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Q5 Backpropagation Consider the following neural network for regression:

Q5 Backpropagation Consider the following neural network for regression:

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Q5 Backpropagation Consider the following neural network for regression:
Transcribed Image Text:Q5 Backpropagation Consider the following neural network for regression:
1 Vo Wo W1 V1 ni n2 where all weights have value 1. Both neurons have a linear function f (x) = x , so that, the output of neuron n1 is Z1 = Wot WX, and the output of neuron n2 is y = Z 2 = Vot VIZ 1. Compute the value of the weights V1 and W1 after one step of gradient descent with learning rate n = 0.1 on the mean squared %3D t)2, where y(x) is the output of the network on point X and t is the desired value for X, on the input point (1, 2) error E = (where the first value of the vector is the input, and the second value the corresponding target). Q5.1 V1 = =0.8+-0.001 Q5.2 W1= =0.9+-0.001
Transcribed Image Text:1 Vo Wo W1 V1 ni n2 where all weights have value 1. Both neurons have a linear function f (x) = x , so that, the output of neuron n1 is Z1 = Wot WX, and the output of neuron n2 is y = Z 2 = Vot VIZ 1. Compute the value of the weights V1 and W1 after one step of gradient descent with learning rate n = 0.1 on the mean squared %3D t)2, where y(x) is the output of the network on point X and t is the desired value for X, on the input point (1, 2) error E = (where the first value of the vector is the input, and the second value the corresponding target). Q5.1 V1 = =0.8+-0.001 Q5.2 W1= =0.9+-0.001
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