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, ... 🧠
What it does
This project is a free, open library of over 60 working recreations of the most important AI research papers, each paired with plain-English explanations displayed side-by-side with the code on a companion website. Think of it as a textbook that also shows you the actual working blueprints behind technologies like ChatGPT, image generators, and other modern AI systems.
Why it matters for PMs
With 65,000+ stars on GitHub, this is one of the most popular AI education resources in the world, signaling massive demand from engineers wanting to deeply understand the AI techniques powering today's products. For PMs and founders, it's a window into the foundational building blocks—transformers, optimizers, reinforcement learning—that teams are using to build competitive AI features, making it a useful reference for evaluating technical roadmaps and engineering proposals.
Early stage — limited signal data
Score updated Feb 18, 2026
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