♪ (music) ♪ You've probably heard a lot
about AI machine learning over the last few months. And maybe you've been inspired
by videos showing what's possible with AI machine learning. But what is it really? Once you go beyond the hype
and get down to writing code, what does AI really look like? Well, that's what we're going
to go through in this video series, where we'll teach you what it's like
to write code for machine learning, and how it provides different,
new, and exciting scenarios that will help you write applications that behave more like a human being,
giving you artificial intelligence. I'm Laurence,
and I'm going to be your guide. You don't need
to know a lot to get started, and we'll be using the Python language. Don't worry if you've never used it,
it's super simple to understand, and you'll be up and running in no time. So let's start with a very simple example. Consider you're creating a game
of <i>Rock, Paper, Scissors</i>. When you play this
with a human, it's very basic; every child can learn it
in just a few minutes. Now, let's take a look
at the most basic part of a game that the human brain is really good at, and that's recognizing
what it's actually looking at. So consider these images. Most people can look at them
and instantly recognize which ones are rock, which ones are paper, and which ones are scissors. But how would you program
a computer to recognize them? Think about all of the diversity
of hand types, skin color, and even people who do scissors
like me, with their thumb sticking out, and people who do scissors
with their thumb in. If you've ever written any kind of code,
you'll instantly realize that this is a really,
really difficult task. It might take you thousands
or tens of thousands of lines of code, and that's just to play
rock, paper, or scissors. So what if there was
a different way to teach a computer to recognize what it sees? What if you could have
a computer learn in the same way that a human does? That's the core of machine learning
and the path to artificial intelligence. So traditional programming
looks like this. You have data, for example,
a feed from the webcam, and you have rules that act on this data. These rules are expressed
in a programming language and are the bulk
of any code that you write. Ultimately, these rules will act
on the data and give you an answer. Maybe it sees a rock,
maybe it sees a paper, and maybe it sees scissors. But what if you turn this diagram around, and instead of you as the programmer
figuring out the rules, you instead give
it answers with the data and have the computer
figure out what the rules are. That's machine learning. So now, I can have
lots of pictures of rocks and tell a computer
that this is what a rock looks like, and this is what paper looks like, and this is what scissors looks like. And I can have a computer
figure out the patterns that match them to each other. Then, my computer will have learned
to recognize a rock, paper, and scissors. That's the core of building something
that uses machine learning. You get a set of data
that has patterns inherent in it, and you have a computer learn
what those patterns are. Before we write a neural network that learns something as complex
as rock, paper, and scissors, let's use a much simpler example. Take a look at these numbers-- there's a relationship
between the <i>X</i> and <i>Y</i> values. Can you see it? It's actually <i>Y = 2X - 1.</i> So if you saw it, how did you get that? Maybe you noticed
that the <i>Y</i> value increases by 2, while the <i>X</i> value only increases by 1. So it was <i>Y = 2X </i>
plus a minus something. And then, you may have seen
that when <i>X</i> was zero, <i>Y</i> was minus one, so you figured <i>Y = 2X - 1</i>
would be a good guess, and then you took a look
at the other numbers and saw that it worked. That's exactly the principle
that all machine learning works on. So let's take a look. This is the entire code that you can use
to create a machine-learned model that figures out what matches
these numbers to each other. Don't worry if some of it
doesn't look very familiar right now, you'll be able to pick that up in no time. This first line defines the model itself. A model is a trained neural network, and here we have
the simplest possible neural network, which, in this case, is a single layer
indicated by the <i>keras.layers.Dense</i> code. And that layer has a single neuron in it,
indicated by <i>units = 1.</i> We also feed a single value
into the neural network, which is the <i>X</i> value, and we'll have the neural network
predict what the <i>Y</i> would be for that <i>X.</i> So that's why we just say
that <i>input_shape</i> is one value. When you compile the model,
there are two functions: the <i>loss</i> and the <i>optimizer.</i> These are the key to machine learning. How machine learning works
is that the model will make a guess about the relationship
between the numbers. For example, it might guess
that <i>Y = 5X + 5</i>. And when training, it will then calculate how good or how bad that guess is,
using the <i>loss</i> function. And then, it will use
the <i>optimizer</i> function to generate another guess. The logic is that the combination
of these two functions will slowly get us closer and closer
to the correct formula. And, in this case, it will go through
that loop 500 times, making a guess, calculating
how accurate that guess is, and then using the <i>optimizer</i>
to enhance that guess, and so on. The data itself is set up
as an array of <i>Xs</i> and <i>Ys,</i> and our process
of matching them to each other is in the <i>fit</i> method of the model. We literally say, "<i>fit</i> the <i>Xs</i>
to the <i>Ys</i> and try this 500 times." When it's done,
we'll have a trained model. So now you can try to predict
a <i>Y</i> value for a given <i>X.</i> What do you think would happen
if you tried this line of code predict the <i>Y</i> when <i>X</i> equals 10? You might think
that the answer is 19, right? But it isn't. It's actually something like 18.9998. It's close to 19,
but it's not quite there. Why do you think that would be? Well, the computer was trained
to match only six pairs of numbers. It looks like a straight-line relationship
between them, for those six, but it may not be a straight line
for values outside of those six. There's a very high probability
that it's a straight line, but we can't be certain. And this probability is built
into the prediction, so it's telling us a value very close
to 19, instead of exactly 19. Try the code out using the link
in the description below this video to see it for yourself. This is something you'll see
a lot more of in machine learning. And in the next video in this series,
we'll take what you've learned and apply that
to a more interesting problem-- computer vision-- and seeing how you can
teach a computer to see things, using exactly the same methodology
as you used here. We'll see you in that video, and don't forget to hit
that <i>subscribe</i> button. Thank you! ♪ (music) ♪
