Name: TensorFlow 2.0速成班 (TensorFlow 2.0 Crash Course)
Uploaded: 2021-01-14T10:23:55.000Z
Duration: 2 h 13 min 17 s
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And welcome to a brand new tutorial, Siris on neural networks with python and tensorflow.

Oh, now, tensorflow 2.0, is the brand new version of tensorflow still actually in the Alfa stages right now?

But it should be released within the next few weeks.

But because it's an Alfa TENSORFLOW has been kind enough to release us that Alfa version.

So that's what we're gonna be working with in this tutorial.

And this will work for all future versions of tensorflow to point out.

So don't be worried about that now, before I get too far into this first video, I just want to quickly give you an overview of exactly what I'm gonna be doing throughout this serious You guys have an idea of what to expect and what you're going to learn Now, the beginning videos, and especially this one are gonna be dedicated to understanding how a neural network works.

And I think this is absolutely fundamental and that you have to have some kind of basis on the math behind a neural network before you're really able to actually properly implement one.

Now, Tensor float is a really nice job of making it super easy to implement neural networks and use them.

But to actually have a successful and complex no network, you have to understand how they work on the lower level, so that's going to be doing for the first few videos.

After that, what we'll do is we'll start designing our own neural networks that can solve the very basic M nus datasets that tensor vote provides to us.

Now these air pretty straightforward and pretty simple, but they give us a really good building block on understanding how the architecture of a neural network works.

What are some of the different activation functions, how you can connect layers and all of that which will transition us nicely into creating our own neural networks, using our own data for something like playing a game.

Now, personally, I'm really interested with neural networks playing games, and I'm sure a lot of you are as well.

And that's what I'm gonna be aiming to do near the end of the Siris, on kind of our larger project will be designing a neural network and tweaking it so they can play a very basic game that I have personally designed in python with Pie Game.

Now with that being sent, that's kind of it for what we'll be doing in this series.

I may continue this on future in later videos into, like, very specific neural network.

Serious maybe chat about or something like that.

But I need you guys to let me know what you'd like to see in the comments down below.

With that being said, if you're excited about the Siri's, make sure you drop a like on this video and subscribe to the channel to be notified when I post the new videos.

And with that being said, let's get into this first video on how a neural network works and what a neural network is.

So let's start talking about what a neural network is and how they work.

Now, when you hear a neural network, you usually think of neurons.

Now neurons are what compose our brain, and I believe don't quote me on this.

We have billions of them in our body or in our break.

Now, the way that neurons work on a very simple and high level is you have a bunch of them that are connected in some kind of waste.

Let's say these are four neurons and they're connected in some kind of pattern.

Now, in this case, our pattern is completely like, uh, like, random, where just arbitrary.

But this is the way that they're connected.

Okay, Now, neurons can either fire or not fire, so you need to be on her off, just like a one or a zero.

Okay, so let's say that for some reason this neuron decides to fire, maybe you touch something.

Something fires in your brain, and this neuron decides to fire.

Now it's connected to, in this case, all of the other neurons.

So what it will do is it will look at its other neurons and the connection, and it will possibly cause it's connected neurons to fire or to not fire.

So in this case, let's say, maybe that's what this one firing causes this connected neuron to fire this one to fire.

And now it's decided it turned it off or something like that.

Okay, so that's what happened Now when this neuron fires well, it's connected to listener on and it's connected to this now.

But let's say that maybe when this one fires, it causes this one to UN fire because it was just fire something like that, right?

Now that it's off, it causes this one to fire back up and then it goes.

It's just a chain of firing and unfired, and that's just kind of how it works, Right?

Now, that's a far as I'm gonna go into explaining neurons.

But this kind of gives us a little bit of a basis for a neural network.

Now, a neural network essentially is a connected layer of neurons were connected layers so multiple of neurons.

So in this case, let's say that we have a first layer.

We're gonna call this our input layer that has for nerves.

And we have one more layer that only contains one now.

Now in our neural network, we can have our connections happening in different ways.

We could have each, uh, woody coats neuron connected to each other, neurons so from layer to layer, or we could have, like some connected to others, some not connected, some connected multiple times.

It really depends on the type type of neural network we're doing now.

In most cases, what we do is we have what's called a fully connected neural network, which means that each neuron in one layer is connected to each neuron in the next layer.

So if I were to add another neuron here, then what would happen is each of these neurons would also connect to this neuron one time so it would have a total of eight connections because four times two is right, and that's how that would work.

We're just gonna use one neuron in the next layer just to make things a little bit easier to understand.

Now all of these connections have what is known as a wait.

Now, this is in a neural network specifically OK, so we're going to say this is known as Wait one.

This is wait three, and this is wait for and again just to re emphasize this is known as our input layer because it is the first layer in our connected layers of neurons okay, and going with that, the last layer in our connected layer of knowns is known as our output layer.

Now, these are the only two layers that we really concern ourselves with when we look and use a neural network.

Now, obviously, when we create them, we have to determine what layers we're gonna have in the connection type.

But when we're actually using the neural network to make predictions or to train it were only concerning ourselves with the input layer and the output layer.

Will essentially, given some kind of input, we want to do something with it and get some kind of output, right?

In this case, we have four inputs and we have one output.

But we could have a case where we have four inputs and we have 25 outputs, right?

It really depends on the kind of problem we're trying to solve.

So this is a very simple example, But what I'm going to do is show you how we would or how a neural network would work to train a very basic snaking.

But, like, this is what the position the snake looks like, where this is the tale.

What I want to do is I want to train a neural network that will allow this snake to stay alive.

So essentially, its output will be what direction to go in or like to follow a certain direction or not.

Okay, essentially, just keep this snake a lot.

Well, the first step is to decide what our input is gonna be and then to decide what our output is gonna be.

So in this case, I think a clever input is gonna be.

Do we have something in front of the snake?

Do we have something to the left of the snake?

And do we have something to the right to the snake?

Because in this case, all that's here is just the snake, and he just needs to be able to survive.

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TensorFlow 2.0速成班 (TensorFlow 2.0 Crash Course)

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