Showing 5 open source projects for "machine learning platform"

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  • Forever Free Full-Stack Observability | Grafana Cloud Icon
    Forever Free Full-Stack Observability | Grafana Cloud

    Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.

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  • Build Securely on AWS with Proven Frameworks Icon
    Build Securely on AWS with Proven Frameworks

    Lay a foundation for success with Tested Reference Architectures developed by Fortinet’s experts. Learn more in this white paper.

    Moving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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  • 1
    NeuralProphet

    NeuralProphet

    A simple forecasting package

    NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in...
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  • 2
    PyTorch Forecasting

    PyTorch Forecasting

    Time series forecasting with PyTorch

    PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with...
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  • 3
    Premium Markets

    Premium Markets

    Premium Markets is an automated financial technical analysis system.

    ...Full installation instructions can be found at http://www.premiummarkets.uk/html/swtui.html#Download . I would also like to bring to your attention that, in its advanced version, Premium Markets also provides a Forecast machine learning engine based on neural networks. See http://www.premiummarkets.uk/ for documentation and a workable demo of the trend forecast and prediction engine.
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  • 4
    Deep learning time series forecasting

    Deep learning time series forecasting

    Deep learning PyTorch library for time series forecasting

    Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series...
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  • Ship Agents Faster Icon
    Ship Agents Faster

    Transform your applications and workflows into powerful agentic systems at global scale.

    Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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  • 5
    Cenobi

    Cenobi

    cost estimation and management accounting, using neural networks

    ...Although Cenobi is particularly suitable for management accounting purposes, it can also be used as a general machine learning tool. The neural networks at the heart of the program are fully object-oriented and therefore highly adaptable. You are very welcome to use them under the GPLv3.
    Downloads: 0 This Week
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