Titlebook: Linear Algebra and Optimization for Machine Learning; A Textbook Charu C. Aggarwal Textbook 2020 Springer Nature Switzerland AG 2020 Linear

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The Linear Algebra of Similarity,this important class of methods in machine learning. The real utility of such methods arises when the similarity matrix is chosen differently from the use of dot products (and the data matrix is sometimes not even available).

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Textbook 2020 A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this text

Forehead-Lift

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没花的是打扰

Constrained Optimization and Duality,over the region of the optimization space that satisfies these constraints. This region is referred to as the . in optimization parlance. The straightforward use of a gradient-descent procedure does not work, because an unconstrained step might move the optimization variables outside the feasible region of the optimization problem.

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The Linear Algebra of Graphs,pplications are conceptually represented as optimization problems on graphs. Graph matrices have a number of useful algebraic properties, which can be leveraged in machine learning. There are close connections between kernels and the linear algebra of graphs; a classical application that naturally belongs to both fields is . (cf. Section 10.5).

含沙射影

Eigenvectors and Diagonalizable Matrices,Any square matrix . of size . × . can be considered a linear operator, which maps the .-dimensional column vector . to the .-dimensional vector .. A linear transformation . is a combination of operations such as rotations, reflections, and scalings of a vector ..

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Charu C. AggarwalFirst textbook to provide an integrated treatment of linear algebra and optimization with a special focus on machine learning issues.Includes many examples to simplify exposition and facilitate in lea