Dimensionality Reduction in Data Science

Dimensionality Reduction in Data Science 1st ed. 2022 Edition
Author: Max Garzon (Editor), Ching-Chi Yang (Editor), Deepak Venugopal (Editor), Nirman Kumar (Editor), Kalidas Jana (Editor), Lih-Yuan Deng (Editor)
Publisher ‏ : ‎ Springer; 1st ed. 2022 edition (July 29, 2022)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 276 pages
ISBN-10 ‏ : ‎ 3031053702
ISBN-13 ‏ : ‎ 9783031053702

Book Description
This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.
The ability to generate, gather and store volumes of data in the order of tera- and exo Author:tes daily has far outpaced our ability to derive useful information with available computational resources for many domains.
This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.
This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed Author: precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed Author: re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded Author: these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.

下载地址 Download隐藏内容需1积分,VIP免费,请先 !没有帐号? 注 册 一个!
觉得文章有用就打赏一下
未经允许不得转载:finelybook » Dimensionality Reduction in Data Science

评论 抢沙发

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址

觉得文章有用就打赏一下

非常感谢你的打赏,我们将继续给力更多优质内容,让我们一起创建更加美好的网络世界!

支付宝扫一扫打赏

微信扫一扫打赏