9789819929504 - linear algebra with python: theory and applications di tsukada, makoto; kobayashi, yuji; kaneko, hiroshi; takahasi, sin-ei; shirayanagi, kiyoshi (23 risultati)
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LINEAR ALGEBRA WITH PYTHON
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-Ei; Shirayanagi, Kiyoshi; Noguchi, Masato
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Linear Algebra With Python : Theory and Applications
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-ei; Shirayanagi, Kiyoshi
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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
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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
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Linear Algebra With Python : Theory and Applications
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-ei; Shirayanagi, Kiyoshi
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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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
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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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
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Da: California Books, Miami, FL, U.S.A.California Books
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Linear Algebra with Python
Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi
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Da: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Hardback. Condizione: New. 2023 ed. 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 mappings 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.Read more Continue reading Read less FROM THE BACK COVERThis 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 mappings 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 symboli.
Linear Algebra With Python : Theory and Applications
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-ei; Shirayanagi, Kiyoshi
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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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
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Linear Algebra With Python : Theory and Applications
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-ei; Shirayanagi, Kiyoshi
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Linear Algebra with Python
Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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Hardback. Condizione: New. 2023 ed. 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 mappings 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.
Linear Algebra With Python: Theory and Applications
Tsukada, Makoto/ Kobayashi, Yuji/ Kaneko, Hiroshi/ Takahasi, Sin-ei/ Shirayanagi, Kiyoshi
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 324 pages. 10.00x7.01x0.98 inches. In Stock.
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Hardcover. Condizione: New Books. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will…be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Linear Algebra with Python
Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi
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Da: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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Hardback. Condizione: New. 2023 ed. 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 mappings 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.
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
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Da: BestAroundDeals, Grand Rapids, MI, U.S.A.BestAroundDeals
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Hardcover. Condizione: New. 1st Edition.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: 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 mappings… 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.
Linear Algebra with Python
Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi
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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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Hardback. Condizione: New. 2023 ed. 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 mappings 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.
Linear Algebra with Python
Tsukada, Makoto; Kobayashi, Yuji; Kaneko, Hiroshi; Takahasi, Sin-Ei; Shirayanagi, Kiyoshi; Noguchi, Masato
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- Print on Demand
Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condizione: Brand New. New. Delivery takes 20-25 days. Print on Demand.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 mappings 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. 324 pp. Englisch.
Linear Algebra with Python
Makoto Tsukada|Yuji Kobayashi|Hiroshi Kaneko|Sin-Ei Takahasi|Kiyoshi Shirayanagi|Masato Noguchi
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gives a unified overview of various phenomena with linear structure from the perspective of functional analysisMakes it enjoyable to learn linear algebra with Python by performing linear calculations without manual c…alculationsHandles large.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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: lin…ear mappings 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 324 pp. Englisch.
