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    $300 Free Credits to Build on Google Cloud

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    Build Securely on AWS with Proven Frameworks

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  • 1
    mlforecast

    mlforecast

    Scalable machine learning for time series forecasting

    mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates...
    Downloads: 3 This Week
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  • 2
    Hyperformer

    Hyperformer

    Hypergraph Transformer for Skeleton-based Action Recognition

    ...To relax such a restriction, Self-Attention (SA) mechanism has been adopted to make the topology of GCNs adaptive to the input, resulting in the state-of-the-art hybrid models. Concurrently, attempts with plain Transformers have also been made, but they still lag behind state-of-the-art GCN-based methods due to the lack of structural prior.
    Downloads: 0 This Week
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  • 3
    Ultroid

    Ultroid

    Telegram UserBot, Built in Python Using Telethon lib

    ...Ultroid has minimal amount of plugins (just the necessary ones) in the main repository, and all the other less-useful stuff in the addons repository. This facilitates quick deployments and lag-free use. Ultroid can install any plugin from the most of the other 'userbots' without any issue.
    Downloads: 17 This Week
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  • 4
    Scalable Distributed Deep-RL

    Scalable Distributed Deep-RL

    A TensorFlow implementation of Scalable Distributed Deep-RL

    ...In this architecture, multiple actor processes interact with their environments in parallel to collect trajectories, which are then asynchronously sent to a centralized learner for policy updates. The learner uses importance weighting to correct for policy lag between actors and the learner, enabling stable off-policy training at scale. This design allows the system to scale efficiently to hundreds of environments and billions of frames while maintaining sample efficiency and stability. The implementation supports training in DeepMind Lab (DMLab) and has also been adapted for other environments like Atari and Street View.
    Downloads: 0 This Week
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    Veeam Data Platform v13.1

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