# What is an activation function? What is the use of it?

An activation function is a non-linear function applied to the output of a neural network layer. It is used to introduce non-linearity to the model, which enables it to learn more complex patterns in the data. Without an activation function, the neural network would simply be a linear function, and it would not be able to model complex relationships between the input and output.

There are several types of activation functions, including sigmoid, tanh, ReLU, and softmax. Each has its own advantages and disadvantages.

- The
**sigmoid**activation function is a common choice for binary classification problems. It maps the output of a neural network layer to a value between 0 and 1, which can be interpreted as the probability of the input belonging to the positive class. - The
**tanh**activation function is similar to the sigmoid function, but it maps the output to a value between -1 and 1. It is commonly used in recurrent neural networks because it can capture long-term dependencies. - The
**Rectified Linear Unit (ReLU)**activation function is a popular choice for deep neural networks. It maps negative values to 0 and leaves positive values unchanged. This simple non-linearity has been shown to work well in practice and can speed up training by avoiding the vanishing gradient problem. - The
**softmax**activation function is used in the output layer of a neural network for multiclass classification problems. It maps the output of a neural network layer to a probability distribution over the classes.

In summary, activation functions are an essential component of neural networks, and their choice depends on the problem at hand. The right choice can speed up training and improve the accuracy of the model, while the wrong choice can hinder performance.