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

Python Notebook

Jupyter Notebook is an open source web application that is the de facto choice for AI, ML, and data scientists. Jupyter Notebook supports multiple computer languages, and the most popular is Python.

Throughout this book, the term Python Notebook will be used synonymously for Jupyter Notebook, JupyterLab, and Google Colab Jupyter Notebook.

For Python developers, there are many choices of integrated development environment (IDE) platforms, such as Integrated Development and Learning Environment (IDLE), PyCharm, Microsoft Visual Studio, Atom, Sublime, and many more. Still, a Python Notebook is the preferred choice for AI, ML, and data scientists. It is an interactive IDE fit for exploring, coding, and deploying AI projects.

Fun fact

The easiest learning method is reading this book, running the code, and hacking it. This book cannot cover all scenarios; therefore, you must be comfortable with hacking the code so that it matches your real-world dataset. The Python Notebook is designed for interactivity. It gives us the freedom to play, explore, and make mistakes.

Python Notebook is the development tool of choice, and in particular, we will review the following:

  • Google Colab
  • Python Notebook options
  • Installing Python Notebook

Let’s begin with Google Colab.