Showing 4 open source projects for "simple-draw"

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  • 1
    MagicMirror²

    MagicMirror²

    Modular smart mirror platform with a list of installable modules

    MagicMirror² is Open Source, free and maintained by a big group of enthusiasts. Got a nice idea? Send us a pull request and become a part of the big list of contributors. The core of MagicMirror² contains a strong API which allows 3rd party developers to build additional modules. Modules you can use. Modules you can develop. Read our extensive documentation to find out everything you want to know about the MagicMirror² project. The full API description allows you to build your own modules....
    Downloads: 1 This Week
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  • 2
    CNN Explainer

    CNN Explainer

    Learning Convolutional Neural Networks with Interactive Visualization

    In machine learning, a classifier assigns a class label to a data point. For example, an image classifier produces a class label (e.g, bird, plane) for what objects exist within an image. A convolutional neural network, or CNN for short, is a type of classifier, which excels at solving this problem! A CNN is a neural network: an algorithm used to recognize patterns in data. Neural Networks in general are composed of a collection of neurons that are organized in layers, each with their own...
    Downloads: 0 This Week
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  • 3
    AI-Blocks

    AI-Blocks

    A powerful and intuitive WYSIWYG to create Machine Learning models

    A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models! The concept of AI-Blocs is to have a simple scene with draggable objects that have scripts attached to them. The model can be run directly on the editor or be exported to a standalone script that runs on Tensorflow. Variables are parsed from python scripts and can be edited from the AI-Blocs properties panel. To run your model simply press the "Play" button and let the magic happen! ...
    Downloads: 1 This Week
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  • 4
    This project intends to create a bacteria simulator framework, with some realistic bacteria control methods based on chemical signaling, simple sensors, motors and neural networks. The bacteria will evolve in a genetic algorithm environment.
    Downloads: 0 This Week
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