What is the purpose of an activation function in a neural network?

The purpose of an activation function in a neural network is primarily to assign an output signal based on the total input. This function takes the weighted sum of inputs and applies a specific transformation to produce an output that can be understood by the next layer of the network.

Activation functions introduce non-linearity into the model, allowing neural networks to learn and approximate complex patterns in the data. For example, commonly used activation functions like the sigmoid, ReLU (Rectified Linear Unit), and tanh transform the weighted input sum into a range that is suitable for the next processing layer or for making a final prediction. This transformation is crucial as it enables the network to make decisions and predictions based on patterns that are not merely linear.

Other available options do not accurately capture the primary role of an activation function. While minimizing errors, increasing network complexity, and deriving the loss function are important components of neural network training and operation, they are not the direct purpose of an activation function itself. Hence, the correct understanding of the role of the activation function aligns with the idea that it assigns an output signal based on total input, enabling the network to perform effectively.