The Generative Deep Learning Book
Written by David Foster.
Introduction to Generative Deep Learning
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.
Key Concepts and Tools
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. 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.
Get your copy today!
Learn how to build your very own creative generative models with this practical guide.
About the Author
Education and professional experience
David is a data scientist, entrepreneur, and educator focused on AI in creative fields. He holds an MA in Mathematics from Trinity College, Cambridge, and an MSc in Operational Research from the University of Warwick. David is a faculty member of the Machine Learning Institute, specialising in practical AI applications and real-world problem-solving. His research includes improving the transparency and interpretability of AI algorithms, with published work on explainable machine learning in healthcare.
David Foster
Founding Partner, ADSP
