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amhotw t1_jc0mf55 wrote

If you are serious, I would recommend working on Rudin's Principles of Math Analysis. It might take a day (or more...) to wrap your head around a single proof but at the end you'll be ready to read anything (of course you might need to check some definitions.)

For KL divergence, entropy etc., Info Theory book by Mackay is great.

For hessian, well it is just calculus; the second derivative of a multivariate function. To understand its uses, you would need some understanding of numerical analysis and concave programming. For the latter, Boyd's optimization book is a classic. I don't remember a good book on numerical analysis but some diff. eqn.s books have nice chapters on it.

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nirnamous OP t1_jc0mvz9 wrote

Thank you very much.

Your comment is very helpful.

I'll refer these sources.

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nirnamous OP t1_jc0n7vx wrote

Just asking (Not trying to offend you or anything)
Why you asked whether this is serious ? Are above are very basic things ?

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amhotw t1_jc0r0nn wrote

I just meant this would take significant amount of time. I think it is impossible to do research in a quantitative field without understanding these so I would say it is well worth the investment. But most people are not concerned with research or even understanding the methods.

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