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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
Languages
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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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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. Training a Prediction Model FREE CHAPTER 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

Summary

We have covered a lot of options for training deep learning models in this chapter! We discussed options for running it locally and showed the importance of having a GPU card. We used the three main cloud providers to train deep learning models in R on the cloud. Cloud computing is a fantastic resource we gave an example of a super-computer costing $149,000. A few years ago, such a resource would have been out of reach for practically everyone, but now thanks to cloud computing, you can rent a machine like this on an hourly basis.

For AWS, Azure, and Paperspace, we installed MXNet on the cloud resources, giving us the option of which deep learning library to use. I encourage you to use the examples in the other chapters in this book and try all the different cloud providers here. It is amazing to think that you could do so and your total cost could be less than...

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