Overview

Welcome to this course!Here, you will go through a "journey" of the Top 50 Dynamic Programming Java Algorithm questions that are asked in coding interviews as well as competitive programming. So let's start by answering the most fundamental questions:What is Dynamic Programming Algorithm?Dynamic programming is a powerful optimization technique for plain recursion problems. If you can break down a problem into simpler sub-problems (optimal substructure) and these sub-problems are repetitive in nature, then you can convert this problem into a Dynamic Programming Solution.In this course, we will start with a basic introduction to Dynamic Programming. We will understand what Dynamic Programming is and how to find out whether a recursive problem can be solved using Dynamic Programming.Can Dynamic Programming solve all problems?Dynamic Programming cannot solve all problems. The dynamic programming algorithm is meant to solve only those problems that have optimal substructure and overlapping subproblem properties.Why is Dynamic Programming faster?Dynamic Programming is faster because we break down a problem into various smaller subproblems. Then, we store the solutions of these subproblems in a table or cache whenever we encounter them first. Hence, if we get the same subproblem later on, instead of recomputing it, we fetch it from the storage and return it. This saves us a lot of time.Moreover, since a Dynamic Programming algorithm required optimal substructure property. It means that the best solution to the initial subproblem can be constructed from the optimal solutions of its subproblems. Hence, we can easily eliminate the need to consider all the possible subproblem solutions. This reduces the time complexity of the algorithm.What will be our approach when you take this course?We will solve the 50 most popular Dynamic Programming examples asked in coding interviews. For each question, our approach will be: Understand the problem statement with a few examples.Check if it can be solved recursively.Build a recursion tree and determine the recursive pattern.Check for optimal substructure and overlapping subproblem properties using the recursive tree.Derive the recursive code for the solution.Using the recursive code, convert the problem to a Top-Down (Memoization) approach.Then solve the same problem with the Bottom-Up (Tabulation) approach.Come up with an optimized DP solution if possible (space or time).Discuss the time and space complexities of all the approaches.Don't worry, we won't bore you with slides and PPTs. We will understand each algorithm using a whiteboard explanation of approaches and the code in parallel. This will help you to be more expressive during your interviews.In any interview, the interviewer wants to know the thinking or the approach that you are going to take to solve a problem given by him. Thus, it becomes very necessary to keep reciting the approach out loud. The format of these videos will help you to think out loud and will promote discussions and you will be able to question back the interviewer.What are the top 50 Dynamic Programming Problems or Examples asked in Coding Interview and Competitive Programming that we will discuss?Lecture 1: IntroductionLecture 2: Introduction to Dynamic Programming - Recursive, Memoization, TabulationLecture 3: Binomial Coefficient ProblemLecture 4: Maximize the Cut SegmentsLecture 5: Friends Pairing ProblemLecture 6: Rod Cutting ProblemLecture 7: Gold Mine ProblemLecture 8: Nth Catalan NumberLecture 9: Largest Sum Contiguous SubArray (Kadane's Algorithm)Lecture 10: Climbing StairsLecture 11: Maximum Sum Increasing SubsequenceLecture 12: House RobberLecture 13: Subset Sum ProblemLecture 14: Longest Common SubsequenceLecture 15: Longest Increasing SubsequenceLecture 16: Weighted Job SchedulingLecture 17: Maximum Length Chain of PairsLecture 18: Maximum Sum of Disjoint Pairs with Specific DifferencesLecture 19: Egg-Dropping ProblemLecture 20: Minimum Removals from Array to make Max - Min

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