I am an electrical engineer interested in Machine Learning applications.

So far, based on the myriad of literature I have read about Machine Learning stuff, a lot of results and their quality depends on the type of network, training, error correction methods, etc.

Say I learn how to make computers build and train models, but is that enough to get good results? Are there any resources that will guide me into choosing a good topology or Network parameters (say like number of hidden layers, etc)

How do developers who use Machine Learning in production environments confidently come to a particular network topology/parameter set for a given class of problem?