What is Reddit's opinion of Algorithms to Live By: The Computer Science of Human Decisions?

From 3.5 billion Reddit comments

Pls get started with statistics and probability theory, then python. Practice python to make sure you understand statistics correctly, on simple examples with known solutions/answers.

Otherwise you'll be another "algo trader" asking what's wrong with his "16 trades per 10 years backtest that yields 1000%" with tripple leverage.

There's a great book "Algorithms to Live By" (https://www.amazon.com/Algorithms-Live-Computer-Science-Decisions-ebook/dp/B015CKNWJI/), ideally you'll need to apply similar level of problem solving approach to anything you develop

I used to spend 3 hours per day on Leetcode, but these days not anymore. I did solve like 500 questions. I think Leetcode is a waste of time but it is what it is, that's the game we need to play sadly.

But these days if I were to Leetcode, I probably only spend max 1 hour per day, and that's maybe around 3 months before my interviewing spree.

Untuk senior, fokus ke medium. Aim to solve a random medium in under 25 minutes (starting from reading the question) and I think you should be good.

Hmm, apa ya tipsnya. Coba pake Python aja biar gampang, fokus ke logikanya daripada tetek bengeknya. Python juga gampang nulisnya, jadi bisa pake tulis tangan kalau mau.

Maybe baca buku atau nonton YouTube soal algoritma yang bisa dilihat dalam hidup sehari2? Kyknya kalau tau aplikasinya jadi lebih semangat belajarnya

https://www.amazon.com/Algorithms-Live-Computer-Science-Decisions-ebook/dp/B015CKNWJI/ref=sr_1_3?crid=264GRKTMPQOZ0&keywords=daily+life+algorithms&qid=1655361033&sprefix=daily+life+algoritms%2Caps%2C51&sr=8-3

In the secretary problem's wiki, See:

>1/e-law of best choice

There is a great intuitive explanation of general optimal stopping theory in https://www.amazon.com/Algorithms-Live-Computer-Science-Decisions-ebook/dp/B015CKNWJI

If you create fixed constraints, like time or budget, it allows you to estimate the overall best, with confidence associated with the size of the constraint.

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