The learning rate is a hyperparameter that controls the size of the steps the model takes when updating its weights during training. A high learning rate can lead to the model overshooting the optimal solution, while a low learning rate may result in slower convergence and longer training times.
The learning rate is typically tuned by trial and error or using techniques like learning rate schedules or adaptive methods such as Adam. Common strategies to adjust the learning rate include reducing it over time or using learning rate annealing to stabilize training.
Setting an appropriate learning rate is crucial to ensure the model converges to a good solution without either oscillating around or getting stuck in local minima.