Day 27 : Locality-Sensitive Hashing (LSH) Explained: Fast Vector Search in High-Dimensional Data

Описание: Welcome to Day 27 of my 150 Days of AI journey! 🚀 Today, we are diving deep into a critical concept for modern AI and search engines: Locality-Sensitive Hashing (LSH).
If you have ever wondered how systems quickly find similar items in massive datasets—like image retrieval or recommendation engines—LSH is often the secret sauce.
In this video, we cover:
• What is LSH? An indexing technique used for approximate nearest neighbour (ANN) search in high-dimensional spaces.
• The Core Logic: Unlike traditional hashing, LSH is designed so that similar vectors map to the same buckets with high probability, while dissimilar vectors map to different hashes.
• How It Works: We explore how the algorithm uses multiple hash tables to improve accuracy and why querying only items in the same buckets drastically reduces computational costs.
• Cosine Similarity & Random Hyperplanes: A look at how vectors are converted into a binary hash signature based on which side of a hyperplane they lie.
• Pros and Cons: We discuss the trade-offs between speed and recall, and why LSH requires careful tuning of parameters like hash bits and table numbers.
Key Takeaways: ✅ Efficiency: Highly effective for high-dimensional data. ✅ Simplicity: A relatively simple indexing method to implement. ⚠️ Limitations: It provides approximate results rather than exact ones and can see performance drops with highly dense or skewed datasets.
Whether you are building a recommendation system or just curious about vector databases, understanding LSH is a game-changer for AI development.
Join me on my journey! This is part of my 150-day challenge to master Artificial Intelligence. If you’re learning AI too, let’s connect in the comments!
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