Showing 3 open source projects for "website developing software"

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    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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    Add Two Lines of Code. Get Full APM.

    AppSignal installs in minutes and auto-configures dashboards, alerts, and error tracking.

    Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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    Coframe

    Coframe

    Coframe brings your UX to life with AI-powered optimization

    Bring your UX to life with AI-powered optimization and personalization. Coframe brings the content of your app or website to life through AI-powered optimization, personalization, and overall self-improvement. It takes minutes to integrate, and the ROI is clear to measure. Your website or app gains self-enhancing abilities with Coframe, learning from real-world performance. It's A/B testing, but with a serious upgrade. Coframe uses the latest in AI to generate copy that is tailored to your...
    Downloads: 0 This Week
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  • 2
    TorchGAN

    TorchGAN

    Research Framework for easy and efficient training of GANs

    The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to...
    Downloads: 0 This Week
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  • 3
    ML for Trading

    ML for Trading

    Code for machine learning for algorithmic trading, 2nd edition

    On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms,...
    Downloads: 5 This Week
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