Magenta is an open source research project exploring the role of machine learning as a tool in the creative process, focused on art and music. It includes Magenta Studio, a collection of music plugins for Ableton Live, and Magenta RealTime 2, an open model and real-time neural synthesis engine.
Stanford MLSys Seminar Series is a seminar series on the frontier of machine learning and systems. Its 2025 "Hidden Layer" series schedule lists talks, and earlier limited series covered systems for machine learning and foundation models. Stanford students could sign up for associated classes, and an email list provides speaker and livestream notifications.
A collaboration platform where the machine learning community shares models, datasets and applications. It hosts public models, datasets and Spaces, and offers an open source stack including libraries such as Transformers, Datasets and Accelerate. It also provides paid Compute and Enterprise solutions, with an Inference Providers API giving access to models from multiple AI providers.
astroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib and astropy, distributed under the 3-clause BSD license. It provides statistical and machine learning routines for analyzing astronomical data, loaders for open astronomical datasets, and examples of analyzing and visualizing datasets. The project was started in 2012 to accompany a textbook on statistics, data mining and machine learning in astronomy.
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LLM Explorer is a directory of open-source language models and AI agents. It lists 150 AI agents and 64,811 open-source large and small language models, grouped into categories with benchmarks and analytics.
Apache Superset is an open-source data exploration and visualization platform. It offers a no-code visualization builder and a SQL IDE, connects to SQL-based databases, and ships with more than 40 pre-installed visualization types. It includes dashboards, a chart builder, SQL Lab and datasets, with a plug-in architecture for custom visualizations.
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Stanford MLSys Seminar Series is a seminar series on the frontier of machine learning and systems. Its 2025 "Hidden Layer" series schedule lists talks, and earlier limited series covered systems for machine learning and foundation models. Stanford students could sign up for associated classes, and an email list provides speaker and livestream notifications.
A collaboration platform where the machine learning community shares models, datasets and applications. It hosts public models, datasets and Spaces, and offers an open source stack including libraries such as Transformers, Datasets and Accelerate. It also provides paid Compute and Enterprise solutions, with an Inference Providers API giving access to models from multiple AI providers.
astroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib and astropy, distributed under the 3-clause BSD license. It provides statistical and machine learning routines for analyzing astronomical data, loaders for open astronomical datasets, and examples of analyzing and visualizing datasets. The project was started in 2012 to accompany a textbook on statistics, data mining and machine learning in astronomy.
spaCy is a free open-source library for Natural Language Processing in Python. It features named entity recognition, part-of-speech tagging, dependency parsing and word vectors, supports over 75 languages and provides 84 trained pipelines for 25 languages.
LLM Explorer is a directory of open-source language models and AI agents. It lists 150 AI agents and 64,811 open-source large and small language models, grouped into categories with benchmarks and analytics.
Apache Superset is an open-source data exploration and visualization platform. It offers a no-code visualization builder and a SQL IDE, connects to SQL-based databases, and ships with more than 40 pre-installed visualization types. It includes dashboards, a chart builder, SQL Lab and datasets, with a plug-in architecture for custom visualizations.
