It goes through the measurements. It assumes it has as many motions as measurements.

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And thus gradient descent on this type of cost function which you get whenever you're using linear regression, it will always convert to the global optimum, because there are no other local optima other than global optimum.

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And then we apply the hash function, oops, let me write it like this.

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And if you run gradient descent on a cost function like this, then gradient descent, you can show mathematically, you can find a much more direct path to the global minimum rather than taking a much more convoluted path where you're sort of trying to follow a much more complicated trajectory to get to the global minimum.

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In the next video, we'll start to talk about an algorithm for trying to optimize the cost function.

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So what is the area of this triangle going to be? Well, we apply the same formula.

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

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