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A deep learning-based translation library between 50 languages, built with transformers.
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Ready to use deeplearning docker images.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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dowel is a logger for machine learning research. It is installed with pip and used by importing dowel and adding outputs such as StdOutput and TensorBoardOutput, then logging messages and tabular records during training runs.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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DVClive is an open-source tool that logs and tracks machine learning metrics, parameters, and models using Git and/or DVC. It is hosted on GitHub as the repository treeverse/dvclive. It provides a simple interface for experiment tracking.
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Scala Library/REPL for Machine Learning Research.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Deep learning operations reinvented (for pytorch, tensorflow, jax and others).
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Python package which helps to debug machine learning classifiers and explain their predictions.
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emel is a simple and functional machine learning library built for the Erlang ecosystem and implemented in Gleam. It turns data into functions and supports installing through rebar.config, mix.exs or gleam add. Implemented algorithms include linear regression, k nearest neighbors, decision tree, naive Bayes, k means, perceptron, logistic regression and neural network, along with mathematics, algebra, geometry and statistics.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Machine learning development environment for data science and AI/ML engineering teams.
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EvalML is an AutoML library written in Python that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. It includes data quality checks, cross-validation, preprocessing, feature engineering, feature selection, and model understanding tools. Install from PyPI or conda-forge.
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A fast Evolution Strategy implementation in Python.
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Facets is a project containing two visualizations, Facets Overview and Facets Dive, for understanding and analyzing machine learning datasets. The visualizations are Polymer web components backed by TypeScript that can be embedded in Jupyter notebooks or webpages. Overview produces feature-by-feature statistical analysis and compares statistics across datasets. The repository was archived on Jul 27, 2024 and is now read-only.
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Fairlearn is a Python package hosted on a GitHub repository maintained by the Fairlearn project, designed to assess and improve the fairness of machine learning models. The repository includes documentation covering fairness definitions and a getting started guide for the tool.
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High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Feast is an open source feature store for machine learning. It manages an offline store for historical data, a low-latency online store, and a feature server. It generates point-in-time correct feature sets to avoid data leakage and provides a single data access layer abstracting feature storage from retrieval. Installable with pip and initialized through a CLI.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Set of tools for creating and testing machine learning features, with a scikit-learn compatible API.