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@@ -59,7 +59,7 @@
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17. [Statistical Machine Learning - CMU](https://www.youtube.com/watch?v=azaLcvuql_g&list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) by Prof. Larry Wasserman
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18. [Deep Learning Course](https://www.college-de-france.fr/site/en-yann-lecun/course-2015-2016.htm) by Yann LeCun (2016)
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19. [Bay area DL school](http://www.bayareadlschool.org/) by Andrew Ng, Yoshua Bengio, Samy Bengio, Andrej Karpathy, Richard Socher, Hugo Larochelle and many others @ Stanford, CA (2016)
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-20.[Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley](https://www.youtube.com/playlist?list=PLkFD6_40KJIxopmdJF_CLNqG3QuDFHQUm)
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+20. [Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley](https://www.youtube.com/playlist?list=PLkFD6_40KJIxopmdJF_CLNqG3QuDFHQUm)
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21. [UVA Deep Learning Course] (http://uvadlc.github.io) MSc in Artificial Intelligence for the University of Amsterdam.
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22. [MIT 6.S094: Deep Learning for Self-Driving Cars] (http://selfdrivingcars.mit.edu/)
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15. [A beginners Guide to Deep Neural Networks](http://googleresearch.blogspot.com/2015/09/a-beginners-guide-to-deep-neural.html) By Natalie Hammel and Lorraine Yurshansky
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16. [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M) by Steve Jurvetson (and panel) at VLAB in Stanford.
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17. [Introduction to Artificial Neural Networks and Deep Learning](https://www.youtube.com/watch?v=FoO8qDB8gUU) by Leo Isikdogan at Motorola Mobility HQ
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+18. [NIPS 2016 lecture and workshop videos](https://nips.cc/Conferences/2016/Schedule) - NIPS 2016
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### Papers
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*You can also find the most cited deep learning papers from [here](https://github.com/terryum/awesome-deep-learning-papers)*
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2. [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf)
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3. [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)
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4. [CMU’s list of papers](http://deeplearning.cs.cmu.edu/)
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-5. [Neural Networks for Named Entity
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-Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stanford.edu/~socherr/pa4-ner.zip)
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+5. [Neural Networks for Named Entity Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stanford.edu/~socherr/pa4-ner.zip)
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6. [Training tricks by YB](http://www.iro.umontreal.ca/~bengioy/papers/YB-tricks.pdf)
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7. [Geoff Hinton's reading list (all papers)] (http://www.cs.toronto.edu/~hinton/deeprefs.html)
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8. [Supervised Sequence Labelling with Recurrent Neural Networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
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@@ -271,6 +271,7 @@ Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stan
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22. [visualqa.org](http://www.visualqa.org/)
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23. [www.mpi-inf.mpg.de/departments/computer-vision...](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/)
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24. [Deep Learning News](http://news.startup.ml/)
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+25. [Machine Learning is Fun! Adam Geitgey's Blog](https://medium.com/@ageitgey/)
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### Datasets
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Christos