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  • Sách/Book

  • Introduction to algorithms

    • Authors: Thomas H. Cormen (2022)

  • Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout.
  • 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

  • Hesitant Fuzzy Set: Theory and Extension

    • Authors: Bahram Farhadinia (2021)

  • Presenting the review of many and important types of hesitant fuzzy extensions, and including references to a large number of related publications, this book will serve as a useful reference book for researchers in this field.
  • Sách/Book

  • A Guide to Design and Analysis of Algorithms

    • Authors: Soubhik Chakraborty (2023)

  • As there can be more than one algorithm for the same problem, designing and analyzing an algorithm becomes important in order to make it as efficient and robust as possible. This book will serve as a guide to design and analysis of computer algorithms.
  • Sách/Book

  • Fuzzy Sets, Fuzzy Logic and Their Applications

    • Authors: Michael Gr. Voskoglou (2020)

  • The present book contains 20 articles collected from amongst the 53 total submitted manuscripts for the Special Issue “Fuzzy Sets, Fuzzy Loigic and Their Applications” of the MDPI journal Mathematics. The articles, which appear in the book in the series in which they were accepted, published in Volumes 7 (2019) and 8 (2020) of the journal, cover a wide range of topics connected to the theory and applications of fuzzy systems and their extensions and generalizations.
  • 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.