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# 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.