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Více informací o knize Fast Kernel Expansions with Applications to CV and DL. Part 1b

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• Anotace
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Anotace knihy

The scope of the manuscript is to give a review of kernel expansions, FOURIER features and fast numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in large-scale datasets. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. Applications to Computer Vision (CV) and Deep Learning (DL) are enclosed with practical hints on the topic. Specifically, we give a primer on facial recognition and a foundation for the use of Vision in Robotics. The scope of the manuscript is to give a review of kernel expansions, FOURIER features and fast numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in large-scale datasets. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. Applications to Computer Vision (CV) and Deep Learning (DL) are enclosed with practical hints on the topic. Specifically, we give a primer on facial recognition and a foundation for the use of Vision in Robotics.

Parametry knihy

Zařazení knihy Knihy v němčině Naturwissenschaften, Medizin, Informatik, Technik Mathematik Mathematik - Sonstiges

• Plný název: Fast Kernel Expansions with Applications to CV and DL. Part 1b
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 76
• EAN: 9786203925395
• ISBN: 620392539X
• ID: 36676346
• Nakladatelství: LAP LAMBERT Academic Publishing
• Hmotnost: 131 g
• Rozměry: 220 × 150 × 5 mm
• Datum vydání: 21. June 2021

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