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# Papers 
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 - [Models for Understanding versus Models for Prediction](papers/RC1581.pdf) 
 - [End to End Learning for Self-Driving Cars] (https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/)
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 - [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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# Links 
 - [Yann LeCun’s Keynote on Predictive Learning](http://alumni.media.mit.edu/~jorkin/aibooks.html)
 - [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/)
 - [A cource of machine learning](http://ciml.info/)
 - [machine-learning-for-everyday-tasks](http://blog.mailgun.com/machine-learning-for-everyday-tasks/)
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# Videos 
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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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# Code 
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- [TensorFlow](https://www.tensorflow.org/)