Dynamic programming (DP) is an optimization technique for solving problems by breaking them into simpler, interdependent subproblems. By solving each subproblem once and storing its result, DP avoids redundant calculations. This approach is crucial for coding interviews because many real-world problems, especially optimization and decision-making, rely on DP to find efficient solutions.
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Grokking Dynamic Programming Interview in C++
The ultimate guide to dynamic programming interviews in C++ with strategies developed by FAANG engineers. Practice with real-world questions and get interview-ready in just a few hours.
4.8
53 Lessons
25h
Also available in
Also available in
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets
- The ability to identify and apply the underlying pattern in an interview question by assessing the problem statement
- Familiarity with dynamic programming techniques with hands-on practice in a setup-free coding environment
- The ability to efficiently evaluate the tradeoffs between time and space complexity in different solutions
- A flexible conceptual framework for solving any dynamic programming question, by connecting problem characteristics and possible solution techniques
Learning Roadmap
1.
Getting Started
Getting Started
Get familiar with dynamic programming essentials, its applications, and key optimization techniques.
2.
0/1 Knapsack
0/1 Knapsack
Discover the logic behind solving optimization problems using dynamic programming techniques.
3.
Unbounded Knapsack
Unbounded Knapsack
6 Lessons
6 Lessons
Go hands-on with dynamic programming for optimized solutions to unbounded knapsack challenges.
4.
Recursive Numbers
Recursive Numbers
12 Lessons
12 Lessons
Enhance your skills in solving recursive number problems using dynamic programming techniques.
5.
Longest Common Substring
Longest Common Substring
16 Lessons
16 Lessons
Map out the steps for solving comprehensive string matching problems using dynamic programming techniques.
6.
Palindromic Subsequence
Palindromic Subsequence
6 Lessons
6 Lessons
Tackle finding and counting palindromic subsequences, minimizing deletions, and partitioning strings efficiently.
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Developed by MAANG Engineers
ABOUT THIS COURSE
Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an advanced optimization technique applied to recursive solutions. However, DP is not a one-size-fits-all technique, and it requires practice to develop the ability to identify the underlying DP patterns. With a strategic approach, coding interview prep for DP problems shouldn’t take more than a few weeks.
This course starts with an introduction to DP and thoroughly discusses five DP patterns. You’ll learn to apply each pattern to several related problems, with a visual representation of the working of the pattern, and learn to appreciate the advantages of DP solutions over naive solutions.
After completing this course, you will have the skills you need to unlock even the most challenging questions, grok the coding interview, and level up your career with confidence.
This course is also available in JavaScript, Python, and Java—with more coming soon!
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Frequently Asked Questions
What is dynamic programming, and how does it help in coding interviews?
What are some common dynamic programming patterns I should know for interviews?
Why is dynamic programming emphasized in technical interviews?
How can dynamic programming proficiency enhance my performance in coding interviews?
How should I explain a dynamic programming solution in an interview?
