Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron J. Weiss, Vincent Dubourg low, Jake Vanderplas, Alexandre Passos low, David Cournapeau, Matthieu Brucher low, Matthieu Perrot low, Édouard Duchesnay
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- SciPy 1.0: fundamental algorithms for scientific computing in Python→Scikit-learn: Machine Learning in PythonLegacy edge — source detail unavailable
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- AutoML: A survey of the state-of-the-art→Scikit-learn: Machine Learning in PythonLegacy edge — source detail unavailable
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- Orange: data mining toolbox in python→Scikit-learn: Machine Learning in PythonLegacy edge — source detail unavailable
- Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine …→Scikit-learn: Machine Learning in PythonLegacy edge — source detail unavailable
- Anomaly Detection with Robust Deep Autoencoders→Scikit-learn: Machine Learning in PythonLegacy edge — source detail unavailable
- Automated Machine Learning→Scikit-learn: Machine Learning in PythonLegacy edge — source detail unavailable
- AutoML: A survey of the state-of-the-art→Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…Legacy edge — source detail unavailable
- Array programming with NumPy→SciPy 1.0: fundamental algorithms for scientific computing in PythonLegacy edge — source detail unavailable
- Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Researc…→Machine Learning: Algorithms, Real-World Applications and Research DirectionsLegacy edge — source detail unavailable
- Automated Machine Learning→Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…Legacy edge — source detail unavailable
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| Computational Physics and Python Applications | Computer Science |
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