Models of Neural Networks

This book is set up in a non-traditional way, yet it takes a systematic approach. There are four parts. The first part is historical and deals with the changes that have taken place in recent years in the relationship between mathematics and sociology: an analysis is made of Paul F. Lazarsfeld’s mathematical models, models of simulation and artificial societies, and models of artificial neural networks.

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.

DEMYSTIFYING DEEP LEARNING