aktivaatioita
Aktivaatioita, also known as activation functions, are mathematical functions used in artificial neural networks to introduce non-linearity into the model, enabling it to learn and represent complex patterns in data. They are applied to the output of a neuron, which is the result of a linear combination of inputs and weights. The primary purpose of activation functions is to transform this output into a form that can be used for further processing or as the final output of the network.
Common activation functions include the sigmoid function, which maps inputs to a range between 0 and 1,
Activation functions play a crucial role in the performance and training dynamics of neural networks. They