CNNs: Convolution, Pooling, Receptive Fields

Convolutional neural networks replace dense layers with small filters slid across an image, sharing weights so the same edge detector applies everywhere. This buys parameter efficiency, translation equivariance, and a receptive field that grows with depth, which is the inductive bias that makes CNNs data-efficient for vision. Applied AI interviews probe this to check you understand why architecture choice encodes assumptions about the data, not just how to call a library.