# Distributed GLM

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## Event Information

Event: [Atlanta Big Data Science](http://www.meetup.com/Atlanta-Big-Data/events/219651141/)

Date: January 27, 2015

Place: Atlanta, GA @ Polygon

Speakers: Tom Kraljevic

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## Content

* [Slides PDF](DistributedGLM_TK_2015_01_27_ATL.pdf)
* [Slides PPT](DistributedGLM_TK_2015_01_27_ATL.pptx)

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## Q & A

There will be time for questions at the event.

After the event concludes, please send questions to <h2ostream@googlegroups.com>.

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## References

#### Books

* [Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) ([PDF](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf))
* [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) ([PDF](http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf))

#### Journal articles

* [Zou, H and Hastie, T. (2005) Regularization and variable selection via the elastic net](<http://web.stanford.edu/~hastie/Papers/B67.2%20(2005)%20301-320%20Zou%20&%20Hastie.pdf>)

* [Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent](http://www.stanford.edu/~hastie/Papers/glmnet.pdf)

* [Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato and Jonathan Eckstein. (2011) Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers](http://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf)

* [Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2012) Strong Rules for Discarding Predictors in Lasso-type Problems, JRSSB, vol 74](http://www-stat.stanford.edu/~tibs/ftp/strong.pdf)


#### Software Documentation

* [H2O Mirzakhani Release Documentation](http://h2o-release.s3.amazonaws.com/h2o/rel-mirzakhani/2/docs-website/index.html)
* [H2O Software Architecture](http://h2o-release.s3.amazonaws.com/h2o/rel-mirzakhani/2/docs-website/developuser/h2o_sw_arch.html)
* [H2O GLM Documentation](http://h2o-release.s3.amazonaws.com/h2o/rel-mirzakhani/2/docs-website/datascience/glm.html) (Contains many references)
* [H2O GLM Vignette](https://github.com/h2oai/h2o/blob/master/docs/glm/GLM_Vignette.pdf) (Contains many references)

#### Software Downloads

(H2O latest version at the date of the talk.  All demos from this talk will use this version of H2O.)

* [H2O Mirzakhani Release](http://h2o-release.s3.amazonaws.com/h2o/rel-mirzakhani/2/index.html)
* [H2O package for R (Mirzakhani Release)](http://h2o-release.s3.amazonaws.com/h2o/rel-mirzakhani/2/index.html#R)

(H2O older versions)

* [H2O package for R (Markov Release in CRAN, which is a release behind on the date of the talk)](http://cran.r-project.org/web/packages/h2o/index.html)

(Other)

* [glmnet package for R](http://cran.r-project.org/web/packages/glmnet/index.html) (Contains many references)

#### Talks

* [Nykodym, T. (2013) Distributed GLM Implementation](../2013_06_13_GLM/glm_talk2.pdf)
* [Kraljevic, T. (2014) H2O World 2014: H2O in Big Data Environments](https://github.com/h2oai/h2o-training/blob/master/tutorials/bigdataenv/H2OinBigDataEnvironments.pdf)
* [Nykodym, T. and Maj, P. (2014) GOTO Berlin: Fast Analytics on Big Data](../2014_11_06_GOTO_Berlin/PetrMaj_and_TomasNykodym_FastAnalyticsOnBigData.pdf)

#### Web

* [Trevor Hastie's Stanford Page](http://web.stanford.edu/~hastie/)
* [Rob Tibshirani's Stanford Page](http://statweb.stanford.edu/~tibs/)
* [Stephen Boyd's Stanford Page](http://stanford.edu/~boyd) and [details about ADMM](http://stanford.edu/~boyd/admm.html)
* [Wikipedia GLM](http://en.wikipedia.org/wiki/Generalized_linear_model)