Help me, Froh!

How fitting.

Froh will protect you

Come hither, Froh!

And on this slide, I'm basically explaining all the problems you have.

We talked about the fit from data and we even talked about multivariate Gaussians.

I'm going to be fitting very complex high order polynomials that might fit the training set with much more complex functions

The algorithm is amazingly simple.

Reconnect frame to current flow

And that reconciliation is going to be done on the cash flow statement, and I'll do that in the next video.

There are also statements that influence what gets executed and under which conditions.

It's not very challenging any more.

You're actually generating revenue.

Courage, Froh, it is but early yet!

Suppose we're fitting a high order polynomial like that shown here, but to prevent overfitting, we're going to use regularization, like that shown here, so we have this regularization term to try to keep the values of the parameters small.

For this quiz we will assume a degenerative case of 3 data points and just 1 cluster center.

So, that's the second path. The first path, positive emotion.

So a river is designed to channel the flow of water, and the lightning bolt that comes out of a cloud channels the flow of electricity, and a leaf is designed to channel the flow of nutrients to the tree, sometimes even having to route around an obstacle, but to get that nutrition flowing.

And this is indeed what Mike Csikszentmihalyi has been talking about, about flow.

Suppose we fit a linear regression model with a very high order polynomial, but to prevent overfitting, we are going to use regularization as shown here.

In that case, given that we're fitting a high order polynomial, this is a usual overfitting setting.

As for the discussion of most of the learning algorithms, we're going to begin by talking about the cost function for fitting the parameters of the network.

If you think about Abstract, his style is about freestyle, flow and finesse.

Donner and Froh are dreaming of shelter

You learned about what a Gaussian is.

So, that's Jcv of theta because with high bias we won't be fitting.

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.

So, we'll do that for this figure, where maybe d equals 1, were going to be fitting very simple functions where as we are the right of this this may be d equals 4 or relatively may be even larger numbers.

And so this degree of polynomial, so the parameter is no longer fit to the test set.

If you think about just that,

So I can look at my first model, my second model, fits with these different values of the regularization parameter and evaluate them on my cross validation set basically measure the average squared error of each of these parameter vectors theta on my cross validation set.

And to this day, I am ambivalent, but when you have instead games like Flow, there's no doubt.

And the reason is, what we've done is, we've fit this extra the parameter d, that is this degree of polynomial, and we'll fit that parameter d using the test set.

And by having fit the parameter to the test set, this means that the performance of the hypothesis on that test set may not be a fair estimate of how well the hypothesis is likely to do on examples we haven't seen before.

As we increase the degree of the polynomial, we're going to fit our training set better and better and so, if d equals 1 that ever rose to the high training error.

So here are examples of two models with, different amounts of Baryonic matter.

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.

We don't need to invent solutions because we have them cash flow loans based in income rather than assets,

So what will follow is a student made film about just that.

And in this video, we also stepped through an entire process of giving data set, we have, fitting the parameters, doing parameter estimations.

Formally, these statements are said to refer to control flow because they guard how execution flows through your procedure.

In this video, we're going to put together gradient descent with our cost function, and that will give us an algorithm for

And the key that distinguishes these two cases is if you have a high bias problem your training set error will also be high as your hypothesis just not fitting the training set well.

So those two are ideal and complementary areas from which flow is easy to go into.

We'll get a chance to see in class commenting out parts of a test file to see if it still causes an error.