pgmpy is a Python toolkit for causal and probabilistic reasoning using graphical models. It implements data structures for models such as DAGs, PDAGs, MAGs, PAGs, Bayesian Networks, Dynamic Bayesian Networks and Structural Equation Models, with algorithms for tasks including causal discovery, causal identification, inference, model validation, parameter estimation and simulations. Algorithms follow a unified composable API and are scikit-learn compatible when possible. It is available on PyPI and conda.
Infer.NET is a framework for running Bayesian inference in graphical models. It can be used for machine learning problems such as classification, recommendation or clustering, and for probabilistic programming. It is published as open source on GitHub under the MIT license and available as NuGet packages.
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.
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.
Keras is a deep learning API that focuses on debugging speed, code conciseness and maintainability. It uses a multi-backend approach that allows work with JAX, TensorFlow and PyTorch, and the KerasHub library provides Keras 3 implementations of popular model architectures with pretrained checkpoints.
Maige is open-source infrastructure for running natural language workflows on a codebase. After connecting a repository, users write rules describing what should happen when issues and pull requests are opened, and the tool can label, assign, comment, review code, and run simple code snippets. It works with the GitHub API. A standard plan is offered after free use, with usage-based payment.
HandyAI is a collection of free browser-based tools for AI and Claude developers, including token counters, prompt analyzers, JSON to tool-use converters and cost calculators. It requires no sign-up and lists the LLM Token Counter and AI Cost Calculator as live tools, with others announced for later release.
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Infer.NET is a framework for running Bayesian inference in graphical models. It can be used for machine learning problems such as classification, recommendation or clustering, and for probabilistic programming. It is published as open source on GitHub under the MIT license and available as NuGet packages.
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.
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.
Keras is a deep learning API that focuses on debugging speed, code conciseness and maintainability. It uses a multi-backend approach that allows work with JAX, TensorFlow and PyTorch, and the KerasHub library provides Keras 3 implementations of popular model architectures with pretrained checkpoints.
Maige is open-source infrastructure for running natural language workflows on a codebase. After connecting a repository, users write rules describing what should happen when issues and pull requests are opened, and the tool can label, assign, comment, review code, and run simple code snippets. It works with the GitHub API. A standard plan is offered after free use, with usage-based payment.
HandyAI is a collection of free browser-based tools for AI and Claude developers, including token counters, prompt analyzers, JSON to tool-use converters and cost calculators. It requires no sign-up and lists the LLM Token Counter and AI Cost Calculator as live tools, with others announced for later release.
