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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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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

Running Deep Learning Models in the Cloud

Up till now, we have only briefly discussed the hardware requirements for training deep learning models, as almost all of the examples in this book run on any modern computer. While you do not need a GPU (Graphical Processing Unit) based computer to run the examples in this book, there is no getting away from the fact that training complicated deep learning models requires a computer with a GPU. Even if you have a suitable GPU on your machine, installing the necessary software to train deep learning models using GPUs is not a trivial task. This section will briefly discuss how to install the necessary software to run deep learning models on GPUs and also discusses the advantages and disadvantages of using cloud computing for deep learning. We will use various cloud providers to create virtual instances or access services that will allow...

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