Answer
The function takes the weighted sum of a neuron's inputs and transforms it, often adding non linearity so the network can learn complex patterns instead of only straight line relationships.
Real-world example The ReLU activation function is widely used because it is simple and helps a network trained on image data learn faster than older functions.