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  • ♪ (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 Rock, Paper, Scissors.

  • 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 X and Y values.

  • Can you see it?

  • It's actually Y = 2X - 1.

  • So if you saw it, how did you get that?

  • Maybe you noticed that the Y value increases by 2,

  • while the X value only increases by 1.

  • So it was Y = 2X plus a minus something.

  • And then, you may have seen that when X was zero, Y was minus one,

  • so you figured Y = 2X - 1 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 keras.layers.Dense code.

  • And that layer has a single neuron in it, indicated by units = 1.

  • We also feed a single value into the neural network,

  • which is the X value,

  • and we'll have the neural network predict what the Y would be for that X.

  • So that's why we just say that input_shape is one value.

  • When you compile the model, there are two functions:

  • the loss and the optimizer.

  • 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 Y = 5X + 5.

  • And when training, it will then calculate

  • how good or how bad that guess is, using the loss function.

  • And then, it will use the optimizer 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 optimizer to enhance that guess, and so on.

  • The data itself is set up as an array of Xs and Ys,

  • and our process of matching them to each other

  • is in the fit method of the model.

  • We literally say, "fit the Xs to the Ys and try this 500 times."

  • When it's done, we'll have a trained model.

  • So now you can try to predict a Y value for a given X.

  • What do you think would happen if you tried this line of code

  • predict the Y when X 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 subscribe button.

  • Thank you!

  • ♪ (music) ♪

♪ (music) ♪

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A2 初級

機器學習入門(ML Zero to Hero,第一部分) (Intro to Machine Learning (ML Zero to Hero, part 1))

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    林宜悉 發佈於 2021 年 01 月 14 日
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