B1 Intermediate 3 Folder Collection
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SPEAKER 1: Hey, everybody.
Welcome to the ML & AI sandbox here at I/O 2019.
So in the keynote, we saw some amazing stuff
that you can accomplish with on-device ML.
And I'm here with TensorFlow Lite,
showing developers how they can build that same stuff
So you've got everything from object detection
to image classification and voice recognition.
And we're showing people how they can deploy that
down to devices that are really tiny
and run for weeks on a single battery.
So to really show you what's possible with TensorFlow Lite
on-device, we built this amazing experience called Dance Like.
Behind me is Dance Like.
It's super fun application using TensorFlow Lite, which
is TensorFlow's mobile and embedded systems framework
for running machine learning.
It basically teaches people how to dance.
And so it does this by running pose segmentation on the GPU.
We have a bunch of GPU-related ops
that we just released for TensorFlow Lite,
so you should check those out.
It enables you to run super fast models.
We're running two in real time and giving the user
a heap of feedback, which helps them dance better.
So to go and build really interesting applications
on mobile phones, embedded systems,
and executing this all on-device,
you can go to TensorFlow.org/Lite.
There's heaps of sample code.
There's lots of documentation and tutorials.
And you can go and build an amazing application like Dance
Like and release it to the world.
SPEAKER 3: What we're looking at here is a model called PoseNet.
PoseNet is a model that's trained
on images of human beings, and is trained
to predict their skeletal pose.
This model here is actually running entirely
in the browser with a library called TensorFlow.js.
TensorFlow.js is a machine learning library for JavaScript
that can run entirely in the browser
and use your GPU through WebGL.
Now, TensorFlow.js also runs in Node.js using the entire
TensorFlow C++ binary and all of its hardware acceleration
storage behind it.
Piano Genie is a model that runs in TensorFlow.js, entirely
in the browser as well, that generates MIDI notes
as part of a piano performance.
Now, this model is interesting because it
brings a human in the loop and lets
the human condition where the model is actually going to go.
TensorFlow.js is open source and is available online
on TensorFlow.org/js, as are multiple more examples
and demos of how to use it.
Head over there today to start building machine learning
for the browser.
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ML & AI sandbox demos at Google I/O 2019

3 Folder Collection
林宜悉 published on March 25, 2020
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