Dimensionality Reduction in Machine Learning
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Form:Einzelkauf Download
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Sprache:Englisch
167,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
04.02.2025
Herausgeber
Jamal Amani Rad + weitereVerlag
Elsevier Science & Techn.Seitenzahl
250 (Printausgabe)
Dateigröße
25829 KB
Sprache
Englisch
EAN
9780443328190
Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.
- Provides readers with a comprehensive overview of various dimension reduction algorithms, including linear methods, non-linear methods, and deep learning methods
- Covers the implementation aspects of algorithms supported by numerous code examples
- Compares different algorithms so the reader can understand which algorithm is suitable for their purpose
- Includes algorithm examples that are supported by a Github repository which consists of full notebooks for the programming code
- Provides readers with a comprehensive overview of various dimension reduction algorithms, including linear methods, non-linear methods, and deep learning methods
- Covers the implementation aspects of algorithms supported by numerous code examples
- Compares different algorithms so the reader can understand which algorithm is suitable for their purpose
- Includes algorithm examples that are supported by a Github repository which consists of full notebooks for the programming code
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