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Attention
Attention
 
 
Overfitting
Overfitting
 
 
Reinforcement Learning
Reinforcement Learning
 
 
dask
dask
 
 
fmri_vae
fmri_vae
 
 
generalization
generalization
 
 
gmm
gmm
 
 
gradients
gradients
 
 
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
 
 
Function Approximation.ipynb
Function Approximation.ipynb
 
 
GP overfitting.ipynb
GP overfitting.ipynb
 
 
Gradient.ipynb
Gradient.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
 
 
Saddle Point LBFGS.ipynb
Saddle Point LBFGS.ipynb
 
 
Semi-supervised Extra Trees.ipynb
Semi-supervised Extra Trees.ipynb
 
 
Text Classification.ipynb
Text Classification.ipynb
 
 
Time Series.ipynb
Time Series.ipynb
 
 
Untitled Diagram.drawio
Untitled Diagram.drawio
 
 
Variable Importance with Completely Randomized Trees.ipynb
Variable Importance with Completely Randomized Trees.ipynb
 
 
cloudstorage.ini.example
cloudstorage.ini.example
 
 
environment.yml
environment.yml
 
 
nmf_topics.ipynb
nmf_topics.ipynb
 
 
quantile_regression_as_classification.ipynb
quantile_regression_as_classification.ipynb
 
 
structure_digits.ipynb
structure_digits.ipynb
 
 
test.drawio
test.drawio
 
 
ubuntu-quickstart.sh
ubuntu-quickstart.sh
 
 

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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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Some sample IPython notebooks for scikit-learn

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