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2. Neural Networks Apply the back-propagation algorithm to the following network. Your training example is: El: Il = 1, 12 = 3, o=0; All weights are set to value 1 initially; learning rate is set to 0.2. Show the updated weight value after the training of the above two examples. (In the figure below, "in^2" means the square of the sum of the inputs.) Output o o= (in^2) ws W6 al a2 al-fin 2) a2-(in^2) w2 W wy 11 12

2. Neural Networks Apply the back-propagation algorithm to the following network. Your training example is: El: Il = 1, 12 = 3, o=0; All weights are set to value 1 initially; learning rate is set to 0.2. Show the updated weight value after the training of the above two examples. (In the figure below, "in^2" means the square of the sum of the inputs.) Output o o= (in^2) ws W6 al a2 al-fin 2) a2-(in^2) w2 W wy 11 12

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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2. Neural Networks Apply the back-propagation algorithm to the following network. Your training example is: El: Il = 1, 12 = 3, o=0; All weights are set to value 1 initially; learning rate is set to 0.2. Show the updated weight value after the training of the above two examples. (In the figure below, "in^2" means the square of the sum of the inputs.) Output o o = (in^2) WS W6 al al-(in^2) a2-(in^2) w2 W3 W4 11 12
Transcribed Image Text:2. Neural Networks Apply the back-propagation algorithm to the following network. Your training example is: El: Il = 1, 12 = 3, o=0; All weights are set to value 1 initially; learning rate is set to 0.2. Show the updated weight value after the training of the above two examples. (In the figure below, "in^2" means the square of the sum of the inputs.) Output o o = (in^2) WS W6 al al-(in^2) a2-(in^2) w2 W3 W4 11 12
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