you mean material specific to deep learning that is more general and less math heavy? Hm, that's a good question, the resources I'd reference are all a bit math

you mean material specific to deep learning that is more general and less math heavy? Hm, that's a good question, the resources I'd reference are all a bit math heavy. However, don't be afraid of diving into TensorFlow, it's really a nice library that takes care of all the tedious, mathematical details. E.g., in contrast/addition to NumPy (leaving out the comp. efficiency part out of the discussion for now), it already implements several optimzation algorithms, so you wouldn't have to worry about implementing backpropagation from scratch or so. Sure, it still requires a bit of linear algebra, but it's really more straight-forward than it seems at first glance :). Maybe, you'd be interested in Keras (http://keras.io); http://keras.io); it's a wrapper around Theano and TensorFlow which provides a really intuitive interface for building neural nets! Haha, btw. I really enjoyed Joel Gruus, post ;)