Linear algebra is at the heart of data science, machine learning, deep learning, statistics, and pretty much everything on computers. This unique text

Linear Algebra: Theory… by Mike X Cohen [PDF/iPad/Kindle]

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2021-05-22 18:30:04

Linear algebra is at the heart of data science, machine learning, deep learning, statistics, and pretty much everything on computers.

This unique textbook combines in-depth comprehensive explanations, visualizations, examples, and code (Python and MATLAB) to explain concepts in linear algebra. It's all you need.

Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.

The way linear algebra is presented in traditional textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you!

If you are interested in learning the mathematical concepts linear algebra and matrix analysis, but also want to apply those concepts to data analyses on computers (e.g., statistics or signal processing), then this book is for you. You'll see all the math concepts implemented in MATLAB and in Python.

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