Sigmoid Activation

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.