Isbn: 9786630304220 - application of differential geometry in artificial intelligence (5 risultati)

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

Taschenbuch. Condizione: 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. Condizione: 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. …

Taschenbuch. Condizione: 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).…