At this point in the chapter, we've suggested that it is, for the most part, computationally impossible, or at least impractical, to try every single combination of hyperparameters we might want to try. Deep neural networks can certainly take a long time to train. While you can parallelize and throw computational resources at the problem, it's likely that your greatest limiter in searching for hyperparameters will continue to be time.
If time is our greatest constraint, and we can't reasonably explore all possibilities in the time we have, then we will have to create a strategy where we get the most utility in the time we have.
In the remainder of this section, I'll cover some common strategies for hyperparameter optimization and then I'll show you how to optimize hyperparameters in Keras with two of my favorite methods...
