Neural Network Principles
Download Neural Network Principles PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Neural Network Principles book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Neural Network Principles
Using models of biological systems as springboards to a broad range of applications, this volume presents the basic ideas of neural networks in mathematical form. Comprehensive in scope, Neural Network Principles outlines the structure of the human brain, explains the physics of neurons, derives the standard neuron state equations, and presents the consequences of these mathematical models. Author Robert L. Harvey derives a set of simple networks that can filter, recall, switch, amplify, and recognize input signals that are all patterns of neuron activation. The author also discusses properties of general interconnected neuron groups, including the well-known Hopfield and perception neural networks using a unified approach along with suggestions of new design procedures for both. He then applies the theory to synthesize artificial neural networks for specialized tasks. In addition, Neural Network Principles outlines the design of machine vision systems, explores motor control of the human brain and presents two examples of artificial hand-eye systems, demonstrates how to solve large systems of interconnected neurons, and considers control and modulation in the human brain-mind with insights for a new understanding of many mental illnesses.
The Principles of Deep Learning Theory
Author: Daniel A. Roberts
language: en
Publisher: Cambridge University Press
Release Date: 2022-05-26
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Principles of Artificial Neural Networks
This book should serves as a self-study course for engineers and computer scientist in the industry. The features include major neural network approaches and architectures with theories and detailed case studies for each of the approaches acompanied by complete computer codes and the corresponding computed results. There is also a chapter on LAMSTAR neural network.