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