Data augmentation with Python: enhance accuracy in deep learning with practical data augmentation for image, text, audio and tabular data
1st ed.. - Birmingham, England: Packt Publishing, [2023]
Online
Monographie, Elektronische Ressource
- 1 online resource (394 pages)
Ermittle Ausleihstatus...
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.
Cover -- Title Page -- Copyright -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Data Augmentation -- Chapter 1: Data Augmentation Made Easy -- Data augmentation role -- Data input types -- Image definition -- Text definition -- Audio definition -- Tabular data definition -- Python Notebook -- Google Colab -- Additional Python Notebook options -- Installing Python Notebook -- Programming styles -- Source control -- The PacktDataAug class -- Naming convention -- Extend base class -- Referencing a library -- Exporting Python code -- Pluto -- Summary -- Chapter 2: Biases in Data Augmentation -- Computational biases -- Human biases -- Systemic biases -- Python Notebook -- Python Notebook -- GitHub -- Pluto -- Verifying Pluto -- Kaggle ID -- Image biases -- State Farm distracted drivers detection -- Nike shoes -- Grapevine leaves -- Text biases -- Netflix -- Amazon reviews -- Summary -- Part 2: Image Augmentation -- Chapter 3: Image Augmentation for Classification -- Geometric transformations -- Flipping -- Cropping -- Resizing -- Padding -- Rotating -- Translation -- Noise injection -- Photometric transformations -- Basic and classic -- Advanced and exotic -- Random erasing -- Combining -- Reinforcing your learning through Python code -- Pluto and the Python Notebook -- Real-world image datasets -- Image augmentation library -- Geometric transformation filters -- Photographic transformations -- Random erasing -- Combining -- Summary -- Chapter 4: Image Augmentation for Segmentation -- Geometric and photometric transformations -- Real-world segmentation datasets -- Python Notebook and Pluto -- Real-world data -- Pandas -- Viewing data images -- Reinforcing your learning -- Horizontal flip -- Vertical flip -- Rotating -- Resizing and cropping -- Transpose -- Lighting -- FancyPCA -- Combining -- Summary.
Part 3: Text Augmentation -- Chapter 5: Text Augmentation -- Character augmenting -- Word augmenting -- Sentence augmentation -- Text augmentation libraries -- Real-world text datasets -- The Python Notebook and Pluto -- Real-world NLP datasets -- Pandas -- Visualizing NLP data -- Reinforcing learning through Python Notebook -- Character augmentation -- Word augmenting -- Summary -- Chapter 6: Text Augmentation with Machine Learning -- Machine learning models -- Word augmenting -- Sentence augmenting -- Real-world NLP datasets -- Python Notebook and Pluto -- Verify -- Real-world NLP data -- Pandas -- Viewing the text -- Reinforcing your learning through the Python Notebook -- Word2Vec word augmenting -- BERT -- RoBERTa -- Back translation -- Sentence augmentation -- Summary -- Part 4: Audio Data Augmentation -- Chapter 7: Audio Data Augmentation -- Standard audio augmentation techniques -- Time stretching -- Time shifting -- Pitch shifting -- Polarity inversion -- Noise injection -- Filters -- Low-pass filter -- High-pass filter -- Band-pass filter -- Low-shelf filter -- High-shelf filter -- Band-stop filter -- Peak filter -- Audio augmentation libraries -- Real-world audio datasets -- Python Notebook and Pluto -- Real-world data and pandas -- Listening and viewing -- Reinforcing your learning -- Time shifting -- Time stretching -- Pitch scaling -- Noise injection -- Polarity inversion -- Low-pass filter -- Band-pass filter -- High-pass and other filters -- Summary -- Chapter 8: Audio Data Augmentation with Spectrogram -- Initializing and downloading -- Audio Spectrogram -- Various Spectrogram formats -- Mel-spectrogram and Chroma STFT plots -- Spectrogram augmentation -- Spectrogram images -- Summary -- Part 5: Tabular Data Augmentation -- Chapter 9: Tabular Data Augmentation -- Tabular augmentation libraries -- Augmentation categories.
Real-world tabular datasets -- Exploring and visualizing tabular data -- Data structure -- First graph view -- Checksum -- Specialized plots -- Exploring the World Series data -- Transforming augmentation -- Robust scaler -- Standard scaler -- Capping -- Interaction augmentation -- Regression augmentation -- Operator augmentation -- Mapping augmentation -- Extraction augmentation -- Summary -- Index -- About Packt -- Other Books You May Enjoy.
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Data augmentation with Python: enhance accuracy in deep learning with practical data augmentation for image, text, audio and tabular data
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Verantwortlichkeitsangabe: | Duc Haba |
Autor/in / Beteiligte Person: | Haba, Duc [author.] |
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Ausgabe: | 1st ed. |
Veröffentlichung: | Birmingham, England: Packt Publishing, [2023] |
Medientyp: | Monographie |
Datenträgertyp: | Elektronische Ressource |
Umfang: | 1 online resource (394 pages) |
ISBN: | 1-80323-591-8 |
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