Convolutional Neural Networks (CNNs) work by processing data hierarchically, learning features through convolutional layers. These layers apply filters to detect patterns like edges, shapes, and textures in the input.
Pooling layers reduce spatial dimensions, retaining essential features while making computations efficient. Fully connected layers at the end interpret these features to produce outputs like classifications or predictions.
By stacking multiple convolutional layers, CNNs can extract increasingly complex features, making them ideal for tasks like image classification, object detection, and semantic segmentation.