相關主題
商品描述
This book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others.
The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications.
By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing.
What You Will Learn
- Be able to answer the question: What are Large Language Models?
- Understand techniques such as prompt engineering, fine-tuning, RAG, and vector databases
- Know the best practices for effective implementation
- Know the metrics and frameworks essential for evaluating the performance of Large Language Models
Who This Book Is For
An essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs
商品描述(中文翻譯)
這本書深入探討了廣泛的主題，涵蓋了大型語言模型（Large Language Models, LLMs）的基礎方面，例如 PaLM、LLaMA、BERT 和 GPT 等。
本書帶您了解創建和部署基於 LLM 的應用程序所涉及的複雜性，並提供對模型架構的深入理解。您將探索如微調（fine-tuning）、提示工程（prompt engineering）和檢索增強生成（retrieval augmented generation, RAG）等技術。本書還討論了評估 LLM 輸出的不同方法，以及大型模型的優勢和限制。本書專注於開發大型語言模型所需的工具、技術和方法，並包含實作範例和提示，以指導您使用最新的自然語言處理（Natural Language Processing, NLP）技術來構建應用程序。它提供了一個路線圖，幫助您應對與構建和部署基於 LLM 的應用程序相關的挑戰。
在本書結束時，您將理解 LLM 並構建符合新興商業需求的應用程序，解決語言處理領域的各種問題。
您將學到的內容：
- 能夠回答問題：什麼是大型語言模型？
- 理解提示工程、微調、RAG 和向量數據庫等技術
- 知道有效實施的最佳實踐
- 知道評估大型語言模型性能所需的指標和框架
本書適合對象：
這是一本對於渴望在此領域獲得實踐經驗的 AI-ML 開發者和愛好者的重要資源；同樣適用於尋求對大型語言模型（LLMs）進行技術理解的個人，以及那些希望使用 LLM 構建應用程序的人。
作者簡介
Bhawna Singh, a Data Scientist at CeADAR (UCD), holds both a bachelor and master degree in computer science. During her master's program, she conducted research focused on identifying gender bias in Energy Policy data across the European Union. With prior experience as a Data Scientist at Brightflag in Ireland and a Machine Learning Engineer at AISmartz in India, Bhawna brings a wealth of expertise from both industry and academia. Her current research interests center on exploring diverse applications of Large Language Models. Over the course of her career, Bhawna has built models on extensive datasets, contributing to the development of intelligent systems addressing challenges such as customer churn, propensity prediction, sales forecasting, recommendation engines, customer segmentation, pdf validation, and more. She is dedicated to creating AI systems that are accessible to everyone, promoting inclusivity regardless of race, gender, social status, or language.
作者簡介(中文翻譯)
Bhawna Singh 是 CeADAR (UCD) 的數據科學家，擁有計算機科學的學士和碩士學位。在她的碩士課程中，她進行了針對歐盟能源政策數據中性別偏見的研究。Bhawna 曾在愛爾蘭的 Brightflag 擔任數據科學家，以及在印度的 AISmartz 擔任機器學習工程師，帶來了來自產業和學術界的豐富專業知識。她目前的研究興趣集中在探索大型語言模型的多樣化應用。在她的職業生涯中，Bhawna 在大量數據集上建立模型，為解決客戶流失、傾向預測、銷售預測、推薦引擎、客戶細分、PDF 驗證等挑戰的智能系統發展做出了貢獻。她致力於創造對所有人都可及的 AI 系統，促進無論種族、性別、社會地位或語言的包容性。
