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 NLP datasets

This chapter will use the same Netflix and Twitter real-world NLP datasets from Chapter 5. In addition, both datasets have been vetted, cleaned, and stored in the pluto_data directory in this book’s GitHub repository. The startup sequence is similar to the previous chapters. It is as follows:

  1. Clone the Python Notebook and Pluto.
  2. Verify Pluto.
  3. Locate the NLP data.
  4. Load the data into pandas.
  5. View the data.

Let’s start with the Python Notebook and Pluto.

Python Notebook and Pluto

Start by loading the data_augmentation_with_python_chapter_6.ipynb file into Google Colab or your chosen Jupyter Notebook or JupyterLab environment. From this point onward, we will only display code snippets. The complete Python code can be found in the Python Notebook.

The next step is to clone the repository. We will reuse the code from Chapter 5. The !git and %run statements are used to instantiate Pluto:

# clone Packt GitHub...