The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R
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- Автор: Colleen M. Farrelly | Yaé Ulrich Gaba
- ISBN-10: 1718503083
- ISBN-13: 978-1718503083
- Publisher: No Starch Press
- Publication date: September 12, 2023
- Language: English
- Dimensions: 7.06 x 0.6 x 9.25 inches
- Print length: 264 pages
From the Publisher
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'A great source'“A great source for the application of topology and geometry in data science. Topology and geometry advance the field of machine learning on unstructured data, and The Shape of Data does a great job introducing new readers to the subject.” —Uchenna “Ike” Chukwu, Senior Quantum Developer |
'Phenomenal'“A novel perspective and phenomenal achievement in the application of geometry to the field of machine learning. . . . Even as a more veteran data scientist who has been in the industry for years now, having read this book I've come away with a deeper connection to and new understanding of my field." —Kurt Schuepfer, Ph.D., McDonalds Corporation |
'Walks you through'"The title says it all. Data is bound by many complex relationships not easily shown in our two-dimensional, spreadsheet filled world. The Shape of Data walks you through this richer view and illustrates how to put it into practice." —Stephanie Thompson, Data Scientist and Speaker |
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 University of Miami with an MS in Biostatistics, Colleen has worked as a data scientist in a variety of industries, including health care, 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 doing any sort of water sport.
About the Author
Yaé Ulrich Gaba completed his doctoral studies at the University of Cape Town (UCT, South Africa) with specialization in Topology and is presently a research associate at Quantum Leap Africa (QLA, Rwanda). His research interests are computational geometry, applied algebraic topology (topological 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/shape analysis using geometric and topological ideas and methods.
About the Publisher
No Starch Press has published the finest in geek entertainment since 1994, creating both timely and timeless titles like Python Crash Course, Python for Kids, How Linux Works, and Hacking: The Art of Exploitation. An independent, San Francisco-based publishing company, No Starch Press focuses on a curated list of well-crafted books that make a difference. They publish on many topics, including computer programming, cybersecurity, operating systems, and LEGO. The titles have personality, the authors are passionate experts, and all the content goes through extensive editorial and technical reviews. Long known for its fun, fearless approach to technology, No Starch Press has earned wide support from STEM enthusiasts worldwide.