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Data Augmentation with Python

You're reading from   Data Augmentation with Python Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246451
Length 394 pages
Edition 1st Edition
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Author (1):
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Duc Haba Duc Haba
Author Profile Icon Duc Haba
Duc Haba
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Data Augmentation
2. Chapter 1: Data Augmentation Made Easy FREE CHAPTER 3. Chapter 2: Biases in Data Augmentation 4. Part 2: Image Augmentation
5. Chapter 3: Image Augmentation for Classification 6. Chapter 4: Image Augmentation for Segmentation 7. Part 3: Text Augmentation
8. Chapter 5: Text Augmentation 9. Chapter 6: Text Augmentation with Machine Learning 10. Part 4: Audio Data Augmentation
11. Chapter 7: Audio Data Augmentation 12. Chapter 8: Audio Data Augmentation with Spectrogram 13. Part 5: Tabular Data Augmentation
14. Chapter 9: Tabular Data Augmentation 15. Index 16. Other Books You May Enjoy

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...
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