Multi-Layer Perceptrons Explained and Illustrated

In the previous article we talked about perceptrons as one of the earliest models of neural networks. As we have seen, single perceptrons are limited in their computational power since they can solve only linearly separable problems. In this article we will discuss multi-layer perceptrons (MLPs), which are networks consisting of multiple layers of perceptrons and are much more powerful than single-layer perceptrons. We will see how these networks operate and how to use them to solve complex tasks such as image classification. A multi-layer perceptron (MLP) is a neural network that has at least three layers: an input layer, an hidden layer and an output layer.