Which activation function is commonly used in RNNs and why?

The choice of the hyperbolic tangent function (tanh) as the activation function commonly used in Recurrent Neural Networks (RNNs) is primarily due to its properties related to how it behaves during training and its suitability for capturing sequence data. The tanh function is centered at zero, which means it outputs values ranging from -1 to 1. This centering is beneficial because it helps in maintaining a smoother gradient during the backpropagation process, reducing the chances of the vanishing gradient problem that RNNs can face.

When inputs are centered, it leads to faster convergence and better model performance because the gradients for weights associated with inputs close to zero do not shrink as drastically as they might with non-centered functions. The output values of tanh provide a balanced activation range, which allows for both positive and negative influences on the next layer, facilitating the learning of complex relationships in sequential data.

In contrast, while ReLU is widely used in other types of networks due to its simplicity and ease of training, it has limitations in recurrent architectures, particularly in dealing with negative values. The sigmoid function, while also common in older models, suffers from saturation issues leading to gradients that can be very small, which can hinder learning in deep networks