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


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


access token 31

additive Chi2 kernel 358

Albumentations 81

reference link 81

Amazon reviews dataset 53-57

American Multi-Cinema (AMC) 7

American National Standards Institute (ANSI) 153

amplitude 236

Antares Auto-Tune Pro 237


artificial neural network (ANN) algorithm 4

ASCII value 237

audio augmentation 235

libraries 243

audio control clip 248

audio definition 7

audio filters 240

Audiomentations 243, 244

reference link 243

audio Spectrogram 294-297

audio Waveform graph

listening 249-252

viewing 249-252

Augly 81, 155, 243

reference link 81

autoencoders 358

autoregressive integrated moving average (ARIMA) 317

Autotune 237


background noise 240

background noise injection method 273