📚 Mathematics of Data Science is an open book about the mathematical foundations behind modern data science. Its authors organize the material as a path from linear algebra and probability to deep learning.
The book covers high-dimensional phenomena, singular value decomposition and PCA, linear regression and regularization, graphs and clustering, nonlinear reduction, random projections, and optimization.
It also addresses classification, a mathematical introduction to deep learning, concentration of measure, matrix inequalities, compressed sensing, and low-rank matrix recovery.
🧠 The approach connects techniques that are often taught separately. For example, SVD explains PCA, regularization links geometry to generalization, and graphs can be used to study communities and data representations.
💡 Explanation in a nutshell#
Data science is not only about using libraries: many of its tools rely on a few mathematical ideas. This book is a map for understanding how matrices, probability, optimization, and geometry become algorithms.
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