What defines a Convolutional Neural Network (CNN)?

A Convolutional Neural Network (CNN) is specifically designed for processing visual data, and its primary function is to apply filters to images. This enables the network to detect and learn various features within an image, such as edges, textures, and patterns, which are crucial for tasks like image classification, object detection, and segmentation.

The convolutional layers in a CNN utilize a mathematical operation called convolution, where filters move across the input image and compute the output feature map. This process helps preserve the spatial relationships between pixels, making CNNs particularly powerful for image-related tasks compared to other neural network architectures.

The other options describe methods or models that do not align with the primary focus of CNNs. For instance, processing textual data refers more to Recurrent Neural Networks (RNNs) or Transformers, regression analysis typically involves different types of models that are not solely focused on visual data, and reinforcement learning involves different techniques that are unrelated to the convolutional processes found in CNNs.