So far, we've examined all the building blocks needed to build a Convolutional Neural Network (CNN), and that's exactly what we are going to do in this section, where we explain why convolution is so efficient and widely used.
Here's the architecture of a CNN:
First, we start with a 28 x 28 grayscale image, so we have one channel that's just a black-and-white image. For now, it doesn't really matter, but these are handwritten digit images taken from the MNIST dataset that we saw in the previous chapter.
In the first layer, we'll apply a 5 x 5 filter, a convolution feed filter, with a stride of 1 and no padding, and, applying the formula we saw in the previous section will give us a 24 x 24 output matrix. But since we want a higher number of channels, in order to capture more features, we will apply...
