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Key benefits

Explore beautiful, customized charts and infographics in full color

Work with fully functional OO code using open source libraries in the Python Notebook for each chapter

Unleash the potential of real-world datasets with practical data augmentation techniques

Description

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.

What you will learn

Write OOP Python code for image, text, audio, and tabular data

Access over 150,000 real-world datasets from the Kaggle website

Analyze biases and safe parameters for each augmentation method

Visualize data using standard and exotic plots in color

Discover 32 advanced open source augmentation libraries

Explore machine learning models, such as BERT and Transformer

Meet Pluto, an imaginary digital coding companion

Extend your learning with fun facts and fun challenges

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Amazon verified reviews

Dr. Matthias Nagel Aug 11, 2023

Amazon Verified review

Ady Ngom Jun 14, 2023

The author's passion for teaching really shines through this piece. A definitive must read if you want to start navigating the world of possibilities with AI and need a solid compass to guide you through.

Y. XIA Jun 07, 2023

"Data Augmentation with Python" is a comprehensive overview of various data augmentation techniques and a demonstration of how to implement them using Python. This book strikes a balance between theoretical discussions and hands-on examples. The chapters are well-sequenced and accompanied by well-structured code snippets and illustrations, which are easy to understand and follow. Throughout the exercises, the author provides numerous fun facts, challenges, and real-world examples, making learning even more engaging.Overall, I enjoyed reading it and I believe this is a great resource for anyone interested in enhancing their understanding of the topic.

crawdaddie Jul 05, 2023

"Boosting AI Accuracy with Real-World Datasets" is a helpful and practical guide for people who want to use Python and improve their AI models using data augmentation techniques. The book has lots of useful information, easy-to-understand explanations, and practical examples that allow readers to learn and become skilled in enhancing images, text, audio, and tabular data. This book is a valuable resource because of its practical approach.Data augmentation is a required technique for improving AI accuracy, and Duc Haba does an excellent job of explaining various augmentation methods using real-world datasets. Whether it's image, text, audio, or tabular data, readers will find detailed explanations, code samples, and insights into the biases and safe parameters for each augmentation method.The book covers a wide range of topics, providing over 150 functional object-oriented methods and open-source libraries to help readers achieve optimal results. This book is accessible to aspiring data scientists and those interested in the AI discipline, even without prior AI or deep learning skills.

Poker Nanny Jun 07, 2023

This book effectively introduces readers to the topic of Data Augmentation. While the subject itself is vast and cannot be thoroughly explored in a single book, the author takes a practical approach by presenting various recipes that demonstrate different techniques for augmenting data across a variety of categories. Considering its scope, this book serves its purpose well in acquainting readers with Data Augmentation. Personally, I found the chapter on Biases in Data Augmentation to be the highlight of the book.

Data Augmentation with Python: Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data

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