Shallow Learning vs. Deep Learning: A Practical Guide for Machine Learning Solutions
暫譯: 淺層學習與深層學習：機器學習解決方案的實用指南
Ertuğrul, Ömer Faruk, Guerrero, Josep M., Yilmaz, Musa
- 出版商: Springer
- 出版日期: 2025-10-13
- 售價: $6,560
- 貴賓價: 9.5 折 $6,232
- 語言: 英文
- 頁數: 274
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031695011
- ISBN-13: 9783031695018
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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 explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.
In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.
商品描述(中文翻譯)
這本書探討了機器學習領域中淺層學習與深層學習之間的持續辯論。它提供了從淺層學習到深層學習的機器學習方法的全面調查，並檢視它們在各個領域的應用。《淺層學習與深層學習：機器學習解決方案的實用指南》強調，選擇機器學習方法應該根據數據集的特定特徵、操作環境以及每個應用的獨特需求來決定，而不是受到當前趨勢的影響。
在每一章中，這本書深入探討不同的應用領域，例如工程、現實世界場景、社會應用、影像處理、生物醫學應用、異常檢測、自然語言處理、語音識別、推薦系統、自主系統和智慧電網應用。通過比較淺層學習和深層學習在這些領域的有效性，這本書提供了一個深思熟慮的機器學習策略選擇和應用的框架。本指南旨在幫助研究人員、實務工作者和學生加深對何時以及如何有效應用不同機器學習技術的理解。通過比較研究和詳細分析，讀者將獲得寶貴的見解，以便在各自的領域做出明智的決策。
作者簡介
Omer Faruk Ertugrul, was born in Batman, Turkey in 1978. He received the B.S. degree from the Hacettepe University, Department of Electrical and Electronics Engineering in 2001, M.S. and Ph.D. degrees in Electrical and Electronics Engineering in 2010, and 2015, respectively. His research interests include machine learning and signal processing. He is in 100,000 top-scientists list in the world, %2 top-scientist list in the world, in Turkey Top 10.000 Scientists, and in AD Scientific Index - 2022 Turkey Top 10.000 Scientists, in 2019, 2020, 2021, and 2022 respectively. He is currently associate editor in NC&A (SCI-E indexed-Q1) in Middle East excluding Iran. He is also co-founder/co-owner and CTO in INSENSE, ABRH and SOFTSENSE.
Josep M. Guerrero received his B.Sc. (1997), M.Sc. (2000), and Ph.D. (2003) in engineering from the Technical University of Catalonia, Barcelona. He is currently pursuing an M.Sc. in Psychobiology and Cognitive Neuroscience. Since 2011, he has been a Full Professor at AAU Energy, Aalborg University, Denmark, leading the Microgrid Research Program. In 2019, he founded the Center for Research on Microgrids (CROM). His research covers microgrids, IoT, cybersecurity, maritime and space microgrids, and smart medical systems. He is an Associate Editor for IEEE TRANSACTIONS and has over 900 papers with 117,000 citations. Recognized as a Highly Cited Researcher (2014-2022), he received the IEEE Bimal Bose Award (2021) and IEEE PES Douglas M. Staszesky Award (2022).
Musa Yilmaz received his Associate Professor certificate in Electrical-Electronics and Communication Engineering. He works at the University of California, Riverside, and Batman University. He received his M.Sc. degree from Marmara University, Istanbul, Turkey, in 2004, and his Ph.D. degree from the same institution in 2013. From 2015 to 2016, Dr. Yilmaz was a visiting scholar at the Smart Grid Research Center (SMERC) at the University of California, Los Angeles (UCLA). His primary research interests include smart grid technologies, renewable energy, machine learning, and signal processing. Dr. Yilmaz is a partner of the medical company Biosys LLC. He has served as Editor-in-Chief of the Balkan Journal of Electrical and Computer Engineering (BAJECE) and the European Journal of Technique (EJT). Additionally, he is the owner of INESEG, a publishing organization. Dr. Yilmaz has authored over 50 research articles, several book chapters, and frequently delivers invited keynote lectures at international conferences. He has also led his research team as the Principal Investigator in several European projects. He is an IEEE Senior Member.
作者簡介(中文翻譯)
Omer Faruk Ertugrul 於1978年出生於土耳其的巴特曼。他於2001年獲得哈吉特佩大學（Hacettepe University）電氣與電子工程系的學士學位，並於2010年和2015年分別獲得電氣與電子工程的碩士和博士學位。他的研究興趣包括機器學習和信號處理。他在全球10萬名頂尖科學家名單中排名前2%，在土耳其的前10,000名科學家中名列前茅，並在AD Scientific Index - 2022土耳其前10,000名科學家中於2019、2020、2021和2022年均有上榜。他目前擔任中東地區（不包括伊朗）NC&A（SCI-E索引-Q1）的副編輯。他也是INSENSE、ABRH和SOFTSENSE的共同創辦人/共同擁有者及首席技術官。
Josep M. Guerrero 於1997年、2000年和2003年分別在巴塞隆納的加泰羅尼亞理工大學獲得工程學士、碩士和博士學位。他目前正在攻讀心理生物學和認知神經科學的碩士學位。自2011年以來，他一直是丹麥奧爾堡大學（Aalborg University）AAU Energy的全職教授，負責微電網研究計劃。2019年，他創立了微電網研究中心（CROM）。他的研究涵蓋微電網、物聯網、網絡安全、海洋和太空微電網以及智能醫療系統。他是IEEE TRANSACTIONS的副編輯，發表了超過900篇論文，引用次數達117,000次。被認定為高被引研究者（2014-2022），他獲得了IEEE Bimal Bose獎（2021年）和IEEE PES Douglas M. Staszesky獎（2022年）。
Musa Yilmaz 獲得電氣電子與通信工程的副教授證書。他在加州大學河濱分校（University of California, Riverside）和巴特曼大學工作。他於2004年在土耳其伊斯坦堡的馬爾馬拉大學獲得碩士學位，並於2013年在同一機構獲得博士學位。2015年至2016年，Yilmaz博士曾在加州大學洛杉磯分校（UCLA）的智能電網研究中心（SMERC）擔任訪問學者。他的主要研究興趣包括智能電網技術、可再生能源、機器學習和信號處理。Yilmaz博士是醫療公司Biosys LLC的合作夥伴。他曾擔任《巴爾幹電氣與計算機工程期刊》（BAJECE）和《歐洲技術期刊》（EJT）的主編。此外，他還是出版機構INESEG的擁有者。Yilmaz博士已發表超過50篇研究文章，數篇書籍章節，並經常在國際會議上發表受邀主題演講。他還作為主要研究者領導他的研究團隊參與多個歐洲項目。他是IEEE的資深會員。
