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R with Parallel Computing from User Perspectives

September 10, 2016
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R with Parallel Computing from User Perspectives

Share This: This article is originally published in Capital of Statistic by Chinese and I would like to thank He Tong for lots of great suggestions. All code in this post can be found on GitHub . Data scientists are already very familiar with statistical software like R, SAS, SPSS, MATLAB; however, some of them are relatively

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R for Deep Learning (III): CUDA and MultiGPUs Acceleration

May 8, 2016
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R for Deep Learning (III): CUDA and MultiGPUs Acceleration

Notes: 1. The entire source code of this post in here 2. The PDF version of this post in here In previous two blogs (here and here), we illustrated several skills to build and optimize artificial neural network (ANN) with R and speed up by parallel BLAS libraries in modern hardware platform including Intel Xeon

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R for Deep Learning (II): Achieve High-Performance DNN with Parallel Acceleration

February 20, 2016
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R for Deep Learning (II): Achieve High-Performance DNN with Parallel Acceleration

I would like to thank  Jun Ma  and all other technical reviewers and readers for their informative comments and suggestions in this post. 50 years of Data Science, David Donoho, 2015 Computing with Data.Every data scientist should know and use several languages for data analysis and data processing. These can include popular languages like R

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R for Deep Learning (I): Build Fully Connected Neural Network from Scratch

February 13, 2016
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R for Deep Learning (I):  Build Fully Connected Neural Network from Scratch

I would like to thank Feiwen, Neil  and all other technical reviewers and readers for their informative comments and suggestions in this post. Backgrounds Deep Neural Network (DNN) has made a great progress in recent years in image recognition, natural language processing and automatic driving fields, such as Picture.1 shown from 2012  to 2015 DNN improved

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The R Parallel Programming Blog

February 2, 2016
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The R Parallel Programming Blog

Today, parallel computing truly is a mainstream technology. But, stock R is still a single-thread and main memory (RAM) limited software, which really restricts its usage and efficiency against the challenges from very complex model architectures, dynamically configurable analytics models and big data input with billions of parameters and samples. Therefore, ParallelR dedicated on accelerate

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