market_hacker

i just implemented a project in elixir with LLM support and would never have considered that before. (i had never used elixir before) - So who knows maybe it will help adoption?

Do any JAX experts know if there is an equivalent to https://captum.ai/ - a model interpretability library for pytorch?

In particular i want to be able to measure feature importance on both inputs and internal layers on a sample by sample basis. This is the only thing currently holding me back from using JAX right now.

Alternatively a simle to read/understand/port implementation of DeepLIFT would work too.

thanks

you probably know this but you're building a nice labelled training set for machine learning to help you automate the process later

maybe i'm missing the point, but i can't see the advantage of using this over pandas

I think there may be a problem with this kind of analysis - it seems to me that the "riskier" plays (2 point conversion, going for it, etc.) - are more likely attempted when coaches think they will work - not randomly. To really do a fair analysis of expectancy you would need trials where the play selection is chosen randomly. Anyone else agree with me?

no.

that would only work if every traded product on the planet was in a single matching engine.