aktivaatiofunktion

An activation function is a mathematical function applied to the output of a neuron in a neural network to introduce non-linearity into the model. This non-linearity allows the network to learn and represent complex patterns in data. Without activation functions, a neural network would essentially be a linear model, regardless of the number of layers, as the composition of linear functions is still a linear function.

Common activation functions include the sigmoid function, which maps input values to a range between 0 and 1, and the hyperbolic tangent (tanh) function, which maps input values to a range between -1 and 1. Another popular activation function is the rectified linear unit (ReLU), which outputs the input directly if it is positive, otherwise, it outputs zero. ReLU is widely used due to its simplicity and effectiveness in mitigating the vanishing gradient problem.

Activation functions play a crucial role in the training process of neural networks. They help in backpropagation by providing a way to compute the gradient of the loss function with respect to the weights of the network. This gradient is then used to update the weights through optimization algorithms like gradient descent.

The choice of activation function can significantly impact the performance of a neural network. Different activation functions are suited to different types of problems and network architectures. For example, ReLU is often preferred for hidden layers in deep networks, while sigmoid and tanh are more commonly used in the output layer for binary and multi-class classification tasks, respectively.