What is a Restricted Boltzmann Machine?

A Restricted Boltzmann Machine is a type of artificial neural network that falls under the category of generative models. It was introduced by Geoffrey Hinton and Terry Sejnowski in the 1980s. RBMs consist of two layers: visible units and hidden units. The units within each layer are fully connected, but there are no connections between units within the same layer.

RBMs are called “restricted” because of the restrictions imposed on the connections between units. This restriction ensures that the visible units are only connected to the hidden units and vice versa, making RBMs a bipartite graph. This architectural constraint simplifies the training and inference procedures of RBMs.

In the world of machine learning, one algorithm that has gained significant attention is the Restricted Boltzmann Machine (RBM). RBMs are powerful generative models that have been widely used for various applications, such as dimensionality reduction, feature learning, and collaborative filtering. In this article, we will explore the concepts and steps involved in training and using RBMs, along with some good examples to solidify our understanding.