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No transparency here. What is it considering to come up with these scores? Are they prioritizing growth, value, financial health, or something else?


With deep learning, it's very hard to get the formula from the model, we don't know how it looks at the data.

But, we heard the same feedback from a few other users and we're thinking some ways to show how the model thinks and why it scores like this.

Thank you for trying and provide your valuable feedback.


Haven't read the paper yet, but it's clear that the brain is much more sample efficient than artificial neural networks. We should be spending more time on figuring out why than scaling up LLMs.


I'm risking just posting another process that you might have seen, but I thought it was helpful. Maybe you or someone else will too.

Farnam Street annual review https://fs.blog/annual-review/ (Direct link without giving your email: https://fsmisc.s3.ca-central-1.amazonaws.com/2022+AR.pdf)


https://sullivantm.com/

Mostly me writing about different machine learning ideas (and other topics) I'm exploring. It might be useful to others, but the primary reason is writing helps me understand things better and reveal which parts I don't.


I know you asked for books, but this Knowledge Project podcast is too good to leave out if you haven't heard it.

https://fs.blog/knowledge-project-podcast/the-ultimate-barga...


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