davidADSP/Generative_Deep_Learning_2nd_Edition
Official code repository for the second edition of Generative Deep Learning, covering VAEs, GANs, diffusion models, and Transformers with TensorFlow implementations.
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This repository accompanies the O’Reilly book teaching machines to paint, write, compose, and play through generative deep learning. It provides Jupyter Notebook implementations covering variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, energy-based models, diffusion models, Transformers, and multimodal approaches. The code is designed to run with Docker and includes TensorFlow implementations alongside dataset setup for hands-on learning.
Frequently asked
- What is davidADSP/Generative_Deep_Learning_2nd_Edition?
- Official code repository for the second edition of Generative Deep Learning, covering VAEs, GANs, diffusion models, and Transformers with TensorFlow implementations.
- Is Generative_Deep_Learning_2nd_Edition open source?
- Yes — davidADSP/Generative_Deep_Learning_2nd_Edition is open source, released under the Apache-2.0 license.
- What language is Generative_Deep_Learning_2nd_Edition written in?
- davidADSP/Generative_Deep_Learning_2nd_Edition is primarily written in Jupyter Notebook.
- How popular is Generative_Deep_Learning_2nd_Edition?
- davidADSP/Generative_Deep_Learning_2nd_Edition has 1.5k stars on GitHub.
- Where can I find Generative_Deep_Learning_2nd_Edition?
- davidADSP/Generative_Deep_Learning_2nd_Edition is on GitHub at https://github.com/davidADSP/Generative_Deep_Learning_2nd_Edition.