Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs (Paperback)
暫譯: 使用 LangChain 的生成式 AI:用 Python、ChatGPT 和其他大型語言模型 (LLM) 建立應用程式 (平裝本)
Auffarth, Ben
- 出版商: Packt Publishing
- 出版日期: 2023-12-22
- 售價: $1,850
- 貴賓價: 9.5 折 $1,757
- 語言: 英文
- 頁數: 360
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1835083463
- ISBN-13: 9781835083468
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相關分類:
LangChain
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相關翻譯:
LangChain 大模型應用開發 (簡中版)
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商品描述
Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants, integrating with web searches and code execution.
Purchase of the print or Kindle book includes a free PDF eBook
Key Features:
- GitHub repository updated regularly to stay abreast of LangChain developments
- Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges
- Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality
Book Description:
The ChatGPT and the GPT models by OpenAI have brought about a revolution in the way we think about the world - and not only in how we write and research, but in how we can process information.
This book discusses the functioning, capabilities, and limitations of LLMs including ChatGPT and Bard. It also demonstrates how to use the LangChain framework to implement production-ready applications based on these models, such as agents and personal assistants, and integrate with other tools such as web searches and code execution.
As you progress through the chapters, you'll use transformer models and diverse attention mechanisms, refining the intricate process of training and fine-tuning. You'll get to grips with data-driven decision-making with automated analysis and visualization using pandas and Python. You'll also take a closer look at the heuristics of how to use these models, prompting, training and fine-tuning, and deploying at scale.
By the time you've finished this book, you'll have a deep understanding of what makes LLMs tick and how to make the most of them.
What You Will Learn:
- Gain an understanding of LLMs and their legal implications
- Understand transformer models and different attention mechanisms
- Train and fine-tune LLMs and get to know the tools for using them
- Build applications with LangChain like question-answering systems and chatbots
- Implement automated data analysis and visualization with pandas and Python
- Grasp prompt engineering to improve prompts and evaluation strategies
- Deploy LLMs as a service with LangChain
- Interact privately with your documents without data leaks using ChatGPT
Who this book is for:
The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will be a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.
Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.
商品描述(中文翻譯)
掌握 LangChain 框架以開發生產就緒的應用程式,包括代理和個人助理,並整合網路搜尋和程式碼執行。
購買印刷版或 Kindle 版書籍包括免費 PDF 電子書
主要特色:
- 定期更新的 GitHub 倉庫,以跟上 LangChain 的最新發展
- 深入探索 LLM 的領域,了解其基本原理、倫理維度和應用挑戰
- 提升使用 ChatGPT 和 GPT 模型的能力,從啟發式方法和訓練到可擴展的部署,幫助您將想法變為現實
書籍描述:
OpenAI 的 ChatGPT 和 GPT 模型徹底改變了我們思考世界的方式——不僅在於我們如何寫作和研究,還在於我們如何處理資訊。
本書討論了 LLM 的運作、能力和限制,包括 ChatGPT 和 Bard。它還展示了如何使用 LangChain 框架來實現基於這些模型的生產就緒應用程式,例如代理和個人助理,並與其他工具如網路搜尋和程式碼執行進行整合。
隨著您逐步閱讀各章,您將使用變壓器模型和多樣的注意力機制,精煉訓練和微調的複雜過程。您將掌握基於數據的決策制定,並使用 pandas 和 Python 進行自動化分析和可視化。您還將更深入地了解如何使用這些模型的啟發式方法、提示、訓練和微調,以及大規模部署。
當您完成本書時,您將對 LLM 的運作有深入的理解,並知道如何充分利用它們。
您將學到的內容:
- 了解 LLM 及其法律影響
- 理解變壓器模型和不同的注意力機制
- 訓練和微調 LLM,並熟悉使用它們的工具
- 使用 LangChain 構建應用程式,如問答系統和聊天機器人
- 使用 pandas 和 Python 實現自動化數據分析和可視化
- 掌握提示工程以改善提示和評估策略
- 使用 LangChain 將 LLM 部署為服務
- 使用 ChatGPT 私密地與您的文件互動,避免數據洩漏
本書適合誰:
本書適合開發人員、研究人員以及任何對 LLM 感興趣的人。無論您是初學者還是有經驗的開發人員,如果您想充分利用 LLM 並希望在 LLM 和 LangChain 領域保持領先,本書將是您寶貴的資源。
具備基本的 Python 知識是前提,而對機器學習的先前接觸將有助於您更輕鬆地跟上內容。