Isbn: 9789811529122 - accelerated optimization for machine learning: first-order algorithms (4 Ergebnisse)

Zustand: New. In.

Taschenbuch. Zustand: Neu. Accelerated Optimization for Machine Learning | First-Order Algorithms | Zhouchen Lin (u. a.) | Taschenbuch | xxiv | Englisch | 2021 | Springer Nature B.V. | EAN 9789811529122 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Zustand: Brand New. 299 pages. 9.25x6.10x0.63 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where thealgorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.…