Inhaltsangabe
"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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Optimizing Retrieval (Paperback)
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Paperback. Zustand: 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798306867977
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Paperback. Zustand: 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798306867977
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