Activation functions, such as tanh, are used in artificial neural networks to introduce non-linearity into the output of a neuron. Without a non-linear activation function, a neural network would simply be a linear function of its inputs, which would severely limit its ability to model complex relationships between input and output.
Tanh, short for hyperbolic tangent, is one of several activation functions that can be used in neural networks. It is a non-linear function that maps the input to an output in the range of -1 to +1. Tanh is a popular choice of activation function because it is differentiable, which allows for the use of gradient-based optimization algorithms during the training of the neural network. Additionally, it has a nice property of being zero-centered, which can help in reducing the bias in the neural network.
Other activation functions, such as ReLU (rectified linear unit) and sigmoid, are also commonly used in neural networks. Each activation function has its own strengths and weaknesses, and the choice of activation function depends on the specific problem and network architecture.