Statistical Mechanics of Neural Networks

This book provides a comprehensive introduction to statistical mechanics in neural networks, covering key concepts and techniques such as cavity method, mean-field theory, replica techniques, and more. It focuses on quantitative frameworks and aims to isolate the underlying mechanisms through physics and mathematical beauty. It is a valuable resource for students, researchers, and practitioners in the field of neural networks.

Format: Paperback / softback
Length: 296 pages
Publication date: 06 January 2023
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the intricate realm of fundamental statistical mechanics, providing a thorough introduction to the inner workings of neural networks. It explores in depth essential concepts and techniques such as the cavity method, 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. By walking new learners through these theories and essential skill sets, the book aims to empower them to understand and utilize neural networks effectively.

The book specifically emphasizes quantitative frameworks of neural network models, where the underlying mechanisms can be precisely isolated and understood through the lens of physics and mathematical beauty. It serves as a valuable reference for students, researchers, and practitioners in the field of neural networks, offering a deep understanding of the underlying principles and applications of these powerful tools.

Weight: 486g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811675720
Edition number: 1st ed. 2021