RBM is often used as part of a multi-layer deep belief network. The output of the RBM is used as an input to another layer. The use of the RBM is repeated until the final layer is reached.
Deep Belief Networks (DBNs) consist of several RBMs stacked together. Each hidden layer provides the input for the subsequent layer. Within each layer, the nodes cannot communicate laterally and it becomes essentially a network of other single-layer networks. DBNs are especially helpful for classifying, clustering, and recognizing image data.
The term, continuous restricted Boltzmann machine, refers an RBM that uses values other than integers. Input data is normalized to values between zero and one.
Each node of the input layer ...