This document discusses common problems that can occur in hyperparameter optimization including trusting default values too much, using the wrong evaluation metric, overfitting hyperparameters to validation data, optimizing too few hyperparameters, relying too heavily on manual tuning, using grid search which scales poorly, using random search which has high variance, and provides recommendations such as using Bayesian optimization which can efficiently search large hyperparameter spaces and intelligently determine the most promising configurations to evaluate next.