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No Bullshit Guide to Linear Algebra 2nd Edition by Ivan Savov 0992001021 978-0992001025

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No Bullshit Guide to Linear Algebra Ivan Savov Digital Instant Download

Author(s): Ivan Savov
ISBN(s): 9780992001018, 0992001013
Edition: draft
File Details: PDF, 4.31 MB
Year: 2016
Language: english
SKU: EB-9974708 Category: Tag:

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Product details:

ISBN 10: 0992001021

ISBN 13: 978-0992001025

Author: Ivan Savov

Linear algebra is the foundation of science and engineering. Knowledge of linear algebra is a prerequisite for studying statistics, machine learning, computer graphics, signal processing, chemistry, economics, quantum mechanics, and countless other applications. Indeed, linear algebra offers a powerful toolbox for modelling the real world.

The NO BULLSHIT GUIDE TO LINEAR ALGEBRA shows the connections between the computational techniques of linear algebra, their geometric interpretations, and the theoretical foundations. This university-level textbook contains lessons on linear algebra written in a style that is precise and concise. Each concept is illustrated through definitions, formulas, diagrams, explanations, and examples of real-world applications. Readers build their math superpowers by solving practice problems and learning to use the computer algebra system SymPy to speed up tedious matrix arithmetic tasks.

“The book explains the concepts in a way that gives a strong intuitive understanding.” — Joe Nestor, student

“It’s very well written and a fun read!” — Felix Kwok, professor

“I used this book in multiple big data courses when I needed a deeper understanding of the material.” — Zane Zakraisek, student

The author, Ivan Savov, combines 15 years of tutoring experience with a B.Eng. in electrical engineering, an M.Sc. in physics, and a Ph.D. in computer science from McGill University.

Table of contents:

1. Math fundamentals (equations, algebra, functions, geometry, trigonometry, complex, linear equations, set notation)

2. Intro to linear algebra (definitions, vectors, matrices, linearity)

3. Computational linear algebra (RREF, multiplication, determinants, inverses)

4. Geometric aspects of linear algebra (lines, planes, projections, vector spaces)

5. Linear transformations (matrix representations, change of basis for matrices, invertible matrix theorem)

6. Theoretical linear algebra (eigenvectors, abstract vector spaces, Gram–Schmidt, decompositions)

7. Applications (chemical equations, economics, circuits, graphs, least squares, computer graphics, cryptography, error-correcting codes, Fourier analysis)

8. Probability theory (distributions, Markov chains, PageRank)

9. Quantum mechanics (Dirac notation, postulates, polarizing lenses experiment, applications)

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Tags: Ivan Savov, Bullshit, Linear, Algebra