3 Repos
Awesome GitHub RepositoriesMachine Learning Resampling
Techniques for oversampling or undersampling training data to balance class distributions.
Distinct from Machine Learning Training: Focuses on data resampling for balance, whereas Machine Learning Training focuses on the training process itself.
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Awesome Machine Learning Resampling GitHub Repositories
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- scikit-learn-contrib/imbalanced-learn
scikit-learn-contrib/imbalanced-learn
7,104Auf GitHub ansehenimbalanced-learn is a dataset balancing framework and Python machine learning extension designed to resample training data and reduce the impact of class imbalance. It provides a toolkit of algorithms for adjusting class distributions to improve model performance on minority class prediction. As a scikit-learn resampling library, it extends the ecosystem with specialized tools for balancing datasets through over-sampling and under-sampling techniques. This allows for the correction of skewed class proportions to reduce model bias toward the majority class. The library implements the scikit-l
Applies oversampling or undersampling techniques to create a balanced training set for scikit-learn models.
Python - lululxvi/deepxde
lululxvi/deepxde
3,874Auf GitHub ansehenDeepXDE is a scientific machine learning library and deep learning PDE solver used to compute solutions for forward and inverse ordinary, partial, and integro-differential equations. It functions as a physics-informed neural network library that embeds physical laws and boundary conditions directly into the neural network loss function. The project provides a deep operator network framework for learning operator mappings that approximate relationships between functions in multiphysics problems. It is implemented as a multi-backend tensor library, allowing the system to switch between differen
Implements adaptive resampling of training coordinates to improve accuracy in high-residual regions of the spatial domain.
Pythondeep-learningdeeponetjax - kmkolasinski/deep-learning-notes
kmkolasinski/deep-learning-notes
1,348Auf GitHub ansehenThis repository is an educational collection of implementations and research notes focused on deep learning architectures and optimization techniques. It provides modular code examples designed to demonstrate foundational and advanced concepts in machine learning, ranging from basic neural network structures to complex training strategies. The project distinguishes itself by offering practical implementations of specialized research methods, including capsule-based feature aggregation, gradient direction decoupling, and self-normalizing weight regularization. These materials allow for the stu
Provides programmatic resampling techniques to balance minority class representation in training data pipelines.
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