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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2018/08/videos-from-nyc-r-conference.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>The <a rel="nofollow" href="https://www.rstats.nyc/2018/" target="_blank">videos from the NYC R conference</a> have been published, and there are <em>so</em> many great talks there to explore. I highly recommend checking them out: you'll find a wealth of interesting R applications, informative deep dives on using R (and a few other applications as well), and some very entertaining deliveries. In this post, I wanted to highlight a couple of talks in particular. </p>
<p>The talk by Jonathan Hersh (Chapman University), <a rel="nofollow" href="https://www.youtube.com/watch?v=pLqL7qhi6pw" target="_blank">Applying Deep Learning to Satellite Images to Estimate Violence in Syria and Poverty in Mexico</a> is both fascinating and a real technical achievement. In the first part of the talk, he uses convolutional neural networks to identify buildings, roads, and vegetation in satellite images, and use that to predict poverty rates across all of Mexico. In the second part, he uses a similar technique to identify violent hotspots in Syria, this time by identifying damaged buildings and roads from the satellite images. In both cases, analyzing satellite images is a lot less expensive (and more consistent) than collecting samples at the ground level. The neural networks were implemented in <a rel="nofollow" href="http://deeplearning.net/software/theano/" target="_blank">Theano</a> running on a <a rel="nofollow" href="https://docs.microsoft.com/azure/virtual-machines/linux/sizes-gpu?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">GPU-enabled Microsoft Azure server</a>.</p>
<p><iframe allowfullscreen="" src="https://www.youtube.com/embed/pLqL7qhi6pw" width="450" height="315"></iframe></p>
<p>Moving from applications to platforms, my colleague Marck Vaisman gave a great talk on R for Big Data in the cloud. His talk offers some practical considerations related to working with large data sets in R, and details some cloud technologies for big data that you can connect with R, like Spark and sparklyr (which is easy to use in the cloud with <a rel="nofollow" href="https://docs.microsoft.com/azure/azure-databricks/connect-databricks-excel-python-r?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Azure Databricks</a>). </p>
<p><iframe allowfullscreen="" src="https://www.youtube.com/embed/pWX_Hxrbng4" width="450" height="283"></iframe></p>
<p>My talk on <a rel="nofollow" href="http://blog.revolutionanalytics.com/2018/04/minecraft-robot-in-r.html" target="_blank">R and Minecraft</a> is there as well, if you want to <a rel="nofollow" href="https://www.youtube.com/watch?v=r_JgPF8MJpY" target="_blank">check it out</a>. That talk and all of the rest from the conference are available for viewing at the link below.</p>
<p>New York R Conference: <a rel="nofollow" href="https://www.rstats.nyc/2018/" target="_blank">2018</a> </p>
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