What to read after Generative Deep Learning?

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 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, 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 architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN