What is ReLU? How is it better than sigmoid or tanh?‍

ReLU is an abbreviation for Rectified Linear Unit. It is an activation function which has the value 0 for all negative values and the value f(x) = x for all positive values. The ReLU has a simple activation function which makes it fast to compute and while the sigmoid and tanh activation functions saturate at higher values, the ReLU has a potentially infinite activation, which addresses the problem of vanishing gradients.

The derivative of the sigmoid function for large positive or negative numbers is almost zero. From this comes the problem of vanishing gradient — during the backpropagation our net will not learn (or will learn drastically slow). One possible way to solve this problem is to use ReLU activation function.

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