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Every layer is a set of neurons whose values come from a weighted sum of the previous layer's activations, squashed through a sigmoid.
A neuron holds a number between 0 and 1 — its activation.
Each connection has a weight; each neuron a bias — the two knobs the network turns.
Compresses any real number to 0–1 so activations stay bounded.
In a neural network, what determines a neuron's activation value?