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Now Publishers Spectral Learning on Matrices and Tensors (Foundations and Trends® in Machine Learning)

Name: Spectral Learning on Matrices and Tensors (Foundations and Trends(R) in Machine Learning)
Brand: Now Publishers

Architecture & Microprocessors

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Description

The authors of this monograph survey recent progress in using spectral methods including matrix and tensor decomposition techniques to learn many popular latent variable models. With careful implementation, tensor-based methods can run efficiently in practice, and in many cases they are the only algorithms with provable guarantees on running time and sample complexity.The focus is on a special type of tensor decomposition called CP decomposition, and the authors cover a wide range of algorithms to find the components of such tensor decomposition. They also discuss the usefulness of this decomposition by reviewing several probabilistic models that can be learned using such tensor methods.The second half of the monograph looks at practical applications. This includes using Tensorly, an efficient tensor algebra software package, which has a simple python interface for expressing tensor operations. It also has a flexible back-end system supporting NumPy, PyTorch, TensorFlow, and MXNet.Spectral Learning on Matrices and Tensors provides a theoretical and practical introduction to designing and deploying spectral learning on both matrices and tensors. It is of interest for all students, researchers and practitioners working on modern day machine learning problems.

Product Specifications

Brand: Now Publishers
Format: paperback
ASIN: 1680836404
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 30 November 2019
Listed Since: 27 November 2019

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Now Publishers Spectral Learning on Matrices and Tensors (Foundations and Trends® in Machine Learning)

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