# Why does gradient descent work faster with ReLU compared to using with Signoid? [duplicate]

As far as I understand, Signoid function is used for mapping the outputs of neural network to the values between 0 and 1. Why is using rectified linear unit(ReLU) as activation function in deep neural networks, works faster? Can you please explain the mathematical concept behind it?

## marked as duplicate by Jakub Bartczuk, Sycorax, Ferdi, kjetil b halvorsen, jbowmanJan 2 at 17:14

• "Faster" in what sense? If you ask about computation, max in ReLU just compares two numbers, while exp does a number of different computations. – Tim Jan 2 at 14:28