Gradient descent

Gradient descent is a first-order optimization algorithm. To find a local minimum of a function using gradient descent, ...

Gradient Descent: Picking the Best Learning Rate
For linear networks, E is quadratic then we can write
so that we have ...

gradient descent Understanding this term depends to some extent on the error surface metaphor.

Gradient Descent. Optimization techniques for non-linear functions (e.g. the error function of a neural network as the weights are varied) which attempt to move incrementally to successively lower points in search space, in order to locate a minimum.

Although, theoretically, the back-propagation algorithm performs gradient descent on the total error only if the weights are adjusted after the full set of learning patterns has been presented, ...

Just as gradient descent has the parameter at that determines how much xt changes with each timestep, evolution has the parameters r and g that determine how much noise changes children from parents.

For very large data sets, using more advanced optimization techniques is often slower than using gradient descent, if the weights of the network are updated by gradient descent after each training example.

Back propagation is effectively utilizing a search technique called gradient descent to search for the best possible improvement in the link weights to reduce the error.

Training of a network can be done by most types of standard, non-linear optimisation algorithms such as gradient descent or BFGS2.

We then explore an alternative way to compute linear parameters---gradient descent.

• On-line learning algorithms rooted in stochastic gradient descent; small-scale and large-scalelearning problems.
• Kernel methods, including support vector machines, and the representer theorem.

See also: Neural network, Classification, Likelihood, Regression, Distribution