└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Top GitHub Repositories for AI and ML Enthusiasts 2 | 3 | Here is an extended list of important GitHub repositories related to AI and Machine Learning (ML) across various domains, including natural language processing (NLP), computer vision, reinforcement learning, and more: 4 | 5 | --- 6 | 7 | ### **General Machine Learning** 8 | 9 | 1. **Machine Learning From Scratch** 10 | Implementations of ML models from scratch using NumPy only. 11 | [Link](https://github.com/eriklindernoren/ML-From-Scratch) 12 | 13 | 2. **100-Days-Of-ML-Code** 14 | A comprehensive guide to learn ML in 100 days. 15 | [Link](https://github.com/Avik-Jain/100-Days-Of-ML-Code) 16 | 17 | 3. **fastai/fastai** 18 | High-level library built on PyTorch, focused on making DL accessible. 19 | [Link](https://github.com/fastai/fastai) 20 | 21 | 4. **ml-course** 22 | Open materials for studying machine learning algorithms. 23 | [Link](https://github.com/yandexdataschool/ml-course) 24 | 25 | 5. **Hands-On ML with Scikit-Learn, Keras, and TensorFlow** 26 | Notebooks for the book "Hands-On Machine Learning." 27 | [Link](https://github.com/ageron/handson-ml3) 28 | 29 | --- 30 | 31 | ### **Deep Learning** 32 | 33 | 6. **Deep Learning Specialization on Coursera** 34 | Jupyter notebooks from Andrew Ng's Coursera course. 35 | [Link](https://github.com/Kulbear/deep-learning-coursera) 36 | 37 | 7. **PyTorch Examples** 38 | A set of example scripts for PyTorch. 39 | [Link](https://github.com/pytorch/examples) 40 | 41 | 8. **TensorFlow Models** 42 | TensorFlow implementations of ML and DL models. 43 | [Link](https://github.com/tensorflow/models) 44 | 45 | 9. **Dive into Deep Learning** 46 | Interactive deep learning book with code, math, and discussions. 47 | [Link](https://github.com/d2l-ai/d2l-en) 48 | 49 | 10. **Deep Learning for Computer Vision** 50 | DL implementations focused on computer vision. 51 | [Link](https://github.com/PracticalDL/Practical-Deep-Learning-Book) 52 | 53 | --- 54 | 55 | ### **Natural Language Processing (NLP)** 56 | 57 | 11. **Transformers** 58 | Hugging Face's popular library for transformers in NLP. 59 | [Link](https://github.com/huggingface/transformers) 60 | 61 | 12. **spaCy** 62 | Industrial-strength NLP library in Python. 63 | [Link](https://github.com/explosion/spaCy) 64 | 65 | 13. **stanfordnlp** 66 | Official Python wrapper for Stanford NLP tools. 67 | [Link](https://github.com/stanfordnlp/stanfordnlp) 68 | 69 | 14. **NLTK** 70 | Natural Language Toolkit — classic library for NLP. 71 | [Link](https://github.com/nltk/nltk) 72 | 73 | 15. **OpenNLP** 74 | Apache library for processing natural language text. 75 | [Link](https://github.com/apache/opennlp) 76 | 77 | --- 78 | 79 | ### **Computer Vision** 80 | 81 | 16. **OpenCV** 82 | Library for computer vision, image processing, and machine learning. 83 | [Link](https://github.com/opencv/opencv) 84 | 85 | 17. **Detectron2** 86 | Facebook's research library for object detection and segmentation. 87 | [Link](https://github.com/facebookresearch/detectron2) 88 | 89 | 18. **DeepLab** 90 | Semantic image segmentation using DeepLab models. 91 | [Link](https://github.com/tensorflow/models/tree/master/research/deeplab) 92 | 93 | 19. **StyleGAN2** 94 | Official repository for StyleGAN2, used for generating images. 95 | [Link](https://github.com/NVlabs/stylegan2) 96 | 97 | 20. **YOLOv5** 98 | Real-time object detection with YOLOv5. 99 | [Link](https://github.com/ultralytics/yolov5) 100 | 101 | --- 102 | 103 | ### **Reinforcement Learning** 104 | 105 | 21. **OpenAI Gym** 106 | Toolkit for developing RL environments and algorithms. 107 | [Link](https://github.com/openai/gym) 108 | 109 | 22. **Stable-Baselines3** 110 | A set of RL algorithms based on PyTorch. 111 | [Link](https://github.com/DLR-RM/stable-baselines3) 112 | 113 | 23. **RLlib** 114 | A scalable library for reinforcement learning. 115 | [Link](https://github.com/ray-project/ray/tree/master/rllib) 116 | 117 | 24. **DeepMind Lab** 118 | A 3D environment for RL research. 119 | [Link](https://github.com/deepmind/lab) 120 | 121 | 25. **dopamine** 122 | Google's lightweight RL framework. 123 | [Link](https://github.com/google/dopamine) 124 | 125 | --- 126 | 127 | ### **AI Ethics and Fairness** 128 | 129 | 26. **AI Fairness 360** 130 | IBM's toolkit to detect and mitigate bias in ML models. 131 | [Link](https://github.com/Trusted-AI/AIF360) 132 | 133 | 27. **Themis-ML** 134 | A library for fairness-aware ML. 135 | [Link](https://github.com/cosmicBboy/themis-ml) 136 | 137 | 28. **What-If Tool** 138 | Visual interface for ML fairness and interpretability. 139 | [Link](https://github.com/PAIR-code/what-if-tool) 140 | 141 | --- 142 | 143 | ### **Miscellaneous** 144 | 145 | 29. **Awesome Machine Learning** 146 | A curated list of awesome ML frameworks, libraries, and software. 147 | [Link](https://github.com/josephmisiti/awesome-machine-learning) 148 | 149 | 30. **Awesome AI** 150 | A curated list of AI-related tools, datasets, and resources. 151 | [Link](https://github.com/h2oai/awesome-ai) 152 | 153 | 31. **Data Science Notebooks** 154 | Jupyter notebooks for data analysis, visualization, and ML. 155 | [Link](https://github.com/donnemartin/data-science-ipython-notebooks) 156 | 157 | 32. **Model Zoo for PyTorch** 158 | Pre-trained PyTorch models for various tasks. 159 | [Link](https://github.com/pytorch/vision/tree/main/references) 160 | 161 | 33. **AI Papers with Code** 162 | A collection of AI papers accompanied by their implementations. 163 | [Link](https://github.com/paperswithcode/paperswithcode) 164 | 165 | --- 166 | 167 | These repositories cover a wide range of applications and techniques in AI and ML, making them valuable resources for learning and project development. Let me know if you want recommendations specific to a particular area! 168 | --------------------------------------------------------------------------------