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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

Text biases

By now, you should recognize the patterns for fetching real-world image datasets and importing metadata into pandas. It is the same pattern for text datasets. Pluto will guide you through two sessions and use his power of observation to name the biases. He could employ the latest in generative AI such as OpenAI GPT3 or GPT4 to list the biases in the text. Maybe he will do that later, but for now, he will use his noggin. Nevertheless, Pluto will attempt to write Python code to gain insight into the texts' structures, such as the word count and misspelled words. It is not the fairness matrix but a step in the right direction.

Pluto searches the Kaggle website for the Natural Language Processing (NLP) dataset, and the result consists of over 2,000 datasets. He chooses the Netflix Shows and the Amazon Reviews datasets. Retrieving and viewing the NLP dataset follows the same fetching, importing, and printing steps outlined in the image dataset.

Let’s start...

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