A Beginner's Tutorial for Restricted Boltzmann Machines - Deeplearning4j: Open-source, distributed deep learning for the JVM

Invented by Geoff Hinton, a Restricted Boltzmann machine is an algorithm useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. Given their relative simplicity and historical importance, restricted Boltzmann machines are the first neural network we'll tackle. In the paragraphs below, we describe in diagrams and plain language how they work. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The first layer of the RBM is called the visible, or input, layer, and the second is the hidden layer. Each circle in the graph above represents a neuron-like unit called a node, and nodes are simply where calculations take place.