The tanh function, short for hyperbolic tangent, is a mathematical function that maps real numbers to a range between -1 and 1. It is defined as the ratio of the hyperbolic sine to the hyperbolic cosine. The function is often used in neural networks as an activation function because it helps to normalize the output, making it easier for the model to learn complex patterns. The tanh function is particularly useful because it is symmetric around the origin, meaning that it outputs negative values for negative inputs and positive values for positive inputs. This property helps in reducing the likelihood of issues like the vanishing gradient problem during training, making it a popular choice in various machine learning applications.