Advanced Methods for Optimizing and Accelerating Deep Learning Models

Erscheinungsjahr 2027
ISBN: 978-0-443-48495-7
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

Advanced Optimization and Acceleration Techniques for Deep Learning Models provides a comprehensive guide to enhancing deep learning models' efficiency, scalability, and performance, including large language models (LLMs). As AI systems grow in complexity, optimizing their training and deployment has become critical for achieving higher accuracy, faster inference, and reduced computational costs. This book explores cutting-edge optimization strategies, from gradient descent refinements and hyperparameter tuning to model compression, pruning, and hardware acceleration. AI is evolving rapidly, but existing deep learning resources often focus on building models rather than optimizing them for efficiency and scalability. As deep learning applications expand into cloud computing, edge AI, and real-time decision-making, a dedicated resource on optimization is essential. This book addresses this gap by providing a structured approach to making deep learning networks faster, more cost-effective, and more sustainable.

Autoren/Hrsg.

Siarry, Patrick

Fourati, Rahma

Tmamna, Jihene

Baghdadi, Asma

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

1. Foundations of AI and Deep Learning Systems
2. Optimization in Deep Learning: Motivation and Scope
3. Neural Architecture Search (NAS) and Green AI
4. Pruning Techniques for Model Compression
5. Quantization for Efficient Inference
6. Knowledge Distillation for Compact Models
7. Sparsity and Efficient Architectures
8. Hardware Acceleration for Deep Learning
9. Federated Learning for Privacy-Preserving AI
10. Split Learning for Collaborative Model Training
11. Fog Computing for Edge AI

Über Autor(innen)

Fourati, Rahma
Rahma Fourati (Member, IEEE) received her PhD degree in computer engineering systems from the National Engineering School of Sfax (ENIS), Tunisia, with the Research Group in Intelligent Machines (REGIM), in 2021. She has been serving as an assistant professor at the Faculty of Law, Economics, and Management Sciences of Jendouba, Tunisia, since 2023. Additionally, she served as the chair of the IEEE Computational Intelligence Society for the period 2021-2022. Her research interests include affective computing, physiological signals, healthcare applications, deep neural network compression, and evolutionary computation.

Tmamna, Jihene
Dr. Jihene Tmamna received her Computer Science Engineering degree in 2017 and her Ph.D. in Computer Systems Engineering in 2023 from the National Engineering School of Sfax (ENIS), Tunisia. Her research interests include deep neural networks, neural network compression, and optimization algorithms. She has extensively worked on pruning and quantizing Convolutional Neural Network (CNN) architectures to develop Lightweight models. Additionally, she has applied pruning techniques to optimize multimodal CNN-based models in the context of the Audio-Visual Speech Enhancement (AVSE) challenge. She is currently a Postdoctoral Researcher at the University of Sfax, Tunisia.

Baghdadi, Asma
Asma Baghdadi holds a degree in Software Engineering and a Ph.D. in Information Sciences and Technologies. Since January 2022, she has served as an Assistant Professor at Esprit School of Business. Her research contributions shows her dedication to advancing knowledge at the intersection of technology and healthcare. As a researcher, she is affiliated with REGIM Lab, LISSI, and the AI4U research units.

Siarry, Patrick
Patrick Siarry was born in France in 1952. He received the Ph.D. degree in computer science and optimization from University Paris VI, Paris, France, in 1986, and the Doctorate of Sciences (Habilitation) degree in computer science and optimization from University Paris XI, Orsay, France, in 1994.,He was first involved in the development of analog and digital models of nuclear power plants with Electricité de France, Paris. Since 1995, he has been a Professor of Automatics and Informatics with Université Paris-Est Créteil, Créteil, France. His main research interests include computer-aided design of electronic circuits, cognitive intelligence, and the applications of new stochastic global optimization heuristics to various engineering fields, also including the fitting of process models to experimental data, the learning of fuzzy rule bases, and of neural networks.