jmtomczak/intro_dgm
"Deep Generative Modeling": Introductory Examples
This project provides practical, hands-on Python code examples for understanding how Generative AI models work. It takes foundational mathematical concepts and turns them into runnable code, illustrating how these models can create new data, compress information, or even power large language models. This is ideal for students, engineers, and researchers across fields like computer science, data science, and bioinformatics who want to bridge the gap between theory and implementation in deep generative modeling.
1,295 stars. Actively maintained with 1 commit in the last 30 days.
Use this if you want to understand, implement, and experiment with different types of deep generative models from scratch, with clear, simplified examples.
Not ideal if you are looking for a high-level API or a pre-built solution to deploy generative AI without needing to understand the underlying mechanisms.
Stars
1,295
Forks
204
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
1
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