I'm trying to optimize a neural network architecture for a particular problem, but there just seems to be so many hyperparameters that I'm concerned that there are much better options that I'm not exploring (e.g. I might be getting trapped in a local minima for hyperparams).Essentially, I'd like some standard bounds for hyperparameter search. Ideally, if a person were to see the breadth of explored parameters, they might be reasonably certain to try another class of machine learning models. In particular, I am looking for concrete advice on at leastWhat size neurons to include in the searchThe number of hidden layers, and the width of the layers Theoptimizer and learning rates Activation functions and loss functionsThank you so much!
Bounds for MLP hyperparameter search
neural network;hyperparameter