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🚀 Welcome to Part 72 of Code & Debug’s DSA in Python Course! In this lecture, we extend our Advanced Recursion concepts by solving Count All Subsequences with Sum K using Recursion & Backtracking. This is a critical problem in Dynamic Programming, Combinatorics, and Optimization Problems. 📚 What you’ll learn in this video: ✅ Understanding the problem statement & constraints ✅ Recursive approach to count valid subsequences ✅ Optimizing using Backtracking to reduce redundant computations ✅ Time complexity analysis of the recursive approach ✅ Handling edge cases efficiently 💡 Why is this important? This problem is fundamental for Subset Sum Problems, Combinatorial Counting, and Dynamic Programming. It is frequently asked in FAANG interviews and helps in efficiently solving counting-based problems using recursion and backtracking. 👉 Link to solve this question: https://www.geeksforgeeks.org/problems/perfect-sum-problem5633/1 👉 Refer the article below for better understanding: https://codeanddebug.in/blog/count-all-subsequences-with-sum-k/ 👉 📄 Access the full YouTube DSA Playlist Sheet (All Questions in Order): 🔗 https://docs.google.com/spreadsheets/d/1AWE15Fy3wD2iqu2vjK_R7cCiuvSsjYQclcdZmHpF66o/edit?usp=sharing 👉 Enroll in the free DSA Python course here: https://codeanddebug.in/course/master-dsa-with-leetcode 👉 Enroll for Self-Paced Advance DSA course here: https://codeanddebug.in/course/zero-to-hero-python-dsa 🙏 Thank you for supporting Code & Debug! Don’t forget to like, share, and subscribe to our channel. Hit the 🔔 bell icon to stay updated with our latest lectures. #Recursion #CountSubsequences #SubsetSum #Backtracking #PythonDSA #DSAPythonCourse #CodeAndDebug #CompetitiveProgramming #CodingInterviews #Part72
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