Confusing use of gradient in Pattern recognition and machine learning -

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i'm reading prml , gradient notation seems confusing. in chapter 2, page 116, a column vector: enter image description here

and on appendix e page 707, column vector: enter image description here however, in chapter 3, during derivation of least-square, page 141, row vector: enter image description here

can clarify these confusing details me? have read posts on web, of them says gradient strictly column vector, says depends on calculation being carried out, says depends on author, , couldn't come conclusive answer

the answer stated in question - gradient vector of partial derivatives, treating column/row vector not matter. people use orientation best particular use/derivation, same applies putting data points in data matrix row/column wise, defining linear projections column/row vectors etc. thus, answer it depends on particular use, have check kind of notation author uses. why that? because not matter, , calculations - row notation reduces amount of transpose operations needed, , - column notation helps. that's all.


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