I don't fit in anywhere I go ?

A city fit for a king

Cats fit on the windowsill

It did not let up.

Either way they were going to eat it.

Some crazy guy is giving a commencement speech.

So this seems reasonable so far.

Children fit in the snow

Here is what we see coming.

We keep the inner slope, but change the outer slope.

Now let's consider the model selection problem.

So the we have the neutron.

So Jawbone is a project that you're familiar with, and it has a humanistic technology.

like vacuum tube technologies, and relay based technologies.

lambda is large, then you have a high bias problem and you might not fit your training set so well.

Concretely, if you fit some set of parameters theta 0, theta 1, theta 2 and so on to your training set then, the fact that your hypothesis does well in the training set, well, this doesn't mean much in terms of predicting how well your hypothesis will generalize to new examples not seen in the training set.

But somehow adding that example out there caused linear regression to change in straight line fit to the data from this magenta line out here to this blue line over here, and caused it to give us a worse hypothesis.

It seems like a reasonable fit to the data, because, you know, it

He loses a leg I lose a face!

An ill beseeming semblance for a feast.

You're gonna lose it.

Step on it, Dave. You're losing him.

Do not lose him.

I probably lost my job, all of my friends and my mother disowned me.

No problem. Sorry. Sorry about that.

Oh, yeah, you're right. My bad.

Ruins your ambition, keeps you from what you want in life.

I'm sorry.

Lose Formatting

That's all right.

I got to go.

Excuse me. Thank you.

You're gonna lose.

What do we have to lose?

Just keep sight of him.

Lose her?

Everything.

And the hose on that van is just about a perfect match...

And the house? Gone.

In later videos, we will build on this to talk about how to prevent in the problems of overfitting and underfitting as well.

But settling isn't always such a bad thing.

lambda corresponds a high bias where you might not even fit your training set well, whereas a small value of lambda corresponds to, if you can you know freely fit to very high degree polynomials, your data, let's say.

So, if you take this training set and fill a straight

Window loses focus

We mustn't lose them in these caves, Alex! Fire!