> I thought it was interesting the same technique (convolution) was used in two different applications: photoshop/gimp for image filters and in convolutional ne

> I thought it was interesting the same technique (convolution) was used in two different applications: photoshop/gimp for image filters and in convolutional neural networks (CNNs). What is the purpose of convolution in CNNs? IMO, convolution in CNNs would be better denoted as correlation. In CNNs the feature maps are convolved (correlated), eg swept over the image and multiple at each spot with the results being accumulated, in order to find where in the image these filters fit. The output produces a map of how strongly each filter (there are usually multiple in a convolution layer, that is the third dimension of the layer), fits with the image. These become the weights passed onto the next layers. As an electrical engineer by training, who used convolution all the time, I didn't understand how the two were related, especially because of the third dimension. I read this https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/ https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Unders... and that lead me to how I understand it today.