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Consider the back propagation neural network as shown in figure 1, assume that the neurons have a logistic sigmoid activation function, do the following: a) Perform a forward pass on the network. b) Perform a reverse pass (training) once (target =1, x=1, n = 0.9) 2 0.004 0.01 0.101 0.35 Z1 0.66 0.205 h 0.703 0.404 0.595 0.55 0855 0.9 0.5 0.072 h 0.246 Z2 0.567 0.445 Figure 1 0.057 0.343 0.007

Consider the back propagation neural network as shown in figure 1, assume that the neurons have a logistic sigmoid activation function, do the following: a) Perform a forward pass on the network. b) Perform a reverse pass (training) once (target =1, x=1, n = 0.9) 2 0.004 0.01 0.101 0.35 Z1 0.66 0.205 h 0.703 0.404 0.595 0.55 0855 0.9 0.5 0.072 h 0.246 Z2 0.567 0.445 Figure 1 0.057 0.343 0.007

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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Consider the back propagation neural network as shown in figure 1, assume that the neurons have a logistic sigmoid activation function, do the following: a) Perform a forward pass on the network. b) Perform a reverse pass (training) once (target =1, x=1, n = 0.9) 2 0.004 0.01 0.101 0.35 Z1 0.66 0.205 h 0.703 0.404 0.595 0.55 0855 0.9 0.5 0.072 h 0.246 Z2 0.567 0.445 Figure 1 0.057 0.343 0.007
Transcribed Image Text:Consider the back propagation neural network as shown in figure 1, assume that the neurons have a logistic sigmoid activation function, do the following: a) Perform a forward pass on the network. b) Perform a reverse pass (training) once (target =1, x=1, n = 0.9) 2 0.004 0.01 0.101 0.35 Z1 0.66 0.205 h 0.703 0.404 0.595 0.55 0855 0.9 0.5 0.072 h 0.246 Z2 0.567 0.445 Figure 1 0.057 0.343 0.007
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