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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

Python Notebook

This chapter’s coding lessons primarily focus on downloading real-world datasets from the Kaggle website. The later chapters rely on or reuse these fetching functions.

In the previous chapter, you learned about this book’s general rules for development on the Python Notebook. The object-oriented class named Pluto contains the methods and attributes, and you add new methods to Pluto as you learn new concepts and techniques. Review Chapter 1 if you are uncertain about the development philosophy.

In this book, the term Python Notebook is used synonymously for Jupyter Notebook, JupyterLab, and Google Colab Notebook.

Fun challenge

Pluto challenges you to change the object’s name from Pluto to any other name. If you do change the name, then substitute that name where you see Pluto in your text and code. For example, if you change the object name to Sandy, then pluto.draw_batch_image() becomes sandy.draw_batch_image().

Starting with this...

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