(15pts) Bishop 3.4. Consider a linear model of the form D y(x, w) = wo + Wixi i=1 together with a sum-of-squares error f

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(15pts) Bishop 3.4. Consider a linear model of the form D y(x, w) = wo + Wixi i=1 together with a sum-of-squares error f

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15pts Bishop 3 4 Consider A Linear Model Of The Form D Y X W Wo Wixi I 1 Together With A Sum Of Squares Error F 1
15pts Bishop 3 4 Consider A Linear Model Of The Form D Y X W Wo Wixi I 1 Together With A Sum Of Squares Error F 1 (46.56 KiB) Viewed 178 times
15pts Bishop 3 4 Consider A Linear Model Of The Form D Y X W Wo Wixi I 1 Together With A Sum Of Squares Error F 2
15pts Bishop 3 4 Consider A Linear Model Of The Form D Y X W Wo Wixi I 1 Together With A Sum Of Squares Error F 2 (99.48 KiB) Viewed 178 times
(15pts) Bishop 3.4. Consider a linear model of the form D y(x, w) = wo + Wixi i=1 together with a sum-of-squares error function of the form N 1 ED(W): Σ{y(xn, w) - tn}² 2 n=1 =
i. (5 points) Derive the vectorized analyitical solution (also called the closed form solution): w* = [D_1] ¹ t ii. (5 points) Derive the gradient of a single parameter: N JED (w) δω; Σ{y(xn, w) - tn}xnj n=1 iii. (10 points) Vectorize the gradient to get the following formula: [y(x₁, w) - t₁ JED(w) VwED(w) [D_1] (x2, w) - t2 Əw y(xn, w) - tn = = =
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