Statistical Learning Using Neural Networks, de Braganca Pereira, Basi

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: McElreath, Richard
Название: Statistical Rethinking
ISBN: 036713991X ISBN-13(EAN): 9780367139919
Издательство: Taylor&Francis
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Цена: 83690.00 T
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Описание: Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.

Автор: Dickison
Название: Multilayer Social Networks
ISBN: 1107079497 ISBN-13(EAN): 9781107079496
Издательство: Cambridge Academ
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Цена: 87650.00 T
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Описание: Multilayer networks are an emerging and active interdisciplinary area. This book unifies and consolidates existing practical and theoretical knowledge on the topic including data collection and analysis, modeling, and mining of multilayer social network systems, and the evolution of dynamic processes such as information spreading.

Автор: Okabe
Название: Spatial Analysis Along Networks - Statistical and Computational Methods
ISBN: 0470770813 ISBN-13(EAN): 9780470770818
Издательство: Wiley
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Цена: 88650.00 T
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Описание: * Presents a much-needed practical guide to statistical spatial analysis on a network, in a logical, user-friendly order. * Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics.

Автор: Hofstad
Название: Random Graphs and Complex Networks
ISBN: 110717287X ISBN-13(EAN): 9781107172876
Издательство: Cambridge Academ
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Цена: 54910.00 T
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Описание: Network science is one of the fastest growing areas in science and business. This classroom-tested, self-contained book is designed for master`s-level courses and provides a rigorous treatment of random graph models for networks, featuring many examples of real-world networks for motivation and numerous exercises to build intuition and experience.

Автор: Lyons
Название: Probability on Trees and Networks
ISBN: 1107160154 ISBN-13(EAN): 9781107160156
Издательство: Cambridge Academ
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Цена: 61240.00 T
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Описание: This authoritative state-of-the-art account of probability on networks for graduate students and researchers in mathematics, statistics, computer science, and engineering, brings together sixty years of research, including many developments where the authors played a leading role. The text emphasizes intuition, while also giving complete proofs.

Автор: Okechukwu A. Uwechue; Abhijit S. Pandya
Название: Human Face Recognition Using Third-Order Synthetic Neural Networks
ISBN: 0792399579 ISBN-13(EAN): 9780792399575
Издательство: Springer
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Цена: 144370.00 T
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Описание: Explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. This book serves as a reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns.

Автор: Thomas Lindblad; Jason M. Kinser
Название: Image Processing Using Pulse-Coupled Neural Networks
ISBN: 3642063438 ISBN-13(EAN): 9783642063435
Издательство: Springer
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Цена: 97820.00 T
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Описание: and Theory.- Theory of Digital Simulation.- Automated Image Object Recognition.- Image Fusion.- Image Texture Processing.- Image Signatures.- Miscellaneous Applications.- Hardware Implementations.

Автор: Loy, James
Название: Neural network projects with python
ISBN: 1789138906 ISBN-13(EAN): 9781789138900
Издательство: Неизвестно
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Цена: 53940.00 T
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Описание: This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network, implementing a facial recognition security system using Siamese Neural Networks, and more.

Автор: Concha Bielza, Pedro Larranaga
Название: Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
ISBN: 110849370X ISBN-13(EAN): 9781108493703
Издательство: Cambridge Academ
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Цена: 85530.00 T
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Описание: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods.

Автор: Michel Denuit; Donatien Hainaut; Julien Trufin
Название: Effective Statistical Learning Methods for Actuaries III
ISBN: 3030258262 ISBN-13(EAN): 9783030258269
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible.Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting.Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

Автор: Helias, Moritz Dahmen, David
Название: Statistical field theory for neural networks
ISBN: 3030464431 ISBN-13(EAN): 9783030464431
Издательство: Springer
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Цена: 69870.00 T
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Описание: This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks.