diff --git a/extra_autodiff.ipynb b/extra_autodiff.ipynb index e67a164..6ef643d 100644 --- a/extra_autodiff.ipynb +++ b/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": [ + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/extra_gradient_descent_comparison.ipynb b/extra_gradient_descent_comparison.ipynb index 69643b4..d6dac91 100644 --- a/extra_gradient_descent_comparison.ipynb +++ b/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": [ + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, { "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, diff --git a/index.ipynb b/index.ipynb index 09a1f2b..91842dc 100644 --- a/index.ipynb +++ b/index.ipynb @@ -10,6 +10,17 @@ "\n", "[Prerequisites](#Prerequisites) (see below)\n", "\n", + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, + { + "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", - "\n", - "## Scientific Python tutorials\n", - "* [NumPy](tools_numpy.ipynb)\n", - "* [Matplotlib](tools_matplotlib.ipynb)\n", - "* [Pandas](tools_pandas.ipynb)\n", - "\n", - "## Math Tutorials\n", - "* [Linear Algebra](math_linear_algebra.ipynb)\n", - "* [Differential Calculus](math_differential_calculus.ipynb)\n", - "\n", - "## Extra Material\n", - "* [Auto-differentiation](extra_autodiff.ipynb)\n", - "\n", - "## Misc.\n", - "* [Equations](book_equations.pdf) (list of equations in the book)\n" + "19. [Training and Deploying TensorFlow Models at Scale](19_training_and_deploying_at_scale.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scientific Python tutorials\n", + "* [NumPy](tools_numpy.ipynb)\n", + "* [Matplotlib](tools_matplotlib.ipynb)\n", + "* [Pandas](tools_pandas.ipynb)" + ] + }, + { + "cell_type": "markdown", + "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", - "\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." + ] + }, + { + "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": { diff --git a/math_linear_algebra.ipynb b/math_linear_algebra.ipynb index 501a176..6c33d02 100644 --- a/math_linear_algebra.ipynb +++ b/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": [ + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, { "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, diff --git a/tools_matplotlib.ipynb b/tools_matplotlib.ipynb index 1cf655a..949f5c2 100644 --- a/tools_matplotlib.ipynb +++ b/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": [ + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, { "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, diff --git a/tools_numpy.ipynb b/tools_numpy.ipynb index ddd3ab6..7b5c22e 100644 --- a/tools_numpy.ipynb +++ b/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", - "\n", - "# Creating arrays" + "*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.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, + { + "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, diff --git a/tools_pandas.ipynb b/tools_pandas.ipynb index 7e87472..2e93943 100644 --- a/tools_pandas.ipynb +++ b/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": [ + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ] + }, { "cell_type": "markdown", "metadata": {},