But let's generalize even more.

Now we can generalize the idea.

let's generalize this into a function.

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.

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

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.

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

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.

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

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

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.

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.

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.

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 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.

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.

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.

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.

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.

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?

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.

Concretely, if you fit some set of parameters theta 0, theta 1, theta 2 and so on to your training set then, the fact that your hypothesis does well in the training set, well, this doesn't mean much in terms of predicting how well your hypothesis will generalize to new examples not seen in the training set.

The mistake of the international aid industry is to pick these isolated incidents of success, generalize them, pour billions and trillions of dollars into them, and then spread them across the whole world, ignoring the specific and unique circumstances in a given village, the skills, the practices, the norms and habits that allowed that small aid project to succeed

And the more general principal is that, once your parameters were fit to some set of data maybe the training set, maybe something else then the error of your hypothesis as measured on that same data set, such as the training error, that's unlikely to be a good estimate of your actual generalization error, that is, of how well the hypothesis will generalize to new examples.

So we could say in general the loan amount, if we now generalize it to n instead of and n equals a number, we could say that it's equal to I'll actually take the p out of the equation, so it's equal to p, times 1 plus 1 over 1 plus i plus 1 over 1 plus i squared plus, and you just keep doing this n times, plus 1 over 1 plus i to the n.