Think Linear Algebra (2023)

6 hours ago

Think Linear Algebra offers a code-first, case-based approach to learning linear algebra, using real-world problems like traffic modeling, flocking bird simulations, and electrical circuits.
It leverages Python and libraries such as NumPy, SciPy, SymPy, and NetworkX to build practical solutions, emphasizing hands-on learning through Jupyter notebooks for immediate feedback.
Designed for those who struggled with traditional math or prefer intuitive learning, the book starts with applications and builds theory as needed, focusing on linear algebra's role in machine learning, computing, and graphics.
Available under a Creative Commons license, it allows free copying, distribution, and modification with attribution, excluding commercial use.
Key learning outcomes include formulating problems with vectors/matrices, using Python for simulations and decompositions, visualizing concepts, and applying linear algebra in fields like engineering and data science.
The book includes chapters on topics like matrix multiplication, eigenvectors, vector operations, projections, transformations, systems of equations, null space, and regression, each with interactive Colab notebooks.

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