Bishop Machine Learning

R Machine Learning solutions  eBooks & eLearning

Posted by naag at Dec. 10, 2016
R Machine Learning solutions

R Machine Learning solutions
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 8.5 Hours | 1.81 GB
Genre: eLearning | Language: English

R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R.

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
Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI

Practical Machine Learning with H20: Powerful, Scalable Techniques for Deep Learning and AI by Darren Cook
English | 31 Dec. 2016 | ISBN: 149196460X | 300 Pages | AZW3/MOBI/EPUB/PDF (conv) | 17.61 MB

In Practical Machine Learning with H2O, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community.
"Pattern Recognition and Machine Learning: Solutions Exercises" by Christopher M. Bishop

"Pattern Recognition and Machine Learning: Solutions Exercises" by Christopher M. Bishop
Information Science and Statistics
Sрringеr Science+Business Media | 2006 | ISBN: 0387310732 0387310738 | 101 pages | PDF | 1 MB

Example solutions for a subset of the exercises are available from the book. This issue is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. A volume deals with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets.
"Pattern Recognition and Machine Learning" by Christopher M. Bishop

"Pattern Recognition and Machine Learning" by Christopher M. Bishop
Information Science and Statistics
Sрringеr Science+Business Media | 2006 | ISBN: 0387310738 9780387310732 | 761 pages | PDF | 5 MB

This textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Pattern Recognition and Machine Learning (Repost)  eBooks & eLearning

Posted by foosaa at July 22, 2009
Pattern Recognition and Machine Learning (Repost)

Christopher M. Bishop, "Pattern Recognition and Machine Learning"
Springer | 2007 | ISBN: 0387310738 | English | 738 Pages | PDF | 9.5 MB

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Building Machine Learning Projects with TensorFlow  eBooks & eLearning

Posted by AlenMiler at Dec. 9, 2016
Building Machine Learning Projects with TensorFlow

Building Machine Learning Projects with TensorFlow by Rodolfo Bonnin
English | 24 Nov. 2016 | ISBN: 1786466589 | 282 Pages | AZW3/MOBI/EPUB/PDF (conv) | 34.32 MB

Machine Learning: Algorithms and Applications  eBooks & eLearning

Posted by arundhati at Dec. 8, 2016
Machine Learning: Algorithms and Applications

Mohssen Mohammed, Muhammad Badruddin Khan, "Machine Learning: Algorithms and Applications"
2016 | ISBN-10: 1498705383 | 226 pages | EPUB | 6 MB

Genetic Algorithms in Search, Optimization, and Machine Learning  eBooks & eLearning

Posted by Willson at Dec. 8, 2016
Genetic Algorithms in Search, Optimization, and Machine Learning

David E. Goldberg, "Genetic Algorithms in Search, Optimization, and Machine Learning"
English | 1989 | ISBN: 0201157675, 817758829X | 432 pages | DJVU | 4 MB

Financial Signal Processing and Machine Learning  eBooks & eLearning

Posted by roxul at Dec. 8, 2016
Financial Signal Processing and Machine Learning

Ali N. Akansu, "Financial Signal Processing and Machine Learning"
English | ISBN: 1118745671 | 2016 | 320 pages | PDF | 4 MB