OK, so we now know that convolutions are important in the use of filters. But how does this relate to neural networks?
Recall that a neural network is defined as a linear transform (
) with a non-linearity applied on it (written as 
). Note that x, the input image, is acted upon as a whole. This would be like having a single filter across the entire image. But what if we could process the image one small section at a time?
To add to that, in the preceding section, I showed how a simple filter could be used to blur an image. ...
