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## Uses of Interface
org.encog.neural.networks.training.LearningRate

Packages that use LearningRate
org.encog.neural.cpn.training |
org.encog.neural.networks.training.propagation.back |
org.encog.neural.networks.training.propagation.manhattan |
org.encog.neural.networks.training.propagation.quick |
org.encog.neural.networks.training.simple |
org.encog.neural.som.training.basic |

Uses of LearningRate in org.encog.neural.cpn.training

Classes in org.encog.neural.cpn.training that implement LearningRate
`class` | `TrainInstar` Used for Instar training of a CPN neural network.
`class` | `TrainOutstar` Used for Instar training of a CPN neural network.

Uses of LearningRate in org.encog.neural.networks.training.propagation.back

Classes in org.encog.neural.networks.training.propagation.back that implement LearningRate
`class` | `Backpropagation` This class implements a backpropagation training algorithm for feed forward neural networks.

Uses of LearningRate in org.encog.neural.networks.training.propagation.manhattan

Classes in org.encog.neural.networks.training.propagation.manhattan that implement LearningRate
`class` | `ManhattanPropagation` One problem that the backpropagation technique has is that the magnitude of the partial derivative may be calculated too large or too small.

Uses of LearningRate in org.encog.neural.networks.training.propagation.quick

Classes in org.encog.neural.networks.training.propagation.quick that implement LearningRate
`class` | `QuickPropagation` QPROP is an efficient training method that is based on Newton's Method.

Uses of LearningRate in org.encog.neural.networks.training.simple

Classes in org.encog.neural.networks.training.simple that implement LearningRate
`class` | `TrainAdaline` Train an ADALINE neural network.

Uses of LearningRate in org.encog.neural.som.training.basic

Classes in org.encog.neural.som.training.basic that implement LearningRate
`class` | `BasicTrainSOM` This class implements competitive training, which would be used in a winner-take-all neural network, such as the self organizing map (SOM).

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