Dynamic Programming Crash Course | Advanced Data Structures And Algorithms Tutorial | Simplilearn
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This video from simplilearn is based on the dynamic programming crash course. This advanced data structures and algorithms tutorial will help you learn all the major concepts of algorithms and data structures with theoretical as well as practical approaches.
Introduction to dynamic programming crash course
What Is Dynamic Programming?
Longest Common Subsequence Problem Using Dynamic Programming
Longest Increasing Subsequence
Subset Sum Problem
Longest Common Substring
Matrix Chain Multiplication
Minimum Edit Distance In Dynamic Programming
Knapsack Problem Using Dynamic Programming
Coin Change Problem Using Dynamic Programming
Floyd Warshall Algorithm
Tower Of Hanoi Problem
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What Is Dynamic Programming?
In computer science, something is said to be efficient if it is quick and uses minimal memory. By storing the solutions to subproblems, we can quickly look them up if the same problem arises again. Because there is no need to recompute the solution, this saves a significant amount of calculation time. But hold on! Efficiency comprises both time and space difficulty. But, why does it matter if we reduce the time required to solve the problem only to increase the space required?
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