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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

Tabular augmentation libraries

Tabular augmentations are not established as image, text, or audio augmentations. Typically, data scientists develop tabular augmentation techniques specific to a project. There are a few open source projects on the GitHub website. Still, DL and generative AI will continue to advance in forecasting for time series and tabular data predictions, and so will tabular augmentations. The following open source libraries can be found on the GitHub website:

  • DeltaPy is a tabular augmentation for generating and synthesizing data focusing on financial applications such as time series stock forecasting. It fundamentally applies to a broad range of datasets. The GitHub website link is https://github.com/firmai/deltapy. The published scholarly paper is called DeltaPy: A Framework for Tabular Data Augmentation in Python, by Derek Snow, The Alan Turing Institute, in 2020.
  • The Synthetic Data Vault (SDV) is for augmenting tabular data by generating synthetic...
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