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Learn how to implement and optimize topological sort and cycle detection algorithms for graphs using DFS, union-find, queue, and heuristics.
Learn the pros and cons of using graphs and matrices for different types of recommender systems, and how to choose the right data structure for your data and goal.
Learn how to improve the performance and scalability of divide and conquer sorting algorithms by following six steps.
Learn how to use graph algorithms for solving different types of matching problems in data science, such as bipartite matching, weighted matching, stable matching…
Learn how to apply quick sort to different types of data structures, such as linked lists or trees, and what are the advantages and disadvantages of doing so.
Learn about the factors that affect the trade-offs between speed and compression ratio in string algorithms, and some of the common methods for data compression.
Learn how to use two common algorithms on directed graphs to solve problems in ordering, scheduling, dependency analysis, and deadlock prevention.
Learn how string matching algorithms and data structures can help you perform various tasks and overcome difficulties in natural language processing.
Learn how to use dynamic programming algorithms for string matching in two data structures: matrix and graph. Find patterns in texts with multiple dimensions.
Learn what skip graphs are, how they work, and how they can support graphs, trees, sets, and more in this article. Explore skip graphs applications and examples.
Learn how hashing can help or hinder string matching, and compare some common hashing algorithms for data structures.
Learn about five divide and conquer sorting methods, their performance, and their real-world applications in this article.
Learn some best practices and tips for writing binary search tree traversal code using different methods, techniques, and examples.
Learn about the advantages and disadvantages of using skip trie trees for fast string matching, and how to overcome some common challenges or pitfalls.
Learn how to avoid some common pitfalls or misconceptions about topological sort, a technique to order DAG nodes based on dependencies.
Learn how to use arrays, lists, sets, maps, trees, and graphs to filter noisy, incomplete, or inconsistent data in big data analysis.
Learn how to handle text, image, and audio data with string algorithms for data compression and other applications.
Learn how to compare and contrast different types of tree traversals and when to use them for various problems with tree data structures.
Learn how to design, test, fix, and improve your data structures for edge cases and corner cases, using examples from competitive programming.
Learn how to choose a hash function, handle collisions, resize the table, and test for correctness and complexity in this article on hash table best practices.
Learn about edit distance and cosine similarity, two methods for measuring string similarity and distance, and how to choose the best one for your text data.