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This volume features a collection of peer-reviewed contributions from the biannual conference organized by the Mexican... Læs mere
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This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using... Læs mere
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The excellent generalizability of deep learning is like a “cloud” to conventional complexity-based learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous.
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This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming... Læs mere
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This Element introduces the basics of Bayesian regression modeling using modern computational tools and assumes that the reader has taken a... Læs mere
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This third edition explores both the leading software tools, methodology of spatial data analysis and the suite of R, for the analysis of vector... Læs mere
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The goals of the 2nd edition are to develop the skills and an appreciation for the versatility of modern time series analysis as a tool for analyzing dependent data. This edition emphasizes a variety of methodological techniques to illustrate solutions to data analysis problems.
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This book is designed for intermediate learners with a basic understanding of Python. Python programming is broken down here into easily digestible lessons of less than 5 minutes each, following a tried and tested BiteSize approach.
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Mathematical Foundations of Artificial Intelligence: Basics of Manifold Theory is the first volume in a two-part series. Together, they establish a unifying mathematical framework based on smooth manifold theory and Riemannian geometry.
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This book focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization... Læs mere
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Distinct from similar books, unique sections are covered in each chapter such as solution of nonlinear algebraic systems,... Læs mere
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The topics covered include, but are not limited to, statistical theory and methods, sampling theory, Bayesian statistics, statistical modeling, computational statistics, classification, data analysis, gender statistics and applied statistics.