The Shape of Data: Network Science, Geometry-Based Machine Learning, and Topological Data Analysis in R

類型： 簡裝書 語言： 英語（English） 作者： Colleen M. Farrelly 頁數： 264 頁 出版于： 2023-09-12 ISBN13： 9781718503083 ISBN10： 1718503083 出版社： No Starch Press 規格： 23.1902 x 17.8054 x 1.1938 cm 重量： 498 克

<b>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.</b><br><br>Whether you&rsquo;re a mathematician, seasoned data scientist, or marketing professional, you&rsquo;ll find <i>The Shape of Data </i>to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning.<br><br>This book&rsquo;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&nbsp;geometry shapes the algorithms that drive data analysis.<br><br>In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you&rsquo;ll explore:<br><br><ul><li>Supervised and unsupervised learning algorithms and their application to network data analysis</li><li>The way distance metrics and dimensionality reduction impact machine learning</li><li>How to visualize, embed, and analyze survey and&nbsp;text data with topology-based algorithms</li><li>New approaches to computational solutions, including distributed computing and quantum algorithms</li></ul>
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<b>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.</b> <p/>Whether you're a mathematician, seasoned data scientist, or marketing professional, you'll find <i>The Shape of Data </i>to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning. <p/>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. <p/>In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you'll explore: <p/><ul><li>Supervised and unsupervised learning algorithms and their application to network data analysis</li><li>The way distance metrics and dimensionality reduction impact machine learning</li><li>How to visualize, embed, and analyze survey and text data with topology-based algorithms</li><li>New approaches to computational solutions, including distributed computing and quantum algorithms</li></ul>

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