Question: Assume a linear model y = wo+w and we use linear regression to train this model. The cost function is MSE = (w.2() -

$Assume a linear model $ y=w_{0}+w_{1} x $ and we use linear regression to train this model. The cost function is \( M S E=\$

Assume a linear model y = wo+w and we use linear regression to train this model. The cost function is MSE = (w.2() - y)2 (the gradient function is: *, (w.y() g()) 2003). m OMSE Duy Consider stochastic mode of linear regression and initial values wo respectively after training with the following two examples of (x, y) in the given order? (1, 3.05) (4,8.80) O w0-2.9291, w1-2.7287 Ow0-2.6753, w1-2.4825 Ow0-2.8229, w1-2.4686 O WO-1.8732, w1-2.4332 3, w = 3, and the learning rate = 0.01. What are two and w

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