Consisting of accidental and systematic errors.

This is a right angle this over here is gonna be a vertical angle

This is an altitude

Variance

Error diffusion

And, if we go further say, beyond local supercluster, most of the velocities due to Hubble expansion.

But then the tricky part is the distances.

Monochrome error diffusion

Having done that, finally, what

Concretely, for the high bias case, that is, the case of under fitting, what we find is that both the cross validation error and the training error are going to be high.

Give or take a few years

Concretely, let our training error and cross validation error be defined as in the previous videos, just say, the squared error, the average squared error as measured on the 20 sets or as measured on the cross validation set.

If your hypothesis basically classified the example y correctly.

If the prediction of our function f equals our target label, and the error is zero, then no update occurs.

Degrees

Radius

Angle

Angle

X angle

Y angle

Angle

X angle

Y angle

Do I do it upside down?

Now, we can use that exact same logic

So we could draw that here.

So, if your algorithm is suffering from a bias problem, the training set error would be high and you may find that the cross validation error will also be high.

Values given over accidental errors.

This angle plus this angle is going to be equal to this angle.

Trigonometric functions use degree mode for angles.

But this is just general terminology for different types of angles

Angular velocity

Seed radius

Cut off angle

Angle Mode

Watermark Angle

And then j subscript cv is my cause validation error is pretty much what you'd expect.

Now the other thing that we know

Now what else can we figure out?

J C V and J

The answer is this.

A. This is indeed the identity matrix with essentially ones on the diagonals and zeros on the off diagonals, up to a numerical round off.

Intersect this polygon with another polygon

You have to have points B and C before you can draw those equal arcs.

And similarly, I'm then also going to define the cross validation set error when the test set error, as before to be the average sum of squared errors on the cross validation and the test sets.