Name: Accelerated Optimization for Machine Learning: First-Order Algorithms (en Inglés)
Price: 477.29 PEN
Availability: InStock
Author: Lin, Zhouchen ; Li, Huan ; Fang, Cong
ISBN: 9789811529122

portada Accelerated Optimization for Machine Learning: First-Order Algorithms (en Inglés)

Accelerated Optimization for Machine Learning: First-Order Algorithms (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

275

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 1.6 cm

Peso

0.42 kg.

ISBN13

9789811529122

Categorías

Ciencia y análisis de datos: generalidades
Matemáticas aplicadas
Análisis y captura de datos
Inteligencia artificial

Accelerated Optimization for Machine Learning: First-Order Algorithms (en Inglés)

Zhouchen Lin (Autor) · Huan Li (Autor) · Cong Fang (Autor) · Springer · Tapa Blanda

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Reseña del libro "Accelerated Optimization for Machine Learning: First-Order Algorithms (en Inglés)"

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.