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📘 Welcome to Part 207 of Code & Debug’s DSA in Python Course! In this episode, we crack the canonical optimization DP: the 0/1 Knapsack problem from GeeksforGeeks. Given weights, values, and capacity W, the goal is to maximize total value without exceeding capacity, choosing to take or skip each item entirely. We’ll progress from brute-force recursion to the optimal O(W) space DP, aligning with the complete guide in our article. This problem is foundational for knapsack-style patterns and interviews, reinforcing state design, transitions, and space rolling techniques. 🔗 GFG Problem – 0/1 Knapsack: https://www.geeksforgeeks.org/problems/0-1-knapsack-problem0945/1 🔗 In-Depth Article with Code & Explanations: https://codeanddebug.in/blog/0-1-knapsack-problem/ 📄 FULL Playlist Sheet (Every Video Listed): https://docs.google.com/spreadsheets/d/1AWE15Fy3wD2iqu2vjK_R7cCiuvSsjYQclcdZmHpF66o/edit?usp=sharing 🎓 Enroll Free: Master Python DSA Course https://codeanddebug.in/course/master-dsa-with-leetcode 🚀 Advance Python DSA for FAANG (Zero to Hero Course) https://codeanddebug.in/course/zero-to-hero-python-dsa Stay focused and keep coding with Code & Debug. Like | Share | Subscribe | Hit the 🔔 #Knapsack01 #DynamicProgramming #DPonSubsequences #OptimizationDP #GFGPractice #Tabulation #Memoization #SpaceOptimization #PythonDSA #CodeAndDebug #Part207 #InterviewPrep
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