Mathematics for Machine Learning

This book PDF is perfect for those who love Computers genre, written by Marc Peter Deisenroth and published by Cambridge University Press which was released on 31 March 2020 with total hardcover pages 398. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Mathematics for Machine Learning books below.

Mathematics for Machine Learning
Author : Marc Peter Deisenroth
File Size : 45,6 Mb
Publisher : Cambridge University Press
Language : English
Release Date : 31 March 2020
ISBN : 9781108470049
Pages : 398 pages
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Mathematics for Machine Learning by Marc Peter Deisenroth Book PDF Summary

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Mathematics for Machine Learning

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

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Mathematics for Machine Learning

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

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Mathematics for Machine Learning

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"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book

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