When optimizing a machine learning model, hyperparameter tuning is crucial. One of the most important hyperparameters is the learning rate, which controls how much the model updates its weights during training. A learning rate that is too high can cause the model to become unstable and overfit the training data, while a learning rate that is too low can slow down the training process and prevent the model from reaching its full potential.
There are a number of different methods for tuning the learning rate. One common approach is to use a learning rate schedule, which gradually decreases the learning rate over the course of training. Another approach is to use adaptive learning rate algorithms, which automatically adjust the learning rate based on the performance of the model.
