jmtomczak/intro_dgm
A Jupyter Notebook companion for the textbook "Deep Generative Modeling," providing implementations of deep generative model architectures.
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This repository contains Jupyter Notebook implementations for the book “Deep Generative Modeling.” It covers major deep generative model classes: mixture models, probabilistic circuits, autoregressive models, flow-based models, VAEs, GANs, energy-based models, score-based models, and large language models. The materials are designed for readers with a background in calculus, linear algebra, probability theory, and basics of machine learning and PyTorch, combining conceptual explanations with executable code examples.
Frequently asked
- What is jmtomczak/intro_dgm?
- A Jupyter Notebook companion for the textbook "Deep Generative Modeling," providing implementations of deep generative model architectures.
- Is intro_dgm open source?
- Yes — jmtomczak/intro_dgm is open source, released under the MIT license.
- What language is intro_dgm written in?
- jmtomczak/intro_dgm is primarily written in Jupyter Notebook.
- How popular is intro_dgm?
- jmtomczak/intro_dgm has 1.3k stars on GitHub.
- Where can I find intro_dgm?
- jmtomczak/intro_dgm is on GitHub at https://github.com/jmtomczak/intro_dgm.