Maximum gradient

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

Draw scrollbar bevel

And essentially, the apartments cover the parking.

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

Sixpercent grade, fast greens, breaks to the left.

In B, it's about zero.

How can we describe this mathematically?

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.

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.

If we now do the MDP trick in this new belief space, and you pour water in here, it kind of flows through here and creates all these gradients as we had before.

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

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.

Adapt maximum gradient

How can we optimize these two terms over here?

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

Of course we don't.

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

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

The gradient with respect to W0 is very similar.

So he's getting citizens to check on them.

Here is a prototypical loss function and the method for interation will be called gradient descent.

It is able to move around its environment.

It's on very steep ground.

You do this until you find yourself with what's called a local minimum, where B resides.

So, in gradient descent, you know, when computing derivatives, we're computing these sums, this sum of.

That's going to equal our old location, yi, and then this gradient, and the gradient here is just going to be the derivative with respect to yi.

Horz. align. betw. cartr.

We already know that this has a closed form solution, but just for the fun of it, let's look at gradient descent.

And these expressions look nasty, but what it really means is we subtract an expression like this every time we do gradient descent from W1 and an expression like this every time we do gradient descent from W0, which is easy to implement, and that implements gradient descent.

Is it linear or nonlinear?

And I have also moved to a new point on my cost function.

It's actually a stand up product from Switzerland, because in Switzerland they have a natural need for diagonal elevators.

In this video, let's talk about how to fit the parameters of that hypothesis.

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

That's where this occasion comes from in our update step. This would be simultaneous update.

We want to minimize this, and we're going to use gradient descent.

Lay Out Children Horizontally

The ocean drives climate and weather, stabilizes temperature, shapes Earth's chemistry.

Take the tangent to that point, like that straight line, the red line, just, just touching this function and let's look at the slope of this red line.

So, this is what we worked out in the previous videos.

So, let's see how gradient descent works.