[1] http://en.wikipedia.org/wiki/Deep_learning#Convolutional_neu...
[2] http://deeplearning.net/reading-list/
[3] http://en.wikipedia.org/wiki/Deep_learning#Results
[4] http://www.wired.com/wiredscience/2012/06/google-x-neural-ne...
Deep Learning generally refers to machine learning algorithms that deal with stacking multiple layers of simpler functions to enable more complicated functions, and optimizing all the parameters to best fit your training set and generalize to new samples (the hard part). Though it usually refers to neural networks, I dont think there's any reason it doesn't also apply to other layered approaches as long as there's a relatively unified learning algorithm applied across the whole system.
There are clearly many different deep learning algorithms, even if you just count the permutations of tricks you can choose from to improve layered NN generalization. Though to be fair I think very good progress is being made towards developing "better" algorithms in the sense that new ones (e.g. RBM pretraining + dropout) usual perform better than than older algorithms, no matter what data you use it on (now network architecture is another matter entirely).
But I do agree with your point.