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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

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781788992893

Length 378 pages

Edition 2nd Edition

Languages

R

Tools

H2O

Concepts

Deep Learning

Authors (2):

Hodnett

Wiley

View More author details

Table of Contents (17) Chapters

Title Page

Packt Upsell

Contributors

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

1. Other Books You May Enjoy

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Index

Deploying TensorFlow models

Historically, one of the perceived disadvantages of using R for data science projects was the difficulty in deploying machine learning models built in R. This often meant that companies used R mainly as a prototyping tool to build models which were then rewritten in another language, such as Java and .NET. It is also one of the main reasons cited for companies switching to Python for data science as Python has more glue code, which allows it to interface with other programming languages.

Thankfully, this is changing. One interesting new product from RStudio, called RStudio Connect, allows companies to create a platform for sharing R-Shiny applications, reports in R Markdown, dashboards, and models. This allows companies to serve machine learning models using a REST interface.

The TensorFlow (and Keras) models we have created in this book can be deployed without any runtime dependency on either R or Python. One way of doing this is TensorFlow Serving, which is an...

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Authors (2)

Hodnett

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

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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