Rectified Linear Unit (ReLU) is a popular activation function used in Machine Learning for neural network models. The activation function is added to the output layer of a neural network and is used to introduce non-linearity into the output that helps improve the accuracy of the model.

Simply put, ReLU function is a threshold function that activates a neuron only if the input value is greater than zero. If the input is less than zero, the function will output zero. This property of ReLU function is popularly known as Rectification of the input signal.

In the early days of neural networks, nonlinear functions like the sigmoid function were used as activation functions. However, such functions have certain disadvantages, including vanishing gradient problem and computational inefficiency.

ReLU, on the other hand, has proven to be superior to other activation functions for several reasons. Firstly, ReLU is a linear function for all positive input. This means that the model can be trained more easily as there is no diminishing gradient problem that happens with more complex activation functions.

Secondly, ReLU has a very simple mathematical formula, making it computationally efficient. This is because the function doesn’t involve complex mathematical operations that can slow down the training process.

Another advantage of using ReLU is that it helps in regularization. Regularization is a technique used to prevent overfitting, which is a common problem in machine learning. The Rectification property of ReLU helps prevent the model from memorizing data rather than generalizing it by reducing the likelihood of overfitting.

Lastly, the use of the ReLU function also speeds up the training process of neural networks. This is because the function is computationally less demanding than other functions like the sigmoid function. Therefore, it allows for faster convergence of the model during the training phase, resulting in faster prediction times.

In conclusion, ReLU is a powerful and popular activation function used in machine learning due to its simplicity, training speed, regularization, and capacity for preventing the vanishing gradient problem. It has become the go-to activation function for most neural network models, and it is a technique that every machine learning enthusiast should have in their arsenal.