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 Ciano and TensorFlow to implement them.
Now, as of today, piano and TensorFlow 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 GPU enabled libraries like Nampai and Mat Plot.
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 100 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 Nampai side 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 piano or TensorFlow,
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 Phyno and TensorFlow.
So it's possible to do on Windows.
With this 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.
And we have deep learning in Python, part one, which is mostly in Nampai and a little bit of TensorFlow.
We've got easy natural language processing in Python data analytics.
Ask you all for newbies, beginners and marketers, cluster analysis and unsupervised machine learning
in Python and unsupervised machine learning.
Hidden Markov 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 TensorFlow or both.
We've got practical deep learning and piano and TensorFlow convolutional neural networks and 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 piano and TensorFlow.
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 Nampai say by Panda's Matt Plot, Lib Piano and TensorFlow 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 StackOverflow.
If you're on a Mac, you can just do pseudo pip install Nampai say by a Python Panda's map plot live
in Seattle.
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 too easy and stop it if you want
to install TensorFlow.
Just copy the command at TensorFlow dot org.
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 TensorFlow 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 VirtualBox and a lightweight version of Ubuntu.
I would recommend Ubuntu or Lubuntu.
I'm going to use the 64 bit version of Ubuntu for this tutorial, so download these first and then returned
to the tutorial.
All right, so now that you've got VirtualBox installed, we're going to create a new machine with 64
bit lubuntu.
So you want to hit new type in a name for your machine.
And this stuff is already correct.
I'm going to choose a.
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 eight 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 going to load up the Lubuntu ISO that you downloaded from
Lubuntu Dot Net.
All right, so hit OK.
And it's start.
Hey, so it's going to take you through the installation of everyone to so you want to hit install stall.
All right, so Element two is going to take you through some prompts.
All right, so you want to erase this and install Lubuntu, and I'm not going to check any of these
sit continue.
All right, so once that's done, it's going to ask you to restart.
So just to 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 resizable 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 Flex Terminal.
Now, when a city into.
That folder.
Cool.
So the correct command was siru dot slash v box Linux editions that run.
Notice how there was something that failed in there, so we need to install.
So you want to run sudo apt get update.
And then run sudo apt get upgrade.
So the next thing is, you want to sudo apt get install, build essential.
All right, once you've done that, we can try to run VBox editions again, so that Sudoku.
Dot slash.
V-Bucks, Linux.
All right, so everything worked 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.
OK, so now we can install the actual data science stuff.
So you want to open again Alex Terminal to a pseudo apt get update again.
And you want to install.
So it's a long list, so Python, Nampai, Python sci fi.
Python Mat Plot Lib I Python.
Python, PIP, Python, Dev and Python set up tools.
Yes.
All right, so now that that's installed, you can test it out and type in a python.
So a python is open, import Nampai.
Implies working in broad sci fi import pandas.
Right, we don't have a panacea.
We will install our next import.
I plot lib.
Cool.
So we have everything we've installed so far.
So now we're going to install
pandas and piano.
So that's pip install minus minus upgrade pandas, the A..
All right, so now we've got Fiona, the last thing we need is TensorFlow.
To get this, you guys want to go to the TensorFlow website and just grab the latest command since they're
updating the package all the time.
So let's open a browser.
Let's search for install TensorFlow.
All right, you take this code.
Copy it, you paste it in here.
All right, now, you've got piano and TensorFlow.
You can test them out.
Go to GitHub 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 setpiece version.
OK, so this one works.
All right, so you see into machine learning examples go into and in class two.
So this has some introductory.
TensorFlow and the andno code.
So just run Python Fino one Pi, since this does not require any data.
Cool, so it seems everything is working now, let's try the TensorFlow example.
So it's TensorFlow one.
All right, so you want to make sure you install the correct TensorFlow.
You want the CPU 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 or.
So you had download.
You go to Ubuntu 64bit.
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.
So you hope in machine learning examples, and this is all of our code.