NEU

Matrix Methods in Data Analysis

Angebot€117,69
inkl. MwSt. • Kein physischer Versand
Sofort per Download lieferbar
Ihr Downloadlink kommt direkt per E-Mail.
PDF: lesbar auf Smartphone, Tablet, Computer und in vielen PDF-Apps.

E-Book
eBook-Format:PDF
14 Tage Widerrufsrecht
Sicher bezahlen – auch auf Rechnung mit Klarna
PayPal Klarna Visa Mastercard Maestro Apple Pay Google Pay
Inhaltsangabe

Part I: Linear Algebra And Machine Learning.- Why Should We Care?.- What You May Have Learned Before..- Core Topics.- Supplementary Topics.- Part II: Matrix Multiplication And Partitioned Matrices.- Why Should We Care?.- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part III: Norms, Distances, And Similarities.- Why Should We Care?.- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part IV: The Four Fundamental Subspaces Of A Matrix, And Gram-Matrices.-  Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part V: The Lu Factorization Of A Matrix.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part VI: Orthogonality And The Qr Factorization.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part VII: Orthogonal Projections And The Least Squares Problem.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part VIII: Eigenvalues, Eigenvectors, And Algorithms.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part IX: Symmetric And Positive Definite Matrices.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Part X: Singular Value Decomposition.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.-From The Classroom To Real Life.- Part XI: Nonnegative Matrices And Perron Theory.- Why Should We Care? .- What You May Have Learned Before.- Core Topics.- Supplementary Topics.- From The Classroom To Real Life.- Index.

Produktdetails
  • Erscheinungsdatum: 22.09.2026
  • Autor/Autorin: Maria Isabel Bueno Cachadina,Javier Perez Alvaro
  • Reihe: Mathematics and Statistics (R0)
  • Format: E-Book
  • Dateiformat: PDF
  • Kopierschutz: Wasserzeichen
  • Dateigröße: 46.1 MB
  • Verlag: SPRINGER
  • Sprache: Englisch
  • Umfang: 1004 Seiten
  • ISBN: 9783032113146
  • Lieferung: Sofort per Download
  • Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
  • Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.
Darum lohnt sich dieses Buch

Illustrates how principles of linear algebra can be used to understand topics in data science Presents a balanced view of both theory and applications Includes supplementary github content through python labs

Herstellerinformationen
Springer Nature Customer Service Center GmbH

Email: ProductSafety@springernature.com