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Manually train a linear function ho (x) 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. = X1 0 0 1 1 x2 0 1 0 1 y 2 = Tx based on the following 3 3 4

Manually train a linear function ho (x) 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. = X1 0 0 1 1 x2 0 1 0 1 y 2 = Tx based on the following 3 3 4

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 \( h_\theta(\vec{x}) = \vec{\theta}^T \cdot \vec{x} \) based on the following training instances using **batch gradient descent** algorithm. The initial values of parameters are \( \theta_0 = 0.1 \), \( \theta_1 = 0.1 \), \( \theta_2 = 0.1 \). The learning rate \( \alpha \) is 0.1. Please update each parameter at least five times. | \( x_1 \) | \( x_2 \) | \( y \) | |:---:|:---:|:---:| | 0 | 0 | 2 | | 0 | 1 | 3 | | 1 | 0 | 3 | | 1 | 1 | 4 |
Transcribed Image Text:Manually train a linear function \( h_\theta(\vec{x}) = \vec{\theta}^T \cdot \vec{x} \) based on the following training instances using **batch gradient descent** algorithm. The initial values of parameters are \( \theta_0 = 0.1 \), \( \theta_1 = 0.1 \), \( \theta_2 = 0.1 \). The learning rate \( \alpha \) is 0.1. Please update each parameter at least five times. | \( x_1 \) | \( x_2 \) | \( y \) | |:---:|:---:|:---:| | 0 | 0 | 2 | | 0 | 1 | 3 | | 1 | 0 | 3 | | 1 | 1 | 4 |
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