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Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python

By: Publication details: Shroff Publishers & Distributors Pvt. Ltd. 2022 MumbaiEdition: 1st EdDescription: 311ISBN:
  • 9789355422187
Subject(s): DDC classification:
  • 005.74015 COH
Summary: 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. Source: https://www.amazon.in/Practical-Linear-Algebra-Data-Science/dp/9355422180/ref=sr_1_1?crid=2FN6CJ81PDFLX&dib=eyJ2IjoiMSJ9.ggJB6Le-STGZGnE74Vr_-Q.vTTt5f5WSrHXBViQ4sezP_neOUHLQmLOFsv2GiLnk9c&dib_tag=se&keywords=9789355422187&qid=1732545198&sprefix=9789355420039%2Caps%2C354&sr=8-1
List(s) this item appears in: New Arrivals December 2024
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Book Book Main Library Analytics Reference book 005.74015 COH (Browse shelf(Opens below)) Available 119579

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.

Source: https://www.amazon.in/Practical-Linear-Algebra-Data-Science/dp/9355422180/ref=sr_1_1?crid=2FN6CJ81PDFLX&dib=eyJ2IjoiMSJ9.ggJB6Le-STGZGnE74Vr_-Q.vTTt5f5WSrHXBViQ4sezP_neOUHLQmLOFsv2GiLnk9c&dib_tag=se&keywords=9789355422187&qid=1732545198&sprefix=9789355420039%2Caps%2C354&sr=8-1

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