Hello everyone and welcome to the tents flow overview lecture this lecture will discuss what tests flow
actually is and will go ahead and explore the documentation Web site.
Let's talk about tensor flow in general tensor flow is an open source software library that was developed
by Google and it's quickly become one of the most popular deep learning libraries in the field and it
can run on either C.P. or Pue.
And as a quick side note typically deep neural network models run much faster on GPU versus CPQ just
because of the way computations are done in this sort of machine learning model.
Now the basic idea of tensor flow is to be able to create what's known as a data flow graph.
These graphs have nodes and edges just as we saw in the previous lecture for neural networks the arrays
or the data that's passed along from layer of nodes to layer of nodes along these edges is known as
a tensor.
So that's how tensor flow gets its name these tensors are flowing through this data graph.
Now there's two ways to use tensor flow.
There is the customizable graph session method which is the typical method you would use for tensor
flow and there is a psychic learned type interface.
We have contrib that learn that will also talk about the sikat learn method is a lot easier to use but
it's not as customizable as your typical graph session method.
We're going to go over both.
But keep in mind that the session method it can feel a little overwhelming if you don't have the strong
background in the mathematics behind these deep learning topics.
All right.
Let's go ahead and explore that documentation.
You can find it at WDW thought tensor float or it wouldn't go ahead and jump to my browser now.
OK here I am at the tensor float dot org home page and you'll see some information about what tensor
flow is.
There's updates on recent additions to tensor flow.
It gets updated pretty often or some pretty cool stuff.
So I would recommend checking this every once in awhile.
And then there's an explanation of what the data flow graph is and how it relates to tensor flow.
And then the main features of tensor flow so the main selling points at this library.
OK so if we scroll back up here there's a get started button you can go out and click on that or click
on that gets started tab and it should take you to this get started page will give you a brief introduction
such as a little Python program of how to use tensor flow but more importantly it actually has download
and setup.
So if you're on a Mac or Linux computer and you want to go ahead and jump right to it you can click
on whatever works for you.
Pip install virtual environment install or most likely Anaconda installation.
If you use the Anaconda distribution you'll go ahead and click on that.
We'll take you to the Anaconda installation and this will basically tell you step by step how to install
tensor flow with Python on an anaconda installation and some of the steps actually include installing
Anaconda.
You don't need to do that but you probably do want to create a Conda environment and it'll explain how
to do that as you go along with whatever version of Python you want to use.
So basically just come here and follow step by step on what they ask you to do.
Pretty straightforward instructions but essentially you're just going to install it.
So up an environment and do it the install.
You don't actually have to set up a candy environment if you don't want to you can skip that step.
You could just jump straight to here and either type condo install Dasch see the forge tensor flow if
you want CPU only or you can continue on here with some Pipp installs if you want to mess around the
GPS stuff.
OK so after that we check out some of the other tabs on the home page.
Coming back here there is a tutorials link which if you click on that I'll take you to this page and
this page has basic tutorials such as basic neural networks and just for M-L beginners deep them just
for experts some tensor for mechanics 101.
And then a little conversation about TFT contrib learn.
Now we're actually going to be explaining the sort of tutorials that's what's coming up in our actual
lectures.
But you can check this out if you want a different viewpoint to actually apply tensor flow.
Then on the next tab going back here there's the how to tab and this how to tab just has some basic
how tos for some common mechanics.
They are going to be doing with tensor flow and also some information on what's known as tensor board
which is a way to visualize the training and evaluation of your models.
Then if you go ahead and continue on there's the immobile tab that takes you here.
So tense flow is actually designed with immobile and embedded platforms in mind.
It has some sample code if you want to use deep learning with either an Android iOS or a Raspberry Pi
operating system.
Then finally there's the API.
The API documentation is just literally API for the library.
You can check that out.
And then there's resources so additional resources such as a tensor flow of white paper citation.
What is senseful used for the community put some nice links here.
Development help support etc..
All right.
So go ahead and mess around Explorer documentation page a little bit.
But coming up next is going to be the installation walk through.
We'll briefly discuss one more time which you have to do for Mac OS or Linux operating systems.
But most of that lecture is going to be focused on setting up a virtual box on your Windows machine
.
If you're a Windows user in order to run tensor flow.
OK thanks everyone and I'll see at the next lecture.