Convolutional Neural Networks: Feature Extraction and Spatial Convolutions

Spatial hierarchies in visual data require specialized architectural priors. Convolutional Neural Networks (CNNs) utilize local receptive fields, shared weights, and spatial pooling to build shift-invariant representation spaces. We dissect kernel convolutions, feature map dimensions, activation mapping, and backpropagation through pooling layers.