Increase the Diversity of Your Dataset with Data Augmentation

Authors :: David Paper
Source :: State-of-the-Art Deep Learning Models in TensorFlow ISBN: 9781484273401
Publication Year :: 2021
Publisher :: Apress, 2021.
Abstract: We guide you in the creation of augmented data experiments to increase the diversity of a training set by applying random (but realistic) transformations. Data augmentation is very useful for small datasets because deep learning models crave a lot of data to perform well.

Subjects :: Training set
business.industry
Computer science
Deep learning
Artificial intelligence
business
Machine learning
computer.software_genre
computer
Diversity (business)

ISBN :: 978-1-4842-7340-1
ISBNs :: 9781484273401
Database :: OpenAIRE
Journal :: State-of-the-Art Deep Learning Models in TensorFlow ISBN: 9781484273401
Accession number :: edsair.doi...........de7efae3b99babe8118c2d5c62f145f4
Full Text :: https://doi.org/10.1007/978-1-4842-7341-8_2