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Some sample IPython notebooks for scikit-learn

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letor_cluster
letor_cluster
 
 
representations
representations
 
 
screenshots
screenshots
 
 
sklearn_demos
sklearn_demos
 
 
.gitignore
.gitignore
 
 
Adjusted Variable Importances with Randomized Trees.ipynb
Adjusted Variable Importances with Randomized Trees.ipynb
 
 
Bootstrap.ipynb
Bootstrap.ipynb
 
 
Clustering Model Selection.ipynb
Clustering Model Selection.ipynb
 
 
Data Preprocessing for the Learning to Rank example.ipynb
Data Preprocessing for the Learning to Rank example.ipynb
 
 
Distributed Aggregate and Join.ipynb
Distributed Aggregate and Join.ipynb
 
 
Distributed Learning of Extra Trees with IPython.parallel.ipynb
Distributed Learning of Extra Trees with IPython.parallel.ipynb
 
 
Explained variances.ipynb
Explained variances.ipynb
 
 
Labeled Faces in the Wild recognition.ipynb
Labeled Faces in the Wild recognition.ipynb
 
 
Learning to Rank.ipynb
Learning to Rank.ipynb
 
 
MNIST8M Chunking and Upload to Cloud Blob Storage.ipynb
MNIST8M Chunking and Upload to Cloud Blob Storage.ipynb
 
 
Non IID cross-validation.ipynb
Non IID cross-validation.ipynb
 
 
Numa-aware computation experiments.ipynb
Numa-aware computation experiments.ipynb
 
 
Numba Parakeet Cython.ipynb
Numba Parakeet Cython.ipynb
 
 
Numpy intro.ipynb
Numpy intro.ipynb
 
 
Parameter search for Extra Trees on the MNIST classificationt task.ipynb
Parameter search for Extra Trees on the MNIST classificationt task.ipynb
 
 
Patch-Based Feature Extraction for Image Classification.ipynb
Patch-Based Feature Extraction for Image Classification.ipynb
 
 
README.md
README.md
 
 
SGD stuff.ipynb
SGD stuff.ipynb
 
 
Semi-supervised Extra Trees.ipynb
Semi-supervised Extra Trees.ipynb
 
 
Text Classification.ipynb
Text Classification.ipynb
 
 
Time Series.ipynb
Time Series.ipynb
 
 
Variable Importance with Completely Randomized Trees.ipynb
Variable Importance with Completely Randomized Trees.ipynb
 
 
cloudstorage.ini.example
cloudstorage.ini.example
 
 
nmf_topics.ipynb
nmf_topics.ipynb
 
 
structure_digits.ipynb
structure_digits.ipynb
 
 
ubuntu-quickstart.sh
ubuntu-quickstart.sh
 
 

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Sample IPython 0.12+ notebooks for machine learning stuff

Screenshots

Digits Topics

Install dependencies

sudo pip install -U tornado
sudo pip install -U pyzmq
sudo pip install -U git+https://github.com/ipython/ipython.git

Run the notebook

Then cd into this folder and run:

ipython notebook --pylab=inline

Then click on a notebook. The focus is on the first cell: hit Shift-Enter to execute the current cell an move on to the next. You can also click on Run > All in the left panel.

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Some sample IPython notebooks for scikit-learn

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