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The Deep Learning Workshop

You're reading from   The Deep Learning Workshop Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839219856
Length 474 pages
Edition 1st Edition
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Authors (5):
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Nipun Sadvilkar Nipun Sadvilkar
Author Profile Icon Nipun Sadvilkar
Nipun Sadvilkar
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Mohan Kumar Silaparasetty Mohan Kumar Silaparasetty
Author Profile Icon Mohan Kumar Silaparasetty
Mohan Kumar Silaparasetty
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
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Toc

Table of Contents (9) Chapters Close

Preface
1. Building Blocks of Deep Learning 2. Neural Networks FREE CHAPTER 3. Image Classification with Convolutional Neural Networks (CNNs) 4. Deep Learning for Text – Embeddings 5. Deep Learning for Sequences 6. LSTMs, GRUs, and Advanced RNNs 7. Generative Adversarial Networks Appendix

Data Augmentation

In the previous section, you were introduced to data generators that can do a lot of the heavy lifting, such as feeding the model from folders rather than columnar data for you regarding data processing for neural networks. So far, we have seen how to create them, load data from a structured folder, and feed the model by batch. We only performed one image transformation with it: rescaling. However, data generators can perform many more image transformations.

But why do we need to perform data augmentation? The answer is quite simple: to prevent overfitting. By performing data augmentation, we are increasing the number of images in a dataset. For one image, we can generate, for instance, 10 different variants of the same image. So, the size of your dataset will be multiplied by 10.

Also, with data augmentation, we have a set of images with a broader range of visuals. For example, selfie pictures can be taken from different angles, but if your dataset only contains...

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