Welcome to this brand new module.
So let's talk about what's coming up.
If you remember we did a whole lot of work in the previous modules.
Now we weren't exactly tilling the land but we did the machine learning equivalent with the amnesty
data set for recognizing handwritten digits namely we built a neural network in the form of a multilayer
perception.
And we did all of this in Jupiter notebook.
There we set up the layers and then we defined our loss and optimization metrics.
And then we went on to train our model and while our training was happening we were analyzing and evaluating
our model's performance using tensor board.
Now all of this hard work is going to be the foundation for what we're going to do now.
At this point we are ready to really reap the fruits of our labor because in this module we're going
to take the final step in our machine learning workflow.
We're actually going to deploy our model and what I mean by that I mean that at the moment our model
is sitting on our local computer.
But we could also send it out into the world and get it to do some real work.
And by that I mean creating a Web site where our model gets to predict some handwritten digits on some
real life data but this will allow us to do is turn our machine learning model into an actual product.
And we're going to do just that by creating a Web site called Math garden.
And on this website kids or anyone really can practice their math skills by writing a digit into this
black box here.
So four plus two that is equal to six.
And I can check my answer and if my answer is correct then our garden will start to grow.
Two plus three is equal to five.
And if I click check answer then we can see that our garden continues to grow.
But what if we get it wrong three plus zero.
What if that's equal to two instead of three then machine learning model.
Well take a look at what's in this black box.
And because we've got a wrong our garden will start to wither.
So that's awesome right.
So this is a really really neat application of the model that you've trained and that you've got already
because now you can put it to work and make it predict what's in this box here and give it a purpose
make it go live.
And by the end of this module you'll be able to publish this Web site so that you get an actual your
URL that you can then send to your friends and family and also show it off to potential employers as
part of your portfolio.
So now let's talk a little bit about the workflow of what we're going to be doing in this module how
we're going to get to that final step where we publish our Web site and bring our product into the world
first.
Let's talk about saving and loading our models.
Why is this useful.
Well oftentimes training takes an incredibly long time.
And after you've trained a model you want to be able to save or export that trained model.
And conversely oftentimes you want to be able to load a model so that you can pick up training where
you left off.
This is the bit of the module where we're still going to be working in Jupiter notebook.
I'm going to show you how you can save and export your amnesty model that you've trained.
But step two is going to be deploying this model to the browser and this means getting our model to
be able to work in Chrome Firefox or Microsoft Edge for example.
Now there's one caveat for this step because we can't actually use regular tensor flow in our browsers
for this.
We need a version of tensor flow called tensor float J S and this will mean converting our model into
a tensor flow.
Yes so that it can be used on the web because that J.S. Pont stands for JavaScript.
Speaking of JavaScript that's the programming language that we're gonna be using to build our Web site.
And that's going to be step three as part of this workflow.
We're going to be using HDMI all CSX and javascript to create our math garden Web site.
This will involve writing the code to style our Web site writing the code to create this little canvas
here where you can draw an image and also writing the logic of course to check the answers.
Now this is not a web development course but not to worry we're gonna be keeping it simple and we'll
stick to the basics.
And besides now that you already know Python you're going to see all the same programming concepts apply
to JavaScript.
It's just gonna be a slightly different syntax but you're going to pick it up in no time.
No problem.
Trust me in the next part of the module it's going to be back to data science because we're going to
have to pre process some data.
What kind of data.
And why do we need to pre process things.
Well we're gonna have somebody draw a number onto a canvas right.
That's how the website's gonna work.
But drawing this on a canvas will give us an image.
But what we need to give to our tensor flow Jesus model is in fact a tensor.
It's gonna be an array of the pixel values that we've used to train our model on.
So how do we go from an image to a tensor.
And what additional steps do we need to take.
Well we might have to crop the image we might have to scale the pixel values and we're also going to
have to create an actual tensor and much much more.
Now a lot of this work is actually incredibly involved but this is an opportunity to learn about an
incredibly popular machine learning library called Open CV the CV stands for computer vision and open
CV will be the library that will help us work with our images.
It's incredibly powerful and also quite popular because it was originally developed by Intel back in
nineteen ninety nine having pre processed our image data and turn it into a tensor.
We can of course feed it into our tensor flow Jesus model.
And at that point we can make a prediction based on the image and compare it to the correct result.
If the answer is correct our garden will blossom and grow.
And that brings us to the final step which is publishing our Web site.
And the last part of the module we're going to go from working on our local machines and using our local
host to publishing our Web site and giving it a real U.R.L..
I'm going to show you how you can make your website go live on the Internet so that you can send a link
to your friends and family and co-workers and Twitter followers and everybody that you want to show
your product to.
So I hope you're as excited as I am.
I'll see you in the next lesson.
Let's get started.