How do you efficiently choose the hyper-parameters of a neural network (e.g. the learning rate, number of layer, weights, etc.)?
In deep learning era, there are two possible choices. Caveat approach and Panda approach.
In this approach, it is supposed that you have a very powerful cluster system that enables you to run different models simultaneously on different nodes. In this manner, you construct a d-dimensional space which each corresponds to a special hyperparameter. Then you can have a grid approach to partition the space and for each intersection of the grids, you can have a possible initialisation although there are some better ways in order to find better initialisations.
This approach is widely used among students due to using desktop computers. In this approach, you usually try to find an initialisation of hyperparameters based on your experience or the other architectures' which are available and you try to refine them step by step by using cross-validation.
To answer your question, the efficiency depends on your computing power. For more details, you can take a look at the contents of the third week.