Computer Engineering Machine Learning and Neural Networks: A Computer Engineering Perspective
暫譯: 計算機工程中的機器學習與神經網絡：計算機工程的視角

Chen, Yiran, Li, Hai, Yang, Huanrui

Computer Engineering Machine Learning and Neural Networks: A Computer Engineering Perspective
  • 出版商: Springer
  • 出版日期: 2026-05-24
  • 售價: $3,440
  • 貴賓價: 9.5 折 $3,268
  • 語言: 英文
  • 頁數: 312
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3032209781
  • ISBN-13: 9783032209788
  • 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 is the first textbook focusing on practicality of machine learning (ML) and deep neural networks (DNN), by introducing methods that enable engineering applications of ML and DNN models. The authors describe many methodologies that are widely used in designing, training, and deploying of these models and discuss their applicability under various contexts. Coverage begins with the basic knowledge of machine learning and deep neural networks and their applications in solving practical engineering problems. It then proceeds through a series of computer engineering methods commonly used in developing machine learning and deep neural network models. The book also explains how to improve the training and inference performance in terms of model accuracy, size, runtime, etc. by considering various requirements and availability of data in the applications. Techniques that are widely adopted in both industry and academia are discussed. Tutorials and projects designed to practice the introduced techniques are provided using popular development frameworks of machine learning.

  • Emphasizes practice over theoretical foundations, making content accessible to engineering students and engineers;

  • Includes in-depth discussion of popular DNN models and their applications;

  • Discusses engineering methods and tricks widely adopted in practice for using ML and DNN to solve engineering problems.

商品描述(中文翻譯)

這是第一本專注於機器學習（ML）和深度神經網絡（DNN）實用性的教科書，通過介紹使 ML 和 DNN 模型能夠應用於工程的各種方法。作者描述了許多在設計、訓練和部署這些模型中廣泛使用的方法論，並討論了它們在不同情境下的適用性。內容從機器學習和深度神經網絡的基本知識及其在解決實際工程問題中的應用開始，然後進一步介紹一系列在開發機器學習和深度神經網絡模型中常用的計算機工程方法。書中還解釋了如何考慮應用中的各種需求和數據可用性，以提高模型的準確性、大小、運行時間等方面的訓練和推理性能。討論了在業界和學術界廣泛採用的技術。提供了使用流行的機器學習開發框架來實踐所介紹技術的教程和項目。

- 強調實踐而非理論基礎，使內容對工程學生和工程師更易於理解；
- 包含對流行 DNN 模型及其應用的深入討論；
- 討論在實踐中廣泛採用的工程方法和技巧，以使用 ML 和 DNN 解決工程問題。

作者簡介

Yiran Chen is the John Cocke Distinguished Professor of Electrical and Computer Engineering at Duke University. He serves as the Principal Investigator and Director of the NSF AI Institute for Edge Computing Leveraging Next Generation Networks (Athena) and Co-Director of the Duke Center for Computational Evolutionary Intelligence (DCEI). His research group focuses on innovations in emerging memory and storage systems, machine learning and neuromorphic computing, and edge computing. Dr. Chen has authored over 700 publications and holds 96 U.S. patents. His work has received widespread recognition, including two Test-of-Time Awards and 14 Best Paper/Poster Awards. He is the recipient of the IEEE Circuits and Systems Society's Charles A. Desoer Technical Achievement Award and the IEEE Computer Society's Edward J. McCluskey Technical Achievement Award. He also serves as the inaugural Editor-in-Chief of the IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI) and the founding Chair of the IEEE Circuits and Systems Society's Machine Learning Circuits and Systems (MLCAS) Technical Committee. Dr. Chen is a Fellow of the AAAS, ACM, IEEE, and NAI, and a member of the European Academy of Sciences and Arts.

Hai (Helen) Li is the Marie Foote Reel E'46 Distinguished Professor and Department Chair of the Electrical and Computer Engineering Department at Duke University. She received her B.S. and M.S. from Tsinghua University and her Ph.D. from Purdue University. Her research interests include neuromorphic circuits and systems for brain-inspired computing, machine learning acceleration and trustworthy AI, conventional and emerging memory design and architecture, and software and hardware co-design. Dr. Li served/serves as the Associate Editor-in-Chief and Associate Editor for multiple IEEE and ACM journals. She was the General Chair or Technical Program Chair of numerous IEEE/ACM conferences and the Technical Program Committee member of over 30 international conference series. Dr. Li is a Distinguished Lecturer of the IEEE CAS Society and a Distinguished Speaker of ACM. Dr. Li is a recipient of the IEEE Edward J. McCluskey Technical Achievement Award, Ten Year Retrospective Influential Paper Award from ICCAD, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, nine best paper awards, and another ten best paper nominations. Dr. Li is a fellow of ACM, AAAS, IEEE, and NAI.

作者簡介(中文翻譯)

Yiran Chen 是杜克大學電機與計算機工程系的 John Cocke 傑出教授。他擔任 NSF AI Institute for Edge Computing Leveraging Next Generation Networks (Athena) 的首席研究員和主任，以及杜克計算進化智能中心 (DCEI) 的共同主任。他的研究團隊專注於新興記憶和儲存系統、機器學習和類神經計算以及邊緣計算的創新。陳博士已發表超過 700 篇論文，並擁有 96 項美國專利。他的工作獲得廣泛認可，包括兩項 Test-of-Time 獎和 14 項最佳論文/海報獎。他是 IEEE Circuits and Systems Society 的 Charles A. Desoer 技術成就獎和 IEEE Computer Society 的 Edward J. McCluskey 技術成就獎的獲得者。他還擔任 IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI) 的首任主編，以及 IEEE Circuits and Systems Society 的機器學習電路與系統 (MLCAS) 技術委員會的創始主席。陳博士是 AAAS、ACM、IEEE 和 NAI 的會士，並且是歐洲科學與藝術學院的成員。

Hai (Helen) Li 是杜克大學電機與計算機工程系的 Marie Foote Reel E'46 傑出教授及系主任。她在清華大學獲得學士和碩士學位，並在普渡大學獲得博士學位。她的研究興趣包括用於腦啟發計算的類神經電路和系統、機器學習加速和可信 AI、傳統及新興記憶設計和架構，以及軟硬體共同設計。李博士曾擔任多個 IEEE 和 ACM 期刊的副主編和副編輯。她曾擔任多個 IEEE/ACM 會議的總主席或技術程序主席，並且是超過 30 個國際會議系列的技術程序委員會成員。李博士是 IEEE CAS Society 的傑出講者和 ACM 的傑出演講者。李博士獲得 IEEE Edward J. McCluskey 技術成就獎、ICCAD 的十年回顧影響力論文獎、德國 TUM-IAS Hans Fischer 獎學金、ELATE 獎學金、九項最佳論文獎和另外十項最佳論文提名。李博士是 ACM、AAAS、IEEE 和 NAI 的會士。

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