...two, one, mark.

Load the catapults.

How fitting.

On my mark. Five...

Torpedo away.

Launch tube status?

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

What about you? He's the priority.

Radioactive isotopes have a heat signature

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

I'm going to be fitting very complex high order polynomials that might fit the training set with much more complex functions

The algorithm is amazingly simple.

Hot Dog and Apollo are in the tubes.

Escape capsule ready for transport.

Several of the escape pods have been jettisoned.

The explosion flattened buildings and swept bodies away as well as emitting intense heat and radiation.

Did you give the order to shoot?

They made her radioactive.

Get back to me when you've activated the launch cycle.

In a fighter, you have an ejection seat.

In reality, they could hit South Korea, Japan, and possibly American bases in the Pacific.

Casings? Ejection port marks show that you're looking for a Glock or a Beretta.

(Machine gun fire)

Crosslocking now. Prelaunch auto cycle engaged.

Ejection seat is safe. Arm handle.

I could shoot my rifle here.

What was I supposed to do... fire off a flare?

So here are examples of two models with, different amounts of Baryonic matter.

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.

For this quiz we will assume a degenerative case of 3 data points and just 1 cluster center.

So plan B, always a plan B.

The archer killed the deer.

And the key that distinguishes these two cases is if you have a high bias problem your training set error will also be high as your hypothesis just not fitting the training set well.

He's the one who ordered it. Liar!

Navcon Green, interval failure. Abort launch. Frack!

Caused by the explosions and meltdowns in the Fukushima Dai ichi Nuclear Power Plants,

Suppose we fit a linear regression model with a very high order polynomial, but to prevent overfitting, we are going to use regularization as shown here.

In that case, given that we're fitting a high order polynomial, this is a usual overfitting setting.

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.

Refraction

Refraction

Reflection

Reflections

Shots

Reflect