Consisting of accidental and systematic errors.

Variance

Error diffusion

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.

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.

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

Control

3D controller

RF controller

J C V and J

The answer is this.

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.

Bridge control

CardBus control

Message control

Mass storage controller

Display controller

Multimedia controller

Memory controller

Bridge

Serial bus controller

Wireless controller

Intelligent controller

SCSI storage controller

IPI bus controller

Ethernet controller

WorldFip controller

Serial controller

Parallel controller

DMA controller

Keyboard controller

Mouse controller

Scanner controller

USB controller

Control key

Access Control

If you draw a curve like this, your quadratic error becomes 2.

If I go to my output for 100 steps, I find that the y value is between 0.7 and 0.9.