Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
暫譯: R語言機器學習與深度學習的超參數調整：實用指南
Bartz, Eva, Bartz-Beielstein, Thomas, Zaefferer, Martin
- 出版商: Springer
- 出版日期: 2023-01-02
- 售價: $2,610
- 貴賓價: 9.5 折 $2,480
- 語言: 英文
- 頁數: 323
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9811951691
- ISBN-13: 9789811951695
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相關分類:
R 語言、DeepLearning
海外代購書籍(需單獨結帳)
商品描述
This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required.
The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.
商品描述(中文翻譯)
這本開放存取的書籍提供了大量的實作範例，說明如何在實務中應用超參數調整，並深入探討機器學習（ML）和深度學習（DL）方法的運作機制。這本書的目的是讓讀者能夠以顯著更少的時間、成本、努力和資源，利用這裡描述的方法來達成更好的結果。本書中呈現的案例研究可以在一般的桌上型電腦或筆記型電腦上運行，無需高效能計算設施。
這本書的構思源自於Bartz & Bartz GmbH為德國聯邦統計局（Destatis）進行的一項研究。在該研究的基礎上，本書針對業界的實務工作者以及學術界的研究人員、教師和學生。內容專注於ML和DL演算法的超參數調整，並分為兩個主要部分：理論（第一部分）和應用（第二部分）。涵蓋的基本主題包括：重要模型參數的調查；四個參數調整研究和一個廣泛的全局參數調整研究；基於嚴重性對ML和DL方法性能的統計分析；以及一種基於共識排名的新方法，用於聚合和分析多個演算法的結果。本書分析了六種相關的ML和DL方法中超過30個超參數，並提供源代碼，以便用戶能夠重現結果。因此，它同時作為手冊和教科書。
作者簡介
Prof. Dr. Thomas Bartz-Beielstein is an artificial intelligence expert with 30+ years of experience. He is a professor of applied mathematics at TH Köln in Germany and the director of the Institute for Data Science, Engineering, and Analytics (IDE+A). His research lies in artificial intelligence, machine learning, simulation, and optimization. He developed the Sequential Parameter Optimization (SPO). SPO integrates approaches from surrogate model-based optimization and evolutionary computing. He has worked on diverse topics from applied mathematics and statistics, design of experiments, simulation-based optimization and applications in domains as water industry, elevator control, or mechanical engineering.
Prof. Dr. Martin Zaefferer is a professor at Duale Hochschule Baden-Württemberg Ravensburg, teaching subjects related to data science in business informatics. Previously, he worked as a consultant at Bartz & Bartz GmbH and as a researcher at TH Köln, where he also studied electrical engineering and automation. He received a PhD from the Department of Computer Science at TU Dortmund University. Subsequently, he developed a keen interest in researching methods from the intersection of optimization and machine learning algorithms. He is passionate about the analysis of complex processes and finding novel solutions to challenging real-world problems.
Prof. Dr. Olaf Mersmann is a professor of data science at TH Köln-University of Applied Sciences in Germany and a member of the Institute for Data Science, Engineering, and Analytics (IDE+A). Having studied physics, statistics and data science, his research interests include landscape analysis for black box optimization problems and industrial machine learning applications. He is one of the developers of the exploratory landscape analysis approach to characterize continuous function landscapes.
作者簡介(中文翻譯)
Eva Bartz 是法律與數據保護方面的專家。在廣泛的數據保護領域中，她特別專注於人工智慧的應用及其帶來的好處與風險。基於這豐富的經驗，她於2014年與 Thomas Bartz-Beielstein 共同創立了 Bartz & Bartz GmbH，並為各種客戶提供諮詢服務。她將 Bartz & Bartz GmbH 顧問的學術專業知識——這些顧問在各自領域中都是領先的專家——轉化為客戶的利益。其中一位客戶是德國聯邦統計局（Destatis），為他們進行的研究為本書奠定了基礎。
Prof. Dr. Thomas Bartz-Beielstein 是一位擁有超過30年經驗的人工智慧專家。他是德國科隆應用科技大學（TH Köln）應用數學的教授，也是數據科學、工程與分析研究所（IDE+A）的主任。他的研究領域包括人工智慧、機器學習、模擬和優化。他開發了序列參數優化（Sequential Parameter Optimization, SPO）。SPO 整合了基於代理模型的優化方法和進化計算。他曾在應用數學和統計學、實驗設計、基於模擬的優化以及水產業、電梯控制或機械工程等領域的應用等多個主題上工作。
Prof. Dr. Martin Zaefferer 是德國巴登-符騰堡雙元大學（Duale Hochschule Baden-Württemberg Ravensburg）的教授，教授與商業資訊學相關的數據科學科目。此前，他曾在 Bartz & Bartz GmbH 擔任顧問，並在科隆應用科技大學（TH Köln）擔任研究員，並在該校學習電機工程與自動化。他在多特蒙德工業大學（TU Dortmund University）計算機科學系獲得博士學位。隨後，他對優化與機器學習算法交叉領域的方法研究產生了濃厚的興趣。他熱衷於分析複雜過程並尋找解決現實世界挑戰問題的新方法。
Prof. Dr. Olaf Mersmann 是德國科隆應用科技大學（TH Köln-University of Applied Sciences）的數據科學教授，也是數據科學、工程與分析研究所（IDE+A）的成員。他曾學習物理學、統計學和數據科學，他的研究興趣包括針對黑箱優化問題的景觀分析和工業機器學習應用。他是探索性景觀分析方法的開發者之一，該方法用於描述連續函數的景觀。
