Hey guys this is Caleb with Dev's slopes dot com and welcome to the Cornell section of the iOS 11 course.
I'm so excited to get you into using Core M-L because it's seriously so cool.
Apple has made it so easy to integrate machine learning into iOS apps and that's actually what you are
here to learn.
So let's dive right into it.
I want to show you some parts of the apple developer documentation for core M-L and we're going to basically
learn what you will learn in this section.
I'm also going to tell you what core melt is and why you should care as a developer.
So I'm going to pull open Safari here and I'm just on the core M-L web site developer dot apple dot
com slash machine learning if you're interested and as you can see there are already a couple of ways
that Apple is using machine learning.
They use it in quick type in messages to predict what words you're going to type next.
They use it in the camera to identify faces so that it can focus on your face automatically without
you having to tap on the screen.
They also use it here to identify landmarks that they use it in a lot of really cool ways already but
you're here to learn it for yourself.
So let's go ahead and let's just talk quickly about what is Corum out.
Carmel is a massively helpful framework from Apple that allows you to put machine learning into your
app.
Simple as that.
If we go here to the core page you can see it allows you to integrate machine learning models into your
app and we're going to be using what's called a trained model.
OK.
And Apple provides some trained models that we can use in each of them.
Kind of has a specific super power.
One might be really good at identifying objects in a scene.
One might be good at identifying faces another one might be good at identifying like different types
of scenes.
OK we're going to basically use this train model.
We're going to pass it an input case maybe like a photo or a block of text.
It's going to run it through all kinds of complex calculations in the trained model and give us an output
of our desired result.
So if we took a photo of something it would pass the photo into the model and it would output a label
or a piece of text with what it thinks that is in the photo.
If we were to pass in a word that was a proper noun it could tell us that is someone's name it could
identify that type of information.
So that's really cool.
So that's Corum.
There's another framework we're going to use called vision and this is really cool.
It allows you to use what is called Computer Vision machine learning computer vision allows you to do
things like face tracking face detection landmarks text detection rectangle detection barcode detection.
Now as you're hearing this you're probably thinking of some apps you know that use this.
If you've ever used an app called Word Lens I think that's what it was called before Google bought them
and put it into Google translate.
But with Word Lens what you can do is you can take a picture of a street sign and it can translate that
word in your in real time on that street sign.
It's very cool.
So that kind of thing could be done with text detection with the vision framework.
Not only that but it can do object tracking If you've ever posted on Instagram or snapchat story you
know that you can drop on an emoji or even words and you can pin them to a certain object in the frame.
And then as you move that object follows you.
That's how object tracking can be done with vision.
And the last one is image registration and that's partly what we will use to create an app that lets
us take a photo of something and then using that photo the app can identify what it is.
It's really really cool.
OK.
So that's vision.
And we've talked briefly about core M-L but the last one we're going to talk about is called and that's
linguistic tagger.
And that's a class that Apple has provided to allow us to analyze language and identify different things
about it like we could identify proper names.
We could do all kinds of other cooling sticks stuff like we could determine whether a word is a noun
if it's used a certain way in a sentence we could determine if it's a verb an adjective we can detect
what language is being spoken.
So imagine that if you're sitting somewhere you you're not quite sure what language someone near you
is speaking.
You can just hold up your phone and it could listen and detect Oh that's Spanish or that's Italian.
K. There's lots of really cool ways.
Machine learning can be used in your application.
The coolest thing is that core M-L is stored locally on your device so it doesn't even need to pull
data from some kind of cloud server or reach out to an API.
The male model can do all of the thinking for us.
That's the coolest thing it's sort of like we're putting a brain into our application and it's very
very cool so I'm so excited to tell you more about it.
But let's go ahead and let's move on to the next video so you can actually get started learning core
m-L.
All right so head over to the next video.
We'll see you there.