
@@ -29,34 +40,65 @@
     "16. [Natural Language Processing with RNNs and Attention](16_nlp_with_rnns_and_attention.ipynb)\n",
     "17. [Representation Learning Using Autoencoders](17_autoencoders.ipynb)\n",
     "18. [Reinforcement Learning](18_reinforcement_learning.ipynb)\n",
-    "19. [Training and Deploying TensorFlow Models at Scale](19_training_and_deploying_at_scale.ipynb)\n",
+    "19. [Training and Deploying TensorFlow Models at Scale](19_training_and_deploying_at_scale.ipynb)"
-    "\n",
+   ]
-    "## Scientific Python tutorials\n",
+  },
-    "* [NumPy](tools_numpy.ipynb)\n",
+  {
-    "* [Matplotlib](tools_matplotlib.ipynb)\n",
+   "cell_type": "markdown",
-    "* [Pandas](tools_pandas.ipynb)\n",
+   "metadata": {},
-    "\n",
+   "source": [
-    "## Math Tutorials\n",
+    "## Scientific Python tutorials\n",
-    "* [Linear Algebra](math_linear_algebra.ipynb)\n",
+    "* [NumPy](tools_numpy.ipynb)\n",
-    "* [Differential Calculus](math_differential_calculus.ipynb)\n",
+    "* [Matplotlib](tools_matplotlib.ipynb)\n",
-    "\n",
+    "* [Pandas](tools_pandas.ipynb)"
-    "## Extra Material\n",
+   ]
-    "* [Auto-differentiation](extra_autodiff.ipynb)\n",
+  },
-    "\n",
+  {
-    "## Misc.\n",
+   "cell_type": "markdown",
-    "* [Equations](book_equations.pdf) (list of equations in the book)\n"
+   "metadata": {},
+   "source": [
+    "## Math Tutorials\n",
+    "* [Linear Algebra](math_linear_algebra.ipynb)\n",
+    "* [Differential Calculus](math_differential_calculus.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extra Material\n",
+    "* [Auto-differentiation](extra_autodiff.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Misc.\n",
+    "* [Equations](book_equations.pdf) (list of equations in the book)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Prerequisites\n",
     "### To understand\n",
     "* **Python** – you don't need to be an expert python programmer, but you do need to know the basics. If you don't, the official [Python tutorial](https://docs.python.org/3/tutorial/) is a good place to start.\n",
     "* **Scientific Python** – We will be using a few popular python libraries, in particular NumPy, matplotlib and pandas. If you are not familiar with these libraries, you should probably start by going through the tutorials in the Tools section (especially NumPy).\n",
-    "* **Math** – We will also use some notions of Linear Algebra, Calculus, Statistics and Probability theory. You should be able to follow along if you learned these in the past as it won't be very advanced, but if you don't know about these topics or you need a refresher then go through the appropriate introduction in the Math section.\n",
+    "* **Math** – We will also use some notions of Linear Algebra, Calculus, Statistics and Probability theory. You should be able to follow along if you learned these in the past as it won't be very advanced, but if you don't know about these topics or you need a refresher then go through the appropriate introduction in the Math section."
-    "\n",
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "### To run the examples\n",
     "* **Jupyter** – These notebooks are based on Jupyter. You can run these notebooks in just one click using a hosted platform such as Binder, Deepnote or Colaboratory (no installation required), or you can just view them using Jupyter.org's viewer, or you can install everything on your machine, as you prefer. Check out the [home page](https://github.com/ageron/handson-ml2/) for more details."
    ]
@@ -85,7 +127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.9"
   },
   "nav_menu": {},
   "toc": {

math_linear_algebra.ipynb

@@ -11,6 +11,17 @@
     "*Machine Learning relies heavily on Linear Algebra, so it is essential to understand what vectors and matrices are, what operations you can perform with them, and how they can be useful.*"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/math_linear_algebra.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3063,7 +3074,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.9"
   },
   "toc": {
    "toc_cell": false,

tools_matplotlib.ipynb

@@ -9,6 +9,17 @@
     "*This notebook demonstrates how to use the matplotlib library to plot beautiful graphs.*"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1242,7 +1253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.9"
   },
   "toc": {
    "toc_cell": true,

tools_numpy.ipynb

@@ -6,9 +6,25 @@
    "source": [
     "**Tools - NumPy**\n",
     "\n",
-    "*NumPy is the fundamental library for scientific computing with Python. NumPy is centered around a powerful N-dimensional array object, and it also contains useful linear algebra, Fourier transform, and random number functions.*\n",
+    "*NumPy is the fundamental library for scientific computing with Python. NumPy is centered around a powerful N-dimensional array object, and it also contains useful linear algebra, Fourier transform, and random number functions.*"
-    "\n",
+   ]
-    "# Creating arrays"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_numpy.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Arrays"
    ]
   },
   {
@@ -2833,7 +2849,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.9"
   },
   "toc": {
    "toc_cell": false,

tools_pandas.ipynb

@@ -12,6 +12,17 @@
     "* NumPy – if you are not familiar with NumPy, we recommend that you go through the [NumPy tutorial](tools_numpy.ipynb) now."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_pandas.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},