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.

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

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

So here's the 100,000 question.

All right.

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.

Use Normalized Name

So, for small values of

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

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

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.

We do the same thing with not A.

I should mention one important point with normal interpolation.

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

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,

The reason why that is the case is it relates to the 0.9 probability of speaking the truth.

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.

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

We normalize this by the same expression plus the probability for the non spam case.

And we're going to apply the exact same mechanics as we did before.

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.

So that's model selection applied to selecting the regularization parameter

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.

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.

All that's missing is the normalizer.

When we now normalize, we α 0.25 0.05, which is 0.3.

The same for the opposite event of no fire given the neighbor just said, yes, it burns.

This over here is kind of a mixed variance. This is often called the covariance.

Because the product might violate the fact that probabilities add up to 1, there was a product followed by normalization.

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.