QUANTUM PHYSICS (2版)
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書名:Quantum Physics of Atoms, Molecules, Solids, Nuclei, and Particles 2/E
作者:EISBERG
出版社:WILEY
出版日期:1985/02/14
ISBN:9780471873730
原價:
2300
售價:
2254
現省:
46元
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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。
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THERMAL PHYSICS(台灣版) (2版)
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【原文書】
書名:Thermal Physics 2/E
作者:Charles Kittel
ISBN:0716710889
Synopsis
CONGRATULATIONS TO HERBERT KROEMER, 2000 NOBEL LAUREATE FOR PHYSICS
For upper-division courses in thermodynamics or statistical mechanics, Kittel and Kroemer offers a modern approach to thermal physics that is based on the idea that all physical systems can be described in terms of their discrete quantum states, rather than drawing on 19th-century classical mechanics concepts.
"synopsis" may belong to another edition of this title.
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THE PRINCIPLES OF CIRCULAR ACCELERATORS AND STORANE RINGS 1993
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This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.
Detailed step-by-step explanations for all equations and clear exposition of both old and new concepts in deep learning theory make the book accessible to readers with a minimal prerequisite of linear algebra, calculus, and informal probability theory
Many novel results that appear for the first time in the literature, taking readers to the forefront of deep learning theory
Provides a unique approach that bridges deep learning and theoretical physics, demonstrating to the ML community how a theoretical physics approach can be useful, while also teaching techniques that are valuable for theoretical physicists
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