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Python自然语言处理食谱：使用Python构建强大NLP解决方案的60多种食谱，第2版

English | 2024 | ISBN: 1803245743 | 400 pages | True EPUB | 5.91 MB

Updated to include three new chapters on transformers, natural language understanding (NLU) with explainable AI, and dabbling with popular LLMs from Hugging Face and OpenAI

Key Features - Leverage ready-to-use recipes with the latest LLMs, including Mistral, Llama, and OpenAI models

- Use LLM-powered agents for custom tasks and real-world interactions

- Gain practical, in-depth knowledge of transformers and their role in implementing various NLP tasks with open-source and advanced LLMs

Book Description Harness the power of Natural Language Processing to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess.

You'll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you'll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You'll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs.

This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)-fostering trust and transparency in your NLP models.

By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.

What You Will Learn - Understand fundamental NLP concepts along with their applications using examples in Python

- Classify text quickly and accurately with rule-based and supervised methods

- Train NER models and perform sentiment analysis to identify entities and emotions in text

- Explore topic modeling and text visualization to reveal themes and relationships within text

- Leverage Hugging Face and OpenAI LLMs to perform advanced NLP tasks

- Use question-answering techniques to handle both open and closed domains

- Apply XAI techniques to better understand your model predictions

Who this book is for This updated edition of the Python Natural Language Processing Cookbook is for data scientists, machine learning engineers, and developers with a background in Python. Whether you're looking to learn NLP techniques, extract valuable insights from textual data, or create foundational applications, this book will equip you with basic to intermediate skills. No prior NLP knowledge is necessary to get started. All you need is familiarity with basic programming principles. For seasoned developers, the updated sections offer the latest on transformers, explainable AI, and Generative AI with LLMs.

中文| 2024 | ISBN:1803245743 | 400页| True EPUB | 5.91 MB已更新，新增了三章内容，分别涉及变压器、具有可解释人工智能的自然语言理解（NLU）以及涉猎Hugging Face和OpenAI的流行LLM 主要功能-利用最新LLM的即用型食谱，包括Mistral、Llama和OpenAI模型 -使用LLM驱动的代理进行自定义任务和现实世界的交互 -深入了解变压器及其在使用开源和高级LLM实现各种NLP任务中的作用 书籍描述利用自然语言处理的力量，通过两位经验丰富的NLP专家撰写的基于食谱的路线图克服现实世界的文本分析挑战，他们凭借NLP能力在改变各个行业方面拥有丰富的经验。 您将能够充分利用NLP的最新进展，包括大型语言模型（LLM），并通过Hugging Face转换器利用它们的功能。通过一系列实践食谱，您将掌握提取实体和可视化文本数据等基本技术。作者将熟练地指导您使用spaCy、Gensim和NLTK等流行库构建情感分析、主题建模和问答的管道。您还将学习如何实现RAG管道，以使用LLM从文本语料库中提取精确的答案。 第二版通过GPT-4、自然语言理解（NLU）和可解释人工智能（XAI）等前沿LLM的新章节扩展了您的技能，从而在NLP模型中培养了信任和透明度。 在本书结束时，您将掌握应用高级文本处理技术、使用预训练的变换器模型、构建自定义NLP管道以从文本数据中提取有价值的见解以推动明智决策的技能。 你将学到什么-使用Python中的示例了解基本的NLP概念及其应用 -使用基于规则和监督的方法快速准确地对文本进行分类 -训练NER模型并执行情感分析，以识别文本中的实体和情感 -探索主题建模和文本可视化，以揭示文本中的主题和关系 -利用Hugging Face和OpenAI LLM执行高级NLP任务 -使用问答技术来处理开放域和封闭域 -应用XAI技术更好地理解您的模型预测 这本更新版的Python自然语言处理烹饪书是为数据科学家、机器学习工程师和具有Python背景的开发人员编写的。无论你是想学习NLP技术，从文本数据中提取有价值的见解，还是创建基础应用程序，这本书都将为你提供基本到中级的技能。无需事先掌握NLP知识即可开始。你所需要的就是熟悉基本的编程原理。对于经验丰富的开发人员来说，更新的部分提供了最新的变压器、可解释的AI和带LLM的生成AI。
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