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, ... 🧠
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SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #235 of 2,841 repositories GStars tracks this week (top 8%).
The project lists 39 contributors on GitHub.
Use Cases & Benefits
- Provides over 60 PyTorch implementations of deep learning papers with side-by-side explanatory notes for better understanding.
- Combines code and detailed documentation to facilitate learning and experimentation with state-of-the-art neural network architectures and algorithms.
- Use for studying and reproducing transformer models including GPT, Vision Transformer, and Switch Transformer with clear tutorials.
- Use for exploring generative models like GANs, diffusion models, and reinforcement learning algorithms with practical implementations.
- Use for experimenting with advanced optimizers, normalization techniques, and adaptive computation methods in deep learning research.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
Watch-outs
- No commits in the last six months (last push Jan 2026).
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,
The website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.
We are actively maintaining this repo and adding new implementations almost weekly. for updates.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
6.8K+18
Open issues
44+11
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 4, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| rohitg00/ai-engineering-from-scratch | 68.7K | +3.5K | 75 | 3 days ago |
| p-e-w/heretic | 35.1K | +1.5K | 41 | 121 days ago |
| pytorch/pytorch | 104.9K | +422 | 75 | 13 days ago |
| labmlai/annotated_deep_learning_paper_implementations | 67.5K | +0 | 75 | — |
| roboflow/supervision | 51.2K | +0 | 75 | 1 day ago |
| pyg-team/pytorch_geometric | 24.1K | +0 | 40 | today |
| Category median | 51.2K | +422 | 75 | — |
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