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Practical Linear Algebra for Data Science: From Core Concepts to Applications
USD24,95
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En muy buen estado
Libro que se ha leído y que no tiene un aspecto nuevo, pero que está en un estado excelente. No hay desperfectos visibles en la tapa y se incluye sobrecubierta, si procede, para las tapas duras. Todas las páginas están en perfecto estado, sin arrugas ni roturas y no falta ninguna. El texto no está subrayado ni resaltado de forma alguna, y no hay anotaciones en los márgenes. Puede presentar marcas de identificación mínimas en la contraportada o las guardas. Muy poco usado. Consulta el anuncio del vendedor para obtener más información y la descripción de cualquier posible imperfección.
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Envío:
USD5,88 (aprox. 5,04 EUR) USPS Media MailTM.
Ubicado en: Las Vegas, Nevada, Estados Unidos
Entrega:
Entrega prevista entre el vie. 10 oct. y el mar. 14 oct. a 94104
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N.º de artículo de eBay:157224988872
Características del artículo
- Estado
- Brand
- Unbranded
- Book Title
- Practical Linear Algebra for Data Science: From Core Concepts to
- MPN
- Does not apply
- ISBN
- 9781098120610
Acerca de este producto
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1098120612
ISBN-13
9781098120610
eBay Product ID (ePID)
16057246179
Product Key Features
Number of Pages
300 Pages
Language
English
Publication Name
Practical Linear Algebra for Data Science : from Core concepts to Applications Using Python
Subject
Algebra / Linear, Data Processing
Publication Year
2022
Type
Textbook
Subject Area
Mathematics, Computers
Format
Trade Paperback
Dimensions
Item Height
0.9 in
Item Weight
19.9 Oz
Item Length
9.1 in
Item Width
7 in
Additional Product Features
Intended Audience
Trade
LCCN
2022-301984
Illustrated
Yes
Synopsis
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. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis
LC Classification Number
QA185.D37C64 2022
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