Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems. 2nd Edition AurĂŠlien GĂŠron

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Spis treści książki

• Preface
  • The Machine Learning Tsunami
  • Machine Learning in Your Projects
  • Objective and Approach
  • Prerequisites
  • Roadmap
  • Changes in the Second Edition
  • Other Resources
  • Conventions Used in This Book
  • Code Examples
  • Using Code Examples
  • 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
    • Supervised/Unsupervised Learning
      • Supervised learning
      • Unsupervised learning
      • Semisupervised learning
      • Reinforcement Learning
    • Batch and Online Learning
      • Batch learning
      • Online learning
    • Instance-Based Versus Model-Based Learning
      • Instance-based learning
      • Model-based learning
  • 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
    • Create the Workspace
    • Download the Data
    • Take a Quick Look at the Data Structure
    • Create a Test Set
  • Discover and Visualize the Data to Gain Insights
    • Visualizing Geographical Data
    • Looking for Correlations
    • Experimenting with Attribute Combinations
  • Prepare the Data for Machine Learning Algorithms
    • Data Cleaning
    • Handling Text and Categorical Attributes
    • Custom Transformers
    • Feature Scaling
    • Transformation Pipelines
  • Select and Train a Model
    • Training and Evaluating on the Training Set
    • Better Evaluation Using Cross-Validation
  • Fine-Tune Your Model
    • Grid Search
    • Randomized Search
    • Ensemble Methods
    • Analyze 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 Matrix
    • Precision and Recall
    • 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
    • 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
    • Computational Complexity
  • SVM Regression
  • Under the Hood
    • Decision Function and Predictions
    • Training Objective
    • Quadratic Programming
    • The Dual Problem
    • Kernelized SVMs
    • Online 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
  • Instability
  • 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
  • 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
  • Kernel PCA
    • Selecting a Kernel and Tuning Hyperparameters
  • LLE
  • Other Dimensionality Reduction Techniques
  • Exercises
• 9. Unsupervised Learning Techniques
  • Clustering
    • 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 Preprocessing
    • Using Clustering for Semi-Supervised Learning
    • DBSCAN
    • Other Clustering Algorithms
  • Gaussian Mixtures
    • Anomaly Detection Using Gaussian Mixtures
    • 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
    • Installing TensorFlow 2
    • 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
    • Nonsaturating Activation Functions
    • 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 Optimization
    • Nesterov Accelerated Gradient
    • AdaGrad
    • RMSProp
    • Adam and Nadam Optimization
    • 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 Data API
    • Chaining Transformations
    • Shuffling the Data
      • Interleaving lines from multiple files
    • Preprocessing the Data
    • Putting Everything Together
    • Prefetching
    • Using the Dataset with tf.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
  • Preprocessing the Input Features
    • Encoding Categorical Features Using One-Hot Vectors
    • Encoding Categorical Features Using Embeddings
    • Keras Preprocessing Layers
  • TF Transform
  • The TensorFlow Datasets (TFDS) Project
  • Exercises
• 14. Deep Computer Vision Using Convolutional Neural Networks
  • The Architecture of the Visual Cortex
  • Convolutional Layers
    • Filters
    • Stacking Multiple Feature Maps
    • TensorFlow Implementation
    • Memory Requirements
  • Pooling Layers
    • TensorFlow Implementation
  • CNN Architectures
    • LeNet-5
    • AlexNet
    • GoogLeNet
    • VGGNet
    • ResNet
    • Xception
    • SENet
  • 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 (YOLO)
  • 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
    • Baseline Metrics
    • Implementing a Simple RNN
    • Deep RNNs
    • Forecasting Several Time Steps Ahead
  • Handling Long Sequences
    • Fighting the Unstable Gradients Problem
    • Tackling the Short-Term Memory Problem
      • LSTM cells
      • Peephole connections
      • 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
    • How to Split a Sequential Dataset
    • Chopping the Sequential Dataset into Multiple Windows
    • Building and Training the Char-RNN Model
    • Using the Char-RNN Model
    • Generating Fake Shakespearean Text
    • Stateful RNN
  • Sentiment Analysis
    • Masking
    • Reusing Pretrained Embeddings
  • An EncoderDecoder Network for Neural Machine Translation
    • Bidirectional RNNs
    • Beam Search
  • Attention Mechanisms
    • Visual Attention
    • Attention Is All You Need: The Transformer Architecture
      • Positional embeddings
      • Multi-Head Attention
  • Recent Innovations in Language Models
  • Exercises
• 17. Representation Learning and Generative Learning Using Autoencoders and GANs
  • 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
  • Recurrent 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
  • 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
  • The TF-Agents Library
    • Installing TF-Agents
    • TF-Agents Environments
    • Environment Specifications
    • Environment Wrappers and Atari Preprocessing
    • Training Architecture
    • Creating the Deep Q-Network
    • Creating the DQN Agent
    • Creating the Replay Buffer and the Corresponding Observer
    • Creating Training Metrics
    • Creating the Collect Driver
    • Creating the Dataset
    • Creating the Training Loop
  • 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 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 GCP AI Platform
    • Using the Prediction Service
  • Deploying a Model to a Mobile or Embedded Device
  • Using GPUs to Speed Up Computations
    • Getting Your Own GPU
    • Using a GPU-Equipped Virtual Machine
    • Colaboratory
    • 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 Google Cloud AI Platform
    • Black Box Hyperparameter Tuning on AI Platform
  • Exercises
  • Thank You!
• A. Exercise Solutions
  • Chapter 1: The Machine Learning Landscape
  • Chapter 2: End-to-End Machine Learning Project
  • Chapter 3: Classification
  • Chapter 4: Training Models
  • Chapter 5: Support Vector Machines
  • Chapter 6: Decision Trees
  • Chapter 7: Ensemble Learning and Random Forests
  • Chapter 8: Dimensionality Reduction
  • Chapter 9: Unsupervised Learning Techniques
  • Chapter 10: Introduction to Artificial Neural Networks with Keras
  • Chapter 11: Training Deep Neural Networks
  • Chapter 12: Custom Models and Training with TensorFlow
  • Chapter 13: Loading and Preprocessing Data with TensorFlow
  • Chapter 14: Deep Computer Vision Using Convolutional Neural Networks
  • Chapter 15: Processing Sequences Using RNNs and CNNs
  • Chapter 16: Natural Language Processing with RNNs and Attention
  • Chapter 17: Representation Learning and Generative Learning Using Autoencoders and GANs
  • Chapter 18: Reinforcement Learning
  • Chapter 19: Training and Deploying TensorFlow Models at Scale
• B. 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!
• C. SVM Dual Problem
• D. Autodiff
  • Manual Differentiation
  • Finite Difference Approximation
  • Forward-Mode Autodiff
  • Reverse-Mode Autodiff
• E. Other Popular ANN Architectures
  • Hopfield Networks
  • Boltzmann Machines
  • Restricted Boltzmann Machines
  • Deep Belief Nets
  • Self-Organizing Maps
• F. Special Data Structures
  • Strings
  • Ragged Tensors
  • Sparse Tensors
  • Tensor Arrays
  • Sets
  • Queues
• G. 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 tf.keras (or Not)
• Index

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