Maximum gradient

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

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

Draw scrollbar bevel

And essentially, the apartments cover the parking.

AII right. Stake out the railroad and bus depots.

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

What is the point?

This is state property!

The road.

Sixpercent grade, fast greens, breaks to the left.

For a fugitive there are no freeways.

In B, it's about zero.

We made a deal.

Out on the road.

How can we describe this mathematically?

So at any point, the gradient is a vector.

The railroad is parallel to the road.

Trunk road

Primary road

Secondary road

Unclassified road

At the Safari motel

Road trip!

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

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.

June 17 comes.

This road is not for tourists.

Road under Repair.

Unsurfaced road

It's all that new stuff.

I heard them.

It just blew right through the street.

Traffic.

and land routes east?

And, you notice that my line changed a little bit.

Meet me on the corner of Santa Monica and Century West.

What we're going to do is apply gradient descent to minimize our squared error cost function.

But for now, using the algorithm you just learned, now we're using batch gradient descent, you now know how to implement gradient descent, or linear regression.

Louis Pasteur. ( playful theme playing ) ( slow, dramatic theme playing ) ( rock slaps water )

Cycleway

I can't see. KT There's a wall, there's a wall.

Wait. Do you see that? Is that an asphalt road?

Adapt maximum gradient