Distributed optimization learning (116 résultats)

Paperback. Etat : New.

Paperback. Etat : New.

Etat : New.

Etat : New.

Paperback or Softback. Etat : New. Optimization Algorithms for Distributed Machine Learning. Book.

Etat : As New. Unread book in perfect condition.

paperback. Etat : Fine.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

Hardcover. Etat : Wie neu. 1. Auflage. New book, remains sealed in plastic. Language - English. Ships from Berlin.

Etat : New. 1st ed. 2023 edition NO-PA16APR2015-KAP.

Paperback. Etat : Brand New. 140 pages. 9.45x6.61x0.33 inches. In Stock.

Etat : New.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : New.

Etat : New.

Etat : New. pp. IX, 171 38 illus. Softcover reprint of the original 1st ed. 2017 edition NO-PA16APR2015-KAP.

Hardcover. Etat : Brand New. 171 pages. 9.25x6.25x0.75 inches. In Stock.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.…

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.…

Paperback. Etat : Brand New. reprint edition. 171 pages. 9.25x6.10x0.42 inches. In Stock.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system's state space.…

Paperback. Etat : New.

Taschenbuch. Etat : Neu. Optimization Algorithms for Distributed Machine Learning | Gauri Joshi | Taschenbuch | Synthesis Lectures on Learning, Networks, and Algorithms | xiii | Englisch | 2023 | Springer | EAN 9783031190698 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Etat : Neu. Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems | Tatiana Tatarenko | Taschenbuch | ix | Englisch | 2018 | Springer | EAN 9783319880396 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.…

Etat : As New. Unread book in perfect condition.

Paperback. Etat : New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.