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.

Partial derivatives Gradient and directional derivatives Partial derivative and gradient (articles) Differentiating parametric curves Multivariable chain rule Curvature

Learn the basics of slope and its application in linear equations and graphs with this introductory video tutorial.

So... a gradient is a vector of partial derivatives, and the Jacobian matrix is the matrix form of that?

What is the partial derivative, how do you compute it, and what does it mean?

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.

Gradient descent is a general-purpose algorithm that numerically finds minima of multivariable functions.

Slope-intercept form (y=mx+b) of linear equations highlights the slope (m) and the y-intercept (b) of a line. Watch this video to learn more about it and see some examples.

If anyone can help, I am getting different gradients on the same line. My line is between (3,2) and (4,-3). If I take the gradient between (3.25, 1) and (4,-3), I get -4/0.75 = -5.333.. If I take the …

Discover how energy is released when electrons transition from high to low energy states, creating a proton gradient that drives ATP synthase. Dive into the cutting-edge research in this …