, 21 October 2014 (UTC)
 As I said above, Deep learning is the name of a class of algorithms. Feedforward neural nets, restricted Boltzmann machines, deep neural networks and deep belief networks are all mathematical models. How the models are trained, that is, how model parameters are chosen to optimize some loss function, is another topic entirely. People had looked at deep neural network architectures before deep learning techniques were developed, but it was the deep learning techniquesunsupervised pre-training, layer by layerthat made deep networks more practical at the time. With today's GPU based algorithms, the need for pre-training has lessened. The hype surrounding deep learning has muddied the concept to point that people like Michael Jordan claim it is just a synonym for neural network. But the backlash from current hype doesn't change the historical importance of deep learning algorithms in generating interest and good results in deep neural network models or representational learning. Schmidhuber's review has a good discussion of these issues.   21:55