Læringsrente

Læringsrente, also known as the learning rate, is a parameter in machine learning algorithms that determines the step size at each iteration while moving toward a minimum of a loss function. It is a crucial hyperparameter that influences the convergence speed and the quality of the final model. A high learning rate can cause the algorithm to converge too quickly to a suboptimal solution, while a low learning rate may result in slow convergence or getting stuck in a local minimum. The optimal learning rate is often determined through experimentation and can vary depending on the specific problem and dataset. Techniques such as learning rate schedules and adaptive learning rate methods, like Adam or RMSprop, are used to dynamically adjust the learning rate during training to improve performance.