BoltzmannEntropy/DDPM

Book: The Art and Science of Diffusion Models in Generative AI

13
/ 100
Experimental

This textbook provides graduate students in physics and computer science with a deep understanding of Denoising Diffusion Probabilistic Models (DDPMs) for generative AI. It explains complex mathematical and physical concepts in a conversational tone, providing theoretical knowledge along with programming projects. The book takes in foundational concepts like Brownian motion and outputs the ability to develop and implement advanced generative AI models.

No commits in the last 6 months.

Use this if you are a graduate student or professional who needs a comprehensive, focused textbook to master diffusion models and their practical applications in generative AI, complete with solved problems and coding projects.

Not ideal if you are looking for a quick reference guide or an introductory overview of generative AI that covers multiple model types beyond just diffusion models.

Generative AI Deep Learning Stochastic Processes Computational Physics Machine Learning Engineering
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 0 / 25

How are scores calculated?

Stars

14

Forks

Language

License

Last pushed

Sep 17, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/generative-ai/BoltzmannEntropy/DDPM"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.