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Machine Learning Home Works 2 and 3 1. Apply Linear Regression with Gradient Descent to the following data points for 3 iterations (Learning Rate = 0.1) 2. Add x² as a feature to the previous problem and solve it again.

Machine Learning Home Works 2 and 3 1. Apply Linear Regression with Gradient Descent to the following data points for 3 iterations (Learning Rate = 0.1) 2. Add x² as a feature to the previous problem and solve it again.

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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Machine Learning Home Works 2 and 3 1. Apply Linear Regression with Gradient Descent to the following data points for 3 iterations (Learning Rate = 0.1) x 2. Add x² as a feature to the previous problem and solve it again. 3. Use Logistic Regression to seperate the following data points. Iterate the algorithm for three iterations wih Learning Rate = 0.1. Then plot the decision boundry. ₁ 1 >x 4. Add x² as a feature to the previous problem and solve it again. X
Transcribed Image Text:Machine Learning Home Works 2 and 3 1. Apply Linear Regression with Gradient Descent to the following data points for 3 iterations (Learning Rate = 0.1) x 2. Add x² as a feature to the previous problem and solve it again. 3. Use Logistic Regression to seperate the following data points. Iterate the algorithm for three iterations wih Learning Rate = 0.1. Then plot the decision boundry. ₁ 1 >x 4. Add x² as a feature to the previous problem and solve it again. X
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hi,please solve it by useing formula of gradient descent  and h(theta) not programming, with many thanks

Machine Learning Home Works 2 and 3 1. Apply Linear Regression with Gradient Descent to the following data points for 3 iterations (Learning Rate = 0.1) x 2. Add x² as a feature to the previous problem and solve it again. 3. Use Logistic Regression to seperate the following data points. Iterate the algorithm for three iterations wih Learning Rate = 0.1. Then plot the decision boundry. ₁ 1 >x 4. Add x² as a feature to the previous problem and solve it again. X
Transcribed Image Text:Machine Learning Home Works 2 and 3 1. Apply Linear Regression with Gradient Descent to the following data points for 3 iterations (Learning Rate = 0.1) x 2. Add x² as a feature to the previous problem and solve it again. 3. Use Logistic Regression to seperate the following data points. Iterate the algorithm for three iterations wih Learning Rate = 0.1. Then plot the decision boundry. ₁ 1 >x 4. Add x² as a feature to the previous problem and solve it again. X
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