Founded by Josh Reich and Drew Conway, the New York Open Statistical Programming Meetup started as the New York R Meetup with a handful of people in an office at Union Square Ventures. Since then it has grown to over 11,000 members and has been hosted at NYU, Columbia, AOL, iHeartRadio, eBay, Work-Bench and other locations.
Our mission is to spread knowledge of statistical programming techniques in open-source languages such as R, Python, Julia and Go, and data science in general. Another important aspect is community building and socializing. The meetups start with pizza, followed by a 45-90 minute talk, ending with a trip to the local bar.
To attend please visit the meetup page.
Whenever possible we make presentations available at the Presentations page.
We now stream and host videos of meetups on Facebook and YouTube and older videos are scattered on a variety of services. They are also listed on the Presentations page.
Job openings and other announcements are on the meetup discussion board.
The week after rstudio::global(2021) we have a talk about the latest deep learning framework in R, {torch}.
Thank you to EcoHealth Alliance for providing the Zoom link.
Conversation during the meetup are encouraged in the Zoom chat and on the nyhackr slack.
About the Talk:
In this talk we are going to discuss the implementation of torch, introduce its main components and show some exciting new features that we plan to implement in 2021. We will also discuss the ecosystem that we want to build around the torch project.
About Daniel:
Daniel is a software engineer at RStudio and co-author of the torch package. He is also the maintainer of the TensorFlow for R project.
The talk will begin at 7 PM EST and we will start admitting people to the event shortly before. Since this is completely remote there will be no pizza but everyone is encouraged to have pizza individually.
The nyhackr website was built as a RMarkdown website and the source code can accessed by the community on GitHub.
If you wish to contribute to the website the process is pretty simple.
rmarkdown::render_site()