Locality Sensitive Hashing Essentials
Master Vector Search and Similarity Detection in Modern AI Systems
Finding similar items in massive datasets is one of the biggest challenges in modern AI and machine learning. Traditional approaches that compare every vector to every other vector become impossibly slow as your data grows. Locality Sensitive Hashing (LSH) solves this problem by enabling you to find approximate nearest neighbors in high-dimensional spaces with remarkable speed and efficiency. This book demystifies LSH and shows you exactly how to apply it to real-world problems. You'll learn the core principles behind why LSH works, explore different LSH families for various distance metrics, and discover practical implementation strategies for production systems. Whether you're building recommendation engines, detecting duplicate content, powering semantic search, or scaling vector databases, this guide provides the knowledge and techniques you need to implement LSH effectively. With clear explanations, concrete examples, and actionable insights, you'll understand not just how to use LSH, but when to use it and how to optimize it for your specific use case.
Est. Length
238 pages (11 chapters)
Language
English
Publication date
Dec 24, 2025
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