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Practical Linear Algebra for Data Science

Om Practical Linear Algebra for Data Science

"If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms."--Publisher marketing.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9781098120610
  • Bindende:
  • Paperback
  • Sider:
  • 300
  • Utgitt:
  • 20. september 2022
  • Dimensjoner:
  • 232x177x21 mm.
  • Vekt:
  • 576 g.
  På lager
Leveringstid: 4-7 virkedager
Forventet levering: 13. desember 2024
Utvidet returrett til 31. januar 2025

Beskrivelse av Practical Linear Algebra for Data Science

"If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms."--Publisher marketing.

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