Let's say we're using the tumor classification example.

Suppose I have a patient come in and, you know they have some tumor size and I feed their feature vector X into my hypothesis and suppose my hypothesis outputs the number 0.7.

You may notice they're wearing very thick gloves.

The perfect desert machine. I'm smartly designed.

Anything else?

Any description?

That sure narrows it down.

Can you give me a description of the car, sir?

Other features

Okay, what does your husband look like?

How will I recognize the person I'm meeting?

We saw left feature number 1.

Scrums are a feature of Rugby football.

So what are these characteristics that I'm talking about?

There'sbeen a massive explosion in midcity washington.

I mean, we've all seen apish people.

Well, we sent out an all points.

It's just the female.

So why don't we leverage that? And that's why we built this.

DiNozzo. How many cabdrivers in DC fit this general description?

Match the rest against our victims' stats.

This is an image I took a while back in Amsterdam, and it's interesting because there's a lot of interesting features

They really loved it.

But you've never seen this one before.

And he was really gagging. I thought he might throw up on me, actually.

That was characteristic.

What else? Aside from they hear you?

And you should observe, that the perceived order of features in the scene, by virtue of using a pinhole, will be inverted inside the pinhole camera.

Great speed is a feature of the Concorde.

Describe the level characteristics

Suppose in camera A we see from left to the right the following sequence 1, 2, 3.

Young. Well, he moved Like he was young.

My own website.

Well, and you're glad I didn't show you this.

Characteristic of a fourfooted animal.

What about other distinguishing features?

found two nights ago at the Pier?

For a different configuration let's now assume we get to see 1, 2, 3 in camera B, and we care about the feature indices we see in camera A that corresponds to those features over here.

They did report one distinctive feature which bears mentioning.

By combining both of them, we get a gradient s l magnet? image, and we have our very first feature detector, which is a feature detector of any edge in the image.

Bats have learned and evolved how to deal with this.

It enters a new state of matter called a superfluid.

He look familiar to you?

These are the basic features of my device, or our device.

Start from what they know. Build on what they have.