Hyperparaméternek
Hyperparaméternek is a Hungarian term that translates to "hyperparameter" in English. In the field of machine learning and artificial intelligence, hyperparameters are external configuration variables that are used to control the learning process of a model. Unlike model parameters, which are learned from the data during training, hyperparameters are set before the training begins.
Examples of hyperparameters include the learning rate in gradient descent, the number of hidden layers in a
Finding optimal hyperparameters is a crucial step in model development. This process is often referred to as