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

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

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

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.

I don't fit in anywhere I go ?

like vacuum tube technologies, and relay based technologies.

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.

Here is what we see coming.

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.

Cats fit on the windowsill

It did not let up.

And in fact, if you compare it to data, it fits very well with the development of cities and economies.

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 look at some more examples.

Children fit in the snow

Either way they were going to eat it.

A city fit for a king

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

The other thing I like about Coursera's approach is pedagogically, they emphasize mastery of material and again that fits perfectly with how I approach teaching

Now let's consider the model selection problem.

Is a value that's out here that's you know pretty far from the minimum right it's pretty far this is a pretty high cost because this is just not that good a fit to the data.

So the we have the neutron.

Here's one more, you know, at this value of theta zero, and at that value of theta one, we end up with this hypothesis, h(x) and again, not a great fit to the data, and is actually further away from the minimum.

Some crazy guy is giving a commencement speech.

So this seems reasonable so far.

We can convert nondeterministic finite state machines down into deterministic finite state machines.

We could trade off the relative way between how much we want to fit the training set well, as minimizing A, versus how much we care about keeping the values of the parameters small.

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

I've added some whitespace and another exclamation mark and everything worked.

Hello, Udacity.

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

It looks a bit like a chess piece functions absolutely brilliantly.

And the Vaucouleurs profile looks like a straight line in those coordinates.

X1 through X M, we then fit the parameters, mu 1 through mu n, and sigma squared 1 through sigma squared n, and so these were the formulas similar to the formulas we have in the previous video, that we're going to use the estimate each of these parameters, and just to give some interpretation, mu J, that's my average value of the j feature.

So really, just an amazing image, slowed down extremely, to extremely slow speeds.

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

So happy.

So happy.

It's well hidden.

One thing a learning algorithm might be able to do is put a straight line through the data or to fit a straight line to the data and, based on that, it looks like maybe the house can be sold for maybe about 150,000.

1 WATCH Here is a friar, and slaughter'd Romeo's man,

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

Suppose you like to decide what degree of polynomial to fit to a data set, sort of what features to include to give you a learning algorithm.

if you do this, then you get that , they're great!

An ill beseeming semblance for a feast.