Hands-On Large Language Models: Language Understanding and Generation (Paperback)

Alammar, Jay, Grootendorst, Maarten

  • 出版商: O'Reilly|英文2書85折
  • 出版日期: 2024-10-15
  • 售價: $2,750
  • 貴賓價: 9.5$2,613
  • 語言: 英文
  • 頁數: 425
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1098150961
  • ISBN-13: 9781098150969
  • 相關分類: LangChain
  • 海外代購書籍(需單獨結帳)

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商品描述

AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. Through the visually educational nature of this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.

You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings.

This book also shows you how to:

  • Build advanced LLM pipelines to cluster text documents and explore the topics they belong to
  • Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers
  • Learn various use cases where these models can provide value
  • Understand the architecture of underlying Transformer models like BERT and GPT
  • Get a deeper understanding of how LLMs are trained
  • Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning

Jay Alammar is Director and Engineering Fellow at Cohere (pioneering provider of large language models as an API).

Maarten Grootendorst is a Senior Clinical Data Scientist at Netherlands Comprehensive Cancer Organization (IKNL).

商品描述(中文翻譯)

AI 在過去幾年中獲得了驚人的新語言能力。隨著深度學習的快速進步,語言 AI 系統能夠比以往更好地撰寫和理解文本。這一趨勢促進了新功能、產品和整個產業的興起。通過本書的視覺教育特性,Python 開發者將學習到使用這些能力所需的實用工具和概念。

您將學會如何利用預訓練的大型語言模型來應對文案撰寫和摘要等用例;創建超越關鍵字匹配的語義搜索系統;構建分類和聚類文本的系統,以實現對大量文本文件的可擴展理解;以及使用現有的庫和預訓練模型進行文本分類、搜索和聚類。

本書還將向您展示如何:
- 構建先進的 LLM 管道以聚類文本文件並探索它們所屬的主題
- 構建超越關鍵字搜索的語義搜索引擎,使用密集檢索和重排序等方法
- 學習這些模型可以提供價值的各種用例
- 理解基礎 Transformer 模型(如 BERT 和 GPT)的架構
- 更深入地了解 LLM 的訓練過程
- 使用生成模型微調、對比微調和上下文學習等方法,為特定應用優化 LLM

Jay Alammar 是 Cohere 的總監和工程研究員(大型語言模型作為 API 的先驅提供商)。

Maarten Grootendorst 是荷蘭綜合癌症組織(IKNL)的高級臨床數據科學家。