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 23 April 2020 with total hardcover pages 391. 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 : 42,8 Mb
Publisher : Cambridge University Press
Language : English
Release Date : 23 April 2020
ISBN : 9781108470049
Pages : 391 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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Hands On Mathematics for Deep Learning

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Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side

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Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

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Inequality has become an essential tool in many areas of mathematical research, for example in probability and statistics where it is frequently used in the proofs. "Probability Inequalities" covers inequalities related with events, distribution functions, characteristic functions, moments and random variables (elements) and their sum. The book shall serve as

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