The Practical Guide to Large Language Models: Hands-On AI Applications with Hugging Face Transformers
暫譯: 大型語言模型實用指南：使用 Hugging Face Transformers 的實作 AI 應用

Gridin, Ivan

The Practical Guide to Large Language Models: Hands-On AI Applications with Hugging Face Transformers
  • 出版商: Apress
  • 出版日期: 2025-12-27
  • 售價: $2,390
  • 貴賓價: 9.5 折 $2,271
  • 語言: 英文
  • 頁數: 360
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798868822155
  • ISBN-13: 9798868822155
  • 相關分類: Large language model

    • 尚未上市，無法訂購

相關主題

商品描述

This book is a practical guide to harnessing Hugging Face's powerful transformers library, unlocking access to the largest open-source LLMs. By simplifying complex NLP concepts and emphasizing practical application, it empowers data scientists, machine learning engineers, and NLP practitioners to build robust solutions without delving into theoretical complexities.

The book is structured into three parts to facilitate a step-by-step learning journey. Part One covers building production-ready LLM solutions introduces the Hugging Face library and equips readers to solve most of the common NLP challenges without requiring deep knowledge of transformer internals. Part Two focuses on empowering LLMs with RAG and intelligent agents exploring Retrieval-Augmented Generation (RAG) models, demonstrating how to enhance answer quality and develop intelligent agents. Part Three covers LLM advances focusing on expert topics such as model training, principles of transformer architecture and other cutting-edge techniques related to the practical application of language models.

Each chapter includes practical examples, code snippets, and hands-on projects to ensure applicability to real-world scenarios. This book bridges the gap between theory and practice, providing professionals with the tools and insights to develop practical and efficient LLM solutions.

What you will learn:

    What are the different types of tasks modern LLMs can solve How to select the most suitable pre-trained LLM for specific tasks How to enrich LLM with a custom knowledge base and build intelligent systems What are the core principles of Language Models, and how to tune them How to build robust LLM-based AI Applications
Who this book is for:

Data scientists, machine learning engineers, and NLP specialists with basic Python skills, introductory PyTorch knowledge, and a primary understanding of deep learning concepts, ready to start applying Large Language Models in practice.

商品描述(中文翻譯)

本書是一本實用指南，旨在利用 Hugging Face 強大的 transformers 函式庫，開啟對最大開源 LLM（大型語言模型）的訪問。通過簡化複雜的自然語言處理（NLP）概念並強調實際應用，它使數據科學家、機器學習工程師和 NLP 從業者能夠構建穩健的解決方案，而無需深入理論的複雜性。

本書分為三個部分，以便於逐步學習。第一部分涵蓋構建生產就緒的 LLM 解決方案，介紹 Hugging Face 函式庫，並使讀者能夠解決大多數常見的 NLP 挑戰，而無需深入了解 transformer 的內部運作。第二部分專注於利用 RAG（檢索增強生成）和智能代理來增強 LLM，探索檢索增強生成模型，展示如何提高答案質量並開發智能代理。第三部分涵蓋 LLM 的進展，專注於專家主題，如模型訓練、transformer 架構原則及其他與語言模型實際應用相關的前沿技術。

每一章都包括實用範例、程式碼片段和實作專案，以確保與現實世界場景的適用性。本書彌合了理論與實踐之間的鴻溝，為專業人士提供開發實用且高效的 LLM 解決方案所需的工具和見解。

您將學到的內容：
- 現代 LLM 可以解決的不同類型任務
- 如何為特定任務選擇最合適的預訓練 LLM
- 如何用自定義知識庫豐富 LLM 並構建智能系統
- 語言模型的核心原則及其調整方法
- 如何構建穩健的基於 LLM 的 AI 應用程式

本書適合對象：
具備基本 Python 技能、入門 PyTorch 知識和對深度學習概念有初步理解的數據科學家、機器學習工程師和 NLP 專家，準備開始在實踐中應用大型語言模型。

作者簡介

Ivan Gridin is an artificial intelligence expert, researcher, and author with extensive experience in applying advanced machine-learning techniques in real-world scenarios. His expertise includes natural language processing (NLP), predictive time series modeling, automated machine learning (AutoML), reinforcement learning, and neural architecture search. He also has a strong foundation in mathematics, including stochastic processes, probability theory, optimization, and deep learning. In recent years, he has become a specialist in open-source large language models, including the Hugging Face framework. Building on this expertise, he continues to advance his work in developing intelligent, real-world applications powered by natural language processing.

He is a loving husband and father and collector of old math books.

You can learn more about him on LinkedIn: https: //www.linkedin.com/in/survex/.

作者簡介(中文翻譯)

伊凡·格里丁（Ivan Gridin） 是一位人工智慧專家、研究員和作者，擁有在實際場景中應用先進機器學習技術的豐富經驗。他的專業領域包括自然語言處理（NLP）、預測時間序列建模、自動化機器學習（AutoML）、強化學習和神經架構搜索。他在數學方面也有堅實的基礎，包括隨機過程、概率論、優化和深度學習。近年來，他成為開源大型語言模型的專家，包括 Hugging Face 框架。基於這些專業知識，他持續推進開發由自然語言處理驅動的智能實際應用的工作。

他是一位慈愛的丈夫和父親，也是舊數學書籍的收藏家。

您可以在 LinkedIn 上了解更多關於他的資訊：https://www.linkedin.com/in/survex/。

類似商品

最後瀏覽商品 (7)