Statistical Mechanics of Neural Networks (1st ed. 2021)

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Synopsis
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.
Copyright:
2021

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9789811675706
Related ISBNs:
9789811675690
Publisher:
Springer Singapore, Singapore
Date of Addition:
Copyrighted By:
Higher Education Press
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Science, Computers and Internet, Technology, Earth Sciences, Mathematics and Statistics
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

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