Deep belief networks

A DBN is a graphical model, constructed using multiple stacked RBMs. While the first RBM trains a layer of features based on input from the pixels of the training data, subsequent layers treat the activations of preceding layers as if they were pixels and attempt to learn the features in subsequent hidden layers. This is frequently described as learning the representation of data and is a common theme in deep learning.

How many multiple RBMs there should be depends on what is needed for the problem at hand. From a practical perspective, it's a trade-off between increasing accuracy and increasing computational cost. It is the case that each layer of RBMs will improve the lower bound of the log probability of the training data. ...

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