Data-Level Deep Learning Methods

You learned about various sampling methods in the previous chapters. Collectively, we call these methods data-level methods in this book. These methods include random undersampling, random oversampling, NearMiss, and SMOTE. We also explored how these methods work with classical machine learning algorithms.

In this chapter, we’ll explore how to apply familiar sampling methods to deep learning models. Deep learning offers unique opportunities to enhance these methods further. We’ll delve into elegant techniques to combine deep learning with oversampling and undersampling. Additionally, we’ll learn how to implement various sampling methods with a basic neural network. We’ll also cover dynamic sampling, which involves adjusting the data sample across multiple training iterations, using varying balancing ratios for each iteration. Then, we will learn to use some data augmentation techniques for both images and text. We’...