ReLU (Rectified Linear Unit) is a piecewise linear activation that outputs zero for negative inputs and the input itself for positive inputs. It speeds up training by avoiding saturated states seen in sigmoid or tanh functions. However, it can cause dead neurons if inputs are consistently non-positive.