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Neural Networks

A neural network is layers of simple units, each passing weighted signals to the next. Training adjusts those weights until the network produces the right outputs. No single unit knows anything, and the ability emerges from the whole. The guide here lets you watch one learn to recognize digits, which makes the idea concrete without any heavy math.

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Reference Guide · 20 min read

How Neural Networks Work

The canonical 784-16-16-10 MNIST network as an explorable 3D machine: 16 stations across 4 acts, a 16-step guided journey from pixels to the brain question, all 12,960 weight connections really drawn, and a training row where gradient descent is really computed on an illustrative loss terrain, and a Train mode where the network genuinely learns 10,000 real MNIST digits in your browser.

Neural Networks Deep Learning Backpropagation Gradient Descent MNIST 3D Interactive AI Explainer

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3D Interactive AI Explainer Backpropagation Deep Learning Gradient Descent MNIST

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Neural Networks

How Neural Networks Work

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