Shape of Data Book by Colleen M Farrelly - Bookswagon
Shape of Data

Shape of Data


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International Edition


About the Book

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 book dives into how geometry, network science, and topology drive machine-learning algorithms--and how you can use them in all kinds of data analysis. Through vivid case studies in the R programming language, and detailed illustrations of major concepts, you'll learn practical scientific applications for these algorithms across many types of data and fields of study. There are also plenty of hands-on examples of relevant code covered throughout, which you'll use for your own natural language processing project in one of the book's final chapters.
About the Author: 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 processing. Since graduating from the University of Miami with an MS in biostatistics, Colleen has worked as a data scientist in a vari- ety of industries, including healthcare, consumer packaged goods, biotech, nuclear engineering, marketing, and education. Colleen often speaks at tech conferences, including PyData, SAS Global, WiDS, Data Science Africa, and DataScience SALON. When not working, Colleen can be found writing haibun/haiga or swimming.

Yaé Ulrich Gaba completed his doctoral studies at the University of Cape Town (UCT, South Africa) with a specialization in topology and is currently a research associate at Quantum Leap Africa (QLA, Rwanda). His research interests are computational geometry, applied algebraic topology (topologi- cal data analysis), and geometric machine learning (graph and point-cloud representation learning). His current focus lies in geometric methods in data analysis, and his work seeks to develop effective and theoretically justified algorithms for data and shape analysis using geometric and topological ideas and methods.


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Product Details
  • ISBN-13: 9781718503083
  • Publisher: No Starch Press
  • Publisher Imprint: No Starch Press
  • Height: 234 mm
  • No of Pages: 264
  • Spine Width: 18 mm
  • Weight: 560 gr
  • ISBN-10: 1718503083
  • Publisher Date: 12 Sep 2023
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: Network Science, Geometry-Based Machine Learning, and Topological Data Analysis in R
  • Width: 177 mm


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