A
annotated_deep_learning_paper_implementations
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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Python
MIT
Updated: Today
📋 Project at a Glance
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What's this?A , built with Python open-source project in the Learning & Resources category, core strengths: attention/deep-learning
Who made it?Maintained by labmlai team, 67.3K⭐ on GitHub, #34 out of 3640 in Learning & Resources
Why does it exist?In the Learning & Resources space, attention workflows faced efficiency bottlenecks. annotated_deep_learning_paper_implementations was built by labmlai to address these deep-learning challenges.
What can it do?Key use cases: deep-learning-tutorial, gan, literate-programming
How to install with AI?Use an AI coding assistant (Claude Code, Cursor, Copilot) to automatically set up pip dependencies and virtual env. Follow the README — the AI handles the rest.
🔗 github.com/labmlai/annotated_deep_learning_paper_implementations | 官网 https://nn.labml.ai
🔗 github.com/labmlai/annotated_deep_learning_paper_implementations | 官网 https://nn.labml.ai
Topics
attentiondeep-learningdeep-learning-tutorialganliterate-programmingloramachine-learningneural-networksoptimizerspytorchreinforcement-learningtransformertransformers