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SKU/Artículo: AMZ-B0DHV6NQC7

Novel Tensor Fields: Beyond Classical Tensor Calculus in AI (Mastering Machine Learning)

Format:

Kindle

Hardcover

Kindle

Paperback

Detalles del producto

Disponibilidad:
Fuera de stock

Peso con empaque:
0.36 kg

Devolución:
No

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

Venturing into novel territory, we explore advanced tensor field theories that extend traditional mathematical frameworks. These include: - Hypercomplex Tensor Fields: Exploring tensors defined over hypercomplex number systems, enabling more efficient representations of multidimensional data. - Non-Euclidean Tensor Spaces: Discussing tensor fields in curved spaces and their applications in modeling data with underlying geometric complexities. - Dynamic Tensor Fields: Presenting tensors that evolve over time, crucial for temporal data analysis and sequential decision-making processes. - Stochastic Tensor Fields: Integrating probabilistic approaches within tensor calculus to address uncertainties inherent in real-world data. The core of the book focuses on how these novel tensor fields can be harnessed in AI: - Deep Learning Innovations: Demonstrating how advanced tensor operations can enhance neural network architectures, leading to more powerful and interpretable models. - Geometric Machine Learning: Applying tensor field concepts to develop algorithms that respect the geometric structure of data, improving performance in areas like computer vision and graphics.

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