Which of the following is a commonly used activation function?

The ReLU (Rectified Linear Unit) is widely used as an activation function in neural networks, particularly in deep learning models. Its mathematical representation is simple: it outputs the input directly if it is positive; otherwise, it outputs zero. This characteristic helps to introduce non-linearity into the model while also allowing for efficient training due to its properties of enabling faster convergence and mitigating the vanishing gradient problem, which can occur with other activation functions.

ReLU has become popular because it performs well in practice across various tasks, including image recognition and natural language processing. Its straightforward implementation and ability to handle the initialization of the weights make it a default choice in many architectures.

In contrast to ReLU, while the sigmoid function and the tanh function do introduce non-linearity, they can lead to issues like saturation in deeper layers, contributing to slower training times. The softmax function is specifically designed for multi-class classification problems, providing a probability distribution over classes, but it is not a general-purpose activation function like ReLU, sigmoid, or tanh.