Bellot Learning Probabilistic

Learning Probabilistic Graphical Models in R  eBooks & eLearning

Posted by Grev27 at May 14, 2016
Learning Probabilistic Graphical Models in R

David Bellot, "Learning Probabilistic Graphical Models in R"
English | ISBN: 1784392057 | 2016 | PDF/EPUB/MOBI | 250 pages | 4 MB/7 MB/11 MB
From Algorithms to z-scores: Probabilistic and Statistical Modeling in Computer Science

From Algorithms to z-scores: Probabilistic and Statistical Modeling in Computer Science
by Norm Matloff
English | 2009 | ISBN: 1616100362 | 274 pages | PDF | 8.48 MB
Probability and Computing: Randomized Algorithms and Probabilistic Analysis (repost)

Michael Mitzenmacher, Eli Upfal, "Probability and Computing: Randomized Algorithms and Probabilistic Analysis"
English | 2005 | ISBN: 0521835402 | 307 pages | PDF | 25 MB

Stanford University - Introduction to Artificial Intelligence [repost]  eBooks & eLearning

Posted by ParRus at Nov. 5, 2016
Stanford University - Introduction to Artificial Intelligence [repost]

Stanford University - Introduction to Artificial Intelligence
WEBRip | English | MP4 + PDF Guide | 640 x 360 | AVC ~250 kbps | 30 fps
AAC | 123 Kbps | 44.1 KHz | 2 channels | ~24 hours | 4.42 GB
Genre: eLearning Video / Science, Cybernetics, Probability

Online Introduction to Artificial Intelligence is based on Stanford CS221, Introduction to Artificial Intelligence. This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing. The objective of this class is to teach you modern AI. You learn about the basic techniques and tricks of the trade, at the same level we teach our Stanford students. We also aspire to excite you about the field of AI. Whether you are a seasoned professional, a college student, or a curious high school student - everyone can participate.

Probabilistic Finite Element Model Updating Using Bayesian Statistics  eBooks & eLearning

Posted by Underaglassmoon at Nov. 1, 2016
Probabilistic Finite Element Model Updating Using Bayesian Statistics

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering
Wiley | Mechanical Engineering | December 12 2016 | ISBN-10: 1119153034 | 248 pages | pdf | 6.23 mb

by Tshilidzi Marwala (Author), Ilyes Boulkaibet (Author), Sondipon Adhikari (Author)
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (repost)

Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Series in Representation and Reasoning)"
English | 1988 | ISBN: 1558604790 | 552 pages | PDF | 56 MB

Probabilistic Methods in Geotechnical Engineering (repost)  eBooks & eLearning

Posted by interes at Sept. 27, 2016
Probabilistic Methods in Geotechnical Engineering (repost)

Probabilistic Methods in Geotechnical Engineering (CISM International Centre for Mechanical Sciences) by D. V. Griffiths and G. A. Fenton
English | October 12, 2007 | ISBN-10: 3211733655 | 346 pages | PDF | 11,7 Mb
Behavioral Social Choice: Probabilistic Models, Statistical Inference, and Applications

Behavioral Social Choice: Probabilistic Models, Statistical Inference, and Applications
by Michel Regenwetter, Bernard Grofman, A. A. J. Marley, Ilia Tsetlin
English | 2006 | ISBN: 0521536669 | 258 pages | PDF | 13.55 MB

Studies in Expansive Learning: Learning What Is Not Yet There  eBooks & eLearning

Posted by roxul at Sept. 11, 2016
Studies in Expansive Learning: Learning What Is Not Yet There

Yrjö Engeström, "Studies in Expansive Learning: Learning What Is Not Yet There"
English | ISBN: 110710520X | 2016 | 288 pages | PDF | 4 MB

Deep Learning: Recurrent Neural Networks in Python  eBooks & eLearning

Posted by AlenMiler at Sept. 10, 2016
Deep Learning: Recurrent Neural Networks in Python

Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) by LazyProgrammer
English | 8 Aug 2016 | ASIN: B01K31SQQA | 86 Pages | AZW3/MOBI/EPUB/PDF (conv) | 1.44 MB

Like Markov models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.