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We take the values of those probabilities and we enter them into another RBM as input data. This RBM will also give out some other probabilities for its hidden layer, and these probabilities are also filters for its own inputs. These filters will be of a higher level and more complex. We repeat this a couple of times, we stack the resulting RBMs and, on top of the last one, we add a classification layer (such as logistic regression) and we get ourselves a Deep Belief Network\cite{Hinton_Teh_2006}.   The idea that started the deep learning revolution was this: you can learn layer by layer filters that get more and more complex and at the end you don’t work directly with pixels, but with high level features, that are much better indicators of what objects are there in an image.   The learning of the parameters of a RBM is done using an algorithm called “contrastive divergence”. This starts with an example from the input data, calculates the values for the hidden layer and then these values are used to simulate what input data they would produce. The weights are then adjusted with the difference between the original input data and the “dreamed” input data (with some inner products around there). This process is repeated for each example of the input data, several times, until either the error is small enough or a predetermined number of iterations has passed.\subsection{Autoencoders}