Hey guys in this lecture I'm going to show you how to set up your development environment so that you
can follow along in these courses.
First let's talk about operating systems the three big ones are Windows Linux and Mac.
Most but not all of my courses are part of the Deep Learning series.
Eventually the deep learning models were going to build will become so complex that we will need specialized
libraries like Fino and tensor flow to implement them.
Now as of today piano intensive flow are not officially supported on Windows.
So if you are interested in deep learning you will discover pretty soon that there is not much you can
do without a ton of work.
Even working with non GP you enabled libraries like Nam pi and Matt Platt lib can be a challenge.
But these are at least possible on windows if you really must use windows and have your python environment
inside windows and you don't want to try the virtual machine method which I'm going to discuss next.
Then one good library I know of is called Anaconda.
You can get Anaconda at continuum.
I O slash downloads.
I can't vouch for this one hundred percent because I haven't used it myself but I've known others who
have found it at least usable.
Now I'm telling you this from experience.
I've worked with a ton of clients one on one and Python development especially when it involves the
number pi side pi stack is not easy when it's on Windows.
The method I'm going to describe using virtual machines will work on most modern computers and as of
right now if you want to do any deep learning stuff whether that be with V.A. or tensor flow you cannot
do this on Windows.
With that out of the way I'm going to quickly go over which courses I've released so far or plan to
release in the near future that do not require V.A. intensive flow so it's possible to do on windows
with these in mind you can decide if you want to install a virtual machine or not but I would highly
recommend it because it's free and everyone can follow the same instructions so we've got linear regression
in Python logistic regression in Python which are the prerequisites to deep learning part one.
We have deep learning in Python part 1 which is mostly in number pi and a little bit of tensor flow
we've got easy natural language processing and Python data analytics.
Ask you all for newbies beginners and marketers cluster analysis an unsupervised machine learning in
Python and unsupervised machine learning hit and Markoff models in python.
This course is going to have a little bit of piano in it next.
Here are some courses that depend heavily upon piano or tensor flow or both.
We've got practical deep learning in piano intensive flow convolution on neural networks in Python unsupervised
deep learning in Python and recurrent neural networks in Python.
As you can see a lot of interesting and complex stuff can be done in Vienna intensive flow.
All right.
So hopefully I've convinced you that using a virtual machine is a good idea.
Windows is a great operating system but it tends to not be as developer friendly since it tries to be
more consumer friendly.
Note that if you're on a Mac this probably isn't necessary.
You can just install num pie side by Panda's Matlab lib piano and tensor flow using easy installer Pip
sometimes this stuff can fail depending on the many combinations of versions of each thing that are
possible but usually just googling your error message will lead you to the right solution on Stack Overflow.
If you're on a Mac you can just do pseudo pip install num by Skype I Python Panda's map live in Vienna.
Alternatively you can use easy install which might have a more recent version you may need to use easy
install to install Pip itself which is just pseudo easy install Pip if you want to install tensor flow.
Just copy the command at tensor flow dawg.
It's just a pip install command that points to their custom installation location.
I won't put it here since it corresponds to a specific version and that version may become out of date
by the time you watch this.
That is good news because the people working on intensive flow are updating it all the time.
All right so if you've made it this far that means you want to install a virtual machine with Linux
on Windows or that you're already using Linux and you want to know what commands to use to install these
libraries.
We are going to need two things to start for this tutorial.
Virtual Box and a lightweight version of Ubuntu.
I would recommend X Ubuntu or element two.
I'm going to use the 64 bit version of L2 Bantu for this tutorial so download these first and then return
to the tutorial.
All right.
So now that you've got virtual box installed we're going to create a new machine with 64 bit L2 buttons.
So you want to hit new type in a name for your machine
and this stuff is already correct.
I'm going to choose two gigs of memory.
You can choose more memory if your computer has more memory
I'm going to create a virtual hard disk.
Now
I'm going to choose dynamically allocated and 8 gigs is good enough for this example
all right so I'm going to go to settings.
If we go to storage you can choose
the iso file that you downloaded.
So I'm gonna load up the Blue Band to ISO that you downloaded from every bunch you've done it all right.
So here.
OK and it start so it's gonna take you through the installation of L1 to see you want to hit install.
All right.
So element who is gonna take you through some prompts
right.
So you want to erase this.
Get install Ubuntu and I'm not going to check any of these.
Continue.
All right.
So once that's done it's going to ask you to restart.
So just it restart.
Now
all right now that you're in the machine what I always like to do if you notice how this window is too
big for my Mac window you can make the inner window re sizable by installing guest editions.
And this also lets you do useful things like cut and paste between machines.
So you want to open a terminal you go to System Tools select El X terminal.
Now when a city into that folder
goes to the correct command was suru dot slash v box Linux editions that run notice how there was something
that failed in there.
So we need to install GCSE.
So you want to run sudo apt get update
and then run sudo apt get upgrade
so the next thing is you want to suru apt get install build central
all right once you've done that we can try to run Redbox box editions again so that pseudo dot slash
v box Linux.
All right.
So everything work this time so we're going to restart this machine.
All right.
So now the window is only as big as I want to drag it.
So now we can install the actual data science stuff.
So you want to open again Alex Terminal 2 a Sue apps get update again.
Now you want to install
so long lists so fell num pi Python side by
pi fine matte plot lib I python I found Pip python dev and Python set up tools
sit.
Yes.
All right.
So now that that's installed you can test it out.
Can type in a python so I python is open.
Import num pi then PIs working import side by import pandas right.
We don't have pandas yet.
We will install the next import apply live
call so we have everything we've installed so far.
So now we're going to install
pandas MVNO so that it's pip install minus minus upgrade pandas the ANO
All right.
So now we've got Fiona.
The last thing we need is tensor flow to get this.
You guys wanna go to the tensor flow Web site and just grab the latest command since they're updating
the package all the time.
So let's open a browser
and let's search for install tensor flow
all right.
Take this code
copy and paste it in here
all right.
Now you've got V.A. intensive flow you can test them out to get her dot com slash lazy programmer slash
machine learning examples
all right.
So you need to get if you want to check out this code with get so let's install get
all right so let's run our clone command again.
Let's try the CTP s version
OK.
So this one works
all right.
So you see into the machine learning examples go into and in class too.
So this has some introductory tensor flow on the ANO code.
So just run Python free and or one up high since this does not require any data
cool.
So it seems everything is working.
Now let's try the tensor flow example.
So it's tensor flow one
all right.
So you want to make sure you install the correct tensor flow.
You want the ECP you only version
hopefully it just overrides the other version without any trouble.
So let's try the example again
super.
So now everything is working.
Now if you want.
There is a text editor that I highly recommend.
It's called Sublime Text
so you download you go to Ubuntu 64 bit
you save this
and it will automatically be installed
all right.
So now you have the exact same text editor that I use in my lectures
see you open machine learning examples and this is all of our code.