Multilayer Perceptron
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Multilayer Perceptron
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-26
Multilayer Perceptron written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Computers categories.
What Is Multilayer Perceptron A fully connected class of feedforward artificial neural network (ANN), a multilayer perceptron, or MLP, is referred to as a multilayer perceptron. The word "MLP" is used in a way that is rather vague. Sometimes it is used to refer to any feedforward ANN, and other times it is used more specifically to refer to networks that are constructed of several layers of perceptrons; for more information, see "Terminology." When they just contain one hidden layer, multilayer perceptrons are sometimes jokingly referred to as "vanilla" neural networks. This is especially true when the term is used in a slang context. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Multilayer Perceptron Chapter 2: Artificial Neural Network Chapter 3: Perceptron Chapter 4: Artificial Neuron Chapter 5: Activation Function Chapter 6: Backpropagation Chapter 7: Delta Rule Chapter 8: Feedforward Neural Network Chapter 9: Universal Approximation Theorem Chapter 10: Mathematics of Artificial Neural Networks (II) Answering the public top questions about multilayer perceptron. (III) Real world examples for the usage of multilayer perceptron in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of multilayer perceptron. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Multilayer Perceptrons
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Author : Ruth Vang-Mata
language : en
Publisher:
Release Date : 2020
Multilayer Perceptrons written by Ruth Vang-Mata and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Differential equations categories.
"Multilayer Perceptrons: Theory and Applications opens with a review of research on the use of the multilayer perceptron artificial neural network method for solving ordinary/partial differential equations, accompanied by critical comments. A historical perspective on the evolution of the multilayer perceptron neural network is provided. Furthermore, the foundation for automated post-processing that is imperative for consolidating the signal data to a feature set is presented. In one study, panoramic dental x-ray images are used to estimate age and gender. These images were subjected to image pre-processing techniques to achieve better results. In a subsequent study, a multilayer perceptrons artificial neural network with one hidden layer and trained through the efficient resilient backpropagation algorithm is used for modeling quasi-fractal patch antennas. Later, the authors propose a scheme with eight steps for a dynamic time series forecasting using an adaptive multilayer perceptron with minimal complexity. Two different data sets from two different countries were used in the experiments to measure the robustness and accuracy of the models. In closing, a multilayer perceptron artificial neural network with a layer of hidden neurons is trained with the resilient backpropagation algorithm, and the network is used to model a Koch pre-fractal patch antenna"--
Perceptrons
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-25
Perceptrons written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-25 with Computers categories.
What Is Perceptrons The perceptron is a technique for supervised learning of binary classifiers that is used in the field of machine learning. A function known as a binary classifier is one that can determine whether or not an input, which is often portrayed by a vector of numbers, is a member of a particular category. It is a kind of linear classifier, which means that it is a classification method that forms its predictions on the basis of a linear predictor function by combining a set of weights with the feature vector. In other words, it creates its predictions based on a linear predictor function. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Perceptron Chapter 2: Supervised learning Chapter 3: Support vector machine Chapter 4: Linear classifier Chapter 5: Pattern recognition Chapter 6: Artificial neuron Chapter 7: Hopfield network Chapter 8: Backpropagation Chapter 9: Feedforward neural network Chapter 10: Multilayer perceptron (II) Answering the public top questions about perceptrons. (III) Real world examples for the usage of perceptrons in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of perceptrons. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
How To Fine Tune Neural Networks For Classification
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Author : Ionut B. Brandusoiu
language : en
Publisher: GAER Publishing House
Release Date : 2020-08-19
How To Fine Tune Neural Networks For Classification written by Ionut B. Brandusoiu and has been published by GAER Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-19 with Computers categories.
This book covers in the first part the theoretical aspects of neural networks and their functionality, and then based on the discussed concepts it explains how to find-tune a neural network to yield highly accurate prediction results which are adaptable to any classification tasks. The introductory part is extremely beneficial to someone new to learning neural networks, while the more advanced notions are useful for everyone who wants to understand the mathematics behind neural networks and how to find-tune them in order to generate the best predictive performance of a certain classification model.
Second Order Methods For Neural Networks
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Author : Adrian J. Shepherd
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Second Order Methods For Neural Networks written by Adrian J. Shepherd and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
About This Book This book is about training methods - in particular, fast second-order training methods - for multi-layer perceptrons (MLPs). MLPs (also known as feed-forward neural networks) are the most widely-used class of neural network. Over the past decade MLPs have achieved increasing popularity among scientists, engineers and other professionals as tools for tackling a wide variety of information processing tasks. In common with all neural networks, MLPsare trained (rather than programmed) to carryout the chosen information processing function. Unfortunately, the (traditional' method for trainingMLPs- the well-knownbackpropagation method - is notoriously slow and unreliable when applied to many prac tical tasks. The development of fast and reliable training algorithms for MLPsis one of the most important areas ofresearch within the entire field of neural computing. The main purpose of this book is to bring to a wider audience a range of alternative methods for training MLPs, methods which have proved orders of magnitude faster than backpropagation when applied to many training tasks. The book also addresses the well-known (local minima' problem, and explains ways in which fast training methods can be com bined with strategies for avoiding (or escaping from) local minima. All the methods described in this book have a strong theoretical foundation, drawing on such diverse mathematical fields as classical optimisation theory, homotopic theory and stochastic approximation theory.
Data Mining Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2012-11-30
Data Mining Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-30 with Computers categories.
Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.
Proceedings Of International Joint Conference On Advances In Computational Intelligence
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Author : Mohammad Shorif Uddin
language : en
Publisher: Springer Nature
Release Date : 2022-05-18
Proceedings Of International Joint Conference On Advances In Computational Intelligence written by Mohammad Shorif Uddin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-18 with Technology & Engineering categories.
This book gathers outstanding research papers presented at the 5th International Joint Conference on Advances in Computational Intelligence (IJCACI 2021), held online during October 23–24, 2021. IJCACI 2021 is jointly organized by Jahangirnagar University (JU), Bangladesh, and South Asian University (SAU), India. The book presents the novel contributions in areas of computational intelligence and it serves as a reference material for advance research. The topics covered are collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal and natural language processing.
Data Mining
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Author : Ian H. Witten
language : en
Publisher: Elsevier
Release Date : 2005-07-13
Data Mining written by Ian H. Witten and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-13 with Computers categories.
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output
Machine Learning
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Author : T V Geetha
language : en
Publisher: CRC Press
Release Date : 2023-05-17
Machine Learning written by T V Geetha and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-17 with Computers categories.
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.
Security And Privacy In Social Networks And Big Data
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Author : Yang Xiang
language : en
Publisher: Springer Nature
Release Date : 2020-09-25
Security And Privacy In Social Networks And Big Data written by Yang Xiang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-25 with Computers categories.
This book constitutes revised and selected papers from the 6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020, held in Tianjin, China, in September 2020. The 38 full papers presented in this volume were carefully reviewed and selected from a total of 111 submissions. The papers are organized according to the topical sections on big data security; social networks; privacy-preserving and security.