Deep learning is this branch of machine learning

loosely inspired by how the brain works.

We have had experience building software for

deep learning over the last few years.

Although it was initially a research project, we've since

collaborated with about 50 different teams at Google

and deployed these systems in real products

across a really wide spectrum of areas.

Today, it's used heavily in our speech recognition systems,

in the new Google Photos product, in Gmail, in search.

We’ve really taken all that experience and built that into TensorFlow

TensorFlow is this machine learning library that's used across Google

for applying deep learning to a lot of different areas.

Doing both artificial intelligence research

and deploying these production models.

They're really powerful at doing various kinds of perceptual

and language understanding tasks.

These models are able to actually make it so computers can actually see.

And are actually able to understand

what is in an image when you're looking at it.

What is in a short video clip.

And that enables all kinds of powerful product features.

Machine learning is the secret sauce for the products of tomorrow.

It no longer makes sense to have separate tools

for researchers in machine learning and people who are

developing real products.

There should really be one set of tools that researchers can use

to try out their crazy ideas and if those ideas work,

they can move them directly into products

without having to rewrite code.

On the research side, the goal is to

bring new understanding to existing problems,

advance the state of the art on existing problems,

understand new problems that were considered before.

Then on the engineering side, the goal is to take those insights

from the research community

and use them to enable products and product features

that wouldn't have been possible before.

Part of the point of TensorFlow is to allow collaboration

and communication between researchers.

It allows the researcher on one location to develop an idea and explore it.

And then just send code that someone else can use at the other side of the world.

We are making it a lot easier for humans

to be able to use the devices around them.

We think having this as an open source tool really helps that

and speeds that effort up.

So we expect developers to be able to do a lot more than they can do today.

We think we have the best machine learning infrastructure in the world

and we have the opportunity to share that.

And that's what we want to do here.