Random Matrix Methods For Machine Learning(1st Edition)
Authors:
Romain Couillet ,Zhenyu Liao
In Stock: 2 Left
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Solution Manual Includes
Price:
$9.99
Book details
ISBN: 1009123238, 978-1009123235
Book publisher: Cambridge University Press
Book Price $0 : This Book Presents A Unified Theory Of Random Matrices For Applications In Machine Learning, Offering A Large-dimensional Data Vision That Exploits Concentration And Universality Phenomena. This Enables A Precise Understanding, And Possible Improvements, Of The Core Mechanisms At Play In Real-world Machine Learning Algorithms. The Book Opens With A Thorough Introduction To The Theoretical Basics Of Random Matrices, Which Serves As A Support To A Wide Scope Of Applications Ranging From SVMs, Through Semi-supervised Learning, Unsupervised Spectral Clustering, And Graph Methods, To Neural Networks And Deep Learning. For Each Application, The Authors Discuss Small- Versus Large-dimensional Intuitions Of The Problem, Followed By A Systematic Random Matrix Analysis Of The Resulting Performance And Possible Improvements. All Concepts, Applications, And Variations Are Illustrated Numerically On Synthetic As Well As Real-world Data, With MATLAB And Python Code Provided On The Accompanying Website.
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