Thanks for this comment. Just to be clear I am not saying it isn't necessary but am genuinely curious what I'm missing. I think a big influence on my comment is using ideas that touch on linear algebra and possibly even doing the computations one might end up doing via linear algebra but without knowing it.
For example- Least squares regression. Totally use this. Even took a semester in college on just regression. The linear algebra underpinnings though haven't never been shown except for a quick blurb in my linear algebra text book. I still understand the concepts of fitting a model and when it's a bad fit (such as non-normal distribution of residuals, co-linearity) but the theoretical underpinnings are more fuzzy to me.
Representing features as vectors, sure. But that's also a pretty superficial use of linear algebra since from that point forward I'm using something on the trig side to compute results (at least in clustering).
I also tend to lean rather heavily on probability and bayesian approaches to many areas. So Naive Bayes classification is a love of mine, finding ideal parameter values given data coming in becomes an online updating multi-armed bandit problem to me (which also doesn't require explicit linear algebra). A lot of my work is also in experiment design and analysis and for this I use a mixture of Bayseian and frequentist statistical testing.
Canned packages out of the box with parameters to tweak that I can cross validate to evaluate how well my model is working. If I happen to venture out to other models I'm probably reading up on common pitfalls and how to test for them.
To me, it's entirely possible that the gap between you and I is due to experience and even just differences in training/learning (including but not limited to the quantity of it). These discussions are important for me since they help to inform my future learning aspirations.
For example- Least squares regression. Totally use this. Even took a semester in college on just regression. The linear algebra underpinnings though haven't never been shown except for a quick blurb in my linear algebra text book. I still understand the concepts of fitting a model and when it's a bad fit (such as non-normal distribution of residuals, co-linearity) but the theoretical underpinnings are more fuzzy to me.
Representing features as vectors, sure. But that's also a pretty superficial use of linear algebra since from that point forward I'm using something on the trig side to compute results (at least in clustering).
I also tend to lean rather heavily on probability and bayesian approaches to many areas. So Naive Bayes classification is a love of mine, finding ideal parameter values given data coming in becomes an online updating multi-armed bandit problem to me (which also doesn't require explicit linear algebra). A lot of my work is also in experiment design and analysis and for this I use a mixture of Bayseian and frequentist statistical testing.
Canned packages out of the box with parameters to tweak that I can cross validate to evaluate how well my model is working. If I happen to venture out to other models I'm probably reading up on common pitfalls and how to test for them.
To me, it's entirely possible that the gap between you and I is due to experience and even just differences in training/learning (including but not limited to the quantity of it). These discussions are important for me since they help to inform my future learning aspirations.