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Lecture 19: Dynamic Programming I: Fibonacci, Shortest Paths

Lecture 19: Dynamic Programming I: Fibonacci, Shortest Paths on HackerTube: recommended by 4 Hacker News commenters across 4 separate months, 2016–2021.

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Erik Demaine's MIT 6.006 lecture introducing dynamic programming through Fibonacci numbers and shortest paths. It covers memoization, subproblems and optimal substructure.

Why Hacker News recommends it

Recommended as a clear introduction to dynamic programming from a well-regarded instructor.

people recommended it
4
separate months
4
strong recommendations
0
discussed on HN
2016–2021
“Please watch this MIT intro”
— dominotw, Apr 2021, in “Dynamic Programming vs. Divide-and-Conquer (2018)”

What HN commenters said

4 Hacker News comments link this video. Each one links back to its thread.

dominotwApr 2021Recommends

> on interviews they want you to explicitly construct the array very often i.e. it's the different between hire and a strong hire exactly because @lru_cache is much easier are you serious? someone really asked you to "use array". Beyond stupid if they really did. > you can easily blow the stack for real production grade implementations using recursion recursive solution doesn't automatically mean using a the method stack. you can implement recursive solutions using explicit stack . > dynamic programming proper please. There is no "proper" dynamic programming. Please watch this MIT intro ▶ this video .

in “Dynamic Programming vs. Divide-and-Conquer (2018)” · View on HN

dehrmannFeb 2021Recommends

I don't think I've ever been asked a DP problem in an interview, and only had to use it while programming once . That said, I liked the MIT lecture on it: ▶ this video

in “Algorithms by Jeff Erickson” · View on HN

gbaconApr 2019Recommends

You may also like a couple of lectures by Erik Demaine: Dynamic Programming I: Fibonacci, Shortest Paths ▶ this video Dynamic Programming II: Text Justification, Blackjack [link]

in “Graphical Introduction to Dynamic Programming” · View on HN

philippebackAug 2016Recommends

When you deal with large data sets (e.g. Hadoop/Spark...), BigO starts to hit badly. Dynamic Programming also requires you to have a clue about it. See ▶ this video for some cool explanations.

in “The Imposter's Handbook” · View on HN

Topics & tags

Algorithms & data structuresUniversity lectures & coursesalgorithmsmemoizationshortest pathsmit ocwerik demaine