RevoPemaR: The Definitive Guide

RevoPemaR: The Definitive Guide

Code Efficient Parallel External Memory Algorithms

About the Book

Get started in your journey to writing scalable, distributable, and efficient Parallel External Memory Algorithms using RevoPemaR framework from Revolution Analytics. Unleash statistical analysis unbounded by memory constraints.

This book targets advanced R users with a minimum of 6 months of experience in Statistics. You should have worked with large datasets of size 8 GB or more to appreciate the importance of the material presented.

About the Author

Naimish Agarwal
Naimish Agarwal

I'm currently a 3rd year student in Indian Institute of Information Technology, Allahabad, India pursuing a 5 year course in Integrated Masters of Technology in Information Technology with Specialization in Robotics from 2013-2017. I'm quite passionate about Data Science and Machine Learning especially Neural Networks and Deep Learning.

Table of Contents

  • Preface
  • Software Requirements
  • Audience
  • About the Author
  • Introduction
    • What are some of the challenges in modern day statistical data analysis?
    • What are External Memory Algorithms (EMAs)?
    • What are Parallel External Memory Algorithms (PEMAs)?
    • What is RevoPemaR?
    • When should I use RevoPemaR?
    • What makes RevoPemaR an awesome framework for coding PEMAs?
  • RevoPemaR
    • Reference Classes
    • PemaBaseClass
    • Useful functions in RevoPemaR
  • Demo Scripts
    • PemaMean
    • PemaMeanEx
    • PemaPopularWords
    • PemaPopularWordsEx
    • PemaWordCount
    • PemaWordCountEx
    • PemaGradDescent
    • PemaLogitGD
    • PemaLogitGDEx
    • PemaByGroup
    • FineFoodsImport
  • What should I do next?

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