jdchang1

milo

kmodes

Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

colab_images

curl

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

cyclegan_implementation

d3-plugins

[DEPRECATED] A repository for sharing D3.js V3 plugins.

ddpo-pytorch

DDPO for finetuning diffusion models, implemented in PyTorch with LoRA support

drqv2

DrQ-v2: Improved Data-Augmented Reinforcement Learning

Elevator-Unity

GEM-metrics

Automatic metrics for GEM tasks

gym

A toolkit for developing and comparing reinforcement learning algorithms.

imageclef-brown

implementing-machine-learning-algorithms-from-scratch

This is where I want to play around with some algorithms and implement them to get a better understanding of the algorithms. I do not make use of libraries to optimize functionality, and in now way are these good for use with real datasets.

IQ-Learn-benchmark

(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation

jdchang1.github.io

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration