The case for R, for AI developers

April 12, 2018

[This article was first published on Revolutions, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I had a great time this week at the conference in San Francisco, where I had the pleasure of presenting to an audience of mostly Java and Python developers. It's unfortunate that videos won't be available for a while, because there were some amazing presentations: those by Matt Ranney, Mike Williams and Rachel Thomas were particular standouts.

My goal for the presentation I gave was to encourage developers to take a look at R (and its community) for developing AI applications, and in particular to bring a statistical perspective to data, inference and prediction as used by AI applications:

I also delivered a workshop on using R to interface with a couple of the Cognitive Services vision APIs, to generate captions from random images in Wikipedia, and to train a custom image recognizer with images of hotdogs. The workshop is hosted as a Jupyter Notebook, so it's easy to try out yourself — all you need is a browser. You can find all the files and instructions at the link below.

Azure Notebooks: AI for R users 

To leave a comment for the author, please follow the link and comment on their blog: Revolutions. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)