labmlai/annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
This project helps deep learning researchers and practitioners understand complex deep learning algorithms. It provides clear, side-by-side explanations alongside PyTorch code implementations for various neural networks, including Transformers, GANs, and Reinforcement Learning models. You get working code and detailed notes, making it easier to grasp how these advanced systems function.
65,913 stars.
Use this if you are a deep learning researcher or practitioner who needs to understand and implement advanced neural network architectures, like those found in recent academic papers, with clear explanations.
Not ideal if you are looking for a high-level overview or an out-of-the-box solution without diving into the underlying code and mathematical details.
Stars
65,913
Forks
6,621
Language
Python
License
MIT
Category
Last pushed
Jan 22, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/labmlai/annotated_deep_learning_paper_implementations"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related models
SwanHubX/SwanLab
⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports...
mdsrqbl/omnihuman
AI model that understands text & humanoids.
stas00/ml-engineering
Machine Learning Engineering Open Book
analyticalrohit/AI-ML-Cheatsheets
All Stanford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine...
avikumart/LLM-GenAI-Transformers-Notebooks
An repository containing all the LLM notebooks with tutorial and projects