XOR Neural Network Interactive Visualizer

Decision Space (2D) Grid: [0,1] × [0,1]

Epoch

0.5000

Loss (MSE)

50%

Accuracy

0 ep/s

Speed

Network Architecture & Weights 2 → Hidden → 1

Input (X1, X2)	Target	Output	Status

Why XOR is special: A single perceptron can only learn linearly separable functions (like AND, OR). It fails on XOR because no single straight line can separate `(0,1)` and `(1,0)` from `(0,0)` and `(1,1)`. Adding a non-linear hidden layer transforms the 2D coordinate space into a higher-dimensional representation where the classes become linearly separable.