evolution of weights

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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3.
Suppose a simple single layer perceptron with 2-d input X= (x1, x2), a bias., ReLU for activation
function, and MSE (mean squared error) for loss function.
D D
D®)
D4)
X1
-1
1
X2
-1
Show evolution of weights according to stochastic gradients algorithm with batch size =1
Show evolution of weights according to stochastic gradients algorithm with batch sizel=2
a.
b.
Transcribed Image Text:3. Suppose a simple single layer perceptron with 2-d input X= (x1, x2), a bias., ReLU for activation function, and MSE (mean squared error) for loss function. D D D®) D4) X1 -1 1 X2 -1 Show evolution of weights according to stochastic gradients algorithm with batch size =1 Show evolution of weights according to stochastic gradients algorithm with batch sizel=2 a. b.
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