- 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.
|