PRINCIPLES OF QUANTUM MECHANICS (2版)

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【原文書】 書名：Principles of Quantum Mechanics 2/e 作者:R. Shankar 出版社：Kluwer Academic Plenum Publishers ISBN：9780306447907 About this Textbook Reviews from the First Edition: "An excellent text … The postulates of quantum mechanics and the mathematical underpinnings are discussed in a clear, succinct manner." (American Scientist) "No matter how gently one introduces students to the concept of Dirac’s bras and kets, many are turned off. Shankar attacks the problem head-on in the first chapter, and in a very informal style suggests that there is nothing to be frightened of." (Physics Bulletin) Reviews of the Second Edition: "This massive text of 700 and odd pages has indeed an excellent get-up, is very verbal and expressive, and has extensively worked out calculational details---all just right for a first course. The style is conversational, more like a corridor talk or lecture notes, though arranged as a text. … It would be particularly useful to beginning students and those in allied areas like quantum chemistry." (Mathematical Reviews) R. Shankar has introduced major additions and updated key presentations in this second edition of Principles of Quantum Mechanics. New features of this innovative text include an entirely rewritten mathematical introduction, a discussion of Time-reversal invariance, and extensive coverage of a variety of path integrals and their applications. Additional highlights include: - Clear, accessible treatment of underlying mathematics - A review of Newtonian, Lagrangian, and Hamiltonian mechanics - Student understanding of quantum theory is enhanced by separate treatment of mathematical theorems and physical postulates - Unsurpassed coverage of path integrals and their relevance in contemporary physics The requisite text for advanced undergraduate- and graduate-level students, Principles of Quantum Mechanics, Second Edition is fully referenced and is supported by many exercises and solutions. The book’s self-contained chapters also make it suitable for independent study as well as for courses in applied disciplines.

近代物理導讀與精解 (10版)

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書名：研究所、大學：近代物理導讀與精解 作者：赫龍 出版社：超級科技 出版日期：2014/07/01 ISBN：9785550261682 內容簡介 研究所考試、大學用書,代物理的蓬勃發展，包括愛因斯坦的相對論等，許多理論相繼出籠，全球科技也隨之突飛猛進，尤其是電子科技、光電科技和奈米科技等領域。若想進一步鑽研這些領域，對近代物理的了解更是不可或缺。

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

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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

Fundamentals of Thermodynamics (11版)

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目錄 1 Introduction and Preliminaries 2 Properties of a Pure Substance 3 Energy Equation and First Law of Thermodynamics 4 Energy Analysis for a Control Volume 5 The Second Law of Thermodynamics 6 Entropy 7 Entropy Analysis for a Control Volume 8 Exergy 9 Power and Refrigeration Systems—With Phase Change 10 Power and Refrigeration Systems—Gaseous Working Fluids Appendix A SI Units: Single-State Properties Appendix B SI Units: Thermodynamic Tables Appendix C Ideal Gas Specific Heat Appendix D Equations of State Appendix E Figures Appendix F English Unit Tables