Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning, Gallier Jean H, Quaintance Jocelyn

Автор: Strang Gilbert
Название: Linear Algebra and Learning from Data
ISBN: 0692196382 ISBN-13(EAN): 9780692196380
Издательство: Cambridge Academ
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Цена: 66520.00 T
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Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Автор: Boyd Stephen
Название: Introduction to Applied Linear Algebra
ISBN: 1316518965 ISBN-13(EAN): 9781316518960
Издательство: Cambridge Academ
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Цена: 45410.00 T
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Описание: 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 and artificial intelligence, signal and image processing, navigation, control, and finance.

Автор: Strang
Название: Introduction to Linear Algebra 5 ed.
ISBN: 0980232775 ISBN-13(EAN): 9780980232776
Издательство: Cambridge Academ
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Цена: 0.00 T
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Описание: Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains all the qualities of earlier editions, while at the same time seeing numerous minor improvements and major additions. The latter include: • A new chapter on singular values and singular vectors, including ways to analyze a matrix of data • A revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages • A new section on linear algebra and cryptography • A new chapter on linear algebra in probability and statistics. A dedicated and active website also offers solutions to exercises as well as new exercises from many different sources (including practice problems, exams, and development of textbook examples), plus codes in MATLAB®, Julia, and Python.

Автор: Shalev-Shwartz
Название: Understanding Machine Learning
ISBN: 1107057132 ISBN-13(EAN): 9781107057135
Издательство: Cambridge Academ
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Цена: 92490.00 T
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Описание: Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the `hows` and `whys` of machine-learning algorithms, making the field accessible to both students and practitioners.

Автор: Steven L. Brunton, J. Nathan Kutz
Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
ISBN: 1108422098 ISBN-13(EAN): 9781108422093
Издательство: Amazon Internet
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Цена: 0.00 T
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Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.

Автор: Shouvik Chakraborty, Kalyani Mali
Название: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities
ISBN: 1799827372 ISBN-13(EAN): 9781799827375
Издательство: Mare Nostrum (Eurospan)
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Цена: 134910.00 T
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Описание: Computer vision and object recognition are two technological methods that are frequently used in various professional disciplines. In order to maintain high levels of quality and accuracy of services in these sectors, continuous enhancements and improvements are needed. The implementation of artificial intelligence and machine learning has assisted in the development of digital imaging, yet proper research on the applications of these advancing technologies is lacking.

Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities explores the theoretical and practical aspects of modern advancements in digital image analysis and object detection as well as its applications within healthcare, security, and engineering fields. Featuring coverage on a broad range of topics such as disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers, academicians, practitioners, scientists, industry professionals, scholars, and students seeking research on the current developments in object recognition using artificial intelligence.

Автор: Shouvik Chakraborty, Kalyani Mali
Название: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities
ISBN: 1799827364 ISBN-13(EAN): 9781799827368
Издательство: Mare Nostrum (Eurospan)
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Цена: 174630.00 T
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Описание: Computer vision and object recognition are two technological methods that are frequently used in various professional disciplines. In order to maintain high levels of quality and accuracy of services in these sectors, continuous enhancements and improvements are needed. The implementation of artificial intelligence and machine learning has assisted in the development of digital imaging, yet proper research on the applications of these advancing technologies is lacking.

Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities explores the theoretical and practical aspects of modern advancements in digital image analysis and object detection as well as its applications within healthcare, security, and engineering fields. Featuring coverage on a broad range of topics such as disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers, academicians, practitioners, scientists, industry professionals, scholars, and students seeking research on the current developments in object recognition using artificial intelligence.

Автор: Ramgopal Kashyap, A.V. Senthil Kumar
Название: Challenges and Applications for Implementing Machine Learning in Computer Vision
ISBN: 1799801837 ISBN-13(EAN): 9781799801832
Издательство: Mare Nostrum (Eurospan)
Цена: 173190.00 T
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Описание: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.

Автор: Ramgopal Kashyap, A.V. Senthil Kumar
Название: Challenges and Applications for Implementing Machine Learning in Computer Vision
ISBN: 1799801829 ISBN-13(EAN): 9781799801825
Издательство: Mare Nostrum (Eurospan)
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Цена: 174630.00 T
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Описание: Presents research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. The book highlights a wide range of topics such as video segmentation, object recognition, and 3D modelling.

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: Lax, Peter D.
Название: Linear algebra and its applications
ISBN: 0471751561 ISBN-13(EAN): 9780471751564
Издательство: Wiley
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Цена: 88650.00 T
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Описание: This introduction to linear algebra by world-renowned mathematician Peter Lax is unique in its emphasis on the analytical aspects of the subject as well as its numerous applications. The book grew out of Dr. Lax`s course notes for the linear algebra classes he taught at New York University.

Автор: Anton, Howard; Rorres, Chris
Название: Elementary linear algebra with supplemental applications
ISBN: 0470561572 ISBN-13(EAN): 9780470561577
Издательство: Wiley
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Цена: 192150.00 T
Наличие на складе: Невозможна поставка.
Описание: When it comes to learning linear algebra, engineers trust Anton. The tenth edition presents the key concepts and topics along with engaging and contemporary applications. The chapters have been reorganized to bring up some of the more abstract topics and make the material more accessible. More theoretical exercises at all levels of difficulty are integrated throughout the pages, including true/false questions that address conceptual ideas. New marginal notes provide a fuller explanation when new methods and complex logical steps are included in proofs. Small-scale applications also show how concepts are applied to help engineers develop their mathematical reasoning.