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portada Accelerated Optimization for Machine Learning: First-Order Algorithms
Type
Physical Book
Author
Publisher
Language
English
Pages
275
Format
Paperback
Dimensions
23.4 x 15.6 x 1.6 cm
Weight
0.42 kg.
ISBN13
9789811529122
Categories

Accelerated Optimization for Machine Learning: First-Order Algorithms

Zhouchen Lin (Author) · Huan Li (Author) · Cong Fang (Author) · Springer · Paperback

Accelerated Optimization for Machine Learning: First-Order Algorithms - Lin, Zhouchen ; Li, Huan ; Fang, Cong

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Synopsis "Accelerated Optimization for Machine Learning: First-Order Algorithms"

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 the algorithms 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.

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