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PAIGE BAILEY: Hi,
I'm Paige Bailey
for "The Developer Show."
Today, we're at the
TensorFlow Dev Summit
in Sunnyvale, California.
And if you didn't
already know, TensorFlow
is an open source
machine learning
platform for everyone--
every skill level, every
industry, and every platform.
Whether you're deploying
a machine learning model
to a mobile device or training
a massive neural network
on a cluster of TPUs, TensorFlow
is an entire end-to-end machine
learning ecosystem for you.
The full keynote from
today is linked below,
but if you want just the
highlights, here they are--
the TensorFlow Dev
Summit top five.
Number one, the TensorFlow
2.0 alpha release.
This is really the biggest
announcement of the summit.
TF 2.0 is all about making
TensorFlow simple and easy
to use with higher-level
APIs focused
on Keras and eager execution.
Keras gives you the
ability to train
scalable, multi-platform
neural networks with less
than 10 lines of code.
And if you need more
low-level functionality,
you can use Keras's
subclassing capabilities,
or go even deeper to
TensorFlow's core ops
with TF module.
And if you are a
fan of estimators,
don't worry,
they're still around
and will continue to be for
the lifetime of TensorFlow 2.
To migrate your existing
models to TensorFlow 2.0,
we've created an upgrade
utility called TF Upgrade V2.
From the command
line, you just input
a Python file or
a Jupyter Notebook
and a location for the
file to be exported.
The file will be converted
along with a report.txt
that details changes
that were made
and recommends manual
changes that you'll
have to make yourself.
And if you'd like to upgrade
your notebook on GitHub
in-place, check out
this bookmarklet
created by one of our machine
learning Google Developer
Experts, Sergii Khomenko.
Just replace github.com with
tf2up.ml, and there you go.
The general release
of TensorFlow 2.0
is coming in Q2 later this
year, but try it out now
and make sure to
provide your feedback
to testing@tensorflow.org.
Next up at number two are
the new online courses
from deeplearning.ai
and Udacity.
TensorFlow is
collaborating with partners
like Udacity,
deeplearning.ai and fast.ai
to foster the open source
machine learning ecosystem
and train the next generation
of machine learning developers.
At the dev summit
this week, we're
excited to announce
two new courses focused
on the alpha release
of TensorFlow 2.0,
as well as a complete
curriculum from MIT.
Make sure to check out these
materials at no cost, created
alongside deep learning legends
like Sebastian Thrun and Andrew
Ng.
Number three on the list is
O'Reilly's TensorFlow World,
which will be held in Santa
Clara the week of October 28.
The vision is to bring together
the entire TensorFlow world--
practitioners, contributors,
machine learning experts,
and enterprises--
to connect and to
learn from each other.
The call for
proposals is open now,
so make sure to submit
your talk ideas.
And if you want a fully paid
trip to TensorFlow World,
there's number
four on our list--
the PoweredByTF challenge.
To enter, build a cool
example with TF 2.0
and add it to the
examples repo on GitHub.
More details, of
course, can be found
at tensorflow.devpost.com.
And number five, the one
I'm most excited about.
If you're a student
working in machine learning
or any other
academic discipline,
this is your chance to work with
the TensorFlow team at Google
and get paid for it.
You could work on anything from
TensorFlow.js to TensorFlow
Lite or even TensorFlow core--
the same machine
learning framework
used across Google in
products like Gmail, Photos,
Search, and more.
For all the announcements
and updates,
click the links in
the description below.
And on your way there, take
a moment to share this video
with your friends.
They'll thank you
for it and we'll
thank you by doing more videos.
I'm Paige Bailey for
"The Developer Show,"
and I'll see you next time.
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