Throughout the practical part of this course you will find that we did mention of ghettos and then to
flow in this video.
We will try to understand what get us and into law.
So let's see.
Get us is a deep learning framework that provides a convenient way to define and train almost any kind
of deep learning model.
Basically get us what's at the model level.
It will help you define the model that is how many layers how many layers.
What is the added function.
What is the optimizer etc..
But it does not handle the lower level operations.
If you remember in the previous two lectures we learned that while training a neural network we need
a lot of differentiation metrics manipulation etc. All these are not done by great us.
Instead this low level manipulation and differentiation of data is done by certain specialized and very
optimized libraries.
Good thing about Gates is that it can vote seamlessly with several such lower level libraries.
Currently there are three main backend libraries entered law which is led by Google C indicate which
stands for cognitive toolkit and is the alert by Microsoft and T.A. which is the library Miller lab
at University of Montreal.
Any piece of code written and get us can be done with any of these backend without having to change
anything in the code but as of now pencil law is the most widely adopted most scalable and most production
ready so we will be using pencil law in this course.
Now pencil flow or any other such low level library.
These libraries need processing power from our system to do all these data manipulation.
This processing power can be provided by either you or you which stands for central processing unit
or the graphical processing it.
By default we do all CPI based installation of K doesn't enter law but if you are running on a system
with n really deep you end a properly configured libraries of any media such as CUDA or c you'd be an
n which are for B planning then you can install the DB You based version of data enter low back end
and then as well so that's all we need to know about get us intent to flow.
No need to be overwhelmed by these domes.
No you will see how using get us will define our neural network model and then we will take it us to
use pencil flow back in to print the model in the next video.
We will learn how to install gave us intensive law in our system.