Short story: I read The Elements of Statistical Learning and got frustrated when I was trying to verify some of the results, e.g., given $$\text{RSS}(\beta) = \left(\mathbf{y}-\mathbf{X}\beta\right)^{T}\left(\mathbf{y}-\mathbf{X}\beta\right)\text{,}$$ then $$\begin{align}&\dfrac{\partial\text{RSS}}{\partial \beta} = -2\mathbf{X}^{T}\left(\mathbf{y}-\mathbf{X}\beta\right) \\ &\dfrac{\partial^2\text{RSS}}{\partial \beta\text{ }\partial \beta^{T}} = 2\mathbf{X}^{T}\mathbf{X}\text{.} \end{align}$$ I am looking for a matrix calculus book which is written like your traditional calculus book (i.e., proofs of theorems, examples, exercises on computation, etc.). I have already seen this question and feel that the text by Magnus and Neudecker focuses too much on the theory, and the text I have by Gentle focuses too little on the theory and too much on the computation side.
Is there a happy medium out there which is accessible to someone with a background in undergraduate analysis?