Popular GitHub Repositories for Accelerated Discovery

? Papers & tech blogs by companies sharing their work on data science & machine learning in production.

A WebGL accelerated JavaScript library for training and deploying ML models.

? GPU accelerated Neural networks in JavaScript for Browsers and Node.js

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

Official PyTorch implementation of PTS/PSRN: Fast and efficient symbolic expression discovery through parallelized tree search. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Data & AI Notebook templates catalog organized by tools, following the IMO (input, model, output) framework for easy usage and discovery..

CV-CUDA™ is an open-source, GPU accelerated library for cloud-scale image processing and computer vision.

文本挖掘和预处理工具（文本清洗、新词发现、情感分析、实体识别链接、关键词抽取、知识抽取、句法分析等），无监督或弱监督方法

A portable accelerated data query and LLM-inference engine, written in Rust, for data-grounded AI apps and agents.

Machine Learning University: Accelerated Natural Language Processing Class

Zero shot vulnerability discovery using LLMs

Message Passing Neural Networks for Molecule Property Prediction

BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.

Lightweight inference library for ONNX files, written in C++. It can run Stable Diffusion XL 1.0 on a RPI Zero 2 (or in 298MB of RAM) but also Mistral 7B on desktops and servers. ARM, x86, WASM, RISC-V supported. Accelerated by XNNPACK.

GPU Accelerated t-SNE for CUDA with Python bindings