A Convolutional Neural Network (CNN) is a specialized type of neural network primarily used for processing structured grid data, such as images. CNNs are particularly effective in computer vision tasks, including image classification, object detection, and face recognition. They leverage a series of layers to automatically learn and extract features from input data, which makes them powerful for visual recognition tasks.

The architecture of a CNN typically includes several key layers: convolutional layers, pooling layers, activation layers, and fully connected layers. Convolutional layers apply filters to the input data to extract features, while pooling layers reduce the spatial dimensions of the data, helping to minimize computational load and prevent overfitting. Activation layers introduce non-linearity into the model, allowing it to learn complex patterns. Finally, fully connected layers integrate the features learned by the previous layers to make predictions or classifications ¹²³.

CNNs have become a cornerstone of deep learning in computer vision due to their ability to learn hierarchical representations of data, making them highly effective for various applications in this field ⁴⁵.