I don't fit in anywhere I go ?

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.

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

Either way they were going to eat it.

So the we have the neutron.

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.

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

Let's look at some more examples.

The image will be scaled to fit the desktop. It will be stretched to fit all four corners.

In the previous video, we talked about the form of the hypothesis for linear regression with multiple features or with multiple variables.

Here is what we see coming.

Cats fit on the windowsill

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

And let's just complete the square here.

Let's solve it.

It did not let up.

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.

Now let's consider the model selection problem.

How do you want your hair cut?

Children fit in the snow

I just can't go on any more.

You want to spend it on the poor

A city fit for a king

An ill beseeming semblance for a feast.

One of the things we'll talk about later is how to choose and how to decide do you want to fit a straight line to the data or do you want to fit the quadratic function to the data and there's no fair picking whichever one gives your friend the better house to sell.

I create computer models of the brain to try to understand how the brain makes predictions, how the brain makes decisions, how the brain learns and so on.

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.

This hypothesis, h(x), with these values of theta zero, theta one, it's really not such a good fit to the data.

let's say, 2 minus 3?

line of the ellipses like these, but you certainly cannot fit a more complex data set like that shown here.

We're probably going to go around and do a fish bowl, where we stand around the outside, and we're going to watch and really think about what a good discussion looks like, how we talk and how we listen, how we ask questions, all of those good things that you guys do very, very well.

Your father gave the same sword to two young princes.

How do you like your coffee?

How could you?

So now we have 1 over the square root of the absolute value of x minus 3.

Those channel lights must have been shifted. I wonder it hasn't been reported.

And that change is forcing us to question quite fundamental aspects of our society how we keep ourselves healthy, how we govern ourselves, how we educate ourselves, how we keep ourselves secure.

How can we relate these to these values right here?

What do you do in your free time?

How would you like to pay for it?

So once again that equals a plus bi, divided by c plus di.

Actually, there's a polygon tool here.

Now, so this is how we fit the parameters to this data.

How do you interpret this poem?