This repository serves as an educational collection of practical examples and tutorials designed to facilitate the study of machine learning and data science concepts using Python. It provides a structured environment for learning core algorithms and data analysis techniques through hands-on implementation and iterative exploration. The project covers a broad range of analytical capabilities, including predictive modeling for regression, classification, and clustering tasks, as well as network topology analysis for identifying influence patterns in interconnected data. It also incorporates na
PyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving embedding spaces. It provides a modular framework where interchangeable loss functions, mining strategies, and evaluation tools can be composed to learn representations that map similar items to nearby points and dissimilar items to distant points in the embedding space. The library distinguishes itself through a highly configurable architecture that separates concerns across several interchangeable components. Users can assemble custom loss functions from pluggable distance metrics
CONTRIBUTORS WELCOME Generalized Additive Models in Python
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The main features of scikit-learn-contrib/mapie are: Regression and Classification.
Open-source alternatives to scikit-learn-contrib/mapie include: devamoghs/machine-learning-with-python — This repository serves as an educational collection of practical examples and tutorials designed to facilitate the… dswah/pygam — [CONTRIBUTORS WELCOME] Generalized Additive Models in Python. kevinmusgrave/pytorch-metric-learning — PyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving… koaning/human-learn. madeleineudell/lowrankmodels.jl. scikit-learn-contrib/deslib.