Deep Belief Network

A Deep Belief Network (DBN) is a type of artificial neural network that learns to recognize complex patterns by stacking multiple layers of simpler units called neurons. Each layer learns to identify features from the previous layer, gradually building an understanding from basic to intricate details. This hierarchical structure enables the DBN to effectively analyze large and complex data, making it useful for tasks like image recognition, speech processing, and pattern detection. Think of it as a multi-level filter system that extracts meaningful information from raw data through successive stages of learning.