21 rezultatov
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Hands-On Unsupervised Learning Using Python(2019) PATEL, ANKUR A.Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-rVezava: Mehka82,08 €
PATEL, ANKUR A.
Hands-On Unsupervised Learning Using Python
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-r
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Neural Networks for Pattern Recognition(1995) BISHOP, CHRISTOPHER M. (, MICROSOFT RESEARCH)This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on patternVezava: Mehka138,60 €
BISHOP, CHRISTOPHER M. (, MICROSOFT RESEARCH)
Neural Networks for Pattern Recognition
This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern
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Machine Learning: The Art and Science of Algorithms That Make Sense of Data(2012) PETER FLACHVezava: Mehka81,40 €
PETER FLACH
Machine Learning: The Art and Science of Algorithms That Make Sense of Data
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A Gentle Introduction to Support Vector Machines in Biomedicine: Volume 2: Case Studies and Benchmarks(2012) ALEXANDER STATNIKOVVezava: Trda63,31 €
ALEXANDER STATNIKOV
A Gentle Introduction to Support Vector Machines in Biomedicine: Volume 2: Case Studies and Benchmarks
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High-Dimensional Probability(2018) VERSHYNIN, ROMAN (UNIVERSITY OF CALIFORNIA, IRVINE)The data sciences are moving fast, and probabilistic methods are both the foundation and a driver. This highly motivated text brings beginners up to speed quickly and provides working data scientists with powerful new tools. Ideal for a basic second coursVezava: Trda93,27 €
VERSHYNIN, ROMAN (UNIVERSITY OF CALIFORNIA, IRVINE)
High-Dimensional Probability
The data sciences are moving fast, and probabilistic methods are both the foundation and a driver. This highly motivated text brings beginners up to speed quickly and provides working data scientists with powerful new tools. Ideal for a basic second cours
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Cambridge Series in Statistical and Probabilistic Mathematics(2019) MARTIN J. WAINWRIGHTVezava: Trda110,24 €
MARTIN J. WAINWRIGHT
Cambridge Series in Statistical and Probabilistic Mathematics
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Data Matching(2014) CHRISTEN, PETERThis inaugural volume on a topic of increasing importance collates research on databases, statistics, information retrieval, data mining and machine learning to provide a detailed discussion of the practical aspects and limitations of data matching.Vezava: Mehka165,36 €
CHRISTEN, PETER
Data Matching
This inaugural volume on a topic of increasing importance collates research on databases, statistics, information retrieval, data mining and machine learning to provide a detailed discussion of the practical aspects and limitations of data matching.
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Introduction to Applied Linear Algebra(2018) BOYD, STEPHEN (STANFORD UNIVERSITY, CALIFORNIA),VANDENBERGHE, LIEVEN (UNIVERSITY OF CALIFORNIA, LOS ANGELES)A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning anVezava: Trda69,53 €
BOYD, STEPHEN (STANFORD UNIVERSITY, CALIFORNIA),VANDENBERGHE, LIEVEN (UNIVERSITY OF CALIFORNIA, LOS ANGELES)
Introduction to Applied Linear Algebra
A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning an
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Man-Machine Speech Communication(2018)The papers address issues such as challenging issues in speech recognition and enhancement, speaker and language recognition, speech synthesis, corpus and phonetic in speech technology, speech generation, speech analyzing and modelling, speech processingVezava: Mehka23,85 €
Man-Machine Speech Communication
The papers address issues such as challenging issues in speech recognition and enhancement, speaker and language recognition, speech synthesis, corpus and phonetic in speech technology, speech generation, speech analyzing and modelling, speech processing
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Linear Algebra for Everyone(2020) STRANG, GILBERT (MASSACHUSETTS INSTITUTE OF TECHNOLOGY)Vezava: Trda89,88 €
STRANG, GILBERT (MASSACHUSETTS INSTITUTE OF TECHNOLOGY)
Linear Algebra for Everyone
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An Intuitive Exploration of Artificial Intelligence - Theory and Applications of Deep Learning(2022) DUBE, SIMANTVezava: Mehka83,95 €
DUBE, SIMANT
An Intuitive Exploration of Artificial Intelligence - Theory and Applications of Deep Learning
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Intelligent Mobile Service Computing(2020)This book discusses recent research and applications in intelligent service computing in mobile environments. The book, made up of contributions from academic and industry, aims to present advances in intelligent services, new algorithms and techniques inVezava: Trda23,85 €
Intelligent Mobile Service Computing
This book discusses recent research and applications in intelligent service computing in mobile environments. The book, made up of contributions from academic and industry, aims to present advances in intelligent services, new algorithms and techniques in
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Essentials of Pattern Recognition(2020) WU, JIANXIN (NANJING UNIVERSITY, CHINA)Introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. The text focuses on a relatively small number of core concepts with an abundance of illustrations and examples and provVezava: Trda93,27 €
WU, JIANXIN (NANJING UNIVERSITY, CHINA)
Essentials of Pattern Recognition
Introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. The text focuses on a relatively small number of core concepts with an abundance of illustrations and examples and prov
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Machine Learning Refined(2020) WATT, JEREMY (NORTHWESTERN UNIVERSITY, ILLINOIS),BORHANI, REZA (NORTHWESTERN UNIVERSITY, ILLINOIS),KATSAGGELOS, AGGELOS K. (NORTHWESTERN UNIVERSITY, ILLINOIS)An intuitive approach to machine learning detailing the key concepts needed to build products and conduct research. Featuring color illustrations, real-world examples, practical coding exercises, and an online package including sample code, data sets, lecVezava: Trda98,36 €
WATT, JEREMY (NORTHWESTERN UNIVERSITY, ILLINOIS),BORHANI, REZA (NORTHWESTERN UNIVERSITY, ILLINOIS),KATSAGGELOS, AGGELOS K. (NORTHWESTERN UNIVERSITY, ILLINOIS)
Machine Learning Refined
An intuitive approach to machine learning detailing the key concepts needed to build products and conduct research. Featuring color illustrations, real-world examples, practical coding exercises, and an online package including sample code, data sets, lec
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Scaling up Machine Learning(2018)In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a vaVezava: Mehka76,31 €
Scaling up Machine Learning
In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a va
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Mathematics for Machine Learning(2020) DEISENROTH, MARC PETER (UNIVERSITY COLLEGE LONDON),FAISAL, A. ALDO (IMPERIAL COLLEGE LONDON),ONG, CHENG SOONThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculusVezava: Mehka73,49 €
DEISENROTH, MARC PETER (UNIVERSITY COLLEGE LONDON),FAISAL, A. ALDO (IMPERIAL COLLEGE LONDON),ONG, CHENG SOON
Mathematics for Machine Learning
This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus
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Biometrics(2018) FAIRHURST, MICHAEL (PROFESSOR OF COMPUTER VISION, UNIVERSITY OF KENT)With the rise of digital technologies the need for effective means of identification has grown enormously. Biometrics is the rapidly growing science of identifying individuals through biological characteristics, from iris patterning to voice recognition.Vezava: Mehka12,50 €
FAIRHURST, MICHAEL (PROFESSOR OF COMPUTER VISION, UNIVERSITY OF KENT)
Biometrics
With the rise of digital technologies the need for effective means of identification has grown enormously. Biometrics is the rapidly growing science of identifying individuals through biological characteristics, from iris patterning to voice recognition.
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Algorithmic High-Dimensional Robust Statistics(2023) DIAKONIKOLAS, ILIAS (UNIVERSITY OF WISCONSIN-MADISON),KANE, DANIEL M. (UNIVERSITY OF CALIFORNIA, SAN DIEGO)This reference text offers a clear unified treatment for graduate students, academic researchers, and professionals interested in understanding and developing statistical procedures for high-dimensional data that are robust to idealized modeling assumptioVezava: Trda76,31 €
DIAKONIKOLAS, ILIAS (UNIVERSITY OF WISCONSIN-MADISON),KANE, DANIEL M. (UNIVERSITY OF CALIFORNIA, SAN DIEGO)
Algorithmic High-Dimensional Robust Statistics
This reference text offers a clear unified treatment for graduate students, academic researchers, and professionals interested in understanding and developing statistical procedures for high-dimensional data that are robust to idealized modeling assumptio
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