Generalization

It is now really easy to explain expectation maximization as a generalization of K means.

And then finally, another generalization.

Expectation maximization is a probabilistic generalization of k means.

And this generalization holds true of the testimony of authorities as much as of anything else.

This is a fairly complicated generalization of what we just saw.

For a neural network our cost function is going to be a generalization of this.

Which is a generalization of the Vaucouleurs profile, that log of a surface brightness goes as the radius to the minus 1 over n.

This question involves function generalization in reinforcement learning, and we're operating in a 1 dimensional environment of squares, and we're going to consider a state generalization function, that is a function that takes a state such as this and condenses it into some features to represent that state.

And gives you the best generalization error out of these options.

and Bob Mills put forward, 50 years ago, this generalization of Maxwell's equations, with a new symmetry.

There is no generalization at all from one task to another in robotics.

Not surprisingly the best complexity is obtained where the generalization error is minimum.

If you really wish to understand this math, it happens to be just a generalization of the math

However, in reality your generalization error on unknown data tends to go like this.

So a tempting generalization for a child, for an adult, for a computer is that any verb that can appear in the construction,

So we need to be able to recover that. The generalization of albedo of course is color, but color as in the pigmentation of the object.

But if you see a couple of examples and you see a pattern there you can often extrapolate and get to a kind of broader generalization.

When you implement this, I recommend you use the function arctan2, which is the generalization of arctangent that takes as input delta y and then delta x.

The cost function we use for the neural network is going to be a generalization of the one that we use for logistic regression.

EM is a probabilistic generalization that also allows you to find clusters but also modifies the shapes of the clusters by modifying the covariance matrix.

Insertion is another strategy. This is the generalization of the solution that I gave for the find the second best example before.

As a man and as a lawyer.

It's not clear, by the way, since then, whether that is an accurate generalization, but the data at the time seemed to support that.

As quick as a wink.

As big as a whale

A miss is as good as a mile.

A miss is as good as a mile.

I may never be as virtuosic as a violinist or as heroic as a cellist

Now let's be a little careful here because Sudoku as it's typically designed, doesn't really have an input size all the inputs are of the same size. but you can imagine a generalization of Sudoku where you have a bigger grid and maybe more symbols and the same kind of verification procedure works.

As light as a feather, and as hard as dragon scales.

He's as timid as a rabbit.

He's as strong as a horse.

I'm as hungry as a horse.

It's as lovely as a rose.

I'm as hungry as a bear.

He's as strong as a horse.

He's as tame as a kitten.

A man is as old as he feels and a woman as old as she looks.

A man is as old as he feels, and a woman as old as she looks.

Not as great a general as Napoleon, not as great a statesman as Frederick the Great.

Schweitzer was a musician as well as a doctor.

We can fight as a team, as a unit.

As a Colombian, as a citizen of the world,

And as a sister and later, as a scientist,