Honest Take — Before You Begin
Dynamic programming is the hardest topic in computer science for working programmers. Not because the math is hard — it's not, compared to, say, information theory or type theory.…
Master recursive thinking, then build from simple recursion through backtracking to dynamic programming — the single most important and most feared topic in technical interviews.
Rails uses memoization constantly — @user ||= User.find(id) is the simplest form of caching overlapping subproblems. Rails route matching uses a form of trie-based pattern matching that avoids recomputing prefix matches. Bundler's dependency resolution is a constraint satisfaction problem solved with backtracking. Understanding DP also helps you think about caching strategies: fragment caching, Russian doll caching, and counter caches are all ways of storing computed results to avoid recomputation — which is exactly what DP does.
This course unlocks once you've finished its prerequisite. Open prerequisite →
Dynamic programming is the hardest topic in computer science for working programmers. Not because the math is hard — it's not, compared to, say, information theory or type theory.…
Approach: READ: Ch 14 (Dynamic Programming), Ch 15 (Greedy Algorithms)
Approach: READ: Ch 5 (graph traversal -- needed for backtracking context), Ch 8 (DP), Ch 9 (intractable problems), Ch 10 (how to design algorithms)
Approach: CODE ALONG: implement every DP problem in Ruby
Work through each item before the checkpoint.
Solve 20 DP problems on LeetCode (mix of Easy, Medium, Hard). For each, write the recurrence relation, implement top-down, implement bottom-up, and analyze time/space complexity. …
6 lessons. Read in order; spiral back when you need to. By the end you'll have used the core ideas twice — once on the abstract, once on something you'll meet at work next week.