Download or read book Perceptrons written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-25 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Models of Neurons and Perceptrons Selected Problems and Challenges written by Andrzej Bielecki and published by Springer. This book was released on 2018-05-17 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes models of the neuron and multilayer neural structures, with a particular focus on mathematical models. It also discusses electronic circuits used as models of the neuron and the synapse, and analyses the relations between the circuits and mathematical models in detail. The first part describes the biological foundations and provides a comprehensive overview of the artificial neural networks. The second part then presents mathematical foundations, reviewing elementary topics, as well as lesser-known problems such as topological conjugacy of dynamical systems and the shadowing property. The final two parts describe the models of the neuron, and the mathematical analysis of the properties of artificial multilayer neural networks. Combining biological, mathematical and electronic approaches, this multidisciplinary book it useful for the mathematicians interested in artificial neural networks and models of the neuron, for computer scientists interested in formal foundations of artificial neural networks, and for the biologists interested in mathematical and electronic models of neural structures and processes.
Download or read book Perceptrons Reissue of the 1988 Expanded Edition with a new foreword by L on Bottou written by Marvin Minsky and published by MIT Press. This book was released on 2017-09-22 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first systematic study of parallelism in computation by two pioneers in the field. Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou In 1969, ten years after the discovery of the perceptron—which showed that a machine could be taught to perform certain tasks using examples—Marvin Minsky and Seymour Papert published Perceptrons, their analysis of the computational capabilities of perceptrons for specific tasks. As Léon Bottou writes in his foreword to this edition, “Their rigorous work and brilliant technique does not make the perceptron look very good.” Perhaps as a result, research turned away from the perceptron. Then the pendulum swung back, and machine learning became the fastest-growing field in computer science. Minsky and Papert's insistence on its theoretical foundations is newly relevant. Perceptrons—the first systematic study of parallelism in computation—marked a historic turn in artificial intelligence, returning to the idea that intelligence might emerge from the activity of networks of neuron-like entities. Minsky and Papert provided mathematical analysis that showed the limitations of a class of computing machines that could be considered as models of the brain. Minsky and Papert added a new chapter in 1987 in which they discuss the state of parallel computers, and note a central theoretical challenge: reaching a deeper understanding of how “objects” or “agents” with individuality can emerge in a network. Progress in this area would link connectionism with what the authors have called “society theories of mind.”
Download or read book Support Vector Machines and Perceptrons written by M.N. Murty and published by Springer. This book was released on 2016-08-16 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>
Download or read book Multilayer Perceptrons written by Ruth Vang-Mata and published by . This book was released on 2020 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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"--
Download or read book The Perceptron written by Frank Rosenblatt and published by . This book was released on 1958 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Activity Regulation in Perceptrons written by Neil Mark Branston and published by . This book was released on 1964 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Neural Information Processing Systems 8 written by David S. Touretzky and published by MIT Press. This book was released on 1996 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book
Download or read book A Pilot Study on the Applicability of Perceptrons to the Classification of Bubble Chamber Photographs written by Sherman Ming Chow and published by . This book was released on 1963 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Control Systems written by Jitendra R. Raol and published by CRC Press. This book was released on 2019-07-12 with total page 738 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional control system, chemical reactor, head-disk assembly, pitch control of an aircraft, yaw-damper control, helicopter control, and tidal power control. Decentralized control, game-theoretic control, and control of hybrid systems are discussed. Also, control systems based on artificial neural networks, fuzzy logic, and genetic algorithms, termed as AI-based systems are studied and analyzed with applications such as auto-landing aircraft, industrial process control, active suspension system, fuzzy gain scheduling, PID control, and adaptive neuro control. Numerical coverage with MATLAB® is integrated, and numerous examples and exercises are included for each chapter. Associated MATLAB® code will be made available.
Download or read book Introduction to Neural Networks written by Architecture Technology Architecture Technology Corpor and published by Elsevier. This book was released on 2015-11-24 with total page 73 pages. Available in PDF, EPUB and Kindle. Book excerpt: Please note this is a Short Discount publication. Neural network technology has been a curiosity since the early days of computing. Research in the area went into a near dormant state for a number of years, but recently there has been a new increased interest in the subject. This has been due to a number of factors: interest in the military, apparent ease of implementation, and the ability of the technology to develop computers which are able to learn from experience. This report summarizes the topic, providing the reader with an overview of the field and its potential direction. Included is an introduction to the technology and its future directions, as well as a set of examples of possible applications and potential implementation technologies.
Download or read book Second Order Methods for Neural Networks written by Adrian J. Shepherd and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Pattern Recognition by Self organizing Neural Networks written by Gail A. Carpenter and published by MIT Press. This book was released on 1991 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields ofcognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19articles take up developments in competitive learning and computational maps, adaptive resonancetheory, and specialized architectures and biological connections. Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent articles by Kohonen, applyingthem to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designingadaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks,selforganizing pattern recognition systems whose top-down template feedback signals guarantee theirstable learning in response to arbitrary sequences of input patterns. In Part 4, articles describeembedding ART modules into larger architectures and provide experimental evidence fromneurophysiology, event-related potentials, and psychology that support the prediction that ARTmechanisms exist in the brain. Contributors: J.-P. Banquet, G.A. Carpenter, S.Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C.von der Malsburg, C.L. Winter.
Download or read book Perception of the Visual Environment written by Ronald G. Boothe and published by Springer Science & Business Media. This book was released on 2001-11-16 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aimed at students taking a course on visual perception, this textbook considers what it means for a man, a monkey and a computer to perceive the world. After an introduction and a discussion of methods, the book deals with how the environment produces a physical effect, how the resulting "image" is processed by the brain or by computer algorithms in order to produce a perception of "something out there". It also discusses color, form, motion, distance, and also the sensing of three dimensionality, before dealing with visual perception and its role in awareness and consciousness. The book concludes with discussions of perceptual development, blindness, and visual disorders. Visual perception is by its very nature an interdisciplinary subject that requires a basic understanding of a range of topics from diverse fields, and this is a very readable guide to all students whether they come from a neuroscience, psychology, cognitive science, robotics, or philosophy background.
Download or read book A Computer Program for Simulation of Perceptrons and Similar Neural Networks written by Trevor H. Barker and published by . This book was released on 1966 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of the report is to describe the capabilities of, and give instructions for the use of, a very general program designed for the simulation of perceptrons and perceptron-like networks. The program was written for the IBM 7090/7094.
Download or read book Data Science for Entrepreneurship written by Werner Liebregts and published by Springer Nature. This book was released on 2023-03-23 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fast-paced technological development and the plethora of data create numerous opportunities waiting to be exploited by entrepreneurs. This book provides a detailed, yet practical, introduction to the fundamental principles of data science and how entrepreneurs and would-be entrepreneurs can take advantage of it. It walks the reader through sections on data engineering, and data analytics as well as sections on data entrepreneurship and data use in relation to society. The book also offers ways to close the research and practice gaps between data science and entrepreneurship. By having read this book, students of entrepreneurship courses will be better able to commercialize data-driven ideas that may be solutions to real-life problems. Chapters contain detailed examples and cases for a better understanding. Discussion points or questions at the end of each chapter help to deeply reflect on the learning material.
Download or read book Artificial Neural Networks Formal Models and Their Applications ICANN 2005 written by Wlodzislaw Duch and published by Springer. This book was released on 2005-08-25 with total page 1051 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is the first part of the two-volume proceedings of the International C- ference on Artificial Neural Networks (ICANN 2005), held on September 11–15, 2005 in Warsaw, Poland, with several accompanying workshops held on September 15, 2005 at the Nicolaus Copernicus University, Toru , Poland. The ICANN conference is an annual meeting organized by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society, and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in those fields. In 2005 the ICANN conference was organized by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, and the Nicolaus Copernicus Univ- sity, Toru , Poland. From over 600 papers submitted to the regular sessions and some 10 special c- ference sessions, the International Program Committee selected – after a thorough peer-review process – about 270 papers for publication. The large number of papers accepted is certainly a proof of the vitality and attractiveness of the field of artificial neural networks, but it also shows a strong interest in the ICANN conferences.
