Aktivaatiofunktiota

Aktivaatiofunktiota, also known as an activation function, is a crucial component in artificial neural networks. It introduces non-linearity into the output of a neuron, enabling the network to learn and model complex patterns. Without an activation function, a neural network would behave like a linear regression model, regardless of the number of layers.

Commonly used activation functions include the sigmoid function, which outputs values between 0 and 1, and the hyperbolic tangent (tanh) function, which outputs values between -1 and 1. The rectified linear unit (ReLU) is another popular choice, outputting zero for negative inputs and the input value for positive inputs. More recently, the leaky ReLU and parametric ReLU have been introduced to address the "dying ReLU" problem, where neurons can become inactive during training.

The choice of activation function can significantly impact the performance of a neural network. For example, sigmoid and tanh functions can suffer from vanishing gradient problems, making training deep networks difficult. ReLU, on the other hand, has been shown to mitigate this issue and accelerate training.

In addition to these, there are specialized activation functions designed for specific tasks, such as the softmax function for multi-class classification problems. The selection of an appropriate activation function is a key consideration in the design and training of neural networks.