Generative Deep Learning (2nd Edition)
FreeGANs, VAEs, diffusion models (David Foster).
FreeFree tier
About Generative Deep Learning (2nd Edition)
Generative Deep Learning (2nd Edition) by David Foster is a practical guide to building generative models using TensorFlow and Keras. It covers the fundamentals of generative deep learning, including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, with hands-on code examples and real-world applications. Published by O'Reilly Media, the book is part of the O'Reilly learning platform, which offers books, courses, and live training.
Key Features
Covers GANs, VAEs, and diffusion models
Hands-on code examples in TensorFlow and Keras
Practical implementation from basics to advanced techniques
Includes real-world applications of generative AI
Part of O'Reilly's trusted technical learning platform
Pros & Cons
Pros
- Comprehensive coverage of major generative model families
- Practical, code-driven approach suitable for developers and researchers
- Authored by a recognized expert in generative AI
- Integrates with O'Reilly's interactive learning features (labs, sandboxes)
Cons
- Access to full content requires O'Reilly subscription or purchase
- May assume prior knowledge of deep learning basics
Best For
Learning generative deep learning techniquesBuilding image generation and synthesis modelsUnderstanding variational inference and adversarial trainingExploring diffusion models for high-quality sample generation
FAQ
What topics does Generative Deep Learning (2nd Edition) cover?
The book covers variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, with practical implementations in TensorFlow and Keras.
Is this book suitable for beginners?
The book assumes some prior experience with deep learning and Python; it is best suited for developers and researchers looking to apply generative techniques.