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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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Please do this by hand and not by code. Please....

**Training a Linear Function Using Batch Gradient Descent** **Objective:** Manually train a linear function \( h_{\theta}(\vec{x}) = \vec{\theta}^T \cdot \vec{x} \) using the batch gradient descent algorithm. **Initial Parameters:** - \( \theta_0 = 0.1 \) - \( \theta_1 = 0.1 \) - \( \theta_2 = 0.1 \) **Learning Rate:** - \( \alpha = 0.1 \) **Instructions:** Update each parameter at least five times. **Training Dataset:** | \( x_1 \) | \( x_2 \) | \( y \) | |-----------|-----------|---------| | 0 | 0 | 2 | | 0 | 1 | 3 | | 1 | 0 | 3 | | 1 | 1 | 4 | In this task, you will utilize the training dataset to compute updates to the parameters \(\theta_0\), \(\theta_1\), and \(\theta_2\) using the batch gradient descent algorithm. Each update involves using the entire dataset to calculate the cost gradient and then adjusting the parameters accordingly. Repeat this process for at least five iterations.
Transcribed Image Text:**Training a Linear Function Using Batch Gradient Descent** **Objective:** Manually train a linear function \( h_{\theta}(\vec{x}) = \vec{\theta}^T \cdot \vec{x} \) using the batch gradient descent algorithm. **Initial Parameters:** - \( \theta_0 = 0.1 \) - \( \theta_1 = 0.1 \) - \( \theta_2 = 0.1 \) **Learning Rate:** - \( \alpha = 0.1 \) **Instructions:** Update each parameter at least five times. **Training Dataset:** | \( x_1 \) | \( x_2 \) | \( y \) | |-----------|-----------|---------| | 0 | 0 | 2 | | 0 | 1 | 3 | | 1 | 0 | 3 | | 1 | 1 | 4 | In this task, you will utilize the training dataset to compute updates to the parameters \(\theta_0\), \(\theta_1\), and \(\theta_2\) using the batch gradient descent algorithm. Each update involves using the entire dataset to calculate the cost gradient and then adjusting the parameters accordingly. Repeat this process for at least five iterations.
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