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)
1
Part 1: Data Augmentation
4
Part 2: Image Augmentation
7
Part 3: Text Augmentation
10
Part 4: Audio Data Augmentation
13
Part 5: Tabular Data Augmentation

To get the most out of this book

I designed this book to be a hands-on journey. It will be more effective to read a chapter, run the code on the Python Notebook, re-read the chapter’s part that confused you, and jump back to hacking the code until the concept or technique is firmly understood.

Software/hardware covered in the book

Operating system requirements

Python

Chrome, Edge, Safari, or FireFox browser on Windows, macOS, or Linux.

Jupyter Notebook (Python Notebook)

Python standard libraries, Panda, Matplotlib, and Numpy

Python image, text, audio, and tabular data augmentation libraries.

The default online Jupyter Notebook is the Google Colab. You need a Google account. For other online Jupyter Notebook, like Kaggle, Microsoft, or other online Jupyter Notebook, you need sign up or have an account to their services.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Downloading real-world dataset from the Kaggle website requires a Kaggle username and key.