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https://github.com/austinvhuang/awesome-haskell-deep-learning

In the tradition of "awesome" (curated) lists, this is a list of references and code for doing deep learning in Haskell.
https://github.com/austinvhuang/awesome-haskell-deep-learning

List: awesome-haskell-deep-learning

Last synced: about 1 month ago
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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.

Host: GitHub
URL: https://github.com/austinvhuang/awesome-haskell-deep-learning
Owner: austinvhuang
Created: 2017-03-12T22:12:57.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2021-10-18T03:06:30.000Z (almost 3 years ago)
Last Synced: 2024-05-21T16:08:32.604Z (4 months ago)
Size: 20.5 KB
Stars: 288
Watchers: 19
Forks: 16
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - awesome-haskell-deep-learning - In the tradition of "awesome" (curated) lists, this is a list of references and code for doing deep learning in Haskell. (Other Lists / PowerShell Lists)

README

        # Awesome Haskell Deep Learning [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](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 of Automatic Differentiation](https://www.youtube.com/watch?v=ne99laPUxN4) | Conal Elliott

## Haskell Packages

### Packages Under Active Development

- [backprop](http://hackage.haskell.org/package/backprop) - Automatic heterogeneous back-propagation that can be used either implicitly (in the style of the ad library) or using explicit graphs built in monadic style. | [Justin Le](https://github.com/mstksg) 

- [arrayfire-haskell](http://hackage.haskell.org/package/arrayfire) - High-level Haskell bindings to the [ArrayFire](https://github.com/arrayfire/arrayfire) General-purpose GPU library. | [David Johnson](https://github.com/dmjio) 

- [backprop-hmatrix](http://hackage.haskell.org/package/backprop) - Automatic heterogeneous back-propagation that can be used either implicitly (in the style of the ad library) or using explicit graphs built in monadic style. | [Justin Le](https://github.com/mstksg)

- [dex](https://github.com/google-research/dex-lang) - a research language for typed, functional array processing.

- [diffhask](https://github.com/o1lo01ol1o/diffhask) - DSL for forward and reverse mode automatic differentiation via a version of operator overloading. Port of DiffSharp to Haskell; currently a work in progress. | [Tim Pierson](https://github.com/o1lo01ol1o)

- [funn](https://github.com/nshepperd/funn) - This is an experimental library exploring a combinator approach for building and training neural networks in haskell. | [Neil Shepperd](https://github.com/nshepperd)

- [grenade](https://github.com/HuwCampbell/grenade) - Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. | [Huw Campbell](https://github.com/HuwCampbell)

- [gym-http-api](https://github.com/openai/gym-http-api) This project provides a local REST API to the gym open-source library, includes a Haskell client by [Sam Stites](https://github.com/stites)

- [hasktorch](https://github.com/hasktorch/hasktorch) Tensors and neural networks in Haskell, leverages the libtorch backend. | [Hasktorch Contributor Team](https://github.com/hasktorch/hasktorch/graphs/contributors)

- [hasktorch-yolo](https://github.com/junjihashimoto/hasktorch-yolo) yolov3 implementaiton in hasktorch | [Junji Hashimoto](https://github.com/junjihashimoto)

- [hnn](http://hackage.haskell.org/package/hnn) - A neural network library implemented purely in Haskell, relying on the hmatrix library. | [Alp Mestan](https://github.com/alpmestan)

- [rc](http://hackage.haskell.org/package/rc) - Reservoir computing library. | [Bogdan Penkovsky](https://github.com/masterdezign)

- [synthesis](https://gitlab.com/tycho01/hasktorch/tree/synthesis/synthesis/) - Implementation for [Typed Neuro-Symbolic Program Synthesis for the Typed Lambda Calculus](https://arxiv.org/abs/2008.12613)

- [tensor-safe](https://github.com/leopiney/tensor-safe) - A framework to define valid deep neural network models and export them to specific languages | [Leonardo Pineyro](https://github.com/leopiney)

- [tensorflow](https://github.com/tensorflow/haskell) - The tensorflow-haskell package provides Haskell bindings to TensorFlow. | [Judah Jacobson](https://github.com/judah) and [Greg Steuk](https://github.com/blackgnezdo)

- [TypedFlow](https://github.com/GU-CLASP/TypedFlow) - TypedFlow is a typed, higher-order frontend to TensorFlow and a high-level library for deep-learning. Generates python. | [Jean-Philippe Bernardy](https://github.com/jyp)

### Legacy Packages

- [convoluted](https://github.com/jonascarpay/convoluted) - Dependently typed convolutional neural networks in pure Haskell. Uses the repa library for high-performance arrays, with a static wrapper that ensures networks are valid at compile-time. | [Jonas Carpay](https://github.com/jonascarpay)

- [deeplearning-hs](https://hackage.haskell.org/package/deeplearning-hs)

- [dnngraph](https://github.com/ajtulloch/dnngraph)

- [lambdanet](https://hackage.haskell.org/package/LambdaNet)

- [neural](http://hackage.haskell.org/package/neural) - The goal of neural is to provide a modular and flexible neural network library written in native Haskell. | [Lars Brünjes](https://github.com/brunjlar)

## Related Topics & Links

- [Chris Olah's blog](http://colah.github.io/archive.html)

- [dataHaskell site](http://www.datahaskell.org/) and [gitter chat](https://gitter.im/dataHaskell/Lobby)

- [idontgetoutmuch blog](https://idontgetoutmuch.wordpress.com/)

- [Justin Le's blog](https://blog.jle.im/)

- [Monday Morning Haskell](https://mmhaskell.com/)

## Contribution

Feel free to send a pull request.