Linear Algebra for Data Science, Machine Learning, and Signal Processing

Linear Algebra for Data Science, Machine Learning, and Signal Processing
Author: Jeffrey A. Fessler
Publisher: Cambridge University Press
Total Pages: 453
Release: 2024-04-30
Genre: Computers
ISBN: 1009418149


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Master matrix methods via engaging data-driven applications, aided by classroom-tested quizzes, homework exercises and online Julia demos.


Linear Algebra for Data Science, Machine Learning, and Signal Processing
Language: en
Pages: 453
Authors: Jeffrey A. Fessler
Categories: Computers
Type: BOOK - Published: 2024-04-30 - Publisher: Cambridge University Press

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Master matrix methods via engaging data-driven applications, aided by classroom-tested quizzes, homework exercises and online Julia demos.
Machine Learning for Signal Processing
Language: en
Pages: 378
Authors: Max A. Little
Categories: Computers
Type: BOOK - Published: 2019 - Publisher: Oxford University Press, USA

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Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the mos
Linear Algebra and Learning from Data
Language: en
Pages: 0
Authors: Gilbert Strang
Categories: Computers
Type: BOOK - Published: 2019-01-31 - Publisher: Wellesley-Cambridge Press

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Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes
Linear Algebra for Signal Processing
Language: en
Pages: 193
Authors: Adam Bojanczyk
Categories: Technology & Engineering
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

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Signal processing applications have burgeoned in the past decade. During the same time, signal processing techniques have matured rapidly and now include tools
Fast Algorithms for Signal Processing
Language: en
Pages: 469
Authors: Richard E. Blahut
Categories: Technology & Engineering
Type: BOOK - Published: 2010-06-24 - Publisher: Cambridge University Press

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Efficient signal processing algorithms are important for embedded and power-limited applications since, by reducing the number of computations, power consumptio