These are often called multivariate gaussians.

We call it the cloud of normality.

I wouldn't want to change with normality.

And how we see is by continually redefining normality.

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

And, just to give this another name, this is also called multivariate linear regression.

And if you implement this you will get a working implementation of gradient descent for multivariate linear regression.

Everyone's got a gift inside of us, and in all honesty, the pursuit of normality is the ultimate sacrifice of potential.

So, let me show you how quickly our brains can redefine normality, even at the simplest thing the brain does, which is color.

Normality overlooks the beauty that differences give us, and the fact that we are different doesn't mean that one of us is wrong.

And computing these partial derivatives, so going from this equation to either of these equations down there, computing those partial derivative terms requires some multivariate calculus.

The desperate try to restore order and normality, and if we are not able to cut this circle of violence, it will be transferred to the next generation without a doubt.

You have to familiarize yourself with the matrix class and then go and fill in the filter code in accordance to the things that I showed you for the multivariate Kalman filter.