How Large Language Models Work
暫譯: 大型語言模型的運作原理
Raff, Edward, Farris, Drew, Biderman, Stella
- 出版商: Manning
- 出版日期: 2025-08-05
- 售價: $1,790
- 貴賓價: 9.5 折 $1,701
- 語言: 英文
- 頁數: 200
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1633437086
- ISBN-13: 9781633437081
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相關分類:
Large language model
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商品描述
Learn how large language models like GPT and Gemini work under the hood in plain English. How Large Language Models Work translates years of expert research on Large Language Models into a readable, focused introduction to working with these amazing systems. It explains clearly how LLMs function, introduces the optimization techniques to fine-tune them, and shows how to create pipelines and processes to ensure your AI applications are efficient and error-free. In How Large Language Models Work you will learn how to: - Test and evaluate LLMs
- Use human feedback, supervised fine-tuning, and Retrieval augmented generation (RAG)
- Reducing the risk of bad outputs, high-stakes errors, and automation bias
- Human-computer interaction systems
- Combine LLMs with traditional ML Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. How Large Language Models Work is written by some of the best machine learning researchers at Booz Allen Hamilton, including researcher Stella Biderman, Director of AI/ML Research Drew Farris, and Director of Emerging AI Edward Raff. In clear and simple terms, these experts lay out the foundational concepts of LLMs, the technology's opportunities and limitations, and best practices for incorporating AI into your organization. About the book How Large Language Models Work is an introduction to LLMs that explores OpenAI's GPT models. The book takes you inside ChatGPT, showing how a prompt becomes text output. In clear, plain language, this illuminating book shows you when and why LLMs make errors, and how you can account for inaccuracies in your AI solutions. Once you know how LLMs work, you'll be ready to start exploring the bigger questions of AI, such as how LLMs "think" differently that humans, how to best design LLM-powered systems that work well with human operators, and what ethical, legal, and security issues can--and will--arise from AI automation. About the reader Includes examples in Python. No knowledge of ML or AI systems is required. About the author Edward Raff is a Director of Emerging AI at Booz Allen Hamilton, where he leads the machine learning research team. He has worked in healthcare, natural language processing, computer vision, and cyber security, among fundamental AI/ML research. The author of Inside Deep Learning, Dr. Raff has over 100 published research articles at the top artificial intelligence conferences. He is the author of the Java Statistical Analysis Tool library, a Senior Member of the Association for the Advancement of Artificial Intelligence, and twice chaired the Conference on Applied Machine Learning and Information Technology and the AI for Cyber Security workshop. Dr. Raff's work has been deployed and used by anti-virus companies all over the world. Drew Farris is a Director of AI/ML Research at Booz Allen Hamilton. He works with clients to build information retrieval, as well as machine learning and large scale data management systems, and has co-authored Booz Allen's Field Guide to Data Science, Machine Intelligence Primer and Manning Publications' Taming Text, the 2013 Jolt Award-winning book on computational text processing. He is a member of the Apache Software Foundation and has contributed to a number of open source projects including Apache Accumulo, Lucene, Mahout and Solr. Stella Biderman is a machine learning researcher at Booz Allen Hamilton and the executive director of the non-profit research center EleutherAI. She is a leading advocate for open source artificial intelligence and has trained many of the world's most powerful open source artificial intelligence algorithms. She has a master's degree in computer science from the Georgia Institute of Technology and degrees in Mathematics and Philosophy from the University of Chicago.
- Use human feedback, supervised fine-tuning, and Retrieval augmented generation (RAG)
- Reducing the risk of bad outputs, high-stakes errors, and automation bias
- Human-computer interaction systems
- Combine LLMs with traditional ML Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. How Large Language Models Work is written by some of the best machine learning researchers at Booz Allen Hamilton, including researcher Stella Biderman, Director of AI/ML Research Drew Farris, and Director of Emerging AI Edward Raff. In clear and simple terms, these experts lay out the foundational concepts of LLMs, the technology's opportunities and limitations, and best practices for incorporating AI into your organization. About the book How Large Language Models Work is an introduction to LLMs that explores OpenAI's GPT models. The book takes you inside ChatGPT, showing how a prompt becomes text output. In clear, plain language, this illuminating book shows you when and why LLMs make errors, and how you can account for inaccuracies in your AI solutions. Once you know how LLMs work, you'll be ready to start exploring the bigger questions of AI, such as how LLMs "think" differently that humans, how to best design LLM-powered systems that work well with human operators, and what ethical, legal, and security issues can--and will--arise from AI automation. About the reader Includes examples in Python. No knowledge of ML or AI systems is required. About the author Edward Raff is a Director of Emerging AI at Booz Allen Hamilton, where he leads the machine learning research team. He has worked in healthcare, natural language processing, computer vision, and cyber security, among fundamental AI/ML research. The author of Inside Deep Learning, Dr. Raff has over 100 published research articles at the top artificial intelligence conferences. He is the author of the Java Statistical Analysis Tool library, a Senior Member of the Association for the Advancement of Artificial Intelligence, and twice chaired the Conference on Applied Machine Learning and Information Technology and the AI for Cyber Security workshop. Dr. Raff's work has been deployed and used by anti-virus companies all over the world. Drew Farris is a Director of AI/ML Research at Booz Allen Hamilton. He works with clients to build information retrieval, as well as machine learning and large scale data management systems, and has co-authored Booz Allen's Field Guide to Data Science, Machine Intelligence Primer and Manning Publications' Taming Text, the 2013 Jolt Award-winning book on computational text processing. He is a member of the Apache Software Foundation and has contributed to a number of open source projects including Apache Accumulo, Lucene, Mahout and Solr. Stella Biderman is a machine learning researcher at Booz Allen Hamilton and the executive director of the non-profit research center EleutherAI. She is a leading advocate for open source artificial intelligence and has trained many of the world's most powerful open source artificial intelligence algorithms. She has a master's degree in computer science from the Georgia Institute of Technology and degrees in Mathematics and Philosophy from the University of Chicago.
商品描述(中文翻譯)
了解大型語言模型如 GPT 和 Gemini 的運作原理，通俗易懂。
大型語言模型的運作原理 將多年專家對大型語言模型的研究轉化為可讀性強、重點明確的入門介紹，幫助讀者理解這些驚人的系統如何運作。它清楚地解釋了 LLM 的功能，介紹了優化技術以進行微調，並展示了如何創建管道和流程，以確保您的 AI 應用程序高效且無錯誤。 在 大型語言模型的運作原理 中，您將學到如何： - 測試和評估 LLM- 使用人類反饋、監督微調和檢索增強生成 (RAG)
- 降低不良輸出、高風險錯誤和自動化偏見的風險
- 人機互動系統
- 將 LLM 與傳統機器學習結合 購買印刷書籍可獲得 Manning Publications 提供的免費 PDF 和 ePub 格式電子書。 大型語言模型的運作原理 由 Booz Allen Hamilton 的一些頂尖機器學習研究人員撰寫，包括研究員 Stella Biderman、AI/ML 研究主任 Drew Farris 和新興 AI 主任 Edward Raff。這些專家用清晰簡單的術語闡述了 LLM 的基礎概念、技術的機會與限制，以及將 AI 融入組織的最佳實踐。 關於本書 大型語言模型的運作原理 是一本介紹 LLM 的書籍，探索 OpenAI 的 GPT 模型。該書帶您深入 ChatGPT，展示提示如何轉化為文本輸出。這本啟發性的書籍用清晰的通俗語言展示了 LLM 何時以及為何會出錯，以及如何在您的 AI 解決方案中考慮不準確性。一旦您了解 LLM 的運作方式，您將準備好開始探索 AI 的更大問題，例如 LLM 如何與人類「思考」不同、如何設計與人類操作員良好協作的 LLM 驅動系統，以及 AI 自動化可能會出現的倫理、法律和安全問題。 關於讀者 包含 Python 範例。不需要具備機器學習或 AI 系統的知識。 關於作者 Edward Raff 是 Booz Allen Hamilton 的新興 AI 主任，負責領導機器學習研究團隊。他在醫療保健、自然語言處理、計算機視覺和網絡安全等領域工作，並從事基礎 AI/ML 研究。作為《Inside Deep Learning》的作者，Raff 博士在頂尖人工智慧會議上發表了超過 100 篇研究文章。他是 Java 統計分析工具庫的作者，並且是人工智慧促進協會的資深成員，曾兩次擔任應用機器學習與信息技術會議及 AI 與網絡安全研討會的主席。Raff 博士的工作已被全球的防病毒公司部署和使用。 Drew Farris 是 Booz Allen Hamilton 的 AI/ML 研究主任。他與客戶合作建立信息檢索、機器學習和大規模數據管理系統，並共同撰寫了 Booz Allen 的《數據科學實地指南》、《機器智能入門》和 Manning Publications 的《駕馭文本》，這本書在 2013 年獲得 Jolt 獎。 他是 Apache 軟體基金會的成員，並對多個開源項目做出了貢獻，包括 Apache Accumulo、Lucene、Mahout 和 Solr。 Stella Biderman 是 Booz Allen Hamilton 的機器學習研究員，也是非營利研究中心 EleutherAI 的執行董事。她是開源人工智慧的主要倡導者，並訓練了許多世界上最強大的開源人工智慧算法。她擁有喬治亞理工學院的計算機科學碩士學位，以及芝加哥大學的數學和哲學學位。
作者簡介
Edward Raff is a Director of Emerging AI at Booz Allen Hamilton, where he leads the machine learning research team. He has worked in healthcare, natural language processing, computer vision, and cyber security, among fundamental AI/ML research. The author of Inside Deep Learning, Dr. Raff has over 100 published research articles at the top artificial intelligence conferences. He is the author of the Java Statistical Analysis Tool library, a Senior Member of the Association for the Advancement of Artificial Intelligence, and twice chaired the Conference on Applied Machine Learning and Information Technology and the AI for Cyber Security workshop. Dr. Raff's work has been deployed and used by anti-virus companies all over the world. Drew Farris is a professional software developer and technology consultant whose interests focus on large scale analytics, distributed computing and machine learning. Previously, he worked at TextWise where he implemented a wide variety of text exploration, management and retrieval applications combining natural language processing, classification and visualization techniques. He has contributed to a number of open source projects including Apache Mahout, Lucene and Solr, and holds a master's degree in Information Resource Management from Syracuse University's iSchool and a B.F.A in Computer Graphics. Stella Biderman is a machine learning researcher at Booz Allen Hamilton and the executive director of the non-profit research center EleutherAI. She is a leading advocate for open source artificial intelligence and has trained many of the world's most powerful open source artificial intelligence algorithms. She has a master's degree in computer science from the Georgia Institute of Technology and degrees in Mathematics and Philosophy from the University of Chicago.
作者簡介(中文翻譯)
Edward Raff 是 Booz Allen Hamilton 的新興人工智慧部門主任，負責領導機器學習研究團隊。他在醫療保健、自然語言處理、計算機視覺和網絡安全等領域工作，並從事基礎的人工智慧/機器學習研究。作為 Inside Deep Learning 的作者，Raff 博士在頂尖的人工智慧會議上發表了超過 100 篇研究文章。他是 Java 統計分析工具庫的作者，也是人工智慧促進協會的資深會員，曾兩次擔任應用機器學習與資訊技術會議及人工智慧在網絡安全研討會的主席。Raff 博士的工作已被全球的防病毒公司部署和使用。
Drew Farris 是一位專業的軟體開發人員和技術顧問，專注於大規模分析、分散式計算和機器學習。他曾在 TextWise 工作，實現了各種文本探索、管理和檢索應用，結合了自然語言處理、分類和可視化技術。他參與了多個開源項目，包括 Apache Mahout、Lucene 和 Solr，並擁有雪城大學資訊資源管理碩士學位及計算機圖形學的美術學士學位。Stella Biderman 是 Booz Allen Hamilton 的機器學習研究員，也是非營利研究中心 EleutherAI 的執行董事。她是開源人工智慧的主要倡導者，並訓練了許多世界上最強大的開源人工智慧算法。她擁有喬治亞理工學院的計算機科學碩士學位，以及芝加哥大學的數學和哲學學位。
