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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, ... 🧠
$ git clone https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
stars
67,201
forks
6,743
language
Python
license
MIT License
What is labmlai/annotated_deep_learning_paper_implementations?
This repository contains over 60 implementations of deep learning papers with detailed side-by-side explanations, covering transformers, optimizers, GANs, reinforcement learning, and other modern neural network architectures. Developers can use it to understand how influential papers work in practice by studying well-commented PyTorch code alongside educational notes. It's useful for learning implementation details, referencing specific algorithms, or building on existing implementations for research and projects.
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Topics
#attention #deep-learning #deep-learning-tutorial #gan #literate-programming #lora #machine-learning #neural-networks #optimizers #pytorch #reinforcement-learning #transformer #transformers
Activity
34 open issues · last updated Jul 21, 2026