R Deep Learning Projects
This is the code repository for R Deep Learning Projects, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
About the Book
In this book, we have tried to make a case for using deep learning within R. Most deep learning power-users would shun R and go for other languages, such as Python. We, however, believe that R has a solid ecosystem of packages, and visualization and manipulation tools, that can be combined with deep learning libraries to create interesting projects. R also has a huge base of users without a software engineering background (perhaps you are one of them?), and those users are increasingly interested in applications that deep learning is making possible, but they do not have time to learn Python.
Instructions and Navigation
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
All chapters have codes. Support data files are in the data folder. The large data files can be downloaded via the links provided in the chapters.
The code will look like the following:
> 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)
+ )
+ }
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.