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Hello and welcome to this art tutorial.

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So we have a very exciting tutorial
ahead of us because in this tutorial

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we are going to build the A
and an and fit it to the training set.

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So right now it's time for action

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and let's build
our first artificial neural network.

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All right.

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So as I told you in the previous tutorial,
we are going to use the best package

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for that.

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That is the most efficient one
that offers the most options,

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and also one package
that can run your models very efficiently.

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That's indeed very important
because the models that we're going

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to build are complex and,
you know, they can run over many epochs.

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So we need a package
that will be efficient

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for these highly compute

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intensive tasks that are inevitable
when we build a deep learning model.

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In this section, in this tutorial,
we're not going to build a very deep

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learning model.

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We are going to have indeed
two hidden layers, but still.

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And if you need to work with a deeper
artificial neural

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network, well, of course
it's better if you have the best package.

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So there are several deep learning
packages in R, so we have neural net

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to build deep learning models
that are regressors and not classifiers.

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So we cannot use this one.

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Then we have a net
that allows us to build a deep

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learning classifier model,
but with only one hidden layer.

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And then we have deep net, another
very good deep learning package

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that can build deep learning models
with many hidden layers.

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But that's still not
the one we're going to use.

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And so now I'm going to tell you
the one that we're going to use.

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It is called the H2O package.

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So why is it, in my opinion, the best
package to build deep learning models?

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Well, it's for three reasons.

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The first reason,
and that's the most important reason,

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is that H2O is an open source
software platform

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that allows you to connect
to an instance of a computer system

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that therefore allows you
to run your model very efficiently.

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So thanks to this connection
to this computer system,

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we will be able to train

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a deep learning model very efficiently,
like in a few seconds.

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For those of you who follow the Python
tutorials, it's like connecting to a GPU,

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you know, that allows you to run highly
compute intensive parallel computations.

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So that's the first reason efficiency.

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Now, the second reason
is that this package

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offers a lot of options
to build your deep learning model.

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So, you know, it will be very easy for us
to choose different numbers of hidden

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layers and choose different numbers
of neurons in the hidden layers,

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as well as other options
to develop your model.

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And speaking of options,
that leads me to the third reason why.

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To me,
it is the best deep learning package.

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Well, the third reason is that one
option of this H2O package

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is that it contains a parameter
tuning argument that allows you

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to choose some optimal numbers
to build your deep learning model.

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So we will see that by building the model.

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And so let's start creating it.

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So the first thing we have to do
is of course to install the H2O package.

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So let's do this.

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We're going to use
the install dot packages command.

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Here we go.

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And remember in this install the packages
function

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we need to input the name of the package
in quotes.

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That is h to o.

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So that's all H2O written
this simple way.

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And so you just need to select this line
and execute it.

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To install the package I already have

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installed on my our studio here, it is.

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And it's also already imported.

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But of course this will
install this package without any issue.

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And of course if you have any issue
you can ask me some questions in the Q&A.

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I'll help you fix that.

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So I'm going to put that in comment.

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And now let's move on to the next line,

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which is to import this package
in case we have to automate the scripts.

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So to import it as usual we use library
and in parentheses not in quotes.

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The name of the package H to. Oh.

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All right. Great.

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And now we need to do something special.

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It's not the usual package that
we've been using throughout this course.

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It's a new kind of package

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because it's a package that we're taking
from an open source platform,

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and therefore that requires a connection
to an EC2 instance.

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And therefore what we'll do right now
before starting to create

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the model is to establish this connection.

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And don't worry, that's very easy.

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We need to take our package H to
oh then dot.

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And then as you can see here
it contains many functions.

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And the function we're interested in
to make this connection

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and in fact initialize an H2O instance.

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Well we need to take the init function.

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All right.

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And as you can see in this function
we have several parameters that allows you

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to connect to a specific server
for example, this first argument

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here is IP which you can use
to specify the IP address of the server

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you want to connect to, in
which your H2O instance will be running.

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But that's only if you have an idea
of which server you want to connect to.

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But in our case,
we will connect to some sort of a default

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available server, and therefore
we want to use these parameters here.

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However, there is one that we'll use and
that is the end thread's parameter here.

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And what is this argument.

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Well this argument is the number of cores
in the system that you're connecting to

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that will be used to build the deep
learning model.

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Because, you know,
building a deep learning

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model requires a lot of computations.

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Highly compute intensive ones.

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And therefore we need many cores
to run these computations.

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And again, that's the reason why we prefer
GPUs or CPUs.

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Because simply GPUs have many more cores.

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So here
we'll take this argument and threads.

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And now the trick is to input the value
minus one.

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Because specifying this
minus one value will take

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all the available cores of the system
you're connecting to.

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So don't even think about it.

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I strongly recommend to use minus one here
because this will optimize the number

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of course to use
and therefore the future computations.

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Great.

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And that's actually the only argument
we need to input here in this

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init function.

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So we are ready to execute
that to connect to the system.

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All right. So let's execute.

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Starting h2o JVM and connecting.

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And here we go.

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All the connections are established.

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And now we're working on another system

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much more powerful because we have
an optimized number of cores.

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And so we are ready to build
any kind of complex deep learning models.

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Great. So now we have everything we need.

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We have a powerful tool with us.

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So we are definitely ready
to start building the deep learning model.

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All right.

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So that's what we're going to do
starting from the next tutorial.

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Until then, enjoy machine learning.