Why is an activation function useful in a neural network?

Correct answer: It helps learn complex relationships by adding nonlinearity

An activation function introduces nonlinearity into a neuron's output. This allows a neural network to learn complex patterns instead of being limited to simple linear relationships. Deleting data, shutting down the network, or hiding images is not the role of an activation function. Exam tip: associate activation functions primarily with nonlinearity and learning complex patterns.