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)
1
Part 1: Data Augmentation
4
Part 2: Image Augmentation
7
Part 3: Text Augmentation
10
Part 4: Audio Data Augmentation
13
Part 5: Tabular Data Augmentation

Text Augmentation with Machine Learning

Text augmentation with machine learning (ML) is an advanced technique compared to the standard text augmenting methods we covered in the previous chapter. Ironically, text augmentation aims to improve ML model accuracy, but we used a pre-trained ML model to create additional training NLP data. It’s a circular process. ML coding is not in this book’s scope, but understanding the difference between using libraries and ML for text augmentation is beneficial.

Augmentation libraries, whether for image, text, or audio, follow the traditional programming methodologies with structure data, loops, and conditional statements in the algorithm. For example, as shown in Chapter 5, the pseudocode for implementing the _print_aug_reserved() method could be as follows:

# define synonym words, pseudo-code
reserved = [['happy', 'joyful', 'cheerful'],
  ['sad', 'sorrowful', 'regretful...