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A demo of K-Means clustering on the handwritten digits data.ipynb
A demo of K-Means clustering on the handwritten digits data.ipynb
 
 
A demo of structured Ward hierarchical clustering on Lena image.ipynb
A demo of structured Ward hierarchical clustering on Lena image.ipynb
 
 
AstroML - Density Estimation.ipynb
AstroML - Density Estimation.ipynb
 
 
AstroML - Huber Loss Function.ipynb
AstroML - Huber Loss Function.ipynb
 
 
AstroML - SDSS Stripe 82 Moving Object Catalog.ipynb
AstroML - SDSS Stripe 82 Moving Object Catalog.ipynb
 
 
AstroML - SDSS imaging data and the SDSS Stripe 82 standard stars.ipynb
AstroML - SDSS imaging data and the SDSS Stripe 82 standard stars.ipynb
 
 
AstroML- Bayesian Blocks for Histograms.ipynb
AstroML- Bayesian Blocks for Histograms.ipynb
 
 
AstrolML- Linear Sum of Gaussians.ipynb
AstrolML- Linear Sum of Gaussians.ipynb
 
 
AstronomyBook.ipynb
AstronomyBook.ipynb
 
 
Cluster - ParallelMCOptions.ipynb
Cluster - ParallelMCOptions.ipynb
 
 
Computing with neural synchrony.ipynb
Computing with neural synchrony.ipynb
 
 
Faces dataset decompositions.ipynb
Faces dataset decompositions.ipynb
 
 
Gaussian Processes regression.ipynb
Gaussian Processes regression.ipynb
 
 
HMM Stock Analysis.ipynb
HMM Stock Analysis.ipynb
 
 
Hand writing recognition using SVM.ipynb
Hand writing recognition using SVM.ipynb
 
 
Hiden Markov Model.ipynb
Hiden Markov Model.ipynb
 
 
Hierarchical clustering-structured vs unstructured ward.ipynb
Hierarchical clustering-structured vs unstructured ward.ipynb
 
 
K-Neighbors for Photometric Redshifts of 50,000 galaxies.ipynb
K-Neighbors for Photometric Redshifts of 50,000 galaxies.ipynb
 
 
Linear and Quadratic Discriminant Analysis with confidence ellipsoid.ipynb
Linear and Quadratic Discriminant Analysis with confidence ellipsoid.ipynb
 
 
Manifold learning .ipynb
Manifold learning .ipynb
 
 
Non-linear SVM.ipynb
Non-linear SVM.ipynb
 
 
ObsPy Test.ipynb
ObsPy Test.ipynb
 
 
ParallelMCOptions-cluster.ipynb
ParallelMCOptions-cluster.ipynb
 
 
QuantLib - Basket Options.ipynb
QuantLib - Basket Options.ipynb
 
 
QuantLib Bond Pricing.ipynb
QuantLib Bond Pricing.ipynb
 
 
QuantLib-American Option.ipynb
QuantLib-American Option.ipynb
 
 
QuantLib-European Option.ipynb
QuantLib-European Option.ipynb
 
 
Quantlib-Swap.ipynb
Quantlib-Swap.ipynb
 
 
README
README
 
 
SDSS “Great Wall”.ipynb
SDSS “Great Wall”.ipynb
 
 
SVD Semantic analysis.ipynb
SVD Semantic analysis.ipynb
 
 
Species distribution modeling.ipynb
Species distribution modeling.ipynb
 
 
Visualizing the stock market structure.ipynb
Visualizing the stock market structure.ipynb
 
 
Weighted SVM.ipynb
Weighted SVM.ipynb
 
 
demo2 of the K Means clustering algorithm.ipynb
demo2 of the K Means clustering algorithm.ipynb
 
 
histogram.ipynb
histogram.ipynb
 
 
lot classification probability.ipynb
lot classification probability.ipynb
 
 
outlier - SVM and robust estimator of covariance.ipynb
outlier - SVM and robust estimator of covariance.ipynb
 
 

README

collection of samples to be used with IPython notebook. 
Instructions:  Install IPython notebook.
http://www.windowsazure.com/en-us/develop/python/tutorials/ipython-notebook/

For the additional packages required in the demos you will need to install the packages individually.   


Use one of the following depending on the situation.
apt-get install 
easy_install
pip


For questions please send tweets to:
@wenmingye