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‎chapter02_mathematical-building-blocks.ipynb

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"colab_type": "text"
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"source": [
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](TODO). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**"
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io)."
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"os.environ[\"KERAS_BACKEND\"] = \"jax\""
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## The mathematical building blocks of neural networks"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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"source": [
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"### The engine of neural networks: gradient-based optimization"
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"### The engine of neural networks: Gradient-based optimization"
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"colab_type": "text"
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"#### Chaining derivatives: the Backpropagation algorithm"
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"#### Chaining derivatives: The Backpropagation algorithm"
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"colab_type": "text"
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"source": [
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"### Chapter summary"
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"### Summary"
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}
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‎chapter03_introduction-to-ml-frameworks.ipynb

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"colab_type": "text"
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](TODO). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**"
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io)."
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"os.environ[\"KERAS_BACKEND\"] = \"jax\""
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## Introduction to TensorFlow, PyTorch, JAX, and Keras"
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]
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},
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"cell_type": "markdown",
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"colab_type": "text"
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"##### Tensors and Variables in TensorFlow"
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"##### Tensors and variables in TensorFlow"
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"colab_type": "text"
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"###### Tensor assignment and the `Variable` class"
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"###### Tensor assignment and the Variable class"
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"colab_type": "text"
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"##### Tensor operations: doing math in TensorFlow"
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"##### Tensor operations: Doing math in TensorFlow"
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"colab_type": "text"
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"##### Gradients in TensorFlow: a second look at the `GradientTape` API"
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"##### Gradients in TensorFlow: A second look at the GradientTape API"
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"colab_type": "text"
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"#### An end-to-end example: a linear classifier in pure TensorFlow"
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"#### An end-to-end example: A linear classifier in pure TensorFlow"
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"colab_type": "text"
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"##### Tensors and Parameters in PyTorch"
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"##### Tensors and parameters in PyTorch"
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"colab_type": "text"
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"##### Tensor operations: doing math in PyTorch"
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"##### Tensor operations: Doing math in PyTorch"
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"colab_type": "text"
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"#### An end-to-end example: a linear classifier in pure PyTorch"
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"#### An end-to-end example: A linear classifier in pure PyTorch"
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"colab_type": "text"
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"source": [
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"##### Packaging state and computation with the `Module` class"
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"##### Packaging state and computation with the Module class"
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"colab_type": "text"
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"source": [
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"##### Tensor operations: doing math in JAX"
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"##### Tensor operations: Doing math in JAX"
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"colab_type": "text"
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"##### Making JAX functions fast with `@jax.jit`"
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"##### Making JAX functions fast with @jax.jit"
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"colab_type": "text"
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"#### An end-to-end example: a linear classifier in pure JAX"
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"#### An end-to-end example: A linear classifier in pure JAX"
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"colab_type": "text"
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"source": [
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"#### Layers: the building blocks of deep learning"
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"#### Layers: The building blocks of deep learning"
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"colab_type": "text"
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"##### Automatic shape inference: building layers on the fly"
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"##### Automatic shape inference: Building layers on the fly"
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"colab_type": "text"
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"source": [
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"#### The \"compile\" step: configuring the learning process"
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"#### The \"compile\" step: Configuring the learning process"
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"colab_type": "text"
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"#### Understanding the `fit` method"
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"#### Understanding the fit method"
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"colab_type": "text"
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"#### Monitoring loss & metrics on validation data"
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"#### Monitoring loss and metrics on validation data"
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"colab_type": "text"
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"#### Inference: using a model after training"
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"#### Inference: Using a model after training"
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"colab_type": "text"
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"### Chapter summary"
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"### Summary"
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‎chapter04_classification-and-regression.ipynb

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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](TODO). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**"
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io)."
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"colab_type": "text"
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"### Classifying movie reviews: a binary classification example"
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"## Classification and regression"
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]
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},
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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"### Classifying movie reviews: A binary classification example"
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"colab_type": "text"
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"### Classifying newswires: a multiclass classification example"
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"### Classifying newswires: A multiclass classification example"
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"colab_type": "text"
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"### Chapter summary"
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"### Summary"
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‎chapter05_fundamentals-of-ml.ipynb

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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](TODO). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**"
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io)."
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"### Generalization: the goal of machine learning"
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"## Fundamentals of machine learning"
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"### Generalization: The goal of machine learning"
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"#### Using better architecture priors"
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"original_val_loss = history_original.history[\"val_loss\"]\n",
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"l2_val_loss = history_l2_reg.history[\"val_loss\"]\n",
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"dropout_val_loss = history_dropout.history[\"val_loss\"]\n",
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