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Results 81-89 of 89 (Search time: 1.273 seconds).
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  • 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

  • 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

  • Fundamentals of Logic and Computation: With Practical Automated Reasoning and Verification

    • Authors: Hou, Zhe (2021)

  • This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started.
  • Sách/Book

  • Machine Learning with Quantum Computers

    • Authors: Maria Schuld (2021)

  • This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data.
  • Sách/Book

  • Grokking Deep Reinforcement Learning

    • Authors: Miguel Morales (2021)

  • This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment.