English | 314 pages | Springer; 1st ed. 2021 edition (January 5, 2022) | 9811675694 | PDF | 41.75 Mb
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 n-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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