I am a data scientist with interests in dimension reduction, computational linear algebra, and high performance computing. I am passionate about open source software and programming. I spend a lot of time thinking about text mining, machine learning, distributed systems, and profiling. We do stuff with R on supercomputers, like fitting a linear model with 50,000 cores (it's pretty neat).
http://wrathematics.github.io/
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Drew Schmidt | wrathematics.github.io Reviews
https://wrathematics.github.io
I am a data scientist with interests in dimension reduction, computational linear algebra, and high performance computing. I am passionate about open source software and programming. I spend a lot of time thinking about text mining, machine learning, distributed systems, and profiling. We do stuff with R on supercomputers, like fitting a linear model with 50,000 cores (it's pretty neat).
wrathematics.github.io
wrathematics
http://wrathematics.github.io/index.html
My stack runneth over. Comparing Symmetric Eigenvalue Performance. Password Input with getPass. Perform co-operations with the coop package. Controlling a Remote R Session from a Local One. Text Analytics Webapp Webinar. Floating Point Arithmetic Is Hilarious. Fibonacci series by goto in fortran 77. Cache Rules Everything Around Me. One of My Favorite R Oddities: dump(). A "did you mean? Comparing Symmetric Eigenvalue Performance. Password Input with getPass. Perform co-operations with the coop package.
wrathematics
http://wrathematics.github.io/code.html
My stack runneth over. Programming with Big Data in R (pbdR). I am currently the co-lead developer of the pbdR ecosystem of packages. These are a collection of utilities and services for leveraging R in HPC environments. For more information, see the main project website http:/ r-pbd.org/. System Hardware Info for R and Analytics (SHINRA). SHINRA is a set of tools for benchmarkers working with R. For more information about SHINRA, see https:/ shinra-dev.github.io/. A collection of utilities that make dea...
wrathematics
http://wrathematics.github.io/presentations.html
My stack runneth over. An Introduction to Programming in R. Half-day tutorial in service to the Business Analytics and Statistics Program, University of Tennessee, Knoxville, TN, November 13, 2015. Scaling R Computation for Big Data Processing with XSEDE Resources. Full-day tutorial at XSEDE15. St Louis, MO, July 27, 2015. A Beginner’s Guide to Programming in R. Half-day tutorial in service to the CURRENT project’s DAnCE curriculum, University of Tennessee, Knoxville, TN, June 9, 2015. pdfbutton. Half-da...
wrathematics
http://wrathematics.github.io/publications.html
My stack runneth over. Drew Schmidt, Wei-Chen Chen, George Ostrouchov. Introducing a New Client/Server Framework for Big Data Analytics with the R Language. XSEDE16, Miami, Florida, July 2016. pdfbutton. Pragnesh Patel, Drew Schmidt, Wei-Chen Chen, George Ostrouchov. High-Level Analytics with R and pbdR on Cray Systems. CUG 2014, Lugano, Switzerland, January, 2014. George Ostrouchov, Drew Schmidt, Wei-Chen Chen, and Pragnesh Patel. Combining R with scalable libraries to get the best of both for big data.
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From a (set.)seed grows a mighty dataset – Irregularly Scheduled Programming
https://jcarroll.com.au/2016/05/30/seed
From a (set.)seed grows a mighty dataset Irregularly Scheduled Programming. From a (set.)seed grows a mighty dataset. May 30, 2016. Can you predict the output from this code? PrintStr - function(str) paste(str, collapse=) set.seed(12173423); x - sample(LETTERS, 5, replace=TRUE) set.seed(7723132); y - sample(LETTERS, 5, replace=TRUE) paste(printStr(x), printStr(y). Function then sample the pool of. What are the odds of that! Of all the possible letters we could have sampled, we get that! Which is a lot, b...
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Drew Schmidt
I am a data scientist with interests in dimension reduction, computational linear algebra, and high performance computing. I am passionate about open source software and programming. I spend a lot of time thinking about text mining, machine learning, distributed systems, and profiling. We do stuff with R on supercomputers, like fitting a linear model with 50,000 cores (it's pretty neat).
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