Negative to positive, symmetric around zero. Tanh sharpens the contrast, mapping every input into a tight range from -1 to 1. It centers your activations, promoting faster learning in hidden layers. Once the mainstay of deep nets, Tanh still finds its place in architectures needing balanced, zero-anchored transitions. For the coder who seeks symmetry in chaos, for the mathematician who knows every pattern needs a center.
