Book Image

Data Augmentation with Python

By : Duc Haba
Book Image

Data Augmentation with Python

By: Duc Haba

Overview of this book

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.
Table of Contents (17 chapters)
Part 1: Data Augmentation
Part 2: Image Augmentation
Part 3: Text Augmentation
Part 4: Audio Data Augmentation
Part 5: Tabular Data Augmentation

Word augmenting

Word augmentations carry the same bias and safe level warning as character augmentations. Over half of these augmentation methods inject errors into the text, but other functions generate new text using synonyms or a pretrained AI model. The standard word augmentation functions are listed as follows:

  • The Misspell augmentation function uses a predefined dictionary to simulate spelling mistakes. It is based on the scholarly paper Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs by Claude Coulombe, which was published in 2018.
  • The Split augmentation function splits words into two tokens randomly.
  • The Random word augmentation method applies random behavior to the text with four parameters: substitute, swap, delete, and crop. It is based on two scholarly papers: Synthetic and Natural Noise Both Break Neural Machine Translation by Yonatan Belinkov and Yonatan Bisk, published in 2018, and Data Augmentation via Dependency Tree Morphing for Low...