Dynamic Programming — ZeroCourse

reading · we frame, you read MIT or the canonical taught · we author, no canonical fits ↺ spirals back to earlier lessons

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reading · 40 min + ~300 min canonical

What Dynamic Programming Actually Is: Overlapping Subproblems and Optimal Substructure

Dynamic programming has the worst name in computer science. It's not particularly dynamic; it's not programming in the modern sense. Richard Bellman (who invented it in 1953) pick…

primary Educative Grokking Dynamic Programming Patterns

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reading · 30 min + ~300 min canonical L1

1D DP Patterns: Fibonacci, Coin Change, Longest Increasing Subsequence

The previous lesson said DP is "brute-force recursion plus a cache." This lesson is where you learn to recognise the most common shapes of 1D DP problems — meaning problems whose …

primary Educative Grokking Dynamic Programming Patterns

after L1 locked

reading · 40 min + ~300 min canonical L1

The DP Recipe: How to Convert Any Recursive Brute Force into a DP Solution

Lesson 1 told you what DP is. Lesson 2 gave you three families to recognise. This lesson is the procedure — when you sit down with a new problem, what do you actually do? The hone…

primary Educative Grokking Dynamic Programming Patterns

after L2 locked

reading · 35 min + ~300 min canonical L1

2D DP and the Classics: Edit Distance, LCS, Matrix Chain Multiplication

One-dimensional DP gave you Fibonacci-shaped, decision-at-each-step, and subsequence-shaped patterns. Two-dimensional DP doubles the state and roughly squares the problem space. T…

primary Educative Grokking Dynamic Programming Patterns

after L3 locked

reading · 35 min + ~300 min canonical L1

Knapsack and the DP Pattern Menu: 0/1, Unbounded, and the Patterns You'll See Most

Knapsack is the textbook DP problem. Most of the DP problems you'll meet in production and interviews are knapsack-shaped, even when they're not called that. "Can I make change fo…

primary Educative Grokking Dynamic Programming Patterns

after L4 locked

exercise reading · 15 min L1

Exercises: Easy (Warm-up)

- [ ] Climbing Stairs (#70) — NC/LC — Fibonacci variant, bottom-up DP - [ ] Min Cost Climbing Stairs (#746) — NC/LC — DP with two choices per step - [ ] House Robber (#198) — NC/L…

after L5 locked

exercise reading · 30 min L1

Exercises: Medium (Core)

- [ ] House Robber II (#213) — NC/LC — Circular array: run House Robber twice - [ ] Coin Change (#322) — NC/LC — Classic unbounded knapsack - [ ] Longest Increasing Subsequence (#…

after L6 locked

exercise reading · 45 min L1

Exercises: Hard (Stretch)

- [ ] Edit Distance (#72) — NC/LC — 2D DP: insert, delete, replace operations - [ ] Burst Balloons (#312) — NC/LC — Interval DP, think in reverse - [ ] Regular Expression Matching…

after L7 locked

exercise reading · 60 min L1

Mini-Project

- [ ] Build a sequence alignment tool in Ruby — implement Needleman-Wunsch (global alignment) and Smith-Waterman (local alignment) algorithms; visualize the DP table and traceback…

after L8 locked

L10

project reading · 480 min L1

Project: DP Pattern Catalog

Solve 12 DP problems (4 from each pattern: 1D, 2D, knapsack) and write up the pattern for each. For every problem, document: the recurrence relation, base cases, state diagram, an…

after L9 locked

L11

project reading · 600 min L1

Capstone: Smart Text Editor

- [ ] Milestone 1: Basic terminal text buffer — insert, delete, cursor movement, display - [ ] Milestone 2: File I/O — save and load files - [ ] Milestone 3: Find-and-replace with…

after L10 locked

L12

assessment reading · 77 min L1

Self-Assessment: Dynamic Programming

- [ ] What are the two properties that make a problem solvable with DP? - [ ] Explain the difference between memoization (top-down) and tabulation (bottom-up). When would you pref…

after L11 locked

12 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.