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@miguelgfierro @gramhagen @nikhilrj @WessZumino @loomlike
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Model

In this directory, notebooks are provided to give a deep dive into training models using different algorithms such as Alternating Least Squares (ALS) and Singular Value Decomposition (SVD) using Surprise python package. The notebooks make use of the utility functions (reco_utils) available in the repo.

Notebook Environment Description
als_deep_dive PySpark Deep dive on the ALS algorithm and implementation.
mmlspark_lightgbm_criteo PySpark LightGBM gradient boosting tree algorithm implementation in MML Spark with Criteo dataset.
baseline_deep_dive --- Deep dive on baseline performance estimation.
ncf_deep_dive Python CPU, GPU Deep dive on a NCF algorithm and implementation.
rbm_deep_dive Python CPU, GPU Deep dive on the rbm algorithm and its implementation.
sar_deep_dive Python CPU Deep dive on the SAR algorithm and implementation.
surprise_svd_deep_dive Python CPU Deep dive on a SVD algorithm and implementation.
vowpal_wabbit_deep_dive Python CPU Deep dive into using Vowpal Wabbit for regression and matrix factorization.

Details on model training are best found inside each notebook.

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