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007    cr |n||||||||| 
008    230506s2023    enk     o     000 0 eng d 
020    9781803235912|q(electronic bk.) 
020    1803235918|q(electronic bk.) 
029 1  AU@|b000074281602 
035    (OCoLC)1378301601 
037    9781803246451|bO'Reilly Media 
037    10251193|bIEEE 
040    YDX|beng|cYDX|dORMDA|dOCLCF|dIEEEE|dOCLCO 
049    INap 
082 04 006.3/1 
082 04 006.3/1|223 
099    eBook O'Reilly for Public Libraries 
100 1  Haba, Duc,|eauthor. 
245 10 DATA AUGMENTATION WITH PYTHON|h[electronic resource] :
       |benhance deep learning accuracy with data augmentation 
       methods for image, text, audio, and tabular data /|cDuc 
       Haba.|h[O'Reilly electronic resources] 
250    1st edition. 
260    [England] :|bPACKT PUBLISHING LIMITED,|c2023. 
300    1 online resource 
505 0  Table of Contents Data Augmentation Made Easy Biases in 
       Data Augmentation Image Augmentation for Classification 
       Image Augmentation for Segmentation Text Augmentation Text
       Augmentation with Machine Learning Audio Data Augmentation
       Audio Data Augmentation with Spectrogram Tabular Data 
       Augmentation. 
520    Boost your AI and generative AI accuracy using real-world 
       datasets with over 150 functional object-oriented methods 
       and open source libraries Purchase of the print or Kindle 
       book includes a free PDF eBook 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. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Deep learning (Machine learning) 
650  0 Python (Computer program language) 
650  6 Apprentissage profond. 
650  6 Python (Langage de programmation) 
650  7 Deep learning (Machine learning)|2fast 
650  7 Python (Computer program language)|2fast 
776 08 |iPrint version:|z9781803235912 
776 08 |iPrint version:|z1803246456|z9781803246451
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856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
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       |zAvailable on O'Reilly for Public Libraries 
938    YBP Library Services|bYANK|n305300507 
994    92|bJFN