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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

Reinforcing your learning through Python code

We will pursue the same approach as in Chapter 2. Start by loading the data_augmentation_with_python_chapter_3.ipynb file in Google Colab or your chosen Jupyter Notebook or JupyterLab environment. From this point onward, the code snippets will be from the Python Notebook, which contains all the functions.

This chapter’s coding lessons topics are as follows:

  • Pluto and the Python Notebook
  • Real-world image dataset
  • Image augmentation library
  • Geometric transformations
  • Photometric transformations
  • Random erasing
  • Combining

The next step is to download, set up, and verify that Pluto and the Python Notebook are working adequately.

Pluto and the Python Notebook

Before loading Pluto from Chapter 2, we must retrieve him by cloning this book’s GitHub repository. Using the Python Notebook’s %run magic command, we can invoke Pluto. If you improved or hacked Pluto, load that file. You...

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