Example Concept: Dropout works by randomly setting a fraction of the neurons in a layer to zero during each training iteration. This prevents the network from becoming too reliant on any particular set of neurons, thereby reducing overfitting. During training, dropout creates a form of ensemble learning by effectively training different subnetworks within the main network. At inference time, dropout is turned off, and the full network is used with weights scaled to account for the dropout.