TensorFlow: Open source machine learning


♪♪♪ 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.

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