Advances in Intelligent Data and Information Processing: Proceedings of the International Conference on Intelligent Data and Information Processing (I
暫譯: 智能數據與信息處理的進展:國際智能數據與信息處理會議論文集

Pedrycz, Witold, Wang, John, Tseng, Kuo-Kun

  • 出版商: Springer
  • 出版日期: 2026-02-23
  • 售價: $8,980
  • 貴賓價: 9.5$8,531
  • 語言: 英文
  • 頁數: 304
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3032167019
  • ISBN-13: 9783032167019
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book integrates practical engineering insights with cutting-edge AI/ML methodologies to address real-world intelligent data processing challenges, prioritizing actionable solutions over theoretical abstraction. By bridging algorithmic foundations with industry-specific use cases, it equips readers to translate technical concepts into deployable systems efficiently.

Unlike traditional texts that silo theory and practice, this approach embeds hands-on implementation frameworks, including data preprocessing pipelines, model optimization techniques, and scalability strategies, directly within contextualized problem-solving scenarios. Covering core topics from edge AI deployment to large-scale data analytics, it spans both foundational principles and emerging trends like federated learning and real-time processing. Tailored for IT professionals, computer science practitioners, and engineering researchers, it also serves as a valuable resource for graduate students specializing in data science or intelligent systems. Ideal for upskilling, project reference, or curriculum supplementation, it empowers readers to tackle complex data-intensive tasks with confidence in academic, corporate, or R&D settings.

商品描述(中文翻譯)

本書結合實用的工程見解與尖端的人工智慧/機器學習(AI/ML)方法論,以應對現實世界中的智能數據處理挑戰,優先考慮可行的解決方案而非理論抽象。通過將算法基礎與行業特定的應用案例相結合,幫助讀者有效地將技術概念轉化為可部署的系統。

與傳統文本將理論與實踐隔離的方式不同,本書的做法將實作框架嵌入於具體的問題解決情境中,包括數據預處理管道、模型優化技術和可擴展性策略。涵蓋從邊緣AI部署到大規模數據分析的核心主題,既包括基礎原則,也涉及聯邦學習和實時處理等新興趨勢。本書專為IT專業人士、計算機科學從業者和工程研究人員量身打造,同時也為專攻數據科學或智能系統的研究生提供了寶貴的資源。無論是提升技能、項目參考還是課程補充,本書都能幫助讀者在學術、企業或研發環境中自信地應對複雜的數據密集型任務。