Restricted Boltzmann Machines (RBMs) are shallow, two-layer neural networks that constitute the building blocks of deep-belief networks. The first layer of the RBM is the visible (input) layer, and the second is the hidden layer. Because inputs from all visible nodes are passed to all hidden nodes, an RBM can be defined as a symmetrical bipartite graph.

During the forward pass, each node of the visible layer is multiplied by a separate weight, the products are summed, added to a bias, and the result is passed through an activation function to produce the hidden layer's output. In the reconstruction phase, the hidden layer's activations become the input in a backward pass. They are multiplied by the same weights, one per internode edge. The sum of those products is added to a visible-layer bias at each visible node, producing a reconstruction (an approximation of the original input). This can be represented by the following diagram: