Waching daily Aug 16 2017

Om Shanti !

Today's Murli Date Is 17th August 2017

Essence: Sweet children, wake up early in the morning and remember the Father with a lot of love

and your intellect will change from stone to divine.

Question: What is the way to become prosperous for 21 births?

Answer: Donate the imperishable jewels of knowledge and you will become prosperous

because each jewel of knowledge is worth hundreds of thousands of rupees.

The more one donates & stays in remembrance of the Father, the higher the mercury of one's happiness will rise.

Question: What precautions must you take so that you don't perform any sinful actions?

Answer: You need to take a lot of precautions with your food.

When you take in food cooked by a sinful soul, it affects you.

The Father gives advice on seeing the circumstances of each one.

Essence for dharna: 1. Develop the practice of waking up early in the morning.

Wake up early in the morning and definitely churn knowledge. Sit for half an hour or even 45 minutes and talk to yourself.

Make your intellect full of knowledge.

2. In order to receive blessings from many, open a hospital or college on three square feet of land.

Become egoless, the same as the Father and do service.

Blessing: May you experience every power and virtue through your elevated effort and become an embodiment of experience.

The greatest authority is the authority of experience.

Just as you think and say that a soul is an embodiment of peace or an embodiment of happiness,

similarly, experience every virtue and power and become lost in those experiences.

Since you say that you are an embodiment of peace, then let yourself and others experience that peace.

To become an embodiment of experience is a sign of elevated effort. So, increase your experiences.

Slogan: Become a contented soul and by experiencing being full, no name or trace of anything lacking will remain.

To the sweetest, beloved, long-lost and now-found children, love, remembrance and good morning from the Mother, the Father, BapDada.

The spiritual Father says namaste to the spiritual children.

We spiritual children convey to spiritual Baapdada, our love our remembrance, our good morning & our namaste namaste

Om Shanti !

For more infomation >> Essence of Murli 17-08-2017 - Duration: 5:43.

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TAEYANG - Wake Me Up (Piano Cover) - Duration: 3:57.

For more infomation >> TAEYANG - Wake Me Up (Piano Cover) - Duration: 3:57.

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Learn Colors with Baby Born Doll Sand Molds Toys Finger family song nursery rhymes for children 遊戲 - Duration: 2:25.

I

Am how do you do? Hey?

Here I am here. I am how do you do?

Hmm

Donna made you yeah yeah. Yeah. I am how do you do huh?

Learn Colors with Baby Born Doll Sand Molds Toys Finger family song nursery rhymes for children 遊戲

oh

For more infomation >> Learn Colors with Baby Born Doll Sand Molds Toys Finger family song nursery rhymes for children 遊戲 - Duration: 2:25.

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Nếu Có Duyên nghe được Kinh này Ph.ật T.ổ luôn Phù Hộ Gia Đình Bạn - Duration: 31:27.

For more infomation >> Nếu Có Duyên nghe được Kinh này Ph.ật T.ổ luôn Phù Hộ Gia Đình Bạn - Duration: 31:27.

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Clarify | Die Lochis über das Wählen - Duration: 1:17.

For more infomation >> Clarify | Die Lochis über das Wählen - Duration: 1:17.

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Starting Your SAS Developer Trial: Python Example - Duration: 7:15.

In this video, I'll show how to start the SAS Developer trial

and launch a Jupyter notebook.

In the notebook, I'll use Python to invoke SAS data management

and analytics capabilities on SAS Viya.

As a Python developer, this environment

should be very comfortable to you.

To begin, from the Getting Started:

SAS Viya Developer page, click the Get

Started with Python, R, and SAS API via Jupyter Notebook link.

On the next page, click Start My Trial Now.

The folders shown here contain program examples broken out

by language: SAS, Python, and R. The examples

are here to help you start exploring this environment.

Although uploading your own data is not

enabled in this trial environment,

you have access to sample data for a variety of examples,

including banking, sales, and movie ratings.

Note that many of these examples are available on the SAS GitHub

page.

A link to these examples is on the main SAS Developer Trial

page, as well as here in the notebook.

Let's open a notebook and look at an example that

uses the hmeq data set.

This is a banking example in which

you will use SAS determine which cases are bad credit risks.

In this program, we import the Python packages,

create a session with the SAS Cloud Analytics Services

(or CAS) server, and then load the data and explore it.

To prepare the data, we impute the missing values

and partition the data into training and validation data

sets.

Then we build several models, assess them,

and compare the results.

Notice there's a handy link to the documentation.

Refer to the documentation to help you

understand the SAS Python APIs for the CAS actions.

A CAS action is the smallest unit

of work for the CAS server.

CAS actions are analogous to Python functions.

CAS actions are organized into groups

called action sets, which are analogous to Python packages.

Let's review and submit each code block

as we go through the program.

In the first code block, we load the Python packages

that are needed, as well as assign variables

that we need for our modeling.

Next, we start a CAS session and load

the action sets that we use in this program.

You need to load an action set before you can call the CAS

actions contained within it.

Here, we load the data into CAS.

Now let's explore the data.

We have 11 numeric variables, and our target variable

is a binary variable BAD, which indicates

whether a loan is good or bad.

Here we'll look at the descriptive statistics

of the numeric variables.

Next, we look at the cardinality,

or the number of distinct values,

and build a graph of the missingness.

The graph shows that we have missing values

for every variable.

In order to use the data effectively,

for some of our algorithms, we impute the missing values

using a variety of imputation methods

for different variables.

Now that we have a complete data set with no missing values,

we partition the data into training and validation data

sets using stratified sampling with respect

to our target variable.

In this table, we can see that we've

divided the data into a 70/30 split

within each level of the target variable BAD.

Next, we build a decision tree model.

We also score the current data now using this model

by calling the score action for the decision tree.

Because we've saved the model as "tree model" here,

we could score new observations at a later point

using this model as well.

We also run a SAS program to add columns to the data table

for predicted probabilities of each event, which

will be used for comparing with other models

later in this program.

We submit similar code for forest,

gradient boosting machine, and neural network models.

Note that you can edit the code to configure the modeling

algorithm options as desired.

For example, let's change the number of hidden neurons

here and run this code block.

Now we assess the models using the assess action.

We also define a Python function to make it easier

to assess all four of the models with the same code.

Having assessed all four models, we

can use the assessment statistics

to create a receiver operator characteristic, or ROC

plot and a Lift plot.

They're our standard graphs for visually depicting the accuracy

and effectiveness of a model.

First, we print the area under the ROC curve for each model.

The higher the area under the curve the better,

so we can see that the Forest model outperformed

the other three.

And now, let's draw the ROC curves and Lift plots.

The ROC plot also reflects that the Forest model is the best

with the highest curve.

On the Lift chart, we see that the Forest model

has a lift as high as 5 and at the first decile

the cumulative lift is just over 3.5.

It is also fairly consistent across

the different algorithms.

When you're done making calls to CAS actions in your session,

it's a best practice to close your session.

In a non-trial environment, closing the session

releases resources for others to use.

Here in the trial environment, you have access to the system

only for the duration of your session

and no information is saved on the system.

When you come back, you start with a fresh session.

You do have the option to download notebooks for use

at a later time.

In this video, we performed some basic modeling tasks

using Python.

We encourage you to expand upon the examples that

are provided in the trial and explore the environment.

For more information, please visit us at developer.sas.com.

For more infomation >> Starting Your SAS Developer Trial: Python Example - Duration: 7:15.

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Dembele | Bild: ¿se está mudando Dembelé? - Duration: 1:52.

For more infomation >> Dembele | Bild: ¿se está mudando Dembelé? - Duration: 1:52.

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Hot girl run super new car auto quotes 2018 - Duration: 10:02.

super new car auto quotes

So across your fingers buckle up. We're getting in a car. I'm one heading out. See you guys inside

All right, so these are the buttons to see I wonder if it goes

bad

All right, okay, okay retry button. They're Gonna move

If you go to see over the steering I think I got it. It's on our seatbelt

Come on it

you

I

See you ready foot on the brake okay, and then you hit the start engine button on the especi, er. Well yeah

all right, so

We're gonna go through some of the functionalities, okay?

We're actually in neutral and the parking brakes up, so we could just take the foot off the brake

Okay, so if we want to roll down the windows in the center console you have the window buttons right there easter

Yup, you want to roll down your window. That's

right

Okay, and then right there you have

Automatic you see where's this auto?

Yup, that's if you do not want to use the paddle shifters on the steering wheel or is for reverse? Oh?

Okay, so when I would press when I try to reverse

Yeah, you exactly

Auto is automatic and a Ps is for parking sensor

When you back up if you get close to something, it'll be

And if you want it off, because you don't want it to beat you just turn it hit that button

And then on the steering wheel there's different modes that you can drive in Stanislavski up there little button

All right, so we're gonna put down the top see see see the button in the center

See that button this is

Let me get it right there yup to open it

You want to just push it back up and hold it hold it you hold it

There you go you hold it till it goes all the way down

And the reason they do that is like a child safety feature because God forbid something's in the way when the sunroofs going down

Yup, you're all done

All right, so now we did the decide whether we drive the top down on the top up

I think we're gonna go with the top up so you want you want to go the exact opposite Direction

And just hold it until everything's back up

You can perfect yup now. We're ready for our ride, okay?

All right

so

Let's say I want to move the steering wheel okay like to adjust it how its electric on the left hand side

You'll see it like a little stick that comes out reach under you see they feel a little stick that comes out. Oh

Yeah, yep, so just move that moves in out backwards forward

Okay, you play around?

Get yourself comfortable

Well short

All right, and then we're gonna stick with comfort sport for right now. Why don't we put in comfort switch in comfort?

Oh, and I changed it over here. Yes, it does it always will light up. Yeah, right there

It says comfort automatic mode yeah, I see you right there

It says pete with an automatic which means we're ready to go

All right, so then you were just in park. So what you want to do is

You click that one time okay?

And then that'll put you in first gear all right so nas has one and auto underneath. No it says one and auto and

just

Hit the gas you should ready to go

Okay, there you go

Okay, so I think it's a great idea for go, we'll pick up a burger

Boy a hamburger, okay?

Drive-through eat in the car. I drive through whoppers. Let's do it, okay

So are you dude put your foot on the brake and stop at the stop sign?

It's pretty easy, so this automatically changes gears on its own signal assuming that when I was driving it had a number 2 correct

So when we're in automatic, it's gonna do everything for you. All you do is hit that gas and brake yeah, hey wait straight

Let's go make a right. Where's your

Signal on the right on the left hand side. Yeah, okay. Just like in any other car Joe

drives keep coming straight

How's it feel it feels great it drives, so incredibly smooth

you

All right, so get drive. Give it a little gas circular. Oh, yeah, give it gas fill it out

I love it. I love hats. I'm gonna take it home

So we're gonna test out the paddles, okay?

Are you ready ready first time driving paddles are for me okay? So I want you to click the auto button

Do I put my foot on brake yes?

Okay, so now you are ready to do paddles. So we'll go also known as chip shot

Yes, so well right now you're in what gear. What is it sexist, okay?

So I want you to hit both panels together towards you at the same time what just happened teacher correct, okay?

so now you're neutral you're not any gear and you're

just sit here and relax and if would I be able to let go of the

Well, or you can let go on the brake because we're not on a hill

But you really should stand a break really if you want to stop completely

And you're not at a red light like you just want to pull over

You would put the parking brake on which is on the left hand side, but you push on the left, okay, okay?

But so right now. We're in paddle mode, okay?

So really what you want to do is to start it you put your foot on the brake and this is to upshift

And this is the downshift

okay, so if you've driven a stick before

like Ten Years ago okay in the hills

Okay, well a good thing about the Ferrari especially our ferrari is up here

It actually tells you when to shift and option two. Oh here yep

It actually gives you lights, but I'm gonna be the one telling you satellites, okay?

That's a little extra feature on this for our

California that we have to kind of help people out who haven't driven so much with stick

I'm sorry teaching them how to eat dummy proof. Yes

First okay, we'll let that car pass us first because we're gonna catch up them about three seconds

So let him go all right as fast as you can

Let's do it. Okay, no

seat belt on nope

double neutral

This is this is almost better than so what you think about you're riding a Ferrari

You know honey

So amazing you feel like you're please what was your favorite part?

Well, I'm getting ready to go. I'll see you guys later

Thanks for hanging out with me and I go bite while you guys a little bit

you

you

For more infomation >> Hot girl run super new car auto quotes 2018 - Duration: 10:02.

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How To Check Your Internet Speed. - Duration: 2:49.

Link : copy and paste your browser.

now wait for running the browser

after complete open the browser

press the go button

Just wait for a moment.

here showing download speed & upload speed color by color

thanks for watchinh

don't forget to like comment share with your friends

For more infomation >> How To Check Your Internet Speed. - Duration: 2:49.

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Danh Lam Thắng Cảnh Top 50 2017 - Ca Nhac - How it Begins - Duration: 3:11.

For more infomation >> Danh Lam Thắng Cảnh Top 50 2017 - Ca Nhac - How it Begins - Duration: 3:11.

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Danh Lam Thắng Cảnh Top 50 2017 - Ca Nhac - How it Began - Duration: 3:03.

For more infomation >> Danh Lam Thắng Cảnh Top 50 2017 - Ca Nhac - How it Began - Duration: 3:03.

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Starting Your SAS Developer Trial: SAS Example - Duration: 7:21.

In this video, I'll show how to start the SAS Developer trial

and run a program in SAS Studio to illustrate

the capabilities of SAS Viya.

To begin, from the Getting Started:

SAS Viya Developer page, click Get

started with SAS Studio for the Data Scientist.

On the next page, click Start My Trial Now.

We are now in SAS Studio.

For information on using SAS Studio,

see the links on the SAS Developer trial page.

Although uploading your own data sets in this trial environment

is not enabled, you have access to sample data

for a variety of examples, including banking, sales,

and movie ratings.

Note that many of these examples are available on the SAS GitHub

page.

A link to these examples is on the main SAS Developer Trial

page.

Let's open a program and look at an example that

uses the hmeq data set.

This is a banking example in which

you use SAS to determine which cases are bad credit risks.

In this program, we create a session with the SAS Cloud

Analytics Services (or CAS) server, and then load the data

and explore it.

To prepare the data, we impute the missing values

and partition the data into training and validation

and test data sets.

Then we build several models, assess them,

and compare the results.

Notice there's a handy link to the help center documentation.

Let's review and submit the code blocks

as we go through the program.

In the first code block, we assign macro variables

to make the code easier to follow.

Here, we load the data into CAS.

Now let's explore the data.

We have 11 numeric variables, and our target variable

is the binary variable BAD, which indicates

whether a loan is good or bad.

Here we can look at descriptive statistics

of the numeric variables.

The graph shows that we have missing values

for every variable.

In order to use the data effectively,

for some of our algorithms, we impute the missing values

using a variety of imputation methods

for different variables.

We submit this code and see the output here.

Now that we have a complete data set with no missing values,

we partition the data into training, validation,

and test data sets using stratified sampling

with respect to our target variable.

In this table, we can see that we've

divided the data into a 60/30/10 split

within each level of the variable BAD.

Next, we build a decision tree model.

We'll score the data here and also save the model

so that we can use it for future use

in assessing new observations or new data that come in.

We submit similar code for forest, gradient boosting,

and neural network models.

Note that we can edit the code and configure the modeling

algorithm options as desired.

For example, let's change the number of hidden neurons

here and rerun this code block.

Now we assess the models.

We'll define a SAS macro to make it easier

to assess all four models with the same code.

Having assessed all four models, we

can use the assessment statistics

to create a receiver operating characteristic, or ROC, plot

and a cumulative lift plot.

These are standard graphs for visually depicting the accuracy

and effectiveness of a model.

And now, let's draw the ROC curve and cumulative lift

plots.

The ROC plot reflects that the gradient boosting model

is the best with the highest curve.

On the cumulative lift chart, we see

that the gradient boosting model has a lift starting just over 5

and, at the first decile or 20th percentile,

it has a lift just over 4.

It is also fairly consistent across

the different algorithms.

When you're done submitting code to the CAS

server in your session, it's a best practice

to close your session.

In a non-trial environment, closing the session

releases resources for others to use.

Here in the trial environment, you have access to the system

only for the duration of your session

and no information is saved on the system.

When you come back, you start with a fresh environment.

You do have the option to download these programs

and use them later.

In this video, we performed some basic modeling tasks in SAS.

We encourage you to expand upon the examples that

are provided in the trial and explore the environment.

For more information, please visit us at developer.sas.com.

For more infomation >> Starting Your SAS Developer Trial: SAS Example - Duration: 7:21.

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MOBIL Peugeot 108 TERBARU SUDAH DIRILIS - Duration: 2:38.

For more infomation >> MOBIL Peugeot 108 TERBARU SUDAH DIRILIS - Duration: 2:38.

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Starting Your SAS Developer Trial: R Example - Duration: 7:29.

In this video, I'll show how to start the SAS Developer trial

and launch a Jupyter notebook.

A Jupyter notebook is a web-based interface

used to submit code.

In the notebook, I'll use R to invoke SAS data management

and analytics capabilities on SAS Viya.

As an R developer, the code and the output

will look familiar, even if the interface is new to you.

To begin, from the Getting Started:

SAS Viya Developer page, click the Get

started with Python, R, and SAS API via Jupyter Notebook link.

On the next page, click Start My Trial Now.

The folders shown here contain program examples broken out

by language: SAS, Python, and R. The examples

are here to help you start exploring this environment.

Although uploading your own data sets

is not enabled in the trial environment,

you have access to sample data for a variety of examples,

including banking, sales, and movie ratings.

Note that many of these examples are available on the SAS GitHub

page.

A link to these examples is on the main SAS Developer Trial

page, as well as in the notebook.

Let's open a notebook and look at an example that

uses the hmeq data set.

This is a banking example in which

you use SAS to determine which cases are bad credit risks.

In this program, we load the R packages,

create a session with the SAS Cloud Analytics Services

(or CAS) server, and then load the data to explore it.

To prepare the data, we impute the missing values

and partition the data into training and validation data

sets.

Then we build several models, assess them,

and compare the results.

Notice there's a handy link to the documentation.

Refer to the documentation to help you

understand SAS R APIs for CAS actions.

A CAS action is the smallest unit

of work for the CAS server.

CAS actions are analogous to R functions.

CAS actions are organized into groups

called action sets, which are analogous to R packages.

Let's review and submit each block of code

as we go through the program.

In the first code block, we load the R package that is needed,

as well as assigned variables that we need for our modeling.

Next, we start a CAS session and load

the action sets that we use in this program.

You need to load the action sets before you can call the CAS

actions contained within it.

Here, we load the data into CAS.

Now let's explore the data.

We have 11 numeric variables, and our target variable

is a binary variable BAD, which indicates

whether a loan is good or bad.

Here we'll look at the first rows of our data set

and then we'll use the summary function

to look at the summary statistics for all

of our variables.

Next, we'll look at the cardinality, or the number

of distinct values, and build graphs of the missingness.

The graph shows that we have missing values

for nearly every variable.

In order to use the data effectively,

for some of our algorithms, we impute the missing values

using a variety of imputation methods

for different variables.

Now that we have a complete data set with no missing values,

we partition the data into training and validation data

sets using stratified sampling with respect

to our target variable.

In this table, we can see that we've

divided the data into a 70/30 split

within each level of the target variable BAD.

Next, we are going to map some variables to make it easier

for our code to be reused.

Next, we'll build a decision tree model

and save that model so that we can score

the data set in a future step.

We'll submit similar code for Forest, Gradient Boosting

Machines, and Neural Network models

and then we can score all of these models.

Note that you can edit the code to configure the modeling

algorithm options as desired.

For example, let's change the number of hidden neurons

and rerun this code block.

Now we will score the models that we have just built.

Using the saved score objects that

were created we'll use an R function, the lapply, in order

to concatenate all of these models

together into a single data frame.

Now we assess the models using the assess action.

We also define an R function to make it easier

to assess all four of the models with the same code.

Having assessed all four models, we

can use the assessment statistics

to create the receiver operating characteristics, or ROC plot

and we can create a misclassification chart

and confusion matrix.

And now let's draw the ROC curve.

The ROC plot also reflects that the Gradient Boosting

is the best with the highest curve.

When we're done making calls to CAS actions in your session,

it's best practice to close your session.

In a non-trial environment, closing the session

releases resources for others to use.

Here in the trial environment, you have access to the system

only for the duration of your session

and no information is saved on the system.

When you come back, you start with a fresh session.

You do have the option to download notebooks for use

at a later time.

In this video, we performed some basic modeling tasks

using R. We encourage you to expand upon the examples that

are provided in the trial and explore the environment.

For more information, please visit us at developer.sas.com.