The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R (數據的形狀：基於幾何的機器學習與 R 語言數據分析)

Farrelly, Colleen M., Ulrich Gaba, Yaé

The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R
  • 出版商: No Starch Press
  • 出版日期: 2023-09-12
  • 售價: $1,480
  • 貴賓價: 9.5 折 $1,406
  • 語言: 英文
  • 頁數: 264
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1718503083
  • ISBN-13: 9781718503083
  • 相關分類: Machine Learning

    • 立即出貨 (庫存 < 4)

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商品描述

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.

Whether you're a mathematician, seasoned data scientist, or marketing professional, you'll find The Shape of Data to be the perfect introduction to the critical interplay between the geometry of data structures and machine learning.

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.

In addition to gaining a deeper understanding of how to implement geometry-based algorithms with code, you'll explore:

 

  • Supervised and unsupervised learning algorithms and their application to network data analysis
  • The way distance metrics and dimensionality reduction impact machine learning
  • How to visualize, embed, and analyze survey and text data with topology-based algorithms
  • New approaches to computational solutions, including distributed computing and quantum algorithms

商品描述(中文翻譯)

這本高級機器學習書籍從幾何的角度突出了許多算法，並通過實際應用介紹了網絡科學、度量幾何學和拓撲數據分析的工具。無論您是數學家、經驗豐富的數據科學家還是市場營銷專業人士，您都會發現《數據的形狀》是介紹數據結構幾何和機器學習之間關鍵相互作用的完美入門書籍。本書廣泛的案例研究（來自醫學、教育、社會學、語言學等領域）和對數學背後數十種算法的淺顯解釋，全面而易於理解地展示了幾何如何塑造驅動數據分析的算法。除了深入了解如何使用代碼實現基於幾何的算法之外，您還將探索以下內容：
- 監督和非監督學習算法及其在網絡數據分析中的應用
- 距離度量和降維對機器學習的影響
- 如何使用基於拓撲的算法可視化、嵌入和分析調查和文本數據
- 包括分佈式計算和量子算法在內的計算解決方案的新方法

作者簡介

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.

作者簡介(中文翻譯)

Colleen M. Farrelly是一位高級數據科學家，其學術和行業研究專注於拓撲數據分析、量子機器學習、基於幾何的機器學習、網絡科學、階層建模和自然語言處理。自從從邁阿密大學獲得生物統計學碩士學位以來，Colleen在多個行業擔任數據科學家，包括醫療保健、消費品包裝、生物技術、核工程、市場營銷和教育。Colleen經常在技術會議上發表演講，包括PyData、SAS Global、WiDS、Data Science Africa和DataScience SALON。在工作之餘，Colleen喜歡寫俳文/俳畫或游泳。

Yaë Ulrich Gaba在南非開普敦大學（UCT）完成了博士學位研究，專攻拓撲學，目前是Quantum Leap Africa（QLA，盧旺達）的研究助理。他的研究興趣包括計算幾何、應用代數拓撲（拓撲數據分析）和幾何機器學習（圖形和點雲表示學習）。他目前的重點是在數據分析中應用幾何方法，他的工作旨在開發有效且理論上合理的算法，利用幾何和拓撲的思想和方法進行數據和形狀分析。

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