If you dive into the math of convolution, you'll find that convolution is associative, so you could apply this one to the image and then this one over here, or you can combine these 2 guys over here into a Gaussian gradient kernel and apply this Gaussian gradient kernel to the image.

Now these techniques do quite well.

You might already remember that if I have a plus b times a minus b, that that's equal to a squared minus b squared.

Or 2t plus 3 times 2t plus 3, or you could just say, it's 2t plus 3 squared.

B over Z times f. B over Z is 1 50 times f as 200 gives us 4 over here.

This side is congruent to that side

I get 5 out of 5 heads?

What they needed to train us to do was buy stuff.

Can we apply this rule in this case?

In fact there is something like 50 percent to 60 percent for these cases and that is what we call AP or Average Precision.

And we'll see five years later what turns out to be a good idea or not. So here is a example of a precision inaudible .

This is the set of all possibilities.

And let's say this is the set that meets condition A.

And let's, this is, let me do this in a different color.

And you'll encounter this idea repeatedly in the course. So precision is measured here on the X, on the, on the Y axis.

You don't know what to do with it.

And typically you get a curve, as shown here.

And so what happens is that there is some threshold parameter, and if we have a more lax threshold, you get high recall, if you get more strict threshold, you have high precision.

These settings determines when the filter is applied to text.

Throughout its history, Islam has borrowed and adapted from other civilizations both ancient and modern.

This is feudalism one owner, many workers.

And they had lungs.

Then you can exploit the efficiency of a Kalman filter that is now attached to individual particles.

Print. 51 .

Some settings may affect only newly started applications.

In this case, let's take a look at the 2 sided market and match the market with the appropriate business.

Like maybe that line over there. So how do we come up with values theta zero, theta one that corresponds to a good fit to the data?

One aspect of program semantics or program meanings is the notion of types that we can classify objects or values like 1, 2, and 3 into groups and say, Those are all numbers.

We're able to detect some fraction of the two motorbikes so here, for example, at this point, the precision is 0.7.

Green's theorem this applies when the region is to our left.

The probability of a correct word is from the data.

That rule only applies if he's married.

There are some cases where this rule does not apply.

This rule can't be applied to every situation.

There are some cases where this rule doesn't apply.

And he fits the profile.

That rule holds good in this particular case.

It can't hold 7 because it has to be less than or equal to 3.

So when, can compute the precision recall curve for detectors for cats and dogs and chairs and tables, and so on, and measure the area under that curve.

Statistically, one of you is a beetle.

Well, if I'm here, it means I'm not so good.

I'd like to say a few words on the issue of correspondence, often also called data association.

The move function should be very familiar at this point.

So, there are phenomenas that make life difficult for us. Finally, the Pascal

All the cars were back again, and you even have to admire the car drivers.