Name: Python、TensorFlow、Kerasを使ったディープラーニングのチュートリアル (Deep Learning with Python, TensorFlow, and Keras tutorial)
Uploaded: 2021-01-14T08:09:28.000Z
Duration: 20 min 34 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：

What is going on everybody and welcome to a much-needed

上級比較級を表す句型

Update to the deep learning and Python with tensorflow as well as now chaos tutorial

程度の副詞

it's been a bit over two years since I did just a basic deep learning video in Python and

Since then a lot has changed. It's now much simpler to both like get into it

But then also just to work with deep learning models

So if you want to get into the more nitty gritty details in the lower-level

Tensorflow code you can still check out the older video

But if you're just trying to get started with deep learning that's not necessary anymore because we have these nice high-level

api's like chaos that sit on top of tensorflow and

Make it super super simple. So anybody can follow along if you don't know anything about deep learning that's totally fine

We're going to do a quick run-through of neural networks. Also, you're gonna want Python

3.6 at least as of the release of this video hopefully very very soon

Tensorflow will be supported on three seven and later versions of Python just happens to be the case right now

it isn't I think it's something to do with the

Let's get into it starting with an overview of how neural networks just work

we need to have some sort of balance between treating neural networks like a total black box that we just don't understand at all and

Understanding every single detail to them. So I'm gonna show you guys what I think is just the kind of bare essential to understanding

What's going on? So a neural network is going to consist of the following things. Like what's the goal of any machine learning model?

So let's say X 1 X 2 X 3 and you're just trying to map those

Let's say that output is determining whether something is a dog or that something is a cat

So the output is going to be two neurons in this case. So it's just boom two neurons

Now what we want to do is is figure out how are we going to map to those things?

We could use a single hidden layer. Let's say we're going to do some neurons here and

What's gonna happen is each of these X 1 X 2 and X 3 these are gonna map to that hidden lair

Connected to each of the neurons in that first hidden layer. And each of those connections has its own

Unique weight now from here that first hidden layer could then map and connect to that output layer

the problem is if you did this the relationship between x1 and dog or cat and

All the other ones those relationships would only be linear relationships

so if we're looking to map and nonlinear relationships

Which is probably going to be the case in a complex question. You need to have two or more one

Hidden layer means you just have a neural network two or more hidden layers means you have a quote-unquote deep neural network

So we'll add one more layer and then we're gonna fully connect that one two

And then once that's fully connected again all unique weights, each of those blue lines has a unique weight associated with it

The output and again each blue line has a unique weight associated with it

so now what we're gonna do is talk about what's happening on an

Neuron level. So again that neuron has certain inputs coming to it

It might be the input layer X values or it could be inputs coming from the other neurons

So we're gonna again we're gonna call the inputs x1 x2 and x3

But just keep in mind this could actually not be it might not be your input data

It might be data coming from another neuron

But regardless that data's gonna come in and we're just gonna get the sum of that data

So it's gonna come in and be summed all together

But remember we also have those weights each of the inputs has a unique weight that gets put you know multiplied

Against the input data and then we sum it together finally and this is kind of where the artificial neural network comes into play

we have an activation function and this activation function is kind of meant to

So you can think of the activation function like on a graph, you know?

You got your X and your Y and then a really basic activation function would be like a step or function

So if X is than a certain value boom we step up and we have a value. So let's say here

so if X, you know after being all the inputs are multiplied by their weights sum together if that value is

well, then this activation function returns a 1 but today we tend to use more of a

sigmoid activation function so it's not going to be a 0 or 1 it's going to be some sort of value between

0 and a 1 so instead we might actually return like a point seven nine or something like that

So coming back to this neural network here that we've been drawing

Let's say here on this final output layer. You've got dog and cat

well, this output layer is almost certain to have just a sigmoid activation function and

What's gonna say is maybe dog is a point seven nine and cat is a point two one

these two values are gonna add up to a perfect 1.0 but we're gonna go with whatever the

The neural network is you could say 79 percent confident that it's a dog 21 percent confidence a cat

We're gonna say we're gonna take the Arg max basically and we're gonna say hmm. We think it's a dog

All right. Now that we're all experts on the concepts of neural networks. Let's go ahead and build one. You're gonna need tensorflow

So do a pip install - - upgrade tensorflow you should be on tensorflow version 1.1 or greater. So

one thing you can do is import tensorflow and then

Actually touch flow as TF and then TF dot version will give you your current version

Now let's go ahead and get started. So the first thing we're going to do is import a data set. We're going to use em

Data sets with machine learning. It is a dataset that consists of 28 by 28 sized images

Digits 0 through 9. So it'll be like a 0 1 2 3 and so on and it's a handwritten kind of unique image

soon enough so you can see it's actually an image and the idea is to feed through the pixel values to the neural network and

Which number it actually thinks that image is

So that's our data set, and now what we want to do is

Unpack that data set to training and testing variables

Operation when it's actually a data set that you're kind of bringing in or that you built or whatever

For the sake of this tutorial we want to use something real basic like M inist

so we're gonna unpack it to X train Y train and

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Python、TensorFlow、Kerasを使ったディープラーニングのチュートリアル (Deep Learning with Python, TensorFlow, and Keras tutorial)

sort

slightly

basically

chaos