动手大型语言模型：语言理解和生成

English | October 15th, 2024 | ISBN: 1098150961 | 425 pages | True EPUB | 20.41 MB

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. With 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 amounts 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 • Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

中文| 2024年10月15日|国际标准图书编号：1098150961 | 425页|真正的EPUB | 20.41 MB 就在过去几年里，人工智能已经获得了惊人的新语言能力。在深度学习快速发展的推动下，语言AI系统能够比以往更好地编写和理解文本。这一趋势推动了新功能、新产品和整个行业的兴起。通过这本书，Python开发人员将学习他们今天使用这些功能所需的实用工具和概念。 您将学习如何将预先训练好的大型语言模型的力量用于文案和摘要等用例；创建超越关键字匹配的语义搜索系统；构建对文本进行分类和聚类的系统，以实现对大量文本文档的可扩展理解；并使用现有的库和预训练的模型进行文本分类、搜索和聚类。 本书还向您展示了如何•构建高级LLM管道来对文本文档进行聚类并探索它们所属的主题•构建超越关键字搜索的语义搜索引擎，使用密集检索和重新排序器等方法•学习这些模型可以提供价值的各种用例•了解BERT和GPT等底层Transformer模型的架构•深入了解LLM是如何训练的•了解不同的微调方法如何为特定应用优化LLM（生成模型微调、对比微调、上下文学习等）

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