The Boltzmann machine

A Boltzmann machine is a type of artificial neural network used in machine learning. It consists of interconnected nodes, each representing a binary state (like on or off). The network learns patterns in data by adjusting the connections based on statistical rules, mimicking how particles in thermal equilibrium behave. By exploring different configurations, it can represent complex distributions of data. Boltzmann machines can help with tasks such as dimensionality reduction, feature learning, and generating new data based on learned patterns, making them useful in fields like computer vision, natural language processing, and recommendation systems.