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Laion is a non-profit organization providing open access to large-scale machine learning datasets, tools, and models. It aims to democratize machine learning research and promote environment-friendly resource utilization. Laion offers resources like the LAION-400M and Re-LAION 5B datasets.

causalml is a Python package for uplift modeling and causal inference. It utilizes machine learning algorithms to estimate treatment effects. The tool is designed to help data scientists and researchers. It supports various methods for causal analysis and experimentation.

BayesFlow is a Python library for efficient Bayesian modeling with deep learning. It provides a user-friendly API for amortized Bayesian workflows. It offers a rich collection of neural network architectures. BayesFlow supports multiple backends via Keras3, including PyTorch, TensorFlow, and JAX.

shapiq is a Python library for Shapley interactions and Shapley values in machine learning. It helps explain the output of machine learning models. The library facilitates feature importance analysis and model understanding. It is an open-source tool for machine learning explainability.

Nimfa is a Python module providing various algorithms for nonnegative matrix factorization. It is designed for tasks such as data analysis and feature extraction. Nimfa is distributed under the BSD license. It supports a range of methods for matrix factorization.

photon-ml is a machine learning library built on Apache Spark. It supports training Generalized Linear Models (GLMs) and Generalized Linear Mixed Models (GLMMs). Originally developed by LinkedIn, it is designed for scalable machine learning tasks. photon-ml is suitable for large-scale data analysis and model training.