Hyper-parameters tuning

Following the design of our deep neural network according to the previous sections, we would end up with a bunch of parameters to tune. Some of them have default or recommended values and do not require expensive fine-tuning. Others strongly depends on the underlying data, specific application domain, and a set of other components. Thus, the only way to find best values is to perform a model selection by validating based on the desired metric computed on the validation data fold.

Now we will list a table of parameters that we might want to consider tuning. Please consider that each library or framework may have additional parameters and a custom way of setting them. This table is derived from the available tuning options in H2O. It summarizes the common parameters, but not all of them, when building a deep auto-encoder network in production:

Parameter	Description	Recommended value(s)
activation	The differentiable activation function.	Depends on the data nature...