How Neural Networks Work
It Starts With a Photo
How a neural network sees a picture of a cat
A neural network can't see a photo. First, the image is scanned for features — pointy ears, whiskers, fur texture. Each becomes a number 0–1. These numbers are what the network works with.
We'll follow this cat photo through every step of the network.
Step 1 of 6: It Starts With a Photo
How a neural network sees a picture of a cat
A neural network can't see a photo. First, the image is scanned for features — pointy ears, whiskers, fur texture. Each becomes a number 0–1. These numbers are what the network works with.
We'll follow this cat photo through every step of the network.
Step 2 of 6: What's a Neuron?
One tiny decision-maker that weighs the cat's features
This neuron receives the cat's features. Fur texture (×0.8) and pointy ears (×0.6) get high weights — strong cat signals. Whiskers (×-0.3) are negative — dogs have them too.
Step 3 of 6: Layers & Connections
Tap any neuron to see how it processes the cat features
The cat's 3 features pass through hidden layers that detect combinations of features. Tap any neuron to see the math.
Step 4 of 6: Tune the Weights
Same network — drag the weights and watch the prediction shift
It thinks Dog! Boost cat-signal weights.
Step 5 of 6: The Forward Pass
Watch the cat features flow through to a final answer
Pointy ears, whiskers, fur texture flow forward. Output: Cat=0.57, Dog=0.67. Not confident enough — needs training.
Step 6 of 6: How It Learns
Step through the training loop — the network learns to recognize cats
Forward: Cat features flow forward — ears, whiskers, fur — same as Step 5.