aktivaatiofunktiosta

Activation function in Finnish is aktivaatiofunktio. An activation function is a core component in artificial neural networks. Its purpose is to introduce non-linearity into the output of a neuron. Without activation functions, a neural network would simply be a series of linear transformations, capable of learning only linear relationships. The non-linearity provided by activation functions allows neural networks to learn complex patterns and model intricate data relationships, which is crucial for tasks like image recognition, natural language processing, and more.

There are various types of activation functions, each with its own characteristics and use cases. Some common examples include the sigmoid function, which squashes input values into a range between 0 and 1, making it suitable for binary classification tasks. The hyperbolic tangent (tanh) function is similar to sigmoid but outputs values between -1 and 1. The Rectified Linear Unit (ReLU) is a popular choice due to its simplicity and efficiency; it outputs the input directly if it's positive and zero otherwise. Other variants like Leaky ReLU and Exponential Linear Unit (ELU) address some of the limitations of standard ReLU, such as the "dying ReLU" problem. The choice of activation function can significantly impact a neural network's performance and convergence speed.