Dynamic Programming for Solving Problems

Unlock the power of Dynamic Programming! This course is perfect for programmers with a few months of experience – comfortable with basics like conditional statements and loops – ready to level up their problem-solving skills. We'll focus on understanding the core concepts of dynamic programming and applying them effectively. While we'll implement solutions in C++, the emphasis is on the algorithmic approach.

You'll master dynamic programming by tackling practical problems, including:

• 0-1 Knapsack Problem: A complete explanation with a dedicated 1-hour video.
• Fibonacci Series: Efficiently calculate the Fibonacci sequence using dynamic programming.
• Longest Common Subsequence (LCS): Find the longest common subsequence between two sequences.
• Frog's Staircase to Heaven: Solve the classic frog jumping problem.
• Grid Walking Problem: Navigate a grid to find the optimal path.
• Stock Buy Sell Problem: Develop strategies for maximizing profit in the stock market.
• Coin Change Problem: Determine the minimum number of coins needed to make a certain amount.
• Range Sum Making Queries (No Updates): Efficiently calculate range sums without modifying the input.

What is Dynamic Programming?

Dynamic programming is a powerful technique used in both mathematics and computer science for optimization. It excels when problems can be broken down into overlapping subproblems. If the solution to a larger problem can be built from the solutions to its smaller, overlapping subproblems, then dynamic programming is a suitable approach. This relationship is often described by the Bellman equation.

Two Main Approaches:

• Top-Down Approach (Memoization): Solve subproblems only when needed.
• Bottom-Up Approach (Tabulation): Build solutions to subproblems iteratively.

Ready to conquer complex problems? Start your dynamic programming journey today!

[UPDATE - July 13th, 2025] - Added the House Robber Problem (a popular interview question!).
[UPDATE - July 13th, 2025] - Included a new coding exercise focused on an Interview Question.