What kind of input does a convolutional neural network primarily process?

Convolutional Neural Networks (CNNs) are specifically designed to process data that has a grid-like topology, and they excel particularly with two-dimensional image data. This is because CNNs leverage convolutional layers, which apply filters or kernels that move across the image to detect features such as edges, textures, and shapes. By using multiple layers of convolutions, pooling, and activation functions, CNNs can effectively learn hierarchical representations of data in images, facilitating tasks such as classification, segmentation, and detection.

In contrast, sequential data like time series, text data for natural language processing, and numerical tabular data do not leverage the same spatial structure that images have. While CNNs can be adapted to handle other data types with significant modifications, their foundational architecture and advantages are crafted around processing two-dimensional spatial data, making them the most effective tool for image-related tasks.