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We are given the following training examples in 2D ((-3,5), +), ((-4, –2), +), ((2,1), -), ((4,3),–) Use +1 to map positive (+) examples and -1 to map negative (-) examples. We want to apply the learning algorithm for training a perceptron using the above data with starting weights wo = w1 = w2 = 0 and learning rate 7 = 0.1. In each iteration process the training examples in the order given above. Complete at most 3 iterations over the above training examples. What are the weights at the end of each iteration? Are these weights final

We are given the following training examples in 2D ((-3,5), +), ((-4, –2), +), ((2,1), -), ((4,3),–) Use +1 to map positive (+) examples and -1 to map negative (-) examples. We want to apply the learning algorithm for training a perceptron using the above data with starting weights wo = w1 = w2 = 0 and learning rate 7 = 0.1. In each iteration process the training examples in the order given above. Complete at most 3 iterations over the above training examples. What are the weights at the end of each iteration? Are these weights final

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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We are given the following training examples in 2D ((-3,5), +), ((-4, –2), +), ((2,1), -), ((4,3),–) Use +1 to map positive (+) examples and -1 to map negative (-) examples. We want to apply the learning algorithm for training a perceptron using the above data with starting weights wo = w1 = w2 = 0 and learning rate 7 = 0.1. In each iteration process the training examples in the order given above. Complete at most 3 iterations over the above training examples. What are the weights at the end of each iteration? Are these weights final

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