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

Islam), Ray Islam (Mohammad Rubyet

Retrieval-Augmented Generation (RAG): Empowering Large Language Models (LLMs)
  • 出版商: Dr. Ray Islam (Mohammad Rubyet Islam)
  • 出版日期: 2023-12-28
  • 售價: $430
  • 貴賓價: 9.5 折 $409
  • 語言: 英文
  • 頁數: 70
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798223268512
  • ISBN-13: 9798223268512
  • 相關分類: LangChain

    • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

我們非常高興地宣布這本電子書《檢索增強生成 (RAG)：賦能大型語言模型 (LLMs)》的發行。這本全面的探索揭示了 RAG，這是一種革命性的自然語言處理方法，結合了神經語言模型的力量與先進的檢索系統。

在這本必讀的書中，讀者將深入了解 RAG 的架構和實施，獲得有關其結構和與大型語言模型（如 GPT）整合的詳細資訊。作者還闡明了 RAG 所需的基本基礎設施，包括計算資源、數據存儲和軟體框架。

這項工作的主要亮點之一是對 RAG 中檢索系統的深入探索。讀者將揭示功能、機制以及向量化和輸入理解算法的重要角色。這本書還探討了驗證策略，包括性能評估，並將 RAG 與傳統的機器學習微調技術進行比較，提供對它們各自優缺點的全面分析。從改進的整合和效率到增強的可擴展性，RAG 將填補靜態語言模型與動態數據之間的鴻溝，徹底改變人工智慧和自然語言處理的領域。

《檢索增強生成 (RAG)：賦能大型語言模型 (LLMs)》是自然語言處理領域研究人員、實踐者和愛好者必備的資源。立即獲取您的副本，展開一段變革性的自然語言處理未來之旅。

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