Dynamic Programming (DP) is one of the most powerful algorithmic techniques for solving optimization problems. This topic takes you from understanding the core principles of DP to confidently solving classic problems.
You'll learn to identify when a problem can be solved with DP by recognizing overlapping subproblems and optimal substructure. We'll cover both top-down (memoization) and bottom-up (tabulation) approaches, helping you understand when to use each.
The topic progresses through increasingly complex problem categories: 1D problems, grid-based problems, subsequence problems, the versatile knapsack family, and string manipulation challenges. Each deck builds on previous concepts, reinforcing your understanding through hands-on coding exercises.
By the end, you'll have a solid toolkit for tackling DP problems in technical interviews, competitive programming, and real-world optimization scenarios.