A Rectified Linear Unit is a common name for a neuron (the “unit”) with an activation function of \(f(x) = \max(0,x)\).

Neural networks built with ReLU have the following advantages:

- gradient computation is simpler because the activation function is computationally similar than comparable activation functions like \(\tanh(x)\).
- Neural networks with ReLU are less susceptible to the vanishing gradient problem but may suffer from the dying ReLU problem.