Generative Deep Learning (book code) resource

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1546 stars · 593 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The official companion codebase for the O'Reilly book 'Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play' (2nd Edition), containing Jupyter notebooks organized by chapter. It implements generative modeling techniques such as VAEs, GANs, diffusion models, and transformers using TensorFlow, runnable via Docker.

Use cases

• learn how diffusion models work with hands-on code
• implement variational autoencoders and GANs from scratch
• follow along with the Generative Deep Learning book exercises
• study transformer and autoregressive model implementations
• experiment with music generation and world models in notebooks
• understand generative deep learning techniques like normalizing flows and energy-based models

When to choose

• you are reading the book and want runnable code for each chapter
• you want practical, notebook-based introductions to generative modeling methods
• you prefer a Docker-based environment with GPU support for deep learning experiments

When to avoid

• you need production-ready generative model libraries rather than educational notebooks
• you work primarily in PyTorch rather than TensorFlow
• you want a maintained software package with releases and API stability

Facets

learning-resource · maturity active

machine-learning deep-learning data-science deep-learning machine-learning artificial-intelligence tutorials python generative-models diffusion-models gan variational-autoencoder transformers stable-diffusion jupyter-notebooks oreilly-book docker gpu

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