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

Sentence augmenting

Augmenting at the sentence level is a powerful concept. It was not possible 5 years ago. You had to be working in an ML research company or a billionaire before accessing these acclaimed pre-trained models. Some transformer and large language models (LLMs) became available in 2019 and 2020 as open source, but they are generally for research. Convenient access to online AI servers via a GPU was not widely available at that time. The LLM and pre-trained models have recently become publicly accessible for incorporating them into your projects, such as the HuggingFace website. The salient point is that for independent researchers or students, LLM and pre-trained models only became accessible in mid-2021.

The sentence and word augmenting methods that use ML can’t be done dynamically as with methods using the Nlpaug library. In other words, you have to write and save the augmented text to your local or cloud disk space. The primary reason is that the augmentation...