- 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 boosting is a
machine learning technique based on
boosting in a
functional space,
where the
target is pseudo-residuals
instead of
residuals as...
-
Gradient descent is a
method for
unconstrained mathematical optimization. It is a first-order
iterative algorithm for
minimizing a
differentiable multivariate...
- In mathematics, the
conjugate gradient method is an
algorithm for the
numerical solution of
particular systems of
linear equations,
namely those whose...
- Shai; Singer, Yoram; Srebro,
Nathan (2007). Pegasos:
Primal Estimated sub-
GrAdient SOlver for SVM (PDF). ICML.
Archived (PDF) from the
original on 2013-12-15...
- In
machine learning, the
vanishing gradient problem is the
problem of
greatly diverging gradient magnitudes between earlier and
later layers encountered...
-
Stochastic gradient descent (often
abbreviated SGD) is an
iterative method for
optimizing an
objective function with
suitable smoothness properties (e...
- The two
approaches available are
gradient-based and
gradient-free methods.
Gradient-based
methods (policy
gradient methods)
start with a
mapping from...
-
Geothermal gradient is the rate of
change in
temperature with
respect to
increasing depth in Earth's interior. As a
general rule, the
crust temperature...
- {\displaystyle f(x,y,z)} in three-dimensional
Cartesian coordinate variables, the
gradient is the
vector field: grad ( f ) = ∇ f = ( ∂ ∂ x , ∂ ∂ y , ∂ ∂ z )...
