gradient descent

Both updates can be interpreted as a natural gradient descent.

Gradient descent is applied to the new training set.

The optimal parameters could be found by gradient descent etc.

Gradient descent can also be used to solve a system of nonlinear equations.

Gradient descent also benefits from preconditioning, but this is not done as commonly.

To update the weight using gradient descent, one must chooses a learning rate, .

The adaptation step of the neural gas can be interpreted as gradient descent on a cost function.

The basic intuition behind gradient descent can be illustrated by a hypothetical scenario.

Common methods of estimation include recursive least squares and gradient descent.

His work presents stochastic gradient descent as a fundamental learning algorithm.

The associated process theory of neuronal dynamics is based on minimising free energy through gradient descent.

Gradient descent has problems with pathological functions such as the Rosenbrock function shown here.

These methods are typically slower than gradient descent.

The Evolver evolves the surface toward minimal energy by a gradient descent method.

The gradient descent can be combined with a line search, finding the locally optimal step size on every iteration.

The method used in backpropagation is gradient descent.

In gradient descent, feature scaling can improve the convergence speed of the algorithm.

Another possible training algorithm is gradient descent.

In the example shown, stochastic steepest gradient descent was used to adapt the parameters.

The "hill climbing" strategy of gradient descent is guaranteed to work if there is only one minimum.

Now we should iteratively adjust the points vector by using gradient descent minimization.

For example, one could use gradient descent or the conjugate gradient method.

Stochastic gradient descent runs many greedy searches from random initial locations.

For large and small Hessian, the iterations will behave like gradient descent with step size .

As noted above, gradient descent tells us that our change for each weight should be proportional to the gradient.