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

Text biases

By now, you should recognize the patterns for fetching real-world image datasets and importing metadata into pandas. It is the same pattern for text datasets. Pluto will guide you through two sessions and use his power of observation to name the biases. He could employ the latest in generative AI such as OpenAI GPT3 or GPT4 to list the biases in the text. Maybe he will do that later, but for now, he will use his noggin. Nevertheless, Pluto will attempt to write Python code to gain insight into the texts' structures, such as the word count and misspelled words. It is not the fairness matrix but a step in the right direction.

Pluto searches the Kaggle website for the Natural Language Processing (NLP) dataset, and the result consists of over 2,000 datasets. He chooses the Netflix Shows and the Amazon Reviews datasets. Retrieving and viewing the NLP dataset follows the same fetching, importing, and printing steps outlined in the image dataset.

Let’s start...