Training a Neural Network to Solve XOR
In this interactive tutorial, you'll learn how neural networks learn by training one to solve the XOR (exclusive or) problem - a classic challenge that simple linear classifiers cannot solve.
The XOR Truth Table
| Input A | Input B | Output (A XOR B) |
|---|---|---|
| 0 | 0 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 1 |
| 1 | 1 | 0 |
XOR outputs 1 when inputs are different, and 0 when they're the same.
What You'll Learn
- How neural networks are structured with layers, neurons, and weights
- How the forward pass computes predictions
- How backpropagation adjusts weights to reduce error
- How loss measures prediction accuracy
- How to run inference on a trained network
Scroll down to begin the training journey ↓