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

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781788992893

Length 378 pages

Edition 2nd Edition

Languages

Tools

H2O

Concepts

Deep Learning

Authors (2):

Joshua F. Wiley

Mark Hodnett

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Table of Contents (13) Chapters

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

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Using Paperspace for deep learning

Paperspace is another interesting way to perform deep learning in the cloud. It might be the easiest way to train deep learning models in the cloud. To set up a cloud instance with Paperspace, you can log in to their console, provision a new machine, and connect to it from your web browser:

Start by signing up for a Paperspace account, log in to the console, and go into the Virtual Machine section by selecting Core or Compute. Paperspace has an RStudio TensorFlow template with NVIDIA GPU libraries (CUDA 8.0 and cuDNN 6.0) already installed, along with the GPU version of TensorFlow and Keras for R. You will see this machine type when you select Public Templates, as shown in the following screenshot:

Figure 10.32: Paperspace portal

You will be given a choice of three GPU instances and the choice of pay by the hour or monthly. Select the cheapest...

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Authors (2)

Hodnett

Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.

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Wiley

Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.

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