How fitting.

I should clarify that.

Hull plating's gone.

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

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.

Someone's removed these components.

And the more general principal is that, once your parameters were fit to some set of data maybe the training set, maybe something else then the error of your hypothesis as measured on that same data set, such as the training error, that's unlikely to be a good estimate of your actual generalization error, that is, of how well the hypothesis will generalize to new examples.

look at the SmartBird.

Artificial gravity are ... ad lock cut primary coarse.

They've reinforced their hull plating.

Well, that's what's so exceptional about this device.

Get your packs ... Put it together.

Diplomats are locked in embassy compounds.

Try and remove it, and the device blows.

But there was a big difference.

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.

It's implanted with very conventional techniques.

I lost your smog controls, and 200 pounds of body fat.

They normally remove the restraint after a sale.

Move it! We don't have to actually disarm the device, we just have to remove the cylinders.

His dress betrayed the fact that he was a foreigner.

Hannah will join you as Golitsyn goes exterior.

The transporter won't operate at warp, and docking's out of the question.

Because what I didn't say in my talk is this is actually exoskeletal.

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.

You learned about what a Gaussian is.

Augustin Bouvet, Paint and renovation.

So, that's Jcv of theta because with high bias we won't be fitting.

We will all be caught.

And once again here is as if we fit this parameter theta to my cross validation set, which is why I am saving aside a separate test set that I am going to use to get a better estimate of how well my a parameter vector theta will generalize to previously unseen examples.

So, we'll do that for this figure, where maybe d equals 1, were going to be fitting very simple functions where as we are the right of this this may be d equals 4 or relatively may be even larger numbers.

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