Application of Differential Geometry in Artificial Intelligence - Couverture souple

Name: Application of Differential Geometry in Artificial Intelligence
Author: Vats, Pooja
ISBN: 9786630304220

Vats, Pooja

9786630304220: Application of Differential Geometry in Artificial Intelligence

Couverture souple

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

Editeur : LAP Lambert Academic Publishing, 2026

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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2026
Langue: anglais
ISBN 10: 6630304225
ISBN 13: 9786630304220
Reliure: Broch�
Nombre de pages: 60
Coordonn�es du fabricant: non disponible
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Application of Differential Geometry in Artificial Intelligence

Vats, Pooja

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

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Application of Differential Geometry in Artificial Intelligence

Vats, Pooja

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

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Application of Differential Geometry in Artificial Intelligence (Paperback)

Pooja Vats

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

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Paperback. Etat : new. Paperback. This book, Application of Differential Geometry in Artificial Intelligence, argues that modern AI is best understood not through flat Euclidean assumptions but through the lens of curved manifolds, since data, model parameters, and probability distributions naturally possess non-Euclidean structure. It begins by laying the mathematical foundations-manifolds, Riemannian metrics, geodesics, curvature, and connections-before showing how these tools reshape core areas of machine learning: information geometry and natural gradient optimization, Riemannian optimization on constrained spaces like orthogonal and SPD matrices, geometric deep learning on graphs and meshes via equivariant architectures, and the geometric analysis of neural network loss landscapes. It then turns to manifold learning and dimensionality reduction techniques, geometry-aware architectures such as hyperbolic and Lie-group equivariant networks, and differential-geometric generative models including Riemannian diffusion and optimal transport. A dedicated chapter surveys applications across robotics, computer vision, NLP, and scientific AI (quantum machine learning, physics-informed networks). This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de r�f. du vendeur 9786630304220

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Application of Differential Geometry in Artificial Intelligence

Vats, Pooja

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

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Application of Differential Geometry in Artificial Intelligence

Pooja Vats

Edit� par LAP LAMBERT Academic Publishing Aug 2026, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch. N� de r�f. du vendeur 9786630304220

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Application of Differential Geometry in Artificial Intelligence

Pooja Vats

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book, Application of Differential Geometry in Artificial Intelligence, argues that modern AI is best understood not through flat Euclidean assumptions but through the lens of curved manifolds, since data, model parameters, and probability distributions naturally possess non-Euclidean structure. It begins by laying the mathematical foundations-manifolds, Riemannian metrics, geodesics, curvature, and connections-before showing how these tools reshape core areas of machine learning: information geometry and natural gradient optimization, Riemannian optimization on constrained spaces like orthogonal and SPD matrices, geometric deep learning on graphs and meshes via equivariant architectures, and the geometric analysis of neural network loss landscapes. It then turns to manifold learning and dimensionality reduction techniques, geometry-aware architectures such as hyperbolic and Lie-group equivariant networks, and differential-geometric generative models including Riemannian diffusion and optimal transport. A dedicated chapter surveys applications across robotics, computer vision, NLP, and scientific AI (quantum machine learning, physics-informed networks). N� de r�f. du vendeur 9786630304220

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Application of Differential Geometry in Artificial Intelligence (Paperback)

Pooja Vats

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

Neuf Paperback

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Paperback. Etat : new. Paperback. This book, Application of Differential Geometry in Artificial Intelligence, argues that modern AI is best understood not through flat Euclidean assumptions but through the lens of curved manifolds, since data, model parameters, and probability distributions naturally possess non-Euclidean structure. It begins by laying the mathematical foundations-manifolds, Riemannian metrics, geodesics, curvature, and connections-before showing how these tools reshape core areas of machine learning: information geometry and natural gradient optimization, Riemannian optimization on constrained spaces like orthogonal and SPD matrices, geometric deep learning on graphs and meshes via equivariant architectures, and the geometric analysis of neural network loss landscapes. It then turns to manifold learning and dimensionality reduction techniques, geometry-aware architectures such as hyperbolic and Lie-group equivariant networks, and differential-geometric generative models including Riemannian diffusion and optimal transport. A dedicated chapter surveys applications across robotics, computer vision, NLP, and scientific AI (quantum machine learning, physics-informed networks). This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9786630304220

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Application of Differential Geometry in Artificial Intelligence (Paperback)

Pooja Vats

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

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Vendeur : AussieBookSeller, Truganina, VIC, Australie

�valuation du vendeur 5 sur 5 �toiles

Paperback. Etat : new. Paperback. This book, Application of Differential Geometry in Artificial Intelligence, argues that modern AI is best understood not through flat Euclidean assumptions but through the lens of curved manifolds, since data, model parameters, and probability distributions naturally possess non-Euclidean structure. It begins by laying the mathematical foundations-manifolds, Riemannian metrics, geodesics, curvature, and connections-before showing how these tools reshape core areas of machine learning: information geometry and natural gradient optimization, Riemannian optimization on constrained spaces like orthogonal and SPD matrices, geometric deep learning on graphs and meshes via equivariant architectures, and the geometric analysis of neural network loss landscapes. It then turns to manifold learning and dimensionality reduction techniques, geometry-aware architectures such as hyperbolic and Lie-group equivariant networks, and differential-geometric generative models including Riemannian diffusion and optimal transport. A dedicated chapter surveys applications across robotics, computer vision, NLP, and scientific AI (quantum machine learning, physics-informed networks). This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N� de r�f. du vendeur 9786630304220

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Application of Differential Geometry in Artificial Intelligence

Pooja Vats

Edit� par LAP LAMBERT Academic Publishing Aug 2026, 2026

ISBN 10 : 6630304225 ISBN 13 : 9786630304220

Neuf Taschenbuch

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book, Application of Differential Geometry in Artificial Intelligence, argues that modern AI is best understood not through flat Euclidean assumptions but through the lens of curved manifolds, since data, model parameters, and probability distributions naturally possess non-Euclidean structure. It begins by laying the mathematical foundations-manifolds, Riemannian metrics, geodesics, curvature, and connections-before showing how these tools reshape core areas of machine learning: information geometry and natural gradient optimization, Riemannian optimization on constrained spaces like orthogonal and SPD matrices, geometric deep learning on graphs and meshes via equivariant architectures, and the geometric analysis of neural network loss landscapes. It then turns to manifold learning and dimensionality reduction techniques, geometry-aware architectures such as hyperbolic and Lie-group equivariant networks, and differential-geometric generative models including Riemannian diffusion and optimal transport. A dedicated chapter surveys applications across robotics, computer vision, NLP, and scientific AI (quantum machine learning, physics-informed networks). 60 pp. Englisch. N� de r�f. du vendeur 9786630304220

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