In vector calculus, the gradient of a scalar-valued differentiable function f {\displaystyle f} of several variables is the vector field (or vector-valued...

Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for finding a local minimum of a differentiable...

Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals rather than the typical...

In machine learning, the vanishing gradient problem is encountered when training neural networks with gradient-based learning methods and backpropagation...

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to...

surface gradient is a vector differential operator that is similar to the conventional gradient. The distinction is that the surface gradient takes effect...

A temperature gradient is a physical quantity that describes in which direction and at what rate the temperature changes the most rapidly around a particular...

electrochemical gradient is a gradient of electrochemical potential, usually for an ion that can move across a membrane. The gradient consists of two...

machine learning, backpropagation is a gradient estimation method used to train neural network models. The gradient estimate is used by the optimization...