A Convolutional Neural Network (CNN) is a specialized type of deep neural network predominantly used for analyzing visual imagery. Its architecture is inspired by the organization of the animal visual cortex, where individual neurons respond to specific regions of the visual field. The core building block of a CNN is the convolutional layer, which applies a set of learnable filters (or kernels) to input data, typically images, to extract hierarchical features such as edges, textures, and ultimately, object parts and complete objects. These filters slide across the input, performing dot products, and the results form feature maps. Beyond convolution, CNNs often incorporate pooling layers to reduce dimensionality and fully connected layers for classification or regression tasks. This hierarchical feature learning makes CNNs highly effective for tasks like image classification, object detection, and semantic segmentation, achieving state-of-the-art performance across various computer vision benchmarks.