In this intermediate-level presentation, we will delve into some of the most common problems that arise when training deep neural networks. We’ll provide a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score. We’ll then explore training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help attendees detect and diagnose training problems, we will cover techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.