Name: TensorFlow.js將機器學習帶入網絡及其他領域，由Nick Kreeger和Nikhil Thorat撰寫。 (TensorFlow.js Bringing Machine Learning to the Web and Beyond by Nick Kreeger & Nikhil Thorat)
Uploaded: 2021-01-14T10:25:13.000Z
Duration: 20 min 21 s
Description: 【看影片學英語】數萬部 YouTube 影片，搭配英漢字典即點即查，輕鬆掌握單字發音與用法，長久累積看電影不必再看字幕。

So we're here to talk to boat machine Learning and JavaScript with tensorflow.

So if you're familiar at all with machine learning and data signs, you'll know that most of it happens in Python.

So this is a poll that was given by, ah, popular data science block called Katie Nuggets.

Um, and you can see that python absolutely dominates.

You know, there has been many, many years of tooling built in python for data science from numb pie.

Um, and this field is gonna continue to evolve in python.

And I don't have to convince the folks in this room.

Java script is a very, very popular language.

This is obviously the Octa verse survey by get hope and you could see that Java script absolutely dominates.

Um, so we think that there's actually a lot at the Java script community, and the folks in this room can bring two machine learning.

Okay, A few people, but not a lot of people, so I definitely recommend checking this lingo.

This is an in browser visualization of a neural network.

It was built by one of our colleagues at Google, and the idea here is you can immediately change the number of layers, neurons, learning rate and so forth, and immediately see how the neural network generalizes over a data set.

Now this thing was a huge educational success.

You know, it's being used in universities across the world now and in Google's own machine learning cross course.

So again, I definitely recommend checking us out.

So we step back and we kind of asked ourselves, You know, why was this such a success?

Like, Why do people care about this in browser machine learning thing at all?

And we kind of distilled it down to a few points.

So the obvious things that you just click a link and you get going if you've done any python and it's like really pain in the butt to get your drivers installed, python libraries and stolen that kind of thing, it's super interactive.

You know you have buttons, you have hyper parameters that you can play.

You can immediately see how the changing the knobs affect the learning configuration of the model.

Ah, we didn't take advantage of this in the playground, but, you know, in the Java script world in the browser, you have cameras and microphones and standardize access to these things.

But you don't really get a CZ much in the python.

When and super important to us is that you can actually make predictions locally, and data can stay on the clients.

So we took this and we launched a library called Tensorflow Jess.

So we use Web G L to make everything fast so we actually do all the linear algebra in fragment traders, and I'll talk a little bit about that in a second.

But one of the things that we do is we let you make predictions through machine learning models, but we also let you train them directly in the browser or even in no Jess, and we'll talk about about that as well.

So when designing the library, you know, we had a couple of goals in mind.

One is that we wanted to empower a diverse group of jobs for developers like the folks in this room.

You know, there's a lot of really awesome people in this community, and we want to sort of married these two worlds at the same time.

We wanted the folks who are experienced in the machine learning to build support their work to the web.

Now, these goals air kind of sometimes at conflict.

So we'll talk a little bit about how we resolve this.

Um, okay, so one of the principles that we had was we wanted the library be super easy to use, and we kind of lean towards that over performance at the same time, we didn't want to sacrifice any functionality for simplicity.

Um, so just jumping into what that means, Um, we decided to go with this eager only approach.

I'm not gonna go into what that means, but it's a much simpler way of programming.

And actually, most of the machine learning world is moving towards this eager approach versus a graph based approach where you actually stitched together a computation graph and executed later.

We really wanted to be easy, so we moved towards eager.

We also provide a high level layers, a p i, which is ah, set of best practices in the machine learning community.

So you don't have to think about all the details of your linear algebra when you're constructing a model.

And we also provide a whole repositories of pre trained models that requires zero understanding of machine learning to get started.

And I'm gonna show you a couple of examples of those in a second.

We focus on performance one and where it matters.

Obviously we want matrix multiplies to be fast.

What we did was we took individual models, and we figured out how to make those individual models faster on a use case spaces.

So, as I said, we don't want to actually make the library less functional, So we support radiance.

This is fancy talk for sensitivity of each of the weights, which means we can train through any operation that you use in the Tensorflow Js library sport.

And for any of these models that were about to show you, you can actually dig down and get some of the machine learning constructs out of them if you want to.

Okay, so quickly jumping into what the technical stock looks like.

A TTE the very top of the obstruction AP eyes.

I'll show you a couple of those in a second require a very little understanding of machine learning.

Um, and we'll show you some of that soon to, uh and we also have below that are Cory people, which is orjust linear algebra, Colonel.

So these air matrix multiplies convolutions and their Grady ants, which are derivatives.

So all of these AP eyes, you consort of poke in any of these a structural layers.

All of these sit on top of web jail in the browser.

We use fragment traders to run over mouth in parallel and in node.

We actually bind with the n a p i to tensorflow c++, and what that means is, if you use that same a p i for any of these things, you immediately get the hardware acceleration that Tensorflow has been working hard on for the CPU and Fergie abuse with Kota and eventually we're gonna have tp support.

It was also very important for us not to silo ourselves in the Java script world.

They're trained in the python ecosystem that we want to take advantage of.

So we have converter tools that lets you take a caress model or a tensorflow saved model and bring them back into the JavaScript world.

Okay, so let's quickly take a look at what the models report looks like.

Um, so if you check out this link, these air, all our pre trained models these are hosted on get hub on N p.

So we host all the weights and all the Java script for you, and we have a wealth of models, from object recognition to human pose detection, to localisation, to segmentation, to text classification.

But I want to show you one of the demos because it's fun.

Okay, so this model is called pose in it.

Um, it's running completely in the browser, and nothing is being sent back to a server.

And the idea here is we take RGB images from Webcam.

We pass it through this pose detection model that generates key points for each of you know, some of my body parts.

And then it returns an object that we could just render on the screen.

So this is a lot of fun, and we'll show you how to use one of these models in a minute.

The second model is very similar to pose.

So it's, you know, this background little funny, but basically what it does is it draws a mask of the one where it thinks is a human posing a zero where it does where it thinks there's not.

So this is what this one's a lot of fun, and I don't know if this is gonna show well, here, but one of the effects that really like his portrait mode, you can see this thing blurring.

So we have, you know, a soft core base portrait mode that's running directly in the browser pretty fast.

TensorFlow.js將機器學習帶入網絡及其他領域，由Nick Kreeger和Nikhil Thorat撰寫。 (TensorFlow.js Bringing Machine Learning to the Web and Beyond by Nick Kreeger & Nikhil Thorat)

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TensorFlow.js將機器學習帶入網絡及其他領域，由Nick Kreeger和Nikhil Thorat撰寫。 (TensorFlow.js Bringing Machine Learning to the Web and Beyond by Nick Kreeger & Nikhil Thorat)

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