Mathematics for Machine Learning / Edition 1 by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Free read online books download Mathematics for Machine Learning / Edition 1 by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

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Mathematics for Machine Learning / Edition 1
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Page: 398
Format: pdf, ePub, mobi, fb2
ISBN: 9781108455145
Publisher: Cambridge University Press

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Free read online books download Mathematics for Machine Learning / Edition 1 by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For studentsand otherswith a mathematical background, these derivations provide a starting point to machine learning texts. Forthoselearning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

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There are many reasons why the mathematics of Machine Learning is Applications of Linear Algebra, Part 1 and Part 2. Maybe you run a version of ResNet / Xception / denseNet to classify user images into thousands of categories. Mathematics for Machine Learning - Garrett Thomas
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