Introduction to Perceptron

In the previous chapters, we designed a complex program with a lot of detail. It’s time to take a step back and enjoy the big picture.

In the first part of this course, we built a supervised learning system based on a specific architecture called the perceptron. By the end of this chapter, we’ll know what a perceptron looks like and what it can do. Moreover, we’ll also explore what it cannot do, and why we must move forward to more sophisticated algorithms such as neural networks.

We’ll also learn about the history of the perceptron. It won’t be a boring history lesson, but rather, this will explain a clash of ideas that impacted much of what we know about computers.

What is a perceptron?

To understand what a perceptron is, let’s look back at the binary classifier we discussed in the Getting Real. That program sorted MNIST characters into two classes: “$5$” or “not a $5.$” The following picture shows one way to understand it: