Showing 4 open source projects for "the code distance visualizer"

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    SIA

    SIA

    AI framework to autonomously improve the performance of any AI system

    SIA is a self-improving AI framework designed to improve the performance of models or agents on benchmark tasks. It uses an iterative loop where a meta-agent creates or updates a task-specific target agent, while a feedback agent studies results and proposes improvements. The framework can refine both the harness around the task and the agent implementation itself. It is aimed at research and experimentation across tasks such as machine learning benchmarks, legal classification, code...
    Downloads: 0 This Week
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  • 2
    Visual Blocks

    Visual Blocks

    Visual Blocks for ML is a Google visual programming framework

    ...Because everything lives in the browser, sharing is as simple as exporting a project or link, and collaborators can experiment without installing toolchains. For educators and product teams alike, Visual Blocks reduces the distance from idea to interactive proof-of-concept by turning ML diagrams.
    Downloads: 1 This Week
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  • 3
    Quil

    Quil

    Main repo, Quil source code

    Quil blends Clojure's expressive functional programming with Processing’s creative graphics and animation capabilities, offering a DSL for drawing and interactive visuals in a dynamic, artistic way. In one hand Quil holds Processing, a carefully crafted API for making drawing and animation extremely easy to get your biscuit-loving chops around. In the other she clutches Clojure, an interlocking suite of exquisite language abstractions forged by an army of hammocks and delicately wrapped in...
    Downloads: 1 This Week
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  • 4
    MMdnn

    MMdnn

    Tools to help users inter-operate among deep learning frameworks

    ...We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform.
    Downloads: 0 This Week
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