In the context of neural networks, what is the purpose of an activation function?

The purpose of an activation function in neural networks is to determine the output of a neuron based on its input. This function introduces non-linearity into the model, allowing the network to learn and represent complex relationships in the data. Without an activation function, a neural network would essentially only be able to model linear relationships, as a composition of linear functions remains linear. By applying activation functions, each neuron can transform its input in a way that enables the network to capture intricate patterns and features, paving the way for effective learning and decision-making in various tasks such as classification and regression.

Understanding this function is crucial because it significantly impacts the neural network's ability to learn complex mappings from input to output. Common activation functions include the sigmoid, hyperbolic tangent (tanh), and rectified linear unit (ReLU), each with its characteristics and advantages in different contexts.