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.
|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.