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You're reading from  R Deep Learning Essentials. - Second Edition

Product typeBook
Published inAug 2018
Reading LevelIntermediate
PublisherPackt
ISBN-139781788992893
Edition2nd Edition
Languages
R
Tools
H2O
Concepts
Deep Learning
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Authors (2):
Mark Hodnett
Mark Hodnett
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Mark 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.
Read more about Mark Hodnett

Joshua F. Wiley
Joshua F. Wiley
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Joshua F. 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.
Read more about Joshua F. Wiley

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Neural networks in R

We will build several neural networks in this section. First, we will use the neuralnet package to create a neural network model that we can visualize. We will also use the nnet and RSNNS (Bergmeir, C., and Benítez, J. M. (2012)) packages. These are standard R packages and can be installed by the install.packages command or from the packages pane in RStudio. Although it is possible to use the nnet package directly, we are going to use it through the caret package, which is short for Classification and Regression Training. The caret package provides a standardized interface to work with many machine learning (ML) models in R, and also has some useful features for validation and performance assessment that we will use in this chapter and the next.

For our first examples of building neural networks, we will use the MNIST dataset, which is a classic classification...

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R Deep Learning Essentials. - Second Edition
Published in: Aug 2018Publisher: PacktISBN-13: 9781788992893
© 2018 Packt Publishing Limited All Rights Reserved

Authors (2)

author image
Mark 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.
Read more about Mark Hodnett

author image
Joshua F. 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.
Read more about Joshua F. Wiley

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