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Comment by gbachik | Hacker News Reader

How do you guys understand this stuff!? The topic absolutely fascinated me, but after reading the paper... I just feel dumb. Are there any good resources I could use to better understand machine learning papers such as this? I mean I can't even comprehend the implications this paper could have? Is anyone opening to doing some mentoring? Gbachik@gmail.com

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Machine learning can be quite jargon-heavy, and in my experience the core ideas can often be hidden amongst a bunch of unnecessary maths.

I got frustrated by a paper yesterday which contained function definitions, summations-of-summations, products of sequences, convolutions, set theory, switching back-and-forth between unary-functions/vectors and binary-functions/matrices, converting back-and-forth between {0, 1}, {-1, 1} and {true, false}, weighting elements of a set by 0/1 instead of taking a sub-set, linear programming, etc.

What was their result? To speed up pair-wise comparisons of structured data, only do N% of the comparisons and it will only take N% of the time. To decide which comparisons to discard, see what works well on a small sample of inputs.

Andrew Ng has an introduction class to some deep-learning topics here:

[1]: Wiki with code, exercises and explanation

[2]: Video lecture one with a recap on back-propagation

[3]: Video lecture two on Sparse Auto Encoders

[4]: Handouts

[1]: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

[2]: http://www.stanford.edu/class/cs294a/video1.html

[3]: http://www.stanford.edu/class/cs294a/video2.html

[4]: http://www.stanford.edu/class/cs294a/handouts.html

You can start with:

http://deeplearning.net/tutorial/

And this book (work in progress):

http://www.iro.umontreal.ca/~bengioy/dlbook/

You just need to learn some background. I recommend this hands-on resource: http://karpathy.github.io/neuralnets/

Thanks for all the resources guys! I really appreciate it! I'll start diving in!

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