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"Optimizing Retrieval: From Tokenization to Vector Quantization"
This book provides a deep dive into the core techniques that underpin modern information retrieval systems. It guides readers through the crucial steps, starting with the fundamental process of tokenization – breaking down text into meaningful units. From there, the book explores how these tokens are transformed into numerical representations, a critical step for efficient processing.
The core of the book lies in vector quantization, a powerful technique that compresses and represents high-dimensional data (like text) into lower-dimensional spaces while preserving essential information. This enables faster search, reduced storage requirements, and improved retrieval accuracy.1
Key Topics Covered:
- Tokenization Strategies: Exploring various approaches, including word-level, subword-level (like byte-pair encoding), and character-level tokenization.
- Text Embedding Techniques: Delving into methods like Word2Vec, GloVe, and more recently, Transformer-based models like BERT, which capture semantic relationships between words.2
- Vector Quantization Algorithms: Examining different approaches, such as k-means, product quantization, and hierarchical vector quantization, and their applications in information retrieval.
- Retrieval Models: Exploring how vector quantization is integrated into various retrieval models, including nearest neighbor search, approximate nearest neighbor search, and retrieval augmented generation.
- Practical Applications: Discussing real-world applications of these techniques, such as search engines, recommendation systems, and question answering systems.
"Optimizing Retrieval: From Tokenization to Vector Quantization" is a valuable resource for researchers, practitioners, and students interested in the cutting-edge techniques driving advancements in information retrieval. It provides a comprehensive understanding of the key concepts and their practical implications, empowering readers to build and optimize high-performance retrieval systems.
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- EditoreIndependently published
- Data di pubblicazione2025
- ISBN 13 9798306867977
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine84
- Contatto del produttorenon disponibile
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Optimizing Retrieval: From Tokenization To Vector Quantization
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Taschenbuch. Condizione: Neu. Neuware - 'Optimizing Retrieval: From Tokenization to Vector Quantization'. Codice articolo 9798306867977
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Optimizing Retrieval (Paperback)
Da: CitiRetail, Stevenage, Regno Unito
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Paperback. Condizione: new. Paperback. "Optimizing Retrieval: From Tokenization to Vector Quantization"This book provides a deep dive into the core techniques that underpin modern information retrieval systems. It guides readers through the crucial steps, starting with the fundamental process of tokenization - breaking down text into meaningful units. From there, the book explores how these tokens are transformed into numerical representations, a critical step for efficient processing.The core of the book lies in vector quantization, a powerful technique that compresses and represents high-dimensional data (like text) into lower-dimensional spaces while preserving essential information. This enables faster search, reduced storage requirements, and improved retrieval accuracy.1Key Topics Covered: Tokenization Strategies: Exploring various approaches, including word-level, subword-level (like byte-pair encoding), and character-level tokenization.Text Embedding Techniques: Delving into methods like Word2Vec, GloVe, and more recently, Transformer-based models like BERT, which capture semantic relationships between words.2Vector Quantization Algorithms: Examining different approaches, such as k-means, product quantization, and hierarchical vector quantization, and their applications in information retrieval.Retrieval Models: Exploring how vector quantization is integrated into various retrieval models, including nearest neighbor search, approximate nearest neighbor search, and retrieval augmented generation.Practical Applications: Discussing real-world applications of these techniques, such as search engines, recommendation systems, and question answering systems."Optimizing Retrieval: From Tokenization to Vector Quantization" is a valuable resource for researchers, practitioners, and students interested in the cutting-edge techniques driving advancements in information retrieval. It provides a comprehensive understanding of the key concepts and their practical implications, empowering readers to build and optimize high-performance retrieval systems. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798306867977
Quantità: 1 disponibili
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Optimizing Retrieval (Paperback)
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. "Optimizing Retrieval: From Tokenization to Vector Quantization"This book provides a deep dive into the core techniques that underpin modern information retrieval systems. It guides readers through the crucial steps, starting with the fundamental process of tokenization - breaking down text into meaningful units. From there, the book explores how these tokens are transformed into numerical representations, a critical step for efficient processing.The core of the book lies in vector quantization, a powerful technique that compresses and represents high-dimensional data (like text) into lower-dimensional spaces while preserving essential information. This enables faster search, reduced storage requirements, and improved retrieval accuracy.1Key Topics Covered: Tokenization Strategies: Exploring various approaches, including word-level, subword-level (like byte-pair encoding), and character-level tokenization.Text Embedding Techniques: Delving into methods like Word2Vec, GloVe, and more recently, Transformer-based models like BERT, which capture semantic relationships between words.2Vector Quantization Algorithms: Examining different approaches, such as k-means, product quantization, and hierarchical vector quantization, and their applications in information retrieval.Retrieval Models: Exploring how vector quantization is integrated into various retrieval models, including nearest neighbor search, approximate nearest neighbor search, and retrieval augmented generation.Practical Applications: Discussing real-world applications of these techniques, such as search engines, recommendation systems, and question answering systems."Optimizing Retrieval: From Tokenization to Vector Quantization" is a valuable resource for researchers, practitioners, and students interested in the cutting-edge techniques driving advancements in information retrieval. It provides a comprehensive understanding of the key concepts and their practical implications, empowering readers to build and optimize high-performance retrieval systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798306867977
Quantità: 1 disponibili