Machine Learning Library
Showing 13 of 13 projects
Open source machine learning framework for building contextual text- and voice-based chatbots and assistants.
A modern C++ toolkit for machine learning, computer vision, and data analysis applications.
A fast, header-only C++ machine learning library with bindings for Python, R, Julia, and Go.
A neural network library optimized for dynamic structures that change per training instance, with C++ and Python bindings.
A lightweight deep learning library with a functional API for composing models, compatible with PyTorch, TensorFlow, and MXNet.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
A pure Java machine learning library with no external dependencies, offering a wide collection of algorithms and parallel execution support.
An open-source toolkit for building end-to-end trainable task-oriented dialogue models with neural networks.
A parallel Monte Carlo and machine learning library for scientific inference, available in Python, MATLAB, Fortran, C++, and C.
A simple machine learning framework written in Swift, currently focusing on regression algorithms.
Saul is a declarative domain-specific language in Scala for designing flexible machine learning models with relational feature extraction.
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