Boltzmann Machines (BMs) are stochastic neural networks inspired by the principles of statistical physics. They consist of a network of interconnected binary units known as neurons, organized into visible and hidden layers. Each neuron is associated with a weight that determines its influence on the overall behavior of the model.

BMs employ a process called unsupervised learning, where the model discovers the underlying structure and dependencies of the input data without explicit labels. Through a technique known as Contrastive Divergence, Boltzmann Machines iteratively adjust their weights to maximize the likelihood of generating observed data.

Boltzmann Machines have found applications in a wide range of business domains. In recommendation systems, BMs can learn user preferences and generate personalized recommendations, enhancing customer experiences and driving sales. In finance, these models can analyze complex market data and assist in risk assessment or algorithmic trading. Additionally, BMs have shown promise in drug discovery, image recognition, and natural language processing tasks.

Boltzmann Machines have also paved the way for more advanced generative models, such as Deep Belief Networks (DBNs) and Restricted Boltzmann Machines (RBMs). These models build upon the principles of Boltzmann Machines to learn hierarchical representations and generate more complex outputs.