9781718503083 - The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R di Farrelly, Colleen M.; Ulrich Gaba, Yaé (24 risultati)

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Paperback. Condizione: new. Paperback. This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application.This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application.Whether you're a mathematician, seasoned data scientist, or marketing professional, you'll find The Shape of Data to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning.This book's extensive collection of case studies (drawn from medicine, education, sociology, linguistics, and more) and gentle explanations of the math behind dozens of algorithms provide a comprehensive yet accessible look at how geometry shapes the algorithms that drive data analysis.In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you'll explore-Supervised and unsupervised learning algorithms and their application to network data analysisThe way distance metrics and dimensionality reduction impact machine learningHow to visualize, embed, and analyze survey and text data with topology-based algorithmsNew approaches to computational solutions, including distributed computing and quantum algorithms "The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. The book focuses on practical applications rather than dense mathematical concepts, with coding examples using social network data, text data, medical data, and education data"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. Focused on practical applications rather than dense mathematical concepts, the book progresses through coding examples using social network data, text data, medical data, and education data. Readers will come away with an entirely new toolkit to use in their own machine-learning work, as well as with a solid understanding of some of the most exciting algorithms being used in the field today.

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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New.

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Paperback. Condizione: Brand New. 272 pages. 9.00x7.00x0.75 inches. In Stock.

Condizione: New.

Condizione: New.

Condizione: New.

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Condizione: As New. Unread copy in mint condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback. Condizione: New. The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. Focused on practical applications rather than dense mathematical concepts, the book progresses through coding examples using social network data, text data, medical data, and education data. Readers will come away with an entirely new toolkit to use in their own machine-learning work, as well as with a solid understanding of some of the most exciting algorithms being used in the field today.

Paperback. Condizione: new. Paperback. This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application.This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application.Whether you're a mathematician, seasoned data scientist, or marketing professional, you'll find The Shape of Data to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning.This book's extensive collection of case studies (drawn from medicine, education, sociology, linguistics, and more) and gentle explanations of the math behind dozens of algorithms provide a comprehensive yet accessible look at how geometry shapes the algorithms that drive data analysis.In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you'll explore-Supervised and unsupervised learning algorithms and their application to network data analysisThe way distance metrics and dimensionality reduction impact machine learningHow to visualize, embed, and analyze survey and text data with topology-based algorithmsNew approaches to computational solutions, including distributed computing and quantum algorithms "The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. The book focuses on practical applications rather than dense mathematical concepts, with coding examples using social network data, text data, medical data, and education data"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. Colleen M. Farrelly is a senior data scientist whose academic and industry research has focused on topological data analysis, quantum machine learning, geometry-based machine learning, network science, hierarchical modeling, and natural language proc.

Paperback. Condizione: new. Paperback. This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application.This advanced machine learning book highlights many algorithms from a geometric perspective and introduces tools in network science, metric geometry, and topological data analysis through practical application.Whether you're a mathematician, seasoned data scientist, or marketing professional, you'll find The Shape of Data to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning.This book's extensive collection of case studies (drawn from medicine, education, sociology, linguistics, and more) and gentle explanations of the math behind dozens of algorithms provide a comprehensive yet accessible look at how geometry shapes the algorithms that drive data analysis.In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you'll explore-Supervised and unsupervised learning algorithms and their application to network data analysisThe way distance metrics and dimensionality reduction impact machine learningHow to visualize, embed, and analyze survey and text data with topology-based algorithmsNew approaches to computational solutions, including distributed computing and quantum algorithms "The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. The book focuses on practical applications rather than dense mathematical concepts, with coding examples using social network data, text data, medical data, and education data"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condizione: New. The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. Focused on practical applications rather than dense mathematical concepts, the book progresses through coding examples using social network data, text data, medical data, and education data. Readers will come away with an entirely new toolkit to use in their own machine-learning work, as well as with a solid understanding of some of the most exciting algorithms being used in the field today.

Taschenbuch. Condizione: Neu. Neuware - 'The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. The book focuses on practical applications rather than dense mathematical concepts, with coding examples using social network data, text data, medical data, and education data'.