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

Augmentation categories

It is advantageous to group tabular augmentation into categories. The following concepts are new and particular to the DeltaPy library. The augmentation functions are grouped into the following categories:

  • Transforming techniques can be applied for cross-section and time series data. Transforming techniques in tabular augmentation are used to modify existing rows or columns to create new, synthetic data representative of the original data. These methods can include the following:
    • Scaling: Increasing or decreasing a column value to expand the diversity of values in a dataset
    • Binning: Combining two or more columns into a single bucket to create new features
    • Categorical encoding: Using a numerical representation of categorical data
    • Smoothing: Compensating for unusually high or low values in a dataset
    • Outlier detection and removal: Detecting and removing points farther from the norm
    • Correlation-based augmentation: Adding new features based on correlations between...