Deep Learning In R: Documentation Drives Algorithms Posted on : Apr 09 - 2018

Hard to believe, but the R programming language has been with us since 1993.

A quarter century has now passed since the authors Gentleman and Ihaka originally conceived the R platform as an implementation of the S programming language.

Continuous global software development has taken the original concepts originally inspired by John Chambers’ Scheme in 1975 to now include parallel computing, bioinformatics, social science and more recently complex AI and deep learning methods. Layers have been built on top of layers and today’s R looks nothing like 1990’s R.

So where are we at, especially with the emerging opportunities for deep learning on the horizon?

Current state-of-the-art leadership for deep learning in R is provided by a whole slew of “bolt on” packages, algorithms and methods. Keras, H20.ai and mxNet are currently some of the more visible and well executed methods for integration with R. Each of these three round out an impressive array of other independent development sitting on top of the core R stack. Each are attempting to provide what we here at The Next Platform have been calling “Easy AI”. It is a full on popularity contest for sure. Thousands of results are returned from searching the Rdocumentation project for the keywords “Deep Learning” alone.

Some of the most commonly asked questions today include “Which package should I use for Deep Learning? What’s the best Deep Learning method?”. It feels like every data scientist has been given some sort of homework assignment by their leadership.  Each of them are desperately rooting about aimlessly in the dark recesses of the internet in a vain attempt to get a quick answer before their homework is due to teacher on Monday morning. It’s not right.

Our own research here at The Next Platform showed a number of the usual suspects bubble to the top of any list. But the challenge of answering which are the “best”? Well that is becoming an increasingly more difficult and potentially impossible question to answer. The very fact that there are over 14,500 total packages to choose from in CRAN, Github and Bioconductor tells you how complicated and fragmented development with R is.

For each of these packages, multiple versions also exist, couple that with the fact that a new version of the R core software is released every few months, scientific provenance and reproducibility here is the real challenge. Sure you can just stick it all in a container, but there’s way more to it than that.

The deep learning popularity contest continues, it’s not just how many times a package has been downloaded, but how efficient it is, can it exploit GPU or parallel learning, does it continue to give the same answer after an upgrade? These are the real questions to ask. The only way we can answer these questions effectively is we need to select methods that most efficiently and accurately document themselves. View More