101,39 €
135,19 €
Kaina su kodu: ENG
Linear Algebra with Applications in Machine Learning
Linear Algebra with Applications in Machine Learning
101,39 €
135,19 €
  • Planuojame turėti už 52 d.
This textbook is a comprehensive, application-driven guide to mastering linear algebra from foundational principles to advanced machine learning applications. Designed for students, researchers, and professionals in AI, data science, and engineering, the book blends mathematical rigor with practical implementation using Python and popular libraries such as NumPy, SciPy, Matplotlib, and scikit-learn.Starting with vectors and matrices, the text builds toward systems of linear equations, transform…
  • Kaina galioja įvedus kodą: ENG

Linear Algebra with Applications in Machine Learning (el. knyga) (skaityta knyga) | knygos.lt

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Aprašymas

This textbook is a comprehensive, application-driven guide to mastering linear algebra from foundational principles to advanced machine learning applications. Designed for students, researchers, and professionals in AI, data science, and engineering, the book blends mathematical rigor with practical implementation using Python and popular libraries such as NumPy, SciPy, Matplotlib, and scikit-learn.
Starting with vectors and matrices, the text builds toward systems of linear equations, transformations, determinants, eigenvalues, and vector spaces—then extends to orthogonality, matrix factorizations (e.g., SVD, QR, LU), and optimization.
This book is suitable for either beginner aiming to grasp key ML concepts or an advanced learner exploring spectral methods and tensor decompositions, this book serves as a flexible resource, grounded in mathematics, empowered by code.

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Kaina galioja įvedus kodą: ENG

101,39 €
135,19 €
Planuojame turėti už 52 d.

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This textbook is a comprehensive, application-driven guide to mastering linear algebra from foundational principles to advanced machine learning applications. Designed for students, researchers, and professionals in AI, data science, and engineering, the book blends mathematical rigor with practical implementation using Python and popular libraries such as NumPy, SciPy, Matplotlib, and scikit-learn.
Starting with vectors and matrices, the text builds toward systems of linear equations, transformations, determinants, eigenvalues, and vector spaces—then extends to orthogonality, matrix factorizations (e.g., SVD, QR, LU), and optimization.
This book is suitable for either beginner aiming to grasp key ML concepts or an advanced learner exploring spectral methods and tensor decompositions, this book serves as a flexible resource, grounded in mathematics, empowered by code.

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