Book Image

Data Augmentation with Python

By : Duc Haba
Book Image

Data Augmentation with Python

By: Duc Haba

Overview of this book

Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.
Table of Contents (17 chapters)
Part 1: Data Augmentation
Part 2: Image Augmentation
Part 3: Text Augmentation
Part 4: Audio Data Augmentation
Part 5: Tabular Data Augmentation

Reinforcing your learning through the Python Notebook

Even though NLP ML is highly complex, the implementation for the wrapper code is deceptively simple. This is because of Pluto’s structured object-oriented approach. First, we created a base class for Pluto in Chapter 1 and used the decorator to add a new method as we learned new augmentation concepts. In Chapter 2, Pluto learned to download any of the thousands of real-world datasets from the Kaggle website. Chapters 3 and 4 introduced the wrapper functions process using powerful open source libraries under the hood. Finally, Chapter 5 put forward the text augmentation concepts and methods when using the Nlpaug library.

Therefore, building upon our previous knowledge, the wrapper functions in this chapter use the powerful NLP ML pre-trained model to perform the augmentations.

In particular, this chapter will present wrapper functions and the augmenting results for the Netflix and Twitter real-world datasets using the...