Dimensionality Reduction in Machine Learning

Name: Dimensionality Reduction in Machine Learning
Brand: Elsevier Science
SKU: 9780443328183
Price: 924.49 RON
Availability: InStock

Autor: Snehashish Chakraverty

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Editura: Elsevier Science

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Dimensionality Reduction in Machine Learning provides a comprehensive tutorial on dimension reductio...

Informații despre carte

Autor

Snehashish Chakraverty

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2025

Pagini

250

EAN

9780443328183

ISBN

0443328188

Enbook ID

46434865

Editura

Elsevier Science

Greutate

680

Dimensiuni

191 x 235

Descriere completă

Dimensionality Reduction in Machine Learning provides a comprehensive tutorial on dimension reduction algorithms as the first step of the data life cycle in a machine learning project. This book covers both the mathematical and programming sides of dimension reduction algorithms and compares dimension reduction algorithms in various aspects. Dimension reduction and feature selection is the first step in nearly every machine learning project. The authors provide readers with in-depth understanding of the foundational underpinnings as well as the methods of creating and applying dimension reduction algorithms. The book is divided into four Parts, with chapters from the leading researchers and experts in the field. 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. 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 with each other so that the reader can understand which algorithm is suitable for his/her purpose
All algorithm examples in the book are supported by a Github repository which consists of full notebooks for the programming code

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