This approach was first used for neural networks that worked with images, but it has been successfully used to solve problems from other subject areas. Let's consider using this method for image classification.
Let's assume that the image pixels that are close to each other interact more closely when forming a feature of interest for us (the feature of an object in the image) than pixels located at a considerable distance. Also, if a small trait is considered very important in the process of image classification, it does not matter in which part of the image this trait is found.
Let's have a look at the concept of a convolution operator. We have a two-dimensional image of I and a small K matrix that has a dimension of ...
