I want to build a neural network over a data set. My idea is to use cross-validation on a training set to select the "best" neural network (and evaluate it on a separate test set) and to use nested cross-validation to make some statistical predictions. I'd use nested CV to plot bias and variance of my grid search's hyper-parameters. This way I can estimate my method's performance.
If these assumptions are not wrong, what should I do first? Model selection or estimation?