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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

To get the most out of this book

I designed this book to be a hands-on journey. It will be more effective to read a chapter, run the code on the Python Notebook, re-read the chapter’s part that confused you, and jump back to hacking the code until the concept or technique is firmly understood.

Software/hardware covered in the book

Operating system requirements

Python

Chrome, Edge, Safari, or FireFox browser on Windows, macOS, or Linux.

Jupyter Notebook (Python Notebook)

Python standard libraries, Panda, Matplotlib, and Numpy

Python image, text, audio, and tabular data augmentation libraries.

The default online Jupyter Notebook is the Google Colab. You need a Google account. For other online Jupyter Notebook, like Kaggle, Microsoft, or other online Jupyter Notebook, you need sign up or have an account to their services.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Downloading real-world dataset from the Kaggle website requires a Kaggle username and key.

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