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https://github.com/abonte/awesome-lists-machine-learning

A curated list of awesome lists on Machine Learning.
https://github.com/abonte/awesome-lists-machine-learning

List: awesome-lists-machine-learning

artificial-intelligence awesome-list computer-vision deep-learning machine-learning xai

Last synced: about 1 month ago
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A curated list of awesome lists on Machine Learning.

Host: GitHub
URL: https://github.com/abonte/awesome-lists-machine-learning
Owner: abonte
License: cc0-1.0
Created: 2021-08-27T16:33:25.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-02-21T12:56:39.000Z (7 months ago)
Last Synced: 2024-08-13T08:05:33.647Z (about 1 month ago)
Topics: artificial-intelligence, awesome-list, computer-vision, deep-learning, machine-learning, xai
Homepage:
Size: 66.4 KB
Stars: 25
Watchers: 3
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

ultimate-awesome - awesome-lists-machine-learning - A curated list of awesome lists on Machine Learning. (Other Lists / PowerShell Lists)

README

        # Awesome List of Awesome Lists on Machine Learning







> A curated list of awesome lists on Machine Learning.





[xkcd.com](https://xkcd.com)



## Contents

- [General ML](#general-ml)

- [Application Fields](#application-fields)

- [ML sub-fields](#ml-sub-fields)

  - [Explainability, Interpretability and Fairness](#explainability-interpretability-and-fairness)

- [Computer Vision](#computer-vision)

- [Datasets](#datasets)

- [Summer schools, conferences,...](#events)

- [Outdated](#outdated)

## General ML

Tools, tutorials, software engineering best practices and other.

* [Production machine learning](https://github.com/EthicalML/awesome-production-machine-learning)

  

  \

  Open source libraries to deploy, monitor, version and scale your machine learning

* [Software engineering for machine learning ](https://github.com/SE-ML/awesome-seml)

  

  \

  Articles that cover the software engineering best practices for building machine learning applications

* [MLOps (Machine Learning Operations)](https://github.com/visenger/awesome-mlops)

  

  \

  References for MLOps

* [System for machine learning](https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning)

  

  \

  Research in machine learning system

* [H2O](https://github.com/h2oai/awesome-h2o)

  

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  Research, applications and projects built using the H2O Machine Learning platform

* [Machine learning with Ruby](https://github.com/arbox/machine-learning-with-ruby)

  

  \

  Resouces for ML in Ruby

* [Machine learning tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)

  

  \

  Machine learning and deep learning tutorials, articles and other resources

  

* [Machine learning software](https://github.com/josephmisiti/awesome-machine-learning)

  

  \

  Learning frameworks, libraries and software. 

## Application fields

ML applied to specific fields.

* [Machine learning for cybersecurity](https://github.com/jivoi/awesome-ml-for-cybersecurity)

  

  \

  Tools and resources related to the use of machine learning for cyber security

## ML Sub-fields

  

* [Multimodal machine learning](https://github.com/pliang279/awesome-multimodal-ml)

  

  \

  Research topics in multimodal machine learning

* [Domain adaptation](https://github.com/zhaoxin94/awesome-domain-adaptation)

  

  \

  Papers, applications and resources about domain adaptation

* [Anomaly Detection](https://github.com/hoya012/awesome-anomaly-detection)

  

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  Anomaly detection resources

* [Learning with label noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise)

  

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  Papers, surveys and other resources for learning with noisy labels

* [Open Set Recognition](https://github.com/iCGY96/awesome_OpenSetRecognition_list)

  

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  Papers and resources linked to open set recognition, out-of-distribution, open set domain adaptation, and open world recognition

* [Online Machine Learning](https://github.com/online-ml/awesome-online-machine-learning)

  

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  Papers and resources about machine learning on data arriving sequentially

  

* [AutoML](https://github.com/hibayesian/awesome-automl-papers)

  

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  Papers and resources about automated machine learning

* [Data-centric AI](https://github.com/daochenzha/data-centric-AI)

  

  \

  List of resources on data-centric AI, which focuses on enhancing data quality and quantity to improve AI systems

* [Decision Tree Research Papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers)

  

  \

  A collection of research papers on decision, classification and regression trees with implementations. 

### Explainability, Interpretability and Fairness

* [Machine learning interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability)

  

  \

  Machine learning interpretability resources

* [Fairness papersl](https://github.com/uclanlp/awesome-fairness-papers)

  

  \

  Papers on fairness in NLP

 

* [Explanable AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI)

  

  \

  Research materials on explainable AI/ML

* [XAI](https://github.com/altamiracorp/awesome-xai)

  

  \

  XAI and Interpretable ML papers, methods, critiques, and resources

* [Explanatory supervision](https://github.com/stefanoteso/awesome-explanatory-supervision)

  

  \

  Relevant resources for machine learning from explanatory supervision

## Computer Vision

* [Computer vision](https://github.com/jbhuang0604/awesome-computer-vision)

  

  \

  Computer vision resources

* [Deep learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

  

  \

  Deep Learning tutorials, projects and communities

* [3D machine learning](https://github.com/timzhang642/3D-Machine-Learning)

  

  \

  A resource repository for 3D machine learning

* [Scene understanding](https://github.com/bertjiazheng/awesome-scene-understanding)

  

  \

  Papers for scene understanding

- [Deep learning for video analysis](https://github.com/HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis)

  

  \

  Research on video analysis, especially multimodal learning for video analysis

- [Deep learning object detection](https://github.com/hoya012/deep_learning_object_detection)

  

  \

  Papers about object detection using deep learning

* [Image classification](https://github.com/weiaicunzai/awesome-image-classification)

  

  \

  Deep learning image classification papers and codes since 2014

* [Face recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition)

  

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  Papers about face detection, face alignment, face recognition and other

* [Document understanding](https://github.com/tstanislawek/awesome-document-understanding)

  

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  Resources for document understanding topic related to intelligent document processing

## Datasets

* [Public datasets](https://github.com/awesomedata/awesome-public-datasets)

  

  \

  A topic-centric public data sources in high quality

* [Dataset tools](https://github.com/jsbroks/awesome-dataset-tools)

  

  \

  Labeling tools and libraries for images, audio, time series and text

* [Robotics datasets](https://github.com/sunglok/awesome-robotics-datasets)

  

  \

  Datasets for robotics and computer vision

## Events

* [Summer schools in machine learning + related fields](https://github.com/sshkhr/awesome-mlss).

  

  \

## Outdated

Lists that are either explicitly deprecated by their authors or no longer updated for more than two years, but they are stil a good reference.

* [Adversarial machine learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning)

  

  \

  [Deprecated]

* [Machine learning on source code](https://github.com/src-d/awesome-machine-learning-on-source-code)

  

  \

  [Deprecated]

* [Most cited deep learning papers](https://github.com/terryum/awesome-deep-learning-papers)

  

  \

  The most cited deep learning papers (2012-2016)

  [Deprecated]

 

* [CoreML models](https://github.com/likedan/Awesome-CoreML-Models)

  

  \

  Models for Core ML (for iOS 11+)

* [Quant machine learning trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading)

  

  \

  Quant/Algorithm trading resources with an emphasis on Machine Learning

 

* [Quantum machine learning](https://github.com/krishnakumarsekar/awesome-quantum-machine-learning)

  

  \

  Quantum machine learning algorithms,study materials,libraries and software

  

* [Human pose estimation](https://github.com/wangzheallen/awesome-human-pose-estimation)

  

  \

  Mainly focus on human pose estimation, and will include mesh representation, flow calculation, (inverse) kinematics, affordance, robotics, or sequence learning

  

* [Action recognition](https://github.com/jinwchoi/awesome-action-recognition)

  

  \

  Action recognition and related area resources

  

* [Fairness in AI](https://github.com/datamllab/awesome-fairness-in-ai)

  

  \

  Fairness in AI resources

  

## Contribute

Contributions welcome! Feel free to open a pull-request!

## License