Accelerated Optimization for Machine Learning: First-Order Algorithms
Zhouchen Lin, Huan Li, Cong Fang
Informasi Umum
Kode
21.21.3702
Klasifikasi
006.31 - Machine Learning
Jenis
Buku - Elektronik (E-Book)
Subjek
Machine Learning
No. Rak
Dilihat
139 kali
Informasi Lainnya
Abstraksi
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.
Koleksi & Sirkulasi
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Pengarang
| Nama | Zhouchen Lin, Huan Li, Cong Fang |
| Jenis | Perorangan |
| Penyunting | |
| Penerjemah |
Penerbit
| Nama | Springer Singapore |
| Kota | Singapore |
| Tahun | 2020 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 1.000,00 |
| Jenis | Non-Sirkulasi |
