Items related to Optimizing Retrieval: From Tokenization To Vector Quantizati...
Synopsis
"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.
"synopsis" may belong to another edition of this title.
- PublisherIndependently published
- Publication date2025
- ISBN 13 9798306867977
- BindingPaperback
- LanguageEnglish
- Number of pages84
Search results for Optimizing Retrieval: From Tokenization To Vector Quantizati...
Stock Image
Optimizing Retrieval: From Tokenization To Vector Quantization
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9798306867977_new
Quantity: Over 20 available
Stock Image
Optimizing Retrieval (Paperback)
Seller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: 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. Seller Inventory # 9798306867977
Quantity: 1 available
Stock Image
Optimizing Retrieval: From Tokenization To Vector Quantization
Print on DemandSeller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # I-9798306867977
Quantity: Over 20 available
Stock Image
Lucas, O: Optimizing Retrieval
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware - 'Optimizing Retrieval: From Tokenization to Vector Quantization'. Seller Inventory # 9798306867977
Quantity: 2 available
Stock Image
Optimizing Retrieval (Paperback)
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: 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. Seller Inventory # 9798306867977
Quantity: 1 available