22 rezultatov
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Linear Algebra for Data Science, Machine Learning, and Signal Processing(2024) FESSLER, JEFFREY A. (UNIVERSITY OF MICHIGAN, ANN ARBOR),NADAKUDITI, RAJ RAO (UNIVERSITY OF MICHIGAN, ANN ARBOR)Master basic matrix methods by seeing how the mathematics is used in practice in a range of data-driven applications. Includes a wealth of engaging exercises for quizzes, self-study and interactive learning, as well as online JULIA demos offering a hands-Vezava: Trda84,79 € -
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 €
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Neural Networks for Pattern Recognition(1995) HINTON, GEOFFREYProviding a comprehensive account of neural networks from a statistical perspective, this book emphasizes on pattern recognition, which represents the area of greatest applicability for neural networks in contemporary times.Vezava: Mehka138,60 € -
A Gentle Introduction to Support Vector Machines in Biomedicine: Volume 2: Case Studies and Benchmarks(2012) ALEXANDER STATNIKOVVezava: Trda63,31 € -
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 € -
Cambridge Series in Statistical and Probabilistic Mathematics(2019) MARTIN J. WAINWRIGHTVezava: Trda110,24 € -
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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 € -
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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 € -
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 € -
Linear Algebra for Everyone(2020) STRANG, GILBERT (MASSACHUSETTS INSTITUTE OF TECHNOLOGY)Vezava: Trda89,88 € -
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Introduction to Data Science(2017) IGUAL, LAURA,SEGUI, SANTIThe coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentimeVezava: Mehka59,52 € -
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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 € -
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 € -
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 € -
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 € -
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: Mehka13,88 €
