CNNs: Convolution, Pooling, Receptive Fields

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