Generative AI Foundations in Python: Discover key techniques and navigate modern challenges in LLMs (Python中的生成式AI基礎:探索關鍵技術並應對大型語言模型的現代挑戰)
Rodriguez, Carlos, Shaikh, Samira
- 出版商: Packt Publishing
- 出版日期: 2024-07-26
- 售價: $1,680
- 貴賓價: 9.5 折 $1,596
- 語言: 英文
- 頁數: 190
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1835460828
- ISBN-13: 9781835460825
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相關分類:
LangChain、Python、程式語言、人工智慧
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相關主題
商品描述
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials
Key Features:
- Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation
- Use transformers-based LLMs and diffusion models to implement AI applications
- Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description:
The intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application.
Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You'll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you'll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs.
By the end of this book, you'll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.
What You Will Learn:
- Discover the fundamentals of GenAI and its foundations in NLP
- Dissect foundational generative architectures including GANs, transformers, and diffusion models
- Find out how to fine-tune LLMs for specific NLP tasks
- Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance
- Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG
- Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs
Who this book is for:
This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.
Table of Contents
- Understanding Generative AI: An Introduction
- Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers
- Tracing the Foundations of Natural Language Processing and the Impact of the Transformer
- Applying Pretrained Generative Models: From Prototype to Production
- Fine-Tuning Generative Models for Specific Tasks
- Understanding Domain Adaptation for Large Language Models
- Mastering the Fundamentals of Prompt Engineering
- Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI
商品描述(中文翻譯)
開始您的生成式 AI 之旅,使用 Python 探索大型語言模型,了解負責任的生成式 AI 實踐,並通過指導教程將您的知識應用於現實世界的應用。
主要特點:
- 獲得提示工程、LLM 微調和領域適應的專業知識
- 使用基於 transformers 的 LLM 和擴散模型來實現 AI 應用
- 發現優化模型性能、解決倫理考量和建立 AI 系統信任的策略
- 購買印刷版或 Kindle 書籍包括免費 PDF 電子書
書籍描述:
生成式 AI (GenAI) 和大型語言模型的複雜性和廣度有時會掩蓋其實際應用。理解實施生成式 AI 所需的基礎概念至關重要。本指南通過結合理論和實踐應用,解釋了最先進生成模型背後的核心概念。
《Python 中的生成式 AI 基礎》首先建立基礎理解,介紹生成式 LLM 的基本原理及其歷史演變,同時為更深入的探索奠定基礎。您還將了解如何在現實世界的應用中應用生成式 LLM。本書簡化了複雜性,提供了使用 Python 部署和微調預訓練語言模型的可行指導。隨後,您將深入探討特定任務的微調、領域適應、提示工程、定量評估和負責任的 AI 等主題,重點在於如何有效且負責任地使用生成式 LLM。
在本書結束時,您將熟練掌握將生成式 AI 能力應用於現實世界問題的技巧,自信地以道德和負責任的方式駕馭其巨大潛力。
您將學到的內容:
- 探索 GenAI 的基本原理及其在 NLP 中的基礎
- 剖析基礎生成架構,包括 GAN、transformers 和擴散模型
- 瞭解如何為特定 NLP 任務微調 LLM
- 理解轉移學習和微調以促進領域適應,包括金融等領域
- 探索提示工程,包括上下文學習、模板化和通過思維鏈和 RAG 的合理化
- 實施負責任的生成式 LLM 實踐,以最小化偏見、毒性和其他有害輸出
本書適合對象:
本書適合開發人員、數據科學家和機器學習工程師,適合那些從事生成式 AI 驅動的項目。預期讀者應具備機器學習和深度學習的一般理解,以及一定的 Python 熟練度。
目錄:
- 理解生成式 AI:簡介
- 調查 GenAI 類型和模式:GAN、擴散器和 transformers 概述
- 追溯自然語言處理的基礎及 transformer 的影響
- 應用預訓練生成模型:從原型到生產
- 為特定任務微調生成模型
- 理解大型語言模型的領域適應
- 精通提示工程的基本原理
- 解決倫理考量並為可信的生成式 AI 繪製道路