Selasa, 14 Juni 2016

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R High Performance Programming What You Will LearnBenchmark and profile R programs to solve performance bottlenecksUnderstand how CPU, memory, and disk input/output constraints can limit the performance of R progra

R High Performance Programming


Title:R High Performance Programming
Author:Aloysius Lim
Rating:4.97 (819 Votes)
Id Book:1783989262
Format Type:Paperback
Number of Pages:180 Pages
Publish Date:2015-01-30
Type File:PDF, DOC, RTF, ePub


Overcome performance difficulties in R with a range of exciting techniques and solutionsAbout This Book
  • Benchmark and profile R programs to solve performance bottlenecks
  • Combine the ease of use and flexibility of R with the power of big data tools
  • Filled with practical techniques and useful code examples to process large data sets more efficiently
Who This Book Is ForThis book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.What You Will Learn
  • Benchmark and profile R programs to solve performance bottlenecks
  • Understand how CPU, memory, and disk input/output constraints can limit the performance of R programs
  • Optimize R code to run faster and use less memory
  • Use compiled code in R and other languages such as C to speed up computations
  • Harness the power of GPUs for comp

Aloysius Lim

Aloysius Lim has a knack for translating complex data and models into easy-to-understand insights. As cofounder of About People, a data science and design consultancy, he loves solving problems and helping others to find practical solutions to business challenges using data. His breadth of experience7 years in the government, education, and retail industriesequips him with unique perspectives to find creative solutions.



William Tjhi

William Tjhi is a data scientist with years of experience working in academia, government, and industry. He began his data science journey as a PhD candidate researching new algorithms to improve the robustness of high-dimensional data clustering. Upon receiving his doctorate, he moved from basic to applied research, solving problems among others in molecular biology and epidemiology using machine learning. He published some of his research in peer-reviewed journals and conferences. With the rise o

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