Learning Rate

Controls how quickly the model adapts to the training data.

The learning rate determines the step size when updating model weights during training. This is one of the most important hyperparameters affecting training success.

A learning rate that's too high can cause training to diverge (loss increases or oscillates wildly). A learning rate that's too low will result in very slow training that may get stuck.