Top 50 Dynamic Programming Java Algorithms Coding Questions

Top 50 Dynamic Programming Java Algorithms Coding Questions, available at $74.99, has an average rating of 4.3, with 52 lectures, 2 quizzes, based on 43 reviews, and has 2432 subscribers.

You will learn about 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 This course is ideal for individuals who are Students or professionals who are preparing for software developer related coding interview or Programmers who want to get into competitive programming or Self-taught programmers who want to learn data structures and algorithms It is particularly useful for Students or professionals who are preparing for software developer related coding interview or Programmers who want to get into competitive programming or Self-taught programmers who want to learn data structures and algorithms.

Enroll now: Top 50 Dynamic Programming Java Algorithms Coding Questions

Summary

Title: Top 50 Dynamic Programming Java Algorithms Coding Questions

Price: $74.99

Average Rating: 4.3

Number of Lectures: 52

Number of Quizzes: 2

Number of Published Lectures: 52

Number of Published Quizzes: 2

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: ?1,299

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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: Introduction

Lecture 2:Introduction to Dynamic Programming – Recursive, Memoization, Tabulation

Lecture 3:Binomial Coefficient Problem

Lecture 4:Maximize the Cut Segments

Lecture 5: Friends Pairing Problem

Lecture 6: Rod Cutting Problem

Lecture 7:Gold Mine Problem

Lecture 8: Nth Catalan Number

Lecture 9: Largest Sum Contiguous SubArray (Kadane’s Algorithm)

Lecture 10:Climbing Stairs

Lecture 11:Maximum Sum Increasing Subsequence

Lecture 12: House Robber

Lecture 13:Subset Sum Problem

Lecture 14: Longest Common Subsequence

Lecture 15:Longest Increasing Subsequence

Lecture 16: Weighted Job Scheduling

Lecture 17:Maximum Length Chain of Pairs

Lecture 18:Maximum Sum of Disjoint Pairs with Specific Differences

Lecture 19: Egg-Dropping Problem

Lecture 20: Minimum Removals from Array to make Max – Min <= K (DP + Binary Search)

Lecture 21:Maximum Size Square Sub-Matrix with all 1s

Lecture 22: Largest Area Rectangular Sub-Matrix with Equal 0’s and 1’s

Lecture 23: Largest Rectangular Sub-Matrix whose Sum is 0

Lecture 24:Maximum Sum Rectangle in a 2D Matrix

Lecture 25: Longest Palindromic Subsequence

Lecture 26: Longest Palindromic Substring

Lecture 27:Longest Alternating Subsequence

Lecture 28:Count All Palindromic Subsequences in a Given String

Lecture 29: Longest Common Substring

Lecture 30:Longest Subsequence such that the Difference Between Adjacent is 1

Lecture 31:Longest Repeated Subsequence

Lecture 32:Word Break Problem

Lecture 33:Permutation Coefficient Problem

Lecture 34: Minimum Number of Jumps to Reach End

Lecture 35:Partition Problem

Lecture 36:Count Derangements

Lecture 37:Count the Number of Ways to Reach a Given Score in a Game

Lecture 38: Palindromic Partitioning

Lecture 39:Matrix Chain Multiplication

Lecture 40: Maximum Subsequence Sum such that no 3 Elements are Consecutive

Lecture 41:Painting the Fence

Lecture 42: Largest Independent Set of Nodes in a Binary Tree

Lecture 43: Count Balanced Binary Trees of Height H

Lecture 44:Coin Change Problem

Lecture 45:Optimal Strategy for a Game

Lecture 46:Unique Paths

Lecture 47:Minimum Cost Path

Lecture 48:Coin Game Winner Where Every Player Has 3 Choices

Lecture 49: Edit Distance

Lecture 50:0/1 Knapsack Problem

Lecture 51:Find if a String is Interleaved with 2 Other Strings

Lecture 52:Maximum Profit by Buying and Selling a Share at Most Twice

These 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“.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Introduction to Dynamic Programming – Recursive, Memoization, Tabulation

Chapter 2: Binomial Coefficient Problem

Lecture 1: Binomial Coefficient Problem

Chapter 3: Maximize the Cut Segments

Lecture 1: Maximize the Cut Segments

Chapter 4: Friends Pairing Problem

Lecture 1: Friends Pairing Problem

Chapter 5: Rod Cutting Problem

Lecture 1: Rod Cutting Problem

Chapter 6: Gold Mine Problem

Lecture 1: Gold Mine Problem

Chapter 7: Nth Catalan Number

Lecture 1: Nth Catalan Number

Chapter 8: Largest Sum Contiguous SubArray (Kadanes Algorithm)

Lecture 1: Largest Sum Contiguous SubArray (Kadanes Algorithm)

Chapter 9: Climbing Stairs

Lecture 1: Climbing Stairs

Chapter 10: Maximum Sum Increasing Subsequence

Lecture 1: Maximum Sum Increasing Subsequence

Chapter 11: House Robber

Lecture 1: House Robber

Chapter 12: Subset Sum Problem

Lecture 1: Subset Sum Problem

Chapter 13: Longest Common Subsequence

Lecture 1: Longest Common Subsequence

Chapter 14: Longest Increasing Subsequence

Lecture 1: Longest Increasing Subsequence

Chapter 15: Weighted Job Scheduling

Lecture 1: Weighted Job Scheduling

Chapter 16: Maximum Length Chain of Pairs

Lecture 1: Maximum Length Chain of Pairs

Chapter 17: Maximum Sum of Disjoint Pairs with Specific Difference

Lecture 1: Maximum Sum of Disjoint Pairs with Specific Difference

Chapter 18: Egg Dropping Problem

Lecture 1: Egg Dropping Problem

Chapter 19: Minimum Removals from Array to make Max – Min <= K (DP + Binary Search)

Lecture 1: Minimum Removals from Array to make Max – Min <= K (DP + Binary Search)

Chapter 20: Maximum Size Square Sub-Matrix with all 1s

Lecture 1: Maximum Size Square Sub-Matrix with all 1s

Chapter 21: Largest Area Rectangular Sub-Matrix with Equal 0s and 1s

Lecture 1: Largest Area Rectangular Sub-Matrix with Equal 0s and 1s

Chapter 22: Largest Rectangular Sub-Matrix whose Sum is 0

Lecture 1: Largest Rectangular Sub-Matrix whose Sum is 0

Chapter 23: Maximum Sum Rectangle in a 2D Matrix

Lecture 1: Maximum Sum Rectangle in a 2D Matrix

Chapter 24: Longest Palindromic Subsequence

Lecture 1: Longest Palindromic Subsequence

Chapter 25: Longest Palindromic Substring

Lecture 1: Longest Palindromic Substring

Chapter 26: Longest Alternating Subsequence

Lecture 1: Longest Alternating Subsequence

Chapter 27: Count All Palindromic Subsequence in a Given String

Lecture 1: Count All Palindromic Subsequence in a Given String

Chapter 28: Longest Common Substring

Lecture 1: Longest Common Substring

Chapter 29: Longest Subsequence such that Difference Between Adjacent is 1

Lecture 1: Longest Subsequence such that Difference Between Adjacent is 1

Chapter 30: Longest Repeated Subsequence

Lecture 1: Longest Repeated Subsequence

Chapter 31: Word Break Problem

Lecture 1: Word Break Problem

Chapter 32: Permutation Coefficient Problem

Lecture 1: Permutation Coefficient Problem

Chapter 33: Minimum Number of Jumps to Reach End

Lecture 1: Minimum Number of Jumps to Reach End

Chapter 34: Partition Problem

Lecture 1: Partition Problem

Chapter 35: Count Derangements

Lecture 1: Count Derangements

Chapter 36: Count Number of Ways to Reach a Given Score in a Game

Lecture 1: Count Number of Ways to Reach a Given Score in a Game

Chapter 37: Palindromic Partitioning

Lecture 1: Palindromic Partitioning

Chapter 38: Matrix Chain Multiplication

Lecture 1: Matrix Chain Multiplication

Chapter 39: Maximum Subsequence Sum such that no 3 Elements are Consecutive

Lecture 1: Maximum Subsequence Sum such that no 3 Elements are Consecutive

Chapter 40: Painting the Fence

Lecture 1: Painting the Fence

Chapter 41: Largest Independent Set of Nodes in a Binary Tree

Lecture 1: Largest Independent Set of Nodes in a Binary Tree

Chapter 42: Count Balanced Binary Trees of Height H

Lecture 1: Count Balanced Binary Trees of Height H

Chapter 43: Coin Change Problem

Lecture 1: Coin Change Problem

Chapter 44: Optimal Strategy for a Game

Lecture 1: Optimal Strategy for a Game

Chapter 45: Unique Paths

Lecture 1: Unique Paths

Chapter 46: Minimum Cost Path

Lecture 1: Minimum Cost Path

Chapter 47: Coin Game Winner Where Every Player has 3 Choices

Lecture 1: Coin Game Winner Where Every Player has 3 Choices

Chapter 48: Edit Distance

Lecture 1: Edit Distance

Chapter 49: 0/1 Knapsack Problem

Instructors

Raunak Jain
Instructor at Udemy and a Professional Software Developer

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 7 votes

4 stars: 9 votes

5 stars: 26 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!