Awesome

Awesome AI

A curated list of awesome frameworks, libraries, tools, and resources for Artificial Intelligence (AI). This list covers everything from foundational machine learning and deep learning to specialized areas like NLP, computer vision, and AI ethics.

Contents

• General AI
• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Reinforcement Learning
• AI for Edge Computing
• AI Ethics
• AI Infrastructure
• Learning Resources
• Books
• Community
• Contribute
• License

General AI

• TensorFlow - An open-source platform for machine learning, providing a comprehensive ecosystem of tools.
• PyTorch - An open-source deep learning framework that provides a flexible and dynamic computation graph.
• Scikit-learn - A Python library for machine learning, featuring various classification, regression, and clustering algorithms.
• Keras - A high-level neural networks API, written in Python and capable of running on top of TensorFlow or Theano.
• OpenAI Gym - A toolkit for developing and comparing reinforcement learning algorithms.

Machine Learning

• XGBoost - A scalable and efficient gradient boosting framework.
• LightGBM - A fast, distributed, high-performance gradient boosting framework.
• CatBoost - A gradient boosting library that handles categorical features automatically.
• Dask-ML - A scalable machine learning library that integrates with Dask for parallel computing.
• MLflow - An open-source platform for managing the end-to-end machine learning lifecycle.

Deep Learning

• Hugging Face Transformers - A library for state-of-the-art natural language processing models.
• DeepSpeed - A deep learning optimization library that makes distributed training easy and efficient.
• JAX - A library for high-performance numerical computing and automatic differentiation, designed for deep learning.
• ONNX - An open format for AI models, allowing interoperability between different deep learning frameworks.
• Apache MXNet - A scalable deep learning framework with a flexible programming model.

Natural Language Processing (NLP)

• spaCy - An open-source NLP library for advanced natural language processing in Python.
• NLTK - The Natural Language Toolkit, a comprehensive library for text processing and analysis.
• Stanford NLP - A suite of NLP tools developed by the Stanford NLP Group.
• TextBlob - A simple library for processing textual data.
• AllenNLP - An open-source research library for NLP, built on top of PyTorch.

Computer Vision

• OpenCV - An open-source computer vision library for image and video processing.
• Detectron2 - A high-performance object detection library by Facebook AI Research.
• YOLO (You Only Look Once) - A state-of-the-art, real-time object detection system.
• Fastai - A library simplifying training of fast and accurate neural networks for vision tasks.
• Dlib - A toolkit for machine learning and data analysis, widely used for face detection and recognition.

Reinforcement Learning

• Stable-Baselines3 - A set of reliable implementations of reinforcement learning algorithms in Python.
• Ray RLlib - A scalable library for reinforcement learning built on Ray.
• Acme - A library of reinforcement learning algorithms by DeepMind.
• RLlib - A scalable reinforcement learning library that integrates with Ray.
• TF-Agents - A library for reinforcement learning in TensorFlow.

AI for Edge Computing

• TensorFlow Lite - A framework for running machine learning models on edge devices.
• ONNX Runtime - A cross-platform, high-performance scoring engine for ONNX models.
• Edge Impulse - A platform for developing machine learning models on edge devices.
• OpenVINO - An Intel toolkit for optimizing deep learning models for edge devices.
• AWS IoT Greengrass - A service for deploying machine learning models on edge devices using AWS.

AI Ethics

• AI Fairness 360 - A toolkit for detecting and mitigating bias in machine learning models.
• Explainable AI (XAI) - DARPA’s initiative for explainable AI research.
• EthicsNet - A community-driven project focused on ethical AI.
• FAT Forensics - A toolkit for Fairness, Accountability, and Transparency in AI.
• Papers on AI Ethics - A collection of influential research papers on AI ethics.

Learning Resources

• Coursera: Machine Learning - An introductory machine learning course by Andrew Ng.
• Deep Learning Specialization - A comprehensive deep learning course by Andrew Ng.
• Google AI Hub - A platform for AI research and learning by Google.
• PyTorch Tutorials - Official tutorials for learning PyTorch.
• Fast.ai Courses - Free courses on deep learning and AI.

Books

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - A foundational book on deep learning techniques.
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron - A practical guide to machine learning.
• Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig - A comprehensive textbook on AI.
• Machine Learning Yearning by Andrew Ng - A book on how to structure machine learning projects effectively.
• The Hundred-Page Machine Learning Book by Andriy Burkov - A concise introduction to machine learning.

Community

• Reddit: r/MachineLearning - A subreddit for discussions on machine learning.
• AI Alignment Forum - A community focused on AI alignment and safety research.
• Kaggle - A platform for data science competitions and community.
• PyTorch Forums - A forum for discussing PyTorch-related topics.
• AI Ethics Slack Group - A Slack group for discussions on AI ethics.

Contribute

Contributions are welcome!

License