Leet-Code Notes: Turning Problem Solving into an Algorithm Notebook

The Leet-Code repo is an algorithm practice notebook. It contains Python solutions organized by problem number, with a mix of brute-force approaches, optimized attempts, and exploratory notes.

That makes it valuable as a learning record. The code shows not only final answers, but also the path from direct thinking to better patterns.

Patterns in the Repo

Many problems fall into common categories: arrays, strings, hash maps, sorting, two pointers, recursion, dynamic programming, and basic math.

Examples include Two Sum, Longest Substring Without Repeating Characters, Climbing Stairs, Longest Consecutive Sequence, Two Sum II, Contains-like matching problems, and subarray counting.

Learning Value

Some solutions are intentionally simple. A brute-force solution is often the first version that proves the idea. Later, the same problem can be revisited with a hash map, sliding window, dynamic programming table, or sorted scan.

That progression matters because interview-style problem solving is mostly pattern recognition under constraints.

Knowledge Points

• Hash maps often turn repeated lookup problems from quadratic time into linear time.
• Sliding windows are useful when a substring or subarray must satisfy a moving constraint.
• Two pointers work well when input is sorted or can be scanned from both ends.
• Dynamic programming stores overlapping subproblem results instead of recomputing them.
• Sorting can simplify a problem but usually changes time complexity to O(n log n).
• Recursion is expressive, but memoization is often needed when states repeat.
• Maintaining notes beside code makes it easier to revisit mistakes and improvements.

Takeaway

This repo is less about one polished product and more about building algorithm fluency. The next step would be adding complexity notes and a short reflection for each solved problem.