Can you explain what a convolutional layer in neural networks is?

A convolutional layer is a key component of convolutional neural networks (CNNs), used for processing images and other data with spatial structure. It applies a set of filters (convolution kernels) that slide over the input data and extract local features, such as edges, textures, or shapes.

Each filter is a small array of weights that is multiplied by the corresponding part of the input data and summed to create a feature map. This allows the model to automatically learn to recognize important patterns without manual feature extraction.

Example: if you have an image of 28x28 pixels, a convolutional layer with a 3x3 filter will slide over the image, computing the convolution at each location, forming a new feature map of smaller size.