jmtomczak / intro_dgm
"Deep Generative Modeling": Introductory Examples
Why this job: works in the areas we cover, but does not say which job it does
What it does
This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression, among others.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 6 months ago
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- 1,351
- +1 this week
- Licence
- MIT
- Allows business use
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- Built with
- Jupyter Notebook
- 96% of the code
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- Contributors
- Not stated
- Last 90 days
- Issues
- 43% closed
- 4 still open
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- 4 merged
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More: languages, topics and project record
Languages
- Jupyter Notebook 96%
- TeX 4%
Topics
- autoregressive-modeling
- deep-generative-modelling
- deep-learning
- energy-based-model
- flow-based-modeling
- generative-adversarial-network
- generative-ai
- neural-compression
- pytorch
- score-based-generative-modeling
- variational-autoencoder
Project record
- JobOther AI tools
- StatusListed
- Commits93
- Forks204
- Watching27
- Stars in 28 days+8
- CreatedJan 23, 2021
- First seen by usNov 7, 2026
- CheckedNov 8, 2026