Artificial Neural Network Training and Software Implementation Techniques PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Artificial Neural Network Training and Software Implementation Techniques PDF full book. Access full book title Artificial Neural Network Training and Software Implementation Techniques by Ali Kattan. Download full books in PDF and EPUB format.
Author: Ali Kattan
Publisher: Nova Novinka
ISBN: 9781611229905
Category : Computers
Languages : en
Pages : 58
Get Book Here
Book Description
Artificial neural networks (ANN) are widely used in diverse fields of science and industry. Though there have been numerous techniques used for their implementations, the choice of a specific implementation is subjected to different factors including cost, accuracy, processing speed and overall performance. Featured with synaptic plasticity, the process of training is concerned with adjusting the individual weights between each of the individual ANN neurons until we can achieve close to the desired output. This book introduces the common trajectory-driven and evolutionary-based ANN training algorithms.
Author: Ali Kattan
Publisher: Nova Novinka
ISBN: 9781611229905
Category : Computers
Languages : en
Pages : 58
Get Book Here
Book Description
Artificial neural networks (ANN) are widely used in diverse fields of science and industry. Though there have been numerous techniques used for their implementations, the choice of a specific implementation is subjected to different factors including cost, accuracy, processing speed and overall performance. Featured with synaptic plasticity, the process of training is concerned with adjusting the individual weights between each of the individual ANN neurons until we can achieve close to the desired output. This book introduces the common trajectory-driven and evolutionary-based ANN training algorithms.
Author: Cornelius T. Leondes
Publisher: Academic Press
ISBN: 0080551823
Category : Computers
Languages : en
Pages : 421
Get Book Here
Book Description
This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference. - Recurrent methods - Boltzmann machines - Constructive learning with methods for the reduction of complexity in neural network systems - Modular systems - Associative memory - Neural network design based on the concept of the Inductive Logic Unit - Data classification - Integrated neuron model systems that function as programmable rational approximators
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
Get Book Here
Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Prateek Joshi
Publisher: Packt Publishing Ltd
ISBN: 1786469677
Category : Computers
Languages : en
Pages : 437
Get Book Here
Book Description
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Author: Brian J. Taylor
Publisher: Springer Science & Business Media
ISBN: 0387294856
Category : Computers
Languages : en
Pages : 280
Get Book Here
Book Description
Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668437031
Category : Computers
Languages : en
Pages : 2164
Get Book Here
Book Description
Software development continues to be an ever-evolving field as organizations require new and innovative programs that can be implemented to make processes more efficient, productive, and cost-effective. Agile practices particularly have shown great benefits for improving the effectiveness of software development and its maintenance due to their ability to adapt to change. It is integral to remain up to date with the most emerging tactics and techniques involved in the development of new and innovative software. The Research Anthology on Agile Software, Software Development, and Testing is a comprehensive resource on the emerging trends of software development and testing. This text discusses the newest developments in agile software and its usage spanning multiple industries. Featuring a collection of insights from diverse authors, this research anthology offers international perspectives on agile software. Covering topics such as global software engineering, knowledge management, and product development, this comprehensive resource is valuable to software developers, software engineers, computer engineers, IT directors, students, managers, faculty, researchers, and academicians.
Author: Manjubala Bisi
Publisher: John Wiley & Sons
ISBN: 111922392X
Category : Computers
Languages : en
Pages : 225
Get Book Here
Book Description
This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization. Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.
Author: Stefan Vogt
Publisher: diplom.de
ISBN: 3832420215
Category : Computers
Languages : en
Pages : 142
Get Book Here
Book Description
Inhaltsangabe:Abstract: In the 1980s research efforts and successes made artificial neural networks popular. Since the 1990s engineers have been using this foundation for problem solving. But artifiial neural network solutions for "real-world" problems are sometimes hard to find because of the complexity of the domain and because of the vast number of design attributes the engineer has to deal with. This thesis provides a structured overview of attributes in the design process of artificial neural networks and reviews technical process models. Current development methods for artificial neural networks are then reviewed and critiqued. The thesis concludes with a new design and development method for artificial neural networks. Inhaltsverzeichnis:Table of Contents: List of figuresx List of tablesxi Introduction1 1.Design attributes in ANN3 1.1ANN models4 1.1.1Node level7 1.1.2Network level9 1.1.3Training level9 1.2Data and data representation10 1.3Global system design12 1.4Hardware and software implementation13 1.5Characteristics of ANNs15 1.5.1Advantages of ANNs15 1.5.2Limitations and concerns16 2.Technical process models and engineering methods18 2.1Why use an engineering method?18 2.2Evolutionary model of engineering discipline20 2.3Overview of technical process models22 2.3.1Taxonomy of technical process models24 2.3.2Prototyping25 2.3.3Incremental method26 2.3.4Strict contractual approach26 2.3.5Deciding on process models and methods26 2.3.6Examples of process models27 2.3.7Representation of process models27 2.4Quality criteria of process models29 3.Current engineering methods for ANNs30 3.1Why a special method for ANNs?30 3.1.1Are conventional engineering methodologies suitable for ANNs?30 3.2Methods for expert systems31 3.3System identication methods35 3.4Bailey and Thompson37 3.4.1Criticism43 3.5Medsker and Liebowitz44 3.6Jones and Franklin45 3.7Schalko47 3.8Karayiannis and Nicolaos48 3.8.1Criticism49 3.9Nelson and Illingworth50 3.9.1Criticism51 3.10Whittington and Spracklen52 3.10.1Criticism56 3.11Lawrence and Andriola57 3.11.1Criticism58 3.12General criticism of current methodologies58 4.Proposed design and development method60 4.1Development process61 4.1.1Requirement analysis65 4.1.2Specication68 4.1.3Data and domain analysis70 4.1.4Architectural design76 4.1.5Detailed ANN design84 4.1.6ANN implementation92 4.1.7Training93 4.1.8Monitoring training94 4.1.9ANN quality [...]
Author: Jose R. Dorronsoro
Publisher: Springer
ISBN: 3540460845
Category : Computers
Languages : en
Pages : 1396
Get Book Here
Book Description
The International Conferences on Arti?cial Neural Networks, ICANN, have been held annually since 1991 and over the years have become the major European meeting in neural networks. This proceedings volume contains all the papers presented at ICANN 2002, the 12th ICANN conference, held in August 28– 30, 2002 at the Escuela T ́ecnica Superior de Inform ́atica of the Universidad Aut ́onoma de Madrid and organized by its Neural Networks group. ICANN 2002 received a very high number of contributions, more than 450. Almost all papers were revised by three independent reviewers, selected among the more than 240 serving at this year’s ICANN, and 221 papers were ?nally selected for publication in these proceedings (due to space considerations, quite a few good contributions had to be left out). I would like to thank the Program Committee and all the reviewers for the great collective e?ort and for helping us to have a high quality conference.
Author: Vivienne Sze
Publisher: Springer Nature
ISBN: 3031017668
Category : Technology & Engineering
Languages : en
Pages : 254
Get Book Here
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.
