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

Tabular augmentation libraries

Tabular augmentations are not established as image, text, or audio augmentations. Typically, data scientists develop tabular augmentation techniques specific to a project. There are a few open source projects on the GitHub website. Still, DL and generative AI will continue to advance in forecasting for time series and tabular data predictions, and so will tabular augmentations. The following open source libraries can be found on the GitHub website:

  • DeltaPy is a tabular augmentation for generating and synthesizing data focusing on financial applications such as time series stock forecasting. It fundamentally applies to a broad range of datasets. The GitHub website link is The published scholarly paper is called DeltaPy: A Framework for Tabular Data Augmentation in Python, by Derek Snow, The Alan Turing Institute, in 2020.
  • The Synthetic Data Vault (SDV) is for augmenting tabular data by generating synthetic...