Austin Huang

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.

openmemex. Open source, local-first knowledge platform.

pytorch-sqlite. Example using sqlite with a Pytorch Dataset Interface

HCMTools. Haskell toolbox for research and teaching in classical mechanics. Includes modules for symbolic algebra and automatic differentiation.

abox242. Archive of Andrew J. Turner's Analog Box 2 Source Code - https://sites.google.com/site/analogbox2/download

libtorch-experiments. JavaScript

hello-monad. Trivial (~30 LOC) "hello world" examples of commonly used monad classes.

hylogen. GLSL embedded in Haskell

docker-pytorch-paysage. A docker configuration built off the pytorch image + system dependencies for [paysage](https://github.com/drckf/paysage) pre-installed.

gemma.cpp. lightweight, standalone C++ inference engine for Google's Gemma models.

state-logger. Trivially minimal state log for python, primarily for machine learning experimentation

annotated-transformer. http://nlp.seas.harvard.edu/2018/04/03/attention.html

crayon-hs. A Haskell client to crayon. Implements a servant client implementation of the crayon API.

sketch-minimal-critic. Python

dawn-artifacts.

vkcompute. Minimal starter project for experimenting with vulkan for GPGPU computation