You're struggling to optimize your machine learning model. How do you pinpoint the perfect learning rate?
Optimizing a machine learning model can be a daunting task, especially when it comes to tuning the learning rate. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. Too high a learning rate can cause the model to converge too quickly to a suboptimal solution, while too low a learning rate can cause the process to stall or take too long to converge. You need a strategy to find the perfect balance that leads to the most efficient learning.