Principles and Technologies of Large Models

Erscheinungsjahr 2027
ISBN: 978-981-9248-67-4
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Large models, as a significant direction in artificial intelligence technology, are gradually becoming one of the key trends in future technological development. In light of this, this book focuses on introducing the foundational knowledge, principles, and technologies related to large models. The book is divided into 14 chapters, covering topics such as the basics of deep learning, natural language processing, the architecture of large models, training and optimization of large models, fine-tuning, and related application case studies.

The book emphasizes the scientific and systematic nature of the content, providing a comprehensive and progressive explanation of large model technology from its historical development, theoretical foundations, construction methods, to application scenarios. It concentrates on the extended applications of large models in various fields, offering a comprehensive learning path for application case studies, aiming to cultivate and enhance students' practical and creative abilities. Each chapter includes exercises that allow students to practice and reinforce their knowledge.

This book also offers a rich set of supplementary materials, including lecture slides, videos, and solutions to exercises, making it an ideal textbook for undergraduate programs and research institutes in computer science, artificial intelligence, mechanical engineering, automation, and related disciplines.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Gong, Lina

Wei, Mingqiang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung

Inhaltsverzeichnis

.- Chapter 1 Introduction.

.- Chapter 2 Fundamentals of Deep Learning.

.- Chapter 3 Natural Language Processing.

.- Chapter 4 Large Model Network Structure.

.- Chapter 5 Large Model Training and Optimization.

.- Chapter 6 Fine-Tuning for Large Models.

.- Chapter 7 Large Model Prompt Engineering.

.- Chapter 8 Efficient Large Model Strategy.

.- Chapter 9 Single-modal General Large Model.

.- Chapter 10 Multi-modal General Large Model.

.- Chapter 11 Large Model Evaluation.

.- Chapter 12 Main Application Scenarios of Large Models.

.- Chapter 13 Intelligent Software Development Based on Large Models.

.- Chapter 14 Aerospace Equipment Manufacturing Based on Large Models.

Über Autor(innen)

Mingqiang Wei is an Associate Director of the Key Laboratory of Brain-Computer Intelligence Technology and professor at Nanjing University of Aeronautics and Astronautics. He is a recipient of the National Natural Science Foundation of China’s Excellent Young Scientist Fund, as well as the Jiangsu Province Computer Society Youth Science and Technology Award and the Aerospace Society of China Youth Scientist Award. Professor Wei was also honored with the First Prize for Best Paper at the 14th China Computer Graphics Conference. He is an editorial board member or guest editor for several high-impact journals, including ACM Transactions on Multimedia Computing, IEEE Transactions on Multimedia, and the Journal of Computer-Aided Design and Computer Graphics.
In recent years, Professor Wei has led numerous national and provincial-level projects focusing on large aircraft scanning and measurement, geometric deep learning, and 3D vision. He has published over 150 academic papers in prestigious journals and conferences such as IEEE TPAMI, TVGG, TIP, SIGGRAPH, IJCV, CVPR, and ICCV. His research has significantly contributed to the advancement of key national engineering projects, particularly in the fields of aerospace and computer vision.

Songcan Chen is a professor at Nanjing University of Aeronautics and Astronautics, an expert with special allowances from the State Council, and a Fellow of both the International Association for Pattern Recognition (IAPR) and the Chinese Association for Artificial Intelligence (CAAI). He currently holds prominent positions as the Chair of the Machine Learning Professional Committee of the Chinese Association for Artificial Intelligence and the President of the Second Council of the Jiangsu Provincial Artificial Intelligence Society. Professor Chen has made significant contributions to the field of artificial intelligence, having received the Ministry of Education's Natural Science Award First Prize in 2011 and the National Natural Science Award Second Prize in 2013. He has led 13 projects funded by the National Natural Science Foundation of China, including one key project. His research has been widely recognized, with over 19,650 citations on Google Scholar and an H-index of 66. From 2014 to 2023, he was consistently ranked among China's highly cited scholars for ten consecutive years. 

Lina Gong is an associate professor at Nanjing University of Aeronautics and Astronautics. She has made significant contributions to the fields of deep learning, transfer learning, intelligent software engineering, model reuse, and the security of large models. Over the years, she has led more than 12 research projects, including those supported by the National Key R&D Program, the Military Science and Technology Commission Innovation Project, and the National Natural Science Foundation of China. Dr. Gong has published numerous papers as the first author and corresponding author in prestigious software engineering journals and conferences, such as IEEE Transactions on Software Engineering (TSE) and ACM Transactions on Software Engineering and Methodology (TOSEM). Her research achievements have earned her the Third Prize for Scientific and Technological Progress from China Shipbuilding Group.