The below resources are the ones I used when I started to learn about DL/NNs. Some of them are focused specifically on certain applications, but I found them helpful, too.

Basic NNs:

http://www.wildml.com/2015/09/implementing-a-neural-network-... (a whole series, all worth reading)

https://gist.github.com/sthware/c47824c116e6a61a56d9 (my code based on the above)

http://iamtrask.github.io/2015/07/27/python-network-part2/

http://rolisz.ro/2013/04/18/neural-networks-in-python/

ML:

http://cs229.stanford.edu/materials.html

http://onlinestatbook.com/2/index.html

DL in general, RNNs, RNTS, CNNs, some others:

http://cs224d.stanford.edu/syllabus.html

http://cs231n.stanford.edu/syllabus.html

http://mattmazur.com/2015/03/17/a-step-by-step-backpropagati...

http://arxiv.org/pdf/cs/0205070.pdf

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

http://karpathy.github.io/2015/05/21/rnn-effectiveness/

http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf

http://alexdavies.net/talks/

http://www.socher.org/index.php/Main/SemanticCompositionalit... roughRecursiveMatrix-VectorSpaces

http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/

http://colah.github.io/posts/2014-07-NLP-RNNs-Representation...