An

**activation function**is a decision making**function**that determines the presence of particular neural feature. Non-linearity is needed in**activation functions**because its aim in a neural network is to produce a**nonlinear**decision boundary via non-linear combinations of the weight and inputs.What is ReLU activation function?

In the context of artificial neural networks, the rectifier is an

**activation function**defined as the positive part of its argument: , The rectifier is, as of 2018, the most popular**activation function**for deep neural networks. A unit employing the rectifier is also called a rectified linear unit (**ReLU**).What does a neuron compute activation function?

**Neuron**Output, y: The artificial

**neuron computes**its output according to the equation shown below. This

**is the**result of applying the

**activation function**to the weighted sum of the inputs, less the threshold. This value

**can**be discrete or real depending on the

**activation function**used.