Description: Accelerated Optimization for Machine Learning Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). First-Order Algorithms Author(s): Zhouchen Lin, Huan Li, Cong Fang Format: Hardback Publisher: Springer Verlag, Singapore, Singapore Imprint: Springer Verlag, Singapore ISBN-13: 9789811529092, 978-9811529092 Synopsis 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.
Price: 104.23 GBP
Location: Aldershot
End Time: 2024-08-04T08:08:32.000Z
Shipping Cost: 31.83 GBP
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Book Title: Accelerated Optimization for Machine Learning
Item Height: 235mm
Item Width: 155mm
Author: Huan Li, Cong Fang, Zhouchen Lin
Publication Name: Accelerated Optimization for Machine Learning: First-Order Algorithms
Format: Hardcover
Language: English
Publisher: Springer Verlag, Singapore
Subject: Computer Science, Mathematics
Publication Year: 2020
Type: Textbook
Item Weight: 640g
Number of Pages: 275 Pages
