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…
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.…
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…
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.…