In Generative Deep Learning, David Foster explores how deep learning models can generate art, music, text, and more. This second edition expands on foundational concepts with updated techniques in GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), transformers, and diffusion models. Readers learn how AI can create realistic images, compose music, and even write coherent text, with hands-on examples and implementations using TensorFlow and PyTorch. The foreword by Karl Friston—an influential neuroscientist—adds a theoretical perspective on predictive coding and generative models.

Why Read This Book

• Understand cutting-edge generative AI techniques, including GANs, VAEs, and transformers.
• Learn practical applications of deep learning for image synthesis, text generation, and music composition.
• Work with real-world examples using TensorFlow and PyTorch.
• Gain insights from Karl Friston on the theoretical foundations of generative modeling.
• Suitable for AI researchers, developers, and enthusiasts looking to explore creative AI applications.

About the Author

David Foster is a machine learning expert specializing in generative models. He is a co-founder of Applied Data Science and has extensive experience building AI systems in real-world applications. His work focuses on making generative deep learning accessible through clear explanations and hands-on projects.

Karl Friston is a neuroscientist and the creator of the Free Energy Principle, a groundbreaking theory in predictive coding and artificial intelligence. His work has had a profound impact on the development of generative models and cognitive neuroscience.