Question: Derive the optimal least squares parameter value, (widehat{mathbf{w}}), for the total training loss [mathcal{L}=sum_{n=1}^{N}left(t_{n}-mathbf{w}^{top} mathbf{x}_{n} ight)^{2}] How does the expression compare with that derived from

Derive the optimal least squares parameter value, \(\widehat{\mathbf{w}}\), for the total training loss

\[\mathcal{L}=\sum_{n=1}^{N}\left(t_{n}-\mathbf{w}^{\top} \mathbf{x}_{n}\right)^{2}\]

How does the expression compare with that derived from the average loss?

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