Let's briefly go over some of the basics of machine learning (ML) and neural networks (NNs). In Machine Learning, our goal is to take a collection of data with a particular set of labeled classes or characteristics and use these examples to train our system to predict the values of future data. We call a program or function that predicts classes or labels of future data based on prior training data a classifier.
There are many types of classifiers, but here we will be focusing on NNs. The idea behind NNs is that they (allegedly) work in a way that is similar to the human brain, in that they learn and classify data using a collection of artificial neurons (ANs), all connected together to form a particular structure. Let's step back for a moment, though, and look at what an individual AN is. In mathematics, this is just an affine...