But let's generalize even more.

Now we can generalize the idea.

let's generalize this into a function.

Remember their ancester is SGML, which stands for Standard Generalize Markup Language.

We can generalize this to larger graphs.

If you generalize this, something like this happens.

Let's see if we can generalize to 4.

We'd like to generalize over more data points.

Now let's see if we can generalize this.

But what if I wanted to generalize a little bit ?

Let me zoom in. I'll generalize it a little bit.

So let's generalize it a little bit, to the situation where

But I want to show you that we can generalize them.

They wanted this to generalize to all children in United States.

I'd like now to generalize this to a 2 dimensional problem.

Let's see if we can generalize this somehow to a curve.

And we can even generalize them to vector spaces that aren't normally intuitive for us to actually visualize.

What I want to do in this video is to generalize it.

And that's when you're given a couple of examples and you generalize.

As I said, population was, we want to generalize to all children in the U.S. I mean ultimately, we want to generalize to all children across the world.

We want to be able to generalize these results to all healthy adults.

And therefore fail to generalize the new examples not in the training set.

I mean, this is a very small sample you should not generalize from it.

We have to have a curve before we can generalize it to a curve.

The formulas for calculating Mu and the covariance matrix Sigma generalize the ones we studied before and they are given over here.

The program tried to generalize from that, using supervised learning, and was not able to perform very well.

But, in the next video we're going to take this and generalize this to the case of matrix matrix multiplication.

like in Sauri village, in Kenya, where Jeffrey Sachs is working and therefore generalize this experience as the experience of everybody.

We say that more learning is happening when we generalize a smaller pattern than just copying all of the available input, but that was by no means required for this problem.

So the population is just the entire collection of cases or people that we would like to generalize about in our research.

So to do it, I'm actually going to do this exact technique that we did before, then we'll generalize it so you don't have to do it every time for a particular number.

And seeing what happens, it was OK to generalize, and say, I'm sure that this will happen anytime we make one of these things.

So basically, if you wanted to generalize, you can demonstrate that as the density increases along the bottom there, that the energy consumed reduces dramatically.

So I just want to say a couple more things about that particular story, and then try to generalize how could we have stories of success all over the place for these diseases, as Sam says, these 4,000 that are waiting for answers.

So one typical way of defining the clustering coefficient is to start off by taking about the clustering coefficient for a particular node in a graph, I'm going to generalize this to clustering coefficients for the entire graph next, but let's focus on a single node.

If you generalize this, the most favorite TEDTalks are those that feature topics we can connect with, both easily and deeply, such as happiness, our own body, food, emotions.

Now, if we generalize this and think about not just one or two charter cites, but dozens cities that will help create places for the many hundreds of millions, perhaps billions of people who will move to cities in the coming century is there enough land for them?

So think about this, as it's a little bit nontrivial, but it's going to be important as we go forward and define probabilistic convolution and generalize this to the noisy case.

But normally you'd like to think about the problem, whether you can generalize it, find about the best place to fix, reason about it until you have a very good diagnosis, so this is definitely not true.

In this video, we're going to see if we can generalize this by trying to figure out the Laplace transform of t to the n, where n is any integer power greater than 0, so n is any positive integer greater than 0.

Dotti For, for, for

On the one hand, our need for security, for predictability, for safety, for dependability, for reliability, for permanence.

And once again here is as if we fit this parameter theta to my cross validation set, which is why I am saving aside a separate test set that I am going to use to get a better estimate of how well my a parameter vector theta will generalize to previously unseen examples.

For Turkey, For Europe

'For you, for me'