Convolutional Neural Networks (CNNs) are a specialized class of neural networks primarily used for processing structured grid data, such as images. They are designed to automatically and adaptively learn spatial hierarchies of features from input images, making them particularly effective for tasks in computer vision.

The architecture of CNNs typically consists of multiple layers, including 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, helping to minimize computation and control overfitting. Activation layers introduce non-linearity into the model, allowing it to learn complex patterns. Finally, fully connected layers integrate the features learned by previous layers to make predictions or classifications ¹⁴.

CNNs leverage the spatial structure of images, maintaining the relationship between nearby pixels, which is crucial for effective learning. This approach contrasts with traditional methods that treat images as one-dimensional vectors, losing important spatial information ³. Overall, CNNs have revolutionized the field of computer vision and are widely used in applications such as image recognition, object detection, and more ⁵.