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R Deep Learning Projects

You're reading from   R Deep Learning Projects Master the techniques to design and develop neural network models in R

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788478403
Length 258 pages
Edition 1st Edition
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Authors (2):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
Pablo Maldonado Pablo Maldonado
Author Profile Icon Pablo Maldonado
Pablo Maldonado
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Toc

To get the most out of this book

You should be comfortable with R and RStudio and have some knowledge of college-level mathematics (calculus and linear algebra). Working knowledge of basic machine learning algorithms for classification, regression problems, and clustering might be helpful, but it is not strictly required.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packtpub.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/R-Deep-Learning-Projects. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

 

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Similarly, for the second sample, (a "0"), we run:"

A block of code is set as follows:

> for (i in 1:16) {
+ outputData <- as.array
(executor$ref.outputs$activation15_output)[,,i,1]
+ image(outputData, xaxt='n', yaxt='n',
col=grey.colors(255)
+ )
+ }

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

library(R6)
logit <- function(x){
1/(1+exp(-x))
}

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Once logged in, click on Create New App."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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