Hyperparameter optimization is an important step in getting the very best from our deep neural networks. Finding the best way to search for hyperparameters is an open and active area of machine learning research. While you most certainly can apply the state of the art to your own deep learning problem, you will need to weigh the complexity of implementation against the search runtime in your decision.

There are decisions related to network architecture that most certainly can be searched exhaustively, but a set of heuristics and best practices, as I offered above, might get you close enough or even reduce the number of parameters you search.

Ultimately, hyperparameter search is an economics problem, and the first part of any hyperparameter search should be consideration for your budget of computation time, and personal time, in attempting to isolate the best hyperparameter configuration.

This chapter concludes the basics of deep learning. In the next chapter, we will be moving on to some more interesting and advanced applications of neural networks, starting with computer vision.