Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to create impressive generative deep learning models from scratch using Tensorflow and Keras, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, readers can make their models learn more efficiently and become more creative.
Discover how VAEs can change facial expressions in photos
Train GANs to generate images based on your own dataset
Build diffusion models to produce new varieties of flowers
Train your own GPT for text generation
Learn how large language models like ChatGPT are trained
Explore state-of-the-art architectures such as StyleGAN 2 and Vision Transformer VQ-GAN
Compose polyphonic music using Transformers and MuseGAN
Understand how generative world models can solve reinforcement learning tasks
Dive into multimodal models such as DALL.E 2, Imagen and Stable Diffusion for text-to-image generation
The book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.