Generative Deep Learning by David Foster

Generative Deep Learning by David Foster Book Description 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 https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/ The code repository for examples in the O'Reilly book 'Generative Deep Learning' using Pytorch https://github.com/MLSlayer/Generative-Deep-Learning-Code-in-Pytorch