Linear algebra python theory von tsukada makoto (4 Ergebnisse)
Linear Algebra with Python: Theory and Applications (Springer Undergraduate Texts in Mathematics and Technology)
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-Ei; Shirayanagi, Kiyoshi; Noguchi, Masato
- Hardcover
Anbieter: Majestic Books, Hounslow, Vereinigtes KönigreichMajestic Books
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Linear Algebra with Python: Theory and Applications (Springer Undergraduate Texts in Mathematics and Technology)
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-Ei; Shirayanagi, Kiyoshi; Noguchi, Masato
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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Linear Algebra With Python: Theory and Applications
Tsukada, Makoto/ Kobayashi, Yuji/ Kaneko, Hiroshi/ Takahasi, Sin-ei/ Shirayanagi, Kiyoshi
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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Hardcover. Zustand: Brand New. 324 pages. 10.00x7.01x0.98 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2024
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook is for those who want to learn linear algebra from the basics. After a brief mathematical introduction, it provides the standard curriculum of linear algebra based on an abstract linear space. It covers, among other aspects: linear mapp…ings and their matrix representations, basis, and dimension; matrix invariants, inner products, and norms; eigenvalues and eigenvectors; and Jordan normal forms. Detailed and self-contained proofs as well as descriptions are given for all theorems, formulas, and algorithms.A unified overview of linear structures is presented by developing linear algebra from the perspective of functional analysis. Advanced topics such as function space are taken up, along with Fourier analysis, the Perron-Frobenius theorem, linear differential equations, the state transition matrix and the generalized inverse matrix, singular value decomposition, tensor products, and linear regression models. These all provide a bridge to more specialized theories based on linear algebra in mathematics, physics, engineering, economics, and social sciences.Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding. By using Python's libraries NumPy, Matplotlib, VPython, and SymPy, readers can easily perform large-scale matrix calculations, visualization of calculation results, and symbolic computations. All the codes in this book can be executed on both Windows and macOS and also on Raspberry Pi.
