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 libraries

There are many more Python open source image augmentation libraries than text augmentation libraries. Some libraries are more adaptable to a particular category than others, but in general, it is a good idea to pick one or two and become proficient in them.

The well-known libraries are Nlpaug, Natural Language Toolkit (NLTK), Generate Similar (Gensim), TextBlob, TextAugment, and AugLy:

  • Nlpaug is a library used for textual augmentation for DL. The goal is to improve DL model performance by generating textual data. The GitHub link is
  • NLTK is a platform used for building Python programs to work with human language data. It provides interfaces to over 50 corpora and lexical resources, such as WordNet. NLTK contains text-processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. The GitHub link is
  • Gensim is a popular open source...