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Machine learning development environment for data science and AI/ML engineering teams.
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A library that builds, optimizes, and evaluates ML pipelines using domain-specific functions.
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Interactive reports to analyze ML models during validation or production monitoring.
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Face recognition library that recognizes and manipulates faces from Python or from the command line.
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Foundational Models for State-of-the-Art Speech and Text Translation.
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Industrial Grade Federated Learning Framework.
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Feature store for machine learning.
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Feature engineering package with SKlearn like functionality.
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Set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
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Python library for automated feature engineering.
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A highly-modular C++ machine learning library for embedded electronics and robotics.
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Secure zero-knowledge encrypted file sharing (AES-256-GCM in-browser). No account required, MIT licensed, self-hostable, optional link expiry.
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PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.
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Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.).
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Easy-to-use and flexible AutoML library for Python.
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Friendly Federated Learning Framework.
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Drag & drop UI to build your customized LLM flow using LangchainJS.
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Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
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Unsupervised machine learning with multivariate Gaussian mixture model.
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Julia package for Gaussian processes.
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Large scale Gaussian Mixture Models.
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If this is your first time building with Generative AI models, check out our course, which includes 21 lessons on building with GenAI.
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A Chinese segment based on Conditional Random Field.
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AI code reviewer for GitHub Actions or local use, compatible with any LLM and integrated with Jira/Linear.