Neural Networks: Simplified

Interactive neural network visualiser — adjust inputs, watch signals flow through layers, and see how outputs change. No maths required.

Neural Network Visualiser Signal Flow Playground

Neural Network Visualiser

Drag the sliders to change each input value. Watch how signals travel through the network — brighter connections carry stronger signals, and the output values update in real time.

Input Layer

Raw data enters here. Each node represents one feature or measurement the network looks at.

Hidden Layer

The "thinking" layer. Nodes combine and transform signals to find patterns invisible in the raw data.

Output Layer

The network's answer. Values close to 1 mean high confidence; values close to 0 mean low confidence.

Key insight: Every connection has a "weight" that controls how much influence one node has on the next. When a neural network learns, it is really just adjusting these weights until it gets the right answers. That is the entire secret of AI training.

Signal Flow Playground

Pick a real-world scenario, toggle the inputs on or off, and watch a mini neural network classify the result. Expand the explanation to see how the network made its decision.

Key insight: Real neural networks work exactly like these examples, just at a much larger scale. A spam filter might have thousands of input features and millions of connections. But the fundamental principle is the same: inputs flow in, get transformed through layers, and produce a decision at the end.

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