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R Deep Learning Essentials

You're reading from   R Deep Learning Essentials A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Length 378 pages
Edition 2nd Edition
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Authors (2):
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Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. Training a Prediction Model 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Other Books You May Enjoy

Data augmentation

One approach to increasing the accuracy in a model regardless of the amount of data you have is to create artificial examples based on existing data. This is called data augmentation. Data augmentation can also be used at test time to improve prediction accuracy.

Using data augmentation to increase the training data

We are going to apply data augmentation to the MNIST dataset that we used in previous chapters. The code for this section is in Chapter6/explore.Rmd if you want to follow along. In Chapter 5, Image Classification Using Convolutional Neural Networks, we plotted some examples from the MNIST data, so we won't repeat the code again. It is included in the code file, and you can also refer back...

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