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

Extraction augmentation

The extraction method is a process in time series analysis where multiple constructed elements are used as input, and a singular value is extracted from each time series to create new augmented data. This method uses a package called TSfresh and includes default and custom features. The output of extraction methods differs from the output of transformation and interaction methods, as the latter outputs entirely new time series data. You can use this method when specific values need to be pulled from time series data.

The DeltaPy library contains 34 extraction methods. Writing the wrapper functions for extraction is similar to the wrapper transformation functions. The difficulty is how to discern the forecasting’s effectiveness from tabular augmentation. Furthermore, these methods are components and not complete functions for tabular augmentation.

Pluto will not explain each function, but here is a list of the extraction functions in the DeltaPy...