Neural Networks And Learning Machines

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Neural Networks and Learning Machines

Author : Simon S. Haykin
Publisher : Prentice Hall
ISBN 13 : 0131471392
Total Pages : 938 pages
Book Rating : 4.1/5 (314 download)

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Book Synopsis Neural Networks and Learning Machines by : Simon S. Haykin

Download or read book Neural Networks and Learning Machines written by Simon S. Haykin and published by Prentice Hall. This book was released on 2009 with total page 938 pages. Available in PDF, EPUB and Kindle. Book excerpt: For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Matlab codes used for the computer experiments in the text are available for download at: http: //www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

Neural Networks and Learning Machines

Author : Simon S. Haykin
Publisher : Pearson Education
ISBN 13 : 9780131293762
Total Pages : 934 pages
Book Rating : 4.2/5 (937 download)

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Book Synopsis Neural Networks and Learning Machines by : Simon S. Haykin

Download or read book Neural Networks and Learning Machines written by Simon S. Haykin and published by Pearson Education. This book was released on 2009 with total page 934 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using a wealth of case studies to illustrate the real-life, practical applications of neural networks, this state-of-the-art text exposes students to many facets of Neural Networks.

Neural Networks

Author : Simon Haykin
Publisher :
ISBN 13 : 9788178083001
Total Pages : 842 pages
Book Rating : 4.0/5 (83 download)

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Book Synopsis Neural Networks by : Simon Haykin

Download or read book Neural Networks written by Simon Haykin and published by . This book was released on 1999 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 512 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Networks

Author : Simon Haykin
Publisher :
ISBN 13 : 9780139083853
Total Pages : 842 pages
Book Rating : 4.0/5 (838 download)

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Book Synopsis Neural Networks by : Simon Haykin

Download or read book Neural Networks written by Simon Haykin and published by . This book was released on 1999 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised. *NEW-New chapters now cover such areas as: - Support vector machines. - Reinforcement learning/neurodynamic programming. - Dynamically driven recurrent networks. *NEW-End-of-chapter problems revised, improved and expanded in number. Detailed solutions manual to accompany the text. *Extensive, state-of-the-art coverage exposes students to the many facets of neural networks and helps them appreciate the technologys capabilities and potential applications. *Detailed analysis of back-propagation learning and multi-layer perceptrons. *Explores the intricacies of the learning process-an essential component for understanding neural networks. *Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics. *Integrates computer experiments throughout, giving students the opportunity to see how neural networks are designed and perform in practice. *Reinforces key concepts w

Machine Learning with Neural Networks

Author : Bernhard Mehlig
Publisher : Cambridge University Press
ISBN 13 : 1108849563
Total Pages : 262 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Machine Learning with Neural Networks by : Bernhard Mehlig

Download or read book Machine Learning with Neural Networks written by Bernhard Mehlig and published by Cambridge University Press. This book was released on 2021-10-28 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Efficient Learning Machines

Author : Mariette Awad
Publisher : Apress
ISBN 13 : 1430259906
Total Pages : 263 pages
Book Rating : 4.4/5 (32 download)

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Book Synopsis Efficient Learning Machines by : Mariette Awad

Download or read book Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

Learning and Soft Computing

Author : Vojislav Kecman
Publisher : MIT Press
ISBN 13 : 9780262112550
Total Pages : 556 pages
Book Rating : 4.1/5 (125 download)

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Book Synopsis Learning and Soft Computing by : Vojislav Kecman

Download or read book Learning and Soft Computing written by Vojislav Kecman and published by MIT Press. This book was released on 2001 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

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.

Artificial Neural Networks and Machine Learning – ICANN 2018

Author : Věra Kůrková
Publisher : Springer
ISBN 13 : 303001424X
Total Pages : 866 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2018 by : Věra Kůrková

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2018 written by Věra Kůrková and published by Springer. This book was released on 2018-10-02 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Neural Networks for Beginners

Author : Russel R Russo
Publisher :
ISBN 13 : 9781801693127
Total Pages : 0 pages
Book Rating : 4.6/5 (931 download)

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Book Synopsis Neural Networks for Beginners by : Russel R Russo

Download or read book Neural Networks for Beginners written by Russel R Russo and published by . This book was released on 2021-02-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to understand Neural Networks and learn everything about them but it looks like it is an exclusive club? Are you fascinated by Artificial Intelligence but you think that it would be too difficult for you to learn? If you think that Neural Networks and Artificial Intelligence are the present and, even more, the future of technology, and you want to be part of it... well you are in the right place, and you are looking at the right book. If you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Neural Networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way. In this book you will learn: The types and components of neural networks The smartest way to approach neural network programming Why Algorithms are your friends The "three Vs" of Big Data (plus two new Vs) How machine learning will help you making predictions The three most common problems with Neural Networks and how to overcome them Even if you don't know anything about programming, Neural Networks is the perfect place to start now. Still, if you already know about programming but not about how to do it in Artificial Intelligence, neural networks are the next thing you want to learn. And Neural Networks for Beginners is the best way to do it. Buy Neural Network for Beginners now to get the best start for your journey to Artificial Intelligence.

Introduction to Machine Learning

Author : Ethem Alpaydin
Publisher : MIT Press
ISBN 13 : 0262028182
Total Pages : 639 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Introduction to Machine Learning by : Ethem Alpaydin

Download or read book Introduction to Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2014-08-22 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

Strengthening Deep Neural Networks

Author : Katy Warr
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492044903
Total Pages : 233 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Strengthening Deep Neural Networks by : Katy Warr

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 233 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

Neural Network Learning and Expert Systems

Author : Stephen I. Gallant
Publisher : MIT Press
ISBN 13 : 9780262071451
Total Pages : 392 pages
Book Rating : 4.0/5 (714 download)

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Book Synopsis Neural Network Learning and Expert Systems by : Stephen I. Gallant

Download or read book Neural Network Learning and Expert Systems written by Stephen I. Gallant and published by MIT Press. This book was released on 1993 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: presents a unified and in-depth development of neural network learning algorithms and neural network expert systems

Neural Networks: Tricks of the Trade

Author : Grégoire Montavon
Publisher : Springer
ISBN 13 : 3642352898
Total Pages : 753 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Neural Networks: Tricks of the Trade by : Grégoire Montavon

Download or read book Neural Networks: Tricks of the Trade written by Grégoire Montavon and published by Springer. This book was released on 2012-11-14 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

Machine Learners

Author : Adrian Mackenzie
Publisher : MIT Press
ISBN 13 : 0262036827
Total Pages : 269 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Machine Learners by : Adrian Mackenzie

Download or read book Machine Learners written by Adrian Mackenzie and published by MIT Press. This book was released on 2017-11-16 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.