Chapter 6. Tuning and Optimizing Models

In the last two chapters, we trained deep learning models for classification, regression, and image recognition tasks. In this chapter, we will discuss some important issues in regard to managing deep learning projects. While this chapter may seem somewhat theoretical, if any of the issues discussed are not correctly managed, it can derail your deep learning project. We will look at how to choose evaluation metrics and how to create an estimate of how well a deep learning model will perform before you begin modeling. Next, we will move onto data distribution and the mistakes often made in splitting data into correct partitions for training. Many machine learning projects fail in production use because the data distribution is different to what the model was trained with. We will look at data augmentation, a valuable method to enhance your model's accuracy. Finally, we will discuss hyperparameters and learn how to tune them.

In this chapter, we will...