awesome-embedding-models
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| awesome-embedding-models | LightFM | |
|---|---|---|
| 1 | - | |
| 1,706 | 4,600 | |
| - | 0.9% | |
| 0.0 | 4.8 | |
| about 5 years ago | 4 months ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
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awesome-embedding-models
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Any good libraries for feature extraction?
Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.
LightFM
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Tracking mentions began in Dec 2020.
What are some alternatives?
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Surprise - A Python scikit for building and analyzing recommender systems
Keras - Deep Learning for humans
tensorflow - An Open Source Machine Learning Framework for Everyone
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets
scikit-learn - scikit-learn: machine learning in Python
MLflow - Open source platform for the machine learning lifecycle
PyBrain
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
spotlight - Deep recommender models using PyTorch.