Summary
In this chapter, you learned that data augmentation is essential for achieving higher accuracy prediction in DL and generative AI. Data augmentation is an economical option for extending a dataset without the difficulty of purchasing and labeling new data.
The four input data types are image, text, audio, and tabular. Each data type faces different challenges, techniques, and limitations. Furthermore, the dataset dictates which functions and parameters are suitable. For example, people’s faces and aerial photographs are image datasets, but you can’t expand the data by vertically flipping people’s images; however, you can vertically flip aerial photos.
In the second part of this chapter, you used Python notebooks to reinforce your learning of these augmentation concepts. This involved selecting a Python Notebook as the default IDE to access a cloud-based platform, such as Google Colab or Kaggle, or installing the Python Notebook locally on your laptop.
The Programming styles section lay the foundation for the Python Notebook’s structure. It touched on GitHub as a form of source control, using base classes, extending base classes, long library function names, exporting to Python, and introducing Pluto.
This chapter laid the foundation with Pluto as the main object. Pluto does not start with complete data augmentation functions – he begins with a minimum structure, and as he learns new data augmentation concepts and techniques from chapter to chapter, he will add new methods to his arsenal.
By the end of this book, Pluto and you will learn techniques regarding how to augment image, text, audio, and tabular data. In other words, you will learn how to write a powerful image, text, audio, and tabular augmentation class from scratch using real-world data, which you can reuse in future data augmentation projects.
Throughout this chapter, there were fun facts and fun challenges. Pluto hopes you will take advantage of what’s been provided and expand your experience beyond the scope of this chapter.
In Chapter 2, Biases in Data Augmentation, Pluto and you will explore how data augmentation can increase biases. Using data biases as a guiding principle to data augmentation is an often-overlooked technique.
