Classification
Showing 23 of 23 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.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
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.
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 interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
A fast Support Vector Machine (SVM) library that leverages GPUs and multi-core CPUs for high-performance machine learning.
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