🧑🏫 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, ... 🧠
🧑🏫 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, ... 🧠
Note: use the Annotated Implementation Skill with coding agents to implement any paper with annotations.
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,
Description and README excerpt from labmlai/annotated_deep_learning_paper_implementations on GitHub ↗.
labmlai/annotated_deep_learning_paper_implementations was first seen on GitHub · Trending Python today on Nov 4, 2026. Since then it has appeared in 7 checks on 2 different days. Its best position was #17 of 18, reached on Nov 4, 14:49 UTC. It dropped off the list after Nov 5, 08:49 UTC.
Position on GitHub · Trending Python today at every check (lower is better).
Best rank per day (UTC).
Platform metric as reported by GitHub.
Archived daily, weekly and monthly lists that include this entry.
Not at the moment. It was last on the list on Nov 5, 08:49 UTC, after 2 days of trending.
#17 on GitHub · Trending Python today, on Nov 4, 14:49 UTC. Trendoday checks this list every 180 minutes.
It was first seen on Nov 4, 2026 and has been on the list on 2 days (7 checks) since.
Mostly Python; it is licensed under MIT. It has 67.7k stars and 6781 forks.