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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

Extraction augmentation

The extraction method is a process in time series analysis where multiple constructed elements are used as input, and a singular value is extracted from each time series to create new augmented data. This method uses a package called TSfresh and includes default and custom features. The output of extraction methods differs from the output of transformation and interaction methods, as the latter outputs entirely new time series data. You can use this method when specific values need to be pulled from time series data.

The DeltaPy library contains 34 extraction methods. Writing the wrapper functions for extraction is similar to the wrapper transformation functions. The difficulty is how to discern the forecasting’s effectiveness from tabular augmentation. Furthermore, these methods are components and not complete functions for tabular augmentation.

Pluto will not explain each function, but here is a list of the extraction functions in the DeltaPy...

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