Share
Accelerated Optimization for Machine Learning: First-Order Algorithms (in English)
Zhouchen Lin
(Author)
·
Huan Li
(Author)
·
Cong Fang
(Author)
·
Springer
· Paperback
Accelerated Optimization for Machine Learning: First-Order Algorithms (in English) - Lin, Zhouchen ; Li, Huan ; Fang, Cong
$ 161.04
$ 169.99
You save: $ 8.95
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Monday, July 01 and
Tuesday, July 02.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Accelerated Optimization for Machine Learning: First-Order Algorithms (in English)"
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.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
✓ Producto agregado correctamente al carro, Ir a Pagar.