ANNs employ activation functions (AFs) in the hidden layers to conduct complex computations and then transfer the results to the output layer. The basic goal of AFs is to give the neural network non-linear qualities. They turn a node's linear input signals to non-linear output signals to help deep networks learn high-order polynomials with more than one degree. AFs are distinct in that they are differentiable, which aids their role during neural network backpropagation.