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Könyv A Unifying Theory of Learning: DL Meets Kernel Methods I. de Zarzà

A Unifying Theory of Learning: DL Meets Kernel Methods

Szerző: I. de Zarzà
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: LAP LAMBERT Academic Publishing
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
15 097 Ft
We introduce a framework to use kernel approximates in the mini-batch setting with Stochastic Gradie...

Információk a könyvről

Szerző
I. de Zarzà
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2021
oldal
108
EAN
9786203924954
ISBN
6203924954
Enbook ID
36676329
Kiadó
LAP LAMBERT Academic Publishing
Súly
179
Méretek
150 x 220 x 6

Teljes leírás

We introduce a framework to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. Based on Random Kitchen Sinks, we provide a C++ library for Large-scale ML. It contains a CPU optimized implementation of the algorithm in Le et al. 2013, that allows the computation of approximated kernel expansions in log-linear time. The algorithm requires to compute the product of matrices Walsh Hadamard. A cache friendly Fast Walsh Hadamard that achieves compelling speed and outperforms current state-of-the-art methods has been developed. McKernel establishes the foundation of a new architecture of learning that allows to obtain large-scale non-linear classification combining lightning kernel expansions and a linear classifier. It travails in the mini-batch setting working analogously to Neural Networks. We also propose a new architecture to reduce over-parametrization in Neural Networks. It introduces an operand for rapid computation in the framework of Deep Learning that leverages learned weights. The formalism is described in detail providing both an accurate elucidation of the mechanics and the theoretical implications.We introduce a framework to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. Based on Random Kitchen Sinks, we provide a C++ library for Large-scale ML. It contains a CPU optimized implementation of the algorithm in Le et al. 2013, that allows the computation of approximated kernel expansions in log-linear time. The algorithm requires to compute the product of matrices Walsh Hadamard. A cache friendly Fast Walsh Hadamard that achieves compelling speed and outperforms current state-of-the-art methods has been developed. McKernel establishes the foundation of a new architecture of learning that allows to obtain large-scale non-linear classification combining lightning kernel expansions and a linear classifier. It travails in the mini-batch setting working analogously to Neural Networks. We also propose a new architecture to reduce over-parametrization in Neural Networks. It introduces an operand for rapid computation in the framework of Deep Learning that leverages learned weights. The formalism is described in detail providing both an accurate elucidation of the mechanics and the theoretical implications.

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