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Drawn the spectacle part of this goes, you will find that we didn't mention of get us an authentic

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Lou.

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In this video, we will try to understand what get us and then to blow up.

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So let's see.

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Get US is a deep learning framework that provides a convenient way to define and train almost any kind

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of deep learning model.

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Basically, get us works at any model living.

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It will help you define the model.

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That is how many lives.

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How many lives.

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What does the added function?

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What is the optimizer, etc..

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But it does not handle any lower level operations.

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If you remember in the previous two reflectors, we learned that while training a neural network, we

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need a lot of differentiation, matrix, manipulation, etc., all these are not done by us.

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Instead, this low level manipulation and differentiation of data is done by certain specialized and

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well optimized libraries.

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Good thing about Get US is that it can vote seamlessly with several such lower level libraries.

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Currently, there are three main backend libraries in Tetlow, which is done by Google s.M Begaye,

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which stands for Cognitive Toolkit and is developed by Microsoft and Tierno, which is Dell'Abate Miller

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Lab at University of Montreal.

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Any piece of code written in Get US can be done with any of these back end without having to change

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anything in the code.

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But as of now, denser flow is the most widely adopted, more scalable and most production ready.

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So we will be using pensive of law and discourse.

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Now, denser flow or any other such low level library.

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These libraries need processing power from our system to do all this data manipulation.

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This processing power can be provided by either C.P.U or DIPU, which stands for Central Processing

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Unit or demographical processing it by default.

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We do all C.P.U based installation of care doesn't antiflu.

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But if you are running on a system with End Radio Deepu and a properly configured libraries of Invidia

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such as Kouda or so you'd be an in which I would be Blanning.

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Then you can install the deep you based virgin of data and to flow back in InGen as well.

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So that's all we need to know about.

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Get us intense upload.

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No need to be overwhelmed by these domes now.

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You will see how using guitars will define definer neural network model.

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And then we will take it us to use Tenzer flow backing to train the model in the next video.

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We will learn how to install guitars and tens of low in our system.