Tighten.

The doglegged clincher there.

Put the thing up. Put the gate back up.

And close.

Get your seatbelt on.

Zip up your fly.

How fitting.

Suppose we're fitting a high order polynomial like that shown here, but to prevent overfitting, we're going to use regularization, like that shown here, so we have this regularization term to try to keep the values of the parameters small.

The deadline... November.

The deadline... last week.

And so it goes.

(Laughter)

Give me the seat belts.

You're the one with the problem.

What? We've got deadlines.

And I think, however, we can make good relationships with each other.

Take your time. Do your buckles. All right, tighten that.

Please make sure your seatbelts are securely fastened.

We gotta buckle up. Let's go.

And I was holding a gun.

And on this slide, I'm basically explaining all the problems you have.

Fasten your seat belt, please.

Please fasten your seat belt.

Tom hugged Mary.

Tom wrapped his arms around Mary.

Why? Why are they in the Mid East?

(Applause)

Hold on

Deadlines,maybe.

That's why they shut you out.

You said Norman Bridewell looked good for it.

FASTEN SEAT BELTS.

As for the discussion of most of the learning algorithms, we're going to begin by talking about the cost function for fitting the parameters of the network.

We talked about the fit from data and we even talked about multivariate Gaussians.

Now I come back to you.

(Laughter)

Fasten your seat belt at all times.

She girded her waist with a belt.

You must keep up your guard even after a victory.

Keep your purse and your mouth closed.

It won't be long. It won't be long.

Puts it in reverse. Fifty five out of the driveway, in reverse.

Hang the panel by aligning the drop out holes with the screws

When I was a kid, no one wore a seat belt.

Remember, it must be ready tonight.