Data Augmentation with Python
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Data Augmentation with Python
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Overview of this book
Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset.
The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges.
By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.
Table of Contents (17 chapters)
Preface
Part 1: Data Augmentation
Free Chapter
Chapter 1: Data Augmentation Made Easy
Chapter 2: Biases in Data Augmentation
Part 2: Image Augmentation
Chapter 3: Image Augmentation for Classification
Chapter 4: Image Augmentation for Segmentation
Part 3: Text Augmentation
Chapter 5: Text Augmentation
Chapter 6: Text Augmentation with Machine Learning
Part 4: Audio Data Augmentation
Chapter 7: Audio Data Augmentation
Chapter 8: Audio Data Augmentation with Spectrogram
Part 5: Tabular Data Augmentation
Chapter 9: Tabular Data Augmentation
Index
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