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JASON MAYES: Welcome to the Google AI for JavaScript
Developers.
We have TensorFlow.js course.
I'm Jason Mayes, developer relations engineer
for TensorFlow.js here at Google,
and having used JavaScript throughout my career
and creative innovation, it's been
truly amazing to see how far it has come.
With new browser-level APIs and language features
being added continuously, JavaScript is the world's most
widely-used programming language,
and it makes it super simple for you
to bring your creative ideas to life, no matter what
background you might be from.
And now, the next evolution of how it's going to be used
has arrived--
machine learning.
And for those of you new to this field,
TensorFlow.js is Google's machine learning library
for JavaScript.
And it's just like the original version of TensorFlow
that came out in Python, but it's
aimed at folk who code in JavaScript or Node.js instead.
And this allows more developers to use machine learning
without having to learn a new language
and use their creations in even more places than ever
before, as JavaScript can run pretty much
anywhere from client-side to server-side, mobile native
to desktop native, and even internet
of things devices like a Raspberry Pi.
Coding your creations in JavaScript
allows easy shareability with the reach and scale
of the web, along with a number of benefits that are impossible
server side, as you'll learn later in the course.
Now this course assumes no prior knowledge with this industry
and will kick things off by demystifying
artificial intelligence and machine learning.
I'll then be teaching you how to be
productive using TensorFlow.js, walking you
through how to use common machine-learning models
for things like object detection to spam classification
and much more.
This will enable you to solve real-world problems
that you encounter.
Take this Chrome extension, for example,
that allows you to ask a question on any web page
and then be scrolled to the answer,
even though the answer did not contain the words
in the question asked.
Very powerful stuff.
And with machine-learning models like this
that are capable of natural language processing,
you can deliver more advanced solutions to your customers
than ever before.
And in the use case shown here, no Cloud API
is used because we're using TensorFlow.js in the browser
all on the client side, ensuring your privacy is preserved, too,
as no text needs to be sent to the server for classification.
Now, with a little bit of creativity,
you can take these models or write your own
and use them to bring your existing ideas to life
for your next project by integrating them
with your favorite web technologies,
just like this example does that enables real-time motion
capture to drive virtual avatars using a regular webcam.
And it works on any device with a web browser.
By the end of this course, you'll
understand how to create next-generation web
apps, similar to those that we just saw,
that can give you superpowers versus traditional websites,
allowing you to offer your customers
capabilities that traditional web engineering just could not.
In addition to learning how to use
off-the-shelf pre-made models for many common tasks,
you'll also learn how to make custom models
with your own data.
Now, machine learning is already fast influencing
every major industry out there, and the pace
of innovation in this space will continue to rise in the years
to come.
Traditionally, you may have associated machine learning
with Python and academia, but now it's easier than ever
to use with TensorFlow.js, which enables
you to do all of this in any web browser, anywhere in the world,
and anywhere that JavaScript can run.
Right now, 70% of developers just
like you currently use JavaScript in production.
And combined with the rise in popularity of AI and machine
learning, we've seen exponential growth
for developers taking their first steps with machine
learning in JavaScript.
Now is the perfect time to learn more about it
and take your first steps, too, no matter
what background you might be from, to provide you
with competitive skills for your future career,
as more customers than ever are requesting or expecting smarter
systems powered by machine learning every single day.
In the next part of this chapter,
you'll see what others are already
creating with TensorFlow.js in the browser
to give you a taste of what's possible, along with detailing
what you need to know to get the most out of this course.
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