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GenAI – Approximate Nearest Neighbors (ANN)
GenAI – Approximate Nearest Neighbors (ANN) Table Of Contents: Foundational Concepts What is Nearest Neighbor Search (NNS)? Exact vs Approximate Nearest Neighbors Trade-offs: Speed vs Accuracy vs Memory Use cases in GenAI: Semantic Search, RAG, Recommendation Systems Distance Metrics Euclidean Distance Cosine Similarity Manhattan (L1) Distance Dot Product Similarity Choosing the right metric based on data and task Core ANN Algorithms & Techniques Locality-Sensitive Hashing (LSH) Concept and hash function families MinHash, SimHash Hierarchical Navigable Small World Graphs (HNSW) Graph-based ANN Navigation and hierarchy Product Quantization (PQ) Vector compression for large-scale retrieval IVF (Inverted File Index) + PQ Clustering +
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Machine Learning – Approximate Nearest Neighbors(ANNOY)
ML – Approximate Nearest Neighbors. Table Of Contents: What Is ANNOY Algorithm. Use Case Example. How ANNOY Works Step By Step. Trade Off Controls. Advantages Of Annoy. Limitation Of ANNOY. (1) What Is ANNOY Algorithm ? (2) Example Use Cases. (3) How Annoy Works — Step by Step Step-1: Input Data Preparation Step-2: Build Trees Using Random Projections Step-3: Save The Tree To The Desk Step-4: Querying / Searching (4) Trade-Off Controls (5) Advantages of Annoy (6) Limitation
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GenAI – Creative Co-Pilot Tools
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GenAI – AI for Accessibility
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GenAI – Crisis Management Simulators (Defense)
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GenAI – Synthetic Biology & Chemistry Design
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GenAI – AI Legal Counsels & Advisors
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GenAI – Enterprise Knowledge Management
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GenAI – Digital Humans / AI Companions
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GenAI – Telecommunications Use Cases.
