A neuron takes a vector of numerical features as input, multiplies each feature by a learned weight representing its importance, adds a bias term, and applies an activation function (like sigmoid or ReLU) to produce an output signal between 0 and 1. Multiple neurons with different weights specialize in detecting different patterns, and stacking them in layers enables learning complex relationships. Training adjusts these weights and biases to minimize prediction error.
•5m watch time
125 Impressions