The Mathematical-Function Computation Handbook, Nelson H.F. Beebe

Автор: Riley
Название: Mathematical Methods for Physics and Engineering
ISBN: 0521679710 ISBN-13(EAN): 9780521679718
Издательство: Cambridge Academ
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Цена: 47520.00 T
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Описание: This highly acclaimed undergraduate textbook teaches all the mathematics for undergraduate courses in the physical sciences. Containing over 800 exercises, half come with hints and answers and, in a separate manual, complete worked solutions. The remaining exercises are intended for unaided homework; full solutions are available to instructors.

Автор: Ron Kohavi, Diane Tang, Ya Xu
Название: Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
ISBN: 1108724264 ISBN-13(EAN): 9781108724265
Издательство: Cambridge Academ
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Цена: 45050.00 T
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Описание: Getting numbers is easy; getting trustworthy numbers is hard. From experimentation leaders at Amazon, Google, LinkedIn, and Microsoft, this guide to accelerating innovation using A/B tests includes practical examples, pitfalls, and advice for students and industry professionals, plus deeper dives into advanced topics for experienced practitioners.

Автор: Petr Hajek
Название: Metamathematics of First-Order Arithmetic
ISBN: 1107168414 ISBN-13(EAN): 9781107168411
Издательство: Cambridge Academ
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Цена: 111620.00 T
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Описание: This much-needed monograph on the metamathematics of first-order arithmetic pays particular attention to subsystems (fragments) of Peano arithmetic and gives the reader a deeper understanding of the role of the axiom schema of induction and of the phenomenon of incompleteness.

Автор: Fausto Pedro Garcia Marquez
Название: Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning
ISBN: 1799801063 ISBN-13(EAN): 9781799801061
Издательство: Mare Nostrum (Eurospan)
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Цена: 255030.00 T
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Описание: As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary.

Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

Автор: Pablo Duboue
Название: The Art of Feature Engineering: Essentials for Machine Learning
ISBN: 1108709389 ISBN-13(EAN): 9781108709385
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.

Автор: Tor Lattimore, Csaba Szepesvari
Название: Bandit Algorithms
ISBN: 1108486827 ISBN-13(EAN): 9781108486828
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks.

Автор: E. J. Hinch
Название: Think Before You Compute: A Prelude to Computational Fluid Dynamics
ISBN: 1108479545 ISBN-13(EAN): 9781108479547
Издательство: Cambridge Academ
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Цена: 122460.00 T
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Описание: This guide to computational fluid mechanics is for any beginning graduate student who is thinking of computing a flow. The text starts with a quick introduction to enable students to solve numerically a simple problem by a simple method, before detailing all the key essentials and discussing optional advanced topics.

Автор: Donald B. Percival, Andrew T. Walden
Название: Spectral Analysis for Univariate Time Series
ISBN: 1107028140 ISBN-13(EAN): 9781107028142
Издательство: Cambridge Academ
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Цена: 97150.00 T
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Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.

Автор: Paul H.C. Eilers, Brian D. Marx
Название: Practical Smoothing: The Joys of P-splines
ISBN: 1108482953 ISBN-13(EAN): 9781108482950
Издательство: Cambridge Academ
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Цена: 57030.00 T
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Описание: P-splines are widely used in statistics and machine learning for smoothing out noise in data and to avoid overtraining. This practical guide covers theory and a range of standard and non-standard applications with code in R for professionals and researchers looking for a simple, flexible and powerful smoothing tool.

Автор: Gill, Philip E.,
Название: Numerical linear algebra and optimization /
ISBN: 1611976561 ISBN-13(EAN): 9781611976564
Издательство: Mare Nostrum (Eurospan)
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Цена: 74410.00 T
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Описание: Numerical Linear Algebra and Optimization covers the fundamentals of closely related topics: linear systems (linear equations and least-squares) and linear programming (optimizing a linear function subject to linear constraints). For each problem class, stable and efficient numerical algorithms intended for a finite-precision environment are derived and analyzed. In 1991, when the book first appeared, these topics were rarely taught with a unified perspective, and, somewhat surprisingly, this remains true almost 30 years later. As a result, some of the material in this book can be difficult to find elsewhere—in particular, techniques for updating the LU factorization, descriptions of the simplex method applied to all-inequality form, and the analysis of what happens when using an approximate inverse to solve Ax=b.This book is appropriate for students who want to learn about numerical techniques for solving linear systems and/or linear programming using the simplex method.

Автор: Liao Heng
Название: Mathematics for Future Computing and Communications
ISBN: 1316513580 ISBN-13(EAN): 9781316513583
Издательство: Cambridge Academ
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Цена: 57030.00 T
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Описание: New mathematical ideas go hand-in-hand with innovation in computing, communications and AI. Such innovations are described here in a book that provides a panorama of ideas and applications in computer architecture, software verification, quantum computing, compressed sensing, Bayesian inference, machine learning, reinforcement learning and more.

Автор: Strang Gilbert
Название: Calculus
ISBN: 0980232759 ISBN-13(EAN): 9780980232752
Издательство: Cambridge Academ
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Цена: 67570.00 T
Наличие на складе: Невозможна поставка.
Описание: Gilbert Strang's clear, direct style and detailed, intensive explanations make this textbook ideal as both a course companion and for self-study. Single variable and multivariable calculus are covered in depth. Key examples of the application of calculus to areas such as physics, engineering and economics are included in order to enhance students' understanding. New to the third edition is a chapter on the 'Highlights of calculus', which accompanies the popular video lectures by the author on MIT's OpenCourseWare. These can be accessed from math.mit.edu/~gs.