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 photosTrain GANs to generate images based on your own datasetBuild diffusion models to produce new varieties of flowersTrain your own GPT for text generationLearn how large language models like ChatGPT are trainedExplore state-of-the-art architectures such as StyleGAN 2 and Vision Transformer VQ-GANCompose polyphonic music using Transformers and MuseGANUnderstand how generative world models can solve reinforcement learning tasksDive into multimodal models such as DALL.E 2, Imagen and Stable Diffusion for text-to-image generationThe 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.About the AuthorDavid Foster is a Founding Partner of ADSP, a consultancy delivering bespoke data science and AI solutions. He holds an MA in Mathematics from Trinity College, Cambridge and an MSc in Operational Research from the University of Warwick.Through ADSP, David leads the delivery of high-profile data science and AI projects across the public and private sectors. He has won several international machine-learning competitions and is a faculty member of the Machine Learning Institute. He has given talks internationally on topics related to the application of cutting-edge data science and AI within industry and academia.