Neural
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Neural Networks
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Author : Berndt Müller
language : en
Publisher: Springer Science & Business Media
Release Date : 1995-10-02
Neural Networks written by Berndt Müller 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 1995-10-02 with Computers categories.
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.
Computational Mechanics With Neural Networks
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Author : Genki Yagawa
language : en
Publisher: Springer Nature
Release Date : 2021-02-26
Computational Mechanics With Neural Networks written by Genki Yagawa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.
This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.
Deep Learning With Pytorch
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Author : Vishnu Subramanian
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-23
Deep Learning With Pytorch written by Vishnu Subramanian 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 2018-02-23 with Computers categories.
Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book Description Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is for This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.
Introduction To Deep Learning And Neural Networks With Pythontm
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Author : Ahmed Fawzy Gad
language : en
Publisher: Academic Press
Release Date : 2020-11-25
Introduction To Deep Learning And Neural Networks With Pythontm written by Ahmed Fawzy Gad and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Medical categories.
Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. - Examines the practical side of deep learning and neural networks - Provides a problem-based approach to building artificial neural networks using real data - Describes PythonTM functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in PythonTM - Features math and code examples (via companion website) with helpful instructions for easy implementation
Foundations Of Neural Networks Fuzzy Systems And Knowledge Engineering
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Author : Nikola K. Kasabov
language : en
Publisher: Marcel Alencar
Release Date : 1996
Foundations Of Neural Networks Fuzzy Systems And Knowledge Engineering written by Nikola K. Kasabov and has been published by Marcel Alencar this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.
Neural Computing An Introduction
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Author : R Beale
language : en
Publisher: CRC Press
Release Date : 1990-01-01
Neural Computing An Introduction written by R Beale and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-01-01 with Mathematics categories.
Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function. A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists.
Advanced Neural Computers
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Author : R. Eckmiller
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Advanced Neural Computers written by R. Eckmiller and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
This book is the outcome of the International Symposium on Neural Networks for Sensory and Motor Systems (NSMS) held in March 1990 in the FRG. The NSMS symposium assembled 45 invited experts from Europe, America and Japan representing the fields of Neuroinformatics, Computer Science, Computational Neuroscience, and Neuroscience.As a rapidly-published report on the state of the art in Neural Computing it forms a reference book for future research in this highly interdisciplinary field and should prove useful in the endeavor to transfer concepts of brain function and structure to novel neural computers with adaptive, dynamical neural net topologies.A feature of the book is the completeness of the references provided. An alphabetical list of all references quoted in the papers is given, as well as a separate list of general references to help newcomers to the field. A subject index and author index also facilitate access to various details.
Principles Of Neural Science Sixth Edition
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Author : Eric R. Kandel
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-05
Principles Of Neural Science Sixth Edition written by Eric R. Kandel and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-05 with Medical categories.
The gold standard of neuroscience texts―updated with hundreds of brand-new images and fully revised content in every chapter Doody's Core Titles for 2023! For more than 40 years, Principles of Neural Science has helped readers understand the link between the human brain and behavior. As the renowned text has shown, all behavior is an expression of neural activity and the future of both clinical neurology and psychiatry is dependent on the progress of neural science. Fully updated, this sixth edition of the landmark reference reflects the latest research, clinical perspectives, and advances in the field. It offers an unparalleled perspective on the the current state and future of neural science. This new edition features: Unmatched coverage of how the nerves, brain, and mind function NEW chapters on: - The Computational Bases of Neural Circuits that Mediate Behavior - Brain-Machine Interfaces - Decision-Making and Consciousness NEW section on the neuroscientific principles underlying the disorders of the nervous system Expanded coverage of the different forms of human memory Highly detailed chapters on stroke, Parkinson’s disease, and multiple sclerosis 2,200 images, including 300 new color illustrations, diagrams, radiology studies, and PET scans Principles of Neural Science, Sixth Edition benefits from a cohesive organization, beginning with an insightful overview of the interrelationships between the brain, nervous system, genes, and behavior. The text is divided into nine sections: Part I: Overall Perspective provides an overview of the broad themes of neural science, including the basic anatomical organization of the nervous system and the genetic bases of nervous system function and behavior. Part II: Cell and Molecular Biology of Cells of the Nervous System examines the basic properties of nerve cells, including the generation and conduction of propagated signaling. Part III: Synaptic Transmission focuses on the electrophysiological and molecular mechanism of synaptic transmission with chapters on neuronal excitability, neurotransmitters, and transmitter release. Part IV: Perception discusses the various aspects of sensory perception, including how information from the primary organs of sensation is transmitted to and processed by the central nervous system. Part V: Movement considers the neural mechanisms underlying movement and examines a new treatment that addresses how the basal ganglia regulate the selection of motor actions and instantiate reinforcement learning. Part VI: The Biology of Emotion, Motivation and Homeostasis examines the neural mechanisms by which subcortical areas mediate homeostatic control mechanisms, emotions, and motivation. Part VII: Development and the Emergence of Behavior looks at the nervous system from early embryonic differentiation to the formation and elimination of synapses. Part VIII: Learning, Memory, Language and Cognition expands on the previous section, examining the cellular mechanisms of implicit and explicit memory storage, as well as decision-making and consciousness. Part IX: explores the neural mechanisms underlying diseases and disorders of the nervous system, including autism spectrum disorder, epilepsy, schizophrenia, and anxiety.
Exploring Neural Networks With C
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Author : Ryszard Tadeusiewicz
language : en
Publisher: CRC Press
Release Date : 2014-09-02
Exploring Neural Networks With C written by Ryszard Tadeusiewicz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-02 with Computers categories.
The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations—making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the important properties of neural networks—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand. Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks. Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en
Green Power Materials And Manufacturing Technology And Applications
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Author : Ai Min Yang
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-08-08
Green Power Materials And Manufacturing Technology And Applications written by Ai Min Yang and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-08 with Technology & Engineering categories.
Selected, peer reviewed papers from the International Conference on Green Power, Materials and Manufacturing Technology and Applications (GPMMTA 2011), July 15-18, 2011, Chongqing, China