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

Real-world tabular datasets

There are thousands of real-world tabular datasets on the Kaggle website. Pluto has chosen two tabular datasets for this process.

The Bank Account Fraud Dataset Suite (NeurIPS 2022) contains six synthetic bank account fraud tabular datasets. Each dataset contains 1 million records. They are based on real-world data for fraud detection. Each dataset focuses on a different type of bias. Sergio Jesus, Jose Pombal, and Pedro Saleiro published the dataset in 2022 under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. The Kaggle link is

The World Series Baseball Television Ratings is a dataset for audiences watching the baseball World Series on television from 1969 to 2022. Matt OP published the dataset in 2022 under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license. The Kaggle link is