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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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