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  2. py.svg

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  3. index.rst.txt

    H2O Documentation ========== .. toctree:: intro h2o data frame modeling model_categories tree metrics assembly backend exceptions Indices and tables ========== * :ref:`genindex` * :ref:`modindex` *...
    docs.h2o.ai/h2o/latest-stable/h2o-py/docs/_sources/index.rst.txt
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  4. movielens_implicit.rst.txt

    An implicit feedback recommender for the Movielens dataset ========== Implicit feedback ---------- For some time, the recommender system literature focused on explicit feedback: the Netflix prize f...
    making.lyst.com/lightfm/docs/_sources/examples/movielens_implicit.rst.txt
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  5. datasets.rst.txt

    Datasets ========== .. autofunction:: lightfm.datasets.movielens.fetch_movielens .. autofunction:: lightfm.datasets.stackexchange.fetch_stackexchange
    making.lyst.com/lightfm/docs/_sources/datasets.rst.txt
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  6. learning_schedules.rst.txt

    Using different learning schedules ========== ``lightfm`` implements two learning schedules: adagrad and adadelta. Neither is clearly superior, and, like other hyperparameter choices, the best lear...
    making.lyst.com/lightfm/docs/_sources/examples/learning_schedules.rst.txt
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  7. home.rst.txt

    Welcome to LightFM's documentation! ========== LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback. It also makes it possible...
    making.lyst.com/lightfm/docs/_sources/home.rst.txt
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  8. pydata-sphinx-theme.js

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  9. pydata-sphinx-theme.js

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  10. warp_loss.rst.txt

    Learning-to-rank using the WARP loss ========== LightFM is probably the only recommender package implementing the WARP (Weighted Approximate-Rank Pairwise) loss for implicit feedback learning-to-ra...
    making.lyst.com/lightfm/docs/_sources/examples/warp_loss.rst.txt
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