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 neuron might serve as input to the next artificial neuron, much like how real biological neurons are wired up in the brain. By placing perceptrons side by side, we get a single one-layer neural network. Analogously, by stacking one one-layer...
