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A Walk Through Neural Network - By Heifer Jeffer
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Introduction
Neural network is a computer system modelled on the human brain and nervous system in order to learn the machine how to behave independently like human
Example
Let’s assume that S & U are two sets live in world P , Qs,As,Qu & Au are respectively the inputs of S & U.
b is a bias ,W is weight of (Qs,As,Qu & Au) , L = [W1 Qj + W2 Aj + W3 b
Let’s Design an algorithm to learn our machine how to isolate (classify) the two sets (S, U) of world P
Definition
S = As , Qs
U =Au ,Bu
Qj =Qs + Qu
Aj = As + Au
P = Aj , Qj
L = [W1 Qj + W2 Aj + W3 b]
2013
Stanford University Venture Lab Heider Jeffer PhD Student Operation Research
[A WALK THROUGH NEURAL NETWORK] The Paper starts immediately by Solving a problem in order to explain the meaning of Neural Network
Web :http://goo.gl/FhpHP
Blog:http://goo.gl/9xpqj
E-Mail:[email protected]
Solution
[Au ,Bu ≤ o.5 &
As,Bu ≥ 0.5]
Or
[Au ,Bu ≥ o.5 &
As,Bu ≤ 0.5]
Out (P=Aj,Qj) ← sigmoid [W1 Qj + W2 Aj + W3 b]
YES
End
No
Delta Rule D W1 = dL(Aj,Qj) / d W1 D W2 = dL(Aj,Qj) / d W2 D W3 = O because Bias is a constant
W1 = D W1 + W1
W2 = D W2 + W2
W3 = W3
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