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Feature Request: "best observed" parameters for "best" classification output on test data and some accompanying commentary on why. #195

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wbsp opened this issue Sep 18, 2023 · 0 comments

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@wbsp
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wbsp commented Sep 18, 2023

This is such a rich and helpful illustration! Thanks for building it.

I would love to see a user interface capability where you could just load some parameters that are known to produce a really great outcome in the classification for each dataset. To motivate this, I spent a lot of time experimenting with the "two spirals" example. It took me a while to converge on a set of parameters that did something reasonable. Every step of the way was really valuable in learning, especially seeing the sensitivity of the classification to different activation functions, number of {layers, neurons}, inputs, etc -- but it would really cool to just be able to push a button at the end of it and maybe see a really good set of parameters and maybe even some commentary on why they work.

some really bad inputs and why they are bad could be cool too :)

Again, amazing work. thanks for building and sharing!

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