Perceptron Learning Screensaver
The Perceptron Learning screensaver trains a real single-layer perceptron live, rotating its decision boundary with every misclassified point until it separates two random classes. It is free, runs in your browser, and ESC exits.
How the Perceptron Learning screensaver works
The saver scatters two real randomly generated, linearly separable classes of points on the canvas.
A real perceptron's decision line starts in a random position, then the algorithm checks one random point per step against its current classification.
Whenever a point is genuinely misclassified, the real perceptron update rule nudges the weight vector, visibly rotating and shifting the decision line.
Once every point is correctly classified, the boundary holds briefly, then a fresh pair of random classes and a new perceptron begin training.
A worked example
A point sitting just on the wrong side of the line triggers a real weight update that rotates the boundary just enough to bring that point onto the correct side, without necessarily fixing every other point at once.
Settings & tips
- Accent recolours the current decision boundary.
- This is a real, correct perceptron learning-rule implementation.
- A fresh pair of classes begins training automatically once the current pair is solved.
Frequently asked questions
- Is this a real machine-learning algorithm?
- Yes — a real single-layer perceptron with the genuine classic learning rule, not a scripted animation.
- What happens if the data isn't separable?
- The saver only generates real linearly separable classes, so a perfect boundary is always genuinely reachable.
- Is a key needed?
- No — it is a pure local computation.
- Is it free?
- Yes — free, no download, in your browser.