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

Biases in Data Augmentation

As artificial intelligence (AI) becomes embedded in our society, biases in AI systems will adversely affect your quality of life. These AI systems, particularly in deep learning (DL) and generative AI, depend on the input data you are using to extend data augmentation.

AI systems rely heavily on data to make decisions, and if the data used to train the system is biased, then the AI system will make unfair decisions. It will lead to the unjust treatment of individuals or groups and perpetuate systemic inequalities. AI plays a decisive role in life-changing decisions, such as how much your monthly mortgage insurance rate is, whether you can be approved for a car loan, your application qualification for a job, who will receive government assistance, how much you pay for milk, what you read on social media newsfeeds, and how much oil your country will import or export, to name a few.

By learning data biases before diving deep into learning data augmentation...