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Hinton proposes building deep belief networks by stacking RBMs and training them unsupervised and in ascending order. After that, the network goes into feed-forward mode and backprop can be used to learn the actual task. Thus, some of the problems of backprop are solved by initializing the weights via unsupervised learning.