aktivointikerrosten

Aktivointikerrosten, a Finnish term, translates to "activation layers" in English. In the context of neural networks, activation layers are a crucial component. They introduce non-linearity into the model, allowing it to learn complex patterns and relationships that linear models cannot. Without activation layers, a neural network, regardless of its depth, would essentially behave like a single-layer linear model.

The primary function of an activation layer is to apply a non-linear activation function to the output of the preceding layer. This function determines whether a neuron should be "activated" or not, based on the weighted sum of its inputs. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh. ReLU, for instance, outputs the input directly if it's positive, and zero otherwise. Sigmoid squashes the output to a range between 0 and 1, often used in output layers for binary classification. Tanh, similar to sigmoid, squashes the output but in a range between -1 and 1.

The choice of activation function can significantly impact the performance of a neural network. ReLU is widely used due to its computational efficiency and ability to mitigate the vanishing gradient problem. However, it can suffer from the "dying ReLU" problem where neurons become inactive. Other functions have been developed to address these limitations. Ultimately, activation layers enable neural networks to approximate virtually any function, making them powerful tools for tasks like image recognition, natural language processing, and more.