Convolutional layers are a fundamental component of convolutional neural networks (CNNs), which are widely used in image processing and computer vision tasks. These layers apply a mathematical operation called convolution, where a small filter or kernel slides over the input data, such as an image, to detect patterns like edges or textures. The result is a feature map that highlights important aspects of the input. The primary purpose of convolutional layers is to reduce the dimensionality of the data while preserving essential features. By stacking multiple convolutional layers, CNNs can learn increasingly complex representations, making them effective for tasks like image classification and object detection.