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  • Sách/Book

  • Mathematical methods for physicists.

    • Authors: George B. Arfken (2001)

  • Arfken and Weber's best-selling Mathematical Methods for Physicists has provided upper-level undergraduate and graduate students with the paramount coverage of the mathematics necessary for advanced study in physics and engineering. It provides the essential mathematical methods that aspiring physicists are likely to encounter as students or beginning researchers.
  • Sách/Book

  • Mathematics of fuzzy sets and fuzzy logic

    • Authors: Barnabas Bede (2013)

  • This book presents a mathematically-based introduction into the fascinating topic of Fuzzy Sets and Fuzzy Logic and might be used as textbook at both undergraduate and graduate levels and also as reference guide for mathematician, scientists or engineers who would like to get an insight into Fuzzy Logic. Fuzzy Sets have been introduced by Lotfi Zadeh in 1965 and since then, they have been used in many applications. As a consequence, there is a vast literature on the practical applications of fuzzy sets, while theory has a more modest coverage
  • Sách/Book

  • Algorithmics : the spirit of computing

    • Authors: David Harel (2004)

  • Algorithmics' presents the most important, concepts, methods and results that are fundamental to the science of computing. It starts by introducing the basic ideas of algorithms, including their structures and methods of data manipulation. It then goes on to demonstrate how to design accurate and efficient algorithms, and discusses their inherent limitations. As the author himself says in the preface to the book; 'This book attempts to present a readable account of some of the most important and basic topics of computer science, stressing the fundamental and robust nature of the science in a form that is virtually independent of the details of specific computers, languages and formalisms
  • Sách/Book

  • Equations of Mathematical Physics

    • Authors: A. V. Bitsadze (1980)

  • The present book consists of an introduction and six chapters. The introduction discusses basic notions and definitions of the traditional course of mathematical physics and also mathematical models of some phenomena in physics and engineering
  • Sách/Book

  • Mathematical aspects of deep learning

    • Authors: Philipp Grohs (2023)

  • This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research
  • Sách/Book

  • Mathematics of deep learning : an introduction

    • Authors: Leonid Berlyand; Pierre-Emmanuel Jabin. (2023)

  • The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas.
  • Sách/Book

  • Introduction to Mathematical Statistics and Its Applications

    • Authors: Richard J. Larsen (2013)

  • The authors demonstrate how and when to use statistical methods, while reinforcing the calculus that students have mastered in previous courses. Throughout theFifth Edition, the authors have added and updated examples and case studies, while also refining existing features that show a clear path from theory to practice.
  • Sách/Book

  • Introduction to Mathematical Statistics

    • Authors: Robert Hogg (2020)

  • Substantial changes to the 8th Edition – many based on user feedback – help students appreciate the connection between statistical theory and statistical practice, while other changes enhance the development and discussion of the statistical theory presented.
  • Sách/Book

  • Combinatorial optimization : theory and algorithms

    • Authors: Bernhard Korte (2018)

  • This sixth edition has again been updated, revised, and significantly extended. Among other additions, there are new sections on shallow-light trees, submodular function maximization, smoothed analysis of the knapsack problem, the (ln 4+ɛ)-approximation for Steiner trees, and the VPN theorem. Thus, this book continues to represent the state of the art of combinatorial optimization.