Weight normalization neural networks
- how it trains some neural nets to properly recognize patterns you desire
- how the network processes information
1. a neuron or neurons consist a layer which could become either a input layer or a output layer
in some case, that layer could become a hidden layer or hidden layers
2. the input data or patterns is presented to your neural nets through the input neurons in a input layer in the form of a normalized input vector
3. the input data or pattern is made up by the floating point numbers which should be normalized to the range between -1 and 1
4. the normalization step could be referred to a third layer besides input and output layers as two actual neuron layers
5. the normalization factor can be determined by the vector length of the input data
6. the output data is produced through the output layer consists of output neurons
7. almost every neurons in a layer could interact with other neurons in the other layer and their interactions have some connection weights
8. the output data is generated by using the dot products of the input neurons and their connection weights calculated, then multiplying by the normalization factor, and then converted to bipolar numbers
9. in Kohonen, only one of the output neurons produce
Source: to my round
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That is a misunderstanding of Biblical prophecy
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I posted this story in anothr forum but thought2009-05-07 10:03:30 by ronca
Someone here might gain something from it. It is true.
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Cut and paste the whole link.
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