Original subtitles

Welcome back everyone.

In this lecture we're going to discuss the difference between tensor flow and carries.

So before we begin learning how to code our own neural networks I want to quickly clarify the differences

between tense flow and cares.

Since this is a common point of confusion for students.

So we already learned all the theoretical aspects of neural networks.

Now right before we start coding I want to clarify a few things tensor flow is an open source Deep Learning

library developed by Google with tensor flow 2.0 being officially released in late 2019.

TENSOR flow has large ecosystem of related components including libraries like tech support for visualization

deployment options and production API side as well as support for various programming languages not

just Python cares is a high level python library that can use a variety of Deep Learning libraries underneath

so it can operate on top of libraries such as tensor flow C A.K. or Athena tensor flow one point X or

any of the one point versions had a complex python class system for building models and because it was

a little more complex.

The Caris library ended up being a simpler more abstract API that could run on top of tensor flow so

Caris ended up becoming very popular and it was so popular that when the developers for tensor flow

ended up releasing 2.0 they adopted Caris as the official API for tensor flow.

So while Caris still also remains as a separate library from tensor flow it can also now be officially

imported through tensor flow so there's no need to do an additional installation of carers cares is

now officially part of tensor flow in it's the official API for tensor flow 2.0 and going forward because

it's simpler to use than the more complex subclass system that was used in tensor flow one point X versions

the curious API.

It's easy to use and build models by simply adding layers on top of each other through simple calls.

So in the next lecture let's begin exploring the basics of the cares API for tensor flow.

I'll see you there.