Master the art of Dynamic Programming by learning to break complex challenges into simple, reusable sub-problems. This course features step-by-step animations that bring abstract logic to life, showing you exactly how data flows through tables and recursion trees in real-time. Develop a powerful visual intuition for optimization so you can solve even the toughest algorithmic puzzles with ease. Sheldon created this course.
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⭐️ Contents ⭐️
— 0:00:00 Course Introduction and Visual Intuition
— 0:00:33 Fundamentals of Dynamic Programming
— 0:01:14 The Staircase Problem: Counting Paths
— 0:04:13 Implementing Recursive Solutions
— 0:06:26 The Inefficiency of Simple Recursion
— 0:07:32 Pattern 1: Memoization (Top-Down Approach)
— 0:11:39 Pattern 2: Tabulation (Bottom-Up Approach)
— 0:14:09 Comparing Memoization vs. Tabulation
— 0:16:34 Practice Problem: N-th Tribonacci Number
— 0:20:33 Optimization: Min Cost Climbing Stairs
— 0:28:56 Constant Transition Pattern and Space Optimization
— 0:30:46 Practice Problem: House Robber
— 0:34:08 Pattern 3: Grid Problems (2D DP)
— 0:34:41 Practice Problem: Unique Paths
— 0:39:54 Optimizing Space in Grid Problems
— 0:42:37 Practice Problem: Unique Paths II (With Obstacles)
— 0:45:19 Pattern 4: Two Sequences
— 0:45:47 Practice Problem: Longest Common Subsequence
— 0:57:48 Practice Problem: Edit Distance
— 1:01:34 Pattern 5: Interval DP
— 1:01:46 Practice Problem: Longest Palindromic Subsequence
— 1:11:57 Practice Problem: Palindromic Substrings
— 1:14:47 Pattern 6: Non-Constant Transition
— 1:15:05 Practice Problem: Longest Increasing Subsequence
— 1:23:11 Practice Problem: Partition Array for Maximum Sum
— 1:27:00 Pattern 7: Knapsack-like Problems
— 1:27:14 Practice Problem: Partition Equal Subset Sum
— 1:38:04 Practice Problem: Coin Change
— 1:41:27 Final Summary and Resources
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