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You're reading from  Learning Bayesian Models with R

Product typeBook
Published inOct 2015
Reading LevelBeginner
PublisherPackt
ISBN-139781783987603
Edition1st Edition
Languages
R
Concepts
Machine Learning
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Author (1)
Hari Manassery Koduvely
Hari Manassery Koduvely
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Hari Manassery Koduvely

Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
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Exercises

  1. For the Reuter_50_50 dataset, fit the LDA model using the lda.collapsed.gibbs.sampler function in the lda package and compare performance with that of the topicmodels package. Note that you need to convert the document-term matrix to lda format using the dtm2ldaformat( ) function in the topicmodels package in order to use the lda package.

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Learning Bayesian Models with R
Published in: Oct 2015Publisher: PacktISBN-13: 9781783987603
© 2015 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Hari Manassery Koduvely

Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Read more about Hari Manassery Koduvely

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