In this section, we're going to walk through using a convolutional layer on an example image. We'll graphically see how convolution is just a sliding window. Further we'll learn how to extract multiple features from a window as well as accept multiple layers of input to a window.
In a classic dense layer of a neural network for a given neuron every input feature gets its own weight.
This is great if the input features are totally independent and measure different things, but what if there is structure among your features. The easiest example to imagine this happening is if your input features are pixels from an image. Some pixels are next to each other, others are far away.
For a task like image classification, and font classification especially, it often doesn't matter where a small scale feature occurs in an image. We can look for small scale features in a larger image by sliding a smaller window throughout the image, and this is key to using the same weight...
