konvoluatiokerros

Konvoluatiokerros, often translated as convolutional layer, is a fundamental building block in artificial neural networks, particularly those used for image processing and computer vision tasks. Its primary function is to extract local features from input data, such as images, by applying a set of learnable filters. Each filter, also known as a kernel, is a small matrix of weights that slides over the input data. At each position, the filter performs an element-wise multiplication with the corresponding portion of the input and sums the results, producing a single value in the output feature map.

This sliding operation, known as convolution, allows the layer to detect patterns like edges, corners, or textures, regardless of their position in the input. Multiple filters are typically used in a single convolutional layer, each learning to detect different features. The output of a convolutional layer is a feature map that represents the presence and location of these detected features. Subsequent layers can then learn to combine these simpler features into more complex representations. This hierarchical feature extraction is what makes convolutional neural networks so effective for tasks like image classification, object detection, and segmentation. The architecture of a convolutional layer involves parameters like filter size, stride (the step size the filter takes), and padding (adding zeros around the input to control the output size).