So just to remind you, here's some code for computing the clustering coefficient of a graph with respect to a particular node v.

The last question asked, what is the maximum clustering coefficient for a node in B?

And when you factor by grouping, you're going to split up this term, this 15x.

And X1 and X2. Okay?

Cash converter and calculator

A currency converter and calculator

This doesn't really make sense what we would do with this.

We just have to multiply both sides times the reciprocal of the coefficient on the left hand side.

Two times the number of links divided by length of neighbors times length of neighbors minus one.

And then in the second segment we'll take a more mathematical approach.

If you go back and look at the scatter plot, you sort of see how we would get that value.

If you look at that data between axes x and y,

There are many things which we cannot evaluate in terms of money.

Then, we can apply that to the multiple regression example.

Memory fails me.

A currency converter and calculator

So dollarwise?

You want to group it, one of them with this 2x squared and one of it with the 20.

So that's the back propagation algorithm and how you compute derivatives of your cost function for a neural network.

Modulus

converter, euro, money, cash, calculator

That's easy.

Yes, well, we dream on a budget here, don't we?

35mm equivalent focal length

When you multiply something you can switch around the order.

Like this!

Now it's a level one problem.

Conversion Functions

And actually, as you'll see later, it turns integration to divisions by s.

That assumes we have two variables, x and y, that are continuous.

HKDF.

And the reason we did that is so that we could see exactly how the regression coefficients are estimated in a multiple regression.

And that's true of any sample statistic.

So they store it as 16 bit in memory and then when they had to do floating point they convert to floating point, and they calculate and they store it back in the 16 bit number.

What you then do is for each function you compute the five coefficient for each category as well as for its respective complement.

So the higher the value in 1 diagonal and the lower the value in the other diagonal, the stronger the correlation.

We're still doing ordinary least squares regression just like we did in simple regression but now we'll be using matrix algebra to calculate the coefficients.

Conversion Functions

Correlation

Extinction

Cooling factor

But this time, it took 5 days rather than a minute.

So far, so good. And now one inch, which is pretty small, is going to be a small number relative to a meter, which is huge, right? And so, it is, it's 0.0254 meters.

So if we wanted to compute the clustering coefficient of this graph, we'd actually have to go through node, to node, to node, to node, to node, to node, to node, compute the clustering coefficient for each one and then average.

So I can just rewrite that as A so now A is not a function anymore, it's just a number A times the second derivative of y, with respect to x, plus B times the first derivative, plus C times y.