Title Statistical Mechanics of Neural Networks Author(s) Huang, Haiping Publication Singapore, Springer Nature Singapore, 2021. Description XVIII, 296 p. 62 illus., 40 illus. in color : online resource Abstract Note 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 ISBN,Price 9789811675706 Keyword(s) 1. ARTIFICIAL INTELLIGENCE 2. Computational Intelligence 3. EBOOK 4. EBOOK - SPRINGER 5. Mathematical Models of Cognitive Processes and Neural Networks 6. Neural networks (Computer science)?? 7. STATISTICAL MECHANICS 8. STATISTICAL PHYSICS Item Type eBook Multi-Media Links Please Click here for eBook Circulation Data Accession#   Call# Status Issued To Return Due On Physical Location I11949     On Shelf