Optimization Techniques for Deep Learning: Improving Performance and Efficiency (Springer Optimization and Its Applications, 238)
by Panos M. Pardalos, Atefeh Hemmati, Amir Masoud Rahmani, Fatemeh Bazikar, Hossein Moosaei
Optimization Techniques for Deep Learning focuses on methods for improving the performance and efficiency of deep learning systems. Published as volume 238 in Springer Optimization and Its Applications, this academic work connects deep learning with optimization, offering a focused resource for readers exploring computational methods and algorithmic improvement.
About This Book
Optimization Techniques for Deep Learning examines methods for improving the performance and efficiency of deep learning systems.
The book focuses on the relationship between optimization and deep learning, making the topic relevant to readers studying computational models and algorithmic improvement.
As volume 238 in Springer Optimization and Its Applications, it presents the subject within a broader academic series devoted to optimization research and applications.
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