I am trying to use deep belief networks for face recognition. But I am a beginner in this area, I have read the research papers and documentations available on the Internet and I understood the basic concept for binary images. But still when I sit down to code I find great difficulty because nothing is explained from a programmers perspective all you find is energy functions and all that stuff. Can some body help me design(code) hidden layer for a gray scale Face image ? (To be more specific what should my hidden layer be should it be an array of different filters or something else ....)
how to design feature extraction layer for DBN for face recognition
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I have experience with Neural Networks and Self-Organizing Maps dating back to the late-1980s, but I too find Energy-based Restricted Boltzmann Machines somewhat daunting to just sit down and implement. I found the following websites with either Matlab code (Octave?) and C. They're from the Netflix competition (winner from University of Toronto), but it's a good winning example and should provide some insight.
http://imonad.com/rbm/restricted-boltzmann-machine/
https://code.google.com/p/nprizeadditions/source/browse/trunk/rbm.c
I might also suggest taking Andrew Ng's Coursera on Machine Learning from Stanford (it's free and a new session starts on Jan 19, 2015) I've viewed a number of the lectures and they are very good. Hope this helps.