ML.Neural

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# Package Neural

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ML.Neural.ActFuncs: Activation functions for neural network nodes Activation functions should implement the following API: - _Eval(input)_: returns the value of the function at a given point - _Deriv(input)_: returns the derivative of the function at a given point The current Backprop implementation also requires: - _DerivFromVal(val)_: returns the derivative of the function when its value is val In all cases _input_ is a float as is the value returned. ML.Neural.CrossValidate: handles doing cross validation with neural nets This is, perhaps, a little misleading. ML.Neural.NetNode: Contains the class _NetNode_ which is used to represent nodes in neural nets **Network Architecture:** A tacit assumption in all of this stuff is that we're dealing with feedforward networks. ML.Neural.Network: Contains the class _Network_ which is used to represent neural nets **Network Architecture:** A tacit assumption in all of this stuff is that we're dealing with feedforward networks. ML.Neural.Trainers: Training algorithms for feed-forward neural nets Unless noted otherwise, algorithms and notation are taken from: "Artificial Neural Networks: Theory and Applications", Dan W.