When optimizing a machine studying mannequin, hyperparameter tuning is essential. One of the vital necessary hyperparameters is the educational charge, which controls how a lot the mannequin updates its weights throughout coaching. A studying charge that’s too excessive may cause the mannequin to grow to be unstable and overfit the coaching knowledge, whereas a studying charge 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 educational charge. One widespread method is to make use of a studying charge schedule, which progressively decreases the educational charge over the course of coaching. One other method is to make use of adaptive studying charge algorithms, which robotically alter the educational charge based mostly on the efficiency of the mannequin.