I am working on tuning a machine learning model and want to perform a grid search / hyperparameter tuning on my model to find the best hyperparameters.
The literature I have found it pretty good with explaining the different methods of tuning but now about how they select or justify the ranges they select to tune the parameters.
For example, I want to turn the batch size and learning rate parameters in my model. Right now, the batch size is 8 and the learning rate is 0.0015 (selected arbitrarily). If I were to tune these parameters, how would I know the minimum / maximum values to test.
In the end I need to tune about 7 parameters, so I am looking for some type of rule-of-thumb or justification from literature, or if anyone can offer general advice in this topic.