Mathematics for Machine Learning / Edition 1. Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Mathematics-for-Machine.pdf
ISBN: 9781108455145 | 398 pages | 10 Mb
- Mathematics for Machine Learning / Edition 1
- Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- Page: 398
- Format: pdf, ePub, fb2, mobi
- ISBN: 9781108455145
- Publisher: Cambridge University Press
English book download for free Mathematics for Machine Learning / Edition 1
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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These books are divided in maths, machine learning and deep learning This 2013 edition by Golub and Van Loan, published by The Johns
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But the preface says this is to be "a book on Mathematics for Machine All of the videos from the 2017 edition are available to watch on I've also used and found Mohri's Foundations of Machine Learning quite insightful [1].
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Learn the core mathematical concepts for machine learning and learn to 1 article. 6 downloadable resources. Full lifetime access. Access on mobile and TV.
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