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Manually train a linear function ho (x) = ² ·x based on the following training instances using batch gradient descent algorithm. The initial values of parameters are 0o = 0.1,0₁ = 0.1, 0₂ = 0.1. The learning rate a is 0.1. Please update each parameter at least five times.

Manually train a linear function ho (x) = ² ·x based on the following training instances using batch gradient descent algorithm. The initial values of parameters are 0o = 0.1,0₁ = 0.1, 0₂ = 0.1. The learning rate a is 0.1. Please update each parameter at least five times.

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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Manually train a linear function ho (x) Tx based on the following training instances using batch gradient descent algorithm. The initial values of parameters are o 0.1,0₁ = 0.1, 0₂: = = 0.1. The learning rate a is 0.1. Please update each parameter at least five times. X1 0 0 X2 0 1 0 1 y 2 3 3 4 =
Transcribed Image Text:Manually train a linear function ho (x) Tx based on the following training instances using batch gradient descent algorithm. The initial values of parameters are o 0.1,0₁ = 0.1, 0₂: = = 0.1. The learning rate a is 0.1. Please update each parameter at least five times. X1 0 0 X2 0 1 0 1 y 2 3 3 4 =
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