The ultimate guide to dynamic programming interviews. Developed by FAANG engineers, it equips you with strategic DP skills, practice with real-world questions, and patterns for efficient solutions.

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 C++, JavaScript, and Python—with more coming soon!

Grokking Dynamic Programming Interview

# 0/1 Knapsack

## 0/1 Knapsack
## Target Sum
## Subset Sum
## Count of Subset Sum
## Minimum Subarray Sum Difference
## Minimum Number of Refueling Stops
## Equal Sum Subarrays
## Count Square Submatrices


Let's get introduced to the 0/1 Knapsack pattern.

Getting Started

0/1 Knapsack

Unbounded Knapsack

Recursive Numbers

Longest Common Substring

Palindromic Subsequence

Conclusion

Introduction to 0/1 Knapsack

Overview