  Activation functions shape the outputs of artificial neurons and, therefore,
are integral parts of neural networks in general and deep learning in
particular. Some activation functions, such as logistic and relu, have been
used for many decades. But with deep learning becoming a mainstream research
topic, new activation functions have mushroomed, leading to confusion in both
theory and practice. This paper provides an analytic yet up-to-date overview of
popular activation functions and their properties, which makes it a timely
resource for anyone who studies or applies neural networks.
