Machine Learning for OpenCV 4 - Second Edition

Understanding multilayer perceptrons

In order to create nonlinear decision boundaries, we can combine multiple perceptrons to form a larger network. This is also known as a multilayer perceptron (MLP). MLPs usually consist of at least three layers, where the first layer has a node (or neuron) for every input feature of the dataset, and the last layer has a node for every class label. The layer in between is called the hidden layer.

An example of this feedforward neural network architecture is shown in the following diagram:

In this network, every circle is an artificial neuron (or, essentially, a perceptron), and the output of one artificial ...