Advanced Methods for Optimizing and Accelerating Deep Learning Models
- 1st Edition - February 1, 2027
- Latest edition
- Editors: Patrick Siarry, Rahma Fourati, Jihene Tmamna, Asma Baghdadi
- Language: English
Advanced Optimization and Acceleration Techniques for Deep Learning Models provides a comprehensive guide to enhancing deep learning models' efficiency, scalability, and perfor… Read more
Description
Description
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.
Key features
Key features
- Explains the complexity problem in deep learning and explores optimization techniques tailored to different use cases
- Training Efficiency: How to accelerate training without compromising accuracy using gradient descent optimizations, adaptive learning rates, and parallel processing
- Model Deployment & Scalability: How to efficiently deploy deep learning models in cloud environments, edge devices, and mobile platforms using pruning, quantization, and distillation
- Memory & Computational Constraints: How to reduce model size and inference latency for real-time applications using low-rank factorization, weight sharing, and model compression
- Sustainability & Green AI: How to design energy-efficient AI systems that balance performance and resource consumption, making AI more accessible and cost-effective
Readership
Readership
Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the fields of data science, machine learning, and optimization. The primary audience also includes data analysts and software engineers
Table of contents
Table of contents
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
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 1, 2027
- Language: English
About the editors
About the editors
PS
Patrick Siarry
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.
Affiliations and expertise
University Paris XI, Orsay, FranceRF
Rahma Fourati
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.
Affiliations and expertise
University of Sfax and Université de Jendouba, TunisiaJT
Jihene Tmamna
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.
Affiliations and expertise
University of Sfax, Sfax, TunisiaAB
Asma Baghdadi
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.
Affiliations and expertise
Esprit School of Business, Tunisia