actually that setup should get you to 1.4-1.5 easily.
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in theory Redis could guess after a few tries in most cases? If the “teachers” outputs always look like all 0s but a single 1…
black box ML is a very difficult problem. One size fits all is impossible, you need to constrain the problem upstream.
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Things you need to take into account: 1) number of samples available 2) type of problem 3) class imbalance 4) overfitting ...etc
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here's an example of a (still very imperfect) system that attempts to do black box ML: https://github.com/rhiever/tpot
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yes I agree, what I’m trying to do is to use a dual-weights approach, so that the NN is trained sample-by-sample, as data arrives
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in this way there should be no overfitting issues since the data set in input changes constantly. But it is non trivial.
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