Normalized

Normalize

Normalized Subject

We already talked about this example earlier in class, but now it's in the content of normal distribution so let me take a second to show this to you.

And then you just normalize all of those, so they add up to one.

Then I compute the normalizer, which I'LL call α, is the sum of all these guys over here.

All right.

Use Normalized Name

So, for small values of

This is the quintessential term that occurs in the variance calculation of x.

It's about the same values but, according to this calculation over here, they'd be different.

And if you plug in our Gaussian formula, you get the following

So here's the 100,000 question.

Let's call big W the sum of all these weights, and let's normalize them just for the consideration of what to do, and it's called the normalized weights alpha.

Just to reiterate, here is our model and here is our learning algorithm subjective.

The particle a will get an importance weight of 0.8, nonnormalized.

In this question, we assume that a particle,

We take two patches one from the left image, one from the right image.

We do the same thing with not A.

C plays a role similar to one over Lambda, where Lambda is the regularization parameter we had previously.

The joint probability of cancer and positive is 0.1 0.9. That's the joint that's not normalized.

We multiply those together.

And that's obviously 0.0999.

You programmed a product between the prior probability distribution and a number.

Let's do the same for the non cancer version, pick the number over here to divide and divide it by this same normalizer.

I should mention one important point with normal interpolation.

But this one is just like the variance calculation but it mixes x's and y's whereas these are x² and y².

Recall the standard score for x, zₓ is equal to xᵢ x bar divided by the standard deviation of x.

The normalizer will be 1 over these two things, which is about 9.92.

But whatever this third expression was, that's what I'd want this subtree this subpart of my tuple to be.

I'd like to do in this video, is get a better understanding of how cross validation and training error vary as we as we vary the regularization parameter lambda.

And then we can recover the original probabilities by normalizing based on those values over here, so the probability of A given B, the actual probability, is a normalizer, eta, times this non normalized form over here.

That's the same join for green is 1 times 0.2.

The first is this term which is the cost that comes from the training set and the second is this term, which is the regularization term and what we had, we had to control the trade off between these by saying, you know, that we wanted to minimize A plus and then my regularization parameter lambda, and then times some other term B, right?

We normalize, so the average brightness is zero.

We started off by introducing the concept of a language as a set of strings.

Those sets of strings are called regular languages.

RegExp

Regular Expression

Regular expression

Regexp

Regular expression

RegExp Language

For the weighted average we get 2 times 10 plus 8 times 13.

I get 0.0477.