restricted Boltzmann machine

A Restricted Boltzmann Machine (RBM) is a type of artificial neural network designed to learn patterns in data. It consists of two layers: visible units that represent the data and hidden units that capture underlying features. Unlike regular neural networks, there are no connections between the hidden units. RBMs learn by adjusting the strength of connections based on how the data is structured, allowing them to discover complex patterns. They are commonly used for tasks like image and speech recognition, as well as in collaborative filtering and as building blocks for deep learning models.