What is a convolutional neural network (CNN) primarily used for?

A convolutional neural network (CNN) is specifically designed to process data that has a grid-like topology, making it particularly effective for tasks involving image data. The architecture of a CNN incorporates convolutional layers, which apply various filters to the input image, allowing the network to automatically learn spatial hierarchies of features. This hierarchical feature extraction is vital for identifying patterns such as edges, textures, and more complex structures within images.

CNNs excel in image recognition and classification tasks by leveraging their ability to recognize visual patterns through layers of abstraction. For example, in an image classification scenario, the network can take a raw pixel input and progressively transform it through multiple layers to output a classification label, such as identifying whether the image contains a cat or a dog.

While convolutional neural networks can still be applied in broader contexts — such as video processing or even in certain natural language processing applications by transforming text into a suitable format — their primary and most successful application remains in the realm of image processing.