teivah/algodeck

Features

• Algorithm Study Guides - Reviews key algorithms and data structures through a flash-card format to prepare for technical interviews.
• Algorithm Interview Flash Cards - An open-source collection of flash cards for reviewing algorithms, data structures, and system design concepts.
• Study Aids - A set of flash cards covering arrays, trees, heaps, tries, and graphs with time and space complexity notes.
• Memoization - Describes memoization technique for caching expensive function call results.
• Tabulation - Explains tabulation as a bottom-up dynamic programming caching strategy.
• Data Structures Reference - Flash cards covering arrays, trees, heaps, tries, and graphs with time and space complexity notes.
• Algorithm Complexity Guides - Provides flash cards explaining Big O notation and algorithm complexity analysis.
• Graph Traversal Algorithms - Visits all vertices in a graph using breadth-first or depth-first search, with time complexity O(v e) and space O(v).
• Flash Card Systems - Organises technical concepts as atomic question-answer pairs for self-testing and active recall.
• System Design Decks - Flash cards that explain distributed systems concepts like consensus, replication, caching, and consistency models.
• Flash Card Decks - Presents a curated set of flash cards covering algorithms, data structures, and system design topics for interview preparation.
• Flash Card Decks - Reviewing algorithms and data structures through flash cards with spaced repetition for technical interview readiness.
• Sorting Algorithms - Flash cards describing common sorting algorithms including quicksort, mergesort, heapsort, counting sort, and radix sort.
• Spaced Repetition Systems - Implements spaced repetition scheduling to optimize long-term memory retention of technical concepts.
• System Design Interview Preparation - Studying distributed systems concepts and trade-offs to prepare for system design interviews.
• Technical Interview Preparation - Curates topics specifically for technical interview preparation rather than general computer science education.
• Algorithm Implementations - Provides ready-to-use code snippets of classic algorithms and data structures for interview practice.
• General Optimization Strategies - Chooses between greedy, dynamic programming, or branch-and-bound strategies to find the best solution under given constraints.
• Unweighted Shortest Paths - Finds the shortest path between two nodes in an unweighted graph using breadth-first search.
• Mergesort Implementations - Sorts an array in O(n log n) time using a stable, divide-and-conquer algorithm that works well with linked lists.
• General Dynamic Programming Techniques - Breaks a problem into overlapping subproblems and caches their results to avoid redundant computation.
• DFS-Based Detection - Determines whether a path or a cycle exists in a graph using depth-first search.
• Dynamic Programming Techniques - Covers dynamic programming techniques with memoization and tabulation for interview prep.
• Insertion Sorts - Builds the sorted array one element at a time by inserting each new element into its correct position among already sorted items.
• Quicksorts - Partitions the array around a pivot and recursively sorts the partitions, performing well in practice due to good cache locality.
• Radix Sorts - Processes digits one at a time from least significant to most, using a stable sort per digit to achieve linear time for small digit counts.
• BFS and DFS Traversals - Visits all nodes in a tree using breadth-first or depth-first search, with BFS using O(v) space and DFS using O(h) space.
• Trie Key Searches - Explains trie data structure operations including key insertion and search.
• Flash Card Decks - Using flash cards and spaced repetition to commit data structure operations, properties, and complexity notes to long-term memory.
• In-Place Reversal Operations - Swaps elements from both ends moving inward until the middle is reached, producing the reversed order in place.
• Byzantine Fault Tolerant Protocols - Continues operating correctly even when some nodes malfunction, disobey the protocol, or act maliciously.
• Heapsorts - Builds a heap and repeatedly extracts the root to produce a sorted array in place with O(n log n) time.
• Distributed Systems Concepts - Learning about consistency models, replication, consensus, and other distributed system fundamentals.
• Element Rotations - Reverses the whole array then reverses two sub-sections to shift elements by a given number of positions.
• Bit Manipulation Techniques - Practicing common bitwise operations and tricks used in coding interviews and low-level programming via flash cards.
• Rotated Sorted Array Search - Finds a target value in a rotated sorted array by first locating the pivot and then performing binary search on the appropriate subarray.
• Sorted Array Search Implementations - Repeatedly halves the search range by comparing the middle element to the target, locating an item in logarithmic time.
• Knapsack Problem Solving - Breaks the problem into subproblems and stores their results using memoization or tabulation to find the optimal subset of items.
• Bitwise Operation Libraries - Ships a collection of bit manipulation functions using masks and shifts for interview preparation.
• Counting Sorts - Counts occurrences of each distinct value and uses the counts to place elements in sorted order, stable and linear for small integer ranges.
• Algorithm Foundations - Flashcards for reviewing algorithmic concepts and data structure implementations.
• Algorithms and Data Structures - Flashcards and study materials for mastering algorithmic concepts.

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