You’re interested in learning about Boltzmann machines. A Boltzmann machine is an artificial neural network used for unsupervised learning tasks, such as pattern recognition, feature learning, and data generation. It’s a type of energy-based model that uses stochastic methods for training.
Boltzmann machines consist of nodes interconnected in a network, also known as units. These units can be in two states: on or off. The connections between the teams have associated weights that determine the strength of the links. Learning in Boltzmann machines involves adjusting these weights based on the training data and the energy function of the system.
One common type of Boltzmann machine is the Restricted Boltzmann Machine (RBM). RBMs have a specific structure with no connections within a layer, only between layers. This structure makes training RBMs more tractable.
Boltzmann machines are used in various applications, such as collaborative filtering, dimensionality reduction, and generative modeling. They have also paved the way for more advanced neural network architectures like deep belief networks.