Machine Learning a Probabilistic Perpective

Machine Learning: A Probabilistic Perspective (Repost)  eBooks & eLearning

Posted by nebulae at Nov. 19, 2015
Machine Learning: A Probabilistic Perspective (Repost)

Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective"
English | ISBN: 0262018020 | 2012 | PDF | 1104 pages | 25,7 MB

Machine Learning: A Probabilistic Perspective (Repost)  eBooks & eLearning

Posted by Grev27 at Sept. 6, 2013
Machine Learning: A Probabilistic Perspective (Repost)

Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective"
English | ISBN: 0262018020 | 2012 | PDF | 1104 pages | 25,7 MB

Machine Learning: A Probabilistic Perspective  eBooks & eLearning

Posted by enmoys at Dec. 8, 2012
Machine Learning: A Probabilistic Perspective

Machine Learning: A Probabilistic Perspective By Kevin P. Murphy
2012 | 1104 Pages | ISBN: 0262018020 | PDF | 26 MB

Combinatorial Machine Learning: A Rough Set Approach  eBooks & eLearning

Posted by alt_f4 at Sept. 27, 2015
Combinatorial Machine Learning: A Rough Set Approach

Combinatorial Machine Learning: A Rough Set Approach (Studies in Computational Intelligence) by Mikhail Moshkov
English | June 29, 2011 | ISBN: 3642209947 | 186 Pages | PDF | 2 MB

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in the problem of attribute(feature) selection.

Thoughtful Machine Learning: A Test-Driven Approach  eBooks & eLearning

Posted by ksveta6 at Jan. 5, 2015
Thoughtful Machine Learning: A Test-Driven Approach

Thoughtful Machine Learning: A Test-Driven Approach by Matthew Kirk
2014 | ISBN: 1449374069 | English | 234 pages | PDF + EPUB | 6 MB + 19 MB

Multi-Agent Machine Learning: A Reinforcement Approach  eBooks & eLearning

Posted by Bayron at Oct. 19, 2014
Multi-Agent Machine Learning: A Reinforcement Approach

Multi-Agent Machine Learning: A Reinforcement Approach by H. M. Schwartz
English | 2014 | ISBN: 111836208X | 256 pages | EPUB | 13 MB
Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning)

Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning) by Yoshua Bengio
English | Oct. 28, 2009 | ISBN: 1601982941 | 130 Pages | PDF | 1 MB

Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae.

Financial Signal Processing and Machine Learning  eBooks & eLearning

Posted by ksveta6 at Jan. 12, 2017
Financial Signal Processing and Machine Learning

Financial Signal Processing and Machine Learning (Wiley - IEEE) by Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry M. Malioutov
2016 | ISBN: 1118745671 | English | 320 pages | PDF/EPUB | 4 MB/34 MB

MATLAB Machine Learning  eBooks & eLearning

Posted by AvaxGenius at Dec. 30, 2016
MATLAB Machine Learning

MATLAB Machine Learning By Stephanie Thomas, Michael Paluszek
English | EPUB | 326 Pages | 2017 | ISBN : 1484222490 | 5.17 MB

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning

R: Recipes for Analysis, Visualization and Machine Learning  eBooks & eLearning

Posted by AlenMiler at Dec. 9, 2016
R: Recipes for Analysis, Visualization and Machine Learning

R: Recipes for Analysis, Visualization and Machine Learning by Viswa Viswanathan
English | 24 Nov. 2016 | ASIN: B01N7AE091 | 959 pages | AZW3/MOBI/EPUB/PDF (conv) | 111.63 MB

Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning