A neural network learns to separate two interleaved classes in 2-D space. The colored background shows the network's current decision boundary; training points are drawn on top.
Blue = class 0, red = class 1. Background updates every few steps as the boundary refines.
Percentage of training points correctly classified. Cross-entropy loss drives the boundary to the right position.