Pages
453
Year
2023
Level
intermediate
Read time
12h
David Foster · Shroff Publishers / O'Reilly Media · 2023
Reviewed by Ashish Sheth · Updated April 2026
Generative Deep Learning
Teaching Machines to Paint, Write, Compose, and Play
4.5 / 5
AMAZON · 205 RATINGS
deep learning
SUBJECTS
What you'll come away with
01.
How VAEs, GANs, and diffusion models actually differ under the hood
02.
When each generative architecture is the right choice
03.
Hands-on Keras implementations for every model type covered
04.
How modern image generation (Stable Diffusion-style) works internally
05.
Music and text generation patterns that transfer to LLMs
06.
Why diffusion won the image-generation race
Strengths
+Best single book covering the breadth of generative architectures
+2nd edition adds diffusion models — essential for 2026 readers
+Code-first with Keras implementations you can run
+Strong theoretical grounding without being math-heavy
Caveats
−Keras/TensorFlow focus when much of generative ML is now PyTorch
−Diffusion chapter is solid but the field has moved fast since 2023
−Less coverage of LLM generation than readers may expect from the title
★ 4.5 FROM 205 READERS ON AMAZON
Check Price on Amazon →
Read this if
→ML engineers wanting to understand image generation beyond using APIs
→Developers building Stable Diffusion or music-generation features
→Researchers needing a survey of generative architectures with code
Skip this if
—Engineers who only use generative APIs and don't fine-tune or train
—PyTorch-only shops with no TensorFlow exposure
—Beginners without prior deep learning experience (read Deep Learning with Python first)
Head-to-head comparisons
Generative Deep Learning vs Deep Learning with Python → Generative Deep Learning vs Build a Large Language Model (From Scratch) → Generative Deep Learning vs Hands-On Large Language Models → Frequently asked
Does this book cover Stable Diffusion and modern image generation?
The 2nd edition (2023) added a diffusion-models chapter that covers the architecture behind Stable Diffusion. Specific tools and APIs have evolved since, but the architecture explanations hold up.
Is Generative Deep Learning relevant for LLM developers?
The autoregressive and transformer chapters apply directly to LLMs. The GAN and diffusion content is more relevant if you also work on image or audio generation.
Do I need to know deep learning before reading this?
Yes. This isn't a first deep learning book. Read Hands-On Machine Learning or Deep Learning with Python first, then come back for the generative-specific content.
Read this next
3 alternatives
Ready?
Check Price on Amazon →