Data Augmentation with Python: Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data, 1st Edition

Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries\n\nKey Features\nExplore beautiful, customized charts and infographics in full color\nWork with fully functional OO code using open source libraries in the Python Notebook for each chapter\nUnleash the potential of real-world datasets with practical data augmentation techniques\nBook Description\nData 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.\nThe 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.\nBy the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.\nWhat you will learn\nWrite OOP Python code for image, text, audio, and tabular data\nAccess over 150,000 real-world datasets from the Kaggle website\nAnalyze biases and safe parameters for each augmentation method\nVisualize data using standard and exotic plots in color\nDiscover 32 advanced open source augmentation libraries\nExplore machine learning models, such as BERT and Transformer\nMeet Pluto, an imaginary digital coding companion\nExtend your learning with fun facts and fun challenges\nWho this book is for\nThis 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.\nData Augmentation with Python: Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data 1st Edition is written by Duc Haba and published by Packt Publishing. ISBNs for Data Augmentation with Python are 9781803235912, 1803235918 and the print ISBNs are 9781803246451, 1803246456.\nare 9781804617625, 1804617628.