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In the tradition of "awesome" (curated) lists, this is a list of references and code for doing deep learning in Haskell.
| Date | Stars |
|---|---|
| 2026-07-31 | 299 |
| 2026-08-02 | 299 |
| 2026-08-06 | 299 |
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# Awesome Haskell Deep Learning [](https://github.com/avctrh/awesome-haskell-deep-learning) In the tradition of "awesome" (curated) lists, this is a list of references and code for doing deep learning (and adjacent/related topics) in Haskell. ## Articles - 2020 | [Type-driven Neural Programming by Example](https://arxiv.org/abs/2008.12613) | Kiara Grouwstra - 2019 | [Dex: array programming with typed indices](https://openreview.net/pdf?id=rJxd7vsWPS) | Dougal Maclaurin, Alexey Radul, Matthew J. Johnson, and Dimitrios Vytiniotis - 2018 | [Not-o-matic Differentiation](https://ajknapp.github.io/2018/08/14/notomatic-differentiation.html) | Andrew Knapp - 2018 | [Hasktorch v0.0.1](https://medium.com/@stites/hasktorch-v0-0-1-28d9ab270f3f) | Sam Stites - 2018 | [The Simple Essence of Automatic Differentiation](http://conal.net/papers/essence-of-ad/essence-of-ad-icfp.pdf) | Conal Elliott - 2018 | [A Purely Functional Typed Approach to Trainable Models](https://blog.jle.im/entry/purely-functional-typed-models-1.html) | Justin Le - 2018 | [Introducing the backprop library](https://blog.jle.im/entry/introducing-the-backprop-library.html) | Justin Le - 2017 | [Backprop as Functor: A compositional perspective on supervised learning](https://arxiv.org/abs/1711.10455) | Brendan Fong, David I. Spivak, Rémy Tuyéras - 2017 | [Haskell and AI (multi-part series covering Tensorflow)](https://mmhaskell.com/haskell-ai/) | James Bowen - 2017 | [Backpack for deep learning](http://blog.ezyang.com/2017/08/backpack-for-deep-learning/) | Kaixi Ruan - 2017 | [DeepDarkFantasy: A Programming Language for Deep Learning](https://github.com/ThoughtWorksInc/DeepDarkFantasy) | Marisa Kirisame - 2017 | [Deep Learning, from a Programming Language Perspective](https://marisa.moe/2017/DLPL/) | Marisa Kirisame - 2016 | [Computing symbolic gradient vectors with plain Haskell](http://blog.aloni.org/posts/symbolic-gradients-with-plain-haskell/) | Dan Aloni - 2016 | [Practical Dependent Types in Haskell (Part 2): Existential Neural Networks and Types at Runtime](https://blog.jle.im/) | Justin Le - 2016 | [Practical Dependent Types in Haskell (Part 1): Type-Safe Neural Networks](https://blog.jle.im/entry/practical-dependent-types-in-haskell-1.html) | Justin Le - 2016 | [Reverse-Mode Automatic Differentiation in Haskell Using the Accelerate Library (CS240h project)](http://www.scs.stanford.edu/16wi-cs240h/projects/bradbury_kathawala.pdf) | James Bradbury, Farhan Kathawala - 2015 | [Neural Networks, Types, and Functional Programming](http://colah.github.io/posts/2015-09-NN-Types-FP/) | Christopher Olah - 2014 | [Get a Brain](https://crypto.stanford.edu/~blynn/haskell/brain.html) | Ben Lynn - 2013 | [Backpropogation is Just Steepest Descent with Automatic Differentiation](https://idontgetoutmuch.wordpress.com/2013/10/13/backpropogation-is-just-steepest-descent-with-automatic-differentiation-2/) | Dominic Steinitz ## Talks - 2020 | [PyTorch Developer Day 2020: Torch for R & Hasktorch: Bringing Torch to New Programming Languages](https://www.youtube.com/watch?v=ZnYa99QoznE) | Austin Huang and Daniel Falbel - 2020 | [Berlin Functional Programming Group: Hasktorch](https://www.youtube.com/watch?v=ZnYa99QoznE) | Torsten Scholak - 2020 | [MuniHac 2020: Austin Huang - Hasktorch: Differentiable Functional Programming in Haskell](https://www.youtube.com/watch?v=Qu6RIO02m1U) | Austin Huang - 2019 | [A Functional Reboot for Deep Learning (BOB 2019 Talk)](https://github.com/conal/talk-2018-deep-learning-rebooted) | Conal Elliott - 2019 | [Keynote: Automatic Diferentiation for Dummies](https://www.youtube.com/watch?v=FtnkqIsfNQc) | Simon Peyton Jones - 2018 | [NPFL Numerical Programming in Functional Languages (ICFP Session) 2018 Playlist](https://www.youtube.com/watch?v=0SUvyhbFjeg&list=PLnqUlCo055hWb33k7lJ16TZpG6ZYTOmWj) | Multiple Presenters - 2018 | [The Simple Essence
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