Topic
Algorithms
Learn Algorithms as a connected topic across chapters, concepts, simulations, and interview reasoning.
10 Concepts32 Articles8h 29m
Overview
Learn Algorithms as a connected topic across chapters, concepts, simulations, and interview reasoning.
How this topic helps
Data Structures
Java
Coding Interview
System Design
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Series that contain articles from Algorithms. Select a path to filter the article list.
Articles
32 matched articles
Article 1Algorithms for AI: Trie, Graph Sorting, and K-Way Merge for LLM SystemsTLDR: Generative AI relies heavily on classic computer science algorithms under the hood. In this guide, we explore how to implement Trie prefix filtering for constrained model decoding, Topological S13 minArticle 2HyperLogLog Explained: Counting Billions of Unique Items with 12 KBTLDR: HyperLogLog estimates the number of distinct elements in a dataset using ~12 KB of memory regardless of cardinality — with ±0.81% error. The insight: if you hash every element to a random bit st18 minArticle 3Count-Min Sketch Explained: Frequency Estimation at Streaming ScaleTLDR: Count-Min Sketch (CMS) is a fixed-size d × w counter matrix that estimates how often any element has appeared in a stream. Insert: hash the element with each of the d hash functions to get one c22 minArticle 4Bloom Filters Explained: Membership Testing with Zero False NegativesTLDR: A Bloom filter is a bit array of m bits + k independent hash functions that sets k bits on insert and checks those same k bits on lookup. If any checked bit is 0, the element is definitely not i19 minArticle 5Big O Notation Explained: Time Complexity, Space Complexity, and Why They Matter in InterviewsTLDR: Big O notation describes how an algorithm's resource usage grows as input size grows — not how fast it runs on your laptop. Learn to identify the 7 complexity classes (O(1) through O(n!)), deriv34 minArticle 6Probabilistic Data Structures: A Practical Guide to Bloom Filters, HyperLogLog, and Count-Min SketchTLDR: Probabilistic data structures trade a small, bounded probability of being wrong for orders-of-magnitude better memory efficiency and O(1) speed. Bloom Filters answer "definitely not in this set"14 minPage 1 of 6