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JASON MAYES: This course will be breaking down
high-level concepts and then swiftly move
to hands-on experience using the TensorFlow.js library
and models in the browser.
You will learn what the difference is between concepts
like artificial intelligence, machine learning,
and deep learning, but also how to use
such techniques practically for real examples using
TensorFlow.js.
With this knowledge, you'll be able to supercharge
your next web app to do things like classifying comment
text in a blog post to automatically block spam
or using sensors, such as a webcam on your mobile device,
to alert you when your dog is on the couch
after you left the house.
The knowledge you learn could be applied
to any business or creative idea that you
have for your next project, no matter
what industry you might be working in,
with a little bit of creative thinking.
Better yet, JavaScript is one of the few programming languages
that can run everywhere, enabling
you to leverage the knowledge from this course
and apply it client side, server side, native apps, or even
IoT devices, allowing you to reuse what you learn
across multiple environments.
So let's go ahead and break down each feature chapter
of this course.
In chapter 2, you'll get familiar with common terms
and what they mean, along with the backgrounds
of TensorFlow.js and how it works
and why performing machine learning in JavaScript
can actually be advantageous.
You also learn the different ways
you can leverage machine learning
from using off-the-shelf premade models, retraining existing
models via a concept known as transfer learning,
or writing your own from a blank canvas
and when each might be appropriate.
Chapter 2 is purposely high level,
introducing the key terminology and phrases
that you need to grasp to set you up
for going deeper further in the course
as we unwrap many of these concepts.
Chapter 3 will focus on using premade machine-learning
models.
These are models that someone else has already
trained and made available.
The TensorFlow.js team have created a whole bunch of models
like this that are super easy to use
in just a few lines of JavaScript
and are a great way to get familiar with what
machine-learning models are along with the types of things
that they can do.
You'll also learn the concept of tensors as a core
for sending data in and getting results out
of a machine-learning model.
You'll then learn how to load an arbitrary TensorFlow.js model,
which requires you to work at this tensor level to utilize,
unlike the easier JavaScript classes
you started with at the beginning of this chapter.
By the end of chapter 3, you'll be
competent at loading any model and using it.
Chapter 4 will build on the third
with your new knowledge of tensors.
And you'll learn to create your own simple models
from a blank canvas.
We'll cover concepts like linear regression, perceptrons,
and multilayer perceptrons to predict more advanced things.
Finally, you'll also learn about more modern techniques,
such as convolutional neural networks
and how they can be used to classify imagery.
Now, don't worry if these terms are unknown right now.
All will be revealed as you go through the course.
Chapter 5 will teach you all about transfer learning.
This is the art of taking an existing model
and using that as a starting point to learn
something new, which can often save time and money.
Here you'll learn how to make your own version of Google's
popular Teachable Machine website
to fully appreciate how it works behind the scenes
while learning to recognize custom things.
In chapter 6, you'll learn how to reuse
machine-learning models that may have started out in Python.
While this course is aimed at JavaScript developers,
much academic research is initially launched in Python.
So it's important to know how to take such work
and get it to run in the browser, which we all
know and love, using our easy-to-use Command Line
Conversion Tool.
You also learn how to take a Python model for spam
certification, retrain it, and then use
the newly created version of the model in your web app.
Finally, chapter 7 will take a look at the future and beyond,
taking a high-level view once again of what other model
architectures exist to give you some inspiration of how you
might want to take the knowledge you've learned
and put it into action.
Further resources you may wish to investigate
will also be provided along with how
to get involved in the fast growing TensorFlow.js
community.
And with that, you should be well on your way
to being productive in machine learning and JavaScript.
So let's get started and head on to the next section.
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