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Practical linear algebra for data science: from core concepts to applications using...
Описание
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
Також купити книгу Practical Linear Algebra for Data Science: From Core
Concepts to Applications Using Python, Mike Cohen Ви можете по посиланню
