| 书目名称 | Statistical Mechanics of Neural Networks | | 编辑 | Haiping Huang | | 视频video | | | 概述 | 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 develo | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2021 | | 关键词 | Unsupervised Learning; Mean-field Theory; Cavity Method; Replica Method; Hopfield Model; Restricted Boltz | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-16-7570-6 | | isbn_softcover | 978-981-16-7572-0 | | isbn_ebook | 978-981-16-7570-6 | | copyright | Higher Education Press 2021 |
The information of publication is updating
|
|
发表于 2025-3-21 19:18:47
