Accelerated Optimization for Machine Learning

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: Paperback Publisher: Springer Verlag, Singapore, Singapore Imprint: Springer Verlag, Singapore ISBN-13: 9789811529122, 978-9811529122 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: 103.81 GBP

Location: Aldershot

End Time: 2024-08-07T08:26:22.000Z

Shipping Cost: 31.65 GBP