Data Augmentation with Python: Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data, 1st Edition eBook
$20.00
Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Key Features Explore beautiful, customized charts and infographics in full color Work with fully functional OO…
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About this eTextbook
Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Key Features 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 Book 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 Who this book is for This book is for data scientists and students interested in the AI discipline.
Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book. ISBNs for Data Augmentation with Python are 9781803235912, 1803235918 and the print ISBNs are 9781803246451, 1803246456. are 9781804617625, 1804617628.
Publication details
- Author: Duc Haba
- Publisher: Packt Publishing
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About this eTextbook
Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Key Features 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 Book 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 Who this book is for This book is for data scientists and students interested in the AI discipline.
Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book. ISBNs for Data Augmentation with Python are 9781803235912, 1803235918 and the print ISBNs are 9781803246451, 1803246456. are 9781804617625, 1804617628.
Publication details
- Author: Duc Haba
- Publisher: Packt Publishing
What you receive
- A licensed digital edition of this title, supplied as PDF or EPUB. The format is set by the publisher, so email us the ISBN before ordering and we will confirm which one it is.
- Delivered to the email address on your order as a secure download link, usually within minutes and always within 24 hours of payment.
- Download links do not expire and there is no cap on re-downloads. Readable on any computer, tablet or phone, online or offline.
- This is a digital edition. Nothing is printed, packed or posted, and there is no delivery charge.
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