**0xdata Blog**, and kindly contributed to R-bloggers)

From my perspective the most important event that happened at

useR! 2014 was that I got to meet

the 0xdata team and now, long story short,

here I am introducing the latest version of H_{2}O, labeled

Lagrange (2.6.0.11),

to the R and greater data science communities. Before

joining 0xdata, I was working at a competitor on a rival project and was

repeatedly asked why my generalized linear model analytic didn’t run as fast as

H_{2}O’s GLM. The answer then as it is now is the same — because

H_{2}O has a cutting edge distributed in-memory parallel computing

architecture — but I no longer receive an electric shock every time I say so.

For those hearing about H_{2}O for the first time, it is an open-source

distributed in-memory data analysis tool designed for extremely large data sets

and the H_{2}O Lagrange (2.6.0.11) release provides scalable solutions

for the following

analysis techniques:

- Generalized Linear Model
- K-Means
- Random Forest
- Principal Components Analysis
- Summary
- Gradient Boosted Regression and Classification
- Naive Bayes
- Deep Learning

In my first blog post at 0xdata, I wanted to keep it simple and make sure R

users know how to get the `h2o`

package, which is cross-referenced on the

High-Performance and Parallel Computing

and

Machine and Statistical Learning

CRAN Task Views, up and running on their

computers. To so do, open an R console of your choice and type

```
# Download, install, and initialize the H2O package
install.packages("h2o",
repos = c("http://h2o-release.s3.amazonaws.com/h2o/rel-lagrange/11/R", getOption("repos")))
library(h2o)
localH2O <- h2o.init()
# List and run some demos to see H2O at work
demo(package = "h2o")
demo(h2o.glm)
demo(h2o.deeplearning)
```

After you are done experimenting with the demos in R, you can open up a web

browser to http://localhost:54321/ to give the H_{2}O web interface a

once over and then hop over to

0xdata’s YouTube channel for some

in-depth talks.

Over the coming weeks we at 0xdata will continue to

blog about how to use H_{2}O

through R and other interfaces. If there is a particular use case you would like

to see addressed, join our

h2ostream Google Groups

conversation or e-mail us at support@0xdata.com. Until then, happy analyzing.

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