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Spis treści

Generative Deep Learning. Teaching Machines to Paint, Write, Compose, and Play eBook -- spis treści

• Preface
  • Objective and Approach
  • Prerequisites
  • Other Resources
  • Conventions Used in This Book
  • 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
    • Advances in Machine Learning
    • The Rise of Generative Modeling
    • The Generative Modeling Framework
  • Probabilistic Generative Models
    • Hello Wrodl!
    • Your First Probabilistic Generative Model
    • Naive Bayes
    • Hello Wrodl! Continued
  • The Challenges of Generative Modeling
    • Representation Learning
  • Setting Up Your Environment
  • Summary
• 2. Deep Learning
  • Structured and Unstructured Data
  • Deep Neural Networks
    • Keras and TensorFlow
  • Your First Deep Neural Network
    • Loading the Data
    • Building the Model
    • Compiling the Model
    • Training the Model
    • Evaluating the Model
  • Improving the Model
    • Convolutional Layers
    • Batch Normalization
    • Dropout Layers
    • Putting It All Together
  • Summary
• 3. Variational Autoencoders
  • The Art Exhibition
  • Autoencoders
    • Your First Autoencoder
    • The Encoder
    • The Decoder
    • Joining the Encoder to the Decoder
    • Analysis of the Autoencoder
  • The Variational Art Exhibition
  • Building a Variational Autoencoder
    • The Encoder
    • The Loss Function
    • Analysis of the Variational Autoencoder
  • Using VAEs to Generate Faces
    • Training the VAE
    • Analysis of the VAE
    • Generating New Faces
    • Latent Space Arithmetic
    • Morphing Between Faces
  • Summary
• 4. Generative Adversarial Networks
  • Ganimals
  • Introduction to GANs
  • Your First GAN
    • The Discriminator
    • The Generator
    • Training the GAN
  • GAN Challenges
    • Oscillating Loss
    • Mode Collapse
    • Uninformative Loss
    • Hyperparameters
    • Tackling the GAN Challenges
  • Wasserstein GAN
    • Wasserstein Loss
    • The Lipschitz Constraint
    • Weight Clipping
    • Training the WGAN
    • Analysis of the WGAN
  • WGAN-GP
    • The Gradient Penalty Loss
    • Analysis of WGAN-GP
  • Summary
• II. Teaching Machines to Paint, Write, Compose, and Play
• 5. Paint
  • Apples and Organges
  • CycleGAN
  • Your First CycleGAN
    • Overview
    • The Generators (U-Net)
    • The Discriminators
    • Compiling the CycleGAN
    • Training the CycleGAN
    • Analysis of the CycleGAN
  • Creating a CycleGAN to Paint Like Monet
    • The Generators (ResNet)
    • Analysis of the CycleGAN
  • Neural Style Transfer
    • Content Loss
    • Style Loss
    • Total Variance Loss
    • Running the Neural Style Transfer
    • Analysis of the Neural Style Transfer Model
  • Summary
• 6. Write
  • The Literary Society for Troublesome Miscreants
  • Long Short-Term Memory Networks
  • Your First LSTM Network
    • Tokenization
    • Building the Dataset
    • The LSTM Architecture
    • The Embedding Layer
    • The LSTM Layer
    • The LSTM Cell
  • Generating New Text
  • RNN Extensions
    • Stacked Recurrent Networks
    • Gated Recurrent Units
    • Bidirectional Cells
  • EncoderDecoder Models
  • A Question and Answer Generator
    • A Question-Answer Dataset
    • Model Architecture
    • Inference
    • Model Results
  • Summary
• 7. Compose
  • Preliminaries
    • Musical Notation
  • Your First Music-Generating RNN
    • Attention
    • Building an Attention Mechanism in Keras
    • Analysis of the RNN with Attention
    • Attention in EncoderDecoder Networks
    • Generating Polyphonic Music
  • The Musical Organ
  • Your First MuseGAN
  • The MuseGAN Generator
    • Chords, Style, Melody, and Groove
      • Chords
      • Style
      • Melody
      • Groove
    • The Bar Generator
    • Putting It All Together
  • The Critic
  • Analysis of the MuseGAN
  • Summary
• 8. Play
  • Reinforcement Learning
    • OpenAI Gym
  • World Model Architecture
    • The Variational Autoencoder
    • The MDN-RNN
    • The Controller
  • Setup
  • Training Process Overview
  • Collecting Random Rollout Data
  • Training the VAE
    • The VAE Architecture
    • Exploring the VAE
      • The full model
      • The encoder models
      • The decoder model
  • Collecting Data to Train the RNN
  • Training the MDN-RNN
    • The MDN-RNN Architecture
    • Sampling the Next z and Reward from the MDN-RNN
    • The MDN-RNN Loss Function
  • Training the Controller
    • The Controller Architecture
    • CMA-ES
    • Parallelizing CMA-ES
    • Output from the Controller Training
  • In-Dream Training
    • In-Dream Training the Controller
    • Challenges of In-Dream Training
  • Summary
• 9. The Future of Generative Modeling
  • Five Years of Progress
  • The Transformer
    • Positional Encoding
    • Multihead Attention
    • The Decoder
    • Analysis of the Transformer
    • BERT
    • GPT-2
    • MuseNet
  • Advances in Image Generation
    • ProGAN
    • Self-Attention GAN (SAGAN)
    • BigGAN
    • StyleGAN
  • Applications of Generative Modeling
    • AI Art
    • AI Music
• 10. Conclusion
• Index