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Novel Tensor Fields

Bog
  • Format
  • Bog, paperback
  • Engelsk
  • 390 sider

Beskrivelse

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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Detaljer
  • SprogEngelsk
  • Sidetal390
  • Udgivelsesdato25-09-2024
  • ISBN139798340282057
  • Forlag Independently Published
  • MålgruppeFrom age 0
  • FormatPaperback
  • Udgave0
Størrelse og vægt
  • Vægt521 g
  • Dybde2 cm
  • coffee cup img
    10 cm
    book img
    15,2 cm
    22,8 cm

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