Can I read Accelerated Optimization for Machine Learning : First-Order Algorithms on EtoBox?

Accelerated Optimization for Machine Learning : First-Order Algorithms by Zhouchen Lin & Huan Li & Cong Fang [Lin, Zhouchen & Li, Huan & Fang, Cong] is a nonfiction available to read on EtoBox.

What is Accelerated Optimization for Machine Learning : First-Order Algorithms about?

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 as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

Who reads Accelerated Optimization for Machine Learning : First-Order Algorithms?

Author

Zhouchen Lin & Huan Li & Cong Fang [Lin, Zhouchen & Li, Huan & Fang, Cong]

Publisher

SPRINGER Verlag, SINGAPOR

Published

2020

Language

EN

ISBN

9789811529092

Category

nonfiction

Subjects

Mathematics, Science, Computer Science