Neural Network Playground
Build, train, and watch a neural network learn in real-time
What is a neural network?
Layers of connected units adjust weights to reduce prediction error. Each unit combines inputs, applies an activation, and passes values forward. Training nudges weights so patterns in the data become useful behavior.
Toy scale
This playground is a handful of neurons (XOR/AND/OR). Modern LLMs use billions of parameters and transformer blocks, but the learning idea (adjust weights from error) is the same family of idea. Weights here are seeded for reproducible demos.
Network Visualization
Training status
Epoch
0
Error
1.0000
Error Over Time
Test It
Input 0
Input 1
Output
0.3802
→ 0 (OFF)
Truth Table
[0, 0]→expected: 0got: 0 ✓
[0, 1]→expected: 1got: 0 ✗
[1, 0]→expected: 1got: 0 ✗
[1, 1]→expected: 0got: 0 ✓
Key Insight
This is a tiny network. Modern language models stack many more units and transformer blocks, but still learn by adjusting parameters to reduce error. Next: the Transformer lesson for attention and multi-head structure at LLM scale.
Check your understanding
1/5What does a neuron in a neural network do?
Sources, scope & review statusVerified Jul 29, 2026
Claims this lesson makes
- Stable conceptBackpropagation computes layerwise gradients before an optimizer updates trainable parameters.
Primary reading
Next content review: 2027-07-29. Source links establish the lesson's claims; interactive numbers remain labeled simulations unless explicitly identified as measured data.