Data Augmentation with Python: Enhance accuracy in Deep Learning with practical Data Augmentation for image, text, audio & tabu

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.
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