Generative Deep Learning. 2nd Edition

Spis treści książki

• Foreword
• Preface
  • Objective and Approach
  • Prerequisites
  • Roadmap
  • Changes in the Second Edition
  • Other Resources
  • Conventions Used in This Book
  • Codebase
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Introduction to Generative Deep Learning
• 1. Generative Modeling
  • What Is Generative Modeling?
    • Generative Versus Discriminative Modeling
    • The Rise of Generative Modeling
    • Generative Modeling and AI
  • Our First Generative Model
    • Hello World!
    • The Generative Modeling Framework
    • Representation Learning
  • Core Probability Theory
  • Generative Model Taxonomy
  • The Generative Deep Learning Codebase
    • Cloning the Repository
    • Using Docker
    • Running on a GPU
  • Summary
• 2. Deep Learning
  • Data for Deep Learning
  • Deep Neural Networks
    • What Is a Neural Network?
    • Learning High-Level Features
    • TensorFlow and Keras
  • Multilayer Perceptron (MLP)
    • Preparing the Data
    • Building the Model
      • Layers
      • Activation functions
      • Inspecting the model
    • Compiling the Model
      • Loss functions
      • Optimizers
    • Training the Model
    • Evaluating the Model
  • Convolutional Neural Network (CNN)
    • Convolutional Layers
      • Stride
      • Padding
      • Stacking convolutional layers
      • Inspecting the model
    • Batch Normalization
      • Covariate shift
      • Training using batch normalization
      • Prediction using batch normalization
    • Dropout
    • Building the CNN
    • Training and Evaluating the CNN
  • Summary
• II. Methods
• 3. Variational Autoencoders
  • Introduction
  • Autoencoders
    • The Fashion-MNIST Dataset
    • The Autoencoder Architecture
    • The Encoder
    • The Decoder
    • Joining the Encoder to the Decoder
    • Reconstructing Images
    • Visualizing the Latent Space
    • Generating New Images
  • Variational Autoencoders
    • The Encoder
      • Building the VAE encoder
    • The Loss Function
    • Training the Variational Autoencoder
    • Analysis of the Variational Autoencoder
  • Exploring the Latent Space
    • The CelebA Dataset
    • Training the Variational Autoencoder
    • Analysis of the Variational Autoencoder
    • Generating New Faces
    • Latent Space Arithmetic
    • Morphing Between Faces
  • Summary
• 4. Generative Adversarial Networks
  • Introduction
  • Deep Convolutional GAN (DCGAN)
    • The Bricks Dataset
    • The Discriminator
    • The Generator
    • Training the DCGAN
    • Analysis of the DCGAN
    • GAN Training: Tips and Tricks
      • Discriminator overpowers the generator
      • Generator overpowers the discriminator
      • Uninformative loss
      • Hyperparameters
      • Tackling GAN challenges
  • Wasserstein GAN with Gradient Penalty (WGAN-GP)
    • Wasserstein Loss
    • The Lipschitz Constraint
    • Enforcing the Lipschitz Constraint
    • The Gradient Penalty Loss
    • Training the WGAN-GP
    • Analysis of the WGAN-GP
  • Conditional GAN (CGAN)
    • CGAN Architecture
    • Training the CGAN
    • Analysis of the CGAN
  • Summary
• 5. Autoregressive Models
  • Introduction
  • Long Short-Term Memory Network (LSTM)
    • The Recipes Dataset
    • Working with Text Data
    • Tokenization
    • Creating the Training Set
    • The LSTM Architecture
    • The Embedding Layer
    • The LSTM Layer
    • The LSTM Cell
    • Training the LSTM
    • Analysis of the LSTM
  • Recurrent Neural Network (RNN) Extensions
    • Stacked Recurrent Networks
    • Gated Recurrent Units
    • Bidirectional Cells
  • PixelCNN
    • Masked Convolutional Layers
    • Residual Blocks
    • Training the PixelCNN
    • Analysis of the PixelCNN
    • Mixture Distributions
  • Summary
• 6. Normalizing Flow Models
  • Introduction
  • Normalizing Flows
    • Change of Variables
    • The Jacobian Determinant
    • The Change of Variables Equation
  • RealNVP
    • The Two Moons Dataset
    • Coupling Layers
      • Passing data through a coupling layer
      • Stacking coupling layers
    • Training the RealNVP Model
    • Analysis of the RealNVP Model
  • Other Normalizing Flow Models
    • GLOW
    • FFJORD
  • Summary
• 7. Energy-Based Models
  • Introduction
  • Energy-Based Models
    • The MNIST Dataset
    • The Energy Function
    • Sampling Using Langevin Dynamics
    • Training with Contrastive Divergence
    • Analysis of the Energy-Based Model
    • Other Energy-Based Models
  • Summary
• 8. Diffusion Models
  • Introduction
  • Denoising Diffusion Models (DDM)
    • The Flowers Dataset
    • The Forward Diffusion Process
    • The Reparameterization Trick
    • Diffusion Schedules
    • The Reverse Diffusion Process
    • The U-Net Denoising Model
      • Sinusoidal embedding
      • ResidualBlock
      • DownBlocks and UpBlocks
    • Training the Diffusion Model
    • Sampling from the Denoising Diffusion Model
    • Analysis of the Diffusion Model
      • Generating images
      • Adjusting the number of diffusion steps
      • Interpolating between images
  • Summary
• III. Applications
• 9. Transformers
  • Introduction
  • GPT
    • The Wine Reviews Dataset
    • Attention
    • Queries, Keys, and Values
    • Multihead Attention
    • Causal Masking
    • The Transformer Block
    • Positional Encoding
    • Training GPT
    • Analysis of GPT
      • Generating text
      • Viewing the attention scores
  • Other Transformers
    • T5
    • GPT-3 and GPT-4
    • ChatGPT
  • Summary
• 10. Advanced GANs
  • Introduction
  • ProGAN
    • Progressive Training
      • Minibatch standard deviation
      • Equalized learning rates
      • Pixelwise normalization
    • Outputs
  • StyleGAN
    • The Mapping Network
    • The Synthesis Network
      • Adaptive instance normalization
      • Style mixing
      • Stochastic variation
    • Outputs from StyleGAN
  • StyleGAN2
    • Weight Modulation and Demodulation
    • Path Length Regularization
    • No Progressive Growing
    • Outputs from StyleGAN2
  • Other Important GANs
    • Self-Attention GAN (SAGAN)
    • BigGAN
    • VQ-GAN
    • ViT VQ-GAN
  • Summary
• 11. Music Generation
  • Introduction
  • Transformers for Music Generation
    • The Bach Cello Suite Dataset
    • Parsing MIDI Files
    • Tokenization
    • Creating the Training Set
    • Sine Position Encoding
    • Multiple Inputs and Outputs
    • Analysis of the Music-Generating Transformer
    • Tokenization of Polyphonic Music
      • Grid tokenization
      • Event-based tokenization
  • MuseGAN
    • The Bach Chorale Dataset
    • The MuseGAN Generator
      • The temporal network
      • Chords, style, melody, and groove
      • The bar generator
      • Putting it all together
    • The MuseGAN Critic
    • Analysis of the MuseGAN
  • Summary
• 12. World Models
  • Introduction
  • Reinforcement Learning
    • The CarRacing Environment
  • World Model Overview
    • Architecture
      • The VAE
      • The MDN-RNN
      • The controller
    • Training
  • Collecting Random Rollout Data
  • Training the VAE
    • The VAE Architecture
    • Exploring the VAE
      • The VAE model
      • The encoder models
      • The decoder model
  • Collecting Data to Train the MDN-RNN
  • Training the MDN-RNN
    • The MDN-RNN Architecture
    • Sampling from the MDN-RNN
  • Training the Controller
    • The Controller Architecture
    • CMA-ES
    • Parallelizing CMA-ES
  • In-Dream Training
  • Summary
• 13. Multimodal Models
  • Introduction
  • DALL.E 2
    • Architecture
    • The Text Encoder
    • CLIP
    • The Prior
      • Autoregressive prior
      • Diffusion prior
    • The Decoder
      • GLIDE
      • Upsampler
    • Examples from DALL.E 2
      • Image variations
      • Importance of the prior
      • Limitations
  • Imagen
    • Architecture
    • DrawBench
    • Examples from Imagen
  • Stable Diffusion
    • Architecture
    • Examples from Stable Diffusion
  • Flamingo
    • Architecture
    • The Vision Encoder
    • The Perceiver Resampler
    • The Language Model
    • Examples from Flamingo
  • Summary
• 14. Conclusion
  • Timeline of Generative AI
    • 20142017: The VAE and GAN Era
    • 20182019: The Transformer Era
    • 20202022: The Big Model Era
  • The Current State of Generative AI
    • Large Language Models
    • Text-to-Code Models
    • Text-to-Image Models
    • Other Applications
  • The Future of Generative AI
    • Generative AI in Everyday Life
    • Generative AI in the Workplace
    • Generative AI in Education
    • Generative AI Ethics and Challenges
  • Final Thoughts
• Index

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