Name: はじめに - PythonとPytorchを使ったディープラーニングとニューラルネットワーク p.1 (Introduction - Deep Learning and Neural Networks with Python and Pytorch p.1)
Uploaded: 2021-01-14T12:27:51.000Z
Duration: 24 min 49 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

And welcome to a deep learning with python and, of course, pint torch tutorial.

Siri's is going to start from the very, very basics, assuming maybe you don't know anything about deep learning and then we'll be getting into more complex types of neural networks and applications for them.

So anyways, to get started, of course you're going to need pied towards.

I am assuming you already have at least Python, and my hope is that you at least understand the basics of Python as well as object oriented programming.

So if you don't have either of those two things, probably you should stop and either find someone else's tutorials or I've got tutorials on both the basics.

It's like 13 parts or something like that, Uh, and then also object oriented programming again.

It's not that many videos should be able to get through it pretty quickly.

You should have a basic understanding of python because I'm just assuming you.

I'm not going to be talking about basic python concepts and then object or any programming, because in general, your neural network is going to be a class.

So if you don't understand that you're gonna be fuzzy about certain things, and then you're also probably fuzzy about neural networks.

You should be solid on how object during it programming works first, uh, and then jump into this next up.

At least some of you guys probably don't know about neural networks or at least don't know much.

So I'm gonna go really, really briefly into how neural networks work You.

You can get by here with a pretty high level understanding of neural networks and be just fine.

And then you could If you want to be very successful, you're gonna need a pretty deep understanding, at least for this Siri's.

We're not going to get too deep too quickly, because again, you don't really need that.

But if you're curious or you want to get into more cutting edge research and stuff like that, you're going to need to sew.

Uh, as this serious progresses will be getting a little deeper into things.

But then also, and we'll be talking about concept, so I'm just not gonna hit immediately because there's just no need.

Uh, and then also, at some point, I'm going to do in neural networks from scratch video and buy scratch.

We need something to do, like a ray math or something like that.

Um, just because the list would be really slow.

But if you guys want to get a little deeper that you will be able to both in this series and in the future.

Anyways, assuming you don't know anything about neural networks, I'm going to go briefly over them.

But then again, if you're still kind of fuzzy about just even sort of 100 on networks work, you can look up videos by three Blue one brown that there's some great visuals there.

Also, there's just so much information online a ce faras like how neural networks work at a basic level, but I'll do my best to explain them really quickly.

Let's assume you've got a knish, a situation where you're trying.

You've got images of dogs, cats and humans and you want a neural network that can differentiate between an image of dogs, cats and humans.

So over here, in your input, you've got features and these air going to have to be numerical valued in some way.

It might be the case that they're pixel values and pixel values are already numerical values, right?

022 55 for RGB or if it's gray scaled image, it's just gonna be 022 55 or some other number.

It's usually 022 55 anyways, so they'll already be, um, a new miracle form.

But your features could also be so the input we call that features.

This could also be some sort of categorical type of feature.

So it's like Has four legs, has two legs.

Could be has body of covered in hair or something like that, or what kind of color?

That kind of stuff is going to be like descriptive features, and at some point we have to convert that to numerical, so you could just do like a, like just simply, the first thing is a zero.

The second thing is the one, and so on, and we'll talk much more about that and how how to convert things to numbers later.

But just understand it's always gonna be a number that is being input.

It has to be a number, so anyways, that's your input.

So let's assume they're pixel values, because that's a pretty common task.

You have this import and then it gets passed to these things called hidden layers.

They're called hidden because we don't have control over those layers.

The machine has control over those layers.

After they come through the hidden layers, they get past to an output.

And again, in general, I'm just gonna be super super vague or not vague.

But hopefully not big Super general here.

This is your human neuron and the value that winds up here in general, we do an argon max function that's just gonna ask which of these is the greatest.

So that would mean the largest value is humans.

So we would say this neural network has predicted it's a human.

Okay, um, so how does the machine has a neural network even learned?

We could look at really complicated imagery like this.

It's You don't even really have to think of it this way.

But basically each of these connections you can see how there's a line drawn from every input.

Every neuron here and then every neuron here has a connection to every other one, right?

As you continue going forward here, this is what's referred to as a fully connected neural network.

They don't have to be fully connected, and we can talk more about how to do different things later on.

But in general, this one's fully connected, and each line here each connection is really a wait.

So you've got this input value, and then it gets multiplied by await the biases.

In on a bias is just an extra, you know, bias could be three.

So it's, you know, the input times, the weight plus three.

Okay, and then it becomes a value in here and then X to the same thing is done.

And it's all these air added together into this one neuron.

So I've got another image here and like, this is basically what goes on in a single neuron.

Those inputs are being multiplied by a weight of biases.

Possibly added, All those are added together, So they're just summed together and they're passed through an activation function.

So the activation function kind of mimics of neuron in your brain, right?

But in most cases, we use the sigmoid function.

So is actually a range between zero and one, and that again is just kind of mimic both a neuron firing or not.

And also it serves a purpose of keeping values from just exploding in your neural network.

Um, it just kind of keeps things again between zero and one.

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はじめに - PythonとPytorchを使ったディープラーニングとニューラルネットワーク p.1 (Introduction - Deep Learning and Neural Networks with Python and Pytorch p.1)

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