Archive

Posts Tagged ‘Python’

Actors

The goal of Pykka is to provide easy to use concurrency abstractions for Python by using the actor model.
http://pykka.readthedocs.org/en/latest/index.html

libcppa — An implementation of the Actor Model for C++
http://libcppa.blogspot.com/

Actor Model of Computation: Scalable Robust Information Systems – Carl Hewitt ©2011 – http://carlhewitt.info

http://arxiv.org/ftp/arxiv/papers/1008/1008.1459.pdf

Hewitt, Meijer and Szyperski: The Actor Model (everything you wanted to know, but were afraid to ask)
http://channel9.msdn.com/Shows/Going+Deep/Hewitt-Meijer-and-Szyperski-The-Actor-Model-everything-you-wanted-to-know-but-were-afraid-to-ask

ActorsFoundations for Open System
http://www.erights.org/history/actors.html

Viewing Control Structures as Patterns of Passing Messages – Carl Hewitt
http://dspace.mit.edu/handle/1721.1/6272

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Common Lisp, Prolog, and Python Reference Links

Artificial Intelligence

August 8, 2011 Comments off

Solve for X conference

Markov Models

FANN – Fast Artificial Neural Network Library
http://leenissen.dk/fann/wp/

dlib C++ library
http://dlib.net/

Artificial Intelligence: A Modern Approach
http://aima.cs.berkeley.edu/

Vowpal Wabbit (Fast Learning)
http://worrydream.com/feed.xml

Python Links

Categories: Python Tags: , ,

Machine Learning

Mahoot
http://mahout.apache.org/

Machine Learning and Hadoop by Josh Will
http://www.youtube.com/watch?v=5p06Xg5REj0&feature=youtube_gdata

Pycon 2012 ML and Python Talks
http://aimotion.blogspot.com/2012/03/some-data-and-machine-learning-talks.html

Some ML Packages in Python

Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning
http://www.youtube.com/watch?v=ZmNOAtZIgIk

The Future of Robotics and Artificial Intelligence (Andrew Ng, Stanford University, STAN 2011)
http://www.youtube.com/watch?v=AY4ajbu_G3k

a machine learning definition:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” — Mitchell, Tom M. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7, p.2.

ML Videos
http://videolectures.net/Top/Computer_Science/Machine_Learning/

Guide to Getting Started in Machine Learning
http://abeautifulwww.com/2009/10/11/guide-to-getting-started-in-machine-learning/

Stanford – Artificial Intelligence | Machine Learning
http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

Welcome to the UC Irvine Machine Learning Repository!
http://archive.ics.uci.edu/ml/

MLOSS

http://www.mloss.org/software/

CRAN Task View: Machine Learning & Statistical Learning
http://cran.r-project.org/web/views/MachineLearning.html

MIT – 18.06 Linear Algebra
http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm

Biopython

Ontology and Semantic Web Tools