Retrieval-Augmented Generation (RAG) : Empowering Large Language Models (LLMs)

Ray Islam (Mohammad Rubyet Islam)

ISBN 13: 9798223268512

Editore: Dr. Ray Islam (Mohammad Rubyet Islam), 2023

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nach der Bestellung gedruckt Neuware - Printed after ordering - We are thrilled to announce the release of this Elektronisches Buch, 'Retrieval-Augmented Generation (RAG): Empowering Large Language Models (LLMs)'. This comprehensive exploration unveils RAG, a revolutionary approach in NLP that combines the power of neural language models with advanced retrieval systems.In this must-read book, readers will dive into the architecture and implementation of RAG, gaining intricate details on its structure and integration with large language models like GPT. The authors also shed light on the essential infrastructure required for RAG, covering computational resources, data storage, and software frameworks.One of the key highlights of this work is the in-depth exploration of retrieval systems within RAG. Readers will uncover the functions, mechanisms, and the significant role of vectorization and input comprehension algorithms. The book also delves into validation strategies, including performance evaluation, and compares RAG with traditional fine-tuning techniques in machine learning, providing a comprehensive analysis of their respective advantages and disadvantages.From improved integration and efficiency to enhanced scalability, RAG is set to bridge the gap between static language models and dynamic data, revolutionizing the fields of AI and NLP.'Retrieval-Augmented Generation (RAG): Empowering Large Language Models (LLMs)' is a must-have resource for researchers, practitioners, and enthusiasts in the field of natural language processing. Get your copy today and embark on a transformative journey into the future of NLP.

Codice articolo 9798223268512

Dati bibliografici

• Titolo: Retrieval-Augmented Generation (RAG) : Empowering Large Language Models (LLMs)
• Editore: Dr. Ray Islam (Mohammad Rubyet Islam)
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 13: 9798223268512
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 229 L x 152 A x 5 P (millimetri) 229 millimetri di larghezza per 152 millimetri di altezza per 5 millimetri di profondità
• Peso: 117 grammi

Descrizione articolo

Riassunto

We are thrilled to announce the release of this eBook, "Retrieval-Augmented Generation (RAG): Empowering Large Language Models (LLMs)". This comprehensive exploration unveils RAG, a revolutionary approach in NLP that combines the power of neural language models with advanced retrieval systems.

In this must-read book, readers will dive into the architecture and implementation of RAG, gaining intricate details on its structure and integration with large language models like GPT. The authors also shed light on the essential infrastructure required for RAG, covering computational resources, data storage, and software frameworks.

One of the key highlights of this work is the in-depth exploration of retrieval systems within RAG. Readers will uncover the functions, mechanisms, and the significant role of vectorization and input comprehension algorithms. The book also delves into validation strategies, including performance evaluation, and compares RAG with traditional fine-tuning techniques in machine learning, providing a comprehensive analysis of their respective advantages and disadvantages.From improved integration and efficiency to enhanced scalability, RAG is set to bridge the gap between static language models and dynamic data, revolutionizing the fields of AI and NLP.

"Retrieval-Augmented Generation (RAG): Empowering Large Language Models (LLMs)" is a must-have resource for researchers, practitioners, and enthusiasts in the field of natural language processing. Get your copy today and embark on a transformative journey into the future of NLP.

Informazioni sull'autore

Dr. Ray Islam (Mohammad Rubyet Islam) is a strategist and Generative AI expert, serving as a faculty member in Cyber Security at the University of Maryland, College Park and teaching Generative AI (NLP) as an Adjunct Faculty at George Mason University, VA. He has a rich professional background with leadership roles in AI and ML at prominent organizations such as Deloitte and Raytheon. He has provided consultancy to The National Aeronautics and Space Administration (NASA), The General Services Administration (GSA), and others. He holds the position of associate editor for the Journal of Prognostics and Health Management (JPHM). Additionally, he serves as a reviewer for Elsevier's journal, Reliability Engineering and System Safety, and is poised to be the Editor-in-Chief of an upcoming International Journal for Ethics & AI. His research primarily focuses on Generative AI, Explainable AI (XAI), Cyber Security, and the ethical dimensions of AI.