Generative Deep Learning

Generative Deep Learning Front Cover
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330 pages

Book Description

Generative is one of the hottest topics in . It’s now possible to teach a machine to at human endeavors such as painting, , 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 in the field. Through tips and tricks, you’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 of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Book Details

  • Title: Generative Deep Learning
  • Author:
  • Length: 330 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2019-07-08
  • ISBN-10: 1492041947
  • ISBN-13: 9781492041948
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