How fitting.

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

The algorithm is amazingly simple.

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

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

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

Depth of Root Window

in the depth of the sky

Inheritance depth

Depth

Depth

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.

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.

How deep are they?

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

You learned about what a Gaussian is.

No Depth Limit

Maximum inheritance depth

The river is five feet deep.

Maximum depth to check.

Stop at Depth

Stack depth limits

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.

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.

The pond is 3 meters deep.

The snow was knee deep.

The lake is about 25 meters deep.

Was your father this hairy?

His greed brought him low.

Returns the depth of the current item in the tree. Root items have depth 0.

He ain't deep.

Now, from there we look at A, and we see that there are two pages that A links to.

The pond is 3 meters deep.

So I can look at my first model, my second model, fits with these different values of the regularization parameter and evaluate them on my cross validation set basically measure the average squared error of each of these parameter vectors theta on my cross validation set.

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

And the reason is, what we've done is, we've fit this extra the parameter d, that is this degree of polynomial, and we'll fit that parameter d using the test set.

Let's go back and take a look at this again.

Given the following data, my first question is, can this data be fit exactly using a linear function that maps from X to Y?

No, it's not just about depth.

George, please help me. DAVlD

Once tocrawl is empty, that means that that level is finished, that depth is finished, and we want to go on to start the next depth, and that's it.

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

Watch carefully.

Increase the maximum depth