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Tensor Decompositions for Data Science by Grey Ballard and Tamara G. Kolda Comprehensive Guide to Multilinear Algebra Machine Learning Algorithms New Release 2026 2027 Academic Year
Master the foundations of tensor decompositions with this definitive guide by experts Grey Ballard and Tamara G. Kolda. Designed for advanced undergraduate and graduate students, this resource bridges theoretical mathematics with practical data science applications in machine learning, signal processing, neuroscience, and quantum computing. The text provides a self-contained treatment of 3-way to d-way tensors, featuring real-world open-source datasets, rigorous algorithmic explanations, and extensive appendices on linear algebra, optimization, probability, and statistics. Enhanced for the 2026/2027 academic year, this updated edition serves as an essential reference for researchers and students seeking to demystify high-dimensional data analysis through intuitive notation and precise mathematical typesetting.
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