When optimizing a machine studying mannequin, hyperparameter tuning is essential. One of the necessary hyperparameters is the training price, which controls how a lot the mannequin updates its weights throughout coaching. A studying price that’s too excessive may cause the mannequin to turn out to be unstable and overfit the coaching information, whereas a studying price that’s too low can decelerate the coaching course of and stop the mannequin from reaching its full potential.
There are a variety of various strategies for tuning the training price. One widespread strategy is to make use of a studying price schedule, which steadily decreases the training price over the course of coaching. One other strategy is to make use of adaptive studying price algorithms, which mechanically regulate the training price primarily based on the efficiency of the mannequin.
