Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd Edition

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• Preface
  • The Machine Learning Tsunami
  • Machine Learning in Your Projects
  • Objective and Approach
  • Code Examples
  • Prerequisites
  • Roadmap
  • Changes Between the First and the Second Edition
  • Changes Between the Second and the Third Edition
  • Other Resources
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. The Fundamentals of Machine Learning
• 1. The Machine Learning Landscape
  • What Is Machine Learning?
  • Why Use Machine Learning?
  • Examples of Applications
  • Types of Machine Learning Systems
    • Training Supervision
      • Supervised learning
      • Unsupervised learning
      • Semi-supervised learning
      • Self-supervised learning
      • Reinforcement learning
    • Batch Versus Online Learning
      • Batch learning
      • Online learning
    • Instance-Based Versus Model-Based Learning
      • Instance-based learning
      • Model-based learning and a typical machine learning workflow
  • Main Challenges of Machine Learning
    • Insufficient Quantity of Training Data
    • Nonrepresentative Training Data
    • Poor-Quality Data
    • Irrelevant Features
    • Overfitting the Training Data
    • Underfitting the Training Data
    • Stepping Back
  • Testing and Validating
    • Hyperparameter Tuning and Model Selection
    • Data Mismatch
  • Exercises
• 2. End-to-End Machine Learning Project
  • Working with Real Data
  • Look at the Big Picture
    • Frame the Problem
    • Select a Performance Measure
    • Check the Assumptions
  • Get the Data
    • Running the Code Examples Using Google Colab
    • Saving Your Code Changes and Your Data
    • The Power and Danger of Interactivity
    • Book Code Versus Notebook Code
    • Download the Data
    • Take a Quick Look at the Data Structure
    • Create a Test Set
  • Explore and Visualize the Data to Gain Insights
    • Visualizing Geographical Data
    • Look for Correlations
    • Experiment with Attribute Combinations
  • Prepare the Data for Machine Learning Algorithms
    • Clean the Data
    • Handling Text and Categorical Attributes
    • Feature Scaling and Transformation
    • Custom Transformers
    • Transformation Pipelines
  • Select and Train a Model
    • Train and Evaluate on the Training Set
    • Better Evaluation Using Cross-Validation
  • Fine-Tune Your Model
    • Grid Search
    • Randomized Search
    • Ensemble Methods
    • Analyzing the Best Models and Their Errors
    • Evaluate Your System on the Test Set
  • Launch, Monitor, and Maintain Your System
  • Try It Out!
  • Exercises
• 3. Classification
  • MNIST
  • Training a Binary Classifier
  • Performance Measures
    • Measuring Accuracy Using Cross-Validation
    • Confusion Matrices
    • Precision and Recall
    • The Precision/Recall Trade-off
    • The ROC Curve
  • Multiclass Classification
  • Error Analysis
  • Multilabel Classification
  • Multioutput Classification
  • Exercises
• 4. Training Models
  • Linear Regression
    • The Normal Equation
    • Computational Complexity
  • Gradient Descent
    • Batch Gradient Descent
    • Stochastic Gradient Descent
    • Mini-Batch Gradient Descent
  • Polynomial Regression
  • Learning Curves
  • Regularized Linear Models
    • Ridge Regression
    • Lasso Regression
    • Elastic Net Regression
    • Early Stopping
  • Logistic Regression
    • Estimating Probabilities
    • Training and Cost Function
    • Decision Boundaries
    • Softmax Regression
  • Exercises
• 5. Support Vector Machines
  • Linear SVM Classification
    • Soft Margin Classification
  • Nonlinear SVM Classification
    • Polynomial Kernel
    • Similarity Features
    • Gaussian RBF Kernel
    • SVM Classes and Computational Complexity
  • SVM Regression
  • Under the Hood of Linear SVM Classifiers
  • The Dual Problem
    • Kernelized SVMs
  • Exercises
• 6. Decision Trees
  • Training and Visualizing a Decision Tree
  • Making Predictions
  • Estimating Class Probabilities
  • The CART Training Algorithm
  • Computational Complexity
  • Gini Impurity or Entropy?
  • Regularization Hyperparameters
  • Regression
  • Sensitivity to Axis Orientation
  • Decision Trees Have a High Variance
  • Exercises
• 7. Ensemble Learning and Random Forests
  • Voting Classifiers
  • Bagging and Pasting
    • Bagging and Pasting in Scikit-Learn
    • Out-of-Bag Evaluation
    • Random Patches and Random Subspaces
  • Random Forests
    • Extra-Trees
    • Feature Importance
  • Boosting
    • AdaBoost
    • Gradient Boosting
    • Histogram-Based Gradient Boosting
  • Stacking
  • Exercises
• 8. Dimensionality Reduction
  • The Curse of Dimensionality
  • Main Approaches for Dimensionality Reduction
    • Projection
    • Manifold Learning
  • PCA
    • Preserving the Variance
    • Principal Components
    • Projecting Down to d Dimensions
    • Using Scikit-Learn
    • Explained Variance Ratio
    • Choosing the Right Number of Dimensions
    • PCA for Compression
    • Randomized PCA
    • Incremental PCA
  • Random Projection
  • LLE
  • Other Dimensionality Reduction Techniques
  • Exercises
• 9. Unsupervised Learning Techniques
  • Clustering Algorithms: k-means and DBSCAN
    • k-means
      • The k-means algorithm
      • Centroid initialization methods
      • Accelerated k-means and mini-batch k-means
      • Finding the optimal number of clusters
    • Limits of k-means
    • Using Clustering for Image Segmentation
    • Using Clustering for Semi-Supervised Learning
    • DBSCAN
    • Other Clustering Algorithms
  • Gaussian Mixtures
    • Using Gaussian Mixtures for Anomaly Detection
    • Selecting the Number of Clusters
    • Bayesian Gaussian Mixture Models
    • Other Algorithms for Anomaly and Novelty Detection
  • Exercises
• II. Neural Networks and Deep Learning
• 10. Introduction to Artificial Neural Networks with Keras
  • From Biological to Artificial Neurons
    • Biological Neurons
    • Logical Computations with Neurons
    • The Perceptron
    • The Multilayer Perceptron and Backpropagation
    • Regression MLPs
    • Classification MLPs
  • Implementing MLPs with Keras
    • Building an Image Classifier Using the Sequential API
      • Using Keras to load the dataset
      • Creating the model using the sequential API
      • Compiling the model
      • Training and evaluating the model
      • Using the model to make predictions
    • Building a Regression MLP Using the Sequential API
    • Building Complex Models Using the Functional API
    • Using the Subclassing API to Build Dynamic Models
    • Saving and Restoring a Model
    • Using Callbacks
    • Using TensorBoard for Visualization
  • Fine-Tuning Neural Network Hyperparameters
    • Number of Hidden Layers
    • Number of Neurons per Hidden Layer
    • Learning Rate, Batch Size, and Other Hyperparameters
  • Exercises
• 11. Training Deep Neural Networks
  • The Vanishing/Exploding Gradients Problems
    • Glorot and He Initialization
    • Better Activation Functions
      • Leaky ReLU
      • ELU and SELU
      • GELU, Swish, and Mish
    • Batch Normalization
      • Implementing batch normalization with Keras
    • Gradient Clipping
  • Reusing Pretrained Layers
    • Transfer Learning with Keras
    • Unsupervised Pretraining
    • Pretraining on an Auxiliary Task
  • Faster Optimizers
    • Momentum
    • Nesterov Accelerated Gradient
    • AdaGrad
    • RMSProp
    • Adam
    • AdaMax
    • Nadam
    • AdamW
  • Learning Rate Scheduling
  • Avoiding Overfitting Through Regularization
    • 1 and 2 Regularization
    • Dropout
    • Monte Carlo (MC) Dropout
    • Max-Norm Regularization
  • Summary and Practical Guidelines
  • Exercises
• 12. Custom Models and Training with TensorFlow
  • A Quick Tour of TensorFlow
  • Using TensorFlow like NumPy
    • Tensors and Operations
    • Tensors and NumPy
    • Type Conversions
    • Variables
    • Other Data Structures
  • Customizing Models and Training Algorithms
    • Custom Loss Functions
    • Saving and Loading Models That Contain Custom Components
    • Custom Activation Functions, Initializers, Regularizers, and Constraints
    • Custom Metrics
    • Custom Layers
    • Custom Models
    • Losses and Metrics Based on Model Internals
    • Computing Gradients Using Autodiff
    • Custom Training Loops
  • TensorFlow Functions and Graphs
    • AutoGraph and Tracing
    • TF Function Rules
  • Exercises
• 13. Loading and Preprocessing Data with TensorFlow
  • The tf.data API
    • Chaining Transformations
    • Shuffling the Data
    • Interleaving Lines from Multiple Files
    • Preprocessing the Data
    • Putting Everything Together
    • Prefetching
    • Using the Dataset with Keras
  • The TFRecord Format
    • Compressed TFRecord Files
    • A Brief Introduction to Protocol Buffers
    • TensorFlow Protobufs
    • Loading and Parsing Examples
    • Handling Lists of Lists Using the SequenceExample Protobuf
  • Keras Preprocessing Layers
    • The Normalization Layer
    • The Discretization Layer
    • The CategoryEncoding Layer
    • The StringLookup Layer
    • The Hashing Layer
    • Encoding Categorical Features Using Embeddings
    • Text Preprocessing
    • Using Pretrained Language Model Components
    • Image Preprocessing Layers
  • The TensorFlow Datasets Project
  • Exercises
• 14. Deep Computer Vision Using Convolutional Neural Networks
  • The Architecture of the Visual Cortex
  • Convolutional Layers
    • Filters
    • Stacking Multiple Feature Maps
    • Implementing Convolutional Layers with Keras
    • Memory Requirements
  • Pooling Layers
  • Implementing Pooling Layers with Keras
  • CNN Architectures
    • LeNet-5
    • AlexNet
    • GoogLeNet
    • VGGNet
    • ResNet
    • Xception
    • SENet
    • Other Noteworthy Architectures
    • Choosing the Right CNN Architecture
  • Implementing a ResNet-34 CNN Using Keras
  • Using Pretrained Models from Keras
  • Pretrained Models for Transfer Learning
  • Classification and Localization
  • Object Detection
    • Fully Convolutional Networks
    • You Only Look Once
  • Object Tracking
  • Semantic Segmentation
  • Exercises
• 15. Processing Sequences Using RNNs and CNNs
  • Recurrent Neurons and Layers
    • Memory Cells
    • Input and Output Sequences
  • Training RNNs
  • Forecasting a Time Series
    • The ARMA Model Family
    • Preparing the Data for Machine Learning Models
    • Forecasting Using a Linear Model
    • Forecasting Using a Simple RNN
    • Forecasting Using a Deep RNN
    • Forecasting Multivariate Time Series
    • Forecasting Several Time Steps Ahead
    • Forecasting Using a Sequence-to-Sequence Model
  • Handling Long Sequences
    • Fighting the Unstable Gradients Problem
    • Tackling the Short-Term Memory Problem
      • LSTM cells
      • GRU cells
      • Using 1D convolutional layers to process sequences
      • WaveNet
  • Exercises
• 16. Natural Language Processing with RNNs and Attention
  • Generating Shakespearean Text Using a Character RNN
    • Creating the Training Dataset
    • Building and Training the Char-RNN Model
    • Generating Fake Shakespearean Text
    • Stateful RNN
  • Sentiment Analysis
    • Masking
    • Reusing Pretrained Embeddings and Language Models
  • An EncoderDecoder Network for Neural Machine Translation
    • Bidirectional RNNs
    • Beam Search
  • Attention Mechanisms
    • Attention Is All You Need: The Original Transformer Architecture
      • Positional encodings
      • Multi-head attention
  • An Avalanche of Transformer Models
  • Vision Transformers
  • Hugging Faces Transformers Library
  • Exercises
• 17. Autoencoders, GANs, and Diffusion Models
  • Efficient Data Representations
  • Performing PCA with an Undercomplete Linear Autoencoder
  • Stacked Autoencoders
    • Implementing a Stacked Autoencoder Using Keras
    • Visualizing the Reconstructions
    • Visualizing the Fashion MNIST Dataset
    • Unsupervised Pretraining Using Stacked Autoencoders
    • Tying Weights
    • Training One Autoencoder at a Time
  • Convolutional Autoencoders
  • Denoising Autoencoders
  • Sparse Autoencoders
  • Variational Autoencoders
  • Generating Fashion MNIST Images
  • Generative Adversarial Networks
    • The Difficulties of Training GANs
    • Deep Convolutional GANs
    • Progressive Growing of GANs
    • StyleGANs
  • Diffusion Models
  • Exercises
• 18. Reinforcement Learning
  • Learning to Optimize Rewards
  • Policy Search
  • Introduction to OpenAI Gym
  • Neural Network Policies
  • Evaluating Actions: The Credit Assignment Problem
  • Policy Gradients
  • Markov Decision Processes
  • Temporal Difference Learning
  • Q-Learning
    • Exploration Policies
    • Approximate Q-Learning and Deep Q-Learning
  • Implementing Deep Q-Learning
  • Deep Q-Learning Variants
    • Fixed Q-value Targets
    • Double DQN
    • Prioritized Experience Replay
    • Dueling DQN
  • Overview of Some Popular RL Algorithms
  • Exercises
• 19. Training and Deploying TensorFlow Models at Scale
  • Serving a TensorFlow Model
    • Using TensorFlow Serving
      • Exporting SavedModels
      • Installing and starting TensorFlow Serving
      • Querying TF Serving through the REST API
      • Querying TF Serving through the gRPC API
      • Deploying a new model version
    • Creating a Prediction Service on Vertex AI
    • Running Batch Prediction Jobs on Vertex AI
  • Deploying a Model to a Mobile or Embedded Device
  • Running a Model in a Web Page
  • Using GPUs to Speed Up Computations
    • Getting Your Own GPU
    • Managing the GPU RAM
    • Placing Operations and Variables on Devices
    • Parallel Execution Across Multiple Devices
  • Training Models Across Multiple Devices
    • Model Parallelism
    • Data Parallelism
      • Data parallelism using the mirrored strategy
      • Data parallelism with centralized parameters
        • Synchronous updates
        • Asynchronous updates
      • Bandwidth saturation
    • Training at Scale Using the Distribution Strategies API
    • Training a Model on a TensorFlow Cluster
    • Running Large Training Jobs on Vertex AI
    • Hyperparameter Tuning on Vertex AI
  • Exercises
  • Thank You!
• A. Machine Learning Project Checklist
  • Frame the Problem and Look at the Big Picture
  • Get the Data
  • Explore the Data
  • Prepare the Data
  • Shortlist Promising Models
  • Fine-Tune the System
  • Present Your Solution
  • Launch!
• B. Autodiff
  • Manual Differentiation
  • Finite Difference Approximation
  • Forward-Mode Autodiff
  • Reverse-Mode Autodiff
• C. Special Data Structures
  • Strings
  • Ragged Tensors
  • Sparse Tensors
  • Tensor Arrays
  • Sets
  • Queues
• D. TensorFlow Graphs
  • TF Functions and Concrete Functions
  • Exploring Function Definitions and Graphs
  • A Closer Look at Tracing
  • Using AutoGraph to Capture Control Flow
  • Handling Variables and Other Resources in TF Functions
  • Using TF Functions with Keras (or Not)
• Index

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