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 Data Augmentation

Tabular augmentation supplements tabular data with additional information to make it more useful for predictive analytics. Database, spreadsheet, and table data are examples of tabular data. It involves transforming otherwise insufficient datasets into robust inputs for ML. Tabular augmentation can help turn unstructured data into structured data and can also assist in combining multiple data sources into a single dataset. It is an essential step in data pre-processing for increasing AI predictive accuracy.

The idea of tabular augmentation is to include additional information to a given dataset that can then be used to generate valuable insights. These datasets can come from various sources, such as customer feedback, social media posts, and IoT device logs. Tabular augmentation can add new information columns to the dataset by enriching the existing columns with more informative tags. It increases the completeness of the dataset and provides more accurate...