So, for the overall cost function, we usually, we will also have a sum over all the training examples and 1 over m term.

Uh,warmup gear,training sweats,shiny type? No.

But this expression here.

So I'm also going to use X superscript

Training grounds?

Your training.

It's training videos.

Training is over... Training is over...

Ear training

So, for this issue, we developed this robotic system.

Get to work.

I'm not going to write x1y1 just write this.

layer, layer capital L. Next, we're going to use the output

Suppose we have a data set like this.

X2 is going to be a vector of the features for my second training example.

I trained myself to death every day.

All thanks to your training, Master

Another happy landing.

Ham Radio Examination trainer

Don't feel ashamed.

But then a couple of years later, when I was a company commander,

Oh, boy I'm agonna be trained

with you to training, right?

Now, if I take the definition for the full of my hypothesis and plug it in, over here, the one I get is that each training example contributes this term, right?

Spotted the seller.

I bet they can't. (Laughter)

The first step is attention training.

Outta my boy's glove.

Yes. lt's a training ground.

Training is over... When...

Whoever wore a lab coat and rubber gloves.

So, what we have for the support vector machine is an minimizationminimalization problem of one of 1 over m, sum over my training examples of y(i) times cost 1 of theta transpose x(i) plus 1 minus y(i) times cost zero of theta transpose x(i).

I'm going to use also Xi subscript J to denote the value of the J, of feature number J and the training example.

And what would you be wearing?

I am not training him

Or, the software is GPL, so it's open source as well.

That's Training, yes?

So we saw already that whenever you have a training example with a label of y 1 if you want to make that first term zero, what you need is is to find a value of theta so that theta transpose x i is greater than or equal to 1.

But the term batch gradient descent means that refers to the fact that, in every step of gradient descent we're looking at all of the training examples.

The letter will arrive tomorrow.

There is wonderful inspiration coming from the area itself.

He's smooth, baby. He keeps his cool.

(Laughter) (Applause)

I guess this thing does land itself.

Which is lower than most Olympic athletes.