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Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and ...
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book DescriptionMachine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is forThis book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
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47.240000 USD

Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

by Giuseppe Bonaccorso
Paperback
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This volume constitutes the thoroughly refereed proceedings of the 24th IFIP WG 1.5 International Workshop on Cellular Automata and Discrete Complex Systems, AUTOMATA 2018, held in Ghent, Belgium, in June 2018.The 10 regular papers presented in this book were carefully reviewed and selected from a total of 16 submissions. The ...
Cellular Automata and Discrete Complex Systems: 24th IFIP WG 1.5 International Workshop, AUTOMATA 2018, Ghent, Belgium, June 20-22, 2018, Proceedings
This volume constitutes the thoroughly refereed proceedings of the 24th IFIP WG 1.5 International Workshop on Cellular Automata and Discrete Complex Systems, AUTOMATA 2018, held in Ghent, Belgium, in June 2018.The 10 regular papers presented in this book were carefully reviewed and selected from a total of 16 submissions. The papers highlight the major advances in the field and the development of new tools, support the development of theory and applications of CA and DCS and identify and study within an inter- and multidisciplinary context, the important fundamental aspects, concepts, notions and problems concerning CA and DCS.
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62.990000 USD

Cellular Automata and Discrete Complex Systems: 24th IFIP WG 1.5 International Workshop, AUTOMATA 2018, Ghent, Belgium, June 20-22, 2018, Proceedings

Paperback
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This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving complex biological problems and enhancing treatment of various grave diseases.
Machine Learning and IoT: A Biological Perspective
This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving complex biological problems and enhancing treatment of various grave diseases.
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238.88 USD

Machine Learning and IoT: A Biological Perspective

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Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book DescriptionCyber security is crucial for both businesses and individuals. As systems are getting smarter, we ...
Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python
Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book DescriptionCyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it's important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you've gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you'll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you'll focus on topics such as network intrusion detection and AV and IDS evasion. We'll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is forThis book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
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41.990000 USD

Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python

by Chiheb Chebbi
Paperback
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Be an adaptive thinker that leads the way to Artificial Intelligence Key Features AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Book DescriptionArtificial Intelligence has the potential to replicate ...
Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases
Be an adaptive thinker that leads the way to Artificial Intelligence Key Features AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Book DescriptionArtificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. What you will learn Use adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Acquire advanced AI, machine learning, and deep learning designing skills Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology Understand future AI solutions and adapt quickly to them Develop out-of-the-box thinking to face any challenge the market presents Who this book is forArtificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book.
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41.990000 USD

Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

by Denis Rothman
Paperback
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Guide to Computing Fundamentals in Cyber-Physical Systems: Concepts, Design Methods, and Applications
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115.490000 USD

Guide to Computing Fundamentals in Cyber-Physical Systems: Concepts, Design Methods, and Applications

by Dietmar P. F. Moller
Paperback
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Mathematical Progress in Expressive Image Synthesis III: Selected and Extended Results from the Symposium Meis2015
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230.990000 USD

Mathematical Progress in Expressive Image Synthesis III: Selected and Extended Results from the Symposium Meis2015

Paperback
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Machine Learning for Beginners: Complete Practical Approach to Machine Learning and Its Application for Complete Beginners
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8.130000 USD

Machine Learning for Beginners: Complete Practical Approach to Machine Learning and Its Application for Complete Beginners

by Thomas Wood
Paperback
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Topics in Grammatical Inference
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146.990000 USD

Topics in Grammatical Inference

Paperback
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Compression-Based Methods of Statistical Analysis and Prediction of Time Series
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146.990000 USD

Compression-Based Methods of Statistical Analysis and Prediction of Time Series

by Mikhail Malyutov, Jaakko Astola, Boris Ryabko
Paperback
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Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, ...
Statistical Regression and Classification: From Linear Models to Machine Learning
Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
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92.13 USD

Statistical Regression and Classification: From Linear Models to Machine Learning

by Norman Matloff
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This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance ...
Deep Learning for Biometrics: 2017
This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories. Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.
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167.990000 USD

Deep Learning for Biometrics: 2017

Hardback
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Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are ...
Non-Convex Multi-Objective Optimization: 2017
Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management.
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115.490000 USD

Non-Convex Multi-Objective Optimization: 2017

by Antanas Zilinskas
Hardback
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John Vince explains a wide range of mathematical techniques and problem-solving strategies associated with computer games, computer animation, virtual reality, CAD and other areas of computer graphics in this completely revised and expanded fifth edition. The first five chapters cover a general introduction, number sets, algebra, trigonometry and coordinate systems, ...
Mathematics for Computer Graphics: 2017
John Vince explains a wide range of mathematical techniques and problem-solving strategies associated with computer games, computer animation, virtual reality, CAD and other areas of computer graphics in this completely revised and expanded fifth edition. The first five chapters cover a general introduction, number sets, algebra, trigonometry and coordinate systems, which are employed in the following chapters on vectors, matrix algebra, transforms, interpolation, curves and patches, analytic geometry and barycentric coordinates. Following this, the reader is introduced to the relatively new topic of geometric algebra, followed by two chapters that introduce differential and integral calculus. Finally, there is a chapter on worked examples. Mathematics for Computer Graphics covers all of the key areas of the subject, including: * Number sets * Algebra * Trigonometry * Coordinate systems * Determinants * Vectors * Quaternions * Matrix algebra * Geometric transforms * Interpolation * Curves and surfaces * Analytic geometry * Barycentric coordinates * Geometric algebra * Differential calculus * Integral calculus This fifth edition contains over 120 worked examples and over 320 colour illustrations, which are central to the author's descriptive writing style. Mathematics for Computer Graphics provides a sound understanding of the mathematics required for computer graphics, giving a fascinating insight into the design of computer graphics software and setting the scene for further reading of more advanced books and technical research papers.
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81.89 USD

Mathematics for Computer Graphics: 2017

by John Vince
Paperback
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This book constitutes the refereed proceedings of the 23th International Conference on DNA Computing and Molecular Programming, DNA 23, held Austin, TX, USA, in September 2017. The 16 full papers presented were carefully selected from 23 submissions. Research in DNA computing aims to draw together mathematics, computerscience, physics, chemistry, biology, ...
DNA Computing and Molecular Programming: 23rd International Conference, DNA 23, Austin, TX, USA, September 24-28, 2017, Proceedings
This book constitutes the refereed proceedings of the 23th International Conference on DNA Computing and Molecular Programming, DNA 23, held Austin, TX, USA, in September 2017. The 16 full papers presented were carefully selected from 23 submissions. Research in DNA computing aims to draw together mathematics, computerscience, physics, chemistry, biology, and nanotechnology to address the analysis, design, and synthesis of information-based molecular systems. The papers address all areas related to biomolecular computing such as: algorithms and models for computation with biomolecular systems; computational processes in vitro and in vivo; molecular motors and molecular robotics; studies of fault-tolerance and error correction; software tools for analysis, simulation, and design; synthetic biology and in vitro evolution; applications in engineering, physics, chemistry, biology, and medicine.
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73.490000 USD

DNA Computing and Molecular Programming: 23rd International Conference, DNA 23, Austin, TX, USA, September 24-28, 2017, Proceedings

Paperback
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This book constitutes the refereed proceedings of the 21st International Symposium on Fundamentals of Computation Theory, FCT 2017, held in Bordeaux, France, in September 2017. The 29 revised full papers and 5 invited papers presented were carefully reviewed and selected from 99 submissions. The papers cover topics of all aspects ...
Fundamentals of Computation Theory: 21st International Symposium, FCT 2017, Bordeaux, France, September 11-13, 2017, Proceedings
This book constitutes the refereed proceedings of the 21st International Symposium on Fundamentals of Computation Theory, FCT 2017, held in Bordeaux, France, in September 2017. The 29 revised full papers and 5 invited papers presented were carefully reviewed and selected from 99 submissions. The papers cover topics of all aspects of theoretical computer science, in particular algorithms, complexity, formal and logical methods.
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89.240000 USD

Fundamentals of Computation Theory: 21st International Symposium, FCT 2017, Bordeaux, France, September 11-13, 2017, Proceedings

Paperback
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This book constitutes the proceedings of the 14th International Conference on Quantitative Evaluation Systems, QEST 2017, held in Berlin, Germany, in September 2017.The 20 full papers and 4 tool papers presented were carefully reviewed and selected From 58 submissions. The papers are organized in topical sections entitled: probabilistic modeling; smart ...
Quantitative Evaluation of Systems: 14th International Conference, QEST 2017, Berlin, Germany, September 5-7, 2017, Proceedings
This book constitutes the proceedings of the 14th International Conference on Quantitative Evaluation Systems, QEST 2017, held in Berlin, Germany, in September 2017.The 20 full papers and 4 tool papers presented were carefully reviewed and selected From 58 submissions. The papers are organized in topical sections entitled: probabilistic modeling; smart energy systems over the cloud; Petri nets and performance modeling; parametric verification; machine learning and formal methods; tools.
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83.990000 USD

Quantitative Evaluation of Systems: 14th International Conference, QEST 2017, Berlin, Germany, September 5-7, 2017, Proceedings

Paperback
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This book constitutes the thoroughly refereed post-conference proceedings of the 12fth International Conference on High Performance Computing in Computational Science, VECPAR 2016, held in Porto, Portugal, in June 2016. The 20 full papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on ...
High Performance Computing for Computational Science - VECPAR 2016: 12th International Conference, Porto, Portugal, June 28-30, 2016, Revised Selected Papers
This book constitutes the thoroughly refereed post-conference proceedings of the 12fth International Conference on High Performance Computing in Computational Science, VECPAR 2016, held in Porto, Portugal, in June 2016. The 20 full papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on applications; performance modeling and analysis; low level support; environments/libraries to support parallelization.
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73.490000 USD

High Performance Computing for Computational Science - VECPAR 2016: 12th International Conference, Porto, Portugal, June 28-30, 2016, Revised Selected Papers

Paperback
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This book highlights state-of-the-art developments in metaheuristics research. It examines all aspects of metaheuristic research including new algorithmic developments, applications, new research challenges, theoretical developments, implementation issues, in-depth experimental studies. The book is divided into two sections. Part I is focused on new optimization and modeling techniques based on metaheuristics. ...
Recent Developments in Metaheuristics
This book highlights state-of-the-art developments in metaheuristics research. It examines all aspects of metaheuristic research including new algorithmic developments, applications, new research challenges, theoretical developments, implementation issues, in-depth experimental studies. The book is divided into two sections. Part I is focused on new optimization and modeling techniques based on metaheuristics. The chapters in this section cover topics from multi-objective problems with fuzzy data with triangular-valued objective functions, to hyper-heuristics optimization methodology, designing genetic algorithms, and also the cuckoo search algorithm. The techniques described help to enhance the usability and increase the potential of metaheuristic algorithms. Part II showcases advanced metaheuristic approaches to solve real-life applications issues. This includes an examination of scheduling, the vehicle routing problem, multimedia sensor network, supplier selection, bin packing, objects tracking, and radio frequency identification. In the fields covered in the chapters are of high-impact applications of metaheuristics. The chapters offer innovative applications of metaheuristics that have a potential of widening research frontiers. Altogether, this book offers a comprehensive look at how researchers are currently using metaheuristics in different domains of design and application.
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178.490000 USD

Recent Developments in Metaheuristics

Hardback
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Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 24th Workshop on Logic, Language, Information and Communication, WoLLIC 2017, held in London, UK, in August 2017. The 28 contributed papers were carefully reviewed and selected from 61 submissions. They ...
Logic, Language, Information, and Computation: 24th International Workshop, WoLLIC 2017, London, UK, July 18-21, 2017, Proceedings
Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 24th Workshop on Logic, Language, Information and Communication, WoLLIC 2017, held in London, UK, in August 2017. The 28 contributed papers were carefully reviewed and selected from 61 submissions. They cover interdisciplinary research in pure and applied logic, aiming at interactions between logic and the sciences related to information and computation.
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89.240000 USD

Logic, Language, Information, and Computation: 24th International Workshop, WoLLIC 2017, London, UK, July 18-21, 2017, Proceedings

Paperback
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Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces ...
Sufficient Dimension Reduction: Methods and Applications with R
Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The author Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.
131.39 USD

Sufficient Dimension Reduction: Methods and Applications with R

by Bing Li
Hardback
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The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book ...
Artificial Intelligence: With an Introduction to Machine Learning, Second Edition
The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.
170.62 USD

Artificial Intelligence: With an Introduction to Machine Learning, Second Edition

by Xia Jiang, Richard E. Neapolitan
Hardback
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The science of AI was born a little over 60 years ago, but for most of that time its achievements were modest. In 2012 it experienced a big bang, when a branch of statistics called Machine Learning (and a sub-branch called Deep Learning) was applied to it. Now machines have ...
Artificial Intelligence and the Two Singularities
The science of AI was born a little over 60 years ago, but for most of that time its achievements were modest. In 2012 it experienced a big bang, when a branch of statistics called Machine Learning (and a sub-branch called Deep Learning) was applied to it. Now machines have surpassed humans in image recognition, and they are catching up with us at speech recognition and natural language processing. Every day, the media reports the launch of a new service, a new product, and a new demonstration powered by AI. When will it end? The surprising truth is, the AI revolution has only just begun. Artificial Intelligence and the Two Singularities argues that in the course of this century, the exponential growth in the capability of AI is likely to bring about two singularities - points at which conditions are so extreme that the normal rules break down. The first is the economic singularity, when machine skill reaches a level that renders many of us unemployable and requires an overhaul of our current economic and social systems. The second is the technological singularity, when machine intelligence reaches and then surpasses the cognitive abilities of an adult human, relegating us to the second smartest species on the planet. These singularities will present huge challenges, but this book argues that we can meet these challenges and overcome them. If we do, the rewards could be almost unimaginable. This book covers: * Recent developments in AI and its future potential * The economic singularity and the technological singularity in depth * The risks and opportunities presented by AI * What actions we should take Artificial intelligence can turn out to be the best thing ever to happen to humanity, making our future wonderful almost beyond imagination. But only if we address head-on the challenges that it will raise. Calum Chace is a best-selling author of fiction and non-fiction books and articles, focusing on the subject of artificial intelligence. He is a regular speaker on artificial intelligence and related technologies, and runs a blog on the subject at www.pandoras-brain. com. Prior to becoming a full-time writer and speaker, he spent 30 years in business as a marketer, a strategy consultant, and a CEO. He studied philosophy at Oxford University, where he discovered that the science fiction he had been reading since boyhood was simply philosophy in fancy dress.
52.88 USD

Artificial Intelligence and the Two Singularities

by Calum Chace
Paperback
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A chain event graph (CEG) is an important generalization of the Bayesian Network (BN). BNs have been extremely useful for modeling discrete processes. However, they are not appropriate for all applications. Over the past six years or so, teams of researchers led by Jim Smith have established a strong theoretical ...
Chain Event Graphics
A chain event graph (CEG) is an important generalization of the Bayesian Network (BN). BNs have been extremely useful for modeling discrete processes. However, they are not appropriate for all applications. Over the past six years or so, teams of researchers led by Jim Smith have established a strong theoretical underpinning for CEGs. This book systematically and transparently presents the scope and promise of this emerging class of models, together with its underpinning methodology, to a wide audience.
119.43 USD

Chain Event Graphics

by Rodrigo Collazo, Christiane Gorgen, James Q Smith
Hardback
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Description Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind ...
Deep Learning with R_p1
Description Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It's behind photo tagging, self-driving cars, virtual assistants and other previously impossible applications. Deep Learning with R is for developers and data scientists with some R experience who want to use deep learning to solve real-world problems. The book is structured around a series of practical examples that introduce each new concept and demonstrate best practices. You'll begin by learning what deep learning is, how it connects with AI and Machine Learning, and why it's rapidly gaining in importance right now. You'll then dive into practical applications of computer vision, natural language processing, and more. Key features * Understand key machine learning concepts * Set up a computer environment for deep learning * Visualize neural networks * Use recurrent neural networks for text and sequence Classification Audience You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is required. About the technology Although deep learning can be a challenging subject, new technologies make it much easier to get started than ever before. The Keras deep learning library featured in this book puts ease of use and accessibility front and center, making it a great fit for new practitioners.
52.490000 USD

Deep Learning with R_p1

by Joseph J Allaire
Paperback
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Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn't prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with ...
Introduction to Machine Learning with Applications in Information Security
Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn't prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of -Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis. Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader's benefit, the figures in the book are also available in electronic form, and in color. About the Author Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master's student projects, most of which involve a combination of information security and machine learning.
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85.30 USD

Introduction to Machine Learning with Applications in Information Security

by Mark Stamp
Hardback
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AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that think in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are ...
Human + Machine: Reimagining Work in the Age of AI
AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that think in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a leader's guide with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.
33.600000 USD

Human + Machine: Reimagining Work in the Age of AI

by Paul R. Dougherty, H. James Wilson
Hardback
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Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, ...
Statistical Regression and Classification: From Linear Models to Machine Learning
Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
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157.450000 USD
Hardback
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This volume presents an historical and philosophical revisiting of the foundational character of Turing's conceptual contributions and assesses the impact of the work of Alan Turing on the history and philosophy of science. Written by experts from a variety of disciplines, the book draws out the continuing significance of Turing's ...
Philosophical Explorations of the Legacy of Alan Turing: Turing 100
This volume presents an historical and philosophical revisiting of the foundational character of Turing's conceptual contributions and assesses the impact of the work of Alan Turing on the history and philosophy of science. Written by experts from a variety of disciplines, the book draws out the continuing significance of Turing's work. The centennial of Turing's birth in 2012 led to the highly celebrated Alan Turing Year , which stimulated a world-wide cooperative, interdisciplinary revisiting of his life and work. Turing is widely regarded as one of the most important scientists of the twentieth century: He is the father of artificial intelligence, resolver of Hilbert's famous Entscheidungsproblem, and a code breaker who helped solve the Enigma code. His work revolutionized the very architecture of science by way of the results he obtained in logic, probability and recursion theory, morphogenesis, the foundations of cognitive psychology, mathematics, and cryptography. Many of Turing's breakthroughs were stimulated by his deep reflections on fundamental philosophical issues. Hence it is fitting that there be a volume dedicated to the philosophical impact of his work. One important strand of Turing's work is his analysis of the concept of computability, which has unquestionably come to play a central conceptual role in nearly every branch of knowledge and engineering.
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146.990000 USD

Philosophical Explorations of the Legacy of Alan Turing: Turing 100

Hardback
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This book constitutes the proceedings of the 19th International Conference on Descriptional Complexity of Formal Systems, DCFS 2017, held in Milano, Italy, in July 2017. The 20 full papers presented together with 4 invited talks were carefully reviewed and selected from 26 submissions.Descriptional Complexity is a field in Computer Science ...
Descriptional Complexity of Formal Systems: 19th IFIP WG 1.02 International Conference, DCFS 2017, Milano, Italy, July 3-5, 2017, Proceedings
This book constitutes the proceedings of the 19th International Conference on Descriptional Complexity of Formal Systems, DCFS 2017, held in Milano, Italy, in July 2017. The 20 full papers presented together with 4 invited talks were carefully reviewed and selected from 26 submissions.Descriptional Complexity is a field in Computer Science that deals with the size of all kinds of objects that occur in computational models, such as turing machines, finite automata, grammars, splicing systems and others. The topics of this conference are related to all aspects of descriptional complexity.
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83.990000 USD

Descriptional Complexity of Formal Systems: 19th IFIP WG 1.02 International Conference, DCFS 2017, Milano, Italy, July 3-5, 2017, Proceedings

Paperback
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