  For deep learning practitioners, hyperparameter tuning for optimizing model
performance can be a computationally expensive task. Though visualization can
help practitioners relate hyperparameter settings to overall model performance,
significant manual inspection is still required to guide the hyperparameter
settings in the next batch of experiments. In response, we present a
streamlined visualization system enabling deep learning practitioners to more
efficiently explore, tune, and optimize hyperparameters in a batch of
experiments. A key idea is to directly suggest more optimal hyperparameter
values using a predictive mechanism. We then integrate this mechanism with
current visualization practices for deep learning. Moreover, an analysis on the
variance in a selected performance metric in the context of the model
hyperparameters shows the impact that certain hyperparameters have on the
performance metric. We evaluate the tool with a user study on deep learning
model builders, finding that our participants have little issue adopting the
tool and working with it as part of their workflow.
