Milleks on õppimiskiirus vajalik ja mille eest see vastutab?

Learning rate (learning rate) is a hyperparameter in machine learning algorithms, especially in optimization methods like gradient descent. It determines how strongly the model parameters are updated at each training step.

If the learning rate is too large, the model may "skip" the minimum of the error function and not converge. If it is too small, training will be very slow and may get stuck in local minima.

Example: updating the weights of a neural network

weights = weights - learning_rate * gradient

Here, learning_rate controls the size of the step in the direction of error reduction. Choosing the right learning rate is important for effective and stable model training.