Random Matrix Methods for Machine Learning
Random Matrix Methods for Machine Learning | 9.47 MB
English | 411 Pages
Title: Random Matrix Methods for Machine Learning
Author: Romain Couillet
Year: 2022
Description:
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.
DOWNLOAD:
https://rapidgator.net/file/36addd2ddd317246a9c137d23ebacc62/Random_Matrix_Methods_for_Machine_Learning.rar
https://uploadgig.com/file/download/f34c1c27a998bb12/Random_Matrix_Methods_for_Machine_Learning.rar
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.
DOWNLOAD:
https://rapidgator.net/file/36addd2ddd317246a9c137d23ebacc62/Random_Matrix_Methods_for_Machine_Learning.rar
https://uploadgig.com/file/download/f34c1c27a998bb12/Random_Matrix_Methods_for_Machine_Learning.rar
Information
Users of Guests are not allowed to comment this publication.
