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We want to minimize the residual, we want to minimize our prediction errors.

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Again, we wanna minimize the residuals.

I'm not going to minimize those concerns.

So we're going to try to minimize my average error, which we're going to minimize one by 2M.

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This will minimize the chance of confusing values

For example, here we're doing it to minimize duplicated code.

And now what does min do? Well, try to minimize.

This allows you to minimize the overhead of handling asynchronous events.

But how would we minimize environmental, financial and transportation cost then?

So I'm going to write minimize over theta zero, theta one.

So again, minimize sums of squares residuals, just like in simple regression.

We want to minimize this, and we're going to use gradient descent.

I'm really keen to try and minimize the addition of any chemicals.

We want to minimize the distance. We want to contract that gap.

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There's ways, if it's temporary, to minimize the impact, but it's a problem.

So, that's the gradient descent algorithm, and you can use it to minimize, to try to minimize any cost function J. Not the cost function J to be defined for

Say I wanted to implement Twiddle so as to minimize the average crosstrack error.

Machine and where you minimize that function then what you have is the parameters

Thrun It's interesting to see how to minimize a loss function using gradient descent.

To define linear regression, we need to understand what we are trying to minimize.

Now O has a choice of what to do, and O wants to minimize.

Remember, we talked about that we were trying to minimize the residual sums of squares.

First, we have to figure out what exactly is the function we're trying to minimize.

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And so the height of the surface shows J, and we want to minimize this function.

What we're going to do is apply gradient descent to minimize our squared error cost function.

In order to minimize the weight, it's very thin, it's as thin as a sandwich wrapper.

Maybe it's a cost function from linear regression. Maybe it's some other function we want to minimize.

The primary function of P is to minimize the error. We steer in proportion to the error.

If there is a difference, however, we update in a way so as to minimize the error.

In this video, let's start to talk about an algorithm, for trying to minimize the cost function.

Whereas, your cross validation error, assuming that this say the squared error which we're trying to minimize.