awesome-embedding-models
PyBrain
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| awesome-embedding-models | PyBrain | |
|---|---|---|
| 1 | - | |
| 1,706 | 2,852 | |
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| 0.0 | 0.0 | |
| about 5 years ago | 3 months ago | |
| Jupyter Notebook | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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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.
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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
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
scikit-learn - scikit-learn: machine learning in Python
HotBits Python API - Python API for HotBits random data generator
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
Pylearn2 - Warning: This project does not have any current developer. See bellow.
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.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.