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 text datasets

The Kaggle website is an online community platform for data scientists and machine learning enthusiasts. The Kaggle website has thousands of real-world datasets; Pluto found a little over 2,900 NLP datasets and has selected two NLP datasets for this chapter.

In Chapter 2, Pluto uses the Netflix and Amazon datasets as examples with which to understand biases. Pluto keeps the Netflix NLP dataset because the movie reviews are curated . There are a few syntactical errors, but overall, the input texts are of high quality.

The second NLP dataset is Twitter Sentiment Analysis (TSA). The 29,530 real-world tweets contain many grammatical errors and misspelled words. The challenge is to classify the tweets into two categories: (1) normal or (2) racist and sexist.

The dataset was published in 2021 by Mayur Dalvi, and the license is CC0: Public Domain,

After selecting the two NLP datasets, you can use the...