Network Architecture
Positive weight
Negative weight
Weak connection
Training Loss
Loss (lower = better)
Watch a tiny neural network learn the XOR problem — a classic task that demonstrates how adjusting weights through gradient descent lets a network discover patterns.
This network has 2 input neurons, 4 hidden neurons, and 1 output neuron. It's trying to learn XOR: output 1 when inputs differ, 0 when they're the same. Press Train to watch it learn through trial and error — adjusting its connection weights to reduce mistakes.