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

Text Augmentation

Text augmentation is a technique that is used in Natural Language Processing (NLP) to generate additional data by modifying or creating new text from existing text data. Text augmentation involves techniques such as character swapping, noise injection, synonym replacement, word deletion, word insertion, and word swapping. Image and text augmentation have the same goal. They strive to increase the size of the training dataset and improve AI prediction accuracy.

Text augmentation is relatively more challenging to evaluate because it is not as intuitive as image augmentation. The intent of an image augmentation technique is clear, such as flipping a photo, but a character-swapping technique will be disorienting to the reader. Therefore, readers might perceive the benefits as subjective.

The effectiveness of text augmentation depends on the quality of the generated data and the specific NLP task being performed. It can be challenging to determine the appropriate...