Algorithmic Mathematics In Machine Learning PDF Download
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Author | : Marc Peter Deisenroth |
Publisher | : Cambridge University Press |
Total Pages | : 392 |
Release | : 2020-04-23 |
Genre | : Computers |
ISBN | : 1108569323 |
Download Mathematics for Machine Learning Book in PDF, ePub and Kindle
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 students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning 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.
Author | : Marc Peter Deisenroth |
Publisher | : Cambridge University Press |
Total Pages | : 391 |
Release | : 2020-04-23 |
Genre | : Computers |
ISBN | : 1108470041 |
Download Mathematics for Machine Learning Book in PDF, ePub and Kindle
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Author | : Marc Peter Deisenroth |
Publisher | : |
Total Pages | : |
Release | : 2019-12 |
Genre | : Machine learning |
ISBN | : 9781108679930 |
Download Mathematics for Machine Learning Book in PDF, ePub and Kindle
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Author | : Bastian Bohn (Mathematician) |
Publisher | : |
Total Pages | : 0 |
Release | : 2024 |
Genre | : Machine learning |
ISBN | : 9781611977875 |
Download Algorithmic Mathematics in Machine Learning Book in PDF, ePub and Kindle
"Explores several well-known machine learning and data analysis approaches from a mathematical perspective and also implements and applies the underlying algorithms to achieve a programming and practical perspective"--
Author | : Shai Shalev-Shwartz |
Publisher | : Cambridge University Press |
Total Pages | : 415 |
Release | : 2014-05-19 |
Genre | : Computers |
ISBN | : 1107057132 |
Download Understanding Machine Learning Book in PDF, ePub and Kindle
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author | : Ankur Moitra |
Publisher | : Cambridge University Press |
Total Pages | : 161 |
Release | : 2018-09-27 |
Genre | : Computers |
ISBN | : 1107184584 |
Download Algorithmic Aspects of Machine Learning Book in PDF, ePub and Kindle
Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.
Author | : Stephen Marsland |
Publisher | : CRC Press |
Total Pages | : 407 |
Release | : 2011-03-23 |
Genre | : Business & Economics |
ISBN | : 1420067192 |
Download Machine Learning Book in PDF, ePub and Kindle
Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but
Author | : Justin Solomon |
Publisher | : CRC Press |
Total Pages | : 400 |
Release | : 2015-06-24 |
Genre | : Computers |
ISBN | : 1482251892 |
Download Numerical Algorithms Book in PDF, ePub and Kindle
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig
Author | : Ankur Moitra |
Publisher | : Cambridge University Press |
Total Pages | : 161 |
Release | : 2018-09-27 |
Genre | : Computers |
ISBN | : 1316884821 |
Download Algorithmic Aspects of Machine Learning Book in PDF, ePub and Kindle
This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.
Author | : Jason Brownlee |
Publisher | : Machine Learning Mastery |
Total Pages | : 319 |
Release | : 2019-09-24 |
Genre | : Computers |
ISBN | : |
Download Probability for Machine Learning Book in PDF, ePub and Kindle
Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.