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# Papers
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 - [Deep Learning in Robotics: A Review of Recent Research](https://arxiv.org/ftp/arxiv/papers/1707/1707.07217.pdf) 
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 - [A learning based approach for social force model parameter estimation](http://ieeexplore.ieee.org/abstract/document/7966368/?reload=true)
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 - [Models for Understanding versus Models for Prediction](papers/RC1581.pdf) 
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 - [End to End Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/)
 - [Can we open the black box of AI?](http://www.nature.com/news/can-we-open-the-black-box-of-ai-1.20731)
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 - [Group Behavior from Video: A Data-Driven Approach to Crowd Simulation](http://mrl.snu.ac.kr/research/ProjectVideoCrowd/Group_Behavior_SCA2007.pdf)
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 - [LSTM Human Trajectory Prediction in Crowd](https://gitlab.version.fz-juelich.de/chraibi1/Machine_Learning/blob/master/papers/Social_LSTM_Human_Trajectory_Prediction_in_Cr.pdf)
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 - [Learning Social Etiquette Human Trajectory ](https://link.springer.com/content/pdf/10.1007%2F978-3-319-46484-8_33.pdf)
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 - [Layered Intelligence for Agent-based Crowd Simulation](https://www.researchgate.net/publication/220164541_Layered_Intelligence_for_Agent-based_Crowd_Simulation)
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 - [Research of Crowd Evacuation Simulation Based on the Machine Learning](papers/S0059063.pdf)
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 - [Distill: a modern machine learning journal](http://distill.pub/)
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 - [Machine learning: New tool in the box](http://www.nature.com/nphys/journal/v13/n5/full/nphys4053.html)
 - [Machine learning phases of matter](http://www.nature.com/nphys/journal/v13/n5/full/nphys4035.html)
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 - [Generative Adversarial Nets](http://papers.nips.cc/paper/5423-generative-adversarial-nets)
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# Books
 - [Reinforcement Learning: An Introduction (PDF)](https://webdocs.cs.ualberta.ca/~sutton/book/bookdraft2016sep.pdf)
 - [Understanding Machine Learning: From Theory to Algorithms (PDF)](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)
 - [Introduction to Machine Learning (PDF)](http://ai.stanford.edu/~nilsson/MLBOOK.pdf)
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# Links 
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 - [Yann LeCun’s Keynote on Predictive Learning](https://iot-for-all.com/yann-lecuns-keynote-on-predictive-learning-mixed-reality-as-imagined-by-magic-leap-and-marc-f2adfecf7ab6#.ia8jwksvl)
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 - [The AI Programmer's Bookshelf](http://alumni.media.mit.edu/~jorkin/aibooks.html)
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 - [How to Get Started with machine learning](https://news.ycombinator.com/item?id=12713056) 
 - [Introduction to Machine Learning for Developers](http://blog.algorithmia.com/introduction-machine-learning-developers/)
 - [machine-learning-way-easier-than-it-looks](https://blog.intercom.com/machine-learning-way-easier-than-it-looks/)
 - [Big Data’s Mathematical Mysteries](https://www.quantamagazine.org/20151203-big-datas-mathematical-mysteries/)
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 - [A course in machine learning](http://ciml.info/)
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 - [machine-learning-for-everyday-tasks](http://blog.mailgun.com/machine-learning-for-everyday-tasks/)
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 - [What's Wrong With Deep Learning?](http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf)
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 - [Uncertainty in Deep Learning (PhD Thesis)](http://mlg.eng.cam.ac.uk/yarin/blog_2248.html)
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 - [Cheatsheet – Python & R codes](https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/)
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 - [Machine Learning’s ‘Amazing’ Ability to Predict Chaos](https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/)
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# Videos 
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 - [Autonomous driving](https://www.coursera.org/learn/machine-learning/lecture/zYS8T/autonomous-driving)
 - [R vs PYTHON](https://blog.dominodatalab.com/video-huge-debate-r-vs-python-data-science/)
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# Code 
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- [Understanding Support Vector Machine via Examples (python)](https://sadanand-singh.github.io/posts/svmpython/)
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- [TensorFlow](https://www.tensorflow.org/)
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- [deepmind/learning-to-learn](https://github.com/deepmind/learning-to-learn)
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- [Machine Learning from scratch: Bare bones implementations in Python](https://github.com/eriklindernoren/ML-From-Scratch)
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- [Tutorial on Deep Learning](https://news.ycombinator.com/item?id=13505160)
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Keras  
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- [KERAS Deep Learning for humans](https://github.com/keras-team/keras)