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Optimization Techniques for Deep Learning : Improving Performance and Efficiency.
・ISBN 978-3-032-20702-9 hard EUR 149.99
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| 著者・編者 | Hemmati, Atefeh / Rahmani, Amir Masoud / Bazikar, Fatemeh / Moosaei, Hossein / Pardalos, Panos M., |
|---|---|
| シリーズ | Springer Optimization and Its Applications |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2026 |
| ページ数 | 196 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-24115＞ |
解説
This book offers a comprehensive guide to optimization techniques in deep learning, a transformative branch of artificial intelligence that has revolutionized fields from computer vision to healthcare. By bridging the gap between theoretical concepts and practical applications, it equips readers with the tools needed to harness the full potential of deep neural networks.
The chapters cover a wide range of optimization methods, beginning with the fundamentals of neural networks and key concepts of deep learning. Readers will explore critical topics such as gradient descent, stochastic optimization, and advanced algorithms, while also addressing the inherent challenges of optimization. The book delves into practical aspects, offering insights into how to make training deep models more efficient and stable. Emerging trends and future perspectives are also presented, making this work a must-read for anyone looking to stay at the forefront of the field.
This book is an invaluable resource for researchers and practitioners seeking practical solutions for optimizing neural networks. Students will find a clear path to understanding the principles and building theoretical knowledge, while industry professionals will gain insights into the latest techniques and trends. Whether you're a seasoned expert or new to the field, this book is essential for anyone interested in deep learning optimization.