Statistical Field Theory For Neural Networks
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Statistical Field Theory for Neural Networks
Author | : Moritz Helias,David Dahmen |
Publsiher | : Springer Nature |
Total Pages | : 203 |
Release | : 2020-08-20 |
Genre | : Science |
ISBN | : 9783030464448 |
Download Statistical Field Theory for Neural Networks Book in PDF, Epub and Kindle
This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.
Statistical Mechanics of Neural Networks
![Statistical Mechanics of Neural Networks](https://youbookinc.com/wp-content/uploads/2024/06/cover.jpg)
Author | : Haiping Huang |
Publsiher | : Unknown |
Total Pages | : 0 |
Release | : 2021 |
Genre | : Electronic Book |
ISBN | : 9811675716 |
Download Statistical Mechanics of Neural Networks Book in PDF, Epub and Kindle
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.
Neural Network Modeling
Author | : P. S. Neelakanta,Dolores DeGroff |
Publsiher | : CRC Press |
Total Pages | : 259 |
Release | : 2018-02-06 |
Genre | : Technology & Engineering |
ISBN | : 9781351428965 |
Download Neural Network Modeling Book in PDF, Epub and Kindle
Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.
Statistical Mechanics of Neural Networks
Author | : Haiping Huang |
Publsiher | : Springer Nature |
Total Pages | : 302 |
Release | : 2022-01-04 |
Genre | : Science |
ISBN | : 9789811675706 |
Download Statistical Mechanics of Neural Networks Book in PDF, Epub and Kindle
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.
The Principles of Deep Learning Theory
Author | : Daniel A. Roberts,Sho Yaida,Boris Hanin |
Publsiher | : Cambridge University Press |
Total Pages | : 473 |
Release | : 2022-05-26 |
Genre | : Computers |
ISBN | : 9781316519332 |
Download The Principles of Deep Learning Theory Book in PDF, Epub and Kindle
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Statistical Field Theory
Author | : Giorgio Parisi |
Publsiher | : Westview Press |
Total Pages | : 366 |
Release | : 1998-11-26 |
Genre | : Science |
ISBN | : 0738200514 |
Download Statistical Field Theory Book in PDF, Epub and Kindle
Specifically written to introduce researchers and advanced students to the modern developments in statistical mechanics and field theory, this book's leitmotiv is functional integration and its application to different areas of physics. The book acts as both an introduction to and a lucid overview of the major problems in statistical field theory.
Markov Chain Monte Carlo Methods in Quantum Field Theories
Author | : Anosh Joseph |
Publsiher | : Springer Nature |
Total Pages | : 134 |
Release | : 2020-04-16 |
Genre | : Science |
ISBN | : 9783030460440 |
Download Markov Chain Monte Carlo Methods in Quantum Field Theories Book in PDF, Epub and Kindle
This primer is a comprehensive collection of analytical and numerical techniques that can be used to extract the non-perturbative physics of quantum field theories. The intriguing connection between Euclidean Quantum Field Theories (QFTs) and statistical mechanics can be used to apply Markov Chain Monte Carlo (MCMC) methods to investigate strongly coupled QFTs. The overwhelming amount of reliable results coming from the field of lattice quantum chromodynamics stands out as an excellent example of MCMC methods in QFTs in action. MCMC methods have revealed the non-perturbative phase structures, symmetry breaking, and bound states of particles in QFTs. The applications also resulted in new outcomes due to cross-fertilization with research areas such as AdS/CFT correspondence in string theory and condensed matter physics. The book is aimed at advanced undergraduate students and graduate students in physics and applied mathematics, and researchers in MCMC simulations and QFTs. At the end of this book the reader will be able to apply the techniques learned to produce more independent and novel research in the field.
Statistics and Neural Networks
Author | : Jim W. Kay,D. M. Titterington |
Publsiher | : Oxford University Press, USA |
Total Pages | : 290 |
Release | : 1999 |
Genre | : Computers |
ISBN | : 0198524226 |
Download Statistics and Neural Networks Book in PDF, Epub and Kindle
Providing a broad overview of important current developments in the area of neural networks, this book highlights likely future trends.