How a Neural Network Is Structured

You've seen the diagram a hundred times: circles connected by lines, arranged in columns, labeled "input," "hidden," "output." But a diagram of dots and arrows doesn't tell you what's actually sitting inside each dot, or what happens to a number as it travels along one of those lines. This guide is entirely about that structure — what a neural network is actually built from, piece by piece, and how a single prediction moves through it start to finish. It deliberately stops short of training — how those connections get tuned in the first place is its own guide, and trying to learn both at once is how this topic usually turns into mush. Here, we're just opening the case and looking at the parts.

The phases

1. Neurons, layers, and what "network" means — the layered picture: input, hidden, and output layers, and what one neuron structurally is.
2. Weights, biases, and activation functions — what a neuron actually computes, and why non-linearity is non-negotiable.
3. The forward pass — how one prediction flows through the whole structure, end to end.

Phase 1: Neurons, layers, and what "network" means →