This article describe about Boltzmann Machine, which is a network of symmetrically connected, coupled stochastic binary units that make stochastic decisions about whether to be on or off. Boltzmann Machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. Boltzmann machines with unconstrained connectivity have not proven useful for practical problems in machine learning or inference, but if the connectivity is properly constrained, the learning can be made efficient enough to be useful for practical problems.
Boltzmann Machine
