LSH for Machine Learning: Plagiarism Detection, Recommender Systems MinHash, SimHash & rNNS скачать в хорошем качестве

LSH for Machine Learning: Plagiarism Detection, Recommender Systems MinHash, SimHash & rNNS

Unlock the power of Locality-Sensitive Hashing (LSH) — one of the most efficient techniques for approximate near neighbour search in high-dimensional data. In this video, we cover: ✅ What is document similarity (k-shingles & signatures) ✅ Key similarity measures: Jaccard, Cosine, Hamming, Euclidean ✅ Applications of LSH in plagiarism detection, recommender systems, NLP, knowledge graphs, and market basket analysis ✅ Core techniques: MinHash & SimHash ✅ Theoretical framework: r-Near Neighbor Search (rNNS), c-approximation, hash table construction & lookup ✅ Performance guarantees and correctness proofs Whether you’re a data scientist, researcher, or student, this breakdown makes LSH approachable and practical. 📚 Resources mentioned: Mining of Massive Datasets Algorithm Design – Kleinberg & Tardos MinHash & SimHash Python tutorials (Bogotobogo) If you found this helpful, don’t forget to like, share, and subscribe for more deep dives into Machine Learning, Algorithms, and Data Science. #LocalitySensitiveHashing #MachineLearning #DataScience #Algorithms #MinHash #SimHash #NearestNeighbor