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Consider fitting a linear regression hø(x) = 0' x = 01x1+02x2+03x3 to a training dataset D = {(x1,Y1),... (xn, Yn)}, where 0 = [01, 62, 03]T and x = [¤1, x2, 83]T Q1.1 Let D = {([1, 0, 0]",0), ([0, 1, 0)" , 0.5), ([0, 0, 1]", – 1)}. Our current estimate of the parameter 0 = [0,0,0]T. which training instance makes the smallest contribution to the gradient ? (contribution means the magnitude of gradient change) 01 O 2 03 Q1.2. What is the updated parameter after one step of gradient descent (assuming step size 1)? O (0, 0, 0]T O [0, 1, 0]" O [0, 0.5, – 1]" O (0,0.5, 1]"

Consider fitting a linear regression hø(x) = 0' x = 01x1+02x2+03x3 to a training dataset D = {(x1,Y1),... (xn, Yn)}, where 0 = [01, 62, 03]T and x = [¤1, x2, 83]T Q1.1 Let D = {([1, 0, 0]",0), ([0, 1, 0)" , 0.5), ([0, 0, 1]", – 1)}. Our current estimate of the parameter 0 = [0,0,0]T. which training instance makes the smallest contribution to the gradient ? (contribution means the magnitude of gradient change) 01 O 2 03 Q1.2. What is the updated parameter after one step of gradient descent (assuming step size 1)? O (0, 0, 0]T O [0, 1, 0]" O [0, 0.5, – 1]" O (0,0.5, 1]"

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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Consider fitting a linear regression he(x) = 0Tx = 01x1+ 02x2+ O3x3 to a training dataset D = {(x1,y1),..., (xn, Yn)}, where 0 = [01, 02, 03]T and x = [#1, x2, 83]T Q1.1 Let D = {([1,0, 0]",0), ([0, 1, 0]" , 0.5), ([0, 0, 1]", – 1)}. Our current estimate of the parameter 0 = [0, 0, 0]T. which training instance makes the smallest contribution to the gradient ? (contribution means the magnitude of gradient change) O 1 O 2 O 3 Q1.2. What is the updated parameter after one step of gradient descent (assuming step size = 1)? O [0, 0, 0]T O (0, 1, 0]" О (), 0.5, — 1]7 O [0, 0.5, 1]"
Transcribed Image Text:Consider fitting a linear regression he(x) = 0Tx = 01x1+ 02x2+ O3x3 to a training dataset D = {(x1,y1),..., (xn, Yn)}, where 0 = [01, 02, 03]T and x = [#1, x2, 83]T Q1.1 Let D = {([1,0, 0]",0), ([0, 1, 0]" , 0.5), ([0, 0, 1]", – 1)}. Our current estimate of the parameter 0 = [0, 0, 0]T. which training instance makes the smallest contribution to the gradient ? (contribution means the magnitude of gradient change) O 1 O 2 O 3 Q1.2. What is the updated parameter after one step of gradient descent (assuming step size = 1)? O [0, 0, 0]T O (0, 1, 0]" О (), 0.5, — 1]7 O [0, 0.5, 1]"
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