[PDF.82qb] Mathematical Problems in Data Science: Theoretical and Practical Methods

Mathematical Problems in Data Science: Theoretical and Practical Methods

[PDF.mh21] Mathematical Problems in Data Science: Theoretical and Practical Methods

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 | #2537791 in Books |  2015-12-16 | Original language:English | PDF # 1 |  9.21 x.56 x6.14l,.0 | File type: PDF | 213 pages

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||0 of 0 people found the following review helpful.| Read another book|By Customer|Never made it pass the poorly written introduction. It just a word jumble of buzz words like "Big Data" and "Data science" barely put together to coherent sentences.| ||“Data science includes mathematical and statistical tools required to find relations and principles behind heterogeneous and possibly unstructured data. It is an emerging field, under active research, and the authors here have attempted to explain exist

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods.  For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computation...

You can specify the type of files you want, for your gadget.Mathematical Problems in Data Science: Theoretical and Practical Methods   |  Li M. Chen, Zhixun Su, Bo Jiang. I really enjoyed this book and have already told so many people about it!

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