Showing 15 of 15 projects
A PyTorch-based open-source framework for deep learning in healthcare imaging, providing domain-specific tools and workflows.
A comprehensive Rust machine learning framework focused on preprocessing and classical algorithms, akin to scikit-learn.
A Python library for feature engineering and selection with scikit-learn compatible transformers.
An AutoML implementation and tutorial for automating machine learning pipelines on both static datasets and dynamic data streams, with a focus on IoT anomaly detection.
A TypeScript machine learning library for the web and Node.js with a simple, consistent API.
A high-performance data profiler for discovering and validating complex patterns like functional dependencies, inclusion dependencies, and association rules.
A high-performance data profiler for discovering and validating complex patterns in datasets, enabling data cleaning and quality analysis.
A Python library for feature selection using nature-inspired wrapper algorithms like particle swarm, grey wolf, and genetic optimization.
A fast feature selection algorithm for tree-based models like XGBoost, designed to outperform Boruta in speed and performance.
A Swiss knife collection of utility functions for developing and evaluating machine learning algorithms in Julia.
An R package for developing traditional credit risk scorecard models with functions for data preprocessing, WOE binning, variable selection, and performance evaluation.
A machine learning library for Clojure built on top of Weka, providing filters, classifiers, regression, and clustering algorithms.
A fast, sklearn-like feature processing library for Go that generates optimized transformers from struct tags.
A collection of neural network libraries for functional and mainstream languages, offering efficient lazy evaluation and cross-language compatibility.
A Clojure library providing machine learning algorithms with simple APIs for data preprocessing and modeling.
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