Linear Algebra with Applications in Machine Learning
From Intuitive Understanding to Python Coding
-
GET 12% OFF
- Publisher's listprice EUR 64.19
-
26 622 Ft (25 355 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 12% (cc. 3 195 Ft off)
- Discounted price 23 428 Ft (22 312 Ft + 5% VAT)
23 428 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer Nature Singapore
- Date of Publication 11 May 2026
- ISBN 9789819551668
- Binding Hardback
- No. of pages404 pages
- Size 235x155 mm
- Language English
- Illustrations VIII, 404 p. 91 illus., 87 illus. in color. 700
Categories
Long description:
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.
Table of Contents:
"
""Introduction to Linear Algebra for Machine Learning"".- ""Vectors"".- ""Matrices"".- ""Tensors"".- ""Linear Systems"".- Linear Transformations"".- ""Determinants"".- ""Eigenvalues and Eigenvectors"".- ""Vector Spaces and Subspaces"".- ""Orthogonality"".- ""Matrix Decompositions: Factorization and SVD"".- ""Optimization and Gradients"".- ""Advanced Topics in Linear Algebra for Machine Learning"".
" More
