It's an absolute pleasure to be here with you today.

As Jocelyn mentioned, I'm Joana Carrasqueira.

And I'm a program manager for TensorFlow at Google.

I'm joined by my colleague Nicole Pang.

NICOLE PANG: Yes, I'm Nicole.

I'm a product manager for TensorFlow at Google.

JOANA CARRASQUEIRA: And we're going

to talk about the TensorFlow community

and the many exciting ways by which you can get involved

in the work that we do.

So let me start by saying thank you.

Thank you to you, thank you to the community for all

the hard work that you've done.

Since we've open-sourced TensorFlow in 2015,

we've received so many contributions and so much

support from the community that really the project, where

it is today, is due to you, to all your efforts

and all your hard work.

So thank you for that.

Just on core TensorFlow alone, we've

received more than 6,000 commits from over 2000 contributors.

This is so impressive.

But not just only this.

On Stack Overflow, we have received

more than 50,000 questions.

And we have onboarded more than 120 machine learning experts

through our Google Developer Experts program.

And we have established 50 user groups all around the world.

We've also had 25 guest posts on our TensorFlow

blog, which is fantastic.

And our community only continues to grow.

Here is a snapshot where you can see

that the number of commits from four years ago

has been rapidly growing.

And there's so much support and excitement from the community.

We truly couldn't have gotten this far

if it wasn't for you, for the contributors, for all

the work that you do.

So thank you so much for that.

NICOLE PANG: And it's not just the contributions you see

and the feedback we get from our community on GitHub and Stack

Overflow.

But of course, as you all know, TensorFlow

has a global world-wide community.

And we see a lot of love for TensorFlow

also on other avenues.

You probably have heard a lot about TF 2.0 today, yesterday.

And you certainly will hear more about it tomorrow.

But TF 2.0 is one instance where our global community responds

really positively.

And we see so many cases of that.

And today, we'll touch on these cases and, of course,

how you can get involved in our communities.

So briefly, what we'll talk about today.

We want to tell you how you can learn TensorFlow,

how you can get started in your own journey of using

TensorFlow, whether you're more in the beginning stages,

or you're really advanced user of TensorFlow

in your applications.

Then we want to showcase to you our global community, really

run through some really amazing use cases,

really tell you what we've seen people do with TensorFlow

or people use TensorFlow for.

And hopefully, that can be very inspirational for all of us

in the community.

And, of course, why you're here today--

you want to know how to get involved at TensorFlow.

So we'll walk you through not just the ways that you might

first think of, which might be contributing code

because TensorFlow is open source, but also

a lot of community groups, a lot of special interest groups.

And those, again, are all over the world.

So both for everyone here in this room and, of course,

everyone watching online, there's many, many resources.

And we're so excited to share with you.

JOANA CARRASQUEIRA: So as you could see,

we truly have a vibrant global community

that continues to grow because there's

so much that you can do, so much that we can all

contribute to TensorFlow.

And let's have a look at where our community is phased,

and what they're doing right now.

So the TensorFlow user groups, they

are a wonderful way of getting involved with TensorFlow.

Either online or face-to-face, you

can meet with other like-minded contributors and developers

really to answer questions, to solve problems, challenges

and building those use cases on really

how you can implement TensorFlow across different industries.

So just an example--

one of our user groups in Korea.

That one is the biggest that we have in the world.

And we have engaged more than 46,000 members.

It is very impressive.

And in China alone, it's the country with most user groups.

And they have user groups across 15 different cities.

It's really impressive how the community is growing so fast

all over the world.

And one of the key messages that Nicole and I would

like you to retain from our presentation today

is that if you don't have a user group where you're

based or in your region, feel free to start one,

share your experiences, connect with

other like-minded developers, and start

talking about TensorFlow.

We are here to support you throughout this process

and this journey.

So feel free to reach out to us.

We're very happy to guide you through the process.

And like I mentioned, if you would

like to start your user group, here are

some of the resources that you can have a look online

if you are interested in starting your own group.

We also are sharing our alias, so you can really

get to know the team and how you can

start creating your user group.

NICOLE PANG: So in the spirit of honoring our global community,

we want to briefly touch on what the TensorFlow

team has been doing worldwide.

So like Joana just said, we have so many user groups.

And you really can see that they are global.

And as you heard this morning in the keynote,

the TensorFlow team was really excited

and really lucky to be able to go to many cities,

and meet many of these users, and meet many of the companies,

and meet many of the startups that

are using TensorFlow in so many different cities in the world.

And, of course, we're so excited that you're here today,

on one of our stops on the TensorFlow

roadshow in Santa Clara today.

And we're really, really excited to, again,

be able to see the use cases.

And we'd love to share briefly some

of these use cases with you.

So first off, when we look at Asia and Asia Pacific,

there's a really big, vibrant community there.

And as Joana just said, a lot of people in Korea,

a lot of people in India, a lot of people in China,

they're all using TensorFlow with two amazing applications.

So in China, for instance, TensorFlow is actually not

just active on our applications, but also the community

is really active on our official TensorFlow WeChat channel.

And this WeChat channel showcases

a lot of use cases of TF Lite on mobile.

Like you can see this one example of a video platform

called IT with image segmentation on mobile devices.

So again, they're doing really awesome work.

And not just doing awesome work but also

sharing with all of the community on the WeChat blog.

And we're really, really glad that we're partnering with them

and really glad to see these use cases come up.

JOANA CARRASQUEIRA: Yes, and Nicole and I

were really fortunate that we were able to join the roadshows

and really connect with the local communities worldwide.

So for example, at the roadshow in Latin America,

we connected with ALeRCE, which is a startup in Chile.

And they are trying to detect supernovas and galaxies

through the [INAUDIBLE] of child processes and machine learning.

And this was really cool.

And they used conventional neural networks

to classify astronomical objects contained

in a stream of about 200,000 images per day.

The work that they're doing is so impressive.

And it's absolutely worth sharing

with the rest of the community.

Another example-- in Europe, we connected

with EyeEm, which is a library of photos that uses TensorFlow

for object classification.

And their algorithm scores photos

based on their static quality but also on the relevance

to your brand's visual identity.

And then every photo is automatically tagged

with keywords just to make sure that the entire library is

searchable.

It's really impressive.

And then they use TensorFlow Lite on mobile

to make it easier and also more accessible for their users

to use EyeEm.

And then lastly, in Africa, we met with many exciting startups

trying to find solutions for problems

at a global scale that were relevant to the region.

But we would like to highlight the great work of Tambua

Health, who leverages the power of machine

learning and spectral analysis to really turn

any smartphone into a powerful non-invasive screening

tool for pneumonia, asthma, and other pulmonary diseases.

So they use convolution neural network

for modeling spectrograms that were

generated from audio analysis through their smartphones.

And then to save models, they're frozen and converted

into TensorFlow Lite.

And the converted model is then deployed to a mobile device

to perform interference.

So these were some of the cases that we connected

with during the roadshows.

And it was brilliant to see all these very innovative ways

that the community is using and building around TensorFlow.

So these were just a few pictures of our roadshows,

where we truly engaged with the community.

And it's palpable.

It's very tangible, the excitement

that we see not only from contributors but also

from users of TensorFlow.

It's fantastic to see how many of these startups

and other companies are truly impacting and changing

the world.

And this is all you using TensorFlow.

So thank you for that.

NICOLE PANG: So like we said in the beginning,

we wanted to do a brief overview, just

a very small sample of some of the awesome use

cases of TensorFlow, but then really

dig into what is available for you.

So one of the first pillars that we'll talk about is education.

Now, why is education important for us at TensorFlow,

and also, we hope, it's important for you

in the community?

Well, TensorFlow is, of course, as you are very hardly

knowing, it's open source.

But also another aspect of that open source nature

is that we want to make sure learning

resources are available to everyone in the world.

And we really value not making just the products

better for learners.

So for instance, TF 2.0--

it's easy debugging.

And the usability of Keras is designed for that better

experience for learners.

So not just the product, but also the educational resources.

So I'd love to go into some of them in a bit more detail.

This morning you heard about our launch of the new Learn ML hub

on tensorflow.org.

This is a great tool.

Because we heard people's feedback

that they would like more curated resources

on tensorflow.org.

They would like more path of learning from whatever level

of machine learning and deep learning

knowledge you have into more advanced applications

of TensorFlow.

So we heard you, and we now responded

with this new resource of Learn ML.

So it's not just a compilation of curated resources,