Name: JavaScriptを使ったニューラルネットワーク - Brain.jsを使ったフルコース (Neural Networks with JavaScript - Full Course using Brain.js)
Uploaded: 2021-01-14T10:25:02.000Z
Duration: 1 h 32 min 7 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：

Hey, welcome to neural nets in JavaScript with brain Js.

The goal is to give you a practical introduction to problem solving with neural networks.

What you're gonna be learning in this course propagation both forward and backward layers, neurons, training, error.

All networks are what recurrent neural networks are in a whole lot more.

We're gonna build a Zorg, ate a counter, a basic math network, an image recognized a sentiment analyzer and a Children's book creator.

And how we're gonna do it is with 17 lectures where we're gonna focus on practice over theory.

What that means is you are going to get your hands dirty.

But more than that, you're gonna walk away knowing the ideas behind neural networks.

There's a swell a bunch of interactive challenges along the way, and that brings me to our use of scramble.

Scramble is a fantastic platform for learning, and at any point during the entire lecture, you can stop me.

You can just introduced a brand new script and you compress command plus s if you're on Mac or Control Plus s if you're on Lennox or Windows and it will execute exactly your coat that is super important throughout.

This course is well, I'm gonna make regular reference to the console, which is directly below here.

So if you see some numbers go down there like, I'll go ahead and test that right now, 0.5 just appeared.

Anytime I start talking about if the neural net was good because it had a low error rate or if the neural net was bad because it had a higher rate, just look down there and that will give you a little bit of reference as to what we're doing.

This is our very first known that this is gonna be awesome.

So the first problem we're gonna tackle is called exclusive or and you can do some research on it if you like.

You have inputs that are the same result in a zero output when they differ results in a one.

So let's take this very simple comment and let's translate it into something that the neural mint or rather the Java script can understand.

We're gonna call it training data and theirs are very simple variable that represents all of our training data.

Let's go ahead and imports brain Js brain Js is I'm just gonna grab a leak.

Vets imports it from a Cdn instant delivery network.

Const pressing next equals new rain dogs neural network.

And down here, we're going to say next, uh, train, we're gonna give it our training data.

By the time we get there, that the Net will have understood what are inputs?

Outputs are so we can hear Consul, log out net.

So let's choose the 1st 1 Give it our layers Hidden layers three will get more in the hidden layers later.

And now we have an output that's so awesome.

Now, the reason that this number here is not zero is because we're using a neural net and it's very hard for him to speak specifically zero and one.

So that's exactly what we want is a number close to zero, which is 0.5 Now.

Go ahead and get the next outputs console locked out.

Just kind of play around their values and and see how the Net operates.

We talked about how to build a neural net, a very simple one to solve, exclusive or and in this one we're going to discuss how it did it.

So the neuron that has different stages, you'll notice I used two different methods.

You're the 1st 1 is trained, and the other one's run now in train would do something called Ford Propagation and back propagation.

Those terms may seem scary at first, but they're actually quite simple.

In fact, we're going to reduce their complexity down to something that even a child can understand.

You take a look at my slides here, forward propagation and back propagation.

We have a ball, we're gonna take a ball, and we're gonna throw it at a cool.

Now when we do that we're going to make a prediction as to how far the baldies to go, how much energy to put behind it, the pathway of the ball, et cetera.

I want you to go ahead and pause the video here and to think about the different steps that happen when you throw a ball article.

But the first step is prediction and in prediction, we're going to think about how we're gonna throw the ball where it needs to land, how much power we need to put behind it, that first step and with my str showing us that we did not go far enough with the ball, this step is for propagation.

We ourselves are making a prediction from them.

We can see how far we were off from the actual goal.

We can measure that and that that step was measuring.

Now the next thing that we want to d'oh is make a determination as to what we're going to do next.

That is our second step of back propagation.

That is our learning step and you know how the story goes.

We measure that would make another prediction.

Third time's the charm that illustrates this very first method and everything that goes on inside the net.

Now, in running our net, we no longer have to measure how far we are from the goal we already know.

And because of that, there's no need to back propagate.

Now, throughout this entire training of the neural net, Burnett is measuring, and that measurement is referred to as error.

During its training, we could actually enable something really interesting.

We're gonna console log era, and we're gonna have our log period sex you 200 intervals.

How far off the net waas you can see for a time.

Some of these errors may go down or may go up, but eventually the net catches on and it starts to accelerate its ability to learn until that air rate starts to drop to a ridiculously low number, not zero until training is completed.

There's no need to continue training as we discussed we can then just forward propagate.

We talked about how neural networks learn using four propagation and back propagation.

And this tutorial, we're going to understand more the structure of the programmatic neuron that neural nets are actually quite simple.

They're composed of a function that receive inputs as an argument and produce outputs.

We think of our neural net and this very simplistic way.

Then really, we can reduce the complexity of it down to one of the simplest functions that you can write.

Pause the video here and just look at the structure.

Now we're gonna talk about how the network initiates.

Did you think about when you were first born?

Likely you didn't know very much overtime, though you begin to know more and more, the neural net begins with a bunch of random values, so everything that affects the outputs is just random.

At first you may ask yourself why the reason is because mathematically we've proven that is an effective way to start off with knowledge.

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JavaScriptを使ったニューラルネットワーク - Brain.jsを使ったフルコース (Neural Networks with JavaScript - Full Course using Brain.js)

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