Training a deep network (the main reason RBM’s are interesting)
	• First train a layer of features that receive input directly from the pixels. • Then treat the activations of the trained features as if they were pixels and learn features of features in a second hidden layer. • It can be proved that each time we add another layer of features we improve a variational lower bound on the log probability of the training data. – The proof is slightly complicated. – But it is based on a neat equivalence between an RBM and a deep directed model (described later)