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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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書(shū)目名稱(chēng)Linear Algebra and Optimization for Machine Learning
副標(biāo)題A Textbook
編輯Charu C. Aggarwal
視頻videohttp://file.papertrans.cn/587/586273/586273.mp4
概述First 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
圖書(shū)封面Titlebook: Linear Algebra and Optimization for Machine Learning; A Textbook Charu C. Aggarwal Textbook 2020 Springer Nature Switzerland AG 2020 Linear
描述.This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. 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 textbook are organized as follows:.1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts..2. Optimization and its applications: M
出版日期Textbook 2020
關(guān)鍵詞Linear Algebra; Optimization; Machine Learning; Deep Learning; Neural Networks; Dynamic Programming; Suppo
版次1
doihttps://doi.org/10.1007/978-3-030-40344-7
isbn_softcover978-3-030-40346-1
isbn_ebook978-3-030-40344-7
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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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 re
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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 b
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nsspektrum als gleichwertige oder überlegene Methode im Vergleich zu konservativen oder chirurgischen Therapien der chronischen arteriellen Verschlusskrankheit durchgesetzt [.] (siehe auch Kapitel 2.7). Dabei wird die Ballonangioplastie alleine oder in Kombination mit anderen perkutanen transluminal
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