Multidimensional cluster generation in Python.
Orange is an open-source machine learning and data visualization toolbox. It offers visual programming in which widgets are placed on a canvas and connected to load datasets and analyze data. It supports interactive visualization methods including box plots, scatter plots, decision trees, hierarchical clustering, heatmaps, MDS, t-SNE and linear projections. Extensions add text mining, network analysis and other specialized capabilities.
MEKA is an open source implementation of methods for multi-label learning and evaluation, based on the WEKA Machine Learning Toolkit. It includes dozens of multi-label methods from the scientific literature and a wrapper to the MULAN framework. The project is distributed via SourceForge, with code hosted on GitHub.
LynxKite is a GPU-accelerated platform for graph analytics and drug discovery. It combines graph reasoning, machine learning and molecule analysis in one workflow, and offers 600+ graph operations with 2D and 3D molecule visualization. It is free and open source with a commercial tier for production teams.
LightFM is a Python implementation of recommendation algorithms for implicit and explicit feedback. It allows item and user metadata to be incorporated into matrix factorization algorithms, representing users and items as sums of latent feature representations. Installation is available from PyPI, and a Docker setup is provided for running examples and tests.
PyCUDA is a software project associated with Andreas Klöckner, who teaches and conducts research in scientific computing at the University of Illinois at Urbana-Champaign. The project has a dedicated documentation site.
Course materials and notes for the Stanford class CS231n: Deep Learning for Computer Vision. The notes accompany the Stanford CS class and cover topics organized in modules. Spring 2026 assignments are listed.
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Orange is an open-source machine learning and data visualization toolbox. It offers visual programming in which widgets are placed on a canvas and connected to load datasets and analyze data. It supports interactive visualization methods including box plots, scatter plots, decision trees, hierarchical clustering, heatmaps, MDS, t-SNE and linear projections. Extensions add text mining, network analysis and other specialized capabilities.
MEKA is an open source implementation of methods for multi-label learning and evaluation, based on the WEKA Machine Learning Toolkit. It includes dozens of multi-label methods from the scientific literature and a wrapper to the MULAN framework. The project is distributed via SourceForge, with code hosted on GitHub.
LynxKite is a GPU-accelerated platform for graph analytics and drug discovery. It combines graph reasoning, machine learning and molecule analysis in one workflow, and offers 600+ graph operations with 2D and 3D molecule visualization. It is free and open source with a commercial tier for production teams.
LightFM is a Python implementation of recommendation algorithms for implicit and explicit feedback. It allows item and user metadata to be incorporated into matrix factorization algorithms, representing users and items as sums of latent feature representations. Installation is available from PyPI, and a Docker setup is provided for running examples and tests.
PyCUDA is a software project associated with Andreas Klöckner, who teaches and conducts research in scientific computing at the University of Illinois at Urbana-Champaign. The project has a dedicated documentation site.
Course materials and notes for the Stanford class CS231n: Deep Learning for Computer Vision. The notes accompany the Stanford CS class and cover topics organized in modules. Spring 2026 assignments are listed.
