# What is: Sigmoid Activation?

Year | 2000 |

Data Source | CC BY-SA - https://paperswithcode.com |

**Sigmoid Activations** are a type of activation function for neural networks:

$f\left(x\right) = \frac{1}{\left(1+\exp\left(-x\right)\right)}$

Some drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.