The 'book-la-demo' repository provides demo code in Julia for the book 'Linear Algebra for Data Science, Machine Learning, and Signal Processing.' Authored by Jeff Fessler and Raj Nadakuditi, the demos showcase techniques and applications that enhance understanding of linear algebra in real-world data contexts.
Explore engaging demos from the book "Linear Algebra for Data Science, Machine Learning, and Signal Processing" by Jeff Fessler and Raj Nadakuditi, published by Cambridge University Press in 2024. This repository provides interactive code samples written in Julia, showcasing various applications of linear algebra concepts fundamental to data science and machine learning.
Key Features:
- The repository includes numerous demos that illustrate core concepts through visualizations and hands-on code.
- Expectations: Demos are designed for the Julia language, requiring version 1.9 or above.
- Dive into essential topics like binary classification, video background separation, non-negative matrix factorization, and more.
Featured Demos:
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Binary Classification: Visualize the classification process with ease.
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Video Foreground/Background Separation: Separate moving objects from the background.
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Non-negative Matrix Factorization: Decompose matrices into non-negative factors.
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Photometric Stereo: Understand surface normals through light variation.
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Preconditioning: Analyze how preconditioning affects iterative methods.
Structure of the Demos:
The project organizes its demos into a structured index, targeting various mathematical concepts and applications:
- Julia Fundamentals
- Fundamentals of Linear Algebra
- Advanced Techniques
- Statistical Applications
- Rank-1 Updates and PCA
By utilizing this repository, learners can effectively navigate through the practical applications of linear algebra relevant to data science and machine learning, enhancing their understanding through interactive coding demonstrations and rich visual feedback.