IDS572_Class4_Neural Networks Sept1511

IDS572_Class4_Neural Networks Sept1511 - IDS 572 Neural...

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1 Neural Networks September 15, 2011 IDS 572
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2 Pattern Recognition
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3 Example using the letters “ T ” and “ H Exact pattern match
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4 Partial matches
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5 Indeterminate pattern – fits either T or H imperfectly
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6 X 1 X 2 X 3 X 4 y 1 y 2 Neural Networks: Sample Network Neurons typically “fully connected” Architecture typically “pyramid-like”
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7 Neural Networks: Terminology X 1 X 2 X 3 . . . X n Layer: set of neurons with common inputs Neuron or Node: single combination of inputs y 1 y m . . . . . . y 2 . . . . . .
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8 Neural Networks: Terminology X 1 X 2 X 3 . . . X n Output Layer: output from these neurons is model output(s) Hidden Layers: layer between inputs layer and output layer, outputs are “hidden” to analyst y 1 y m . . . . . . y 2 . . . . . . Input “Layer”: The inputs to the network
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X 1 X 2 X 3 . . . X n Neural Networks: Neuron Linear weighted sum of inputs Sum output is “squashed” by the sigmoid Max. value of neuron output is 1 Min. value of neuron output is 0
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This note was uploaded on 11/01/2011 for the course IDS 572 taught by Professor Staff during the Fall '08 term at Ill. Chicago.

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IDS572_Class4_Neural Networks Sept1511 - IDS 572 Neural...

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