e-books in Neural Networks category
by B. Mehlig - arXiv.org , 2019
These are lecture notes for my course on Artificial Neural Networks. This course describes the use of neural networks in machine learning: deep learning, recurrent networks, and other supervised and unsupervised machine-learning algorithms.
by Alex Pappachen James (ed.) - InTech , 2018
This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: 1) Devices, 2) Models and 3) Applications. Various memristor models are discussed.
by Christian Dawson - MDPI AG , 2016
This Special Issue focuses on the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.
by Martin T. Hagan, et al. , 2014
This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications.
by Kenji Suzuki (ed.) - InTech , 2013
Artificial neural networks may be the single most successful technology in the last two decades. The purpose of this book is to provide recent advances in architectures, methodologies, and applications of artificial neural networks.
- Wikibooks , 2010
Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks.
by Todd Troyer - University of Texas at San Antonio , 2005
These notes have three main objectives: to present the major concepts of computational neuroscience, to present the basic mathematics that underlies these concepts, and to give the reader some idea of common approaches taken by neuroscientists.
by Ben Krose, Patrick van der Smagt , 1996
This manuscript attempts to provide the reader with an insight in artificial neural networks. The choice of describing robotics and vision as neural network applications coincides with the neural network research interests of the authors.
by Rolf Pfeifer, Dana Damian, Rudolf Fuchslin - University of Zurich , 2010
Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, etc.
by Allessandro Treves, Yasser Roudi - SISSA , 2010
We review the common themes, the network models and the mathematical formalism underlying our studies about different stages in the evolution of the human brain. These studies discuss the evolution of cortical networks in terms of their computations.
by Kenji Suzuki - InTech , 2011
The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The target audience includes professors and students in engineering and medical schools, medical doctors, healthcare professionals, etc.
by Robert Fuller - Abo Akademi University , 1995
This text covers inference mechanisms in fuzzy expert systems, learning rules of feedforward multi-layer supervised neural networks, Kohonen's unsupervised learning algorithm for classification of input patterns, and fuzzy neural hybrid systems.
by William Bialek - arXiv , 2002
We all are fascinated by the phenomena of intelligent behavior, as generated by our own brains. As physicists we want to understand if there are some general principles that govern the dynamics of the neural circuits that underlie these phenomena.
by Xiaolin Hu, P. Balasubramaniam - InTech , 2008
The concept of neural network originated from neuroscience, and one of its aims is to help us understand the principle of the central nerve system through mathematical modeling. The first part of the book is dedicated to this aim.
by Jeff Heaton - Heaton Research , 2011
The book is an introduction to Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques are also introduced.
by Ivan F Wilde - King's College London , 2009
These notes are based on lectures given in the Mathematics Department at King's College London. An attempt has been made to present a logical (mathematical) account of some of the basic ideas of the 'artificial intelligence' aspects of the subject.
by Eugene M. Izhikevich, at al. - Scholarpedia , 2009
Neuroscience, Electrophysiology, Neuron, Network Dynamics, Brain Models, Synapse, Memory, Conditioning, Consciousness, Vision, Olfaction, Neuroimaging, Dynamical Systems, Oscillators, Synchronization, Pattern Formation, Chaos, Bifurcations, etc.
by Ben Goertzel - Plenum Press , 1996
This text applies the concepts of complexity science to provide an explanation of all aspects of human creativity. The book describes the model that integrates ideas from computer science, mathematics, neurobiology, philosophy, and psychology.
by David Kriesel - dkriesel.com , 2011
Text and illustrations should be memorable and easy to understand to offer as many people as possible access to the field of neural networks. The chapters are individually accessible to readers with little previous knowledge.
by Raul Rojas - Springer , 1996
A general theory of artificial neural nets. The book starts with the simple nets, and shows how the models change when more general computing elements and net topologies are introduced. Suitable as a basis for university courses in neurocomputing.
by D. Michie, D. J. Spiegelhalter - Ellis Horwood , 1994
The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.
by Mark Watson - Lulu.com , 2008
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).