pphuc25/deep-learning-argorithms-from-sratch

⌨️ Implementations/tutorials of deep learning papers with side-by-side notes

19
/ 100
Experimental

This resource provides clear, step-by-step implementations of various deep learning algorithms and neural network architectures. It takes the mathematical concepts from deep learning papers and translates them into working code, offering detailed explanations alongside. It's designed for students, researchers, or anyone seeking to understand the inner workings of AI models.

No commits in the last 6 months.

Use this if you are an AI/ML student or researcher who wants to gain a deeper, foundational understanding of how deep learning algorithms like Transformers, GANs, or Diffusion Models are constructed from first principles.

Not ideal if you are a practitioner looking to build production-ready deep learning applications or need highly optimized, state-of-the-art model implementations for real-world tasks.

AI education Machine Learning research Neural network architecture Algorithm comprehension Deep Learning principles
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 3 / 25
Maturity 16 / 25
Community 0 / 25

How are scores calculated?

Stars

4

Forks

Language

Python

License

MIT

Last pushed

Jul 23, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/pphuc25/deep-learning-argorithms-from-sratch"

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