Generative modeling is one of the hottest topics in
AI. It’s now possible to teach a machine to excel at human endeavors such as p
ainting, 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 variatio
nal autoencoders,
generative adversarial networks (GANs), encoder-decoder models, and world models.
Author David Foster demo
nstrates 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, you’ll understand how to make your models learn more efficiently and become more creative.
Discover how variatio
nal autoencoders can change facial ex
pressions 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
