Helmholtz Machines

Helmholtz Machines are a type of artificial neural network designed to learn complex patterns and representations from data. They operate by simultaneously developing a "generative" model (which can produce data similar to what they trained on) and an "inference" model (which figures out underlying features from data). Through a process called "weighted updates," they iteratively improve both models, enabling better understanding and generation of data such as images or sounds. Essentially, Helmholtz Machines mimic how the brain might organize and process information, making them a foundational approach in unsupervised learning and deep generative modeling.