ReLU

Quelle: machinelearningmastery.com

The Rectified Linear Unit (ReLU) is a popular activation function commonly used in neural networks and deep learning models. The function returns 0 if it receives any negative input, but for any positive value x it returns that value back. It can be represented as follows:

ReLU is computationally efficient as it allows the network to converge very quickly. It also helps to alleviate the vanishing gradient problem, which is a common issue when training deep neural networks. However, ReLU units can be fragile during training and can "die". A ReLU neuron is said to be "dead" if it's stuck in the negative side and always outputs 0.

Despite this potential issue, ReLU remains a popular choice of activation function in the field of machine learning due to its simplicity and performance across a variety of tasks.`