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‎README.md

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‎applications.md

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# Awesome Projects
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- [15 AI and Machine Learning Events](http://botunity.co/14-ai-and-machine-learning-events/)
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- [188 examples of artificial intelligence in action](https://poo.ai/)
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- [A curated list of automated machine learning papers, articles, tutorials, slides and projects](https://github.com/hibayesian/awesome-automl-papers) :star:
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- [A curated list of awesome Machine Learning frameworks, libraries and software.](https://github.com/josephmisiti/awesome-machine-learning)
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- [A curated list of awesome places to learn and/or practice algorithms.](https://github.com/tayllan/awesome-algorithms)
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- [A curated list of awesome R packages and tools](https://github.com/qinwf/awesome-R)
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- [A curated list of awesome SLAM tutorials, projects and communities.](https://github.com/kanster/awesome-slam)
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- [A curated list of resources dedicated to bridge between coginitive science and deep learning](https://github.com/robi56/awesome-cognitive-science-and-deep-learning)
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- [A curated list of resources dedicated to Natural Language Processing (NLP)](https://github.com/keon/awesome-nlp)
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- [A curated list of resources for NLP (Natural Language Processing) for Chinese](https://github.com/crownpku/awesome-chinese-nlp#corpus-%E4%B8%AD%E6%96%87%E8%AF%AD%E6%96%99)
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- [A list of artificial intelligence tools you can use today](https://hackernoon.com/a-list-of-artificial-intelligence-tools-you-can-use-today-for-personal-use-1-3-7f1b60b6c94f)
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- [A list of deep learning implementations in biology](https://github.com/hussius/deeplearning-biology)
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- [Awesome-2vec](https://github.com/MaxwellRebo/awesome-2vec)
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- [Awesome Action Recognition](https://github.com/jinwchoi/awesome-action-recognition)
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- [Awesome Adversarial Machine Learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning)
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- [Awesome AI Security](https://github.com/RandomAdversary/Awesome-AI-Security)
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- [Awesome ARKit](https://github.com/olucurious/awesome-arkit)
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- [Awesome Autonomous Vehicles](https://github.com/takeitallsource/awesome-autonomous-vehicles)
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- [Awesome bayesian deep learning](https://github.com/robi56/awesome-bayesian-deep-learning)
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- [Awesome Chatbot Projects,Corpus,Papers,Tutorials.](https://github.com/fendouai/Awesome-Chatbot)
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- [Awesome Deep Learning for Natural Language Processing](https://github.com/brianspiering/awesome-dl4nlp)
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- [Awesome deep vision web demo](https://github.com/hwalsuklee/awesome-deep-vision-web-demo)
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- [Awesome Caffe](https://github.com/MichaelXin/Awesome-Caffe)
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- [Awesome Chatbot Projects,Corpus,Papers,Tutorials.](https://github.com/fendouai/Awesome-Chatbot)
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- [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)
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- [Awesome Deep learning papers and other resources](https://github.com/endymecy/awesome-deeplearning-resources)
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- [Awesome Deep Learning Resources](https://github.com/guillaume-chevalier/awesome-deep-learning-resources)
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- [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision)
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- [Awesome Explorables](https://github.com/sp4ke/awesome-explorables)
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- [Awesome Knowledge Distillation](https://github.com/dkozlov/awesome-knowledge-distillation)
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- [Awesome Machine Learning On Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code)
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- [Awesome Machine Learning Projects](https://ml-showcase.com/)
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- [Awesome Most Cited Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers)
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- [Awesome MXNet](https://github.com//chinakook/Awesome-MXNet)
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- [Awesome Public Datasets](https://github.com/caesar0301/awesome-public-datasets)
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- [Awesome PyTorch](https://github.com/rickiepark/awesome-pytorch) :star:
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- [Awesome-question-answering](https://github.com/dapurv5/awesome-question-answering)
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- [Awesome Reinforcement Learning](https://github.com/aikorea/awesome-rl)
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- [Awesome Robotics](https://github.com/Kiloreux/awesome-robotics)
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- [Awesome Sentiment Analysis](https://github.com/xiamx/awesome-sentiment-analysis)
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- [Awesome-SLAM-list](https://github.com/OpenSLAM/awesome-SLAM-list)
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- [Awesome Semantic Segmentation](https://github.com/mrgloom/awesome-semantic-segmentation)
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- [Awesome speech recognition papers](https://github.com/zzw922cn/awesome-speech-recognition-papers)
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- [Awesome speech recognition speech synthesis papers](https://github.com/zzw922cn/awesome-speech-recognition-speech-synthesis-papers)
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- [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow) :star:
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- [Chainer Info](https://github.com/hidetomasuoka/chainer-info)
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- [Chat corpus collection from various open sources](https://github.com/Marsan-Ma/chat_corpus)
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- [Collection of generative models, e.g. GAN, VAE in Tensorflow, Keras, and Pytorch](https://github.com/wiseodd/generative-models) :star:
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- [Collection of Keras models used for classification](https://github.com//titu1994/Keras-Classification-Models)
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- [Collection of papers and other resources for object tracking and detection using deep learning](https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection)
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- [Collection of reinforcement learners implemented in python.](https://github.com/Islandman93/reinforcepy)
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- [Core ML Models](https://github.com/likedan/Awesome-CoreML-Models)
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- [Curated list of python software and packages related to scientific research in audio](https://github.com/faroit/awesome-python-scientific-audio)
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- [Datasets for Natural Language Processing](https://github.com/karthikncode/nlp-datasets)
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- [Datasets, Transforms and Models specific to Computer Vision](https://github.com/pytorch/vision/)
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- [Deep Learning and Time Series](https://github.com//FrancisArgnR/Time-series---deep-learning---state-of-the-art)
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- [Deep Learning for Recommendation Systems](https://github.com/robi56/Deep-Learning-for-Recommendation-Systems)
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- [Deep Learning Model Convertors](https://github.com/ysh329/deep-learning-model-convertor)
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- [Deep Learning Papers Reading Roadmap](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap)
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- [Deep Learning Processor List](https://zhuanlan.zhihu.com/p/28406226)
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- [Deep Learning Toolbox](https://github.com/dmarnerides/dlt)
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- [Enumerate diverse machine learning training tricks.](https://github.com/Conchylicultor/Deep-Learning-Tricks)
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- [First International Workshop on Deep Learning and Music](https://arxiv.org/html/1706.08675)
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- [Generative Models](https://github.com/wiseodd/generative-models)
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- [HEPML Resources](https://github.com/iml-wg/HEP-ML-Resources)
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- [List of articles related to deep learning applied to music](https://github.com/ybayle/awesome-deep-learning-music)
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- [Machine Learning / Deep Learning Conferences](https://tryolabs.com/blog/machine-learning-deep-learning-conferences/)
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- [Machine Learning Videos](https://github.com/dustinvtran/ml-videos)
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- [Machine Learning From Scratch](https://github.com/eriklindernoren/ML-From-Scratch)
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- [Must-read papers on NRL/NE.](https://github.com/thunlp/NRLpapers)
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- [Neural Networks on Silicon](https://github.com/fengbintu/Neural-Networks-on-Silicon)
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- [Notes/links on math and science, including statistics, bayes, cmpsc, quant trading, machine learning, etc](https://github.com/melling/MathAndScienceNotes)
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- [Open-source simulator for autonomous driving research](https://github.com/carla-simulator/carla) :star:
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- [Over 7 million published research papers in Computer Science and Neuroscience.](http://labs.semanticscholar.org/corpus/)
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- [Paper list of multi-agent reinforcement learning (MARL)](https://github.com/LantaoYu/MARL-Papers)
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- [Really-awesome-gan](https://github.com/nightrome/really-awesome-gan)
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- [Really-awesome-semantic-segmentation](https://github.com/nightrome/really-awesome-semantic-segmentation)
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- [Recommended Papers. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Learning (cs.LG)](https://github.com//ArcherFMY/Paper_Reading_List)
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- [Related Paper of Efficient Deep Neural Networks](https://github.com/Zhouaojun/Efficient-Deep-Learning)
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- [Repository to research & share the machine learning articles](https://github.com/arXivTimes/arXivTimes)
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- [Resources and codes about transfer learning and domain adaptation](https://github.com/jindongwang/transferlearning)
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- [SCODE Word Embeddings using Substitute Words](https://github.com/ai-ku/wvec)
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- [Some good resources for NNMT](http://thegrandjanitor.com/2017/09/09/some-useful-links-on-neural-machine-translation/) :star:
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- [Speech and Natural Language Processing](https://github.com/edobashira/speech-language-processing)
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- [Summaries and notes on Deep Learning research papers](https://github.com/dennybritz/deeplearning-papernotes)
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- [TensorFlow and Deep Learning Tutorials](https://github.com/wagamamaz/tensorflow-tutorial)
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- [TensorFlow Tutorial and Examples for beginners](https://github.com/aymericdamien/TensorFlow-Examples) :star:
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- [TensorFlow World Resources](https://github.com/astorfi/TensorFlow-World-Resources)
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- [The GAN Zoo](https://github.com/hindupuravinash/the-gan-zoo)
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- [The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch](https://github.com/ritchieng/the-incredible-pytorch) :star:
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- [Using python to work with time series data](https://github.com/MaxBenChrist/awesome_time_series_in_python)
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- [Use PyTorch to implement some classic frameworks](https://github.com/sunshineatnoon/Paper-Implementations)
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- [Various math-related things in Python code](https://github.com/calebmadrigal/math-with-python)
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- [机器学习资源 Machine learning](https://github.com/allmachinelearning/MachineLearning)
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- [图像文本位置感知与识别的论文资源汇总](https://github.com/whitelok/image-text-localization-recognition/blob/master/README.zh-cn.md)
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- [深度学习进行目标识别的资源列表](http://www.thinkface.cn/thread-4434-1-1.html)

‎books.md

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# Books
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* [Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville](http://www.deeplearningbook.org/). [`中文版本`](https://github.com/exacity/deeplearningbook-chinese)
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* [Deep Learning Tutorial by LISA lab, University of Montreal](http://deeplearning.net/tutorial/deeplearning.pdf)
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* [Documentation on all topics that I learn on both Artificial intelligence and machine learning.](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/)
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* [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)
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* [Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python](https://github.com/rasbt/deep-learning-book)
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* [Neural Networks and Deep Learning by Michael Nielsen](http://neuralnetworksanddeeplearning.com/)
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* [神经网络与深度学习.邱锡鹏](https://nndl.github.io/)
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* [UFLDL Tutorial](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)
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* [Rules of Machine Learning: Best Practices for ML Engineering](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
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* [Reinforcement Learning: An Introduction](http://incompleteideas.net/sutton/book/bookdraft2016sep.pdf). [`code`](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
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* [Reinforcement LearningState-of-the-Art](http://www.ai.rug.nl/~mwiering/RL-state-of-the-art)
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* [A Course in Machine Learning](http://ciml.info/)
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* [深度学习入门 by PaddlePaddle](https://github.com/PaddlePaddle/book)
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* [TensorFlow For Machine Intelligence](https://bleedingedgepress.com/tensor-flow-for-machine-intelligence/)
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* [First Contact With TensorFlow](http://jorditorres.org/first-contact-with-tensorflow/)
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* [Learning scikit-learn: Machine Learning in Python](https://github.com/gmonce/scikit-learn-book)
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* [Neural Networks: Tricks of the Trade (Lecture Notes in Computer Science)](https://link.springer.com/book/10.1007%2F978-3-642-35289-8)
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* [Scikit-learn 秘籍](https://www.gitbook.com/book/wizardforcel/sklearn-cookbook/details)
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* [Python Data Science Handbook](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb)
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* [Mathematical Foundations](https://mathematical-tours.github.io/book/)
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* [Natural Language Processing in Action](https://www.manning.com/books/natural-language-processing-in-action)

‎corpus.md

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# Corpus
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- [用于对话系统的中英文语料](https://github.com/candlewill/Dialog_Corpus)
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- [搜狗实验室](http://www.sogou.com/labs/)
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- [情感分析︱网络公开的免费文本语料训练数据集汇总](http://blog.csdn.net/sinat_26917383/article/details/51321505)
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- [中文情感分析用词语集](http://www.keenage.com/html/c_bulletin_2007.htm)
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- [人民日报切分/标注语料库](http://www.icl.pku.edu.cn/icl_res/)
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- [数据堂](http://more.datatang.com/)
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- [哈工大信息检索研究中心(HIT CIR)语言技术平台共享资源](http://ir.hit.edu.cn/demo/ltp/Sharing_Plan.htm)
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- [语料库在线](http://www.cncorpus.org/index.aspx)
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- [中文句结构树资料库](http://turing.iis.sinica.edu.tw/treesearch/)
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- [中文对白语料 chinese conversation corpus](https://github.com/rustch3n/dgk_lost_conv)
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- [中文语料小数据：Some useful Chinese corpus datasets](https://github.com/crownpku/Small-Chinese-Corpus)
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- [中文人名语料库。中文姓名,姓氏,名字,称呼,日本人名,翻译人名,英文人名](https://github.com/wainshine/Chinese-Names-Corpus)
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- [联合国平行语料库](https://conferences.unite.un.org/UNCorpus/zh)
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- [保险行业语料库](https://github.com/Samurais/insuranceqa-corpus-zh)
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- [用于训练中英文对话系统的语料库 Datasets for Training Chatbot System](https://github.com/candlewill/Dialog_Corpus)
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- [PTT 八卦版問答中文語料](https://github.com/zake7749/Gossiping-Chinese-Corpus)
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- [3 Million Instacart Orders, Open Sourced](https://www.instacart.com/datasets/grocery-shopping-2017)
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- [ACM Multimedia Systems Conference Dataset Archive](http://traces.cs.umass.edu/index.php/Mmsys/Mmsys)
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- [A dataset for book recommendations: ten thousand books, one million ratings](https://www.kaggle.com/zygmunt/goodbooks-10k)
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- [A dataset for personalized highlight detection](https://github.com/gifs/personalized-highlights-dataset)
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- [A dataset of 200k English plaintext jokes.](https://github.com/taivop/joke-dataset)
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- [A large-scale and high-qualityFMA: A Dataset For Music Analysis dataset of annotated musical notes.](https://magenta.tensorflow.org/datasets/nsynth)
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- [A large-scale dataset of manually annotated audio events](https://research.google.com/audioset/) :star:
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- [Alphabetical list of free/public domain datasets with text data for use in NLP](https://github.com/niderhoff/nlp-datasets)
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- [A MNIST-like fashion product database](https://github.com/zalandoresearch/fashion-mnist) :star:
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- [An awesome list of high-quality datasets](https://webhose.io/datasets) :star:
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- [An awesome list of high-quality open datasets in public domains](https://github.com/caesar0301/awesome-public-datasets) :star:
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- [A new dataset for Attribute Based Classification and Zero-Shot Learning](http://cvml.ist.ac.at/AwA2/)
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- [A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset](https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/)
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- [Chat corpus collection from various open sources](https://github.com/Marsan-Ma/chat_corpus)
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- [Cool Datasets](http://cooldataseWolfram Data Repositoryts.com/#Machine-Learning-Datasets) :star:
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- [Cornell NLVR](http://lic.nlp.cornell.edu/nlvr/)
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- [Corpora of misspellings for download](http://www.dcs.bbk.ac.uk/~ROGER/corpora.html)
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- [DATASETS FOR DATA MINING](http://www.inf.ed.ac.uk/teaching/courses/dme/html/datasets0405.html)
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- [Datasets of Annotated Semantic Relationships](https://github.com/davidsbatista/Annotated-Semantic-Relationships-Datasets)
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- [Datasets for Data Science and Machine Learning](https://elitedatascience.com/datasets)
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- [DeepDive Open Datasets](http://deepdive.stanford.edu/opendata/) :star:
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- [DeepFashion: Fashion Landmark Detection](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)
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- [EMOTIC Dataset](http://sunai.uoc.edu/emotic/)
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- [FMA: A Dataset For Music Analysis](https://github.com/mdeff/fma)
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- [Hard Drive Data and Stats](https://www.backblaze.com/b2/hard-drive-test-data.html)
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- [Japanese Word Similarity Dataset](https://github.com/tmu-nlp/JapaneseWordSimilarityDataset)
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- [Kaggle Datasets](https://www.kaggle.com/datasets)
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- [LLD - Large Logo Dataset](https://data.vision.ee.ethz.ch/cvl/lld/)
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- [Movie Review Data](http://www.cs.cornell.edu/people/pabo/movie-review-data/)
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- [Multi-Domain Sentiment Dataset](http://www.cs.jhu.edu/~mdredze/datasets/sentiment/)
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- [Open Speech and Language Resources](http://www.openslr.org/33) :star:
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- [Pixiv Dataset Overview](https://github.com/jerryli27/pixiv_dataset)
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- [SLAC: A Sparsely Labeled ACtions Dataset from MIT and Facebook](http://slac.csail.mit.edu/)
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- [Some good papers I like](https://github.com/hoangcuong2011/Good-Papers)
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- [Street View Image, Pose, and 3D Cities Dataset](https://github.com/amir32002/3D_Street_View)
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- [Taiga Сorpus](https://tatianashavrina.github.io/taiga_site/)
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- [Ten thousand books, six million ratings](https://github.com/zygmuntz/goodbooks-10k)
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- [The DBpedia Knowledge Base](http://wiki.dbpedia.org/about)
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- [The Quick, Draw! Dataset](https://github.com/googlecreativelab/quickdraw-dataset)
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- [TriviaQA: A Large Scale Dataset for Reading Comprehension and Question Answering](http://nlp.cs.washington.edu/triviaqa/)
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- [VGGFace2 Dataset](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/)
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- [Video Dataset Overview](https://www.di.ens.fr/~miech/datasetviz/)
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- [Wolfram Data Repository](https://datarepository.wolframcloud.com/)
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- [Yelp Open Dataset](https://www.yelp.com/dataset)

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