This example demonstrates a three-layer neural network learning to classify patterns through supervised backpropagation. The Processing sketch uses a custom `Neuron` class with a training loop that adjusts connection weights based on error signals. The code shows the core training mathematics, including a hyperbolic tangent activation function, delta computation, and weight updates scaled by a fixed learning rate. The visualization itself is not present in this code fragment, which focuses solely on the algorithmic update logic for each neuron’s error and weight adjustment.