Accelerated Optimization for Machine Learning:First-Order Algorithms Hardcover – 30 May 2020
by:Zhouchen Lin ,Huan Li ,Cong Fang
pages 页数：300 pages
Publisher Finelybook 出版社：Springer; 1st ed. 2020 edition (30 May 2020)
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.
- Real-World iOS by Tutorials: Professional App Development With Swift
- Core Data by Tutorials (Eighth Edition): Persisting iOS App Data with Core Data in Swift
- Mastering Python: Write powerful and efficient code using the full range of Python’s capabilities, 2nd Edition
- Augmented Reality Art: From an Emerging Technology to a Novel Creative Medium
- Mathematical Modeling and Soft Computing in Epidemiology