I am using deeplearning for hyperspectral data and I had to decide between available frameworks.
Theano vs Torch vs Caffe vs deeplearning4j
There really isn't a single one that stands out from others.
Theano offers a compiler that automatically calculates derivatives for you. but s hard to modify and debug, a lot of tutorials online and lots and lots of active community. bunch of kaggle winners used theano
Torch is being used by the big guys: facebook, google, purdue, etc. kinda like matlab but being dependent on Lua is a draw back. there seems to be a bunch of activity on its github but its mostly from big guys and normal users aren't there
caffe seems more popular on github developed by berkeley. itiis very abstract (define a network with json) and high level dedicated to convolution networks, hard to tweak
DL4j can give you the backone of hadoop and spark but it needs more review.
I tried to go with torch but getting up and running fast was not an option. No good tutorial out there. Contrary Theano has plenty of resources to learn and videos to view. So for now I have created a single layer MNIST logistic layer NN. We'll see how it goes from there
Some benchmark and comparison link