Summary of "LeetCode was HARD until I Learned these 15 Patterns"

Main Ideas and Concepts

The video discusses the importance of recognizing patterns in solving LeetCode problems, emphasizing that understanding these patterns can significantly reduce the time and effort required to tackle coding challenges. The speaker presents 15 key patterns that are frequently encountered in coding interviews, particularly at major tech companies like Amazon and Google. Each pattern is explained with examples, use cases, and suggested practice problems.

Key Patterns and Methodologies

Prefix Sum Pattern
- Use Case: Efficiently querying the sum of elements in a subarray.
- Method: Create a prefix sum array where each index contains the sum of all elements up to that index.
- Example Formula: sum(i to j) = P[j] - P[i-1]
- Practice Problems: Suggested problems on LeetCode.
Two Pointers Pattern
- Use Case: Problems involving pairs of elements or checking for conditions (e.g., palindrome).
- Method: Use two pointers moving towards each other or away based on conditions.
- Practice Problems: Suggested problems on LeetCode.
Sliding Window Pattern
- Use Case: Finding subarrays or substrings that meet certain criteria.
- Method: Maintain a window that expands and contracts as needed.
- Example: Finding the maximum sum of a subarray of size K.
- Practice Problems: Suggested problems on LeetCode.
Fast and Slow Pointers Pattern
- Use Case: Detecting cycles in linked lists or arrays.
- Method: Use two pointers moving at different speeds.
- Practice Problems: Suggested problems on LeetCode.
In-Place Linked List Reversal
- Use Case: Reversing a linked list without extra space.
- Method: Use three pointers (previous, current, next) to reverse the links.
- Practice Problems: Suggested problems on LeetCode.
Monotonic Stack Pattern
- Use Case: Finding the next greater or smaller elements in an array.
- Method: Use a stack to track indices and maintain order.
- Practice Problems: Suggested problems on LeetCode.
Top K Elements Pattern
- Use Case: Finding the K largest or smallest elements.
- Method: Use a min-heap for K largest and a max-heap for K smallest.
- Practice Problems: Suggested problems on LeetCode.
Overlapping Intervals Pattern
- Use Case: Problems involving merging or managing intervals.
- Method: Sort intervals and merge overlapping ones.
- Practice Problems: Suggested problems on LeetCode.
Modified Binary Search Pattern
- Use Case: Searching in non-standard arrays (e.g., rotated arrays).
- Method: Adapt binary search with additional checks.
- Practice Problems: Suggested problems on LeetCode.
Binary Tree Traversal
- Use Case: Traversing binary trees in different orders (pre-order, in-order, post-order, level-order).
- Method: Implement recursive or iterative traversals.
- Practice Problems: Suggested problems on LeetCode.
Depth First Search (DFS)
- Use Case: Exploring paths in graphs or trees.
- Method: Use recursion or a stack to traverse nodes.
- Practice Problems: Suggested problems on LeetCode.
Breadth First Search (BFS)
- Use Case: Level-order traversal in trees or graphs.
- Method: Use a queue to explore nodes level by level.
- Practice Problems: Suggested problems on LeetCode.
Matrix Traversal Pattern
- Use Case: Solving grid-related problems.
- Method: Apply graph algorithms like DFS or BFS.
- Practice Problems: Suggested problems on LeetCode.
Backtracking
- Use Case: Exploring all potential solution paths.
- Method: Recursively explore options and backtrack when necessary.
- Practice Problems: Suggested problems on LeetCode.
Dynamic Programming (DP)
- Use Case: Solving optimization problems by breaking them into subproblems.
- Method: Store solutions to avoid redundant calculations.
- Common Patterns: Knapsack, longest common subsequence, etc.
- Practice Problems: Suggested problems on LeetCode.