Introduction To Neural Networks

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An Introduction to Neural Networks

Kevin Gurney
An Introduction to Neural Networks Book PDF

Author : Kevin Gurney
Publisher : CRC Press
Page : 234 pages
File Size : 41,7 Mb
Release : 2018-10-08
Category : Computers
ISBN : 9781482286991

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An Introduction to Neural Networks by Kevin Gurney Pdf

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.

An Introduction to Neural Networks

James A. Anderson
An Introduction to Neural Networks Book PDF

Author : James A. Anderson
Publisher : MIT Press
Page : 680 pages
File Size : 55,5 Mb
Release : 1995
Category : Computers
ISBN : 0262510812

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An Introduction to Neural Networks by James A. Anderson Pdf

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas. Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject. The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.

Neural Networks

Berndt Müller,Joachim Reinhardt,Michael T. Strickland
Neural Networks Book PDF

Author : Berndt Müller,Joachim Reinhardt,Michael T. Strickland
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 43,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642577604

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Neural Networks by Berndt Müller,Joachim Reinhardt,Michael T. Strickland Pdf

Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.

Introduction to Neural Network Verification

Aws Albarghouthi
Introduction to Neural Network Verification Book PDF

Author : Aws Albarghouthi
Publisher : Unknown
Page : 182 pages
File Size : 52,8 Mb
Release : 2021-12-02
Category : Electronic
ISBN : 1680839101

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Introduction to Neural Network Verification by Aws Albarghouthi Pdf

Over the past decade, a number of hardware and software advances have conspired to thrust deep learning and neural networks to the forefront of computing. Deep learning has created a qualitative shift in our conception of what software is and what it can do: Every day we're seeing new applications of deep learning, from healthcare to art, and it feels like we're only scratching the surface of a universe of new possibilities. This book offers the first introduction of foundational ideas from automated verification as applied to deep neural networks and deep learning. It is divided into three parts: Part 1 defines neural networks as data-flow graphs of operators over real-valued inputs. Part 2 discusses constraint-based techniques for verification. Part 3 discusses abstraction-based techniques for verification. The book is a self-contained treatment of a topic that sits at the intersection of machine learning and formal verification. It can serve as an introduction to the field for first-year graduate students or senior undergraduates, even if they have not been exposed to deep learning or verification.

Introduction to Neural Networks with Java

Jeff Heaton
Introduction to Neural Networks with Java Book PDF

Author : Jeff Heaton
Publisher : Heaton Research Incorporated
Page : 380 pages
File Size : 54,5 Mb
Release : 2005
Category : Computers
ISBN : 9780977320608

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Introduction to Neural Networks with Java by Jeff Heaton Pdf

In addition to showing the programmer how to construct Neural Networks, the book discusses the Java Object Oriented Neural Engine (JOONE), a free open source Java neural engine. (Computers)

Neural Networks

Raul Rojas
Neural Networks Book PDF

Author : Raul Rojas
Publisher : Springer Science & Business Media
Page : 511 pages
File Size : 42,6 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9783642610684

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Neural Networks by Raul Rojas Pdf

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

Neural Networks

Phil Picton
Neural Networks Book PDF

Author : Phil Picton
Publisher : Palgrave Macmillan
Page : 209 pages
File Size : 46,8 Mb
Release : 2001-01-06
Category : Science
ISBN : 0333948998

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Neural Networks by Phil Picton Pdf

This updated and revised second edition assumes no prior knowledge and sets out to describe what neural nets are, what they do, and how they do it. The main networks covered include ADALINE, WISARD, the Hopfield Network, Bidirectional Associative Memory, the Boltzmann machine, counter-propogation, ART networks, and Kohonen's self-organizing maps. These networks are discussed by means of examples, giving the reader a good overall knowledge of current developments in the field.

Introduction to Artificial Neural Networks

Sivanandam S., Paulraj M
Introduction to Artificial Neural Networks Book PDF

Author : Sivanandam S., Paulraj M
Publisher : Vikas Publishing House
Page : 236 pages
File Size : 40,8 Mb
Release : 2009-11-01
Category : Computers
ISBN : 9788125914259

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Introduction to Artificial Neural Networks by Sivanandam S., Paulraj M Pdf

This fundamental book on Artificial Neural Networks has its emphasis on clear concepts, ease of understanding and simple examples. Written for undergraduate students, the book presents a large variety of standard neural networks with architecture, algorithms and applications.

Deep Learning

Ian Goodfellow,Yoshua Bengio,Aaron Courville
Deep Learning Book PDF

Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Page : 801 pages
File Size : 45,5 Mb
Release : 2016-11-10
Category : Computers
ISBN : 9780262337373

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Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Introduction to Deep Learning and Neural Networks with PythonTM

Ahmed Fawzy Gad,Fatima Ezzahra Jarmouni
Introduction to Deep Learning and Neural Networks with PythonTM Book PDF

Author : Ahmed Fawzy Gad,Fatima Ezzahra Jarmouni
Publisher : Academic Press
Page : 302 pages
File Size : 44,5 Mb
Release : 2020-11-25
Category : Medical
ISBN : 9780323909341

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Introduction to Deep Learning and Neural Networks with PythonTM by Ahmed Fawzy Gad,Fatima Ezzahra Jarmouni Pdf

Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonTM functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonTM Features math and code examples (via companion website) with helpful instructions for easy implementation

An Introduction to Neural Network Methods for Differential Equations

Neha Yadav,Anupam Yadav,Manoj Kumar
An Introduction to Neural Network Methods for Differential Equations Book PDF

Author : Neha Yadav,Anupam Yadav,Manoj Kumar
Publisher : Springer
Page : 114 pages
File Size : 42,7 Mb
Release : 2015-02-26
Category : Mathematics
ISBN : 9789401798167

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An Introduction to Neural Network Methods for Differential Equations by Neha Yadav,Anupam Yadav,Manoj Kumar Pdf

This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed interest of the 1980s. A general introduction to neural networks and learning technologies is presented in Section III. This section also includes the description of the multilayer perceptron and its learning methods. In Section IV, the different neural network methods for solving differential equations are introduced, including discussion of the most recent developments in the field. Advanced students and researchers in mathematics, computer science and various disciplines in science and engineering will find this book a valuable reference source.

Introduction to Neural Networks with Java

Jeff Heaton
Introduction to Neural Networks with Java Book PDF

Author : Jeff Heaton
Publisher : Heaton Research, Inc.
Page : 440 pages
File Size : 41,7 Mb
Release : 2008
Category : Computers
ISBN : 9781604390087

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Introduction to Neural Networks with Java by Jeff Heaton Pdf

Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward, Hopfield, and Self Organizing Map networks are discussed. Training techniques such as Backpropagation, Genetic Algorithms and Simulated Annealing are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, financial prediction, game strategy, learning mathematical functions and special application to Internet bots. All Java source code can be downloaded online.

Artificial Neural Networks

Kevin L. Priddy,Paul E. Keller
Artificial Neural Networks Book PDF

Author : Kevin L. Priddy,Paul E. Keller
Publisher : SPIE Press
Page : 184 pages
File Size : 41,8 Mb
Release : 2005
Category : Computers
ISBN : 0819459879

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Artificial Neural Networks by Kevin L. Priddy,Paul E. Keller Pdf

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.

Artificial Neural Networks

P.J. Braspenning,F. Thuijsman,A.J.M.M. Weijters
Artificial Neural Networks Book PDF

Author : P.J. Braspenning,F. Thuijsman,A.J.M.M. Weijters
Publisher : Springer Science & Business Media
Page : 320 pages
File Size : 55,9 Mb
Release : 1995-06-02
Category : Computers
ISBN : 3540594884

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Artificial Neural Networks by P.J. Braspenning,F. Thuijsman,A.J.M.M. Weijters Pdf

This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM.

Strengthening Deep Neural Networks

Katy Warr
Strengthening Deep Neural Networks Book PDF

Author : Katy Warr
Publisher : "O'Reilly Media, Inc."
Page : 246 pages
File Size : 50,7 Mb
Release : 2019-07-03
Category : Computers
ISBN : 9781492044901

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Strengthening Deep Neural Networks by Katy Warr Pdf

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come