Top 50 Dynamic Programming Java Algorithms Coding Questions

Solve the top 50 Dynamic Programming Java Algorithms Questions to ace Coding Interview and Competitive Programming.
Free Download What you'll learn
Understand what is Dynamic Programming, Recursion, and back tracking
Learn the criteria to classify a dynamic programming question using optimal substructure and overlapping subproblems
Master the art of recursion and learn how to convert a recursion problem to dynamic programming
Solve top 50 handpicked dynamic programming Java algorithm questions asked in competitive programming and programming interviews
Solve each question in recursive, top-down (memoization) and bottom-up (tabulation) dynamic programming approaches
Get one step closer to competitive programming and acing coding interview
Requirements
Understanding of any basic programming language (preferably Java)
Basics of programming language such as variables, loops, conditionals, arrays, and strings
Basic understanding of time and space complexities
Description
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 question - What is Dynamic Programming?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. What will be our approach when you take this course?We will solve the 50 most popular Dynamic Programming coding interview questions. 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 Coding Interview Questions 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 <= K (DP + Binary Search)Lecture 21: Maximum Size Square Sub-Matrix with all 1sLecture 22: Largest Area Rectangular Sub-Matrix with Equal 0's and 1'sLecture 23: Largest Rectangular Sub-Matrix whose Sum is 0Lecture 24: Maximum Sum Rectangle in a 2D MatrixLecture 25: Longest Palindromic SubsequenceLecture 26: Longest Palindromic SubstringLecture 27: Longest Alternating SubsequenceLecture 28: Count All Palindromic Subsequences in a Given StringLecture 29: Longest Common SubstringLecture 30: Longest Subsequence such that the Difference Between Adjacent is 1Lecture 31: Longest Repeated SubsequenceLecture 32: Word Break ProblemLecture 33: Permutation Coefficient ProblemLecture 34: Minimum Number of Jumps to Reach EndLecture 35: Partition ProblemLecture 36: Count DerangementsLecture 37: Count the Number of Ways to Reach a Given Score in a GameLecture 38: Palindromic PartitioningLecture 39: Matrix Chain MultiplicationLecture 40: Maximum Subsequence Sum such that no 3 Elements are ConsecutiveLecture 41: Painting the FenceLecture 42: Largest Independent Set of Nodes in a Binary TreeLecture 43: Count Balanced Binary Trees of Height HLecture 44: Coin Change ProblemLecture 45: Optimal Strategy for a GameLecture 46: Unique PathsLecture 47: Minimum Cost PathLecture 48: Coin Game Winner Where Every Player Has 3 ChoicesLecture 49: Edit DistanceLecture 50: 0/1 Knapsack ProblemLecture 51: Find if a String is Interleaved with 2 Other StringsLecture 52: Maximum Profit by Buying and Selling a Share at Most TwiceThese questions are taken from popular practice websites and hence, are asked in coding interviews of top tech companies like Google, Apple, IBM, Cisco, Atlassian, Microsoft, etc. In each of these questions, not only you will learn just about the algorithm to solve those questions, but you will also learn about other concepts like working with HashMaps, HashSets, sorting an array of objects using comparators, binary search algorithms, trees, etc. You will be able to understand the basics of Java programming language and other data structures as well.Having said this, let's meet in the first lecture on "Introduction to Dynamic Programming".
Who this course is for
Students or professionals who are preparing for software developer related coding interview
Programmers who want to get into competitive programming
Self-taught programmers who want to learn data structures and algorithms
Homepage

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