learNN package
The learNN package makes it easy to become familiar with the way neural networks work by demonstrating the key concepts using straightforward code examples. The package is based on previous post on this blog.
Andrew Trask wrote an amazing post at I am Trask called:
In the post Hand Coding a Neural Network I’ve translated the Python code into R.
In a follow up post called:
Andrew shows how to improve the network with optimisation through gradient descent.
Installation
The package can now be installed from CRAN using:
install.packages('learNN') # case sensitive!
Usage
After installation, the package can be loaded using:
library(learNN)
For information on using the package, please refer to the help files.
help('learnn')
help(package = 'learNN')
For examples of usage, see the function-specific help pages.
help('learn_bp') # learn Back Propagation
help('learn_gd') # learn Gradient Descent
help('learn_do') # learn DropOut
Additional Information
An overview of the changes is available in the NEWS file.
news(package='wiod')
## Changes in version 0.2.0:
##
## o add WIOD data
Development
A development version can be installed at your own peril, using:
if (!require('devtools')) install.packages('devtools')
devtools::install_github("bquast/learNN")
Development takes place on the GitHub page, bugs can be filed on the Issues page.