Boltzmann Machine

A Boltzmann Machine is a type of stochastic (randomized) neural network that learns to represent complex data distributions by adjusting the weights between interconnected neurons. Each neuron has a binary state (on/off), and learning happens by minimizing the difference between observed data patterns and the network’s internal predictions using a probabilistic energy function. It’s mainly used as a building block for deeper models like Restricted Boltzmann Machines and Deep Belief Networks.