化學(上冊)
ISBN13:9789576160400
出版社:藝軒
作者:曾國輝
裝訂:平裝
出版日:2001/01/01
中國圖書分類:化學
化學(下冊)
ISBN13:9789576160417
出版社:藝軒
作者:曾國輝
裝訂:平裝
出版日:2005/10/01
中國圖書分類:化學
化學問題指引
ISBN13:9789576160271
出版社:藝軒
作者:曾國輝
裝訂:平裝
出版日:2000/05/01
中國圖書分類:化學
目錄
第一章 單位、測量與數目
第二章 原子、分子與離子
第三章 氣態
第四章 參與反應的原子、分子與離子:化學反應與計量化學
第五章 熱化學
第六章 大氣
第七章 電子結構與週期表
第八章 核化學
第九章 化學鍵
第十章 週期透視
第十一章 分子幾何與鍵結
第十二章 液態及固態
第十三章 水與水界
第十四章 溶液與膠體
第十五章 化學動力論
第十六章 化學平衡
第十七章 酸與鹼
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(特價書3折) Foundations of College Chemistry 14/E 2014 (JW) (14版)
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Foundations of Photonic Crystal Fibers
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The focus of this book lies at the meeting point of electromagnetic waveguides and photonic crystals. Although these are both widely studied topics, they have been kept apart until recently. The purpose of the first edition of this book was to give state-of-the-art theoretical and numerical viewpoints about exotic fibres which use “photonic crystal effects” and consequently exhibit some remarkable properties.Since that first edition, photonic crystal fibres have become an important and effective optical device. In this second edition, the description of the theoretical and numerical tools used to study these fibres is enhanced, whilst up-to-date information about the properties, applications and fabrication of these fibres is added.
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Artificial Intelligence: Foundations of Computational Agents (3版)
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【簡介】
Introduces concepts at the right level of detail, using straightforward language and minimum mathematics
Examines the social and ethical impacts of AI, connecting techniques to real-world benefits and harms
Explains state-of-the-art algorithms and examples underlying the theory, demonstrating how concepts are applied
Provides two complementary software systems – AIPython and AILog - for experimentation and extension
Pedagogical features include examples, end-of-chapter reviews, further reading lists, and exercises
【目錄】
Part I - Agents in the World
Chapter 1 - Artificial Intelligence and Agents
Chapter 2 - Agent Architectures and Hierarchical Control
Part II - Reasoning and Planning with Certainty
Chapter 3 - Searching for Solutions
Chapter 4 - Reasoning with Constraints
Chapter 5 - Propositions and Inference
Chapter 6 - Deterministic Planning
Part III - Learning and Reasoning with Uncertainty
Chapter 7 - Supervised Machine Learning
Chapter 8 - Neural Networks and Deep Learning
Chapter 9 - Reasoning with Uncertainty
Chapter 10 - Learning with Uncertainty
Chapter 11 - Causality
Part IV - Planning and Acting with Uncertainty
Chapter 12 - Planning with Uncertainty
Chapter 13 - Reinforcement Learning
Chapter 14 - Multiagent Systems
Part V - Representing Individuals and Relations
Chapter 15 - Individuals and Relations
Chapter 16 - Knowledge Graphs and Ontologies
Chapter 17 - Relational Learning and Probabilistic Reasoning
Part VI - The Big Picture
Chapter 18 - The Social Impact of Artificial Intelligence
Chapter 19 - Retrospect and Prospect
Appendix A - Mathematical Preliminaries and Notation
Appendix B - Mapping to Open-Source Packages
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Foundations of Computer Vision (Adaptive Computation and Machine Learning series) (1版)
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【簡介】
An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances.
Machine learning has revolutionized computer vision, but the methods of today have deep roots in the history of the field. Providing a much-needed modern treatment, this accessible and up-to-date textbook comprehensively introduces the foundations of computer vision while incorporating the latest deep learning advances. Taking a holistic approach that goes beyond machine learning, it addresses fundamental issues in the task of vision and the relationship of machine vision to human perception. Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process. To emphasize intuitive learning, concepts are presented in short, lucid chapters alongside extensive illustrations, questions, and examples. Written by leaders in the field and honed by a decade of classroom experience, this engaging and highly teachable book offers an essential next-generation view of computer vision.
Up-to-date treatment integrates classic computer vision and deep learning
Accessible approach emphasizes fundamentals and assumes little background knowledge
Student-friendly presentation features extensive examples and images
Proven in the classroom
Instructor resources include slides, solutions, and source code
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