Neural Network Binary Classifier Playground
Train a 2-layer neural network in real time to separate 2D datasets (circle, XOR, spiral). Adjust hidden neurons and inspect decision boundaries.
Concept Breakdown: Supervised vs Unsupervised Model Training
Imagine you are hiking down a mountain in pitch-black fog. You feel the slope with your feet and step downward. Gradient Descent does this mathematically — taking steps downhill until reaching the valley floor of lowest error.
When an AI spots spam emails, it can make two mistakes: letting spam through (False Negative) or blocking a real email (False Positive). A Confusion Matrix tracks these tradeoffs.
Click "Step (1 Iteration)" below. Watch the ball take mathematical steps down the parabolic curve until it settles comfortably at the bottom minimum!
Quick Reference & Instructions
Simple steps, pro tips, and execution details
Provide Inputs
Type, paste, or select your values in the form fields below.
Instant Live Analysis
Calculations and formatting happen automatically with zero delay as you type.
Copy or Use Output
Copy results or apply the clean output directly to your projects.
How It Works
Executes forward propagation and backpropagation with sigmoid/ReLU activations, rendering a live 2D decision boundary heatmap.
Frequently Asked Questions
Common questions about calculations, assumptions, and edge cases.
Yes, Neural Network Binary Classifier Playground is 100% free with unlimited calculations and zero paywalls or subscriptions.
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