Reliable large models knowledge von wang wenguang (8 Ergebnisse)

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as transformative tools, yet they face persistent challenges such as hallucination, knowledge obsolescence, and limited reasoning depth. The book 'Knowledge-Enhanced Large Models' addresses these gaps head-on, offering a comprehensive roadmap for integrating structured knowledge systems-particularly knowledge graphs-with cutting-edge AI models to build more reliable, accurate, and context-aware intelligent systems. This book is tailored for AI researchers, data scientists, engineers, students, and practitioners seeking to harness the synergy between large models and knowledge representation technologies. It balances theoretical rigor with practical implementation, making it equally valuable for academic exploration and industrial application.The book is organized into 10 chapters, systematically guiding readers from foundational concepts to advanced techniques and real-world applications. The first two chapters explore the rise of LLMs, their inherent limitations, and the paradigm shift toward knowledge-enhanced models. Chapters 3 to 5 delve into the infrastructure required to augment LLMs with structured knowledge. Chapters 6 to 9 explore cutting-edge methodologies for bridging symbolic knowledge systems with neural networks. The final chapter translates theory into practice, offering actionable guidelines for deploying knowledge-enhanced models across industries.…

Hardcover. Zustand: Brand New. 468 pages. 6.42x1.22x9.65 inches. In Stock.

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as transformative tools, yet they face persistent challenges such as hallucination, knowledge obsolescence, and limited reasoning depth. The book 'Knowledge-Enhanced Large Models' addresses these gaps head-on, offering a comprehensive roadmap for integrating structured knowledge systems-particularly knowledge graphs-with cutting-edge AI models to build more reliable, accurate, and context-aware intelligent systems. This book is tailored for AI researchers, data scientists, engineers, students, and practitioners seeking to harness the synergy between large models and knowledge representation technologies. It balances theoretical rigor with practical implementation, making it equally valuable for academic exploration and industrial application.The book is organized into 10 chapters, systematically guiding readers from foundational concepts to advanced techniques and real-world applications. The first two chapters explore the rise of LLMs, their inherent limitations, and the paradigm shift toward knowledge-enhanced models. Chapters 3 to 5 delve into the infrastructure required to augment LLMs with structured knowledge. Chapters 6 to 9 explore cutting-edge methodologies for bridging symbolic knowledge systems with neural networks. The final chapter translates theory into practice, offering actionable guidelines for deploying knowledge-enhanced models across industries. 443 pp. Englisch. …

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Zustand: New. Print on Demand.

Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as transformative tools, yet they face persistent challenges such as hallucination, knowledge obsolescence, and limited reasoning depth. The book 'Knowledge-Enhanced Large Models' addresses these gaps head-on, offering a comprehensive roadmap for integrating structured knowledge systems-particularly knowledge graphs-with cutting-edge AI models to build more reliable, accurate, and context-aware intelligent systems. This book is tailored for AI researchers, data scientists, engineers, students, and practitioners seeking to harness the synergy between large models and knowledge representation technologies. It balances theoretical rigor with practical implementation, making it equally valuable for academic exploration and industrial application.The book is organized into 10 chapters, systematically guiding readers from foundational concepts to advanced techniques and real-world applications. The first two chapters explore the rise of LLMs, their inherent limitations, and the paradigm shift toward knowledge-enhanced models. Chapters 3 to 5 delve into the infrastructure required to augment LLMs with structured knowledge. Chapters 6 to 9 explore cutting-edge methodologies for bridging symbolic knowledge systems with neural networks. The final chapter translates theory into practice, offering actionable guidelines for deploying knowledge-enhanced models across industries.Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 472 pp. Englisch.…

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