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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
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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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Building neural networks from scratch in R

Although we have already used some neural network algorithms, it's time to dig a bit deeper into how they work. This section demonstrates how to code a neural network from scratch. It might surprise you to see that the core code for a neural network can be written in fewer than 80 lines! The code for this chapter does just that using an interactive web application written in R. It should give you more of an intuitive understanding of neural networks. First we will look at the web application, then we will delve more deeply into the code for the neural network.

Neural network web application

First, we will look at an R Shiny web application. I encourage you to run the application...

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R Deep Learning Essentials. - Second Edition
Published in: Aug 2018Publisher: PacktISBN-13: 9781788992893

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