Farid
Optimization Techniques for Deep Learning
by Atefeh Hemmati, Amir Masoud Rahmani, Fatemeh Bazikar, Hossein Moosaei, Panos M. Pardalos
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ISBN: • Publisher: Springer Nature Switzerland • Year: 2026
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🌐 [color=#55acee]Language: English
📄 [color=#44bb44]Pages: 201
📋 [color=#ff9900]INFO: English | July 5, 2026 | ISBN-10: 3032207029 | 207 pages| Epub PDF (True) | 45 MB
📝 [color=#888888]DESCRIPTION: 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.
📦 [color=#ff9900]Download Info
Folder: Optimization Techniques For Deep Learning Improving Performance And Efficiency
Format: EPUB
Total Size: 44.65 MB
📋 File List:
[size=2]
📌 978-3-032-20703-6.epub (Atefeh Hemmati, Amir Masoud Rahmani, Fatemeh Bazikar, Hossein Moosaei, Panos M. Pardalos) (2026) (26.37 MB)
📌 978-3-032-20703-6.pdf (Atefeh Hemmati, Amir Masoud Rahmani, Fatemeh Bazikar, Hossein Moosaei, Panos M. Pardalos) (18.29 MB)
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