Murty M Representation in Machine Learning 2023
Category: eBooks
- views: 186
- date: 22 January 2023
- posted by: BeMyLove
Murty M Representation in Machine Learning 2023 | 4.07 MB
English | 101 Pages
Title: Representation in Machine Learning (SpringerBriefs in Computer Science)
Author: M. N. Murty
Year: 2023
Description:
This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book.
In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques' effectiveness.
DOWNLOAD:
https://rapidgator.net/file/3026212c6710bb6bd7348db4827fbedf/Murty_M._Representation_in_Machine_Learning_2023.rar
https://uploadgig.com/file/download/c832dac072ef83Db/Murty_M._Representation_in_Machine_Learning_2023.rar
This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book.
In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques' effectiveness.
DOWNLOAD:
https://rapidgator.net/file/3026212c6710bb6bd7348db4827fbedf/Murty_M._Representation_in_Machine_Learning_2023.rar
https://uploadgig.com/file/download/c832dac072ef83Db/Murty_M._Representation_in_Machine_Learning_2023.rar
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