8. Training deep neural networks on Azure

In the previous chapter, we learned how to train and score classical machine learning (ML) models using non-parametric tree-based ensemble methods. While these methods work well on many small and medium-sized datasets with categorical variables, they don't generalize well on large datasets.

In this chapter, we will train complex parametric models using deep learning (DL) for even better generalization with large datasets. This will help you understand which situations Deep Neural Networks (DNNs) perform better in than traditional models.

First, we will give a short and practical overview of why and when DL works well. We will focus more on understanding the general principles and rationale rather than a theoretical approach. This will help you to assess which use cases and datasets have a need for DL and how it works in general.

We will then take a look at the most popular application domain for DL—computer vision...