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That's a great question. I had assumed that the workflow of an ML engineer consisted of managing the data and a relatively high level set of parameters around
by monocasa 5y ago
That's a great question. I had assumed that the workflow of an ML engineer consisted of managing the data and a relatively high level set of parameters around a search space of layers and connectivity, as the whole shtick of ML is that the hyperparameter space of the tensors themselves is too complex to grok or tweak when generated from training. But I only have a passing knowledge of the subject, pretty much just enough to get myself in trouble in these kinds of discussions.
Any chance some fantastic HNer could chime in there?
- srcreigh 5y agoI'm no data scientist but many statistical methods rely on prior knowledge and even computed inputs. Two examples I can think of are doing linear regression on the square of your input. For deep learning, people have improved visual representation by taking samples of the colors at various frequencies. [1] [1]: https://arxiv.org/pdf/2003.08934.pdf https://arxiv.org/pdf/2003.08934.pdf
- monocasa 5y agoYeah, that's a better way of saying what I meant by managing the data. Mentally projecting data through, massaging said data, and building reproducible pipelines rather than manually tweaking the learned weights after the fact.