Generative Deep Learning Teaching Machines to Paint Write Compose and Play 1st Edition by David Foster ISBN 1098134141 9781098134143

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Product details:

ISBN 10: 1098134141

ISBN 13: 978-1098134143

Author:  David Foster 

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play 1st Edition: Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youâ??ll understand how to make your models learn more efficiently and become more creative.

• Discover how variational autoencoders can change facial expressions in photos
• Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
• Create recurrent generative models for text generation and learn how to improve the models using attention
• Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
• Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

 

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play 1st Edition Table of contents:

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
   • Summary

2. Deep Learning

   • Data for Deep Learning
   • Deep Neural Networks
   • TensorFlow and Keras
   • Multilayer Perceptron (MLP)
   • Convolutional Neural Network (CNN)
   • Summary

II. Methods

3. Variational Autoencoders

• • Introduction
  • Autoencoders
  • Variational Autoencoders
  • Summary

4. Generative Adversarial Networks

   • Introduction
   • Deep Convolutional GAN (DCGAN)
   • Wasserstein GAN with Gradient Penalty (WGAN-GP)
   • Conditional GAN (CGAN)
   • Summary

5. Autoregressive Models

   • Introduction
   • Long Short-Term Memory Network (LSTM)
   • Recurrent Neural Network (RNN) Extensions
   • PixelCNN
   • Summary

6. Normalizing Flow Models

   • Introduction
   • Normalizing Flows
   • RealNVP
   • Other Normalizing Flow Models
   • Summary

7. Energy-Based Models

   • Introduction
   • Energy-Based Models
   • Summary

8. Diffusion Models

   • Introduction
   • Denoising Diffusion Models (DDM)
   • Summary

III. Applications

9. Transformers

• • Introduction
  • GPT
  • Other Transformers
  • Summary

10. Advanced GANs

    • Introduction
    • ProGAN
    • StyleGAN
    • StyleGAN2
    • Other Important GANs
    • Summary

11. Music Generation

    • Introduction
    • Transformers for Music Generation
    • MuseGAN
    • Summary

12. World Models

    • Introduction
    • Reinforcement Learning
    • World Model Overview
    • Summary

13. Multimodal Models

    • Introduction
    • DALL.E 2
    • Imagen
    • Stable Diffusion
    • Flamingo
    • Summary

14. Conclusion

 

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