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プロファウンドアカデミーは、学校や大学向けに設計されたインタラクティブなプラットフォームで、実践的な学習と自動化を通じてコンピュータサイエンス教育を強化し、実践的な知識と学生のエンゲージメントに重点を置いています。
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上位5地域
The Sliding Window technique is a key tool in algorithmic problem-solving that can significantly speed up calculations and improve performance. This technique is often used in competitive programming and algorithmic interviews at top tech companies. Mastering it can give you a significant edge. 💻 Practice: https://profound.academy/algorithms-data-structures/R1FvlthxYQc9PRMnepZr 📚 Full DSA Course: https://profound.academy/algorithms-data-structures 🎓 Teach with Profound Academy: https://profound.academy/teach https://profound.academy https://www.instagram.com/profound.academy.inc https://www.facebook.com/profound.academy.inc https://www.linkedin.com/company/profound-academy-inc Chapters: 0:00 Sliding Window Technique Introduction 0:27 Maximum Sum Subarray of Size K 0:38 Naive Approach - O(NK) 1:22 Introducing the Main Idea Behind the Sliding Window Technique 2:23 Sliding Window Implementation 3:18 Hands-on Practice on Profound Academy 3:30 Dynamic Size Sliding Window 4:34 Dynamic Sliding Window Implementation 5:00 Simulating the Algorithm 5:43 Time Complexity Analysis #SlidingWindow #Algorithms #CodingInterview #Programming #ProblemSolving #CompetitiveProgramming #Coding #DataStructures #AlgorithmicInterviews #Python #Algorithm #DataStructures #Algorithms #ProblemSolving #AlgorithmicInterview #InterviewPreparation #DataStructuresInterview #InterviewQuestions #TechInterview #TechInterviews #DSA #GoogleInterview #FAANG
Prefix Sum Arrays or simply Prefix Sums are used to perform fast range sum queries on a given array. The total time complexity of the algorithm is O(N + Q) where N is the number of elements in the given array and Q is the number of range sum queries. Each of the Q queries are answered in O(1) time. This topic often appears during technical interviews at top tech companies (like Google, Facebook, Amazon, etc). You can prepare for those interviews by practicing and solving challenging problems by following the links below. 💻 Practice: https://profound.academy/algorithms-data-structures/Ccj2qt1MCtTjOTF97hlB 📚 Full DSA Course: https://profound.academy/algorithms-data-structures 🎓 Teach with Profound Academy: https://profound.academy/teach https://profound.academy https://www.instagram.com/profound.academy.inc https://www.facebook.com/profound.academy.inc https://www.linkedin.com/company/profound-academy-inc Chapters: 0:00 Prefix Sum and Range Sum Queries Problem Statement 0:18 Example of Video Performance 0:58 Naive Approach 1:29 Reformulation of the Problem 2:02 Naive Prefix Sum Array Calculation 3:41 Prefix Sum Array Calculation 4:36 Range Sum Queries 5:20 Problem with L being 0 5:42 The Final Algorithm 6:51 Time and Memory Complexity #PrefixSum #Algorithm #DataStructures #Algorithms #ProblemSolving #AlgorithmicInterview #InterviewPreparation #DataStructuresInterview #InterviewQuestions #TechInterview #TechInterviews #DSA #GoogleInterview #FAANG #Algorithms
A 2-dimensional prefix sum is a powerful algorithmic technique used in computer science and mathematics to preprocess a given 2D grid or matrix, enabling efficient querying of summed values over rectangular subregions. In essence, it is an extension of the 1-dimensional prefix sum to two dimensions. This method involves constructing an auxiliary matrix of the same dimensions as the original, where each cell (i, j) contains the sum of all elements in the rectangular region formed by the top-left corner (0, 0) and the current position (i, j). Once this preprocessing is complete, the sum of any rectangular subregion can be calculated with only four array lookups and three arithmetic operations, significantly reducing the time complexity of repeated queries. 💻 Practice: https://profound.academy/algorithms-data-structures/8kq1XwTFMTL1fgkYKdkS 📚 Full DSA Course: https://profound.academy/algorithms-data-structures 🎓 Teach with Profound Academy: https://profound.academy/teach https://profound.academy https://www.instagram.com/profound.academy.inc https://www.facebook.com/profound.academy.inc https://www.linkedin.com/company/profound-academy-inc Chapters: 0:00 2D Prefix Sum and Submatrix Sum Queries Problem Statement 0:19 Example Matrix 0:39 2D Prefix Sum 0:57 Submatrix Sum Query 2:05 Trick of Padding the Array With Zeros 2:29 2D Prefix Sum Array Calculation 3:26 Hands-on Practice on Profound Academy 3:37 Algorithm in Action 4:41 Time and Memory Complexity #PrefixSum #Algorithm #DataStructures #2dPrefixSum #Algorithms #ProblemSolving #AlgorithmicInterview #InterviewPreparation #DataStructuresInterview #InterviewQuestions #TechInterview #TechInterviews #DSA #GoogleInterview #FAANG #Algorithms
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ソーシャルリスニング
Sliding Window Technique
The Sliding Window technique is a key tool in algorithmic problem-solving that can significantly speed up calculations and improve performance. This technique is often used in competitive programming and algorithmic interviews at top tech companies. Mastering it can give you a significant edge. 💻 Practice: https://profound.academy/algorithms-data-structures/R1FvlthxYQc9PRMnepZr 📚 Full DSA Course: https://profound.academy/algorithms-data-structures 🎓 Teach with Profound Academy: https://profound.academy/teach https://profound.academy https://www.instagram.com/profound.academy.inc https://www.facebook.com/profound.academy.inc https://www.linkedin.com/company/profound-academy-inc Chapters: 0:00 Sliding Window Technique Introduction 0:27 Maximum Sum Subarray of Size K 0:38 Naive Approach - O(NK) 1:22 Introducing the Main Idea Behind the Sliding Window Technique 2:23 Sliding Window Implementation 3:18 Hands-on Practice on Profound Academy 3:30 Dynamic Size Sliding Window 4:34 Dynamic Sliding Window Implementation 5:00 Simulating the Algorithm 5:43 Time Complexity Analysis #SlidingWindow #Algorithms #CodingInterview #Programming #ProblemSolving #CompetitiveProgramming #Coding #DataStructures #AlgorithmicInterviews #Python #Algorithm #DataStructures #Algorithms #ProblemSolving #AlgorithmicInterview #InterviewPreparation #DataStructuresInterview #InterviewQuestions #TechInterview #TechInterviews #DSA #GoogleInterview #FAANG
Prefix Sum Array and Range Sum Queries
Prefix Sum Arrays or simply Prefix Sums are used to perform fast range sum queries on a given array. The total time complexity of the algorithm is O(N + Q) where N is the number of elements in the given array and Q is the number of range sum queries. Each of the Q queries are answered in O(1) time. This topic often appears during technical interviews at top tech companies (like Google, Facebook, Amazon, etc). You can prepare for those interviews by practicing and solving challenging problems by following the links below. 💻 Practice: https://profound.academy/algorithms-data-structures/Ccj2qt1MCtTjOTF97hlB 📚 Full DSA Course: https://profound.academy/algorithms-data-structures 🎓 Teach with Profound Academy: https://profound.academy/teach https://profound.academy https://www.instagram.com/profound.academy.inc https://www.facebook.com/profound.academy.inc https://www.linkedin.com/company/profound-academy-inc Chapters: 0:00 Prefix Sum and Range Sum Queries Problem Statement 0:18 Example of Video Performance 0:58 Naive Approach 1:29 Reformulation of the Problem 2:02 Naive Prefix Sum Array Calculation 3:41 Prefix Sum Array Calculation 4:36 Range Sum Queries 5:20 Problem with L being 0 5:42 The Final Algorithm 6:51 Time and Memory Complexity #PrefixSum #Algorithm #DataStructures #Algorithms #ProblemSolving #AlgorithmicInterview #InterviewPreparation #DataStructuresInterview #InterviewQuestions #TechInterview #TechInterviews #DSA #GoogleInterview #FAANG #Algorithms
2D Prefix Sum and Submatrix Sum Queries
A 2-dimensional prefix sum is a powerful algorithmic technique used in computer science and mathematics to preprocess a given 2D grid or matrix, enabling efficient querying of summed values over rectangular subregions. In essence, it is an extension of the 1-dimensional prefix sum to two dimensions. This method involves constructing an auxiliary matrix of the same dimensions as the original, where each cell (i, j) contains the sum of all elements in the rectangular region formed by the top-left corner (0, 0) and the current position (i, j). Once this preprocessing is complete, the sum of any rectangular subregion can be calculated with only four array lookups and three arithmetic operations, significantly reducing the time complexity of repeated queries. 💻 Practice: https://profound.academy/algorithms-data-structures/8kq1XwTFMTL1fgkYKdkS 📚 Full DSA Course: https://profound.academy/algorithms-data-structures 🎓 Teach with Profound Academy: https://profound.academy/teach https://profound.academy https://www.instagram.com/profound.academy.inc https://www.facebook.com/profound.academy.inc https://www.linkedin.com/company/profound-academy-inc Chapters: 0:00 2D Prefix Sum and Submatrix Sum Queries Problem Statement 0:19 Example Matrix 0:39 2D Prefix Sum 0:57 Submatrix Sum Query 2:05 Trick of Padding the Array With Zeros 2:29 2D Prefix Sum Array Calculation 3:26 Hands-on Practice on Profound Academy 3:37 Algorithm in Action 4:41 Time and Memory Complexity #PrefixSum #Algorithm #DataStructures #2dPrefixSum #Algorithms #ProblemSolving #AlgorithmicInterview #InterviewPreparation #DataStructuresInterview #InterviewQuestions #TechInterview #TechInterviews #DSA #GoogleInterview #FAANG #Algorithms
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