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Khan Academy ... Khan Academy

The gradient is a way of packing together all the partial derivative information of a function. So let's just start by computing the partial derivatives of this guy.

Let's omit the variables and only use f. Since f is scalar valued, applying the gradient operator is equivalent to scaling the vector ∇ with the scalar f: ∇f = (d/dx, d/dy)f = (df/dx, df/dy). Next, you …

Gradient descent is an algorithm that numerically estimates where a function outputs its lowest values. That means it finds local minima, but not by setting ∇ f = 0 like we've seen before.

Gradient zawiera informacje o wszystkich pochodnych cząstkowych skalarnej funkcji wielu zmiennych. Oprócz tego's ma interesującą interpretację i różne zastosowania.

The gradient stores all the partial derivative information of a multivariable function. But it's more than a mere storage device, it has several wonderful interpretations and many, many uses.

The gradient stores all the partial derivative information of a multivariable function. But it's more than a mere storage device, it has several wonderful interpretations and many, many uses.

And I talked about it in the last few videos, but as a reminder, it's defined as the divergence of the gradient of F, and it's kind of like the second derivative.

Other operators are not infix, such as derivative (x) or grad (x), written with their respective symbols. But how would you define these "functions" with only other symbols, without …