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if you haven't seen it yet, we do recommend

There is a new release of Deep Learning Book by Yoshua Bengio: http://www.iro.umontreal.ca/~bengioy/dlbook/

Do not hesitate to forward these messages to fellow data scientists

In this notebook, we cover the basics of probability theory, and show how to implement the theory in Python. (You should have a little background in probability and Python.) Then we show how to solve some particularly perplexing paradoxical probability problems.

http://vk.com/wall-103600381_5

Hakathon from russian Facebook

The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.
http://www.kdnuggets.com/2015/09/15-math-mooc-data-science.html

for whom it may concern
http://insightdatascience.com

"On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG16 by 49x from 552MB to 11.3MB, again with no loss of accuracy."

Benchmark on classification datasets results

Predicting Daily Activities from Egocentric Images Using Deep Learning

http://www.cc.gatech.edu/cpl/projects/dailyactivities/

Okay, chat. Cool guyz from Georgia Tech taught neural net to classify activities of the subject.

So now Siri/Cortana/Google can predict that you will use car or buy you bus ticket, if you allow AI to track down images you see.

I think that Google Glass was launched too early, cause this the most awesome application for it.

https://github.com/cmusatyalab/openface

Framework for Face recognition

You should really chek it out