Annotated Paper Implementations

60+ papers implemented and explained side by side
Learn AI & machine learningReference implementationsMITRuns locallyPythonAdvanced
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What is Annotated Paper Implementations?

labml.ai's collection of deep learning papers implemented in PyTorch, with the explanation printed alongside the code — transformers, diffusion, RL, optimisers and more.

Why people choose Annotated Paper Implementations

Annotated Paper Implementations at a glance

CategoryLearn AI & machine learning
TypeReference implementations
LicenseMIT
Runs locallyYes
Built withPython
Skill levelAdvanced
Best forreading a paper and seeing exactly how it is built

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Annotated Paper Implementations head-to-head

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FAQ

Is Annotated Paper Implementations free?

Annotated Paper Implementations is free and open-source (MIT license), so you can use, self-host and modify it at no cost.

Can I run Annotated Paper Implementations locally?

Yes. Annotated Paper Implementations is designed to run on your own machine or server, keeping your data private.

What is the best alternative to Annotated Paper Implementations?

Popular open-source alternatives include Virgilio, ML for Beginners, AI for Beginners. See the comparisons above to choose.

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