, , , , , , and have done reddit AMA's.  These are nice places to start to get a Zeitgeist of the field.

 

and lectures at , , and at Stanford, the at , and the at have excellent tutorials, video lectures and programming exercises that should help you get started.

 

The online book by , notes for , and blogs by , and have clear explanations of MLPs, CNNs and RNNs.  The tutorials at and give equations and code. The encyclopaedic book by is a good place to dive into details.  I have a in progress.

 

, , , , , , , , , , , and , , , , , , , , ( ), are some of the many deep learning software libraries and frameworks introduced in the last 10 years.   and compare the performance of many existing packages. I am working on developing an alternative, , written in supporting CNNs and RNNs on GPUs and supporting easy development of original architectures.  More software can be found at .

and homepages of , have further information, background and links.

 

from: http://www.denizyuret.com/2014/11/some-starting-points-for-deep-learning.html