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9 | 10 | 11 | https://xkcd.com/1838/ 12 | 13 |
14 | 15 | ## Contents 16 | 17 | - [General ML](#general-ml) 18 | - [Application Fields](#application-fields) 19 | - [ML sub-fields](#ml-sub-fields) 20 | - [Explainability, Interpretability and Fairness](#explainability-interpretability-and-fairness) 21 | - [Computer Vision](#computer-vision) 22 | - [Datasets](#datasets) 23 | - [Summer schools, conferences,...](#events) 24 | - [Outdated](#outdated) 25 | 26 | 27 | ## General ML 28 | 29 | Tools, tutorials, software engineering best practices and other. 30 | 31 | * [Production machine learning](https://github.com/EthicalML/awesome-production-machine-learning) 32 | GitHub stars 33 | GitHub last commit\ 34 | Open source libraries to deploy, monitor, version and scale your machine learning 35 | 36 | * [Software engineering for machine learning ](https://github.com/SE-ML/awesome-seml) 37 | GitHub stars 38 | GitHub last commit\ 39 | Articles that cover the software engineering best practices for building machine learning applications 40 | 41 | * [MLOps (Machine Learning Operations)](https://github.com/visenger/awesome-mlops) 42 | GitHub stars 43 | GitHub last commit\ 44 | References for MLOps 45 | 46 | * [System for machine learning](https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning) 47 | GitHub stars 48 | GitHub last commit\ 49 | Research in machine learning system 50 | 51 | * [H2O](https://github.com/h2oai/awesome-h2o) 52 | GitHub stars 53 | GitHub last commit\ 54 | Research, applications and projects built using the H2O Machine Learning platform 55 | 56 | * [Machine learning with Ruby](https://github.com/arbox/machine-learning-with-ruby) 57 | GitHub stars 58 | GitHub last commit\ 59 | Resouces for ML in Ruby 60 | 61 | * [Machine learning tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) 62 | GitHub stars 63 | GitHub last commit\ 64 | Machine learning and deep learning tutorials, articles and other resources 65 | 66 | * [Machine learning software](https://github.com/josephmisiti/awesome-machine-learning) 67 | GitHub stars 68 | GitHub last commit\ 69 | Learning frameworks, libraries and software. 70 | 71 | ## Application fields 72 | 73 | ML applied to specific fields. 74 | 75 | * [Machine learning for cybersecurity](https://github.com/jivoi/awesome-ml-for-cybersecurity) 76 | GitHub stars 77 | GitHub last commit\ 78 | Tools and resources related to the use of machine learning for cyber security 79 | 80 | ## ML Sub-fields 81 | 82 | * [Multimodal machine learning](https://github.com/pliang279/awesome-multimodal-ml) 83 | GitHub stars 84 | GitHub last commit\ 85 | Research topics in multimodal machine learning 86 | 87 | * [Domain adaptation](https://github.com/zhaoxin94/awesome-domain-adaptation) 88 | GitHub stars 89 | GitHub stars\ 90 | Papers, applications and resources about domain adaptation 91 | 92 | * [Anomaly detection](https://github.com/hoya012/awesome-anomaly-detection) 93 | GitHub stars 94 | GitHub stars\ 95 | Anomaly detection resources 96 | 97 | * [Out-of-distribution detection](https://github.com/huytransformer/Awesome-Out-Of-Distribution-Detection) 98 | GitHub stars 99 | GitHub stars\ 100 | Out-of-distribution detection, robustness, and generalization resources 101 | 102 | * [Learning with label noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise) 103 | GitHub stars 104 | GitHub stars\ 105 | Papers, surveys and other resources for learning with noisy labels 106 | 107 | * [Open Set Recognition](https://github.com/iCGY96/awesome_OpenSetRecognition_list) 108 | GitHub stars 109 | GitHub stars\ 110 | Papers and resources linked to open set recognition, out-of-distribution, open set domain adaptation, and open world recognition 111 | 112 | * [Online Machine Learning](https://github.com/online-ml/awesome-online-machine-learning) 113 | GitHub stars 114 | GitHub stars\ 115 | Papers and resources about machine learning on data arriving sequentially 116 | 117 | * [AutoML](https://github.com/hibayesian/awesome-automl-papers) 118 | GitHub stars 119 | GitHub stars\ 120 | Papers and resources about automated machine learning 121 | 122 | * [Data-centric AI](https://github.com/daochenzha/data-centric-AI) 123 | GitHub stars 124 | GitHub stars\ 125 | List of resources on data-centric AI, which focuses on enhancing data quality and quantity to improve AI systems 126 | 127 | * [Decision Tree Research Papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers) 128 | GitHub stars 129 | GitHub stars\ 130 | A collection of research papers on decision, classification and regression trees with implementations. 131 | 132 | ### Explainability, Interpretability and Fairness 133 | 134 | * [Machine learning interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability) 135 | GitHub stars 136 | GitHub stars\ 137 | Machine learning interpretability resources 138 | 139 | * [Fairness papersl](https://github.com/uclanlp/awesome-fairness-papers) 140 | GitHub stars 141 | GitHub stars\ 142 | Papers on fairness in NLP 143 | 144 | * [Explanable AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI) 145 | GitHub stars 146 | GitHub stars\ 147 | Research materials on explainable AI/ML 148 | 149 | * [XAI](https://github.com/altamiracorp/awesome-xai) 150 | GitHub stars 151 | GitHub stars\ 152 | XAI and Interpretable ML papers, methods, critiques, and resources 153 | 154 | * [Explanatory supervision](https://github.com/stefanoteso/awesome-explanatory-supervision) 155 | GitHub stars 156 | GitHub stars\ 157 | Relevant resources for machine learning from explanatory supervision 158 | 159 | ## Computer Vision 160 | 161 | * [Computer vision](https://github.com/jbhuang0604/awesome-computer-vision) 162 | GitHub stars 163 | GitHub stars\ 164 | Computer vision resources 165 | 166 | * [Deep learning](https://github.com/ChristosChristofidis/awesome-deep-learning) 167 | GitHub stars 168 | GitHub stars\ 169 | Deep Learning tutorials, projects and communities 170 | 171 | * [3D machine learning](https://github.com/timzhang642/3D-Machine-Learning) 172 | GitHub stars 173 | GitHub stars\ 174 | A resource repository for 3D machine learning 175 | 176 | * [Scene understanding](https://github.com/bertjiazheng/awesome-scene-understanding) 177 | GitHub stars 178 | GitHub stars\ 179 | Papers for scene understanding 180 | 181 | - [Deep learning for video analysis](https://github.com/HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis) 182 | GitHub stars 183 | GitHub stars\ 184 | Research on video analysis, especially multimodal learning for video analysis 185 | 186 | - [Deep learning object detection](https://github.com/hoya012/deep_learning_object_detection) 187 | GitHub stars 188 | GitHub stars\ 189 | Papers about object detection using deep learning 190 | 191 | * [Image classification](https://github.com/weiaicunzai/awesome-image-classification) 192 | GitHub stars 193 | GitHub stars\ 194 | Deep learning image classification papers and codes since 2014 195 | 196 | * [Face recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition) 197 | GitHub stars 198 | GitHub stars\ 199 | Papers about face detection, face alignment, face recognition and other 200 | 201 | * [Document understanding](https://github.com/tstanislawek/awesome-document-understanding) 202 | GitHub stars 203 | GitHub stars\ 204 | Resources for document understanding topic related to intelligent document processing 205 | 206 | ## Datasets 207 | 208 | * [Public datasets](https://github.com/awesomedata/awesome-public-datasets) 209 | GitHub stars 210 | GitHub stars\ 211 | A topic-centric public data sources in high quality 212 | 213 | * [Dataset tools](https://github.com/jsbroks/awesome-dataset-tools) 214 | GitHub stars 215 | GitHub stars\ 216 | Labeling tools and libraries for images, audio, time series and text 217 | 218 | * [Robotics datasets](https://github.com/sunglok/awesome-robotics-datasets) 219 | GitHub stars 220 | GitHub stars\ 221 | Datasets for robotics and computer vision 222 | 223 | ## Events 224 | 225 | * [Summer schools in machine learning + related fields](https://github.com/sshkhr/awesome-mlss). 226 | GitHub stars 227 | GitHub stars\ 228 | 229 | ## Outdated 230 | 231 | Lists that are either explicitly deprecated by their authors or no longer updated for more than two years, but they are still a good reference. 232 | 233 | * [Adversarial machine learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning) 234 | GitHub stars 235 | GitHub stars\ 236 | [Deprecated] 237 | 238 | * [Machine learning on source code](https://github.com/src-d/awesome-machine-learning-on-source-code) 239 | GitHub stars 240 | GitHub stars\ 241 | [Deprecated] 242 | 243 | * [Most cited deep learning papers](https://github.com/terryum/awesome-deep-learning-papers) 244 | GitHub stars 245 | GitHub stars\ 246 | The most cited deep learning papers (2012-2016) 247 | [Deprecated] 248 | 249 | * [CoreML models](https://github.com/likedan/Awesome-CoreML-Models) 250 | GitHub stars 251 | GitHub last commit\ 252 | Models for Core ML (for iOS 11+) 253 | 254 | * [Quant machine learning trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading) 255 | GitHub stars 256 | GitHub stars\ 257 | Quant/Algorithm trading resources with an emphasis on Machine Learning 258 | 259 | * [Quantum machine learning](https://github.com/krishnakumarsekar/awesome-quantum-machine-learning) 260 | GitHub stars 261 | GitHub stars\ 262 | Quantum machine learning algorithms,study materials,libraries and software 263 | 264 | * [Human pose estimation](https://github.com/wangzheallen/awesome-human-pose-estimation) 265 | GitHub stars 266 | GitHub stars\ 267 | Mainly focus on human pose estimation, and will include mesh representation, flow calculation, (inverse) kinematics, affordance, robotics, or sequence learning 268 | 269 | * [Action recognition](https://github.com/jinwchoi/awesome-action-recognition) 270 | GitHub stars 271 | GitHub stars\ 272 | Action recognition and related area resources 273 | 274 | * [Fairness in AI](https://github.com/datamllab/awesome-fairness-in-ai) 275 | GitHub stars 276 | GitHub stars\ 277 | Fairness in AI resources 278 | 279 | ## Contribute 280 | 281 | Contributions welcome! Feel free to open a pull-request! 282 | 283 | 284 | ## License 285 | 286 | 287 | Public Domain Mark 289 | 290 | --------------------------------------------------------------------------------