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Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
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A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
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A PyTorch implementation of CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation".
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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Train and run a computer vision model with 5-10 lines of code.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Library of SAS Enterprise Miner process flow diagrams to help you learn by example about specific data mining topics.
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Many useful features that aren't part of the main nn package.
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Python library for scalable Bayesian optimisation.
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Reading Wikipedia to answer open-domain questions.
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High performance library for time series distances (DTW) and time series clustering.
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Design and Deploy Large Language Model Apps.
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Python library for experiment metrics logging into simply formatted local files.
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An evolutionary optimization library.
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A library that builds, optimizes, and evaluates ML pipelines using domain-specific functions.
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Face recognition library that recognizes and manipulates faces from Python or from the command line.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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FastEdit aims to assist developers with injecting fresh and customized knowledge into large language models efficiently using one single command.
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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 Scala library for constructing probabilistic models.
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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.