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Some sample IPython notebooks for scikit-learn
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Attention
Overfitting
Reinforcement Learning
dask
fmri_vae
generalization
gmm
gradients
letor_cluster
representations
screenshots
sklearn_demos
.gitignore
Adjusted Variable Importances with Randomized Trees.ipynb
Bootstrap.ipynb
Clustering Model Selection.ipynb
Data Preprocessing for the Learning to Rank example.ipynb
Distributed Aggregate and Join.ipynb
Distributed Learning of Extra Trees with IPython.parallel.ipynb
Explained variances.ipynb
Function Approximation.ipynb
GP overfitting.ipynb
Gradient.ipynb
Labeled Faces in the Wild recognition.ipynb
Learning to Rank.ipynb
MNIST8M Chunking and Upload to Cloud Blob Storage.ipynb
Non IID cross-validation.ipynb
Numa-aware computation experiments.ipynb
Numba Parakeet Cython.ipynb
Numpy intro.ipynb
Parameter search for Extra Trees on the MNIST classificationt task.ipynb
Patch-Based Feature Extraction for Image Classification.ipynb
README.md
SGD stuff.ipynb
Saddle Point LBFGS.ipynb
Semi-supervised Extra Trees.ipynb
Text Classification.ipynb
Time Series.ipynb
Variable Importance with Completely Randomized Trees.ipynb
cloudstorage.ini.example
environment.yml
nmf_topics.ipynb
structure_digits.ipynb
ubuntu-quickstart.sh

README.md

ogrisel's notebook

This is a bunch of IPython notebooks documents with mostly unfinished ML related experiments.

Some of them can be executed in a basic numpy / scipy / pandas / matplotlib / scikit-learn environment for instance using:

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