Convolution

The fundamental operation of CNNs that captures spatial patterns through local filters.

The convolution operation relies on sliding small filters over images or other grid-structured data to detect local patterns. This allows the same filter to be reused across positions, creating strong parameter efficiency. It plays a central role in learning edges, textures, and increasingly complex visual structures in a hierarchical way. One of the main reasons CNNs are so powerful is this local and shared learning mechanism.