Overview
Presents major theoretical tools for the analysis of neural networks
Provides concrete examples for the use of the theories in neural networks
Bridges old tools and frontiers in the theoretical development of neural networks
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About this book
This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.
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Keywords
Table of contents (18 chapters)
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Front Matter
Authors and Affiliations
About the author
Haiping Huang
Dr. Haiping Huang received his Ph.D. degree in theoretical physics from the Institute of Theoretical Physics, the Chinese Academy of Sciences. He works as an associate professor at the School of Physics, Sun Yat-sen University, China. His research interests include the origin of the computational hardness of the binary perceptron model, the theory of dimension reduction in deep neural networks, and inherent symmetry breaking in unsupervised learning. In 2021, he was awarded Excellent Young Scientists Fund by National Natural Science Foundation of China.
Bibliographic Information
Book Title: Statistical Mechanics of Neural Networks
Authors: Haiping Huang
DOI: https://doi.org/10.1007/978-981-16-7570-6
Publisher: Springer Singapore
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Higher Education Press 2021
Hardcover ISBN: 978-981-16-7569-0Published: 05 January 2022
Softcover ISBN: 978-981-16-7572-0Published: 06 January 2023
eBook ISBN: 978-981-16-7570-6Published: 04 January 2022
Edition Number: 1
Number of Pages: XVIII, 296
Number of Illustrations: 22 b/w illustrations, 40 illustrations in colour
Topics: Theoretical, Mathematical and Computational Physics, Mathematical Models of Cognitive Processes and Neural Networks, Theoretical and Computational Chemistry, Computational Intelligence, Artificial Intelligence