It is very important to decide what to do about the slope of the roof.

Rushing the roof? !

The road across the mountain was narrow, and what's more, it was a steep slope.

It's on very steep ground.

Maximum gradient

B is going to be his next position so this is how the hill climber decides where to go next.

It makes sense because it's a downward sloping line, but it's not too steep.

Now, they tell us what the slope of this line is.

We've also got sharpshooters securing rooftop positions.

Draw scrollbar bevel

It sends out that at local optimum your derivative would be equal to zero.

What is his method going to be?

And essentially, the apartments cover the parking.

If its lower, throw up onto the roof (Japanese teeth custom)

Notice also that on all of these graphs, the slope is steeper on in group, authority, purity.

Black roof.

On the roof! On the roof!

The roof!

He's on the roof.

And the very last one is a billboard.

The staircase leading to the rooftop is narrow, steep, and dark.

So at any point, the gradient is a vector.

Now, my derivative term, d, d theta one j of theta one, when evaluated at this point, gonna look at right.

In the attic!

Sixpercent grade, fast greens, breaks to the left.

But there is a very curious pattern in the rest of the 20th century, because we see a sharp decline in this Islamic modernist line.

And if you want to eye ball where is equivalence point, you look for the steepest point in the graph.

We talked about the gradient magnitude kernel, which was defined over a square root of the squares of the image gradients.

That's where the derivative is.

And it turns out there are sometimes other versions of gradient descent that are not batch versions but instead do not look at the entire traning set but look at small subsets of the training sets at the time, and we'll talk about those versions later in this course as well.

In B, it's about zero.

He's on the roof, Alan.

Up on the roof?

There goes the roof... it's all over.

100 for roofing.

I'm in the attic.

I'm up in the attic.

learning people, we're not always created has given me some algorithms.

How can we describe this mathematically?

Tom is in the attic.

So if you apply the rule over here, if you were to start at A as your W zero, then your gradient is negative.

He is on the roof.

Did you open the top?

This is what it looks like today.

Where's the top?