Chapter 1. Installation and Quick-Start
You will be happy to know that H2O is very easy to install. First I will show how to install it with R, using CRAN, and then how to install it with Python, using pip.1
After that we will dive into our first machine learning project: load some data, make a model, make some predictions, and evaluate success. By that point you will be able to boast to family, friends, and the stranger lucky enough to sit next to you on the bus that you’re a bit of an expert when it comes to deep learning and all that jazz.
After a detour to look at how random elements can lead us astray, the chapter will close with a look at the web interface, Flow, that comes with H2O.
Preparing to Install
The examples in this book are going to be in R and Python. So you need one of those already installed. And you will need Java. If you have the choice, I recommend you use 64-bit versions of everything, including the OS. (In download pages, 64-bit versions are often labeled with “x64,” while 32-bit versions might say “x86.”)
You may wonder if the choice of R or Python matters? No, and why will be explained shortly. There is also no performance advantage to using scripts versus more friendly GUI tools such as Jupyter or RStudio.
Installing R
On Linux your distro’s package manager should make this trivial: sudo apt-get install r-base on Debian/Ubuntu/Mint/etc., and sudo yum install R on RedHat/Fedora/Centos/etc.
Mac users should head to https://cran.r-project.org/bin/macosx/ ...
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