Hands-On Machine Learning with Scikit-Learn and PyTorch. Concepts, Tools, and Techniques to Build Intelligent Systems Aurélien Géron

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Spis treści książki

• Preface
  • Machine Learning in Your Projects
  • Objective and Approach
  • Code Examples
  • Prerequisites
  • Roadmap
  • Changes Between the TensorFlow and PyTorch Versions
  • 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
    • Deployment Issues
    • 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. 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
• 6. 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
• 7. 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
• 8. Unsupervised Learning Techniques
  • Clustering Algorithms: k-means and DBSCAN
    • k-Means Clustering
      • 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
• 9. Introduction to Artificial Neural Networks
  • From Biological to Artificial Neurons
    • Biological Neurons
    • Logical Computations with Neurons
    • The Perceptron
    • The Multilayer Perceptron and Backpropagation
  • Building and Training MLPs with Scikit-Learn
    • Regression MLPs
    • Classification MLPs
  • Hyperparameter Tuning Guidelines
    • Number of Hidden Layers
    • Number of Neurons per Hidden Layer
    • Learning Rate
    • Batch Size
    • Other Hyperparameters
  • Exercises
• 10. Building Neural Networks with PyTorch
  • PyTorch Fundamentals
    • PyTorch Tensors
    • Hardware Acceleration
    • Autograd
  • Implementing Linear Regression
    • Linear Regression Using Tensors and Autograd
    • Linear Regression Using PyTorchs High-Level API
  • Implementing a Regression MLP
  • Implementing Mini-Batch Gradient Descent Using DataLoaders
  • Model Evaluation
  • Building Nonsequential Models Using Custom Modules
    • Building Models with Multiple Inputs
    • Building Models with Multiple Outputs
  • Building an Image Classifier with PyTorch
    • Using TorchVision to Load the Dataset
    • Building the Classifier
  • Fine-Tuning Neural Network Hyperparameters with Optuna
  • Saving and Loading PyTorch Models
  • Compiling and Optimizing a PyTorch Model
  • Exercises
• 11. Training Deep Neural Networks
  • The Vanishing/Exploding Gradients Problems
    • Glorot Initialization and He Initialization
    • Better Activation Functions
      • Leaky ReLU
      • ELU and SELU
      • GELU, Swish, SwiGLU, Mish, and RELU2
    • Batch Normalization
      • Implementing batch norm with PyTorch
      • Batch norm 1D, 2D, and 3D
    • Layer Normalization
    • Gradient Clipping
  • Reusing Pretrained Layers
    • Transfer Learning with PyTorch
    • Unsupervised Pretraining
    • Pretraining on an Auxiliary Task
  • Faster Optimizers
    • Momentum
    • Nesterov Accelerated Gradient
    • AdaGrad
    • RMSProp
    • Adam
    • AdaMax
    • NAdam
    • AdamW
  • Learning Rate Scheduling
    • Exponential Scheduling
    • Cosine Annealing
    • Performance Scheduling
    • Warming Up the Learning Rate
    • Cosine Annealing with Warm Restarts
    • 1cycle Scheduling
  • Avoiding Overfitting Through Regularization
    • 1 and 2 Regularization
    • Dropout
    • Monte Carlo Dropout
    • Max-Norm Regularization
  • Practical Guidelines
  • Exercises
• 12. Deep Computer Vision Using Convolutional Neural Networks
  • The Architecture of the Visual Cortex
  • Convolutional Layers
    • Filters
    • Stacking Multiple Feature Maps
    • Implementing Convolutional Layers with PyTorch
  • Pooling Layers
  • Implementing Pooling Layers with PyTorch
  • CNN Architectures
    • LeNet-5
    • AlexNet
    • GoogLeNet
    • ResNet
    • Xception
    • SENet
    • Other Noteworthy Architectures
    • Choosing the Right CNN Architecture
    • GPU RAM Requirements: Inference Versus Training
    • Reversible Residual Networks (RevNets)
  • Implementing a ResNet-34 CNN Using PyTorch
  • Using TorchVisions Pretrained Models
  • Pretrained Models for Transfer Learning
  • Classification and Localization
  • Object Detection
    • Fully Convolutional Networks
    • You Only Look Once
  • Object Tracking
  • Semantic Segmentation
  • Exercises
• 13. 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
• 14. Natural Language Processing with RNNs and Attention
  • Generating Shakespearean Text Using a Character RNN
    • Creating the Training Dataset
    • Embeddings
    • Building and Training the Char-RNN Model
    • Generating Fake Shakespearean Text
  • Sentiment Analysis Using Hugging Face Libraries
    • Tokenization Using the Hugging Face Tokenizers Library
    • Reusing Pretrained Tokenizers
    • Building and Training a Sentiment Analysis Model
    • Bidirectional RNNs
    • Reusing Pretrained Embeddings and Language Models
    • Task-Specific Classes
    • The Trainer API
    • Hugging Face Pipelines
  • An Encoder-Decoder Network for Neural Machine Translation
  • Beam Search
  • Attention Mechanisms
  • Exercises
• 15. Transformers for Natural Language Processing and Chatbots
  • Attention Is All You Need: The Original Transformer Architecture
    • Positional Encodings
    • Multi-Head Attention
    • Building the Rest of the Transformer
  • Building an English-to-Spanish Transformer
  • Encoder-Only Transformers for Natural Language Understanding
    • BERTs Architecture
    • BERT Pretraining
    • BERT Fine-Tuning
    • Other Encoder-Only Models
      • RoBERTa by Facebook AI, July 2019 (125M to 355M parameters)
      • DistilBERT by Hugging Face, October 2019 (66M)
      • ALBERT by Google Research, December 2019 (12M235M)
      • ELECTRA by Google Research, March 2020 (14M335M)
      • DeBERTa by Microsoft, January 2021 (139M1.5B)
      • More encoder-only models on Hugging Face Hub
  • Decoder-Only Transformers
    • GPT-1 Architecture and Generative Pretraining
    • GPT-2 and Zero-Shot Learning
    • GPT-3, In-Context Learning, One-Shot Learning, and Few-Shot Learning
    • Using GPT-2 to Generate Text
    • Using GPT-2 for Question Answering
    • Downloading and Running an Even Larger Model: Mistral-7B
  • Turning a Large Language Model into a Chatbot
    • Fine-Tuning a Model for Chatting and Following Instructions Using SFT and RLHF
    • Direct Preference Optimization (DPO)
    • Fine-Tuning a Model Using the TRL Library
    • From a Chatbot Model to a Full Chatbot System
    • Model Context Protocol
    • Libraries and Tools
  • Encoder-Decoder Models
  • Exercises
• 16. Vision and Multimodal Transformers
  • Vision Transformers
    • RNNs with Visual Attention
    • DETR: A CNN-Transformer Hybrid for Object Detection
    • The Original ViT
      • Implementing a ViT from scratch using PyTorch
      • Fine-tuning a pretrained ViT using the Transformers library
    • Data-Efficient Image Transformer
    • Pyramid Vision Transformer for Dense Prediction Tasks
    • The Swin Transformer: A Fast and Versatile ViT
    • DINO: Self-Supervised Visual Representation Learning
    • Other Major Vision Models and Techniques
  • Multimodal Transformers
    • VideoBERT: A BERT Variant for Text plus Video
    • ViLBERT: A Dual-Stream Transformer for Text plus Image
    • CLIP: A Dual-Encoder Text plus Image Model Trained with Contrastive Pretraining
    • DALLE: Generating Images from Text Prompts
    • Perceiver: Bridging High-Resolution Modalities with Latent Spaces
    • Perceiver IO: A Flexible Output Mechanism for the Perceiver
    • Flamingo: Open-Ended Visual Dialogue
    • BLIP and BLIP-2
  • Other Multimodal Models
  • Exercises
• 17. Speeding Up Transformers
• 18. Autoencoders, GANs, and Diffusion Models
  • Efficient Data Representations
  • Performing PCA with an Undercomplete Linear Autoencoder
  • Stacked Autoencoders
    • Implementing a Stacked Autoencoder Using PyTorch
    • Visualizing the Reconstructions
    • Anomaly Detection Using Autoencoders
    • 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
    • Discrete Variational Autoencoders
  • Generative Adversarial Networks
    • The Difficulties of Training GANs
  • Diffusion Models
  • Exercises
• 19. Reinforcement Learning
  • What Is Reinforcement Learning?
  • Policy Gradients
    • Introduction to the Gymnasium Library
    • Neural Network Policies
    • Evaluating Actions: The Credit Assignment Problem
    • Solving the CartPole Using Policy Gradients
  • Value-Based Methods
    • Markov Decision Processes
    • Temporal Difference Learning
    • Q-Learning
    • Exploration Policies
    • Approximate Q-Learning and Deep Q-Learning
    • Implementing Deep Q-Learning
    • DQN Improvements
  • Actor-Critic Algorithms
  • Mastering Atari Breakout Using the Stable-Baselines3 PPO Implementation
  • Overview of Some Popular RL Algorithms
  • Exercises
  • Thank You!
• A. Autodiff
  • Manual Differentiation
  • Finite Difference Approximation
  • Forward-Mode Autodiff
  • Reverse-Mode Autodiff
• B. Mixed Precision and Quantization
  • Common Number Representations
  • Reduced Precision Models
  • Mixed-Precision Training
  • Quantization
    • Linear Quantization
    • Post-Training Quantization Using torch.ao.quantization
      • Dynamic quantization
      • Static quantization
    • Quantization-Aware Training (QAT)
    • Quantizing LLMs Using the bitsandbytes Library
  • Using Pre-Quantized Models
• Index

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