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Data Augmentation with Python
Data Augmentation with Python

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

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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.

Who is this book 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.

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
Estimated delivery fee Deliver to United States

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 28, 2023
Length: 394 pages
Edition : 1st
Language : English
ISBN-13 : 9781803246451
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What do you get with Print?

Product feature icon Instant access to your digital copy whilst your Print order is Shipped
Product feature icon Colour book shipped to your preferred address
Product feature icon Redeem a companion digital copy on all Print orders
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
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Estimated delivery fee Deliver to United States

Economy delivery 10 - 13 business days

Free $6.95

Premium delivery 6 - 9 business days

$21.95
(Includes tracking information)

Product Details

Publication date : Apr 28, 2023
Length: 394 pages
Edition : 1st
Language : English
ISBN-13 : 9781803246451
Category :
Languages :
Concepts :
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Dr. Matthias Nagel Aug 11, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Ok
Amazon Verified review Amazon
Ady Ngom Jun 14, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
crawdaddie Jul 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"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.
Amazon Verified review Amazon
Poker Nanny Jun 07, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Om S Jun 06, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Data Augmentation with Python" is an essential guide for data scientists and students seeking to improve AI accuracy using real-world datasets. With over 150 functional methods and open source libraries, the book covers image, text, audio, and tabular data augmentation. The author emphasizes the importance of robust datasets in AI projects and presents practical techniques for extending data economically. Through Python code examples and Jupyter Notebooks, readers gain hands-on experience with various augmentation methods. The inclusion of colorful charts and custom plots enhances understanding, while fun facts and challenges add an engaging touch. Suitable for beginners and experienced practitioners, this book equips readers with the skills to enhance AI accuracy through effective data augmentation.
Amazon Verified review Amazon
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Shipping Details

USA:

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Economy: Delivery to most addresses in the US within 10-15 business days

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UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
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Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

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Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

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Rest of the World:

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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
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  8. Guinea-bissau
  9. Iran
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  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
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