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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 coul

by gbachik 12y ago

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

ilyaeck 12y ago
You just need to learn some background. I recommend this hands-on resource: http://karpathy.github.io/neuralnets/ http://karpathy.github.io/neuralnets/
ogrisel 12y ago
You can start with: http://deeplearning.net/tutorial/ http://deeplearning.net/tutorial/ And this book (work in progress): http://www.iro.umontreal.ca/~bengioy/dlbook/ http://www.iro.umontreal.ca/~bengioy/dlbook/
maurits 12y ago
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 http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial [2]: http://www.stanford.edu/class/cs294a/video1.html http://www.stanford.edu/class/cs294a/video1.html [3]: http://www.stanford.edu/class/cs294a/video2.html http://www.stanford.edu/class/cs294a/video2.html [4]: http://www.stanford.edu/class/cs294a/handouts.html http://www.stanford.edu/class/cs294a/handouts.html
chriswarbo 12y ago
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.
gbachik 12y ago
Thanks for all the resources guys! I really appreciate it! I'll start diving in!

by gbachik 12y ago

ilyaeck 12y ago

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

ogrisel 12y ago

You can start with: http://deeplearning.net/tutorial/ http://deeplearning.net/tutorial/ And this book (work in progress): http://www.iro.umontreal.ca/~bengioy/dlbook/ http://www.iro.umontreal.ca/~bengioy/dlbook/

maurits 12y ago

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 http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial [2]: http://www.stanford.edu/class/cs294a/video1.html http://www.stanford.edu/class/cs294a/video1.html [3]: http://www.stanford.edu/class/cs294a/video2.html http://www.stanford.edu/class/cs294a/video2.html [4]: http://www.stanford.edu/class/cs294a/handouts.html http://www.stanford.edu/class/cs294a/handouts.html

chriswarbo 12y ago

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.

gbachik 12y ago

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