Learn and practice the Backtracking, Subsets, and Dynamic Programming patterns to solve coding interview problems.

Grokking Grind 75 in Python: Mastery through Coding Patterns

Building a strong foundation for systematically tackling complex coding interview challenges is crucial. This module explores key patterns for addressing complex coding interview challenges. It covers the Backtracking and Subsets patterns for systematic problem-solving, each suited to different types of problems. Backtracking excels in decision tree exploration, while Subsets is ideal for generating power sets. The module also delves into Dynamic Programming, which optimizes complex problems by breaking them into smaller subproblems. Techniques like memoization and tabulation enhance efficiency. Overall, the module teaches us how to dissect challenging problems systematically and arrive at optimal solutions.

Effective Decision-making

Given an integer array, `nums`, find the contiguous subarray that has the largest sum and return its sum.

> **Note:** A subarray is a contiguous part of an array that contains at least one number.

**Constraints:**

* $1 \leq$ `nums.length` $\leq 10^5$

* $-10^4 \leq$ `nums[i]` $\leq 10^4$



Let's solve the Maximum Subarray problem using the Dynamic Programming pattern.

Backtracking

Subsets

Dynamic Programming

Conclusion

Solution: Maximum Subarray

Statement