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portada Random Matrix Methods for Machine Learning
Type
Physical Book
Year
2022
Language
English
Pages
408
Format
Hardcover
Dimensions
24.6 x 16.8 x 2.4 cm
Weight
0.86 kg.
ISBN13
9781009123235
Edition No.
1

Random Matrix Methods for Machine Learning

Romain Couillet (Author) · Zhenyu Liao (Author) · Cambridge University Press · Hardcover

Random Matrix Methods for Machine Learning - Couillet, Romain ; Liao, Zhenyu

New Book Imported to Austria
Delivery: 25 Aug - 27 Aug Shipping: 5 to 6 business days.
129,06 €
Import costs and 10% VAT included in the price ✅
129,06 €

Synopsis "Random Matrix Methods for Machine Learning"

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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