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light of logistic regression, the Support

Again, the regression constant now is zero because we've standardized.

In the previous video, we talked about a cost function for the neural network.

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?

They think they'll be able to rent it five times during the year but their shipping cost was about 9, their cleaning cost a dollar processing cost a dollar and their customer acquisition and activation cost 25 a team and so if you take a look at their unit costs you add all this up it's 230 but their revenue was only 199.

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And let's say you've implement and regularize linear regression.

we take this first model with lambda equals 0, and minimize my cos function j of theta and this would give me some parameter vector theta and similar to the earlier video,

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I needed tools that were robust, modular, highly efficient and optimized,

It's much like on a jet.

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