Before we begin diving into code, let's cover some basic terminology so that we are all on the same page when referring to things. This terminology applies to CNNs as well as the ConvNetSharp framework.
Convolution: In mathematics, a convolution is an operation performed on two functions. This operation produces a third function, which is an expression of how the shape of one is modified by the other. This is represented visually in the following diagram:
It is important to note that the convolutional layer itself is the building block of a CNN. This layer's parameters consist of a set of learnable filters (sometimes called kernels). These kernels have a small receptive field, which is a smaller view into the total image, and this view extends through the full depth of the input volume. During the forward propagation phase, each filter is convolved...
