I dunno why I find the idea of "finding the hyperplane that maximally separates classes" so unpleasant compared to the approach that makes sense to me "optimize the weights of a parameterized function to minimize an objective function". I imagine under the hood they're not really different. Generally, I think SVMs appear to statisticians while deep learning appeals to physicists and probabilicians.
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I remember being introduced to SVMs in the context of machine learning in biology before deep neural networks: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559884/
I left the ML community in disgust at how unsavory these ML methods were.
I mean you are gonna have to expand on that. How can you be disgusted by some simple math?
I dunno why I find the idea of "finding the hyperplane that maximally separates classes" so unpleasant compared to the approach that makes sense to me "optimize the weights of a parameterized function to minimize an objective function". I imagine under the hood they're not really different. Generally, I think SVMs appear to statisticians while deep learning appeals to physicists and probabilicians.