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Results 11 - 15 of 15 for content_length:[0 TO 9999] (0.01 sec)

  1. 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
    Registered: Fri May 10 08:05:15 UTC 2024
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  2. 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
    Registered: Fri May 10 08:05:20 UTC 2024
    - Last Modified: Wed Mar 09 16:56:08 UTC 2022
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  3. 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
    Registered: Fri May 10 08:06:00 UTC 2024
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  4. lightfm.rst.txt

    LightFM ========== .. autoclass:: lightfm.LightFM :members: :undoc-members:
    making.lyst.com/lightfm/docs/_sources/lightfm.rst.txt
    Registered: Fri May 10 08:05:30 UTC 2024
    - Last Modified: Wed Mar 09 16:56:08 UTC 2022
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  5. dataset.rst.txt

    Building datasets ========== In this example, we'll use LightFM's built-in ``Dataset`` class to build an interaction dataset from raw data. The goal is to demonstrate how to go from raw data (lists...
    making.lyst.com/lightfm/docs/_sources/examples/dataset.rst.txt
    Registered: Fri May 10 08:05:41 UTC 2024
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