電機機械精釋 (7版)

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書名：進修、教學：電機機械精釋 子敬 作者：子敬 出版社：超級科技 出版日期：2015/05/01 ISBN：9789579831208 內容簡介 本書兼具教學及進修的功能，內容著重觀念的建立、理論的探討及問題的解析。

Fundamentals of Cloud Security (1版)

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【簡介】 Fundamentals of Cloud Security offers a structured, framework-based approach to understanding, implementing, and securing cloud environments. Built around the NIST Cybersecurity Framework, it connects identification, protection, detection, response, and recovery to practical applications across AWS, Azure, and Google Cloud. This First Edition text guides readers through readiness assessments, shared responsibility, governance, compliance, and architectural considerations that shape secure, scalable cloud systems. Content explores the cloud threat ecosystem, principles of security and trust, and the strategies, tools, and controls that drive resilient operations. Case studies and real-world examples illustrate how to bridge theory and practice, while coverage of emerging security roles, automated tools, and AI-driven techniques highlights the evolution of cloud protection. Designed to align with cybersecurity and cloud computing programs, it supports modern learning outcomes and prepares learners to address current and future challenges in the field. Features and Benefits Organized around the NIST Cybersecurity Framework, linking key lifecycle stages to practical cloud security strategies. Applies real-world scenarios and provider specifics for AWS, Azure, and Google Cloud to strengthen practical understanding. Covers readiness, governance, and compliance, providing tools to evaluate cloud migration and manage ongoing alignment. Highlights emerging roles and shift-left/shift-right security, guiding modern approaches to multi-cloud threat management. Explores AI-driven and automated security approaches that enhance monitoring, response, and governance across deployments. Offers immersive, scenario-based Cloud Labs that reinforce concepts through real-world, hands-on virtual practice. Instructor resources include PowerPoint slides, test bank, sample syllabi, instructor manual, and time-on-task documentation.

Fundamentals of Probability (5版)

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【簡介】 This one- or two-term calculus-based basic probability text is written for majors in mathematics, physical sciences, engineering, statistics, actuarial science, business and finance, operations research, and computer science. It presents probability in a natural way: through interesting and instructive examples and exercises that motivate the theory, definitions, theorems, and methodology. This book is mathematically rigorous and, at the same time, closely matches the historical development of probability. Whenever appropriate, historical remarks are included, and the 2096 examples and exercises have been carefully designed to arouse curiosity and hence encourage students to delve into the theory with enthusiasm. 【目錄】 1. Axioms of Probability 2. Combinatorial Methods 3. Conditional Probability and Independence 4. Distribution Functions and Discrete Random Variables 5. Continuous Random Variables 6. Special Discrete Distributions 7. Special Continuous Distributions 8. Understanding Relationships: Covariance, Correlations, and Conditional Distributions 9. More Bivariate and Multivariate Topics 10. Inequalities and Limit Theorems Appendix Tables

Fundamentals of Probability and Statistics for Machine Learning (1版)

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【簡介】 An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning.Most curricula have students take an undergraduate course on probability and statistics before turning to machine learning. In this innovative textbook, Ethem Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın accessibly connects machine learning to its roots in probability and statistics, starting with the basics of random experiments and probabilities and eventually moving to complex topics such as artificial neural networks. With a practical emphasis and learn-by-doing approach, this unique text offers comprehensive coverage of the elements fundamental to an empirical understanding of machine learning in a data science context. Consolidates foundational knowledge and key techniques needed for modern data scienceCovers mathematical fundamentals of probability and statistics and ML basicsEmphasizes hands-on learningSuits undergraduates as well as self-learners with basic programming experienceIncludes slides, solutions, and code