Optimization Techniques for Deep Learning: Improving Performance and Efficiency - Couverture rigide

Hemmati, Atefeh; Rahmani, Amir Masoud; Bazikar, Fatemeh; Moosaei, Hossein; Pardalos, Panos M.

Couverture rigide

ISBN 10 : 3032207029 ISBN 13 : 9783032207029

Editeur : Springer Nature Switzerland AG, 2026

Synopsis

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.

A propos du livre

À propos de l'auteur

Atefeh Hemmati received her B.S. degree in Computer Engineering, Information Technology from Central Tehran Branch, IAU, Tehran, Iran in 2020 and received her M.S. degree in Computer Engineering, Software from the Science and Research Branch, IAU, Tehran, Iran in 2023. Her research interests include Internet of Things, LLMs, fog/cloud/edge computing, and artificial intelligence, especially in the fields of machine learning, deep learning. She has authored several publications in these fields

and actively contributes to advanced AI applications in IoT ecosystems.

Amir Masoud Rahmani received his B.S. in computer engineering from Amir Kabir University, Tehran, in 1996, his M.S. in computer engineering from Sharif University of Technology, Tehran, in 1998, and his Ph.D. in computer engineering from IAU University, Tehran, in 2005. Currently, he is a professor of computer engineering. His research interests include Machine Learning, the Internet of Things, cloud/fog computing, and artificial intelligence.

Fatemeh Bazikar received her B.S. degree in Applied Mathematics, Shahid Chamran University, Ahvaz, Iran, in 2011, her M.S. in Applied Mathematics (Optimization), Shahid Chamran University, Ahvaz, Iran, in 2013, her Ph.D. in Applied Mathematics, Guilan University, Rasht, Iran, in 2021, and her Postdoc Researcher in Department of Computer Science, Faculty of Mathematical Sciences, Alzahra University, Tehran, Iran, in 2024. Her research interests include Machine Learning, Optimization, Data Analysis, Mathematical Programming, and artificial intelligence, especially in the fields of machine learning.

Hossein Moosaei

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 3032207029
• ISBN 13: 9783032207029
• Reliure: Relié
• Nombre de pages: 208
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible