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Update all notebooks
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‎chapter02_mathematical-building-blocks.ipynb

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"colab_type": "text"
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},
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"source": [
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"### Rank-3 tensors and higher-rank tensors"
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"### Rank-3 and higher-rank tensors"
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]
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{
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"colab_type": "text"
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"source": [
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"### Chaining derivatives: the Backpropagation algorithm"
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"### Chaining derivatives: The Backpropagation algorithm"
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]
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{
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"colab_type": "text"
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"source": [
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"#### The Gradient Tape in TensorFlow"
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"#### The gradient tape in TensorFlow"
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]
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{
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"learning_rate = 1e-3\n",
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"\n",
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"def update_weights(gradients, weights):\n",
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" for g, w in zip(gradients, model.weights):\n",
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" for g, w in zip(gradients, weights):\n",
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" w.assign_sub(g * learning_rate)"
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]
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},
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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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}
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‎chapter03_introduction-to-keras-and-tf.ipynb

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"colab_type": "text"
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},
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"source": [
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"## Keras and TensorFlow: a brief history"
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"## Keras and TensorFlow: A brief history"
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"colab_type": "text"
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},
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"source": [
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"### Jupyter notebooks: the preferred way to run deep-learning experiments"
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"### Jupyter notebooks: The preferred way to run deep-learning experiments"
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]
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},
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"colab_type": "text"
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"source": [
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"#### Installing packages with `pip`"
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"#### Installing packages with pip"
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"colab_type": "text"
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"source": [
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"#### Constant tensors and Variables"
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"#### Constant tensors and variables"
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"colab_type": "text"
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"source": [
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"**Creating a Variable**"
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"**Creating a TensorFlow variable**"
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]
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"colab_type": "text"
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"source": [
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"**Assigning a value to a Variable**"
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"**Assigning a value to a TensorFlow variable**"
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"colab_type": "text"
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"source": [
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"**Assigning a value to a subset of a Variable**"
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"**Assigning a value to a subset of a TensorFlow variable**"
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"colab_type": "text"
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"source": [
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"**Using assign_add**"
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"**Using `assign_add`**"
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"colab_type": "text"
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"source": [
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"#### Tensor operations: doing math in TensorFlow"
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"#### Tensor operations: Doing math in TensorFlow"
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]
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"colab_type": "text"
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"source": [
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"#### A second look at the `GradientTape` API"
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"#### A second look at the GradientTape API"
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"colab_type": "text"
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"source": [
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"**Using the GradientTape**"
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"**Using the `GradientTape`**"
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]
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"colab_type": "text"
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"source": [
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"**Using the GradientTape with constant tensor inputs**"
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"**Using `GradientTape` with constant tensor inputs**"
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"colab_type": "text"
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"source": [
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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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"source": [
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"num_samples_per_class = 1000\n",
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"negative_samples = np.random.multivariate_normal(\n",
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" mean=[0, 3], cov=[[1, 0.5],[0.5, 1]], size=num_samples_per_class)\n",
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" mean=[0, 3],\n",
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" cov=[[1, 0.5],[0.5, 1]],\n",
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" size=num_samples_per_class)\n",
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"positive_samples = np.random.multivariate_normal(\n",
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" mean=[3, 0], cov=[[1, 0.5],[0.5, 1]], size=num_samples_per_class)"
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" mean=[3, 0],\n",
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" cov=[[1, 0.5],[0.5, 1]],\n",
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" size=num_samples_per_class)"
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"colab_type": "text"
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"source": [
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"## Anatomy of a neural network: understanding core Keras APIs"
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"## Anatomy of a neural network: Understanding core Keras APIs"
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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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"source": [
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"#### The base `Layer` class in Keras"
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"#### The base Layer class in Keras"
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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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},
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"source": [
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"**A `Dense` layer implemented as a `Layer` subclass**"
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"colab_type": "text"
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"source": [
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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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"source": [
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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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"**Calling `fit` with NumPy data**"
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"**Calling `fit()` with NumPy data**"
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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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"source": [
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"**Using the validation data argument**"
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"**Using the `validation_data` argument**"
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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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"source": [
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"## Chapter summary"
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"## Summary"
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‎chapter04_getting-started-with-neural-networks.ipynb

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"colab_type": "text"
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"source": [
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"# Getting started with neural networks: classification and regression"
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"# Getting started with neural networks: Classification and regression"
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"colab_type": "text"
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"source": [
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"## Classifying movie reviews: a binary classification example"
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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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"## Predicting house prices: a regression example"
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"## Predicting house prices: A regression 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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"colab_type": "text"
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"## Generalization: the goal of machine learning"
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"## Generalization: The goal of machine learning"
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"outputs": [],
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"from\u00a0keras\u00a0import\u00a0regularizers\n",
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"from tensorflow.keras import regularizers\n",
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"regularizers.l1(0.001)\n",
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"regularizers.l1_l2(l1=0.001,\u00a0l2=0.001)"
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"regularizers.l1_l2(l1=0.001, l2=0.001)"
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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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