Download or read book Neural Nets and Surroundings written by Bruno Apolloni and published by Springer Science & Business Media. This book was released on 2012-12-25 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop- is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.
Download or read book Neural Nets Applications in Geography written by Bruce C. Hewitson and published by Springer Science & Business Media. This book was released on 1994 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural nets offer a new strategy for spatial analysis, and their application holds enormous potential for the geographic sciences. However, the number of studies that have utilized these techniques is limited. This lack of interest can be attributed, in part, to lack of exposure, to the use of extensive and often confusing jargon, and to the misapprehension that, without an underlying statistical model, the explanatory power of the neural net is very low. This text attacks all three issues, demonstrating a wide variety of neural net applications in geography in a simple manner, with minimal jargon.
Download or read book Neural Network Design written by Martin T. Hagan and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Neural Networks in Biological and Environmental Analysis written by Grady Hanrahan and published by CRC Press. This book was released on 2011-01-18 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound
Download or read book Neural Networks written by Berndt Müller and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Nature of Code written by Daniel Shiffman and published by No Starch Press. This book was released on 2024-09-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: All aboard The Coding Train! This beginner-friendly creative coding tutorial is designed to grow your skills in a fun, hands-on way as you build simulations of real-world phenomena with “The Coding Train” YouTube star Daniel Shiffman. How can we use code to capture the unpredictable properties of nature? How can understanding the mathematical principles behind our physical world help us create interesting digital environments? Written by “The Coding Train” YouTube star Daniel Shiffman, The Nature of Code is a beginner-friendly creative coding tutorial that explores a range of programming strategies for developing computer simulations of natural systems—from elementary concepts in math and physics to sophisticated machine-learning algorithms. Using the same enthusiastic style on display in Shiffman’s popular YT channel, this book makes learning to program fun, empowering you to generate fascinating graphical output while refining your problem-solving and algorithmic-thinking skills. You’ll progress from building a basic physics engine that simulates the effects of forces like gravity and wind resistance, to creating evolving systems of intelligent autonomous agents that can learn from their mistakes and adapt to their environment. The Nature of Code introduces important topics such as: Randomness Forces and vectors Trigonometry Cellular automata and fractals Genetic algorithms Neural networks Learn from an expert how to transform your beginner-level skills into writing well-organized, thoughtful programs that set the stage for further experiments in generative design. NOTE: All examples are written with p5.js, a JavaScript library for creative coding, and are available on the book's website.
Download or read book An Introduction to Neural Networks written by Kevin Gurney and published by CRC Press. This book was released on 2003-12-16 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book How Smart Machines Think written by Sean Gerrish and published by MIT Press. This book was released on 2019-10-22 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Everything you want to know about the breakthroughs in AI technology, machine learning, and deep learning—as seen in self-driving cars, Netflix recommendations, and more. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM’s Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today’s machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson’s famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Talking Nets written by James A. Anderson and published by MIT Press. This book was released on 2000-02-28 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surprising tales from the scientists who first learned how to use computers to understand the workings of the human brain. Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future. The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book. Contributors James A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow
Download or read book The Self Assembling Brain written by Peter Robin Hiesinger and published by Princeton University Press. This book was released on 2022-12-13 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this book, Peter Robin Hiesinger explores historical and contemporary attempts to understand the information needed to make biological and artificial neural networks. Developmental neurobiologists and computer scientists with an interest in artificial intelligence - driven by the promise and resources of biomedical research on the one hand, and by the promise and advances of computer technology on the other - are trying to understand the fundamental principles that guide the generation of an intelligent system. Yet, though researchers in these disciplines share a common interest, their perspectives and approaches are often quite different. The book makes the case that "the information problem" underlies both fields, driving the questions that are driving forward the frontiers, and aims to encourage cross-disciplinary communication and understanding, to help both fields make progress. The questions that challenge researchers in these fields include the following. How does genetic information unfold during the years-long process of human brain development, and can this be a short-cut to create human-level artificial intelligence? Is the biological brain just messy hardware that can be improved upon by running learning algorithms in computers? Can artificial intelligence bypass evolutionary programming of "grown" networks? These questions are tightly linked, and answering them requires an understanding of how information unfolds algorithmically to generate functional neural networks. Via a series of closely linked "discussions" (fictional dialogues between researchers in different disciplines) and pedagogical "seminars," the author explores the different challenges facing researchers working on neural networks, their different perspectives and approaches, as well as the common ground and understanding to be found amongst those sharing an interest in the development of biological brains and artificial intelligent systems"--
Download or read book A Practical Guide to Neural Nets written by Marilyn McCord Nelson and published by Addison Wesley Publishing Company. This book was released on 1994 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on a course given to internal managers at Texas Instruments, this book is an introduction to neural nets for computer science, artificial intelligence and R & D professionals, as well as MIS or DP managers.
Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.
Download or read book Strengthening Deep Neural Networks written by Katy Warr and published by "O'Reilly Media, Inc.". This book was released on 2019-07-03 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Neural Networks written by Richard J. Mammone and published by . This book was released on 1991 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have attracted the interest of scientists from many disciplines: engineering, computer science, mathematics, physics, biology, and cognitive science. This volume collects 15 contributions written by leading international researchers that illustrate important features of various neural network methodologies. Topics discussed include the fundamental principles of neural networks and various modifications of basic neural systems that improve system performance in specific application domains. Where appropriate, improvements are demonstrated by numerical examples.
Download or read book Model Neural Networks and Behavior written by Allen Selverston and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most conspicuous function of the nervous system is to control animal behav ior. From the complex operations of learning and mentation to the molecular con figuration of ionic channels, the nervous system serves as the interface between an animal and its environment. To study and understand the fundamental mecha nisms underlying the control of behavior, it is often both necessary and desirable to employ biological systems with characteristics especially suitable for answering specific questions. In neurobiology, many invertebrates have become established as model systems for investigations at both the systems and the cellular level. Large, readily identifiable neurons have made invertebrates especially useful for cellular studies. The fact that these neurons occur in much smaller numbers than those in higher animals also makes them important for circuit analysis. Although important differences exist, some of the questions that would be tech nically impossible to answer with vertebrates can become experimentally tractable with invertebrates.
Download or read book Make Your Own Neural Network written by Tariq Rashid and published by Createspace Independent Publishing Platform. This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for anyone who wants to understand what neural network[s] are. It's for anyone who wants to make and use their own. And it's for anyone who wants to appreciate the fairly easy but exciting mathematical ideas that are at the core of how they work. This guide is not aimed at experts in mathematics or computer science. You won't need any special knowledge or mathematical ability beyond school maths [sic] ... Teachers can use this guide as a particularly gentle explanation of neural networks and their implementation to enthuse and excite students making their very own learning artificial intelligence with only a few lines of programming language code. The code has been tested to work with a Raspberry Pi, a small inexpensive computer very popular in schools and with young students"--(page 6, Introduction)
