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Master every knapsack variant — 0/1 knapsack, unbounded knapsack, fractional knapsack, subset sum, partition equal subset, and target sum with Python solutions and Big-O analysis.
Master longest subsequence problems — LIS with patience sorting, longest bitonic, chain of pairs, zigzag subsequence, Russian doll envelopes (2D LIS).
Master DP on grids — unique paths, minimum path sum, dungeon game, cherry pickup, and maximum path in grid with step-by-step Python solutions.
Master palindrome DP problems — longest palindromic subsequence, minimum cuts for palindrome partitioning, counting palindromic substrings, and shortest palindrome with KMP.
Complete guide to all stock trading problems — I through IV, with cooldown, and with transaction fee. One unified state machine DP framework covers them all.
Master bitmask dynamic programming — represent subsets as integers, solve the Travelling Salesman Problem, assignment problem, and subset enumeration with Python implementations.
Model dynamic programming as state machines — solve all Buy and Sell Stock variants (I-IV, cooldown, fee), understand state transitions, and build a general framework with Python.
Master string dynamic programming — Longest Common Subsequence, Edit Distance, Longest Palindromic Subsequence, Wildcard and Regex Matching with Python implementations and space optimization.
Master dynamic programming on trees — max independent set, tree diameter via DP, House Robber III, rerooting technique, and post-order traversal patterns with Python implementations.
Master advanced dynamic programming patterns — interval DP, tree DP with rerooting, bitmask DP, digit DP, and optimization techniques like Knuth's and divide-and-conquer optimization.
Master dynamic programming with 1D and 2D patterns, memoization vs tabulation approaches, and solutions to classic LeetCode DP problems.
Solve the multi-transaction stock problem with the greedy peak-valley insight: sum every positive daily delta. Includes complexity analysis and DP alternative.
Build Pascal's Triangle in O(n^2) time using simple row-by-row addition. Includes a clear diagram, edge cases, and complexity analysis.
Solve Jump Game with a single-pass greedy max-reach approach. Compare it against the dynamic programming solution and learn when greedy is provably optimal.
Solve Maximum Product Subarray in linear time by tracking running min and max. Learn why negative numbers flip the role of min and max and see clean code.
Walk through Climbing Stairs from brute force recursion to bottom-up DP, with edge cases, complexity analysis, and an interview script.
Walk through Coin Change with brute force recursion, memoization, and bottom-up DP. Includes complexity analysis and interview script.
Solve House Robber with the pick-or-skip DP recurrence, then optimize to O(1) space. Includes interview script and related variants.
Solve Word Break with bottom-up dynamic programming. Includes brute force, edge cases, complexity analysis, and an interview script.
Eight classic dynamic programming problems — Climbing Stairs, House Robber, Coin Change, LIS, Word Break, 0/1 Knapsack, Edit Distance, and LCS — each with Python solutions and DP tables.
An introduction to dynamic programming — overlapping subproblems, optimal substructure, top-down memoization, and bottom-up tabulation, with worked examples in Python.