Introduction to Neural Networks Using Matlab 6.0
Author : S. N. Sivanandam
Publisher : Tata McGraw-Hill Education
Page : 0 pages
File Size : 11,25 MB
Release : 2006
Category : MATLAB.
ISBN : 9780070591127
MATLAB Deep Learning
Author : Phil Kim
Publisher : Apress
Page : 162 pages
File Size : 17,81 MB
Release : 2017-06-15
Category : Computers
ISBN : 1484228456
Book Description
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
Applications of Neural Networks in Electromagnetics
Author : Christos Christodoulou
Publisher : Artech House Publishers
Page : 544 pages
File Size : 25,2 MB
Release : 2001
Category : Computers
ISBN :
Book Description
The high-speed capabilities and learning abilities of neural networks can be applied to quickly solving numerous complex optimization problems in electromagnetics, and this book shows you how. Even if you have no background in neural networks, this book helps you understand the basics of each main network architecture in use today, including its strengths and limitations. Moreover, it gives you the knowledge you need to identify situations when the use of neural networks is the best problem-solving option.
Neural Network Design
Author : Martin T. Hagan
Publisher :
Page : pages
File Size : 28,15 MB
Release : 2003
Category : Neural networks (Computer science)
ISBN : 9789812403766
Book Description
MATLAB for Neuroscientists
Author : Pascal Wallisch
Publisher : Academic Press
Page : 571 pages
File Size : 19,92 MB
Release : 2014-01-09
Category : Psychology
ISBN : 0123838371
Book Description
MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. - The first complete volume on MATLAB focusing on neuroscience and psychology applications - Problem-based approach with many examples from neuroscience and cognitive psychology using real data - Illustrated in full color throughout - Careful tutorial approach, by authors who are award-winning educators with strong teaching experience
Tutorial on Neural Systems Modeling
Author : Thomas J. Anastasio
Publisher : Sinauer
Page : 0 pages
File Size : 20,4 MB
Release : 2010-03-01
Category : Medical
ISBN : 9780878933396
Book Description
For students of neuroscience and cognitive science who wish to explore the functioning of the brain further, but lack an extensive background in computer programming or maths, this new book makes neural systems modelling truly accessible. Short, simple MATLAB computer programs give readers all the experience necessary to run their own simulations.
Artificial Neural Networks
Author : Kevin L. Priddy
Publisher : SPIE Press
Page : 184 pages
File Size : 43,73 MB
Release : 2005
Category : Computers
ISBN : 9780819459879
Book Description
This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.
An Introduction to Neural Networks
Author : Kevin Gurney
Publisher : CRC Press
Page : 148 pages
File Size : 13,22 MB
Release : 2018-10-08
Category : Computers
ISBN : 1482286998
Book Description
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
Computer Neural Networks on MATLAB
Author : Daniel Okoh
Publisher : Createspace Independent Publishing Platform
Page : 54 pages
File Size : 32,56 MB
Release : 2016-10-07
Category :
ISBN : 9781539360957
Book Description
Computer neural networks are a branch of artificial intelligence, inspired to behave in a manner similar to the human brain; they are trained and they learn from their training. Computer neural networks have a wide variety of applications, mostly hinged around modelling, forecasting, and general predictions. This book illustrates how to use computer neural networks on MATLAB in very simple and elegant manner. The language of the book is elementary as it is meant for beginners, readers are notassumed to have previous skills on the subject. Projects, in varying degrees, have been used to make sure that readers get a practical and hands-on experience on the subject. The book is meant for you if you want to get a quick start with the practical use of computer neural networks on MATLAB without the boredom associated with a lengthy theoretical write-up.
Efficient Processing of Deep Neural Networks
Author : Vivienne Sze
Publisher : Springer Nature
Page : 254 pages
File Size : 17,47 MB
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 3031017668
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
