A Boltzmann Machine is a type of artificial neural network used for learning patterns and making decisions. It is named after the physicist Ludwig Boltzmann and is based on principles from statistical mechanics. This network consists of interconnected units (or nodes) that work together to find hidden patterns in data. The connections between these units have associated weights, which are adjusted to improve learning over time.

A Boltzmann Machine is unique because it is a stochastic (random) network, meaning it introduces randomness in how neurons activate. This randomness helps it escape local optima (bad solutions) and discover better patterns. It is structured with visible units (which take input data) and hidden units (which learn abstract features). The model learns by adjusting the weights between these units to represent the data’s underlying structure more effectively.

One of the best-known variations of the Boltzmann Machine is the Restricted Boltzmann Machine (RBM), which simplifies the original model by restricting certain connections. RBMs have been widely used in applications like feature learning, recommendation systems (such as Netflix’s early algorithms), and deep learning frameworks like Deep Belief Networks.

Although Boltzmann Machines were once a major area of AI research, they have largely been replaced by more efficient deep learning methods, such as transformers and convolutional neural networks (CNNs). However, their influence can still be seen in areas like unsupervised learning and probabilistic modeling.

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