Andere Kunden interessierten sich auch für
![User-friendly Introduction to PAC-Bayes Bounds]( "User-friendly Introduction to PAC-Bayes Bounds")
User-friendly Introduction to PAC-Bayes Bounds84,99 €![Introduction to Multiple Antenna Communications and Reconfigurable Surfaces]( "Introduction to Multiple Antenna Communications and Reconfigurable Surfaces")
Introduction to Multiple Antenna Communications and Reconfigurable Surfaces190,99 €![Introduction to Machine Learning]( "Introduction to Machine Learning")
Introduction to Machine Learning78,99 €![Divided Differences, Falling Factorials, and Discrete Splines]( "Divided Differences, Falling Factorials, and Discrete Splines")
Divided Differences, Falling Factorials, and Discrete Splines99,99 €![A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists]( "A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists")
A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists87,99 €![Machine Learning for Automated Theorem Proving]( "Machine Learning for Automated Theorem Proving")
Machine Learning for Automated Theorem Proving106,99 €![Graph Neural Networks for Natural Language Processing]( "Graph Neural Networks for Natural Language Processing")
Graph Neural Networks for Natural Language Processing99,99 €
As data is a predominant resource in applications, Riemannian geometry is a natural framework to model and unify complex nonlinear sources of data. However, the development of computational tools from the basic theory of Riemannian geometry is laborious. In this monograph the authors present a self-contained exposition of the basic concepts of Riemannian geometry from a computational viewpoint, providing illustrations and examples at each step. They proceed to demonstrate how these concepts are implemented in the open-source project Geomstats, explaining the choices that were made and the conventions chosen. The reader thus learns in one self-contained volume the theory of Riemann geometry and geometric statistics and their implementation to perform statistics and machine learning on manifolds. Containing many practical Python examples, this monograph is a valuable resource both for mathematicians and applied scientists to learn the theory of Riemann geometry and its use in practice implemented with the Geomstats package where most of the difficulties are hidden under high-level functions.