Classification
Showing 36 of 103 projects
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other ML tasks.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other machine learning tasks.
A graphical image annotation tool written in Python for computer vision tasks like segmentation and detection.
A comprehensive Node.js library offering a wide range of natural language processing facilities.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A batteries-included machine learning library for Go with a scikit-learn inspired interface.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
A fast and comprehensive machine learning framework for Java, Scala, and Kotlin with state-of-the-art algorithms and data visualization.
Official code repository for the 'Machine Learning with TensorFlow' book with practical examples.
Detect the language of text with support for up to 419 languages, more than any other library.
A Python machine learning toolkit for time series analysis with scikit-learn compatible API.
Deep neural network to extract structured information from invoice documents with a customizable UI and training tools.
An open source Python library and framework for building computer vision models on satellite, aerial, and large imagery sets.
A web/desktop application for collaborative labeling and annotation of images, text, audio, documents, and other data types.
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
A lambda architecture framework on Apache Spark and Kafka for building and deploying real-time large-scale machine learning applications.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
A fast Support Vector Machine (SVM) library that leverages GPUs and multi-core CPUs for high-performance machine learning.
A Ruby library implementing the ID3 algorithm for decision tree learning with support for continuous and discrete datasets.
A deep learning framework for feature learning directly from point clouds using X-Conv operations, achieving state-of-the-art results in classification and segmentation.
A GPU-accelerated deep learning library for Python using CUDA via PyCUDA, implementing neural networks with various training methods.
A Python library implementing Factorization Machines with a scikit-learn compatible API for regression, classification, and ranking tasks.
A modern, object-oriented machine learning framework for R, providing efficient building blocks for ML workflows.
A fast, ergonomic machine learning library for Rust with broad algorithm coverage and WASM-first defaults.
A scikit-learn compatible Python module for multi-label classification tasks.
A Ruby machine learning library with a Scikit-Learn-like interface for classification, regression, clustering, and dimensionality reduction.
A cross-platform C++ machine learning library for real-time gesture recognition with support for classification, regression, and clustering.
A fast implementation of random forests for classification, regression, and survival analysis, optimized for high-dimensional data.
TensorFlow implementation of arbitrary order (≥2) Factorization Machines for classification and regression tasks.
Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
A Ruby library for text classification with Bayesian, LSI, logistic regression, k-NN, and TF-IDF algorithms.
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