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Intro to H2O Machine Learning in R at Santa Clara University

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Sri Ambati
Sri Ambati

Erin LeDell's presentation on Intro to H2O Machine Learning in R at SCU - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

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H2O.ai
 Machine Intelligence Intro to H2O Machine Learning in . Erin LeDell Ph.D. Santa Clara University January 2016
H2O.ai
 Machine Intelligence Introduction • Statistician & Machine Learning Scientist at H2O.ai in Mountain View, California, USA • Ph.D. in Biostatistics with Designated Emphasis in Computational Science and Engineering from 
 UC Berkeley (focus on Machine Learning) • Worked as a data scientist at several startups • Written a handful of machine learning R packages
H2O.ai
 Machine Intelligence • What/who is H2O.ai? • H2O Overview • H2O Machine Learning • H2O R package
H2O.ai
 Machine Intelligence H2O.ai H2O Company H2O Software • Team: 50. Founded in 2012, Mountain View, CA • Stanford Math & Systems Engineers • Open Source Software
 • Ease of Use via Web Interface • R, Python, Scala, Spark & Hadoop Interfaces • Distributed Algorithms Scale to Big Data
H2O.ai
 Machine Intelligence H2O.ai Founders SriSatish Ambati • CEO and Co-founder at H2O.ai • Past: Platfora, Cassandra, DataStax, Azul Systems, UC Berkeley • CTO and Co-founder at H2O.ai
 • Past: Azul Systems, Sun Microsystems • Developed the Java HotSpot Server Compiler at Sun • PhD in CS from Rice University Dr. Cliff Click
H2O.ai
 Machine Intelligence Scientific Advisory Council Dr. Trevor Hastie Dr. Rob Tibshirani Dr. Stephen Boyd • John A. Overdeck Professor of Mathematics, Stanford University • PhD in Statistics, Stanford University • Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining • Co-author with John Chambers, Statistical Models in S • Co-author, Generalized Additive Models • 108,404 citations (via Google Scholar) • Professor of Statistics and Health Research and Policy, Stanford University • PhD in Statistics, Stanford University • COPPS Presidents’ Award recipient • Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining • Author, Regression Shrinkage and Selection via the Lasso • Co-author, An Introduction to the Bootstrap • Professor of Electrical Engineering and Computer Science, Stanford University • PhD in Electrical Engineering and Computer Science, UC Berkeley • Co-author, Convex Optimization • Co-author, Linear Matrix Inequalities in System and Control Theory • Co-author, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
H2O.ai
 Machine Intelligence H2O Software H2O is an open source, distributed, Java machine learning library. APIs are available for: R, Python, Scala & JSON
H2O.ai
 Machine Intelligence http://h2o.ai/download/h2o/r
H2O.ai
 Machine Intelligence https://github.com/h2oai/h2o-3
H2O.ai
 Machine Intelligence Current Algorithm Overview Statistical Analysis • Linear Models (GLM) • Cox Proportional Hazards • Naïve Bayes Ensembles • Random Forest • Distributed Trees • Gradient Boosting Machine • R Package - Super Learner Ensembles Deep Neural Networks • Multi-layer Feed-Forward Neural Network • Auto-encoder • Anomaly Detection • Deep Features Clustering • K-Means Dimension Reduction • Principal Component Analysis • Generalized Low Rank Models Solvers & Optimization • Generalized ADMM Solver • L-BFGS (Quasi Newton Method) • Ordinary Least-Square Solver • Stochastic Gradient Descent Data Munging • Integrated R-Environment • Slice, Log Transform
H2O.ai
 Machine Intelligence Distributed Key Value Store H2O Frame H2O Distributed Computing • Multi-node cluster with shared memory model. • All computations in memory. • Each node sees only some rows of the data. • No limit on cluster size. • Objects in the H2O cluster such as data frames, models and results are all referenced by key. • Any node in the cluster can access any object in the cluster by key. • Distributed data frames (collection of vectors). • Columns are distributed (across nodes) arrays. • Each node must be able to see the entire dataset (achieved using HDFS, S3, or multiple copies of the data if it is a CSV file). H2O Cluster
H2O.ai
 Machine Intelligence H2O on Amazon EC2 H2O can easily be deployed on an Amazon EC2 cluster. The GitHub repository contains example scripts that 
 help to automate the cluster deployment.
H2O.ai
 Machine Intelligence Design “h2o” R package on CRAN • The only requirement to run the “h2o” R package is R >=3.1.0 and Java 7 or later. • Tested on many versions of Linux, OS X and Windows. • The easiest way to install the “h2o” R package is to install directly from CRAN. • Latest version: http://h2o.ai/download • No computation is ever performed in R. • All computations are performed (in highly optimized Java code) in the H2O cluster and initiated by REST calls from R. Requirements Installation
H2O.ai
 Machine Intelligence Start H2O Cluster from R
H2O.ai
 Machine Intelligence H2O in R: Load Data R code example: Load data
H2O.ai
 Machine Intelligence H2O in R: Train & Test R code example: Train and Test a GBM
H2O.ai
 Machine Intelligence H2O in R: Plotting plot(fit) plots scoring history over time.
H2O.ai
 Machine Intelligence H2O in R: Grid Search R code example: Execute a DL Grid Search
H2O.ai
 Machine Intelligence H2O R Demo https://gist.github.com/ledell Install H2O: http://h2o.ai/download/h2o/r Demo: h2o_higgs_simple_demo.R
H2O.ai
 Machine Intelligence H2O Ensemble Demo https://gist.github.com/ledell Install H2O Ensemble: install_h2oEnsemble.R Demo: lending_club_bad_loans_ensemble.R
H2O.ai
 Machine Intelligence Where to learn more? • H2O Online Training (free): http://learn.h2o.ai • H2O Slidedecks: http://www.slideshare.net/0xdata • H2O Video Presentations: https://www.youtube.com/user/0xdata • H2O Community Events & Meetups: http://h2o.ai/events • Machine Learning & Data Science courses: http://coursebuffet.com
H2O.ai
 Machine Intelligence H2O Booklets https://github.com/h2oai/h2o-3/tree/master/h2o-docs/src/ booklets/v2_2015/PDFs/online
H2O.ai
 Machine Intelligence Thank you! @ledell on Twitter, GitHub erin@h2o.ai http://www.stat.berkeley.edu/~ledell

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Intro to H2O Machine Learning in R at Santa Clara University

  • 1. H2O.ai
 Machine Intelligence Intro to H2O Machine Learning in . Erin LeDell Ph.D. Santa Clara University January 2016
  • 2. H2O.ai
 Machine Intelligence Introduction • Statistician & Machine Learning Scientist at H2O.ai in Mountain View, California, USA • Ph.D. in Biostatistics with Designated Emphasis in Computational Science and Engineering from 
 UC Berkeley (focus on Machine Learning) • Worked as a data scientist at several startups • Written a handful of machine learning R packages
  • 3. H2O.ai
 Machine Intelligence • What/who is H2O.ai? • H2O Overview • H2O Machine Learning • H2O R package
  • 4. H2O.ai
 Machine Intelligence H2O.ai H2O Company H2O Software • Team: 50. Founded in 2012, Mountain View, CA • Stanford Math & Systems Engineers • Open Source Software
 • Ease of Use via Web Interface • R, Python, Scala, Spark & Hadoop Interfaces • Distributed Algorithms Scale to Big Data
  • 5. H2O.ai
 Machine Intelligence H2O.ai Founders SriSatish Ambati • CEO and Co-founder at H2O.ai • Past: Platfora, Cassandra, DataStax, Azul Systems, UC Berkeley • CTO and Co-founder at H2O.ai
 • Past: Azul Systems, Sun Microsystems • Developed the Java HotSpot Server Compiler at Sun • PhD in CS from Rice University Dr. Cliff Click
  • 6. H2O.ai
 Machine Intelligence Scientific Advisory Council Dr. Trevor Hastie Dr. Rob Tibshirani Dr. Stephen Boyd • John A. Overdeck Professor of Mathematics, Stanford University • PhD in Statistics, Stanford University • Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining • Co-author with John Chambers, Statistical Models in S • Co-author, Generalized Additive Models • 108,404 citations (via Google Scholar) • Professor of Statistics and Health Research and Policy, Stanford University • PhD in Statistics, Stanford University • COPPS Presidents’ Award recipient • Co-author, The Elements of Statistical Learning: Prediction, Inference and Data Mining • Author, Regression Shrinkage and Selection via the Lasso • Co-author, An Introduction to the Bootstrap • Professor of Electrical Engineering and Computer Science, Stanford University • PhD in Electrical Engineering and Computer Science, UC Berkeley • Co-author, Convex Optimization • Co-author, Linear Matrix Inequalities in System and Control Theory • Co-author, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
  • 7. H2O.ai
 Machine Intelligence H2O Software H2O is an open source, distributed, Java machine learning library. APIs are available for: R, Python, Scala & JSON
  • 10. H2O.ai
 Machine Intelligence Current Algorithm Overview Statistical Analysis • Linear Models (GLM) • Cox Proportional Hazards • Naïve Bayes Ensembles • Random Forest • Distributed Trees • Gradient Boosting Machine • R Package - Super Learner Ensembles Deep Neural Networks • Multi-layer Feed-Forward Neural Network • Auto-encoder • Anomaly Detection • Deep Features Clustering • K-Means Dimension Reduction • Principal Component Analysis • Generalized Low Rank Models Solvers & Optimization • Generalized ADMM Solver • L-BFGS (Quasi Newton Method) • Ordinary Least-Square Solver • Stochastic Gradient Descent Data Munging • Integrated R-Environment • Slice, Log Transform
  • 11. H2O.ai
 Machine Intelligence Distributed Key Value Store H2O Frame H2O Distributed Computing • Multi-node cluster with shared memory model. • All computations in memory. • Each node sees only some rows of the data. • No limit on cluster size. • Objects in the H2O cluster such as data frames, models and results are all referenced by key. • Any node in the cluster can access any object in the cluster by key. • Distributed data frames (collection of vectors). • Columns are distributed (across nodes) arrays. • Each node must be able to see the entire dataset (achieved using HDFS, S3, or multiple copies of the data if it is a CSV file). H2O Cluster
  • 12. H2O.ai
 Machine Intelligence H2O on Amazon EC2 H2O can easily be deployed on an Amazon EC2 cluster. The GitHub repository contains example scripts that 
 help to automate the cluster deployment.
  • 13. H2O.ai
 Machine Intelligence Design “h2o” R package on CRAN • The only requirement to run the “h2o” R package is R >=3.1.0 and Java 7 or later. • Tested on many versions of Linux, OS X and Windows. • The easiest way to install the “h2o” R package is to install directly from CRAN. • Latest version: http://h2o.ai/download • No computation is ever performed in R. • All computations are performed (in highly optimized Java code) in the H2O cluster and initiated by REST calls from R. Requirements Installation
  • 15. H2O.ai
 Machine Intelligence H2O in R: Load Data R code example: Load data
  • 16. H2O.ai
 Machine Intelligence H2O in R: Train & Test R code example: Train and Test a GBM
  • 17. H2O.ai
 Machine Intelligence H2O in R: Plotting plot(fit) plots scoring history over time.
  • 18. H2O.ai
 Machine Intelligence H2O in R: Grid Search R code example: Execute a DL Grid Search
  • 19. H2O.ai
 Machine Intelligence H2O R Demo https://gist.github.com/ledell Install H2O: http://h2o.ai/download/h2o/r Demo: h2o_higgs_simple_demo.R
  • 20. H2O.ai
 Machine Intelligence H2O Ensemble Demo https://gist.github.com/ledell Install H2O Ensemble: install_h2oEnsemble.R Demo: lending_club_bad_loans_ensemble.R
  • 21. H2O.ai
 Machine Intelligence Where to learn more? • H2O Online Training (free): http://learn.h2o.ai • H2O Slidedecks: http://www.slideshare.net/0xdata • H2O Video Presentations: https://www.youtube.com/user/0xdata • H2O Community Events & Meetups: http://h2o.ai/events • Machine Learning & Data Science courses: http://coursebuffet.com
  • 23. H2O.ai
 Machine Intelligence Thank you! @ledell on Twitter, GitHub erin@h2o.ai http://www.stat.berkeley.edu/~ledell