Climbing Stairs | All 4 DP Approaches | Recursion to Optimal | Part 191 | DSA in Python Course

Описание: 📘 Welcome to Part 191 of Code & Debug's DSA in Python Course!

In this video, we solve our first Dynamic Programming problem: Climbing Stairs from LeetCode. This is the perfect problem to understand the complete evolution of DP thinking - from basic recursion to the optimal space-optimized solution. We'll implement all 4 approaches step-by-step, showing you how each technique builds upon the previous one.

This problem is essentially the Fibonacci sequence in disguise and serves as the foundation for understanding overlapping subproblems and optimal substructure - the core principles of Dynamic Programming!

👨‍🏫 What's covered in this video:
✅ Approach 1: Brute Force Recursion - O(2^n) time complexity
✅ Approach 2: Memoization (Top-Down DP) - O(n) time, O(n) space
✅ Approach 3: Tabulation (Bottom-Up DP) - O(n) time, O(n) space
✅ Approach 4: Space-Optimized Tabulation - O(n) time, O(1) space
✅ Complete Python code walkthrough for each approach
✅ Dry run examples and complexity analysis
✅ Understanding the DP evolution from inefficient to optimal
✅ Interview tips and when to use each approach

By the end of this session, you'll master the fundamental DP problem-solving pattern and be ready to tackle more complex DP challenges!

🔗 LeetCode Problem - Climbing Stairs:
https://leetcode.com/problems/climbin...

🔗 Refer to the link for code:
https://codeanddebug.in/blog/climbing...

📄 Full Playlist Sheet (All Questions in Order):
https://docs.google.com/spreadsheets/...

🎓 Enroll in the FREE Python DSA Course:
https://codeanddebug.in/course/master...

🚀 Advance Python DSA for FAANG (Zero to Hero Course):
https://codeanddebug.in/course/zero-t...

Stay focused and keep coding with Code & Debug.
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