Application Differential Geometry Artificial by Vats Pooja (10 results)

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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: 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.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch.

Taschenbuch. Condition: 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).…

Paperback. Condition: 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.…

Paperback. Condition: 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.…

Taschenbuch. Condition: 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.…

Taschenbuch. Condition: Neu. Application of Differential Geometry in Artificial Intelligence | Pooja Vats | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630304220 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …