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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

Human biases

Human biases are even harder to calculate using Python code. There is no Python or other language library for computing a numeric score for human bias in a dataset. We rely on observation to spot such human biases. It is time-consuming to manually study a particular dataset before deriving possible human biases. We could argue that it is not a programmer’s or data scientist’s job because there is no programable method to follow.

Human biases reflect systematic errors in human thought. In other words, when you develop an AI system, you are limited by the algorithm and data chosen by you. Thus, the prediction of a limited outcome could be biased by your selections. These prejudices are implicit in individuals, groups, institutions, businesses, education, and government.

There is a wide variety of human biases. Cognitive and perceptual biases show themselves in all domains and are not unique to human interactions with AI. There is an entire field of study...

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