Tanh vs. Sigmoid: Why Choose Tanh as Your Activation Function?

Using tanh as an activation function offers advantages over sigmoid due to its wider output range, spanning from -1 to 1. This expanded range allows for better expressive power and stronger non-linearity, enabling the model to handle complex input data more effectively. Additionally, the derivative of tanh is maximized at 0, facilitating faster learning of weight parameters during backpropagation. In contrast, sigmoid's output is confined between 0 and 1, with small derivatives near 0 and 1, leading to the vanishing gradient problem. This issue can significantly slow down training in deep neural networks. Therefore, tanh generally proves more suitable as an activation function compared to sigmoid.