Convolutional Neural Networks (abbreviated as CNN) are a type of artificial neural network specially designed to process data that have a mesh structure, such as images, videos or audio signals. CNNs use a mathematical operation called convolution to extract relevant features from the input data and learn to recognize patterns in it.

The convolutional layers of a CNN are composed of a set of filters or kernels that are repeatedly applied to the input image, each generating a feature map. These feature maps are passed through subsampling layers, also known as pooling layers, which reduce their size to reduce the computational complexity of the network. Finally, the fully connected layers of the CNN are responsible for classifying the image into one or more categories.

CNNs are especially useful in computer vision tasks such as image classification, object detection and semantic segmentation. Their ability to learn and extract relevant features automatically has revolutionized the field of computer vision and enabled practical applications such as face detection in photographs, image classification in social networks, and autonomous driving in vehicles.