Popular GitHub Repositories for Dnn Optimization Study

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Roadmap to becoming an Artificial Intelligence Expert in 2022

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Open standard for machine learning interoperability

Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

A hyperparameter optimization framework

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Fast and Accurate ML in 3 Lines of Code

SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 15+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.

[CVPR 2023, top-10%] Authors official PyTorch implementation of the "Attribute-preserving Face Dataset Anonymization via Latent Code Optimization".

Automated Machine Learning with scikit-learn

Experience, Learn and Code the latest breakthrough innovations with Microsoft AI

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.