Data Augmentation with Python: Enhance accuracy in Deep Learning with practical Data Augmentation for image, text, audio & tabular data
- Length: 307 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2023-05-09
- ISBN-10: 1803246456
- ISBN-13: 9781803246451
- Sales Rank: #437569 (See Top 100 Books)
Unlock the power of data augmentation for AI and Generative AI with real-world datasets. Improve your model’s accuracy and extend images, texts, audio, and tabular using 150+ fully functional OO methods and open-source libraries.
Key Features
- Practical Data augmentation techniques for images, texts, audio, and tabular data using real-world datasets
- Beautiful, customized charts and infographics in full color for image, text, audio, and tabular data
- Fully functional object-oriented code using open-source libraries on the Python Notebook for each chapter
Book Description
Data is paramount in an AI project, especially for Deep Learning and Generative AI. The forecasting accuracy relies on robust input datasets. The traditional method of acquiring additional data is difficult, expensive, and impractical. The only option to extend the dataset economically is data augmentation.
You will learn 20+ Geometric, Photometric, and Random erasing augmentation methods using seven real-world datasets for image classification and segmentation. In addition, we will review eight image augmentation open-source libraries, write OOP wrapper functions on the Python Notebooks, view color image augmentation effects, analyze the safe level and biases, and extend the chapter with Fun facts and Fun challenges.
You will discover 22+ character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The advanced text augmentation chapter uses Machine Learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others.
Similarly, the audio and tabular data chapters have real-world data, open-source libraries, amazing custom plots, Python Notebook, Fun facts, and Fun challenges.
By the end of the 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 websites
- Analyze biases and safe parameters for each augmentation method
- Visualize data using standard and exotics plots in color
- Explore 32 advanced open-source augmentation libraries
- Discover 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
The book is for AI, Data scientists, and students interested in the AI discipline. You don’t need advanced AI or Deep Learning skills, but Python programming and familiarity with Jupyter Notebooks are required.
Data Augmentation with Python Foreword Contributors About the author About the reviewers Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Share your thoughts Download a free PDF copy of this book 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 Why subscribe? Other Books You May Enjoy Packt is searching for authors like you Share your thoughts Download a free PDF copy of this book
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