Computer Neural Networks On Matlab
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Introduction To Neural Networks Using Matlab 6 0
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Author : S. N. Sivanandam
language : en
Publisher: Tata McGraw-Hill Education
Release Date : 2006
Introduction To Neural Networks Using Matlab 6 0 written by S. N. Sivanandam and has been published by Tata McGraw-Hill Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with MATLAB. categories.
Computer Neural Networks On Matlab
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Author : Daniel Okoh
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-10-07
Computer Neural Networks On Matlab written by Daniel Okoh 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 2016-10-07 with categories.
Computer neural networks are a branch of artificial intelligence, inspired to behave in a manner similar to the human brain; they are trained and they learn from their training. Computer neural networks have a wide variety of applications, mostly hinged around modelling, forecasting, and general predictions. This book illustrates how to use computer neural networks on MATLAB in very simple and elegant manner. The language of the book is elementary as it is meant for beginners, readers are notassumed to have previous skills on the subject. Projects, in varying degrees, have been used to make sure that readers get a practical and hands-on experience on the subject. The book is meant for you if you want to get a quick start with the practical use of computer neural networks on MATLAB without the boredom associated with a lengthy theoretical write-up.
Neural Network Architectures Examples Using Matlab
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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-26
Neural Network Architectures Examples Using Matlab written by J. Smith 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 2017-02-26 with Computer architecture categories.
MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: - Deep learning, including convolutional neural networks and autoencoders - Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) - Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) - Unsupervised learning algorithms, including self-organizing maps and competitive layers - Apps for data-fitting, pattern recognition, and clustering - Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance - Simulink(R) blocks for building and evaluating neural networks and for control systems applications Neural networks are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, the connections between elements largely determine the network function. You can train a neural network to perform a particular function by adjusting the values of the connections (weights) between elements.
Soft Computing
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Author : N. P. Padhy
language : en
Publisher: Oxford University Press, USA
Release Date : 2015-07-23
Soft Computing written by N. P. Padhy and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-23 with MATLAB. categories.
Beginning with an overview of intelligent soft computing, the book offers exhaustive coverage of artificial neural networks (ANN), discussing in detail ANN tracking in all the three generations of neural networks and their developments. This is followed by detailed chapters discussing fuzzy logic features and applications. The chapter on genetic algorithms then explains various GA operators such as crossover and mutation with suitable examples and illustrations. Finally, swarm intelligent systems are illustrated in detail, also discussing the engineering applications of particle swarm intelligent systems. MATLAB codes have been used throughout the text to illustrate the applications of the concepts discussed.
Netlab
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Author : Ian Nabney
language : en
Publisher: Springer Science & Business Media
Release Date : 2002
Netlab written by Ian Nabney 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 2002 with Computers categories.
Getting the most out of neural networks and related data modelling techniques is the purpose of this book. The text, with the accompanying Netlab toolbox, provides all the necessary tools and knowledge. Throughout, the emphasis is on methods that are relevant to the practical application of neural networks to pattern analysis problems. All parts of the toolbox interact in a coherent way, and implementations and descriptions of standard statistical techniques are provided so that they can be used as benchmarks against which more sophisticated algorithms can be evaluated. Plenty of examples and demonstration programs illustrate the theory and help the reader understand the algorithms and how to apply them.
Neural Network Design
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Author : Martin T. Hagan
language : en
Publisher:
Release Date : 2003
Neural Network Design written by Martin T. Hagan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Neural networks (Computer science) categories.
Neural Networks And Deep Learning
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2018-08-25
Neural Networks And Deep Learning written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-25 with Computers categories.
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Principles Of Artificial Neural Networks
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Author : Daniel Graupe
language : en
Publisher: World Scientific
Release Date : 2007
Principles Of Artificial Neural Networks written by Daniel Graupe and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
This book should serves as a self-study course for engineers and computer scientist in the industry. The features include major neural network approaches and architectures with theories and detailed case studies for each of the approaches acompanied by complete computer codes and the corresponding computed results. There is also a chapter on LAMSTAR neural network.
Computational Methods For Deep Learning
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Author : Wei Qi Yan
language : en
Publisher:
Release Date : 2021
Computational Methods For Deep Learning written by Wei Qi Yan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Big data categories.
Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.
Practical Matlab Deep Learning
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Author : Michael Paluszek
language : en
Publisher: Apress
Release Date : 2020-02-07
Practical Matlab Deep Learning written by Michael Paluszek and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-07 with Computers categories.
Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. What You Will Learn Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.