I'm reluctant to tell people "no, don't think of it that way," especially if it works for you, because I don't know the best way to think of things. I only know what works well for me. But for me, it'd be ~impossible to use your mental model to do anything useful. That doesn't mean it's bad, just that I don't understand what you mean.
The most straightforward mental model I've ever found for ML is, think of it as 2D matrix operations, like high school linear algebra. Matrix-matrix, matrix-vector, vector-matrix, and vector-vector will get you through 95% of what comes up in practice. In fact I'm having trouble thinking of something that doesn't work that way, because even if you have an RGB image that you multiply against a 2D matrix (i.e. HxWxC multiplied by a mask) the matrix is still only going to apply to 2 of the channels (height and width), since that's the only thing that makes sense. That's why there's all kinds of flattening and rearranging everywhere in practice -- everyone is trying to get a format like N,C,H,W down to a 2D matrix representation.
People like to talk up the higher level maths in ML, but highschool linear algebra (or for the gamedevs in the audience, the stuff you'd normally do in a rendering engine) really will carry you most of the way through your ML journey without loss of generality. The higher level maths usually happens when you start understanding how differentiation works, which you don't even need to understand until way later after you're doing useful things already.
The most straightforward mental model I've ever found for ML is, think of it as 2D matrix operations, like high school linear algebra. Matrix-matrix, matrix-vector, vector-matrix, and vector-vector will get you through 95% of what comes up in practice. In fact I'm having trouble thinking of something that doesn't work that way, because even if you have an RGB image that you multiply against a 2D matrix (i.e. HxWxC multiplied by a mask) the matrix is still only going to apply to 2 of the channels (height and width), since that's the only thing that makes sense. That's why there's all kinds of flattening and rearranging everywhere in practice -- everyone is trying to get a format like N,C,H,W down to a 2D matrix representation.
People like to talk up the higher level maths in ML, but highschool linear algebra (or for the gamedevs in the audience, the stuff you'd normally do in a rendering engine) really will carry you most of the way through your ML journey without loss of generality. The higher level maths usually happens when you start understanding how differentiation works, which you don't even need to understand until way later after you're doing useful things already.