Deep Belief Networks

Deep Belief Networks (DBNs) are a type of artificial neural network designed to recognize complex patterns in data. They consist of multiple layers of interconnected nodes that learn to represent data features at various levels of abstraction. During training, each layer learns to capture features from the layer below, gradually building a hierarchical understanding. This structure allows DBNs to effectively process and analyze large, complex datasets, making them useful for tasks like image recognition, speech processing, and feature extraction. Essentially, they mimic how the brain might learn patterns progressively, improving understanding with each layer.