165 repositories starred since Apr 2010. Mostly machine learning and data, and lately AI and language models.

Implementation of Diffusion Transformer (DiT) in JAX

The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.

Tile primitives for speedy kernels

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

A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.

The official PyTorch implementation of Google's Gemma models

MLX: An array framework for Apple silicon

Code and documentation to train Stanford's Alpaca models, and generate the data.

Train to 94% on CIFAR-10 in <6.3 seconds on a single A100. Or ~95.79% in ~110 seconds (or less!)

Tesseract Open Source OCR Engine (main repository)

JAX implementation ViT-VQGAN

min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch

Version control for machine learning

[ICCV 2019] Monocular depth estimation from a single image

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

[NeurIPS‘2021] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

End-to-End Object Detection with Transformers

Image augmentation for machine learning experiments.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Latex code for making neural networks diagrams

A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Dense image captioning in Torch

Javascript neural networks on top of general scalar/tensor reverse-mode automatic differentiation.

Deep Networks with Stochastic Depth

Efficient, reusable RNNs and LSTMs for torch

Torch implementation of ResNet from http://arxiv.org/abs/1512.03385 and training scripts

From the basics to slightly more interesting applications of Tensorflow

Deep Residual Learning for Image Recognition

GPU Powered BLAS for Browsers :gem:

Adversarial networks in TensorFlow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow

Simple tutorials using Google's TensorFlow Framework

Marvin: A Minimalist GPU-only N-Dimensional ConvNets Framework

An Open Source Machine Learning Framework for Everyone

Faster R-CNN

Torch implementation of neural style algorithm

Neural Slime Volleyball

Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.

DeepWalk - Deep Learning for Graphs

Implements SFO minibatch optimizer in Python and MATLAB, and reproduces figures from paper.

Deep Pink is a chess AI that learns to play chess using deep learning.

The SDK for Jetpac's iOS Deep Belief image recognition framework

A basic implementation of convolutional neural nets

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library