In this book, we will use the Keras R package that uses TensorFlow as a backend for building deep learning networks. An output from a typical R session, used for the examples illustrated in this book, providing various version-related information, is provided in the following code:

# Information from a Keras R session
sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

Random number generation:
 RNG: Mersenne-Twister 
 Normal: Inversion 
 Sample: Rounding 
 
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] keras_2.2.4.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2 lattice_0.20-38 lubridate_1.7.4 zeallot_0.1.0 
 [5] grid_3.6.0 R6_2.4.0 jsonlite_1.6 magrittr_1.5 
 [9] tfruns_1.4 stringi_1.4.3 whisker_0.4 Matrix_1.2-17 
[13] reticulate_1.13 generics_0.0.2 tools_3.6.0 stringr_1.4.0 
[17] compiler_3.6.0 base64enc_0.1-3 tensorflow_1.14.0

As seen previously, for this book we have used the 3.6 version of R that was released in April 2019. The nickname for this R version is Planting of a Tree. The version used for the Keras package is 2.2.4.1. In addition, all the application examples illustrated in the book have been run on a Mac computer with 8 GB of RAM. The main reason for using this specification is that it will allow a reader to go through all the examples without needing advanced computing resources to get started with any deep learning network covered in the book.

In the next section, we will go over the process of developing a deep network model that is broken down into five general steps.