├── .gitignore ├── Adjusted Variable Importances with Randomized Trees.ipynb ├── Attention └── Keras Attention.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 ├── Overfitting └── linear-model-overfitting.ipynb ├── Parameter search for Extra Trees on the MNIST classificationt task.ipynb ├── Patch-Based Feature Extraction for Image Classification.ipynb ├── README.md ├── Reinforcement Learning └── Random walk policy evaluation.ipynb ├── SGD stuff.ipynb ├── Saddle Point LBFGS.ipynb ├── Semi-supervised Extra Trees.ipynb ├── Text Classification.ipynb ├── Time Series.ipynb ├── Untitled Diagram.drawio ├── Variable Importance with Completely Randomized Trees.ipynb ├── bench_gb_libraries.ipynb ├── cloudstorage.ini.example ├── dask └── fold_learn.ipynb ├── environment.yml ├── fmri_vae ├── fmri_autoencoder.ipynb └── fmri_model.py ├── generalization └── run_mnist.py ├── gmm ├── GMM with PyTorch.ipynb ├── GMM with SGD.ipynb ├── Gaussian likelihood landscape.ipynb ├── Model Selection for GMM.ipynb └── gmmsgd.py ├── gradients └── custom optim.ipynb ├── heteroscedastic_optimization_via_surrogate_modeling.ipynb ├── letor_cluster ├── MSLR Grid Search.ipynb ├── letor_gridpoint.py ├── letor_gridresults.json ├── letor_gridresults.py └── letor_gridsearch.py ├── nmf_topics.ipynb ├── probabilistic_regression_figures.ipynb ├── quantile_regression_as_classification.ipynb ├── representations ├── Autoencoder ELMs.ipynb ├── Entangled Manifolds.ipynb ├── MNIST experiments.ipynb ├── Sparse non-linear random projections.ipynb └── Unsupervised feature extraction with the Breiman trick.ipynb ├── screenshots ├── digits.png └── topics.png ├── sklearn_demos ├── Classifier calibration.ipynb ├── Face recognition.ipynb ├── Feature Importances.ipynb ├── Gradient Boosting.ipynb ├── Income classification - Column Transformer Edition.ipynb ├── Income classification.ipynb ├── Language Classification.ipynb ├── Large Scale 2D Clustering-1M.ipynb ├── Large Scale 2D Clustering.ipynb ├── Permutation Importances.ipynb ├── ames_gbrt_search_results.json ├── ames_housing.ipynb ├── fastText.ipynb ├── gbdt_vs_neural_nets_on_tabular_data.ipynb ├── iris.ipynb ├── language │ └── fetch_data.py └── splines_overfitting.ipynb ├── structure_digits.ipynb ├── test.drawio └── ubuntu-quickstart.sh /.gitignore: -------------------------------------------------------------------------------- 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