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Mei Kobayashi – författare

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4 produkter

  1. Mei Kobayashi - Wavelets and Their Applications, Häftad. Tillgänglighet: Lägg i varukorg

    Wavelets and Their Applications

    Case Studies

    Av Mei Kobayashi

    Häftad, 1998

    823 kr

    Lägg i varukorg

    This collection of independent case studies demonstrates how wavelet techniques have been used to solve open problems and develop insight into the nature of the systems under study. Each case begins with a description of the problem and points to the specific properties of wavelets and techniques …

  2. Mei Kobayashi - Federated Learning, Inbunden. Tillgänglighet: Lägg i varukorg

    Federated Learning

    A Primer for Mathematicians

    Av Mei Kobayashi

    Inbunden, 2025

    1534 kr

    Lägg i varukorg

    This book serves as a primer on a secure computing framework known as federated learning. Federated learning is the study of methods to enable multiple parties to collaboratively train machine learning/AI models, while each party retains its own, raw data on-premise, never sharing it with others. …

  3. Mei Kobayashi - Federated Learning, E-bok. Tillgänglighet: Lägg i varukorg

    Federated Learning

    A Primer for Mathematicians

    Av Mei Kobayashi

    E-bok, 2025

    2006 kr

    Lägg i varukorg

    This book serves as a primer on a secure computing framework known as federated learning. Federated learning is the study of methods to enable multiple parties to collaboratively train machine learning/AI models, while each party retains its own, raw data on-premise, never sharing it with others. …

  4. Mei Kobayashi - Federated Learning, Häftad. Tillgänglighet: Lägg i varukorg

    Federated Learning

    A Primer for Mathematicians

    Av Mei Kobayashi

    Häftad, 2026

    1580 kr

    Lägg i varukorg

    This book serves as a primer on a secure computing framework known as federated learning. Federated learning is the study of methods to enable multiple parties to collaboratively train machine learning/AI models, while each party retains its own, raw data on-premise, never sharing it with others. …