machine-learning

All Stanford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine Learning, Probabilities, Statistics, Algebra and Calculus.

Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).

🔍 LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safety & security. 🛡️ Features include text quality, relevance metrics, & sentiment analysis. 📊 A comprehensive tool for LLM observability. 👀

[ICLR 2025 Spotlight] Official implementation of "Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts"

Perception toolkit for sim2real training and validation in Unity

Survival analysis with PyTorch

A tool for converting ONNX files to LiteRT/TFLite/TensorFlow, PyTorch native code (nn.Module), TorchScript (.pt), state_dict (.pt), Exported Program (.pt2), and Dynamo ONNX. It also supports direct conversion from LiteRT to PyTorch.

Pretrained model hub for Keras 3.

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

[Book-2021] Practical MLOps O'Reilly Book

Build AI agents from first principles using a local LLM - no frameworks, no cloud APIs, no hidden reasoning.

Sparkling Water provides H2O functionality inside Spark cluster

The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.

Library for specialized dense and sparse matrix operations, and deep learning primitives.

Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs

R interface for Apache Spark

OmniXAI: A Library for eXplainable AI

Python机器学习算法技术博客，有原创干货！有code实践！ 【更多内容敬请关注公众号 "算法进阶"】

A curated collection of eBooks on Data Science, Machine Learning, and AI to help you learn and grow.

Transformer models from BERT to GPT-4, environments from Hugging Face to OpenAI. Fine-tuning, training, and prompt engineering examples. A bonus section with ChatGPT, GPT-3.5-turbo, GPT-4, and DALL-E including jump starting GPT-4, speech-to-text, text-to-speech, text to image generation with DALL-E, Google Cloud AI,HuggingGPT, and more

Machine Learning Q and AI book

AI Audio Datasets (AI-ADS) 🎵, including Speech, Music, and Sound Effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications.

PyGCL: A PyTorch Library for Graph Contrastive Learning

TensorFlow Lite Samples on Unity

A polyhedral compiler for expressing fast and portable data parallel algorithms

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

NequIP is a code for building E(3)-equivariant interatomic potentials

Source code for end-to-end dialogue model from the MultiWOZ paper (Budzianowski et al. 2018, EMNLP)