I'm not a guru myself, but I'm sure someone will correct me if I'm wrong. :-) The usual approach to supervised ML is to "invent" the model (layers, their param

I'm not a guru myself, but I'm sure someone will correct me if I'm wrong. :-) The usual approach to supervised ML is to "invent" the model (layers, their parameters) or more often copy one from known good reference, then define the cost function and feed it data. "Deep" learning just means that instead of a few layers you use a big number of them. What you describe sounds like an automated way of tweaking the architecture, IIUC? Never done that, usually the cost of a run was too high to let an algorithm do that for me. But I'm curious if this approach is being used?