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https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
😎 A curated list of awesome lists across all machine learning topics. | 机器学习/深度学习/人工智能一切主题 (学习范式/任务/应用/模型/道德/交叉学科/数据集/框架/教程) 的资源列表汇总。
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
List: awesome-machine-learning-resources
artificial-intelligence awesome awesome-list machine-learning machine-learning-algorithms neural-network
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😎 A curated list of awesome lists across all machine learning topics. | 机器学习/深度学习/人工智能一切主题 (学习范式/任务/应用/模型/道德/交叉学科/数据集/框架/教程) 的资源列表汇总。
- Host: GitHub
- URL: https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
- Owner: ZhiningLiu1998
- License: cc0-1.0
- Created: 2021-12-21T11:48:05.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-03-08T16:59:22.000Z (over 1 year ago)
- Last Synced: 2024-08-03T23:11:40.745Z (about 1 month ago)
- Topics: artificial-intelligence, awesome, awesome-list, machine-learning, machine-learning-algorithms, neural-network
- Homepage:
- Size: 114 KB
- Stars: 525
- Watchers: 8
- Forks: 85
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-machine-learning-resources - 😎 A curated list of awesome lists across all machine learning topics. | 机器学习/深度学习/人工智能一切主题 (学习范式/任务/应用/模型/道德/交叉学科/数据集/框架/教程) 的资源列表汇总。. (Programming Language Lists / Python Lists)
README
🚀 Awesome Machine Learning Resources
Language:
[English]
[Chinese/中文]
**A curated list of curated lists of awesome resources across various machine learning and deep learning topics.**
**With 380+ items (Dec 2021), this repository aims to:**
- **help `beginners` understand the branches and latest developments in machine learning;**
- **help `researchers` follow new machine learning research directions;**
- **help `engineers` find suitable tutorials and libraries to solve practical problems.**
**Note:**
- ⭐ **Please leave a STAR if you like this project!** ⭐
- **Contributing:** If you find any incorrect / inappropriate / outdated content, please kindly consider opening an issue or a PR. We would greatly appreciate your contribution to this list!
- **Mark:** ⚠️ indicates **`inactive`**, i.e., the corresponding list has stopped updating (for 12+ months), but can still be a good reference for starters.
What's new:
- [01/2023] Add [Fair Graph Learning](https://github.com/EdisonLeeeee/Awesome-Fair-Graph-Learning) in [Graph Learning](#graph-learning), update section [Time-series/Stream Learning](#time-seriesstream-learning).
- [08/2022] Add Graph for Traffic Benchmark in [Graph Learning](#graph-learning).
- [04/2022] Update section [Graph Learning](#graph-learning).
- [12/2021] Update section [Clustering](#clustering).
- [12/2021] Update section [Natural Language Processing (NLP)](#natural-language-processing-nlp) & [Fairness in AI](#fairness-in-ai).
- [12/2021] The [Chinese version](https://github.com/ZhiningLiu1998/awesome-awesome-machine-learning/blob/main/README_CN.md) is now available!
- [12/2021] Add section [Interdisciplinary](#interdisciplinary-machine-learning--x) - [Software Engineering (MLonCode)](#software-engineering-mloncode).
- [12/2021] Add section [Paradigm](#machine-learning-paradigm) - [Dimensionality Reduction (Feature Selection/Extraction)](#dimensionality-reduction-feature-selectionextraction).
- More
**Check out [Zhining](https://github.com/ZhiningLiu1998)'s other open-source projects!**
Imbalanced-Ensemble [PythonLib]
Imbalanced Learning [Awesome]
Self-paced Ensemble [ICDE]
Meta-Sampler [NeurIPS]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [General Machine Learning](#general-machine-learning)
- [Machine Learning Paradigm](#machine-learning-paradigm)
- [Semi/Self-Supervised Learning](#semiself-supervised-learning)
- [Contrastive Learning](#contrastive-learning)
- [Representation Learning (Embedding)](#representation-learning-embedding)
- [Metric Learning](#metric-learning)
- [Reinforcement Learning](#reinforcement-learning)
- [Transfer Learning](#transfer-learning)
- [Meta-learning](#meta-learning)
- [Multi-task Learning](#multi-task-learning)
- [Imbalanced/Long-tail Learning](#imbalancedlong-tail-learning)
- [Few-shot Learning](#few-shot-learning)
- [Adversarial Learning](#adversarial-learning)
- [Robust Learning](#robust-learning)
- [Active Learning](#active-learning)
- [Lifelong/Incremental/Continual Learning](#lifelongincrementalcontinual-learning)
- [Ensemble Learning](#ensemble-learning)
- [Automated Machine Learning (AutoML)](#automated-machine-learning-automl)
- [Federated Learning](#federated-learning)
- [Anomaly Detection](#anomaly-detection)
- [Clustering](#clustering)
- [Dimensionality Reduction (Feature Selection/Extraction)](#dimensionality-reduction-feature-selectionextraction)
- [Machine Learning Task \& Application](#machine-learning-task--application)
- [Computer Vision (CV)](#computer-vision-cv)
- [Natural Language Processing (NLP)](#natural-language-processing-nlp)
- [Multi-modal \& Cross-modal Learning](#multi-modal--cross-modal-learning)
- [Graph Learning](#graph-learning)
- [Knowledge Graph](#knowledge-graph)
- [Time-series/Stream Learning](#time-seriesstream-learning)
- [Recommender Systems](#recommender-systems)
- [Information Retrieval](#information-retrieval)
- [Gaming \& Searching](#gaming--searching)
- [Machine Learning Model](#machine-learning-model)
- [Pretrained \& Foundation Model](#pretrained--foundation-model)
- [in NLP (BERT, RoBERTa, GPT, etc.)](#in-nlp-bert-roberta-gpt-etc)
- [in CV (Visual Transformers, etc.)](#in-cv-visual-transformers-etc)
- [in other topics](#in-other-topics)
- [Convolutional Neural Network (CNN)](#convolutional-neural-network-cnn)
- [Recurrent Neural Network (RNN, LSTM, GRU, etc.)](#recurrent-neural-network-rnn-lstm-gru-etc)
- [Graph Neural Network (GNN, GCN, GAT, etc.)](#graph-neural-network-gnn-gcn-gat-etc)
- [Generative Model \& Generative Adversarial Network (GAN)](#generative-model--generative-adversarial-network-gan)
- [Variational Autoencoder](#variational-autoencoder)
- [Tree-based \& Ensemble Model](#tree-based--ensemble-model)
- [Machine Learning Interpretability \& Fairness \& Ethics](#machine-learning-interpretability--fairness--ethics)
- [Interpretability in AI](#interpretability-in-ai)
- [Fairness in AI](#fairness-in-ai)
- [Ethics in AI](#ethics-in-ai)
- [Interdisciplinary: Machine Learning + X](#interdisciplinary-machine-learning--x)
- [System (MLSys/SysML)](#system-mlsyssysml)
- [Database (AIDB/ML4DB)](#database-aidbml4db)
- [Software Engineering (MLonCode)](#software-engineering-mloncode)
- [Cyber Security](#cyber-security)
- [Quantum Computing](#quantum-computing)
- [Medical \& Healthcare](#medical--healthcare)
- [Bioinformatics](#bioinformatics)
- [Biology \& Chemistry](#biology--chemistry)
- [Finance \& Trading](#finance--trading)
- [Business](#business)
- [Law](#law)
- [Machine Learning Datasets](#machine-learning-datasets)
- [Production Machine Learning](#production-machine-learning)
- [Open-source Libraries](#open-source-libraries)
- [Big Data Frameworks](#big-data-frameworks)
- [Acknowledgement ✨](#acknowledgement-)
- [Contributors ✨](#contributors-)
General Machine Learning
------------------------
- *Practice*
- [**[List, Library] Awesome Machine Learning**](https://github.com/josephmisiti/awesome-machine-learning) 
- A curated list of awesome machine learning frameworks, libraries and software (by language).
- [**[Library] scikit-learn**](https://github.com/scikit-learn/scikit-learn) 
- scikit-learn: machine learning in Python.
- *Research*
- [**[List] Papers-Literature-ML-DL-RL-AI**](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI) 
- Impactful and widely cited papers and literature on ML/DL/RL/AI.
- [**[List] Awesome Deep Learning**](https://github.com/ChristosChristofidis/awesome-deep-learning) **[⚠️Inactive]** 
- A curated list of awesome deep learning books, courses, videos, lectures, tutorials, and more.
- [**[List] Awesome Deep Learning Papers**](https://github.com/terryum/awesome-deep-learning-papers) **[⚠️Inactive]** 
- A curated list of the most cited deep learning papers (2012-2016).
Machine Learning Paradigm
--------------------------------
#### Semi/Self-Supervised Learning
- *General*
- [**[List] Awesome Semi-Supervised Learning**](https://github.com/yassouali/awesome-semi-supervised-learning) 
- A curated list of awesome Semi-Supervised Learning resources.
- [**[List] Awesome Self-Supervised Learning**](https://github.com/jason718/awesome-self-supervised-learning) 
- A curated list of awesome Self-Supervised Learning resources.
- [**[List] Awesome Self-Supervised Papers**](https://github.com/dev-sungman/Awesome-Self-Supervised-Papers) 
- Collecting papers about Self-Supervised Learning, Representation Learning.
- *Sub-topics*
- [**[List] Awesome Graph Self-Supervised Learning**](https://github.com/LirongWu/awesome-graph-self-supervised-learning) 
- A curated list for awesome self-supervised graph representation learning resources.
- [**[List] Awesome Self-supervised GNN**](https://github.com/ChandlerBang/awesome-self-supervised-gnn) 
- Papers about self-supervised learning on Graph Neural Networks (GNNs).
- *Practice*
- [**[Library] mmselfsup**](https://github.com/open-mmlab/mmselfsup) 
- OpenMMLab Self-Supervised Learning Toolbox and Benchmark.
- [**[Library] unilm**](https://github.com/microsoft/unilm) 
- Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities.
- [**[Library] albert**](https://github.com/google-research/albert) 
- A Lite BERT for Self-supervised Learning of Language Representations.
#### Contrastive Learning
- *General*
- [**[List] PyContrast**](https://github.com/HobbitLong/PyContrast) 
- This repo lists recent contrastive learning papers, and includes code for many of them.
- [**[List] Awesome Contrastive Learning**](https://github.com/asheeshcric/awesome-contrastive-self-supervised-learning) 
- A comprehensive list of awesome contrastive self-supervised learning papers.
- [**[List] Awesome Contrastive Learning Papers & Codes**](https://github.com/coder-duibai/Contrastive-Learning-Papers-Codes) 
- A comprehensive list of awesome Contrastive Learning Papers&Codes.
- *Practice*
- [**[Library] PyGCL**](https://github.com/GraphCL/PyGCL) 
- Graph Contrastive Learning Library for PyTorch.
#### Representation Learning (Embedding)
- *General*
- [**[List] awesome-embedding-models**](https://github.com/Hironsan/awesome-embedding-models) **[⚠️Inactive]** 
- A curated list of awesome embedding models tutorials, projects and communities.
- [**[List] awesome-representation-learning**](https://github.com/Mehooz/awesome-representation-learning) 
- Reading List for Topics in Representation Learning.
- *Sub-topics*
- [**[List] awesome-sentence-embedding**](https://github.com/Separius/awesome-sentence-embedding) 
- A curated list of pretrained sentence and word embedding models.
- [**[List] Awesome Implicit Neural Representations**](https://github.com/vsitzmann/awesome-implicit-representations) 
- A curated list of resources on implicit neural representations.
- [**[List] awesome-2vec**](https://github.com/MaxwellRebo/awesome-2vec) 
- Curated list of 2vec-type embedding models.
- [**[List] Awesome-VAEs**](https://github.com/matthewvowels1/Awesome-VAEs) 
- Awesome work on the VAE, disentanglement, representation learning, and generative models.
- [**[List] Awesome Visual Representation Learning with Transformers**](https://github.com/alohays/awesome-visual-representation-learning-with-transformers) 
- Awesome Transformers (self-attention) in Computer Vision.
- [**[List] Awesome Deep Graph Representation Learning**](https://github.com/zlpure/awesome-graph-representation-learning) 
- A curated list for awesome deep graph representation learning resources.
- [**[List] awesome-network-embedding**](https://github.com/chihming/awesome-network-embedding) **[⚠️Inactive]** 
- Network representation learning, graph embedding, knowledge embedding.
- [**[List] Must-read papers on NRL/NE.**](https://github.com/thunlp/NRLPapers) **[⚠️Inactive]** 
- NRL: network representation learning. NE: network embedding.
- [**[List] disentangled-representation-papers**](https://github.com/sootlasten/disentangled-representation-papers) **[⚠️Inactive]** 
- Papers on disentangled (and an occasional "conventional") representation learning.
- [**[List] Representation Learning on Heterogeneous Graph**](https://github.com/Jhy1993/Representation-Learning-on-Heterogeneous-Graph) **[⚠️Inactive]** 
- Heterogeneous Graph Embedding, Heterogeneous GNNs and Applications.
#### Metric Learning
- *Practice*
- [**[Library] pytorch-metric-learning**](https://github.com/KevinMusgrave/pytorch-metric-learning) 
- The easiest way to use deep metric learning in your application.
- [**[Library] metric-learn**](https://github.com/scikit-learn-contrib/metric-learn) 
- metric-learn: Metric Learning in Python.
- [**[Code Collection] Deep-Metric-Learning-Baselines**](https://github.com/Confusezius/Deep-Metric-Learning-Baselines) **[⚠️Inactive]** 
- PyTorch Implementation for Deep Metric Learning Pipelines.
#### Reinforcement Learning
- *General*
- [**[List] Awesome Reinforcement Learning**](https://github.com/aikorea/awesome-rl) 
- A curated list of resources dedicated to reinforcement learning.
- [**[List] Awesome DL & RL Papers and Other Resources**](https://github.com/endymecy/awesome-deeplearning-resources) 
- A list of recent papers regarding deep learning and deep reinforcement learning.
- [**[List] Awesome Deep RL**](https://github.com/kengz/awesome-deep-rl) 
- A curated list of awesome Deep Reinforcement Learning resources.
- [**[List] Awesome Reinforcement Learning (CH/中文)**](https://github.com/wwxFromTju/awesome-reinforcement-learning-zh) **[⚠️Inactive]** 
- 强化学习从入门到放弃的资料
- *Sub-topics*
- [**[List] Awesome Offline RL**](https://github.com/hanjuku-kaso/awesome-offline-rl) 
- This is a collection of research and review papers for offline reinforcement learning.
- [**[List] Awesome Real World RL**](https://github.com/ugurkanates/awesome-real-world-rl) 
- Great resources for making Reinforcement Learning work in Real Life situations. Papers, projects and more.
- [**[List] Awesome Game AI**](https://github.com/datamllab/awesome-game-ai) 
- A curated, but incomplete, list of game AI resources on multi-agent learning.
- [**[List] Awesome RL Competitions**](https://github.com/seungjaeryanlee/awesome-rl-competitions) 
- Collection of competitions for Reinforcement Learning.
- [**[List] Awesome Robotics**](https://github.com/kiloreux/awesome-robotics) 
- This is a list of various books, courses and other resources for robotics
- [**[List] Awesome RL for Natural Language Processing (NLP)**](https://github.com/adityathakker/awesome-rl-nlp) **[⚠️Inactive]** 
- Curated List of Reinforcement Learning Resources for Natural Language Processing.
- [**[List] Awesome RL for Cybersecurity**](https://github.com/Limmen/awesome-rl-for-cybersecurity) 
- Curated list of resources dedicated to reinforcement learning applied to cyber security.
- *Practice*
- [**[Library] gym**](https://github.com/openai/gym) 
- A toolkit for developing and comparing reinforcement learning algorithms.
- [**[Library] trfl**](https://github.com/deepmind/trfl) 
- TensorFlow Reinforcement Learning.
- [**[Library] rlpyt**](https://github.com/astooke/rlpyt) 
- Reinforcement Learning in PyTorch.
- [**[Library] rlkit**](https://github.com/rail-berkeley/rlkit) 
- Reinforcement learning framework and algorithms implemented in PyTorch.
- [**[Library] MARO**](https://github.com/microsoft/maro) 
- Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
- [**[Library] bandits**](https://github.com/bgalbraith/bandits) 
- Python library for Multi-Armed Bandits.
- [**[Library] BanditLib**](https://github.com/huazhengwang/BanditLib) 
- Library of contextual bandits algorithms.
- [**[Tutorial] reinforcement-learning-an-introduction**](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction) 
- Python replication for Sutton & Barto's book Reinforcement Learning: An Introduction (2nd Edition).
- [**[Code Collection] reinforcement-learning**](https://github.com/dennybritz/reinforcement-learning) **[⚠️Inactive]** 
- Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow.
- [**[Tutorial] Reinforcement-learning-with-tensorflow (English&Chinese)**](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow) **[⚠️Inactive]** 
- Reinforcement Learning Methods and Tutorials.
- [**[Code Collection] reinforcement-learning**](https://github.com/rlcode/reinforcement-learning) **[⚠️Inactive]** 
- Minimal and Clean Reinforcement Learning Examples.
#### Transfer Learning
- *General*
- [**[List] 迁移学习 Transfer Learning**](https://github.com/jindongwang/transferlearning) 
- Everything about Transfer Learning.
- [**[List] Awesome Transfer Learning**](https://github.com/artix41/awesome-transfer-learning) 
- A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general.
- *Sub-topics*
- [**[List] Awesome Domain Adaptation**](https://github.com/zhaoxin94/awesome-domain-adaptation) 
- This repo is a collection of AWESOME things about domain adaptation, including papers, code, etc.
- [**[List] Domain Generalization**](https://github.com/amber0309/Domain-generalization) 
- Domain generalization papers and datasets.
- *Practice*
- [**[Library] Transfer-Learning-Library**](https://github.com/thuml/Transfer-Learning-Library) 
- Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization.
- [**[Code Collection] deep-transfer-learning**](https://github.com/easezyc/deep-transfer-learning) 
- A collection of implementations of deep domain adaptation algorithms
- [**[Tutorial] hands-on-transfer-learning-with-python**](https://github.com/dipanjanS/hands-on-transfer-learning-with-python) **[⚠️Inactive]** 
- Deep learning simplified by transferring prior learning using the Python deep learning ecosystem.
#### Meta-learning
- *General*
- [**[Code Collection] Torchmeta**](https://github.com/tristandeleu/pytorch-meta) 
- A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch.
- [**[List] Meta-Learning Papers**](https://github.com/floodsung/Meta-Learning-Papers) **[⚠️Inactive]** 
- Meta Learning/ Learning to Learn/ One Shot Learning/ Lifelong Learning.
- [**[List] Awesome Meta Learning**](https://github.com/sudharsan13296/Awesome-Meta-Learning) **[⚠️Inactive]** 
- A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
- [**[List] awesome-meta-learning**](https://github.com/dragen1860/awesome-meta-learning) **[⚠️Inactive]** 
- A curated list of Meta-Learning resources.
- *Practice*
- [**[Library] learn2learn**](https://github.com/learnables/learn2learn) 
- A PyTorch Library for Meta-learning Research.
- [**[Code Collection] pytorch-meta**](https://github.com/tristandeleu/pytorch-meta) 
- A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch.
- [**[Tutorial] Hands-On-Meta-Learning-With-Python**](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python) **[⚠️Inactive]** 
- Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow.
#### Multi-task Learning
- *General*
- [**[List] Multitask-Learning**](https://github.com/mbs0221/Multitask-Learning) 
- Multitask-Learning scholars, papers, surveys, slides, proceedings, and open-source projects.
- [**[List] Awesome Multi-Task Learning**](https://github.com/Manchery/awesome-multi-task-learning) 
- 2021 up-to-date list of papers on Multi-Task Learning (MTL), from ML perspective.
- *Sub-topics*
- [**[List] Awesome Multi-Task Learning (for vision)**](https://github.com/SimonVandenhende/Awesome-Multi-Task-Learning) 
- A list of papers on multi-task learning for *computer vision*.
- *Practice*
- [**[Code Collection] Multi-Task-Learning-PyTorch**](https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch) 
- Implement several multi-task learning models and training strategies in PyTorch.
#### Imbalanced/Long-tail Learning
- *General*
- [**[List] Awesome Imbalanced Learning**](https://github.com/ZhiningLiu1998/awesome-imbalanced-learning) 
- Everything about imbalanced (long-tail) learning. Frameworks and libraries (grouped by programming language), research papers (grouped by research field), imbalanced datasets, algorithms, utilities, Jupyter Notebooks, and Talks.
- [**[List] Awesome Long-Tailed Learning**](https://github.com/Stomach-ache/awesome-long-tailed-learning) 
- Related papers are sumarized, including its application in computer vision, in particular image classification, and extreme multi-label learning (XML), in particular text categorization.
- [**[List] Awesome Long-Tailed Learning\***](https://github.com/Vanint/Awesome-LongTailed-Learning) 
- A curated list of awesome deep long-tailed learning resources.
- *Sub-topics*
- [**[List] Awesome Long-tailed Recognition**](https://github.com/zzw-zwzhang/Awesome-of-Long-Tailed-Recognition) 
- A curated list of long-tailed recognition and related resources.
- [**[List] Awesome Imbalanced Time-series Classification**](https://github.com/danielgy/Paper-list-on-Imbalanced-Time-series-Classification-with-Deep-Learning) **[⚠️Inactive]** 
- Paper list of Imbalanced Time-series Classification with Deep Learning.
- *Practice*
- [**[Library] imbalanced-learn**](https://github.com/scikit-learn-contrib/imbalanced-learn) 
- A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning.
- [**[Library] imbalanced-ensemble (English&Chinese)**](https://github.com/ZhiningLiu1998/imbalanced-ensemble) 
- 类别不平衡/长尾机器学习 | Class-imbalanced/Long-tailed ensemble learning in Python
#### Few-shot Learning
- *General*
- [**[List] Awesome Papers Few shot**](https://github.com/Duan-JM/awesome-papers-fewshot) 
- Few-shot learning papers published on top conferences.
- *Sub-topics*
- [**[List] Few Shot Semantic Segmentation Papers**](https://github.com/xiaomengyc/Few-Shot-Semantic-Segmentation-Papers) 
- Papers pertaining to few-shot semantic segmentation.
- [**[List] Awesome Few-Shot Image Generation**](https://github.com/bcmi/Awesome-Few-Shot-Image-Generation) 
- Papers, datasets, and relevant links pertaining to few-shot image generation.
- *Practice*
- [**[Code Collection] Few-shot learning**](https://github.com/oscarknagg/few-shot) **[⚠️Inactive]** 
- Clean, readable and tested code to reproduce few-shot learning research.
#### Adversarial Learning
> **See also:** [Machine Learning Model](#machine-learning-model) -> [**Generative Model & Generative Adversarial Network (GAN)**](#generative-model--generative-adversarial-network-gan)
- *General*
- [**[List] Awesome Adversarial Machine Learning**](https://github.com/yenchenlin/awesome-adversarial-machine-learning) **[⚠️Inactive]** 
- A curated list of awesome adversarial machine learning resources.
- [**[List] Awesome Adversarial Examples for Deep Learning**](https://github.com/chbrian/awesome-adversarial-examples-dl) **[⚠️Inactive]** 
- A list of amazing resources for adversarial examples in deep learning.
- *Sub-topics*
- [**[List] Must-read Papers on Textual Adversarial Attack and Defense (TAAD)**](https://github.com/thunlp/TAADpapers) 
- [**[List] Graph Adversarial Learning Literature**](https://github.com/safe-graph/graph-adversarial-learning-literature) 
- A curated list of adversarial attacks and defenses papers on graph-structured data.
- *Practice*
- [**[Library] adversarial-robustness-toolbox**](https://github.com/Trusted-AI/adversarial-robustness-toolbox) 
- Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security.
- [**[Library] AdversarialDNN-Playground**](https://github.com/QData/AdversarialDNN-Playground) 
- Web-based visualization tool for adversarial machine learning / LiveDemo.
#### Robust Learning
- *General*
- [**[List] Clean, readable and tested code to reproduce few-shot learning research.Awesome Learning with Label Noise**](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise) 
- A curated list of resources for Learning with Noisy Labels
- [**[List] Papers of Robust ML (Defense)**](https://github.com/P2333/Papers-of-Robust-ML) 
- Related papers for robust machine learning (we mainly focus on defenses).
- *Practice*
- [**[Library] adversarial-robustness-toolbox**](https://github.com/Trusted-AI/adversarial-robustness-toolbox) 
- Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security.
- [**[Library] robustness-gym**](https://github.com/robustness-gym/robustness-gym) 
- Robustness Gym is an evaluation toolkit for machine learning.
- [**[Library] robustdg**](https://github.com/microsoft/robustdg) 
- Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
#### Active Learning
- *General*
- [**[List] Awesome Active Learning**](https://github.com/SupeRuier/awesome-active-learning) 
- Previous works of active learning were categorized.
- [**[List] Awesome Active Learning\***](https://github.com/baifanxxx/awesome-active-learning) 
- A curated list of awesome Active Learning.
- [**[List] Awesome Active Learning\*\***](https://github.com/yongjin-shin/awesome-active-learning) 
- A list of resources related to Active learning in machine learning.
- *Practice*
- [**[Library] modAL**](https://github.com/modAL-python/modAL) 
- A modular active learning framework for Python.
- [**[Library] libact**](https://github.com/ntucllab/libact) 
- libact: Pool-based Active Learning in Python
- [**[Library] pytorch_active_learning**](https://github.com/rmunro/pytorch_active_learning) 
- PyTorch Library for Active Learning to accompany Human-in-the-Loop Machine Learning book.
- [**[Code Collection] deep-active-learning**](https://github.com/ej0cl6/deep-active-learning) 
- Python implementations of several active learning algorithms.
- [**[Code Collection] Active Learning Playground**](https://github.com/google/active-learning) **[⚠️Inactive]** 
- A python module for experimenting with different active learning algorithms.
#### Lifelong/Incremental/Continual Learning
- *General*
- [**[List] Awesome Incremental Learning / Lifelong learning**](https://github.com/xialeiliu/Awesome-Incremental-Learning) 
- Papers in Incremental Learning / Lifelong Learning.
- [**[List] Continual Learning Literature**](https://github.com/optimass/continual_learning_papers) 
- Papers in Continual Learning.
- [**[List] Awesome Continual/Lifelong Learning**](https://github.com/prprbr/awesome-lifelong-continual-learning) 
- Papers, blogs, datasets and softwares.
- [**[List] Continual Learning Papers**](https://github.com/ContinualAI/continual-learning-papers) 
- Continual Learning papers list, curated by ContinualAI.
- [**[List] Lifelong Learning Paper List**](https://github.com/floodsung/Lifelong-Learning-Paper-List) **[⚠️Inactive]** 
- Papers in Lifelong Learning / Continual Learning.
- *Practice*
- [**[Code Collection] continual-learning**](https://github.com/GMvandeVen/continual-learning) 
- PyTorch implementation of various methods for continual learning.
- [**[Code Collection] incremental_learning.pytorch**](https://github.com/arthurdouillard/incremental_learning.pytorch) 
- A collection of incremental learning paper implementations.
- [**[Code Collection] Continual-Learning-Benchmark**](https://github.com/GT-RIPL/Continual-Learning-Benchmark) **[⚠️Inactive]** 
- Evaluate three types of task shifting with popular continual learning algorithms.
#### Ensemble Learning
> **See also:** [Machine Learning Model](#machine-learning-model) -> [**Tree-based & Ensemble Model**](#tree-based--ensemble-model)
- *General*
- [**[List] Awesome Ensemble Learning**](https://github.com/yzhao062/awesome-ensemble-learning) **[⚠️Inactive]** 
- Books, papers, courses, tutorials, libraries, datasets.
- *Sub-topics*
- [**[List] Awesome Gradient Boosting Research Papers**](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) 
- A curated list of gradient and adaptive boosting papers with implementations.
- [**[List] Awesome Random Forest**](https://github.com/kjw0612/awesome-random-forest) **[⚠️Inactive]** 
- A curated list of resources regarding tree-based methods and more, including but not limited to random forest, bagging and boosting.
- *Practice*
- [**[Library] xgboost**](https://github.com/dmlc/xgboost) 
- Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library.
- [**[Library] LightGBM**](https://github.com/microsoft/LightGBM) 
- A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework.
- [**[Library] catboost**](https://github.com/catboost/catboost) 
- A fast, scalable, high performance Gradient Boosting on Decision Trees library.
- [**[Library] combo**](https://github.com/yzhao062/combo) 
- A Python Toolbox for Machine Learning Model Combination,
- [**[Library] imbalanced-ensemble (English&Chinese)**](https://github.com/ZhiningLiu1998/imbalanced-ensemble) 
- 类别不平衡/长尾机器学习 | Class-imbalanced/Long-tailed ensemble learning in Python
- [**[Library] mlens**](https://github.com/flennerhag/mlens) **[⚠️Inactive]** 
- A Python library for high performance ensemble learning.
#### Automated Machine Learning (AutoML)
- *General*
- [**[List] Awesome AutoML Papers**](https://github.com/hibayesian/awesome-automl-papers) 
- Automated machine learning papers, articles, tutorials, slides and projects.
- [**[List] Awesome AutoDL**](https://github.com/D-X-Y/Awesome-AutoDL) 
- A curated list of automated deep learning related resources.
- [**[List] Awesome AutoML**](https://github.com/windmaple/awesome-AutoML) 
- Curating a list of AutoML-related research, tools, projects and other resources.
- *Sub-topics*
- [**[List] Awesome Neural Architecture Search Papers**](https://github.com/jackguagua/awesome-nas-papers) 
- Neural Architecture Search Papers
- [**[List] Awesome Architecture Search**](https://github.com/markdtw/awesome-architecture-search) **[⚠️Inactive]** 
- A curated list of awesome architecture search and hyper-parameter optimization resources.
- [**[List] Awesome AutoML and Lightweight Models**](https://github.com/guan-yuan/awesome-AutoML-and-Lightweight-Models) **[⚠️Inactive]** 
- *Practice*
- [**[Library] NNI (Neural Network Intelligence)**](https://github.com/Microsoft/nni) 
- An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
- [**[Library] tpot**](https://github.com/EpistasisLab/tpot) 
- A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
- [**[Library] ludwig**](https://github.com/ludwig-ai/ludwig) 
- Data-centric declarative deep learning framework.
- [**[Library] autokeras**](https://github.com/keras-team/autokeras) 
- AutoML library for deep learning.
- [**[Library] automl**](https://github.com/google/automl) 
- (Google Brain AutoML) list of AutoML related models and libraries.
- [**[Library] autogluon**](https://github.com/awslabs/autogluon) 
- AutoGluon: AutoML for Text, Image, and Tabular Data
- [**[Library] adanet**](https://github.com/tensorflow/adanet) 
- Fast and flexible AutoML with learning guarantees.
- [**[Library] FLAML**](https://github.com/microsoft/FLAML) 
- A fast library for AutoML and tuning.
#### Federated Learning
- *General*
- [**[List] Awesome Federated Learning**](https://github.com/chaoyanghe/Awesome-Federated-Learning) 
- A curated list of federated learning publications, re-organized from Arxiv (mostly).
- [**[List] Awesome Federated Learning\***](https://github.com/poga/awesome-federated-learning) 
- A list of resources releated to federated learning and privacy in machine learning.
- [**[List] Awesome Federated Learning**](https://github.com/weimingwill/awesome-federated-learning) 
- A curated list of research in federated learning.
- [**[List] 联邦学习 Federated Learning**](https://github.com/ZeroWangZY/federated-learning) 
- Everything about federated learning.
- [**[List] Federated Learning**](https://github.com/lokinko/Federated-Learning) 
- Federated Learning Papers (grouped by topic).
- *Sub-topics*
- [**[List] Awesome Federated Computing**](https://github.com/tushar-semwal/awesome-federated-computing) 
- A collection of research papers, codes, tutorials and blogs on ML carried out in a federated manner (distributed;decentralized).
- [**[List] Awesome Federated Learning on Graph and GNN Papers**](https://github.com/huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers) 
- Federated learning on graph, especially on GNNs, knowledge graph, and private GNN.
- *Practice*
- [**[Library] FATE**](https://github.com/FederatedAI/FATE) 
- An Industrial Grade Federated Learning Framework.
- [**[Library] federated**](https://github.com/tensorflow/federated) 
- A framework for implementing federated learning (TensorFlow).
- [**[Code] Federated-Learning-PyTorch**](https://github.com/AshwinRJ/Federated-Learning-PyTorch) 
- Implementation of the vanilla federated learning paper.
- [**[Library] Flower**](https://github.com/adap/flower) 
- A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.
#### Anomaly Detection
- *General*
- [**[List] Anomaly Detection Learning Resources**](https://github.com/yzhao062/anomaly-detection-resources) 
- Books & Academic Papers & Online Courses and Videos & Outlier Datasets & Open-source and Commercial Libraries & Toolkits & Key Conferences & Journals.
- [**[List] Awesome Anomaly Detection**](https://github.com/hoya012/awesome-anomaly-detection) 
- A curated list of awesome anomaly detection resources.
- [**[List] Awesome Anomaly Detection\***](https://github.com/zhuyiche/awesome-anomaly-detection) **[⚠️Inactive]** 
- A list of Papers on anomaly detection.
- *Sub-topics*
- [**[List] Awesome Time-series Anomaly Detection**](https://github.com/rob-med/awesome-TS-anomaly-detection) 
- List of tools & datasets for anomaly detection on time-series data.
- [**[List] Awesome Fraud Detection Research Papers**](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers) 
- A curated list of fraud detection papers.
- [**[List] Awesome Video Anomaly Detection**](https://github.com/fjchange/awesome-video-anomaly-detection) 
- Papers for Video Anomaly Detection, released codes collections.
- [**[List] Awesome Log Analysis**](https://github.com/logpai/awesome-log-analysis) 
- Publications and researchers on log analysis, anomaly detection, fault localization, and AIOps.
- *Practice*
- [**[Library] pyod**](https://github.com/yzhao062/pyod) 
- A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).
- [**[Code] RNN-Time-series-Anomaly-Detection**](https://github.com/chickenbestlover/RNN-Time-series-Anomaly-Detection) 
- RNN based Time-series Anomaly detector model implemented in Pytorch.
- [**[Library (R)] AnomalyDetection**](https://github.com/twitter/AnomalyDetection) **[⚠️Inactive]** 
- Anomaly Detection with R.
- [**[Library] luminol**](https://github.com/linkedin/luminol) **[⚠️Inactive]** 
- Anomaly Detection and Correlation library.
#### Clustering
- *General*
- [**[List] Deep Clustering**](https://github.com/zhoushengisnoob/DeepClustering) 
- Deep Clustering: methods and implements
- *Sub-topics*
- [**[List] Awesome Community Detection Research Papers**](https://github.com/benedekrozemberczki/awesome-community-detection) 
- A collection of community detection papers.
- [**[List] Awesome Multi-view Clustering**](https://github.com/wangsiwei2010/awesome-multi-view-clustering) 
- Collections for state-of-the-art (SOTA), novel multi-view clustering methods (papers, codes and datasets).
- [**[List] Awesome-Deep-Graph-Clustering**](https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering) 
- A collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes, and datasets).
- *Practice*
- [**[Library] faiss**](https://github.com/facebookresearch/faiss) 
- A library for efficient similarity search and clustering of dense vectors.
- [**[Library] hdbscan**](https://github.com/scikit-learn-contrib/hdbscan) 
- A high performance implementation of HDBSCAN clustering.
- [**[Code Collection] time-series-classification-and-clustering**](https://github.com/alexminnaar/time-series-classification-and-clustering) **[⚠️Inactive]** 
- Time series classification and clustering code written in Python.
#### Dimensionality Reduction (Feature Selection/Extraction)
- *General*
- [**[List] Awesome Feature Engineering**](https://github.com/aikho/awesome-feature-engineering) **[⚠️Inactive]** 
- A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning.
- *Practice*
- [**[Library] featuretools**](https://github.com/alteryx/featuretools) 
- An open source python library for automated feature engineering.
- [**[Library] feature-selector**](https://github.com/WillKoehrsen/feature-selector) **[⚠️Inactive]** 
- Feature Selector: Simple Feature Selection in Python.
- [**[Library] scikit-feature**](https://github.com/jundongl/scikit-feature) **[⚠️Inactive]** 
- Open-source feature selection repository in python.
- [**[Tutorial] DimensionalityReduction_alo_codes (Chinese)**](https://github.com/heucoder/dimensionality_reduction_alo_codes) **[⚠️Inactive]** 
- xx特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现xxx.
- [**[Tutorial] feature-engineering-and-feature-selection**](https://github.com/Yimeng-Zhang/feature-engineering-and-feature-selection) **[⚠️Inactive]** 
- A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
- [**[Tutorial] Feature-Selection-for-Machine-Learning**](https://github.com/anujdutt9/Feature-Selection-for-Machine-Learning) **[⚠️Inactive]** 
- Methods with examples for Feature Selection during Pre-processing in Machine Learning.
Machine Learning Task & Application
-----------------------------------
#### Computer Vision (CV)
- *General*
- [**[List] Awesome Computer Vision**](https://github.com/jbhuang0604/awesome-computer-vision) 
- A curated list of awesome computer vision resources.
- [**[List] Awesome Visual-Transformer**](https://github.com/dk-liang/Awesome-Visual-Transformer) 
- Collect some Transformer with Computer-Vision (CV) papers.
- [**[List] Awesome Deep Vision**](https://github.com/kjw0612/awesome-deep-vision) **[⚠️Inactive]** 
- A curated list of deep learning resources for computer vision.
- *Sub-topics*
- [**[List] Awesome Visual Representation Learning with Transformers**](https://github.com/alohays/awesome-visual-representation-learning-with-transformers) 
- Awesome Transformers (self-attention) in Computer Vision.
- [**[List] Awesome Face Recognition**](https://github.com/ChanChiChoi/awesome-Face_Recognition) 
- Face Detection & Segmentation & Alignment & Tracking, and more.
- [**[List] Awesome Neural Radiance Fields**](https://github.com/yenchenlin/awesome-NeRF) 
- A curated list of awesome neural radiance fields papers.
- [**[List] Awesome Neural Rendering**](https://github.com/weihaox/awesome-neural-rendering) 
- A collection of resources on neural rendering.
- [**[List] Awesome Inpainting Tech**](https://github.com/1900zyh/Awesome-Image-Inpainting) 
- A curated list of inpainting papers and resources.
- [**[List] Awesome Image-to-Image Translation**](https://github.com/weihaox/awesome-image-translation) 
- A collection of resources on image-to-image translation.
- [**[List] Deep-Learning-for-Tracking-and-Detection**](https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection) 
- Collection of papers, datasets, code and other resources for object detection and tracking using deep learning.
- [**[List] Awesome Deep Learning for Video Analysis**](https://github.com/HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis) 
- Video analysis, especiall multimodal learning for video analysis research.
- [**[List] Image and Video Deblurring**](https://github.com/subeeshvasu/Awesome-Deblurring) 
- A curated list of resources for Image and Video Deblurring.
- [**[List] Few Shot Semantic Segmentation Papers**](https://github.com/xiaomengyc/Few-Shot-Semantic-Segmentation-Papers) 
- Papers pertaining to few-shot semantic segmentation.
- [**[List] Awesome Few-Shot Image Generation**](https://github.com/bcmi/Awesome-Few-Shot-Image-Generation) 
- Papers, datasets, and relevant links pertaining to few-shot image generation.
- [**[List] Awesome Video Anomaly Detection**](https://github.com/fjchange/awesome-video-anomaly-detection) 
- Papers for Video Anomaly Detection, released codes collections.
- [**[List] 3D Machine Learning**](https://github.com/timzhang642/3D-Machine-Learning) 
- Learn from 3D representations.
- [**[List] awesome-3D-vision (Chinese)**](https://github.com/Tom-Hardy-3D-Vision-Workshop/awesome-3D-vision) 
- 3D视觉算法、SLAM、vSLAM、计算机视觉
- [**[List] 3D-Reconstruction-with-Deep-Learning-Methods**](https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods) 
- The focus of this list is on open-source projects hosted on Github.
- [**[List] Awsome_Deep_Geometry_Learning**](https://github.com/subeeshvasu/Awsome_Deep_Geometry_Learning) 
- A list of resources about deep learning solutions on 3D shape processing.
- [**[List] Awesome Image Classification**](https://github.com/weiaicunzai/awesome-image-classification) **[⚠️Inactive]** 
- A curated list of deep learning image classification papers and codes since 2014.
- [**[List] Awesome Object Detection**](https://github.com/amusi/awesome-object-detection) **[⚠️Inactive]** 
- [**[List] Awesome Face**](https://github.com/polarisZhao/awesome-face) **[⚠️Inactive]** 
- Face releated algorithm, datasets and papers.
- [**[List] Awesome Human Pose Estimation**](https://github.com/wangzheallen/awesome-human-pose-estimation) **[⚠️Inactive]** 
- A collection of resources on human pose related problem.
- [**[List] Awesome Video Generation**](https://github.com/matthewvowels1/Awesome-Video-Generation) **[⚠️Inactive]** 
- A curated list of awesome work (currently 257 papers) a on video generation and video representation learning.
#### Natural Language Processing (NLP)
- *General*
- [**[List] Awesome NLP**](https://github.com/keon/awesome-nlp) 
- A curated list of resources dedicated to Natural Language Processing.
- [**[List] Tracking Progress in Natural Language Processing**](https://github.com/sebastianruder/NLP-progress) 
- Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
- [**[List] Awesome BERT & Transfer Learning in NLP**](https://github.com/cedrickchee/awesome-bert-nlp) 
- Transformers (BERT), attention mechanism, Transformer architectures/networks, and transfer learning in NLP.
- [**[List] funNLP: The Most Powerful NLP-Weapon Arsenal (Chinese)**](https://github.com/fighting41love/funNLP) 
- NLP民工的乐园: 几乎最全的中文NLP资源库
- [**[List, Tutorial] ML-NLP (Chinese)**](https://github.com/NLP-LOVE/ML-NLP) 
- 此项目是机器学习、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识
- [**[List] Awesome Chinese NLP (Chinese)**](https://github.com/crownpku/Awesome-Chinese-NLP) **[⚠️Inactive]** 
- 中文自然语言处理相关资料
- [**[List] Awesome BERT**](https://github.com/Jiakui/awesome-bert) **[⚠️Inactive]** 
- This repository is to collect BERT related resources.
- *Sub-topics*
- [**[List] Machine Translation Reading List**](https://github.com/THUNLP-MT/MT-Reading-List) 
- A machine translation reading list maintained by the Tsinghua Natural Language Processing Group.
- [**[List] Must-Read Papers on Pre-trained Language Models (PLMs)**](https://github.com/thunlp/PLMpapers) 
- List some representative work on PLMs and show their relationship with a diagram.
- [**[List] PromptPapers**](https://github.com/thunlp/PromptPapers) 
- Must-read papers on prompt-based tuning for pre-trained language models.
- [**[List] Must-read papers on NRE**](https://github.com/thunlp/NREPapers) 
- NRE: Neural Relation Extraction.
- [**[List] Awesome Question Answering**](https://github.com/seriousran/awesome-qa) 
- A curated list of the Question Answering (QA) subject.
- [**[List] Textual Adversarial Attack and Defense (TAAD)**](https://github.com/thunlp/TAADpapers) 
- Must-read Papers on Textual Adversarial Attack and Defense.
- [**[List] Machine Reading Comprehension.**](https://github.com/thunlp/RCPapers) 
- Must-read papers on Machine Reading Comprehension.
- [**[List] Legal Intelligence (NLP)**](https://github.com/thunlp/LegalPapers) 
- Must-read Papers on Legal Intelligence.
- [**[List] Awesome NLP Fairness Papers**](https://github.com/uclanlp/awesome-fairness-papers) 
- Papers about fairness in NLP.
- [**[List] Awesome Financial NLP**](https://github.com/icoxfog417/awesome-financial-nlp) 
- Researches for Natural Language Processing for Financial Domain.
- [**[List] Graph4NLP Literature**](https://github.com/graph4ai/graph4nlp_literature) 
- A list of literature regarding Deep Learning on Graphs for NLP.
- [**[List] Awesome Chinese Medical NLP (Chinese)**](https://github.com/GanjinZero/awesome_Chinese_medical_NLP) 
- 中文医学NLP公开资源整理
- [**[List] NLP4Rec-Papers**](https://github.com/THUDM/NLP4Rec-Papers) 
- Paper Collection of NLP for Recommender System.
- [**[List] DataAug4NLP**](https://github.com/styfeng/DataAug4NLP) 
- Collection of papers and resources for data augmentation for NLP.
- [**[List] Must-read papers on KRL/KE.**](https://github.com/thunlp/KRLPapers) **[⚠️Inactive]** 
- KRL: knowledge representation learning. KE: knowledge embedding.
- *Practice*
- [**[Tutorial] NLP Tutorial**](https://github.com/graykode/nlp-tutorial) 
- `nlp-tutorial` is a tutorial for who is studying NLP using Pytorch.
- [**[Datasets] NLP Datasets**](https://github.com/niderhoff/nlp-datasets) 
- Alphabetical list of free/public domain datasets with text data for use in NLP.
#### Multi-modal & Cross-modal Learning
- *Multi-modal*
- [**[List] Awesome Multimodal ML**](https://github.com/pliang279/awesome-multimodal-ml) 
- Reading List for Topics in Multimodal Machine Learning.
- [**[List] Awesome Multimodal Research**](https://github.com/Eurus-Holmes/Awesome-Multimodal-Research) 
- Multimodal Machine Learning research papers.
- *Cross-modal*
- [**[List] Cross-modal Retrieval Tutorial**](https://github.com/Paranioar/Cross-modal_Retrieval_Tutorial) 
- Papers of Cross-Modal Matching and Retrieval.
- [**[List] Awesome Video-Text Retrieval by Deep Learning**](https://github.com/danieljf24/awesome-video-text-retrieval) 
- A curated list of deep learning resources for video-text retrieval.
- [**[List] Awesome Document Understanding**](https://github.com/tstanislawek/awesome-document-understanding) 
- A curated list of resources for Document Understanding (DU) topic related to Intelligent Document Processing (IDP).
- [**[List] Awesome-Cross-Modal-Video-Moment-Retrieval (Chinese)**](https://github.com/yawenzeng/Awesome-Cross-Modal-Video-Moment-Retrieval) 
#### Graph Learning
> **See also:** [Machine Learning Model](#machine-learning-model) -> [**Graph Neural Network (GNN, GCN, GAT, etc.)**](#graph-neural-network-gnn-gcn-gat-etc)
- *General*
- [**[List] Graph-based Deep Learning Literature**](https://github.com/naganandy/graph-based-deep-learning-literature) 
- Conference publications in graph-based deep learning.
- [**[List] Literature of Deep Learning for Graphs**](https://github.com/DeepGraphLearning/LiteratureDL4Graph) 
- This is a paper list about deep learning for graphs.
- [**[List] GNNPapers**](https://github.com/thunlp/GNNPapers) 
- Must-read papers on GNN.
- *Benchmarks*
- [**[Benchmark] Graph-Based Deep Learning Models for Urban Traffic Prediction**](https://github.com/deepkashiwa20/dl-traff-graph) 
- DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
- *Sub-topics*
- [**[List] Awesome Graph Classification**](https://github.com/benedekrozemberczki/awesome-graph-classification) 
- A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations.
- [**[List] Awesome Explainable Graph Reasoning**](https://github.com/AstraZeneca/awesome-explainable-graph-reasoning) 
- A collection of research papers and software related to explainability in graph machine learning.
- [**[List] Awesome Graph Self-Supervised Learning**](https://github.com/LirongWu/awesome-graph-self-supervised-learning) 
- A curated list for awesome self-supervised graph representation learning resources.
- [**[List] Graph Adversarial Learning Literature**](https://github.com/safe-graph/graph-adversarial-learning-literature) 
- A curated list of adversarial attacks and defenses papers on graph-structured data.
- [**[List] Deep Learning for Graphs in Chemistry and Biology**](https://github.com/mufeili/DL4MolecularGraph) 
- A paper list of deep learning on graphs in chemistry and biology.
- [**[List] Awesome Federated Learning on Graph and GNN Papers**](https://github.com/huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers) 
- Federated learning on graph, especially on GNNs, knowledge graph, and private GNN.
- [**[List] Awesome Deep Graph Representation Learning**](https://github.com/zlpure/awesome-graph-representation-learning) 
- A curated list for awesome deep graph representation learning resources.
- [**[List] Graph4NLP Literature**](https://github.com/graph4ai/graph4nlp_literature) 
- A list of literature regarding Deep Learning on Graphs for NLP.
- [**[List] GNN4Traffic**](https://github.com/jwwthu/GNN4Traffic) 
- This is the repository for the collection of Graph Neural Network for Traffic Forecasting.
- [**[List] GNN based Recommender Systems**](https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems) 
- An index of recommendation algorithms that are based on Graph Neural Networks.
- [**[List] Awesome-GNN-Recommendation**](https://github.com/Jhy1993/Awesome-GNN-Recommendation) 
- GNN in Recommendation.
- [**[List] Awesome GNNs on Large-scale Graphs**](https://github.com/Oceanusity/awesome-gnns-on-large-scale-graphs) 
- Large-scale Graph Datasets/Networks.
- [**[List] Awesome Fair Graph Learning**](https://github.com/EdisonLeeeee/Awesome-Fair-Graph-Learning) 
- Paper Lists for Fair Graph Learning (FairGL).
- [**[List] Must-read papers on NRL/NE.**](https://github.com/thunlp/NRLPapers) **[⚠️Inactive]** 
- NRL: network representation learning. NE: network embedding.
- [**[List] Representation Learning on Heterogeneous Graph**](https://github.com/Jhy1993/Representation-Learning-on-Heterogeneous-Graph) **[⚠️Inactive]** 
- Heterogeneous Graph Embedding, Heterogeneous GNNs and Applications.
- *Practice*
- [**[List] awesome-graph**](https://github.com/jbmusso/awesome-graph) 
- A curated list of resources for graph databases and graph computing tools.
#### Knowledge Graph
- *General*
- [**[List] Knowledge Graphs**](https://github.com/shaoxiongji/knowledge-graphs) 
- A collection of knowledge graph papers, codes, and reading notes
- [**[Tutorial] Awesome Knowledge Graph (Chinese)**](https://github.com/husthuke/awesome-knowledge-graph) 
- 整理知识图谱相关学习资料，提供系统化的知识图谱学习路径
- [**[List] Awesome Knowledge Graph**](https://github.com/totogo/awesome-knowledge-graph) 
- Knowledge Graph related learning materials, databases, tools and other resources.
- [**[List] Knowledge Graph Learning**](https://github.com/BrambleXu/knowledge-graph-learning) 
- A curated list of awesome knowledge graph tutorials, projects and communities.
- *Sub-topics*
- [**[List] Knowledge Graph Reasoning Papers**](https://github.com/THU-KEG/Knowledge_Graph_Reasoning_Papers) 
- Knowledge Graph Reasoning Papers.
- [**[List] NLP-Knowledge-Graph (Chinese)**](https://github.com/lihanghang/NLP-Knowledge-Graph) 
- 自然语言处理、知识图谱、对话系统
- *Practice*
- [**[List] awesome-graph**](https://github.com/jbmusso/awesome-graph) 
- A curated list of resources for graph databases and graph computing tools.
#### Time-series/Stream Learning
- *General*
- [**[List] awesome-time-series**](https://github.com/cuge1995/awesome-time-series) 
- List of state of the art papers, code, and other resources focus on time series forecasting.
- [**[List] Awesome-time-series**](https://github.com/cure-lab/Awesome-time-series) 
- A comprehensive survey on the time series domains。
- [**[List] Awesome Time Series Papers (English&Chinese)**](https://github.com/bighuang624/Time-Series-Papers) **[⚠️Inactive]** 
- List of awesome papers from various research fields in time series analysis.
- *Sub-topics*
- [**[List] Awesome Time-series Anomaly Detection**](https://github.com/rob-med/awesome-TS-anomaly-detection) 
- List of tools & datasets for anomaly detection on time-series data.
- [**[List] Deep Learning Time Series Forecasting**](https://github.com/Alro10/deep-learning-time-series) 
- Resources, code and experiments using deep learning for time series forecasting.
- [**[List] Awesome-Deep-Learning-Based-Time-Series-Forecasting**](https://github.com/fengyang95/Awesome-Deep-Learning-Based-Time-Series-Forecasting) 
- Awesome-Deep-Learning-Based-Time-Series-Forecasting.
- [**[List] Awesome Time Series Analysis and Data Mining**](https://github.com/youngdou/awesome-time-series-analysis) 
- A collection list of learning resource, tools and dataset for time series analysis or time series data mining.
- *Practice*
- [**[List, Practice] awesome_time_series_in_python**](https://github.com/MaxBenChrist/awesome_time_series_in_python) 
- Python libraries, datasets, frameworks for time series processing.
- [**[Datasets] Awesome time series database**](https://github.com/xephonhq/awesome-time-series-database) 
- A curated list of time series databases.
- [**[Library] FOST**](https://github.com/microsoft/FOST) 
- An easy-use tool for temporal, spatial-temporal and hierarchical forecasting.
#### Recommender Systems
- *General*
- [**[List] Awesome-RSPapers**](https://github.com/RUCAIBox/Awesome-RSPapers) 
- Recommender System papers in top-conferences.
- [**[List] awesome-RecSys**](https://github.com/jihoo-kim/awesome-RecSys) **[⚠️Inactive]** 
- A curated list of awesome Recommender System (research).
- [**[List] awesome-recommender-systems (Chinese)**](https://github.com/gaolinjie/awesome-recommender-systems) **[⚠️Inactive]** 
- A curated list of awesome resources about Recommender Systems.
- *Sub-topics*
- [**[List] Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement**](https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising) 
- Focus on Embedding, Matching, Ranking (CTR prediction, CVR prediction), Post Ranking, Transfer and Reinforcement Learning.
- [**[List] GNN based Recommender Systems**](https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems) 
- An index of recommendation algorithms that are based on Graph Neural Networks.
- [**[List] NLP4Rec-Papers**](https://github.com/THUDM/NLP4Rec-Papers) 
- Paper Collection of NLP for Recommender System.
- [**[List] Awesome-GNN-Recommendation**](https://github.com/Jhy1993/Awesome-GNN-Recommendation) 
- GNN in Recommendation.
#### Information Retrieval
- [**[List] Awesome Information Retrieval**](https://github.com/harpribot/awesome-information-retrieval) 
- Curated list of information retrieval and web search resources from all around the web.
#### Gaming & Searching
- [**[List] Awesome Monte Carlo Tree Search Papers**](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers) 
- A curated list of Monte Carlo tree search papers with implementations.
Machine Learning Model
----------------------
#### Pretrained & Foundation Model
##### in NLP (BERT, RoBERTa, GPT, etc.)
- *Foundation Models*
- [**[List] Must-Read Papers on Pre-trained Language Models (PLMs)**](https://github.com/thunlp/PLMpapers) 
- List some representative work on PLMs and show their relationship with a diagram.
- [**[List] BERT-related Papers**](https://github.com/tomohideshibata/BERT-related-papers) 
- This is a list of BERT-related papers.
- [**[List] Awesome BERT & Transfer Learning in NLP**](https://github.com/cedrickchee/awesome-bert-nlp) 
- Transformers (BERT), attention mechanism, Transformer architectures/networks, and transfer learning in NLP.
- [**[List] PTMs: Pre-trained-Models in NLP (Chinese)**](https://github.com/loujie0822/Pre-trained-Models) 
- NLP预训练模型的全面总结
- [**[List, Model] Awesome Pretrained Chinese NLP Models (Chinese)**](https://github.com/lonePatient/awesome-pretrained-chinese-nlp-models) 
- 高质量中文预训练模型集合
- [**[List] Vision and Language PreTrained Models**](https://github.com/yuewang-cuhk/awesome-vision-language-pretraining-papers) 
- Recent Advances in Vision and Language PreTrained Models (VL-PTMs).
- [**[List] Awesome BERT**](https://github.com/Jiakui/awesome-bert) **[⚠️Inactive]** 
- This repository is to collect BERT related resources.
- *Pretrained (NOT Foundation) Models*
- [**[List, Model] Awesome Sentence Embedding**](https://github.com/Separius/awesome-sentence-embedding) 
- A curated list of pretrained sentence and word embedding models.
##### in CV (Visual Transformers, etc.)
- *Foundation Models*
- [**[List] Awesome Visual-Transformer**](https://github.com/dk-liang/Awesome-Visual-Transformer) 
- Collect some Transformer with Computer-Vision (CV) papers.
- [**[List] Vision and Language PreTrained Models**](https://github.com/yuewang-cuhk/awesome-vision-language-pretraining-papers) 
- Recent Advances in Vision and Language PreTrained Models (VL-PTMs).
- [**[List] Transformer-in-Vision**](https://github.com/DirtyHarryLYL/Transformer-in-Vision) 
- Recent Transformer-based CV and related works.
- [**[List] Awesome Visual Representation Learning with Transformers**](https://github.com/alohays/awesome-visual-representation-learning-with-transformers) 
- Awesome Transformers (self-attention) in Computer Vision.
- [**[List] Transformer-in-Computer-Vision**](https://github.com/Yangzhangcst/Transformer-in-Computer-Vision) 
- A paper list of some recent Transformer-based CV works.
- *Pretrained (NOT Foundation) Models*
- [**[List, Practice] PyTorch Image Models**](https://github.com/rwightman/pytorch-image-models) 
- A collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.
- [**[List, Model] CV-pretrained-model**](https://github.com/balavenkatesh3322/CV-pretrained-model) 
- Computer Vision Pretrained Models (not papers).
- [**[List, Model] pretrained-models.pytorch**](https://github.com/Cadene/pretrained-models.pytorch) **[⚠️Inactive]** 
- Pretrained vision models for Pytorch.
- [**[Library] segmentation_models**](https://github.com/qubvel/segmentation_models) **[⚠️Inactive]** 
- Python library with Neural Networks for Image Segmentation based on Keras and TensorFlow.
##### in other topics
- [**[List] awesome-pretrained-models-for-information-retrieval**](https://github.com/Albert-Ma/awesome-pretrained-models-for-information-retrieval) 
- Awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR).
#### Convolutional Neural Network (CNN)
> **Note:** This is a big topic and almost all existing lists are outdated. Please refer to [**Computer Vision (CV)**](#computer-vision-cv) in [Machine Learning Task & Application](#machine-learning-task--application) for more recent information.
- [**[Benchmark] convnet-benchmarks**](https://github.com/soumith/convnet-benchmarks) **[⚠️Inactive]** 
- Easy benchmarking of public open-source implementations of convnets.
- [**[Benchmark] cnn-benchmarks**](https://github.com/jcjohnson/cnn-benchmarks) **[⚠️Inactive]** 
- Benchmarks for popular convolutional neural network models on CPU and different GPUs, with and without cuDNN.
#### Recurrent Neural Network (RNN, LSTM, GRU, etc.)
> **Note:** This is a big topic and almost all existing lists are outdated. Please refer to [**Time-series/Stream Learning**](#time-seriesstream-learning) in [Machine Learning Task & Application](#machine-learning-task--application) for more recent information.
- [**[List] Awesome Recurrent Neural Networks**](https://github.com/kjw0612/awesome-rnn) **[⚠️Inactive]** 
- A curated list of resources dedicated to recurrent neural networks (closely related to deep learning).
#### Graph Neural Network (GNN, GCN, GAT, etc.)
> **See also:** [Machine Learning Task & Application](#machine-learning-task--application) -> [**Graph Learning**](#graph-learning)
- *General*
- [**[List] GNNPapers**](https://github.com/thunlp/GNNPapers) 
- Must-read papers on graph neural network.
- [**[List] Graph-based Neural Networks**](https://github.com/sungyongs/graph-based-nn) 
- Important materials about graph-based neural networks and relational networks.
- [**[List] Awesome GCN**](https://github.com/Jiakui/awesome-gcn) 
- This repository is to collect GCN, GAT (graph attention) related resources.
- [**[List] Graph Neural Network (GNN) progress**](https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress) 
- Must-read papers and continuous track on Graph Neural Network (GNN) progress
- [**[List] GNN_Review (Chinese)**](https://github.com/LYuhang/GNN_Review) 
- GNN综述阅读报告
- *Sub-topics*
- [**[List] awesome-self-supervised-gnn**](https://github.com/ChandlerBang/awesome-self-supervised-gnn) 
- Papers about self-supervised learning on Graph Neural Networks (GNNs).
- [**[List] GNN4Traffic**](https://github.com/jwwthu/GNN4Traffic) 
- This is the repository for the collection of Graph Neural Network for Traffic Forecasting.
- [**[List] GNN based Recommender Systems**](https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems) 
- An index of recommendation algorithms that are based on Graph Neural Networks.
- [**[List] Awesome GNNs on Large-scale Graphs**](https://github.com/Oceanusity/awesome-gnns-on-large-scale-graphs) 
- Large-scale Graph Datasets/Networks.
- *Practice*
- [**[Tutorial] GNN-algorithms (Chinese)**](https://github.com/wangyouze/GNN-algorithms) **[⚠️Inactive]** 
- 图神经网络相关算法详述及实现教程
#### Generative Model & Generative Adversarial Network (GAN)
> **See also:** [Machine Learning Paradigm](#machine-learning-paradigm) -> [**Adversarial Learning**](#adversarial-learning)
- *General*
- [**[List] really-awesome-gan**](https://github.com/nightrome/really-awesome-gan) 
- A list of papers and other resources on Generative Adversarial (Neural) Networks.
- [**[List] Awesome Generative Modeling Papers**](https://github.com/zhoubolei/awesome-generative-modeling) 
- Papers on generative modeling.
- [**[List] The GAN Zoo**](https://github.com/hindupuravinash/the-gan-zoo) **[⚠️Inactive]** 
- A list of all named GANs.
- [**[List] AdversarialNetsPapers**](https://github.com/zhangqianhui/AdversarialNetsPapers) **[⚠️Inactive]** 
- Awesome papers about Generative Adversarial Networks. Majority of papers are related to Image Translation.
- [**Awesome GAN Applications**](https://github.com/nashory/gans-awesome-applications) **[⚠️Inactive]** 
- Curated list of awesome GAN applications and demonstrations.
- *Sub-topics & Applications*
- [**[List] awesome-gan-for-medical-imaging**](https://github.com/xinario/awesome-gan-for-medical-imaging) 
- A curated list of awesome GAN resources in medical imaging.
- [**[List] awesome gan-inversion**](https://github.com/weihaox/awesome-gan-inversion) 
- This repo is a collection of resources on GAN inversion.
- *Practice*
- [**[List] Awesome-GANs with Tensorflow**](https://github.com/kozistr/Awesome-GANs) 
- Tensorflow implementation of GANs (Generative Adversarial Networks).
- [**[Tutorial] ganhacks**](https://github.com/soumith/ganhacks) **[⚠️Inactive]** 
- How to Train a GAN? Tips and tricks to make GANs work.
- [**[Tutorial] GAN**](https://github.com/YadiraF/GAN) **[⚠️Inactive]** 
- Resources and Implementations of Generative Adversarial Nets.
- [**[Tutorial] GAN_Theories**](https://github.com/YadiraF/GAN_Theories) **[⚠️Inactive]** 
- How to stabilize training process and generate high quality images.
#### Variational Autoencoder
> **See also:** [Machine Learning Paradigm](#machine-learning-paradigm) -> [**Representation Learning**](#representation-learning)
- [**[List] Awesome-VAEs**](https://github.com/matthewvowels1/Awesome-VAEs) 
- Awesome work on the VAE, disentanglement, representation learning, and generative models.
- [**[Code Collection] PyTorch-VAE**](https://github.com/AntixK/PyTorch-VAE) 
- A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility.
#### Tree-based & Ensemble Model
> **See also:** [Machine Learning Paradigm](#machine-learning-paradigm) -> [**Ensemble Learning**](#ensemble-learning)
- *General*
- [**[List] Awesome Decision Tree Research Papers**](https://github.com/benedekrozemberczki/awesome-decision-tree-papers) 
- A curated list of classification and regression tree research papers with implementations.
- [**[List] Awesome Gradient Boosting Research Papers**](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) 
- A curated list of gradient and adaptive boosting papers with implementations.
- [**[List] Awesome Random Forest**](https://github.com/kjw0612/awesome-random-forest) **[⚠️Inactive]** 
- A curated list of resources regarding tree-based methods and more, including but not limited to random forest, bagging and boosting.
- [**[List] Awesome Ensemble Learning**](https://github.com/yzhao062/awesome-ensemble-learning) **[⚠️Inactive]** 
- Books, papers, courses, tutorials, libraries, datasets.
- *Practice*
- [**[Library] xgboost**](https://github.com/dmlc/xgboost) 
- Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library.
- [**[Library] LightGBM**](https://github.com/microsoft/LightGBM) 
- A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework.
- [**[Library] catboost**](https://github.com/catboost/catboost) 
- A fast, scalable, high performance Gradient Boosting on Decision Trees library.
- [**[Library] mlens**](https://github.com/flennerhag/mlens) 
- A Python library for high performance ensemble learning.
- [**[Library] combo**](https://github.com/yzhao062/combo) 
- A Python Toolbox for Machine Learning Model Combination,
- [**[Library] imbalanced-ensemble (English&Chinese)**](https://github.com/ZhiningLiu1998/imbalanced-ensemble) 
- 类别不平衡/长尾机器学习 | Class-imbalanced/Long-tailed ensemble learning in Python
Machine Learning Interpretability & Fairness & Ethics
-----------------------------------------------------
#### Interpretability in AI
- *General*
- [**[List] Awesome Machine Learning Interpretability**](hhttps://github.com/jphall663/awesome-machine-learning-interpretability) 
- A curated list of awesome machine learning interpretability resources.
- [**[List] Awesome Explainable AI**](https://github.com/wangyongjie-ntu/Awesome-explainable-AI) 
- This repository contains the frontier research on explainable AI (XAI) which is a hot topic recently.
- [**[List] Machine Learning Interpretability**](https://github.com/h2oai/mli-resources) 
- H2O.ai Machine Learning Interpretability Resources.
- [**[List] awesome_deep_learning_interpretability (Chinese)**](https://github.com/oneTaken/awesome_deep_learning_interpretability) **[⚠️Inactive]** 
- 深度学习近年来关于模型解释性的相关论文。
- *Sub-topics*
- [**[List] Awesome Explainable Graph Reasoning**](https://github.com/AstraZeneca/awesome-explainable-graph-reasoning) 
- A collection of research papers and software related to explainability in graph machine learning.
- [**[List] Adversarial Explainable AI**](https://github.com/hbaniecki/adversarial-explainable-ai) 
- Adversarial attacks on model explanations, and evaluation approaches.
- *Practice*
- [**[Tutorial] interpretable-ml-book**](https://github.com/christophM/interpretable-ml-book) 
- Book about interpretable machine learning.
- [**[Tutorial] interpretable_machine_learning_with_python**](https://github.com/jphall663/interpretable_machine_learning_with_python) 
- Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
#### Fairness in AI
- *General*
- [**[List] FairAI**](https://github.com/yongkaiwu/FairAI) 
- This is a collection of papers and other resources related to fairness.
- [**[List] Awesome Fairness in AI**](https://github.com/datamllab/awesome-fairness-in-ai) **[⚠️Inactive]** 
- A curated, but probably biased and incomplete, list of awesome Fairness in AI resources.
- *Sub-topics*
- [**[List] Awesome NLP Fairness Papers**](https://github.com/uclanlp/awesome-fairness-papers) 
- Papers about fairness in NLP.
- *Practice*
- [**[Tutorial]fairness_tutorial**](https://github.com/dssg/fairness_tutorial) 
- Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial.
- [**[Library] ml-fairness-gym**](https://github.com/google/ml-fairness-gym) **[⚠️Inactive]** 
- A set of components for building simple simulations that explore the potential long-run impacts of deploying machine learning-based decision systems in social environments.
#### Ethics in AI
- *General*
- [**[List] criticalML**](https://github.com/rockita/criticalML) **[⚠️Inactive]** 
- Toward ethical, transparent and fair AI/ML: a critical reading list for engineers, designers, and policy makers.
- [**[List] Machine Learning Ethics References**](https://github.com/radames/Machine-Learning-Ethics-References) **[⚠️Inactive]** 
- References about Machine Learning and Data Science discrimination, bias, ethics.
- *Sub-topics*
- [**[List] Awesome-Privacy**](https://github.com/Guyanqi/Awesome-Privacy) **
- Toward ethical, transparent and fair AI/ML: a critical reading list for engineers, designers, and policy makers.
Interdisciplinary: Machine Learning + X
---------------------------------------
#### System (MLSys/SysML)
- [**[List] Awesome System for Machine Learning**](https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning) 
- A curated list of research in machine learning system.
- [**[Tutorial] Machine learning system design pattern**](https://github.com/mercari/ml-system-design-pattern) 
- System design patterns for training, serving and operation of machine learning systems in production.
- [**[Note] CS-Notes (Chinese)**](https://github.com/huangrt01/CS-Notes) 
- MLSys和C++自学笔记，以及算法、操作系统，后续学习分布式系统，终身更新。
- [**[Note] hack-SysML (Chinese)**](https://github.com/Jack47/hack-SysML) 
- Notes on learning and practicing SysML.
- [**[List] SysML-reading-list**](https://github.com/mcanini/SysML-reading-list) **[⚠️Inactive]** 
- A curated reading list of computer science research for work at the intersection of machine learning and systems.
- [**[Tutorial] dive-into-ml-system (Chinese)**](https://github.com/wepe/dive-into-ml-system) **[⚠️Inactive]** 
- Dive into machine learning system, start from reinventing the wheel.
- [**[List] Fast and Scalable Machine Learning: Algorithms and Systems**](https://github.com/ljk628/ML-Systems) **[⚠️Inactive]** 
- This is a collection of papers about recent progress in machine learning and systems, including distributed machine learning, deep learning and etc.
#### Database (AIDB/ML4DB)
- [**[List] ML4DB-paper-list (English&Chinese)**](https://github.com/LumingSun/ML4DB-paper-list) 
- [Paper List] AIDB / ML4DB / Autonomous Database / 智能数据库 / Self-driving Database
#### Software Engineering (MLonCode)
- [**[List] Machine Learning on Source Code (Website)**](https://ml4code.github.io/) 
- Research on machine learning for source code.
- [**[List] awesome-machine-learning-on-source-code**](https://github.com/src-d/awesome-machine-learning-on-source-code) **[⚠️Inactive]** 
- Cool links & research papers related to Machine Learning applied to source code (MLonCode).
#### Cyber Security
- [**[List] Awesome Machine Learning for Cyber Security**](https://github.com/jivoi/awesome-ml-for-cybersecurity) 
- A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security.
- [**[List] Awesome-Cybersecurity-Datasets**](https://github.com/shramos/Awesome-Cybersecurity-Datasets) 
- A curated list of amazingly awesome Cybersecurity datasets.
- [**[List] Machine Learning for Cyber Security**](https://github.com/wtsxDev/Machine-Learning-for-Cyber-Security) **[⚠️Inactive]** 
- A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security.
- [**[List] AI for Security**](https://github.com/nsslabcuus/AI_Security) **[⚠️Inactive]** 
- A paper list about Machine Learning for IDSes.
#### Quantum Computing
- [**[List] Awesome Machine Learning for Cyber Security**](https://github.com/artix41/awesome-quantum-ml) 
- A list of awesome papers and cool resources in the field of quantum machine learning (machine learning algorithms running on quantum devices). It does not include the use of classical ML algorithms for quantum purpose.
#### Medical & Healthcare
- [**[List] healthcare_ml**](https://github.com/isaacmg/healthcare_ml) 
- Relevant resources on applying machine learning to healthcare.
- [**[List, Practice] Awesome Chinese Medical NLP (Chinese)**](https://github.com/GanjinZero/awesome_Chinese_medical_NLP) 
- 中文医学NLP公开资源整理
- [**[List] Awesome Medical Imaging**](https://github.com/fepegar/awesome-medical-imaging) **[⚠️Inactive]** 
- This is an awesome list of software that I use to do research in medical imaging.
#### Bioinformatics
- [**[List] Awesome Bioinformatics**](https://github.com/danielecook/Awesome-Bioinformatics) 
- A curated list of awesome Bioinformatics software, resources, and libraries.
- [**[List] Awesome Bioinformatics Benchmarks**](https://github.com/j-andrews7/awesome-bioinformatics-benchmarks) 
- A curated list of bioinformatics benchmarking papers and resources.
- [**[Tutorial] bioinformatics**](https://github.com/ossu/bioinformatics) 
- Path to a free self-taught education in Bioinformatics (mainly curriculums).
- [**[Code Collection] biocode**](https://github.com/jorvis/biocode) 
- This is a collection of bioinformatics scripts many have found useful and code modules which make writing new ones a lot faster.
#### Biology & Chemistry
- [**[List] deeplearning-biology**](https://github.com/hussius/deeplearning-biology) 
- This is a list of implementations of deep learning methods to biology.
- [**[List] Awesome Python Chemistry**](https://github.com/lmmentel/awesome-python-chemistry) 
- A curated list of awesome Python frameworks, libraries, software and resources related to Chemistry.
- [**[List] Deep Learning for Graphs in Chemistry and Biology**](https://github.com/mufeili/DL4MolecularGraph) 
- A paper list of deep learning on graphs in chemistry and biology.
- [**[List] Awesome DeepBio**](https://github.com/gokceneraslan/awesome-deepbio) **[⚠️Inactive]** 
- A curated list of awesome deep learning applications in the field of computational biology
#### Finance & Trading
- [**[Library] Qlib**](https://github.com/microsoft/qlib) 
- Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
- [**[List, Practice] financial-machine-learning**](https://github.com/firmai/financial-machine-learning) 
- A curated list of practical financial machine learning (FinML) tools and applications.
- [**[List] Awesome AI in Finance**](https://github.com/georgezouq/awesome-ai-in-finance) 
- Research, tools and code that people use to beat the market.
- [**[List] Awesome Financial NLP**](https://github.com/icoxfog417/awesome-financial-nlp) 
- Researches for Natural Language Processing for Financial Domain.
#### Business
- [**[List, Practice] business-machine-learning**](https://github.com/firmai/business-machine-learning) 
- A curated list of applied business machine learning (BML) and business data science (BDS) examples and libraries.
#### Law
- [**[List] Must-read Papers on Legal Intelligence**](https://github.com/thunlp/LegalPapers) 
- Papers and datasets of Legal Artificial Intelligence.
- [**[List, Practice] Legal Text Analytics**](https://github.com/Liquid-Legal-Institute/Legal-Text-Analytics) 
- Resources, methods, and tools dedicated to Legal Text Analytics.
Machine Learning Datasets
-------------------------
- [**[Datasets] Awesome Public Datasets**](https://github.com/awesomedata/awesome-public-datasets) 
- This list of a topic-centric public data sources in high quality.
- [**[Datasets] NLP Datasets**](https://github.com/niderhoff/nlp-datasets) 
- Alphabetical list of free/public domain datasets with text data for use in NLP.
- [**[Datasets] Awesome Dataset Tools**](https://github.com/jsbroks/awesome-dataset-tools) 
- A curated list of awesome dataset tools.
- [**[Datasets] Awesome time series database**](https://github.com/xephonhq/awesome-time-series-database) 
- A curated list of time series databases.
- [**[Datasets] Awesome-Cybersecurity-Datasets**](https://github.com/shramos/Awesome-Cybersecurity-Datasets) 
- A curated list of amazingly awesome Cybersecurity datasets.
- [**[Datasets] Awesome Robotics Datasets**](https://github.com/mint-lab/awesome-robotics-datasets) 
- Robotics Dataset Collections.
Production Machine Learning
---------------------------
#### Open-source Libraries
- [**[List, Library] Awesome Machine Learning**](https://github.com/josephmisiti/awesome-machine-learning) 
- A curated list of awesome machine learning frameworks, libraries and software (by language).
- [**[List, Library] Awesome production machine learning**](https://github.com/EthicalML/awesome-production-machine-learning) 
- This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning 🚀
#### Big Data Frameworks
- [**[List, Practice] Awesome Big Data**](https://github.com/0xnr/awesome-bigdata) 
- A curated list of awesome big data frameworks, resources and other awesomeness.
## Acknowledgement ✨
- 🌟 **Thank you for taking the time to read this far, please leave a STAR if you like this project!** 🌟
- 💬 **If you find any incorrect/inappropriate/outdated content, please kindly consider opening an issue/PR.** 💬
- 💖 **We would greatly appreciate your contribution to this list!** 💖
## Contributors ✨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
Zhining Liu
💻 🤔 🚧
yueliu1999
🖋
Kim Hammar
🖋
Adam Narozniak
🖋
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!