Neural Networks In Computer Intelligence
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Neural Networks In Computer Intelligence
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Author : LiMin Fu
language : en
Publisher: McGraw-Hill Companies
Release Date : 1994
Neural Networks In Computer Intelligence written by LiMin Fu and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Artificial intelligence categories.
This book bridges the gap between artificial intelligence and neural networks. Unlike other network books, this one pioneers the effort to offer a unified perspective which could be used to integrate intelligence technologies. The broad coverage of the book and the emphasis on basic principles can accommodate the diverse background of readers.
Artificial Intelligence In The Age Of Neural Networks And Brain Computing
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Author : Robert Kozma
language : en
Publisher: Academic Press
Release Date : 2023-10-27
Artificial Intelligence In The Age Of Neural Networks And Brain Computing written by Robert Kozma and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-27 with Computers categories.
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks
Neural Network Compu Int Repl
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Author : Usa) Limin Fu (University Of Florida
language : en
Publisher: McGraw-Hill Science, Engineering & Mathematics
Release Date : 1994-01
Neural Network Compu Int Repl written by Usa) Limin Fu (University Of Florida and has been published by McGraw-Hill Science, Engineering & Mathematics this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-01 with Artificial intelligence categories.
This text provides basic concepts, algorithms and analysis of important neural network models, with emphasis on importance of knowledge in intelligent systems design. It bridges the gap between artificial intelligence and neural networks.
Neural Networks With R
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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-09-27
Neural Networks With R written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-27 with Computers categories.
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
Artificial Intelligence And Neural Networks
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Author : F. Acar Savaci
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-07-18
Artificial Intelligence And Neural Networks written by F. Acar Savaci 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 2006-07-18 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the 14th Turkish Symposium on Artificial Intelligence and Neural Networks, TAINN 2005, held in Izmir, Turkey, June 2005. The book presents 26 revised full papers categorized in topical sections on robotics, image processing, classification, learning theory and support vector machines, fuzzy neural networks, robotics, fuzzy logic, machine learning, engineering applications, and neural networks architecture.
Neural Networks
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Author : Herbert Jones
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-08-09
Neural Networks written by Herbert Jones and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-09 with categories.
If you want to learn about Neural Networks then keep reading... Aladdin from "The Arabian Nights" had a magic lamp that fulfilled his every wish when rubbed. Today we have a smartphone that serves as a window to a whole universe of knowledge, entertainment and even wise personal assistants, such as Siri - all we have to do is rub the screen. Aladdin's lamp was powered by a genie, but what powers Siri? Neural networks. It's an astounding concept that tries to mimic the way living brains work by amalgamating human and machine ways of thinking. The goal of this book is to present the reader with a digestible, readable explanation of neural networks while keeping the underlying concepts intact. The reader will acquire fundamental knowledge of neural networks through loosely related chapters that nonetheless reference terms and ideas mentioned throughout the book. The book itself isn't meant to be strictly academic, but a blend of colloquial and technical that brings this exciting, yet eerie, topic to the widest swath of the general public. There is a lot of coding and math behind neural networks, but the reader is presumed to have no prior knowledge or interest in either, so the concepts are broken down and elaborated on as such. Each chapter is made as standalone as possible to allow the reader to skip back and forth without getting lost, with the glossary at the very end serving as a handy summary. Where possible, references have been included to support the presented conclusions and encourage the reader to scrutinize the traditional media in search of clues. Neural Networks: An Essential Beginners Guide to Artificial Neural Networks and their Role in Machine Learning and Artificial Intelligence cover topics such as: Programming a smart(er) computer Composition Giving neural networks legs to stand on The magnificent wetware Personal assistants Tracking users in the real world Self-driving neural networks Taking everyone's job Quantum leap in computing Attacks on neural networks Neural network war Ghost in the machine No backlash And Much, Much More So if you want to learn about Neural Networks without having to go through heavy textbooks, click "add to cart"!
Learning Deep Learning
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Author : Magnus Ekman
language : en
Publisher: Addison-Wesley Professional
Release Date : 2021-07-19
Learning Deep Learning written by Magnus Ekman and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-19 with Computers categories.
NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Advances In Computational Intelligence
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Author : Ignacio Rojas
language : en
Publisher: Springer
Release Date : 2017-06-04
Advances In Computational Intelligence written by Ignacio Rojas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-04 with Computers categories.
This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, held in Cadiz, Spain, in June 2017. The 126 revised full papers presented in this double volume were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on Bio-inspired Computing; E-Health and Computational Biology; Human Computer Interaction; Image and Signal Processing; Mathematics for Neural Networks; Self-organizing Networks; Spiking Neurons; Artificial Neural Networks in Industry ANNI'17; Computational Intelligence Tools and Techniques for Biomedical Applications; Assistive Rehabilitation Technology; Computational Intelligence Methods for Time Series; Machine Learning Applied to Vision and Robotics; Human Activity Recognition for Health and Well-Being Applications; Software Testing and Intelligent Systems; Real World Applications of BCI Systems; Machine Learning in Imbalanced Domains; Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles; End-User Development for Social Robotics; Artificial Intelligence and Games; and Supervised, Non-Supervised, Reinforcement and Statistical Algorithms.
Vlsi For Neural Networks And Artificial Intelligence
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Author : Jose G. Delgado-Frias
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Vlsi For Neural Networks And Artificial Intelligence written by Jose G. Delgado-Frias 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 2013-06-29 with Computers categories.
Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.
Artificial Intelligence For Humans
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Author : Jeff Heaton
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2015
Artificial Intelligence For Humans written by Jeff Heaton and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Algorithms categories.
« Artifical Intelligence for Humans is a book series meant to teach AI to those readers who lack an extensive mathematical background. The reader only needs knowledge of basic college algebra and computer programming. Additional topics are thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and Python. Other languages are planned. »--