© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2021
D. PaperState-of-the-Art Deep Learning Models in TensorFlowhttps://doi.org/10.1007/978-1-4842-7341-8_2

2. Increase the Diversity of Your Dataset with Data Augmentation

David Paper1  
(1)
Logan, UT, USA
 

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.

Notebooks for chapters are located at the following URL:

https://github.com/paperd/deep-learning-models

Data Augmentation

More data typically increases model performance. So what do we do if we ...

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