It got caught on the flange. It's leaking air.

How fitting.

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.

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.

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A banquet?

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.

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Wrong sex!

I'll throw in lunches.

They have shield generators!

What? Valeting.

With a lime. Okay.

You know this thing has a sleep alarm.

Of lying.

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You learned about what a Gaussian is.

Finally, we will perform geometric checks and adjustments to ensure all the machine axes are correctly aligned

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I bought a camera with its accessories.

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Hey, let's just go this way.

Well, the next time think.