Generative Deep Learning

In 'Generative Deep Learning, Second Edition,' David Foster explores how advanced deep learning models create art, music, and text. This updated edition provides hands-on guidance on foundational concepts and cutting-edge techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), transformers, and diffusion models. Readers will learn practical implementations for realistic image synthesis, music composition, and coherent text generation using TensorFlow and PyTorch, complemented by a theoretical foreword from neuroscientist Karl Friston.
Why You Should Read?
Understand and apply state-of-the-art generative AI techniques like GANs, VAEs, transformers, and diffusion models.
Develop practical skills in creating realistic images, composing music, and generating text with deep learning.
Utilize TensorFlow and PyTorch through real-world examples and implementations.
Gain theoretical insights into predictive coding and generative modeling from Karl Friston.