Transformers in Action
暫譯: 行動中的變壓器
Koenigstein, Nicole
- 出版商: Manning
- 出版日期: 2025-11-25
- 售價: $2,090
- 貴賓價: 9.5 折 $1,986
- 語言: 英文
- 頁數: 256
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1633437884
- ISBN-13: 9781633437883
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相關分類:
Large language model
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商品描述
Take a deep dive into Transformers and Large Language Models--the foundations of generative AI! Generative AI has set up shop in almost every aspect of business and society. Transformers and Large Language Models (LLMs) now power everything from code creation tools like Copilot and Cursor to AI agents, live language translators, smart chatbots, text generators, and much more. In Transformers in Action you'll discover: - How transformers and LLMs work under the hood
- Adapting AI models to new tasks
- Optimizing LLM model performance
- Text generation with reinforcement learning
- Multi-modal AI models
- Encoder-only, decoder-only, encoder-decoder, and small language models This practical book gives you the background, mental models, and practical skills you need to put Gen AI to work. What is a transformer?
A "transformer" is a neural network model that finds relationships in sequences of words or other data using a mathematical technique called attention. Because the attention mechanism allows transformers to focus on the most relevant parts of a sequence, transformers can learn context and meaning from even large bodies of text. LLMs like GPT, Gemini, and Claude, are transformer-based models that have been trained on massive data sets, which gives them the uncanny ability to generate natural, coherent responses across a wide range of knowledge domains. About the book Transformers in Action guides you through the design and operation of transformers and transformer-based models. You'll dive immediately into LLM architecture, with even the most complex concepts explained clearly through easy-to-understand examples and clever analogies. Because transformers are based in mathematics, author Nicole Koenigstein carefully guides you through the foundational formulas and concepts one step at a time. You'll also appreciate the extensive code repository that lets you instantly start exploring LLMs hands-on. As you go, you learn how and when to use different model architectures such as decoder-only, encoder-only, and encoder-decoder. You'll also discover when small language models make sense for specific tasks like classification, in resource-constrained environments, or when privacy is paramount. You'll push transformers further with tasks like refining text generation with reinforcement learning, developing multimodal models including building multimodal RAG pipelines, and fine-tuning. You'll even learn how to optimize LLMs to maximize efficiency and minimize cost. About the reader For software engineers and data scientists comfortable with the basics of ML, Python, and common data tools. About the author Nicole Koenigstein is CEO and Chief AI Officer at Quantmate, an agentic ecosystem for hypothesis testing, trading strategy evolution, and dynamic algorithmic intelligence. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
- Adapting AI models to new tasks
- Optimizing LLM model performance
- Text generation with reinforcement learning
- Multi-modal AI models
- Encoder-only, decoder-only, encoder-decoder, and small language models This practical book gives you the background, mental models, and practical skills you need to put Gen AI to work. What is a transformer?
A "transformer" is a neural network model that finds relationships in sequences of words or other data using a mathematical technique called attention. Because the attention mechanism allows transformers to focus on the most relevant parts of a sequence, transformers can learn context and meaning from even large bodies of text. LLMs like GPT, Gemini, and Claude, are transformer-based models that have been trained on massive data sets, which gives them the uncanny ability to generate natural, coherent responses across a wide range of knowledge domains. About the book Transformers in Action guides you through the design and operation of transformers and transformer-based models. You'll dive immediately into LLM architecture, with even the most complex concepts explained clearly through easy-to-understand examples and clever analogies. Because transformers are based in mathematics, author Nicole Koenigstein carefully guides you through the foundational formulas and concepts one step at a time. You'll also appreciate the extensive code repository that lets you instantly start exploring LLMs hands-on. As you go, you learn how and when to use different model architectures such as decoder-only, encoder-only, and encoder-decoder. You'll also discover when small language models make sense for specific tasks like classification, in resource-constrained environments, or when privacy is paramount. You'll push transformers further with tasks like refining text generation with reinforcement learning, developing multimodal models including building multimodal RAG pipelines, and fine-tuning. You'll even learn how to optimize LLMs to maximize efficiency and minimize cost. About the reader For software engineers and data scientists comfortable with the basics of ML, Python, and common data tools. About the author Nicole Koenigstein is CEO and Chief AI Officer at Quantmate, an agentic ecosystem for hypothesis testing, trading strategy evolution, and dynamic algorithmic intelligence. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
商品描述(中文翻譯)
深入探討 Transformers 和大型語言模型——生成式 AI 的基礎！
生成式 AI 已經在商業和社會的幾乎每個方面扎根。Transformers 和大型語言模型（LLMs）現在驅動著從代碼創建工具如 Copilot 和 Cursor 到 AI 代理、即時語言翻譯器、智能聊天機器人、文本生成器等各種應用。 在 Transformers in Action 中，您將發現： - Transformers 和 LLMs 的內部運作- 將 AI 模型適應於新任務
- 優化 LLM 模型性能
- 使用強化學習進行文本生成
- 多模態 AI 模型
- 僅編碼器、僅解碼器、編碼-解碼器和小型語言模型 這本實用的書籍為您提供了背景知識、思維模型和實用技能，幫助您將生成式 AI 應用於實際工作中。 什麼是 transformer？
“transformer” 是一種神經網絡模型，使用一種稱為注意力的數學技術來尋找單詞或其他數據序列中的關係。由於注意力機制使 transformers 能夠專注於序列中最相關的部分，因此 transformers 能夠從大量文本中學習上下文和意義。像 GPT、Gemini 和 Claude 這樣的 LLM 是基於 transformer 的模型，這些模型在龐大的數據集上進行訓練，使它們具備在廣泛知識領域中生成自然且連貫的回應的驚人能力。 關於本書 Transformers in Action 指導您了解 transformers 和基於 transformer 的模型的設計和運作。您將立即深入 LLM 架構，即使是最複雜的概念也會通過易於理解的範例和巧妙的類比清晰解釋。由於 transformers 基於數學，作者 Nicole Koenigstein 會一步一步地仔細引導您了解基礎公式和概念。您還會欣賞到豐富的代碼庫，讓您能夠立即開始動手探索 LLM。 在學習過程中，您將了解何時使用不同的模型架構，如僅解碼器、僅編碼器和編碼-解碼器。您還會發現小型語言模型在特定任務（如分類）、資源受限的環境或隱私至關重要的情況下的適用性。您將進一步推進 transformers，進行如使用強化學習精煉文本生成、開發多模態模型（包括構建多模態 RAG 管道）和微調等任務。您甚至會學習如何優化 LLM，以最大化效率並最小化成本。 關於讀者 適合對機器學習、Python 和常見數據工具有基本了解的軟體工程師和數據科學家。 關於作者 Nicole Koenigstein 是 Quantmate 的首席執行官和首席 AI 官，Quantmate 是一個用於假設測試、交易策略演變和動態算法智能的代理生態系統。 購買印刷版書籍時，您將獲得 Manning 提供的免費電子書（PDF 或 ePub），以及訪問在線 liveBook 格式（及其能用任何語言回答您問題的 AI 助手）的權限。
作者簡介
Nicole Koenigstein is a distinguished Data Scientist and Quantitative Researcher. She is presently the Chief Data Scientist and Head of AI & Quantitative Research at Wyden Capital.
作者簡介(中文翻譯)
妮可·科寧斯坦（Nicole Koenigstein）是一位傑出的數據科學家和定量研究員。她目前擔任Wyden Capital的首席數據科學家及人工智慧與定量研究部門負責人。
