  Tuning machine learning models, particularly deep learning architectures, is
a complex process. Automated hyperparameter tuning algorithms often depend on
specific optimization metrics. However, in many situations, a developer trades
one metric against another: accuracy versus overfitting, precision versus
recall, smaller models and accuracy, etc. With deep learning, not only are the
model's representations opaque, the model's behavior when parameters "knobs"
are changed may also be unpredictable. Thus, picking the "best" model often
requires time-consuming model comparison. In this work, we introduce LAMVI-2, a
visual analytics system to support a developer in comparing hyperparameter
settings and outcomes. By focusing on word-embedding models ("deep learning for
text") we integrate views to compare both high-level statistics as well as
internal model behaviors (e.g., comparing word 'distances'). We demonstrate how
developers can work with LAMVI-2 to more quickly and accurately narrow down an
appropriate and effective application-specific model.
