awesome-haskell-deep-learning:按照“令人敬畏的”(精选)列表的传统,这是在Haskell中进行深度

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很棒的Haskell深度学习 按照“很棒”(精选)列表的传统,这是在Haskell中进行深度学习(以及相邻/相关主题)的参考和代码列表。 2020 | 凯拉·格劳斯特拉(Kiara Grouwstra) 2020 | | 奥斯汀·黄和丹尼尔·法贝尔 2020 | | 托斯滕·斯科拉克(Torsten Scholak) 2020 | | 奥斯汀·黄(Austin Huang) 2019 | Dougal Maclaurin,Alexey Radul,Matthew J.Johnson和Dimitrios Vytiniotis 2019 | | 康纳·埃利奥特(Conal Elliott) 2019 | | 西蒙·佩顿·琼斯 2018 | | 多个演示者 2018 | | 安德鲁·纳普(Andrew Knapp) 2018 | | 山姆·斯特蒂斯 2018 | 康纳
awesome-haskell-deep-learning-master.zip
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# 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. - 2020 | [Type-driven Neural Programming by Example](https://arxiv.org/abs/2008.12613) | Kiara Grouwstra - 2020 | [PyTorch Developer Day 2020: Torch for R & Hasktorch: Bringing Torch to New Programming Languages (talk)](https://www.youtube.com/watch?v=ZnYa99QoznE) | Austin Huang and Daniel Falbel - 2020 | [Berlin Functional Programming Group: Hasktorch (talk)](https://www.youtube.com/watch?v=ZnYa99QoznE) | Torsten Scholak - 2020 | [MuniHac 2020: Austin Huang - Hasktorch: Differentiable Functional Programming in Haskell (talk)](https://www.youtube.com/watch?v=Qu6RIO02m1U) | Austin Huang - 2019 | [Dex: array programming with typed indices](https://openreview.net/pdf?id=rJxd7vsWPS) | Dougal Maclaurin, Alexey Radul, Matthew J. Johnson, and Dimitrios Vytiniotis - 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 (talk)](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 | [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 (talk)](https://www.youtube.com/watch?v=ne99laPUxN4) | Conal Elliott - 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 ## 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/deeplearn
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