Isbn: 9798277971871 - vector database mastery: foundations, indexing, and semantic search with faiss, milvus, and pinecone: 1 (vector database mastery: from foundations to production-ready ai search systems) (11 resultados)

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Condición: New.

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Paperback. Condición: new. Paperback. Build a solid foundation in AI-powered vector search with Vector Database Mastery - Volume 1. This book guides engineers, data scientists, and AI enthusiasts through the essentials of vector representations, similarity search, and indexing techniques for scalable retrieval systems.Learn how to transform text, images, and structured data into high-dimensional vectors and implement efficient search with cosine, Euclidean, and inner product distances. Explore FAISS, Milvus, and Pinecone, and understand the trade-offs between latency, memory, and recall with practical, production-ready Python code.What You'll Learn: Fundamentals of vector embeddings and similarity searchHigh-dimensional indexing with FAISS HNSW, IVF, and PQUsing managed and self-hosted vector databasesBuilding semantic search engines and recommendation systemsMathematical insights behind performance optimizationKey Use Cases: Semantic search beyond keyword queriesPersonalized recommendations and content retrievalMultimodal search with text and imagesRAG pipelines for AI applicationsStart your journey into scalable vector search systems with hands-on examples and theoretical foundations that prepare you for advanced AI retrieval workflows. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: New.

Paperback. Condición: new. Paperback. Build a solid foundation in AI-powered vector search with Vector Database Mastery - Volume 1. This book guides engineers, data scientists, and AI enthusiasts through the essentials of vector representations, similarity search, and indexing techniques for scalable retrieval systems.Learn how to transform text, images, and structured data into high-dimensional vectors and implement efficient search with cosine, Euclidean, and inner product distances. Explore FAISS, Milvus, and Pinecone, and understand the trade-offs between latency, memory, and recall with practical, production-ready Python code.What You'll Learn: Fundamentals of vector embeddings and similarity searchHigh-dimensional indexing with FAISS HNSW, IVF, and PQUsing managed and self-hosted vector databasesBuilding semantic search engines and recommendation systemsMathematical insights behind performance optimizationKey Use Cases: Semantic search beyond keyword queriesPersonalized recommendations and content retrievalMultimodal search with text and imagesRAG pipelines for AI applicationsStart your journey into scalable vector search systems with hands-on examples and theoretical foundations that prepare you for advanced AI retrieval workflows. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…