DeepSeek in Action: LLM Deployment, Fine-Tuning, and Application
暫譯: DeepSeek 實戰：LLM 部署、微調與應用
Dai, Jing
- 出版商: CRC
- 出版日期: 2025-11-18
- 售價: $7,000
- 貴賓價: 9.5 折 $6,650
- 語言: 英文
- 頁數: 14
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1041090005
- ISBN-13: 9781041090007
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相關分類:
Large language model
海外代購書籍(需單獨結帳)
商品描述
From fundamental concepts to advanced implementations, this book thoroughly explores the DeepSeek-V3 model, focusing on its Transformer-based architecture, technological innovations, and applications.
The book begins with a thorough examination of theoretical foundations, including self-attention, positional encoding, the Mixture of Experts mechanism, and distributed training strategies. It then explores DeepSeek-V3's technical advancements, including sparse attention mechanisms, FP8 mixed-precision training, and hierarchical load balancing, which optimize memory and energy efficiency. Through case studies and API integration techniques, the model's high-performance capabilities in text generation, mathematical reasoning, and code completion are examined. The book highlights DeepSeek's open platform and covers secure API authentication, concurrency strategies, and real-time data processing for scalable AI applications. Additionally, the book addresses industry applications, such as chat client development, utilizing DeepSeek's context caching and callback functions for automation and predictive maintenance.
This book is aimed primarily at AI researchers and developers working on large-scale AI models. It is an invaluable resource for professionals seeking to understand the theoretical underpinnings and practical implementation of advanced AI systems, particularly those interested in efficient, scalable applications.
商品描述(中文翻譯)
從基本概念到進階實作，本書深入探討了 DeepSeek-V3 模型，重點在於其基於 Transformer 的架構、技術創新及應用。
本書首先徹底檢視理論基礎，包括自注意力（self-attention）、位置編碼（positional encoding）、專家混合機制（Mixture of Experts mechanism）以及分散式訓練策略。接著探討 DeepSeek-V3 的技術進展，包括稀疏注意力機制（sparse attention mechanisms）、FP8 混合精度訓練（mixed-precision training）及階層負載平衡（hierarchical load balancing），這些技術優化了記憶體和能源效率。透過案例研究和 API 整合技術，檢視該模型在文本生成、數學推理和程式碼補全方面的高效能能力。本書強調 DeepSeek 的開放平台，並涵蓋安全的 API 認證、併發策略及可擴展 AI 應用的即時數據處理。此外，本書還探討了行業應用，例如聊天客戶端開發，利用 DeepSeek 的上下文快取和回調函數進行自動化和預測性維護。
本書主要針對從事大型 AI 模型研究和開發的 AI 研究人員和開發者。對於希望了解先進 AI 系統的理論基礎和實際實作的專業人士來說，這是一本不可或缺的資源，特別是對於那些對高效、可擴展應用感興趣的人士。
作者簡介
Jing Dai graduated from Tsinghua University with research expertise in data mining, natural language processing, and related fields. With over a decade of experience as a technical engineer at leading companies including IBM and VMware, she has developed strong technical capabilities and deep industry insight. In recent years, her work has focused on advanced technologies such as large-scale model training, NLP, and model optimization, with particular emphasis on Transformer architectures, attention mechanisms, and multi-task learning.
作者簡介(中文翻譯)
戴晶畢業於清華大學，專攻資料探勘、自然語言處理及相關領域。她在包括 IBM 和 VMware 等領先公司擔任技術工程師超過十年，擁有強大的技術能力和深厚的行業洞察力。近年來，她的工作專注於先進技術，如大規模模型訓練、自然語言處理（NLP）和模型優化，特別強調 Transformer 架構、注意力機制和多任務學習。
