Linear Algebra and Learning from Data
by Gilbert Strang
Linear Algebra and Learning from Data
by Gilbert Strang
Book Summary
In the rapidly evolving landscape of data science and machine learning, a solid grasp of linear algebra forms the foundational pillar for understanding and developing algorithms that drive modern technologies. This work by Gilbert Strang offers a detailed exploration of linear algebra concepts tailored specifically for those venturing into learning from data, bridging the gap between abstract mathematics and practical application.
- Foundational Concepts: The book introduces core linear algebra topics such as vectors, matrices, and linear transformations with clarity and rigor.
- Matrix Factorizations: Detailed treatment of decompositions like LU, QR, and Singular Value...
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